Data mining and machine learning studies on the subject “defect prediction.”
\\n\\n
Released this past November, the list is based on data collected from the Web of Science and highlights some of the world’s most influential scientific minds by naming the researchers whose publications over the previous decade have included a high number of Highly Cited Papers placing them among the top 1% most-cited.
\\n\\nWe wish to congratulate all of the researchers named and especially our authors on this amazing accomplishment! We are happy and proud to share in their success!
Note: Edited in March 2021
\\n"}]',published:!0,mainMedia:{caption:"Highly Cited",originalUrl:"/media/original/117"}},components:[{type:"htmlEditorComponent",content:'IntechOpen is proud to announce that 191 of our authors have made the Clarivate™ Highly Cited Researchers List for 2020, ranking them among the top 1% most-cited.
\n\nThroughout the years, the list has named a total of 261 IntechOpen authors as Highly Cited. Of those researchers, 69 have been featured on the list multiple times.
\n\n\n\nReleased this past November, the list is based on data collected from the Web of Science and highlights some of the world’s most influential scientific minds by naming the researchers whose publications over the previous decade have included a high number of Highly Cited Papers placing them among the top 1% most-cited.
\n\nWe wish to congratulate all of the researchers named and especially our authors on this amazing accomplishment! We are happy and proud to share in their success!
Note: Edited in March 2021
\n'}],latestNews:[{slug:"webinar-introduction-to-open-science-wednesday-18-may-1-pm-cest-20220518",title:"Webinar: Introduction to Open Science | Wednesday 18 May, 1 PM CEST"},{slug:"step-in-the-right-direction-intechopen-launches-a-portfolio-of-open-science-journals-20220414",title:"Step in the Right Direction: IntechOpen Launches a Portfolio of Open Science Journals"},{slug:"let-s-meet-at-london-book-fair-5-7-april-2022-olympia-london-20220321",title:"Let’s meet at London Book Fair, 5-7 April 2022, Olympia London"},{slug:"50-books-published-as-part-of-intechopen-and-knowledge-unlatched-ku-collaboration-20220316",title:"50 Books published as part of IntechOpen and Knowledge Unlatched (KU) Collaboration"},{slug:"intechopen-joins-the-united-nations-sustainable-development-goals-publishers-compact-20221702",title:"IntechOpen joins the United Nations Sustainable Development Goals Publishers Compact"},{slug:"intechopen-signs-exclusive-representation-agreement-with-lsr-libros-servicios-y-representaciones-s-a-de-c-v-20211123",title:"IntechOpen Signs Exclusive Representation Agreement with LSR Libros Servicios y Representaciones S.A. de C.V"},{slug:"intechopen-expands-partnership-with-research4life-20211110",title:"IntechOpen Expands Partnership with Research4Life"},{slug:"introducing-intechopen-book-series-a-new-publishing-format-for-oa-books-20210915",title:"Introducing IntechOpen Book Series - A New Publishing Format for OA Books"}]},book:{item:{type:"book",id:"8967",leadTitle:null,fullTitle:"Bacterial Biofilms",title:"Bacterial Biofilms",subtitle:null,reviewType:"peer-reviewed",abstract:"This book examines biofilms in nature. Organized into four parts, this book addresses biofilms in wastewater treatment, inhibition of biofilm formation, biofilms and infection, and ecology of biofilms. It is designed for clinicians, researchers, and industry professionals in the fields of microbiology, biotechnology, ecology, and medicine as well as graduate and postgraduate students.",isbn:"978-1-78985-900-3",printIsbn:"978-1-78985-899-0",pdfIsbn:"978-1-83968-819-5",doi:"10.5772/intechopen.82929",price:139,priceEur:155,priceUsd:179,slug:"bacterial-biofilms",numberOfPages:360,isOpenForSubmission:!1,isInWos:null,isInBkci:!1,hash:"e692b520263526cca2b37092c3e8d0b4",bookSignature:"Sadik Dincer, Melis Sümengen Özdenefe and Afet Arkut",publishedDate:"October 7th 2020",coverURL:"https://cdn.intechopen.com/books/images_new/8967.jpg",numberOfDownloads:17958,numberOfWosCitations:36,numberOfCrossrefCitations:50,numberOfCrossrefCitationsByBook:3,numberOfDimensionsCitations:90,numberOfDimensionsCitationsByBook:5,hasAltmetrics:1,numberOfTotalCitations:176,isAvailableForWebshopOrdering:!0,dateEndFirstStepPublish:"March 15th 2019",dateEndSecondStepPublish:"September 23rd 2019",dateEndThirdStepPublish:"November 22nd 2019",dateEndFourthStepPublish:"February 10th 2020",dateEndFifthStepPublish:"April 10th 2020",currentStepOfPublishingProcess:5,indexedIn:"1,2,3,4,5,6",editedByType:"Edited by",kuFlag:!1,featuredMarkup:null,editors:[{id:"188141",title:"Prof.",name:"Sadik",middleName:null,surname:"Dincer",slug:"sadik-dincer",fullName:"Sadik Dincer",profilePictureURL:"https://mts.intechopen.com/storage/users/188141/images/system/188141.png",biography:"For the past 35 years, Prof. Sadık Dincer has been involved in teaching, research, and academic work in numerous distinguished universities in Turkey. Currently, he is working at the Biology and Biotechnology Departments, Cukurova University, Adana, Turkey. His manuscripts and book chapters have been published in national and international journals and his works has been cited 1167 times. To date, he has trained twenty-six MSc and eleven Ph.D. students. He received the Technology Development Award from the Scientific and Technological Research Council of Turkey (TÜBİTAK) in 2013 and a national study patent in 2019. His research is focused on bacteriology, microbial ecology, industrial biotechnology, and microbial genetics.",institutionString:"Cukurova University",position:null,outsideEditionCount:0,totalCites:0,totalAuthoredChapters:"2",totalChapterViews:"0",totalEditedBooks:"1",institution:{name:"Cukurova University",institutionURL:null,country:{name:"Turkey"}}}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,coeditorOne:{id:"292288",title:"Dr.",name:"Melis",middleName:null,surname:"Sümengen Özdenefe",slug:"melis-sumengen-ozdenefe",fullName:"Melis Sümengen Özdenefe",profilePictureURL:"https://mts.intechopen.com/storage/users/292288/images/system/292288.png",biography:"Associate Prof. Dr. Melis Sumengen Ozdenefe received her BSc, MSc, and Ph.D. in Biology from Cukurova University, Adana, Turkey in 2009, 2011, and 2014, respectively. During her MSc, she was an international exchange student and a researcher at Anhalt University, Germany for six months. She has been working in the Department of Biomedical Engineering, Near East University, Northern Cyprus since 2014. Her teaching interests include industrial microbiology, bacteriology, biotechnology, enzymology, and environmental microbiology. Her research areas involve enzymes and biosurfactants, which are produced from various bacteria and fungus, for industrial applications, the production and characterization of bacterial enzymes and bacteriocins, the antimicrobial and antioxidant activity of various plant structures, multiple antibiotic resistance, and heavy metal resistance in Gram-negative bacteria. Dr. Ozdenefe’s works have been published in national and international journals, conferences, congresses, and symposiums and cited 140 times.",institutionString:"Near East University",position:null,outsideEditionCount:0,totalCites:0,totalAuthoredChapters:"0",totalChapterViews:"0",totalEditedBooks:"0",institution:{name:"Near East University",institutionURL:null,country:{name:"Cyprus"}}},coeditorTwo:{id:"292290",title:"Dr.",name:"Afet",middleName:null,surname:"Arkut",slug:"afet-arkut",fullName:"Afet Arkut",profilePictureURL:"https://mts.intechopen.com/storage/users/292290/images/14475_n.jpg",biography:"Afet Arkut Özyapici received a BSc and PhD in Biology from Cukurova University, Turkey, in 2009 and 2013, respectively. During her postgraduate studies, she attended Anhalt University, Germany, for six months as an international exchange student (Erasmus) in 2010–2011. In 2013, she began working as a doctor in the Health Sciences Faculty at Cyprus International University. In March 2014, she became an assistant professor. Dr. Özyapici has been working as head of the Department of Social Work and vice dean of the Health Sciences Faculty at Cyprus International University since 2016 and 2017, respectively. Her teaching and research interests include microbiology, bacteriology, microbial ecology, hospital infection, hygiene, and sanitation.",institutionString:"Cyprus International University",position:null,outsideEditionCount:0,totalCites:0,totalAuthoredChapters:"0",totalChapterViews:"0",totalEditedBooks:"0",institution:{name:"Cyprus International University",institutionURL:null,country:{name:"Cyprus"}}},coeditorThree:null,coeditorFour:null,coeditorFive:null,topics:[{id:"896",title:"Microbial Ecology",slug:"microbial-ecology"}],chapters:[{id:"68476",title:"Biofilm in Moving Bed Biofilm Process for Wastewater Treatment",doi:"10.5772/intechopen.88520",slug:"biofilm-in-moving-bed-biofilm-process-for-wastewater-treatment",totalDownloads:2249,totalCrossrefCites:2,totalDimensionsCites:13,hasAltmetrics:1,abstract:"A brief introduction of the long history of biofilm-based wastewater treatment is given together with basics of biofilm behavior and mechanisms in removal and transformation of pollutants. Moving bed biofilm reactor (MBBR) principles and applications of such are presented. Advantages and limitations of such solutions are given together with evaluations of emerging MBBR applications. The basis of biofilm processes and biofilm layer classification based on dissolved oxygen gradient is discussed. Organisms grow at the protected surface of the biocarrier where oxygen gradients create aerobic, anoxic, and anaerobic layers allowing simultaneous nitrification and denitrification in one MBBR (nitrification, nitritation, autotrophic, and heterotrophic denitrification). Combination of MBBR with activated sludge, continuous flow intermittent cleaning (CFIC®), and integration with anaerobic digestion increases the potential usage of MBBR for enhanced efficiency and energy recovery and is partly discussed as case studies (COD, ammonium, and solid removal). Biofilm thickness and scaling control can be crucial for MBBR performance. The type of carriers, filling degree, and operational conditions play a major role for process performance; hence, the effect of those parameters is presented.",signatures:"Shuai Wang, Sudeep Parajuli, Vasan Sivalingam and Rune Bakke",downloadPdfUrl:"/chapter/pdf-download/68476",previewPdfUrl:"/chapter/pdf-preview/68476",authors:[{id:"253655",title:"Dr.",name:"Shuai",surname:"Wang",slug:"shuai-wang",fullName:"Shuai Wang"},{id:"303855",title:"M.Sc.",name:"Vasan",surname:"Sivalingam",slug:"vasan-sivalingam",fullName:"Vasan Sivalingam"},{id:"303856",title:"MSc.",name:"Sudeep",surname:"Parajuli",slug:"sudeep-parajuli",fullName:"Sudeep Parajuli"},{id:"309355",title:"Prof.",name:"Rune",surname:"Bakke",slug:"rune-bakke",fullName:"Rune Bakke"}],corrections:null},{id:"69536",title:"Effect of Heavy Metals on the Biofilm Formed by Microorganisms from Impacted Aquatic Environments",doi:"10.5772/intechopen.89545",slug:"effect-of-heavy-metals-on-the-biofilm-formed-by-microorganisms-from-impacted-aquatic-environments",totalDownloads:977,totalCrossrefCites:1,totalDimensionsCites:3,hasAltmetrics:0,abstract:"The aquatic environment is highly complex and diverse, consisting of several types of ecosystems that are dynamic products of complex interactions between biotic and abiotic components. Changes in the physical and chemical properties of these ecosystems can significantly affect the balance of life forms present, especially in their microbiota. Among the main pollutants present in these environments are heavy metals. Several studies demonstrate the effects of these minerals on the structure and function of microbial communities, which may develop adaptation mechanisms for survival and permanence in these sites. In addition, the resistance to heavy metals may contribute to the evolution of resistance genes to the different types of antimicrobials due to the increase of the selective pressure in the environment, becoming a public health problem. One of the adaptive mechanisms present in bacteria from impacted environments that has been frequently investigated is the formation of biofilms. Recent studies have reported significant changes in the structure and amount of biofilm formed in the presence of different metals, and consequently, an increase in the tolerance to these pollutants and antimicrobials. This review will discuss the effects of some metals on bacterial biofilms and their consequences for the marine environment.",signatures:"Lívia Caroline Alexandre de Araújo and Maria Betânia Melo de Oliveira",downloadPdfUrl:"/chapter/pdf-download/69536",previewPdfUrl:"/chapter/pdf-preview/69536",authors:[{id:"309527",title:"Dr.",name:"Lívia",surname:"Caroline",slug:"livia-caroline",fullName:"Lívia Caroline"},{id:"310489",title:"Ph.D.",name:"Maria Betânia",surname:"Melo De Oliveira",slug:"maria-betania-melo-de-oliveira",fullName:"Maria Betânia Melo De Oliveira"}],corrections:null},{id:"70686",title:"Natural Compounds Inhibiting Pseudomonas aeruginosa Biofilm Formation by Targeting Quorum Sensing Circuitry",doi:"10.5772/intechopen.90833",slug:"natural-compounds-inhibiting-em-pseudomonas-aeruginosa-em-biofilm-formation-by-targeting-quorum-sens",totalDownloads:789,totalCrossrefCites:4,totalDimensionsCites:5,hasAltmetrics:0,abstract:"The biofilm lifestyle mode certainly represents one of the most successful behaviors to facilitate bacterial survival in diverse inhospitable environments. Conversely, the ability of bacteria to develop effective biofilms represents one of the major obstacles in the fight against bacterial infections. In Pseudomonas aeruginosa, the biofilm formation is intimately connected to the quorum sensing (QS) mechanisms, a mode of cell-to-cell communication that allows many bacteria to detect their population density in order to coordinate common actions. In this chapter, we propose an overview (i) on P. aeruginosa QS mechanisms and their implication in biofilm formation, and (ii) on natural products that are known to interfere with these QS mechanisms, subsequently disrupting biofilm formation. The concluding remarks focus on perspectives of these compounds as possible antibiotherapy adjuvants.",signatures:"Julie Carette, Amandine Nachtergael, Pierre Duez, Mondher El Jaziri and Tsiry Rasamiravaka",downloadPdfUrl:"/chapter/pdf-download/70686",previewPdfUrl:"/chapter/pdf-preview/70686",authors:[{id:"311589",title:"Associate Prof.",name:"Tsiry",surname:"Rasamiravaka",slug:"tsiry-rasamiravaka",fullName:"Tsiry Rasamiravaka"},{id:"311591",title:"Prof.",name:"Mondher",surname:"El Jaziri",slug:"mondher-el-jaziri",fullName:"Mondher El Jaziri"},{id:"311592",title:"Ph.D. Student",name:"Julie",surname:"Carette",slug:"julie-carette",fullName:"Julie Carette"},{id:"311593",title:"Dr.",name:"Amandine",surname:"Nachtergael",slug:"amandine-nachtergael",fullName:"Amandine Nachtergael"},{id:"311594",title:"Prof.",name:"Pierre",surname:"Duez",slug:"pierre-duez",fullName:"Pierre Duez"}],corrections:null},{id:"70594",title:"Inhibition of Bacterial Biofilm Formation",doi:"10.5772/intechopen.90614",slug:"inhibition-of-bacterial-biofilm-formation",totalDownloads:1020,totalCrossrefCites:10,totalDimensionsCites:11,hasAltmetrics:0,abstract:"Biofilm is a complex matrix consisting of extracellular polysaccharides, DNA, and proteins that protect bacteria from a variety of physical, chemical, and biological stresses allowing them to survive in hostile environments. Biofilm formation requires three different stages: cell attachment to a solid substrate, adhesion, and growth. The inhibition of one of these steps by small molecules, such as antimicrobial peptides, or their action on specific targets will leave pathogens armless against classical antibiotics. Any drug impairing crucial processes for bacterial life will inevitably lead to the development of drug-resistant strains, whereas the inhibition of biofilm formation might prevent the onset of bacterial resistance. In this section, we will focus on proteins involved in biofilm formation as useful targets for the development of new drugs that can effectively and specifically impair biofilm formation with slight effects on cell survival, thus avoiding the generation of drug-resistant strains.",signatures:"Angela Di Somma, Antonio Moretta, Carolina Canè, Arianna Cirillo and Angela Duilio",downloadPdfUrl:"/chapter/pdf-download/70594",previewPdfUrl:"/chapter/pdf-preview/70594",authors:[{id:"311520",title:"Ph.D. Student",name:"Angela",surname:"Di Somma",slug:"angela-di-somma",fullName:"Angela Di Somma"},{id:"311653",title:"Dr.",name:"Antonio",surname:"Moretta",slug:"antonio-moretta",fullName:"Antonio Moretta"},{id:"311654",title:"Prof.",name:"Angela",surname:"Duilio",slug:"angela-duilio",fullName:"Angela Duilio"},{id:"311655",title:"Dr.",name:"Carolina",surname:"Canè",slug:"carolina-cane",fullName:"Carolina Canè"},{id:"311656",title:"Dr.",name:"Arianna",surname:"Cirillo",slug:"arianna-cirillo",fullName:"Arianna Cirillo"}],corrections:null},{id:"70055",title:"Approaches for Modeling Anaerobic Granule-Based Reactors",doi:"10.5772/intechopen.90201",slug:"approaches-for-modeling-anaerobic-granule-based-reactors",totalDownloads:649,totalCrossrefCites:0,totalDimensionsCites:0,hasAltmetrics:0,abstract:"Anaerobic granule sludge is a self-forming biofilm. This biofilm can be developed without the presence of bio-carriers. Anaerobic granule sludge-based technologies are dominant technologies in the field of anaerobic wastewater treatment. Although they are successful technologies, many efforts are still needed for a better understanding of the granules and granule-based reactors because reactor failure can occur. Here, reactor modeling is highly helpful in understanding the performance of anaerobic bioreactors. A model that can accurately model bioprocesses in a sludge bed reactor and predict concentrations of effluent components is valuable. This is because the model can provide insights into the reactor and be useful in reactor control. Current models of granules are models on bioprocesses in a single granule sludge or on the hydrodynamics and biokinetics in a sludge bed reactor. Here, we review advances in reactor model and its applications as well as limitations and further improvements in the models.",signatures:"Jixiang Yang",downloadPdfUrl:"/chapter/pdf-download/70055",previewPdfUrl:"/chapter/pdf-preview/70055",authors:[{id:"311779",title:"Dr.",name:"Jixiang",surname:"Yang",slug:"jixiang-yang",fullName:"Jixiang Yang"}],corrections:null},{id:"69086",title:"Innovative Strategies for the Control of Biofilm Formation in Clinical Settings",doi:"10.5772/intechopen.89310",slug:"innovative-strategies-for-the-control-of-biofilm-formation-in-clinical-settings",totalDownloads:880,totalCrossrefCites:1,totalDimensionsCites:4,hasAltmetrics:1,abstract:"Biofilm formation in clinical settings is an increasingly important issue particularly due to the emergence of multidrug-resistant strains, as it resulted in increased mortality, which poses a considerable financial burden on healthcare systems. The bacterial biofilms are quite resistant to the routine antimicrobial-based therapies; therefore, the novel strategies are desired in addition to the conventional antibiotics for the effective control of infections caused by biofilm-forming microbes. So far, the approaches being proposed to control the biofilm formation in clinical practice settings include the use of biofilm inhibitors and the use of modified biomaterials for the development of medical devices to thwart the formation of biofilms. In this chapter, we have focused on the latest developments in the anti-biofilm strategies through the interruption of the quorum-sensing system, which is crucial for biofilm formation and have summarized the various classes of antibacterial compounds for the control of biofilm formation. This agrees with the recent approaches suggested by the National Institute of Health (NIH) that advocates the use of combinational therapies based on the conventional methods and complementary treatment to explore the potential utility and safety concerns of the natural products. The studies regarding these emerging strategies could possibly lead to the establishment of better therapeutic alternates compared to conventional treatments.",signatures:"Aqsa Shahid, Maria Rasool, Naheed Akhter, Bilal Aslam, Ali Hassan, Sadia Sana, Muhammad Hidayat Rasool and Mohsin Khurshid",downloadPdfUrl:"/chapter/pdf-download/69086",previewPdfUrl:"/chapter/pdf-preview/69086",authors:[{id:"201590",title:"Dr.",name:"Bilal",surname:"Aslam",slug:"bilal-aslam",fullName:"Bilal Aslam"},{id:"305891",title:"Dr.",name:"Mohsin",surname:"Khurshid",slug:"mohsin-khurshid",fullName:"Mohsin Khurshid"},{id:"309790",title:"Ms.",name:"Aqsa",surname:"Shahid",slug:"aqsa-shahid",fullName:"Aqsa Shahid"},{id:"309792",title:"Ms.",name:"Maria",surname:"Rasool",slug:"maria-rasool",fullName:"Maria Rasool"},{id:"309793",title:"Dr.",name:"Naheed",surname:"Akhter",slug:"naheed-akhter",fullName:"Naheed Akhter"},{id:"309795",title:"Mr.",name:"Ali",surname:"Hassan",slug:"ali-hassan",fullName:"Ali Hassan"},{id:"309796",title:"Ms.",name:"Sadia",surname:"Sana",slug:"sadia-sana",fullName:"Sadia Sana"},{id:"309797",title:"Prof.",name:"Muhammad Hidayat",surname:"Rasool",slug:"muhammad-hidayat-rasool",fullName:"Muhammad Hidayat Rasool"}],corrections:null},{id:"69533",title:"Formation, Antibiotic Resistance, and Control Strategies of Staphylococcus epidermidis Biofilm",doi:"10.5772/intechopen.89800",slug:"formation-antibiotic-resistance-and-control-strategies-of-em-staphylococcus-epidermidis-em-biofilm",totalDownloads:528,totalCrossrefCites:0,totalDimensionsCites:1,hasAltmetrics:0,abstract:"Staphylococcus epidermidis, member of the group of coagulase-negative staphylococci, belongs to an opportunistic pathogen. It is reported that the major pathogenicity of S. epidermidis is attributed to its biofilm formed on the surface of infected tissues, which enhances bacterial resistance to antibiotics. Thus, how to inhibit biofilm formation and screening biofilm inhibitors will have great value in reducing bacterial drug-resistance, which is beneficial to prevent and treat biofilm-associated infections. In this chapter, we present the current knowledge on formation, antibiotic resistance, and control strategies of S. epidermidis biofilm. First, biofilm formation in S. epidermidis, including factors involved in different phases in the process of biofilm, is analyzed. Second, the mechanisms of antibiotic resistance in S. epidermidis biofilms, such as poor antibiotic penetration, slow growth, and formation of persister cells, are introduced. Finally, control strategies to S. epidermidis biofilm formation are provided.",signatures:"Wei Chen, Ting-Ting Xie and Hong Zeng",downloadPdfUrl:"/chapter/pdf-download/69533",previewPdfUrl:"/chapter/pdf-preview/69533",authors:[{id:"303148",title:"Prof.",name:"Wei",surname:"Chen",slug:"wei-chen",fullName:"Wei Chen"},{id:"306885",title:"Dr.",name:"Ting-ting",surname:"Xie",slug:"ting-ting-xie",fullName:"Ting-ting Xie"},{id:"306886",title:"Dr.",name:"Hong",surname:"Zeng",slug:"hong-zeng",fullName:"Hong Zeng"}],corrections:null},{id:"69590",title:"Combating Biofilm and Quorum Sensing: A New Strategy to Fight Infections",doi:"10.5772/intechopen.89227",slug:"combating-biofilm-and-quorum-sensing-a-new-strategy-to-fight-infections",totalDownloads:1015,totalCrossrefCites:1,totalDimensionsCites:2,hasAltmetrics:0,abstract:"Biofilms are structured aggregates of bacterial cells that are embedded in self-produced extracellular polymeric substances. Various pathogens initiate a disease process by creating organized biofilms that enhance their ability to adhere, replicate to accumulate, and express their virulence potential. Quorum sensing, which refers to the bacterial cell-to-cell communication resulting from production and response to N-acyl homoserine lactone signal molecules, also plays an important role in virulence and biofilm formation. Attenuation of microorganisms’ virulence such that they fail to adapt to the hosts’ environment could be a new strategic fight against pathogens. Thus, agents or products that possess anti-biofilm formation and/or anti-quorum sensing activities could go a long way to manage microbial infections. The incidence of microbial resistance can be reduced by the use of anti-biofilm formation and anti-quorum sensing agents.",signatures:"Cynthia Amaning Danquah, Samuel Osei-Djarbeng, Theresah Appiah, Yaw Duah Boakye and Francis Adu",downloadPdfUrl:"/chapter/pdf-download/69590",previewPdfUrl:"/chapter/pdf-preview/69590",authors:[{id:"186987",title:"Dr.",name:"Yaw Duah",surname:"Boakye",slug:"yaw-duah-boakye",fullName:"Yaw Duah Boakye"},{id:"262750",title:"Dr.",name:"Cynthia",surname:"Amaning Danquah",slug:"cynthia-amaning-danquah",fullName:"Cynthia Amaning Danquah"},{id:"262752",title:"Dr.",name:"Francis",surname:"Adu",slug:"francis-adu",fullName:"Francis Adu"},{id:"297439",title:"Dr.",name:"Theresa",surname:"Appiah",slug:"theresa-appiah",fullName:"Theresa Appiah"},{id:"309609",title:"Dr.",name:"Samuel",surname:"Osei-Djarbeng",slug:"samuel-osei-djarbeng",fullName:"Samuel Osei-Djarbeng"}],corrections:null},{id:"72109",title:"Antibiotic Resistance in Biofilm",doi:"10.5772/intechopen.92388",slug:"antibiotic-resistance-in-biofilm",totalDownloads:1451,totalCrossrefCites:11,totalDimensionsCites:18,hasAltmetrics:0,abstract:"Biofilms can be found on several living and nonliving surfaces, which are formed by a group of microorganisms, complex assembly of proteins, polysaccharides, and DNAs in an extracellular polymeric matrix. By forming a biofilm, bacteria protect themselves from host defense, disinfectants, and antibiotics. Bacteria inside biofilm are much more resistant to antimicrobial agents than planktonic forms since bacteria that are unresisting to antimicrobial agents in any way can turn resistant after forming a biofilm. Low penetration of antibiotics into the biofilm, slow reproduction, and the existence of adaptive stress response constitute the multiphased defense of the bacterium. This antibiotic resistance, which is provided by biofilm, makes the treatments, which use effective antibiotic doses on the bacterium in planktonic shape, difficult. Biofilm formation potential of bacteria appears as an important virulence factor in ensuring the colonization on the living tissues or medical devices and makes the treatment difficult. The aim of this chapter is to overview the current knowledge of antimicrobial resistance mechanisms in biofilms.",signatures:"Sadık Dincer, Fatima Masume Uslu and Anil Delik",downloadPdfUrl:"/chapter/pdf-download/72109",previewPdfUrl:"/chapter/pdf-preview/72109",authors:[{id:"188141",title:"Prof.",name:"Sadik",surname:"Dincer",slug:"sadik-dincer",fullName:"Sadik Dincer"},{id:"315992",title:"MSc.",name:"Fatıma Masume",surname:"Uslu",slug:"fatima-masume-uslu",fullName:"Fatıma Masume Uslu"},{id:"315993",title:"MSc.",name:"Anıl",surname:"Delik",slug:"anil-delik",fullName:"Anıl Delik"}],corrections:null},{id:"71189",title:"Microbial Biofilms",doi:"10.5772/intechopen.90790",slug:"microbial-biofilms",totalDownloads:1087,totalCrossrefCites:4,totalDimensionsCites:5,hasAltmetrics:0,abstract:"Biofilms are the aggregation of microbial cells, which are associated with the surface in almost an irreversible manner. It exists in variety of forms like dental plaque, pond scum, or the slimy build up in sink. Biofilm formation involves sequence of steps like conditioning, attachment, metabolism, and detachment. Biofilm consists of water channels, EPS (Exopolysaccharide), and eDNA (Environmental DNA), which plays an important role in nutrient circulation, its development, and structure stabilization. Resistance of planktonic bacteria against antimicrobial agents gets increased on the formation of biofilm, which may be the presence of diffusive barrier EPS or neutralizing enzyme, cells undergoing starvation, or due to spore formation. There are numerous factors, which affects biofilm formation such as substratum effects, conditioning film on substratum, hydrodynamics, characteristics of the aqueous medium, cell characteristics, and environmental factors. Biofilm can cause industrial, medical, and household damage and is a reason for loss of billions of dollars every year. Development of biofilm on catheters, medical implants, and devices is a major cause of infections and diseases in humans. Examples include Plaque, Native Valve Endocarditis, Otitis media, Prostatitis, Cystic fibrosis, Periodontitis, Osteomyelitis, and many more.",signatures:"Princy Choudhary, Sangeeta Singh and Vishnu Agarwal",downloadPdfUrl:"/chapter/pdf-download/71189",previewPdfUrl:"/chapter/pdf-preview/71189",authors:[{id:"220858",title:"Ms.",name:"Princy",surname:"Choudhary",slug:"princy-choudhary",fullName:"Princy Choudhary"},{id:"251063",title:"Dr.",name:"Sangeeta",surname:"Singh",slug:"sangeeta-singh",fullName:"Sangeeta Singh"},{id:"318847",title:"Dr.",name:"Vishnu",surname:"Agarwal",slug:"vishnu-agarwal",fullName:"Vishnu Agarwal"}],corrections:[{id:"73763",title:"Corrigendum to: Microbial Biofilms",doi:null,slug:"corrigendum-to-microbial-biofilms",totalDownloads:null,totalCrossrefCites:null,correctionPdfUrl:null}]},{id:"71796",title:"Essential Oils as an Innovative Approach against Biofilm of Multidrug-Resistant Staphylococcus aureus",doi:"10.5772/intechopen.91833",slug:"essential-oils-as-an-innovative-approach-against-biofilm-of-multidrug-resistant-em-staphylococcus-au",totalDownloads:663,totalCrossrefCites:1,totalDimensionsCites:3,hasAltmetrics:0,abstract:"Staphylococcus aureus is one of the most common pathogens that cause recurrent, chronic, and biofilm-related diseases. Biofilms are the major form of bacterial structures capable of secreting polysaccharides that provide intrinsic protection against environmental stress like high concentrations of antibiotics. This, along with the emergence of multidrug-resistant strains, has made S. aureus infections a worldwide problem as a result of the inefficiency of the conventional medications. Plant essential oils (EOs) are an important source for drug discovery and pharmaceutical development due to their diverse biological activities, such as antimicrobial agents. The EOs’ microbicide action is extensively reported at the scientific literature and frequently associated with bioactive molecules, such as aldehydes and terpenes. However, the ability of some EOs to inhibit biofilm formation has been poorly explored and it is still unclear how they could be applied in specific treatments against well-known infections. Therefore, this chapter will address virulence factors and biofilm formation of S. aureus, as well as bioprospecting of essential oil as a promising source in the search for new bioactive compounds employed in the fight against this microorganism.",signatures:"Victor Alves Carneiro, Ramaiana Soares Melo, Antônio Mateus Gomes Pereira, Águida Maria Albuquerque Azevedo, Maria Nágila Carneiro Matos, Rafaela Mesquita Bastos Cavalcante, Renan Rhonalty Rocha, Vinícius de Queiroz Albuquerque, Jesús Alberto Pérez Guerrero and Francisco Eduardo Aragão Catunda Junior",downloadPdfUrl:"/chapter/pdf-download/71796",previewPdfUrl:"/chapter/pdf-preview/71796",authors:[{id:"311814",title:"Dr.",name:"Victor Alves",surname:"Carneiro",slug:"victor-alves-carneiro",fullName:"Victor Alves Carneiro"},{id:"311901",title:"MSc.",name:"Ramaiana Soares",surname:"Melo",slug:"ramaiana-soares-melo",fullName:"Ramaiana Soares Melo"},{id:"311902",title:"Dr.",name:"Antonio Mateus",surname:"Gomes Pereira",slug:"antonio-mateus-gomes-pereira",fullName:"Antonio Mateus Gomes Pereira"},{id:"318147",title:"MSc.",name:"Águida Maria",surname:"Albuquerque Azevedo",slug:"aguida-maria-albuquerque-azevedo",fullName:"Águida Maria Albuquerque Azevedo"},{id:"318148",title:"MSc.",name:"Rafaela",surname:"Mesquita Bastos Cavalcante",slug:"rafaela-mesquita-bastos-cavalcante",fullName:"Rafaela Mesquita Bastos Cavalcante"},{id:"318149",title:"BSc.",name:"Renan",surname:"Rhonalty Rocha",slug:"renan-rhonalty-rocha",fullName:"Renan Rhonalty Rocha"},{id:"318150",title:"BSc.",name:"Vinícius",surname:"Queiroz Albuquerque",slug:"vinicius-queiroz-albuquerque",fullName:"Vinícius Queiroz Albuquerque"},{id:"318151",title:"MSc.",name:"Jesús",surname:"Alberto Pérez Guerrero",slug:"jesus-alberto-perez-guerrero",fullName:"Jesús Alberto Pérez Guerrero"},{id:"318230",title:"Dr.",name:"Maria Nágila",surname:"Carneiro Matos",slug:"maria-nagila-carneiro-matos",fullName:"Maria Nágila Carneiro Matos"}],corrections:null},{id:"68020",title:"Methods for Searching of Potential Beneficial Bacteria and Their Products in Dental Biofilm",doi:"10.5772/intechopen.88024",slug:"methods-for-searching-of-potential-beneficial-bacteria-and-their-products-in-dental-biofilm",totalDownloads:832,totalCrossrefCites:0,totalDimensionsCites:1,hasAltmetrics:0,abstract:"Dental microbiota is associated with different types of organisms with dentition including humans and is responsible for many oral diseases all over the world. Bacteria in a dental biofilm are important also in other diseases, i.e., endocarditis, pulmonary fibrosis, and arthritis, and some findings predict the connection of dental microbiota with cancerogenesis. Not all oral bacterial representatives are pathogenic or potentially pathogenic. Dental biofilm consists of numerous different bacteria that may have beneficial characteristics for good condition of dental and oral health. Searching for bacteria or their products with the beneficial effect is important in the development of new biologically based strategies for the prevention or treatment of oral and dental diseases. For searching of potential probiotic candidates are useful methods that could map phenotypic or genotypic characteristics of studied bacteria. This chapter is focused on the spectrum of these basic methods searching for beneficial bacteria and their products.",signatures:"Marián Maďar, Jana Kačírová, Eva Styková, Michaela Maďarová and Radomíra Nemcová",downloadPdfUrl:"/chapter/pdf-download/68020",previewPdfUrl:"/chapter/pdf-preview/68020",authors:[{id:"210430",title:"Dr.",name:"Marián",surname:"Maďar",slug:"marian-madar",fullName:"Marián Maďar"},{id:"300537",title:"Dr.",name:"Jana",surname:"Kačírová",slug:"jana-kacirova",fullName:"Jana Kačírová"},{id:"300539",title:"Dr.",name:"Eva",surname:"Styková",slug:"eva-stykova",fullName:"Eva Styková"},{id:"300540",title:"Dr.",name:"Radomíra",surname:"Nemcová",slug:"radomira-nemcova",fullName:"Radomíra Nemcová"},{id:"307241",title:"Dr.",name:"Michaela",surname:"Maďarová",slug:"michaela-madarova",fullName:"Michaela Maďarová"}],corrections:null},{id:"69968",title:"Composition, Structure, and Formation of Biofilms Constituted by Periodontopathogenic Microorganisms",doi:"10.5772/intechopen.90019",slug:"composition-structure-and-formation-of-biofilms-constituted-by-periodontopathogenic-microorganisms",totalDownloads:544,totalCrossrefCites:1,totalDimensionsCites:1,hasAltmetrics:0,abstract:"Microorganisms that compose the oral microbiota maintain complex interactions with each other, especially pathogens related to periodontal disease. It is possible to characterize the etiology of this multifactorial and polymicrobial disease by the accumulation of biofilms formed in the supra- and subgingival environments associated to the immunological response and the susceptibility of the host, being responsible for a large part of the dental loss especially in the adult phase. Periodontal treatment has been carried out mainly by scaling and root planing. This therapy is limited due to the difficult access in some areas of the teeth, impairing the removal of biofilms. So, this chapter will focus on the composition and formation of the biofilm as well as the host’s immune response to periodontopathogenic microorganisms. Additionally, the therapeutic challenges and the treatments that are currently being studied in order to eliminate this biofilm, such as antimicrobial phototherapy, will be discussed.",signatures:"Juliana Cabrini Carmello, Sarah Raquel de Annunzio and Carla Raquel Fontana",downloadPdfUrl:"/chapter/pdf-download/69968",previewPdfUrl:"/chapter/pdf-preview/69968",authors:[{id:"310060",title:"Ph.D.",name:"Juliana",surname:"Cabrini Carmello",slug:"juliana-cabrini-carmello",fullName:"Juliana Cabrini Carmello"},{id:"310061",title:"MSc.",name:"Sarah Raquel",surname:"De Annunzio",slug:"sarah-raquel-de-annunzio",fullName:"Sarah Raquel De Annunzio"},{id:"310062",title:"Prof.",name:"Carla Raquel",surname:"Fontana",slug:"carla-raquel-fontana",fullName:"Carla Raquel Fontana"}],corrections:null},{id:"70036",title:"Biofilms Formed by Pathogens in Food and Food Processing Environments",doi:"10.5772/intechopen.90176",slug:"biofilms-formed-by-pathogens-in-food-and-food-processing-environments",totalDownloads:1186,totalCrossrefCites:6,totalDimensionsCites:11,hasAltmetrics:0,abstract:"This chapter presents the ability of some pathogenic (Listeria monocytogenes, Escherichia coli, Salmonella enterica, Campylobacter jejuni, Pseudomonas aeruginosa) and toxigenic bacteria (Bacillus cereus, Staphylococcus aureus) to form biofilms and contribute to the persistence of these microorganisms in the food industry. Particularities regarding attachment and composition of biofilms formed in food and food processing environments are presented and genes involved in biofilm production are mentioned. To give a perspective on how to fight against biofilms with new means, nonconventional methods based on bacteriocins, bacteriophages, disruptive enzymes, essential oils, nanoemulsions and nanoparticles, and use of alternative technologies (cold plasma, ultrasounds, light-assisted technologies, pulsed electric field, and high pressure processing) are shortly described.",signatures:"Leontina Grigore-Gurgu, Florentina Ionela Bucur, Daniela Borda, Elena-Alexandra Alexa, Corina Neagu and Anca Ioana Nicolau",downloadPdfUrl:"/chapter/pdf-download/70036",previewPdfUrl:"/chapter/pdf-preview/70036",authors:[{id:"311859",title:"Prof.",name:"Anca",surname:"Nicolau",slug:"anca-nicolau",fullName:"Anca Nicolau"},{id:"311860",title:"Dr.",name:"Elena-Alexandra",surname:"Alexa",slug:"elena-alexandra-alexa",fullName:"Elena-Alexandra Alexa"},{id:"311861",title:"Dr.",name:"Leontina",surname:"Grigore-Gurgu",slug:"leontina-grigore-gurgu",fullName:"Leontina Grigore-Gurgu"},{id:"311862",title:"MSc.",name:"Florentina",surname:"Bucur",slug:"florentina-bucur",fullName:"Florentina Bucur"},{id:"312889",title:"Prof.",name:"Daniela",surname:"Borda",slug:"daniela-borda",fullName:"Daniela Borda"},{id:"312890",title:"Dr.",name:"Corina",surname:"Neagu",slug:"corina-neagu",fullName:"Corina Neagu"}],corrections:null},{id:"68263",title:"Dental Biofilm as Etiological Agent of Canine Periodontal Disease",doi:"10.5772/intechopen.88305",slug:"dental-biofilm-as-etiological-agent-of-canine-periodontal-disease",totalDownloads:935,totalCrossrefCites:3,totalDimensionsCites:5,hasAltmetrics:0,abstract:"Periodontal disease is one of the most common health problem affecting dogs. The disease is more prevalent in small breeds and brachycephalic breeds compared to large breeds, and incidence increases with advancing age. In first stage it affects only the gingival tissue and causes gingivitis. It later develops into periodontitis which involves changes in other periodontium tissues. Main etiological agents of periodontal disease are pathogenic bacteria of dental biofilm, and products of their metabolism. In human, Porphyromonas gingivalis, Treponema denticola and Tannerella forsythia play a key role in the etiology of periodontal disease. Also, there are many other candidates as human periodontal pathogens, including Aggregatibacter actinomycetemcomitans, Prevotella intermedia, Prevotella nigrescens, Fusobacterium nucleatum, Parvimonas micra, Eikenella corrodens, Capnocytophaga gingivalis, Eubacterium nodatum and Campylobacter rectus. Since periodontal diseases in dogs are similar to human diseases in terms of disease progression and clinical manifestation, we can assume their common etiology. This chapter is focused on review about canine dental biofilm and about members of biofilm as potential causative agent of canine periodontal disease.",signatures:"Jana Kačírová, Marián Maďar, Gabriela Štrkolcová, Aladár Maďari and Radomíra Nemcová",downloadPdfUrl:"/chapter/pdf-download/68263",previewPdfUrl:"/chapter/pdf-preview/68263",authors:[{id:"210430",title:"Dr.",name:"Marián",surname:"Maďar",slug:"marian-madar",fullName:"Marián Maďar"},{id:"300537",title:"Dr.",name:"Jana",surname:"Kačírová",slug:"jana-kacirova",fullName:"Jana Kačírová"},{id:"300540",title:"Dr.",name:"Radomíra",surname:"Nemcová",slug:"radomira-nemcova",fullName:"Radomíra Nemcová"},{id:"300579",title:"Dr.",name:"Gabriela",surname:"Štrkolcová",slug:"gabriela-strkolcova",fullName:"Gabriela Štrkolcová"},{id:"300580",title:"Dr.",name:"Aladár",surname:"Maďari",slug:"aladar-madari",fullName:"Aladár Maďari"}],corrections:null},{id:"69138",title:"Biofilm, a Cozy Structure for Legionella pneumophila Growth and Persistence in the Environment",doi:"10.5772/intechopen.89156",slug:"biofilm-a-cozy-structure-for-em-legionella-pneumophila-em-growth-and-persistence-in-the-environment",totalDownloads:527,totalCrossrefCites:0,totalDimensionsCites:1,hasAltmetrics:0,abstract:"Legionella pneumophila (L. pneumophila) is the causative agent of Legionnaires’ disease. Transmission to humans is mediated via inhalation of contaminated water droplets. L. pneumophila is widely distributed in man-made water systems, multiple species of protozoa, and nematodes. L. pneumophila persist within multi-species biofilms that cover surfaces within water systems. Virulence, spread, and resistance to biocides are associated with survival of L. pneumophila within multi-organismal biofilm. Outbreaks of Legionellosis are correlated with the existence of L. pneumophila in biofilms, even after the intensive chemical and physical treatments. Several factors negatively or positively modulate the persistence of L. pneumophila within the microbial consortium-containing L. pneumophila. Biofilm-forming L. pneumophila continue to be a public health and economic burden and directly influence the medical and industrial sectors. Diagnosis and hospitalization of patients and prevention protocols cost governments billions of dollars. Dissecting the biological and environmental factors that promote the persistence and physiological adaptation in biofilms can be fundamental to eliminating and preventing the transmission of L. pneumophila. Herein, we review different factors that promote persistence of L. pneumophila within the biofilm consortium, survival strategies used by the bacteria within biofilm community, gene regulation, and finally challenges associated with biofilm resistance to biocides and anti-Legionella treatments.",signatures:"Arwa Abu Khweek and Amal O. Amer",downloadPdfUrl:"/chapter/pdf-download/69138",previewPdfUrl:"/chapter/pdf-preview/69138",authors:[{id:"117138",title:"Dr.",name:"Amal",surname:"Amer",slug:"amal-amer",fullName:"Amal Amer"},{id:"302360",title:"Associate Prof.",name:"Arwa",surname:"Abu Khweek",slug:"arwa-abu-khweek",fullName:"Arwa Abu Khweek"}],corrections:null},{id:"69502",title:"Oral Microbiota from the Stomatology Perspective",doi:"10.5772/intechopen.89362",slug:"oral-microbiota-from-the-stomatology-perspective",totalDownloads:846,totalCrossrefCites:3,totalDimensionsCites:4,hasAltmetrics:1,abstract:"Besides the properties typical of body cavities, the oral cavity exhibits many differentiating features that allow it to occupy position of an autonomous functional and biological unit, a characteristic ecosystem. An appropriate homeostasis of oral biocenosis and balanced conditions for microorganisms concerning proportions of physiological and pathogenic or potentially pathogenic microbiota play an important role with regard to the oral cavity health and eventually the overall health of an individual. The oral cavity is a constantly changing habitat. The current market offers a number of relevant preparations supporting oral health, and alternative approaches serving these purposes are also available. Results of the studies that focused on microbiocenosis of the dental plaque and interactions between individual bacterial species indicate a probiotic potential of some oral bacteria and their prospective use in prevention of oral cavity diseases. This chapter deals with the state of physiological microbiota found in oral biofilms, with the most important infections of the oral cavity and the potential use of probiotics as a prospective alternative approach to prevention and therapy of oral cavity diseases.",signatures:"Andrea Stašková, Radomíra Nemcová, Stanislav Lauko and Andrej Jenča",downloadPdfUrl:"/chapter/pdf-download/69502",previewPdfUrl:"/chapter/pdf-preview/69502",authors:[{id:"300540",title:"Dr.",name:"Radomíra",surname:"Nemcová",slug:"radomira-nemcova",fullName:"Radomíra Nemcová"},{id:"309897",title:"Dr.",name:"Andrea",surname:"Stašková",slug:"andrea-staskova",fullName:"Andrea Stašková"},{id:"309898",title:"Prof.",name:"Andrej",surname:"Jenča",slug:"andrej-jenca",fullName:"Andrej Jenča"},{id:"310310",title:"Dr.",name:"Stanislav",surname:"Lauko",slug:"stanislav-lauko",fullName:"Stanislav Lauko"}],corrections:null},{id:"72911",title:"The Importance of Biofilms to the Fate and Effects of Microplastics",doi:"10.5772/intechopen.92816",slug:"the-importance-of-biofilms-to-the-fate-and-effects-of-microplastics",totalDownloads:1081,totalCrossrefCites:2,totalDimensionsCites:2,hasAltmetrics:0,abstract:"Microplastics are global pollutants in water media ranging from drinking water to freshwater streams to oceanic pollutant gyres. Besides the obvious appearance involving a scattered presence in the environmental landscape, microplastics are ubiquitous across modern society in products, food, and beginning to have strong economic effects too. Ingestion of microplastics is virtually unavoidable for each of us as we consume food, breathe air, or drink liquids. For example, beer has been found to be contaminated with plastic materials having the dimensions of micro- and nanoparticles. In the environment, the formation of biofilms on microplastics is widely observed and this can significantly alter properties important to environmental and human health. Significant research has been conducted on the role of biofilms in the fate and effect of microplastics on environmental and human health, with a general message to avoid contact with microplastics in the environment until more complete strategies for cleanup are developed.",signatures:"John A. Glaser",downloadPdfUrl:"/chapter/pdf-download/72911",previewPdfUrl:"/chapter/pdf-preview/72911",authors:[{id:"254294",title:"Dr.",name:"John A.",surname:"Glaser",slug:"john-a.-glaser",fullName:"John A. Glaser"}],corrections:null},{id:"70836",title:"Extending an Eco-Evolutionary Understanding of Biofilm-Formation at the Air-Liquid Interface to Community Biofilms",doi:"10.5772/intechopen.90955",slug:"extending-an-eco-evolutionary-understanding-of-biofilm-formation-at-the-air-liquid-interface-to-comm",totalDownloads:700,totalCrossrefCites:0,totalDimensionsCites:0,hasAltmetrics:1,abstract:"Growing bacterial populations diversify to produce a number of competing lineages. In the Pseudomonas fluorescens SBW25 model system, Wrinkly Spreader mutant lineages, capable of colonising the air-liquid interface of static microcosms by biofilm-formation, rapidly appear in diversifying populations with a fitness advantage over the ancestral wild-type strain. Similarly, a biofilm is rapidly produced by a community containing many biofilm-competent members, and selection by serial transfer of biofilm samples across microcosms results in a gradually changing community structure. Both the adaptive radiation producing Wrinkly Spreaders and the succession of biofilm communities in these static microcosms can be understood through evolutionary ecology in which ecological interactions and evolutionary processes are combined. Such eco-evolutionary dynamics are especially important for bacteria, as rapid growth, high population densities and strong selection in the context of infections can lead to fast changes in disease progression and resistance phenotypes, while similar changes in community function may also affect many microbially mediated biotechnological and industrial processes. Evolutionary ecology provides an understanding of why bacterial biofilms are so prevalent and why they are such a successful colonisation strategy, and it can be directly linked to molecular analyses to understand the importance of pathways and responses involved in biofilm-formation.",signatures:"Robyn Jerdan, Olga Iungin, Olena V. Moshynets, Geert Potters and Andrew J. Spiers",downloadPdfUrl:"/chapter/pdf-download/70836",previewPdfUrl:"/chapter/pdf-preview/70836",authors:[{id:"139367",title:"Dr.",name:"Andrew",surname:"Spiers",slug:"andrew-spiers",fullName:"Andrew Spiers"},{id:"315675",title:"Ms.",name:"Robyn",surname:"Jerdan",slug:"robyn-jerdan",fullName:"Robyn Jerdan"},{id:"315676",title:"Dr.",name:"Olga",surname:"Iungin",slug:"olga-iungin",fullName:"Olga Iungin"},{id:"315677",title:"Dr.",name:"Olena",surname:"Moshynets",slug:"olena-moshynets",fullName:"Olena Moshynets"},{id:"315678",title:"Dr.",name:"Geert",surname:"Potters",slug:"geert-potters",fullName:"Geert Potters"}],corrections:null}],productType:{id:"1",title:"Edited Volume",chapterContentType:"chapter",authoredCaption:"Edited by"},subseries:null,tags:null},relatedBooks:[{type:"book",id:"8415",title:"Extremophilic Microbes and Metabolites",subtitle:"Diversity, Bioprospecting and Biotechnological Applications",isOpenForSubmission:!1,hash:"93e0321bc93b89ff73730157738f8f97",slug:"extremophilic-microbes-and-metabolites-diversity-bioprospecting-and-biotechnological-applications",bookSignature:"Afef Najjari, Ameur Cherif, Haïtham Sghaier and Hadda Imene Ouzari",coverURL:"https://cdn.intechopen.com/books/images_new/8415.jpg",editedByType:"Edited by",editors:[{id:"196823",title:"Dr.",name:"Afef",surname:"Najjari",slug:"afef-najjari",fullName:"Afef Najjari"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"8422",title:"Metagenomics",subtitle:"Basics, Methods and Applications",isOpenForSubmission:!1,hash:"82c6409553747ffccd6075f9420e3175",slug:"metagenomics-basics-methods-and-applications",bookSignature:"Wael N. 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Clinically, it is characterized by a progressive loss of articular cartilage and remodeling of the underlying bone remodeling due to a combination of an active response of chondrocytes and the inflammatory cells in the surrounding tissues. Recent research place greater emphasis to promotes our understanding of the OA pathophysiology. A variety of risk factors have been identified. These include gender, traumatic injury, obesity, metabolic dysfunction, nutrition, mechanical factors, and environmental and genetic factors. Extensive research over the past 20 years provided a better understanding, diagnosis, and management of OA. The first part of this book deals with the current progress of OA imaging diagnosis and treatments. The second part discusses how exercise could benefit those with OA. The third part explores potential shreds of evidence of medicinal plants in the strategies for disease prevention.
\r\n\t
Yam (
Unfortunately, because of their high water content [5], fresh yam tubers are highly perishable with postharvest losses of up to 85% [6]. An alternative to the perishability of yam tubers is the transformation into chips, stabilized product by peeling, precooking, and drying in the sun [7]. The manufacture of yam chips, known in Benin, Nigeria, and Togo, makes it possible to preserve the surplus of the tubers for use during the lean season [8]. However, the yam chips are severely attacked by insects that cause significant losses by reducing them to an inconspicuous powder within a few months [7]. Various insect pests such as
The use of synthetic chemical insecticide is the main method of control used by farmers to protect stored yam chips against
Several alternative methods of
Beetles of the
\n
\n
In West Africa, the beetle
Yam chips infested by
Physical control of stored product insects involves the manipulation of physical factors (temperature, relative humidity, atmospheric composition, etc.) to eliminate them or reduce their population to a tolerable level [23]. The control of stored product insect such as
Botanical insecticides are one of promising alternative to the use of chemical synthetic insecticides in pest control because of their minimal costs and ecological side effects [27]. For the protection of stored yam chips against insect pests, Vernier et al. [7] evaluated the level of protection provided by various biological products derived from neem (
The use of resistant varieties is the cheapest, effective, and ecologically safe method of protecting stored products against insect pests [32]. In fact, among the solutions proposed by farmers to fight
Pictures of chips from the five resistant landraces to
Biological control is an important integrated pest management component (IPM), which broadly includes all control types involving living organisms, and represents a good alternative to the use of pesticides. Concerning
Predation by
Synthetic insecticides are the main control method used by farmers and traders, to protect stored yam chips against insect attacks [7, 10]. In Benin, some recommended synthetic insecticides for storage insect pest control such as Sofagrain (1.5% deltamethrin +0.5% pirimiphos methyl) and Antouka (permethrin 3 g/kg + pirimiphos 16 g/kg) showed a good level of stored yam chip protection against
Integrated pest management (IPM) relies on managing insect populations through the combined use of several control methods in a way, which affords the highest priority to the protection of human health as well as the environment. The promising approaches toward effective IPM for
Use resistant landraces (Gaboubaba, Boniwouré, Alahina, Yakanougo, and Wonmangou) for yam chip manufacturing. These five yam landraces have a good agronomic (productivity, number of tubers), culinary (quality of pounded and boiled yam), and technological (quality of yam chips, ease of pounding) characteristics, found in Beninese traditional agriculture [16, 33].
Peel and cut fresh yam tuber in slice ranging from 2 to 3 cm of thickness for fast drying [16].
Precook at around 40°C, and soak during minimum 12 h fresh yam tubers before drying for having the best quality yam flour [42], but also protect chips against insect attacks [43]. Indeed, Nwana and Azodeh [44] showed that the intensity of damage by
Add plants such as leaves and sorghum straw during the parboiling process for red coloration and its insect repellent properties [10]. Indeed, it is known that color largely affects the acceptability of Amala (thick paste obtained after mixed boil water and yam chips flour) by consumers [45].
Dry yam chips on clean surface to avoid insect infestation that could inadvertently be carried to storage; yam chip moisture content must be less than 13% to avoid fungi and insect attacks [46].
Put dried yam chips in polythene-lined jute for lower insect damage and yam chip discoloration [28].
Add leaf powders of
Tightly seal bags and put them in clean and dried place.
Inspection of stored bags should be done monthly.
If stored yam chips are attacked by
In order to develop a good strategy of integrated pest management for
Hermetic storage bags have proven to be a low-cost solution for preventing storage losses due to insects [47]. Storage systems based on the hermetic principle can be used to maintain stored product quality without the need for pesticide application [48]. Apart from testing the effectiveness of the Purdue Improved Crop Storage (PICS bags™) in
Essential oils can have various effects on stored insect pests (repellence, contact toxicity, antifeedant, growth inhibitory, fumigant, etc.) and can be applied as a part of integrated pest management programs for stored products protection. Indeed, plant essential oils can be used in combination with other control techniques for controlling storage insect pest [50]. Essential oil of plants found in the West African flora such as
Entomopathogens have an important place in the biological control because they have a wide host range and are harmless to the environment and human. These include entomopathogenic fungi, nematodes, bacteria, and viruses. These are all widespread in the natural environment and cause infections in many pest species. Entomopathogens contribute to the natural regulation of many populations of arthropods. Much of the research in this area concerns the causal agents of insect diseases and their exploitation for biological pest control. Many entomopathogens can be mass produced, formulated, and applied to pest populations in an analogous manner to chemical pesticides. Also, they can be used more against stored product pests with the development of new biotechnical methods. Indeed, the effectiveness of the formulation of the entomopathogenic fungi,
The parasitoid wasp,
Insect growth regulators are insecticides that mimic hormones in young insects and can be a potential component in integrated pest management against
Methods for detecting and for monitoring
The pest status of
The authors declare that they have no competing interests.
In recent years, researchers in the software engineering (SE) field have turned their interest to data mining (DM) and machine learning (ML)-based studies since collected SE data can be helpful in obtaining new and significant information. Software engineering presents many subjects for research, and data mining can give further insight to support decision-making related to these subjects.
Figure 1 shows the intersection of three main areas: data mining, software engineering, and statistics/math. A large amount of data is collected from organizations during software development and maintenance activities, such as requirement specifications, design diagrams, source codes, bug reports, program versions, and so on. Data mining enables the discovery of useful knowledge and hidden patterns from SE data. Math provides the elementary functions, and statistics determines probability, relationships, and correlation within collected data. Data science, in the center of the diagram, covers different disciplines such as DM, SE, and statistics.
The intersection of data mining and software engineering with other areas of the field.
This study presents a comprehensive literature review of existing research and offers an overview of how to approach SE problems using different mining techniques. Up to now, review studies either introduce SE data descriptions [1], explain tools and techniques mostly used by researchers for SE data analysis [2], discuss the role of software engineers [3], or focus only on a specific problem in SE such as defect prediction [4], design pattern [5], or effort estimation [6]. Some existing review articles having the same target [7] are former, and some of them are not comprehensive. In contrast to the previous studies, this article provides a systematic review of several SE tasks, gives a comprehensive list of available studies in the field, clearly states the advantages of mining SE data, and answers “how” and “why” questions in the research area.
The novelties and main contributions of this review paper are fivefold.
First, it provides a general overview of several SE tasks that have been the focus of studies using DM and ML, namely, defect prediction, effort estimation, vulnerability analysis, refactoring, and design pattern mining.
Second, it comprehensively discusses existing data mining solutions in software engineering according to various aspects, including methods (clustering, classification, association rule mining, etc.), algorithms (k-nearest neighbor (KNN), neural network (NN), etc.), and performance metrics (accuracy, mean absolute error, etc.).
Third, it points to several significant research questions that are unanswered in the recent literature as a whole or the answers to which have changed with the technological developments in the field.
Fourth, some statistics related to the studies between the years of 2010 and 2019 are given from different perspectives: according to their subjects and according to their methods.
Five, it focuses on different machine learning types: supervised and unsupervised learning, especially on ensemble learning and deep learning.
This paper addresses the following research questions:
RQ1. What kinds of SE problems can ML and DM techniques help to solve?
RQ2. What are the advantages of using DM techniques in SE?
RQ3. Which DM methods and algorithms are commonly used to handle SE tasks?
RQ4. Which performance metrics are generally used to evaluate DM models constructed in SE studies?
RQ5. Which types of machine learning techniques (e.g., ensemble learning, deep learning) are generally preferred for SE problems?
RQ6. Which SE datasets are popular in DM studies?
The remainder of this paper is organized as follows. Section 2 explains the knowledge discovery process that aims to extract interesting, potentially useful, and nontrivial information from software engineering data. Section 3 provides an overview of current work on data mining for software engineering grouped under five tasks: defect prediction, effort estimation, vulnerability analysis, refactoring, and design pattern mining. In addition, some machine learning studies are divided into subgroups, including ensemble learning- and deep learning-based studies. Section 4 gives statistical information about the number of highly validated research conducted in the last decade. Related works considered as fundamental by journals with a highly positive reputation are listed, and the specific methods they used and their categories and purposes are clearly expressed. In addition, widely used datasets related to SE are given. Finally, Section 5 offers concluding remarks and suggests future scientific and practical efforts that might improve the efficiency of SE actions.
This section basically explains the consecutive critical steps that should be followed to discover beneficial knowledge from software engineering data. It outlines the order of necessary operations in this process and explains how related data flows among them.
Software development life cycle (SDLC) describes a process to improve the quality of a product in project management. The main phases of SDCL are planning, requirement analysis, designing, coding, testing, and maintenance of a project. In every phase of software development, some software problems (e.g., software bugs, security, or design problems) may occur. Correcting these problems in the early phases leads to more accurate and timely delivery of the project. Therefore, software engineers broadly apply data mining techniques for different SE tasks to solve SE problems and to enhance programming efficiency and quality.
Figure 2 presents the data mining and knowledge discovery process of SE tasks including data collection, data preprocessing, data mining, and evaluation. In the data collection phase, data are obtained from software projects such as bug reports, historical data, version control data, and mailing lists that include various information about the project’s versions, status, or improvement. In the data preprocessing phase, the data are preprocessed after collection by using different methods such as feature selection (dimensionality reduction), feature extraction, missing data elimination, class imbalance analysis, normalization, discretization, and so on. In the next phase, DM techniques such as classification, clustering, and association rule mining are applied to discover useful patterns and relationships in software engineering data and therefore to solve a software engineering problem such as defected or vulnerable systems, reused patterns, or parts of code changes. Mining and obtaining valuable knowledge from such data prevents errors and allows software engineers to deliver the project on time. Finally, in the evaluation phase, validation techniques are used to assess the data mining results such as k-fold cross validation for classification. The commonly used evaluation measures are accuracy, precision, recall, F-score, area under the curve (AUC) for classification, and sum of squared errors (SSE) for clustering.
KDD process for software engineering.
In this review, we examine data mining studies in various SE tasks and evaluate commonly used algorithms and datasets.
A defect means an error, failure, flaw, or bug that causes incorrect or unexpected results in a system [8]. A software system is expected to be without any defects since software quality represents a capacity of the defect-free percentage of the product [9]. However, software projects often do not have enough time or people working on them to extract errors before a product is released. In such a situation, defect prediction methods can help to detect and remove defects in the initial stages of the SDLC and to improve the quality of the software product. In other words, the goal of defect prediction is to produce robust and effective software systems. Hence, software defect prediction (SDP) is an important topic for software engineering because early prediction of software defects could help to reduce development costs and produce more stable software systems.
Various studies have been conducted on defect prediction using different metrics such as code complexity, history-based metrics, object-oriented metrics, and process metrics to construct prediction models [10, 11]. These models can be considered on a cross-project or within-project basis. In within-project defect prediction (WPDP), a model is constructed and applied on the same project [12]. For within-project strategy, a large amount of historical defect data is needed. Hence, in new projects that do not have enough data to train, cross-project strategy may be preferred [13]. Cross-project defect prediction (CPDP) is a method that involves applying a prediction model from one project to another, meaning that models are prepared by utilizing historical data from other projects [14, 15]. Studies in the field of CPDP have increased in recent years [10, 16]. However, there are some deficiencies in comparisons of prior studies since they cannot be replicated because of the difference in utilizing evaluation metrics or preparation way of training data. Therefore, Herbold et al. [16] tried to replicate different CPDP methods previously proposed and find which approach performed best in terms of metrics such as F-score, area under the curve (AUC), and Matthews correlation coefficient (MCC). Results showed that 7- or 8-year approaches may perform better. Another study [17] replicated prior work to demonstrate whether the determination of classification techniques is important. Both noisy and cleaned datasets were used, and the same results were obtained from the two datasets. However, new dataset gave better results for some classification algorithms. For this reason, authors claimed that the selection of classification techniques affects the performance of the model.
Numerous defect prediction studies have been conducted using DM techniques. In the following subsections, we will explain these studies in terms of whether they apply ensemble learning or not. Some defect prediction studies in SE are compared in Table 1. The objective of the studies, the year they were conducted, algorithms, ensemble learning techniques and datasets in the studies, and the type of data mining tasks are shown in this table. The bold entries in Table 1 have better performance than other algorithms in that study.
Ref. | Year | Task | Objective | Algorithms | Ensemble learning | Dataset | Evaluation metrics and results |
---|---|---|---|---|---|---|---|
[18] | 2011 | Classification | Comparative study of various ensemble methods to find the most effective one | NB | Bagging, boosting, RT, | NASA datasets: CM1 JM1 KC1 KC2 KC3 KC4 MC1 MC2 MW1 PC1 PC2 PC3 PC4 PC5 | 10-fold CV, ACC, and AUC |
[19] | 2013 | Classification | Comparative study of class imbalance learning methods and proposed dynamic version of AdaBoost.NC | NB, RUS, RUS-bal, THM, SMB, BNC | RF, SMB, BNC, | NASA and PROMISE repository: MC2, KC2, JM1, KC1, PC4, PC3, CM1, KC3, MW1, PC1 | 10-fold CV Balance, G-mean and AUC, PD, PF |
[20] | 2014 | Classification | Comparative study to deal with imbalanced data | Base Classifiers: C4.5, NB Sampling: ROS, RUS, SMOTE | AdaBoost, Bagging, boosting, RF | NASA datasets: CM1, JM1, KC1, KC2, KC3, MC1, MC2, MW1, PC1, PC2, PC3, PC4, PC5 | 5 × 5 CV, MCC, ROC, results change according to characteristics of datasets |
[17] | 2015 | Clustering/classification | To show that the selection of classification technique has an impact on the performance of software defect prediction models | Statistical: NB, Clustering: KM, EM Rule based: Ripper, Ridor NNs: RBF Nearest neighbor: KNN DTs: J48, | Bagging, AdaBoost, rotation forest, random subspace | NASA: CM1, JM1, KC1, KC3, KC4, MW1, PC1, PC2, PC3, PC4 PROMISE: Ant 1.7, Camel 1.6, Ivy 1.4, Jedit 4, Log4j 1, Lucene 2.4, Poi 3, Tomcat 6, Xalan 2.6, Xerces 1.3 | 10 × 10-fold CV AUC > 0.5 Scott-Knott test α = 0.05, simple logistic, LMT, and RF + base learner outperforms KNN and RBF |
[21] | 2015 | Classification | Average probability ensemble (APE) learning module is proposed by combining feature selection and ensemble learning | RF, GB | NASA: CM1, JM1, KC1, KC3, KC4, MW1, PC1, PC2, PC3, PC4 PROMISE (RQ2): Ant 1.7, Camel 1.6, Ivy 1.4, Jedit 4, Log4j 1, Lucene 2.4, Poi 3, Tomcat 6, Xalan 2.6, Xerces 1.3 | 10 × 10-fold CV, AUC > 0.5 Scott-Knott test α = 0.05, simple logistic, LMT, and RF + base learner outperforms KNN and RBF | |
[22, 23] | 2016 | Classification | Comparative study of 18 ML techniques using OO metrics on six releases of Android operating system | LR, SVM, VP, CART, J48, ADT, Nnge, DTNB | Bagging, random forest, Logistic model trees, | 6 releases of Android app: Android 2.3.2, Android 2.3.7, Android 4.0.4, Android 4.1.2, Android 4.2.2, Android 4.3.1 | 10-fold, inter-release validation AUC for NB, LB, MLP is >0.7 |
[24] | 2016 | Classification | Caret has been applied whether parameter settings can have a large impact on the performance of defect prediction models | NB, KNN, LR, partial least squares, NN, LDA, rule based, DT, SVM | Bagging, boosting | Cleaned NASA JM1, PC5 Proprietary from Prop-1 to Prop-5 Apache Camel 1.2, Xalan 2.5–2.6 Eclipse Platform 2.0–2.1–3.0, Debug 3.4, SWT 3.4, JDT, Mylyn, PDE | Out-of-sample bootstrap validation technique, AUC Caret AUC performance up to 40 percentage points |
[25] | 2017 | Regression | Aim is to validate the source code metrics and identify a suitable set of source code metrics | 5 training algorithms: GD, GDM, GDX, NM, LM | Heterogeneous linear and nonlinear ensemble methods | 56 open-source Java projects from PROMISE Repository | 10-fold CV, t-test, ULR analysis Neural network with Levenberg Marquardt (LM) is the best |
[16] | 2017 | Classification | Replicate 24 CDPD approaches, and compare on 5 different datasets | DT, LR, NB, SVM | LE, RF, BAG-DT, BAG-NB, BOOST-DT, BOOST-NB | 5 available datasets: JURECZKO, NASA MDP, AEEEM, NETGENE, RELINK | Recall, PR, ACC, G-measure, F-score, MCC, AUC |
[26] | 2017 | Classification | Just-in-time defect prediction (TLEL) | NB, SVM, DT, LDA, NN | Bagging, stacking | Bugzilla, Columba, JDT, Platform, Mozilla, and PostgreSQL | 10-fold CV, F-score |
[13] | 2017 | Classification | Adaptive Selection of Classifiers in bug prediction (ASCI) method is proposed. | Base classifiers: LOG (binary logistic regression), NB, RBF, MLP, DT | Voting | Ginger Bread (2.3.2 and 2.3.7), Ice Cream Sandwich (4.0.2 and 4.0.4), and JellyBean (4.1.2, 4.2.2 and 4.3.1) | 10-fold, inter-release validation AUC for NB, LB, MLP is >0.7 |
[27] | 2018 | Classification | MULTI method for JIT-SDP (just in time software defect prediction) | EALR, SL, RBFNet Unsupervised: LT, AGE | Bagging, AdaBoost, Rotation Forest, RS | Bugzilla, Columba, Eclipse JDT, Eclipse Platform, Mozilla, PostgreSQ | CV, timewise-CV, ACC, and POPT MULTI performs significantly better than all the baselines |
[28] | 2007 | Classification | To found pre- and post-release defects for every package and file | LR | — | Eclipse 2.0, 2.1, 3.0 | PR, recall, ACC |
[8] | 2014 | Clustering | Cluster ensemble with PSO for clustering the software modules (fault-prone or not fault-prone) | PSO clustering algorithm | KM-E, KM-M, PSO-E, PSO-M and EM | Nasa MDP, PROMISE | |
[29] | 2015 | Classification | Defect identification by applying DM algorithms | NB, J48, MLP | — | PROMISE, NASA MDP dataset: CM1, JM1, KC1, KC3, MC1, MC2, MW1, PC1, PC2, PC3 | 10-fold CV, ACC, PR, FMLP is the best |
[30] | 2015 | Classification | To show the attributes that predict the defective state of software modules | NB, NN, association rules, DT | Weighted voting rule of the four algorithms | NASA datasets: CM1, JM1, KC1, KC2, PC1 | PR, recall, ACC, F-score NB > NN > DT |
[31] | 2016 | Classification | Authors proposed a model that finds fault-proneness | NB, LR, LivSVM, MLP, SGD, SMO, VP, LR Logit Boost, Decision Stamp, RT, REP Tree | RF | Camel1.6, Tomcat 6.0, Ant 1.7, jEdit4.3, Ivy 2.0, arc, e-learning, berek, forrest 0.8, zuzel, Intercafe, and Nieruchomosci | 10-fold CV, AUC AUC = 0.661 |
[32] | 2016 | Classification | GA to select suitable source code metrics | LR, ELM, SVML, SVMR, SVMP | — | 30 open-source software projects from PROMISE repository from DS1 to DS30 | 5-fold CV, F-score, ACC, pairwise t-test |
[33] | 2016 | — | Weighted least-squares twin support vector machine (WLSTSVM) to find misclassification cost of DP | SVM, NB, RF, LR, KNN, BN, cost-sensitive neural network | — | PROMISE repository: CM1, KC1, PC1, PC3, PC4, MC2, KC2, KC3 | 10-fold CV, PR, recall, F-score, G-mean Wilcoxon signed rank test |
[34] | 2016 | — | A multi-objective naive Bayes learning techniques MONB, MOBNN | NB, LR, DT, MODT, MOLR, MONB | — | Jureczko datasets obtained from PROMISE repository | AUC, Wilcoxon rank test CP MO NB (0.72) produces the highest value |
[35] | 2016 | Classification | A software defect prediction model to find faulty components of a software | Hybrid filter approaches FISHER, MR, ANNIGMA. | — | KC1, KC2, JM1, PC1, PC2, PC3, and PC4 datasets | ACC, ent filters, ACC 90% |
[36] | 2017 | Classification | Propose an hybrid method called TSC-RUS + S | A random undersampling based on two-step cluster (TSC) | Stacking: DT, LR, kNN, NB | NASA MDP: i.e., CM1, KC1, KC3, MC2, MW1, PC1, PC2, PC3, PC4 | 10-fold CV, AUC, (TSC-RUS + S) is the best |
[37] | 2017 | Classification | Analyze five popular ML algorithms for software defect prediction | ANN, PSO, DT, NB, LC | — | Nasa and PROMISE datasets: CM1, JM1, KC1, KC2, PC1, KC1-LC | 10-fold CV ANN < DT |
[38] | 2018 | Classification | Three well-known ML techniques are compared. | NB, DT, ANN | — | Three different datasets DS1, DS2, DS3 | ACC, PR, recall, F, ROC ACC 97% DT > ANN > NB |
[10] | 2018 | Classification | ML algorithms are compared with CODEP | LR, BN, RBF, MLP, alternating decision tree (ADTree), and DT | Max, CODEP, Bagging J48, Bagging NB, Boosting J48, Boosting NB, RF | PROMISE: Ant, Camel, ivy, Jedit, Log4j, Lucene, Poi, Prop, Tomcat, Xalan | F-score, PR, AUC ROC Max performs better than CODEP |
Data mining and machine learning studies on the subject “defect prediction.”
Ensemble learning combines several base learning models to obtain better performance than individual models. These base learners can be acquired with:
Different learning algorithms
Different parameters of the same algorithm
Different training sets
The commonly used ensemble techniques bagging, boosting, and stacking are shown in Figure 3 and briefly explained in this part. Bagging (which stands for bootstrap aggregating) is a kind of parallel ensemble. In this method, each model is built independently, and multiple training datasets are generated from the original dataset through random selection of different feature subsets; thus, it aims to decrease variance. It combines the outputs of each ensemble member by a voting mechanism. Boosting can be described as sequential ensemble. First, the same weights are assigned to data instances; after training, the weight of wrong predictions is increased, and this process is repeated as the ensemble size. Finally, it uses a weighted voting scheme, and in this way, it aims to decrease bias. Stacking is a technique that uses predictions from multiple models via a meta-classifier.
Common ensemble learning methods: (a) Bagging, (b) boosting, (c) stacking.
Some software defect prediction studies have compared ensemble techniques to determine the best performing one [10, 18, 21, 39, 40]. In a study conducted by Wang et al. [18], different ensemble techniques such as bagging, boosting, random tree, random forest, random subspace, stacking, and voting were compared to each other and a single classifier (NB). According to the results, voting and random forest clearly exhibited better performance than others. In a different study [39], ensemble methods were compared with more than one base learner (NB, BN, SMO, PART, J48, RF, random tree, IB1, VFI, DT, NB tree). For boosted SMO, bagging J48, and boosting and bagging RT, performance of base classifiers was lower than that of ensemble learner classifiers.
In study [21], a new method was proposed of mixing feature selection and ensemble learning for defect classification. Results showed that random forests and the proposed algorithm are not affected by poor features, and the proposed algorithm outperforms existing single and ensemble classifiers in terms of classification performance. Another comparative study [10] used seven composite algorithms (Ave, Max, Bagging C4.5, bagging naive Bayes (NB), Boosting J48, Boosting naive Bayes, and RF) and one composite state-of-the art study for cross-project defect prediction. The Max algorithm yielded the best results regarding F-score in terms of classification performance.
Bowes et al. [40] compared RF, NB, Rpart, and SVM algorithms to determine whether these classifiers obtained the same results. The results demonstrated that a unique subset of defects can be discovered by specific classifiers. However, whereas some classifiers are steady in the predictions they make, other classifiers change in their predictions. As a result, ensembles with decision-making without majority voting can perform best.
One of the main problems of SDP is the imbalance between the defect and non-defect classes of the dataset. Generally, the number of defected instances is greater than the number of non-defected instances in the collected data. This situation causes the machine learning algorithms to perform poorly. Wang and Yao [19] compared five class-imbalanced learning methods (RUS, RUS-bal, THM, BNC, SMB) and NB and RF algorithms and proposed the dynamic version of AdaBoost.NC. They utilized balance, G-mean, and AUC measures for comparison. Results showed that AdaBoost.NC and naive Bayes are better than the other seven algorithms in terms of evaluation measures. Dynamic AdaBoost.NC showed better defect detection rate and overall performance than the original AdaBoost.NC. To handle the class imbalance problem, studies [20] have compared different methods (sampling, cost sensitive, hybrid, and ensemble) by taking into account evaluation metrics such as MCC and receiver operating characteristic (ROC).
As shown in Table 1, the most common datasets used in the defect prediction studies [17, 18, 19, 39] are the NASA MDP dataset and PROMISE repository datasets. In addition, some studies utilized open-source projects such as Bugzilla Columba and Eclipse JDT [26, 27], and other studies used Android application data [22, 23].
Although use of ensemble learning techniques has dramatically increased recently, studies that do not use ensemble learning are still conducted and successful. For example, in study [32], prediction models were created using source code metrics as in ensemble studies but by using different feature selection techniques such as genetic algorithm (GA).
To overcome the class imbalance problem, Tomar and Agarwal [33] proposed a prediction system that assigns lower cost to non-defective data samples and higher cost to defective samples to balance data distribution. In the absence of enough data within a project, required data can be obtained from cross projects; however, in this case, this situation may cause class imbalance. To solve this problem, Ryu and Baik [34] proposed multi-objective naïve Bayes learning for cross-project environments. To obtain significant software metrics on cloud computing environments, Ali et al. used a combination of filter and wrapper approaches [35]. They compared different machine learning algorithms such as NB, DT, and MLP [29, 37, 38, 41].
Software effort estimation (SEE) is critical for a company because hiring more employees than required will cause loss of revenue, while hiring fewer employees than necessary will result in delays in software project delivery. The estimation analysis helps to predict the amount of effort (in person hours) needed to develop a software product. Basic steps of software estimation can be itemized as follows:
Determine project objectives and requirements.
Design the activities.
Estimate product size and complexity.
Compare and repeat estimates.
SEE contains requirements and testing besides predicting effort estimation [42]. Many research and review studies have been conducted in the field of SEE. Recently, a survey [43] analyzed effort estimation studies that concentrated on ML techniques and compared them with studies focused on non-ML techniques. According to the survey, case-based reasoning (CBR) and artificial neural network (ANN) were the most widely used techniques. In 2014, Dave and Dutta [44] examined existing studies that focus only on neural network.
The current effort estimation studies using DM and ML techniques are available in Table 2. This table summarizes the prominent studies in terms of aspects such as year, data mining task, aim, datasets, and metrics. Table 2 indicates that neural network is the most widely used technique for the effort estimation task.
Ref. | Year | Task | Objective | Algorithms | Ensemble learning | Dataset | Evaluation metrics and results |
---|---|---|---|---|---|---|---|
[45] | 2008 | Regression | Ensemble of neural networks with associative memory (ENNA) | NN, MLP, KNN | Bagging | NASA, NASA 93, USC, SDR, Desharnais | MMRE, MdMRE and PRED(L) For ENNA PRED(25) = 36.4 For neural network PRED(25) = 8 |
[46] | 2009 | Regression | Authors proposed the ensemble of neural networks with associative memory (ENNA) | NN, MLP, KNN | Bagging | NASA, NASA 93, USC, SDR, Desharnais | Random subsampling, t-test MMRE, MdMRE, and PRED(L) ENNA is the best |
[47] | 2010 | Regression | To show the effectiveness of SVR for SEE | SVR, RBF | — | Tukutuku | LOOCV, MMRE, Pred(25), MEMRE, MdEMRE SVR outperforms others |
[48] | 2011 | Regression | To evaluate whether readily available ensemble methods enhance SEE | MLP, RBF, RT | Bagging | 5 datasets from PROMISE: cocomo81, nasa93, nasa, sdr, and Desharnais 8 datasets from ISBSG repository | MMRE, MdMRE, PRED(25) RTs and Bagging with MLPs perform similarly |
[49] | 2012 | Regression | To show the measures behave in SEE and to create good ensembles | MLP, RBF, REPTree, | Bagging | cocomo81, nasa93, nasa, cocomo2, desharnais, ISBSG repository | MMRE, PRED(25), LSD, MdMRE, MAE, MdAE Pareto ensemble for all measures, except LSD. |
[50] | 2012 | Regression | To use cross-company models to create diverse ensembles able to dynamically adapt to changes | WC RTs, CC-DWM | WC-DWM | 3 datasets from ISBSG repository (ISBSG2000, ISBSG2001, ISBSG) 2 datasets from PROMISE (CocNasaCoc81 and CocNasaCoc81Nasa93) | MAE, Friedman test Only DCL could improve upon RT CC data potentially beneficial for improving SEE |
[51] | 2012 | Regression | To generate estimates from ensembles of multiple prediction methods | CART, NN, LR, PCR, PLSR, SWR, ABE0-1NN, ABE0-5NN | Combining top M solo methods | PROMISE | MAR, MMRE, MdMRE, MMER, MBRE, MIBRE. Combinations perform better than 83% |
[52] | 2012 | Classification/regression | DM techniques to estimate software effort. | M5, CART, LR, MARS, MLPNN, RBFNN, SVM | — | Coc81, CSC, Desharnais, Cocnasa, Maxwell, USP05 | MdMRE, Pred(25), Friedman test Log + OLS > LMS, BC + OLS, MARS, LS-SVM |
[53] | 2013 | Clustering/classification | Estimation of software development effort | NN, ABE, C-means | — | Maxwell | 3-fold CV and LOOCV, RE, MRE, MMRE, PRED |
[54] | 2014 | Regression | ANNs are examined using COCOMO model | MLP, RBFNN, SVM, PSO-SVM Extreme learning Machines | — | COCOMO II Data | MMRE, PRED PSO-SVM is the best |
[55] | 2014 | — | A hybrid model based on GA And ACO for optimization | GA, ACO | — | NASA datasets | MMRE, the proposed method is the best |
[56] | 2015 | Regression | To display the effect of data preprocessing techniques on ML methods in SEE | CBR, ANN, CART Preprocessing rech: MDT, LD, MI, FS, CS, FSS, BSS | — | ISBSG, Desharnais, Kitchenham, USPFT | CV, MBRE, PRED (0.25), MdBRE |
[57] | 2016 | Regression | Four neural network models are compared with each other. | MLP, RBFNN, GRNN, CCNN | — | ISBSG repository | 10-fold CV, MAR The CCNN outperforms the other three models |
[58] | 2016 | Regression | To propose a model based on Bayesian network | GA and PSO | — | COCOMO NASA Dataset | DIR, DRM The proposed model is best |
[59] | 2016 | Classification/regression | A hybrid model using SVM and RBNN compared against previous models | SVM, RBNN | — | Dataset1 = 45 industrial projects Dataset2 = 65 educational projects | LOOCV, MAE, MBRE, MIBRE, SA The proposed approach is the best |
[60] | 2017 | Classification | To estimate software effort by using ML techniques | SVM, KNN | Boosting: kNN and SVM | Desharnais, Maxwell | LOOCV, k-fold CV ACC = 91.35% for Desharnais ACC = 85.48% for Maxwell |
Data mining and machine learning studies on the subject “effort estimation.”
Several studies have compared ensemble learning methods with single learning algorithms [45, 46, 48, 49, 51, 60] and examined them on cross-company (CC) and within-company (WC) datasets [50]. The authors observed that ensemble methods obtained by a proper combination of estimation methods achieved better results than single methods. Various ML techniques such as neural network, support vector machine (SVM), and k-nearest neighbor are commonly used as base classifiers for ensemble methods such as bagging and boosting in software effort estimation. Moreover, their results indicate that CC data can increase performance over WC data for estimation techniques [50].
In addition to the abovementioned studies, researchers have conducted studies without using ensemble techniques. The general approach is to investigate which DM technique has the best effect on performance in software effort estimation. For instance, Subitsha and Rajan [54] compared five different algorithms—MLP, RBFNN, SVM, ELM, and PSO-SVM—and Nassif et al. [57] investigated four neural network algorithms—MLP, RBFNN, GRNN, and CCNN. Although neural networks are widely used in this field, missing values and outliers frequently encountered in the training set adversely affect neural network results and cause inaccurate estimations. To overcome this problem, Khatibi et al. [53] split software projects into several groups based on their similarities. In their studies, the C-means clustering algorithm was used to determine the most similar projects and to decrease the impact of unrelated projects, and then analogy-based estimation (ABE) and NN were applied. Another clustering study by Azzeh and Nassif [59] combined SVM and bisecting k-medoids clustering algorithms; an estimation model was then built using RBFNN. The proposed method was trained on historical use case points (UCP).
Zare et al. [58] and Maleki et al. [55] utilized optimization methods for accurate cost estimation. In the former study, a model was proposed based on Bayesian network with genetic algorithm and particle swarm optimization (PSO). The latter study used GA to optimize the effective factors’ weight, and then trained by ant colony optimization (ACO). Besides conventional effort estimation studies, researchers have utilized machine learning techniques for web applications. Since web-based software projects are different from traditional projects, the effort estimation process for these studies is more complex.
It is observed that PRED(25) and MMRE are the most popular evaluation metrics in effort estimation. MMRE stands for the mean magnitude relative error, and PRED(25) measures prediction accuracy and provides a percentage of predictions within 25% of actual values.
Vulnerability analysis is becoming the focal point of system security to prevent weaknesses in the software system that can be exploited by an attacker. Description of software vulnerability is given in many different resources in different ways [61]. The most popular and widely utilized definition appears in the Common Vulnerabilities and Exposures (CVE) 2017 report as follows:
Vulnerability is a weakness in the computational logic found in software and some hardware components that, when exploited, results in a negative impact to confidentiality, integrity or availability.
Vulnerability analysis may require many different operations to identify defects and vulnerabilities in a software system. Vulnerabilities, which are a special kind of defect, are more critical than other defects because attackers exploit system vulnerabilities to perform unauthorized actions. A defect is a normal problem that can be encountered frequently in the system, easily found by users or developers and fixed promptly, whereas vulnerabilities are subtle mistakes in large codes [62, 63]. Wijayasekara et al. claim that some bugs have been identified as vulnerabilities after being publicly announced in bug databases [64]. These bugs are called “hidden impact vulnerabilities” or “hidden impact bugs.” Therefore, the authors proposed a hidden impact vulnerability identification methodology that utilizes text mining techniques to determine which bugs in bug databases are vulnerabilities. According to the proposed method, a bug report was taken as input, and it produces feature vector after applying text mining. Then, classifier was applied and revealed whether it is a bug or a vulnerability. The results given in [64] demonstrate that a large proportion of discovered vulnerabilities were first described as hidden impact bugs in public bug databases. While bug reports were taken as input in that study, in many other studies, source code is taken as input. Text mining is a highly preferred technique for obtaining features directly from source codes as in the studies [65, 66, 67, 68, 69]. Several studies [63, 70] have compared text mining-based models and software metrics-based models.
In the security area of software systems, several studies have been conducted related to DM and ML. Some of these studies are compared in Table 3, which shows the data mining task and explanation of the studies, the year they were performed, the algorithms that were used, the type of vulnerability analysis, evaluation metrics, and results. In this table, the best performing algorithms according to the evaluation criteria are shown in bold.
Ref. | Year | Task | Objective | Algorithms | Type | Dataset description | Evaluation metrics and results |
---|---|---|---|---|---|---|---|
[71] | 2011 | Clustering | Obtaining software vulnerabilities based on RDBC | RDBC | Static | Database is built by RD-Entropy | FNR, FPR |
[42] | 2011 | Classification/regression | To predict the time to next vulnerability | LR, LMS, MLP, RBF, SMO | Static | NVD, CPE, CVSS | CC, RMSE, RRSE |
[65] | 2012 | Text mining | Analysis of source code as text | RBF, SVM | Static | K9 email client for the Android platform | ACC, PR, recall ACC = 0.87, PR = 0.85, recall = 0.88 |
[64] | 2012 | Classification/text mining | To identify vulnerabilities in bug databases | — | Static | Linux kernel MITRE CVE and MySQL bug databases | BDR, TPR, FPR 32% (Linux) and 62% (MySQL) of vulnerabilities |
[72] | 2014 | Classification/regression | Combine taint analysis and data mining to obtain vulnerabilities | ID3, C4.5/J48, RF, RT, KNN, NB, Bayes Net, MLP, SVM, LR | Hybrid | A version of WAP to collect the data | 10-fold CV, TPD, ACC, PR, KAPPA ACC = 90.8%, PR = 92%, KAPPA = 81% |
[73] | 2014 | Clustering | Identify vulnerabilities from source codes using CPG | — | Static | Neo4J and InfiniteGraph databases | — |
[63] | 2014 | Classification | Comparison of software metrics with text mining | RF | Static | Vulnerabilities from open-source web apps (Drupal, Moodle, PHPMyAdmin) | 3-fold CV, recall, IR, PR, FPR, ACC. Text mining provides benefits overall |
[69] | 2014 | Classification | To create model in the form of a binary classifier using text mining | NB, RF | Static | Applications from the F-Droid repository and Android | 10-fold CV, PR, recall PR and recall ≥ 80% |
[74] | 2015 | Classification | A new approach (VCCFinder) to obtain potentially dangerous codes | SVM-based detection model | — | The database contains 66 GitHub projects | k-fold CV, false alarms <99% at the same level of recall |
[70] | 2015 | Ranking/classification | Comparison of text mining and software metrics models | RF | — | Vulnerabilities from open-source web apps (Drupal, Moodle, PHPMyAdmin) | 10-fold CV Metrics: ER-BCE, ERBPP, ER-AVG |
[75] | 2015 | Clustering | Search patterns for taint-style vulnerabilities in C code | Hierarchical clustering (complete-linkage) | Static | 5 open-source projects: Linux, OpenSSL, Pidgin, VLC, Poppler (Xpdf) | Correct source, correct sanitization, number of traversals, generation time, execution time, reduction, amount of code review <95% |
[76] | 2016 | Classification | Static and dynamic features for classification | LR, MLP, RF | Hybrid | Dataset was created by analyzing 1039 test cases from the Debian Bug Tracker | FPR, FNR Detect 55% of vulnerable programs |
[77] | 2017 | Classification | 1. Employ a deep neural network 2. Combine N-gram analysis and feature selection | Deep neural network | — | Feature extraction from 4 applications (BoardGameGeek, Connectbot, CoolReader, AnkiDroid) | 10 times using 5-fold CV ACC = 92.87%, PR = 94.71%, recall = 90.17% |
[67] | 2017 | Text mining | To analyze characteristics of software vulnerability from source files | — | — | CVE, CWE, NVD databases | PR = 70%, recall = 60% |
[68] | 2017 | Text mining | Deep learning (LSTM) is used to learn semantic and syntactic features in code | RNN, LSTM, DBN | — | Experiments on 18 Java applications from the Android OS platform | 10-fold CV, PR, recall, and F-score Deep Belief Network PR, recall, and F-score > 80% |
[66] | 2018 | Classification | Identify bugs by extracting text features from C source code | NB, KNN, K-means, NN, SVM, DT, RF | Static | NVD, Cat, Cp, Du, Echo, Head, Kill, Mkdir, Nl, Paste, Rm, Seq, Shuf, Sleep, Sort, Tail, Touch, Tr, Uniq, Wc, Whoami | 5-fold CV ACC, TP, TN ACC = 74% |
[78] | 2018 | Regression | A deep learning-based vulnerability detection system (VulDeePecker) | BLSTM NN | Static | NIST: NVD and SAR project | 10-fold CV, PR, recall, F-score F-score = 80.8% |
[79] | 2018 | Classification | A mapping between existing requirements and vulnerabilities | LR, SVM, NB | — | Data is gathered from Apache Tomcat, CVE, requirements from Bugzilla, and source code is collected from Github | PR, recall, F-score LSI > SVM |
Data mining and machine learning studies on the subject “vulnerability analysis.”
Vulnerability analysis can be categorized into three types: static vulnerability analysis, dynamic vulnerability analysis, and hybrid analysis [61, 80]. Many studies have applied the static analysis approach, which detects vulnerabilities from source code without executing software, since it is cost-effective. Few studies have performed the dynamic analysis approach, in which one must execute software and check program behavior. The hybrid analysis approach [72, 76] combines these two approaches.
As revealed in Table 3, in addition to classification and text mining, clustering techniques are also frequently seen in software vulnerability analysis studies. To detect vulnerabilities in an unknown software data repository, entropy-based density clustering [71] and complete-linkage clustering [75] were proposed. Yamaguchi et al. [73] introduced a model to represent a large number of source codes as a graph called control flow graph (CPG), a combination of abstract syntax tree, CFG, and program dependency graph (PDG). This model enabled the discovery of previously unknown (zero-day) vulnerabilities.
To learn the time to next vulnerability, a prediction model was proposed in the study [42]. The result could be a number that refers to days or a bin representing values in a range. The authors used regression and classification techniques for the former and latter cases, respectively.
In vulnerability studies, issue tracking systems like Bugzilla, code repositories like Github, and vulnerability databases such as NVD, CVE, and CWE have been utilized [79]. In addition to these datasets, some studies have used Android [65, 68, 69] or web [63, 70, 72] (PHP source code) datasets. In recent years, researchers have concentrated on deep learning for building binary classifiers [77], obtaining vulnerability patterns [78], and learning long-term dependencies in sequential data [68] and features directly from the source code [81].
Li et al. [78] note two difficulties of vulnerability studies: demanding, intense manual labor and high false-negative rates. Thus, the widely used evaluation metrics in vulnerability analysis are false-positive rate and false-negative rate.
During the past years, software developers have used design patterns to create complex software systems. Thus, researchers have investigated the field of design patterns in many ways [82, 83]. Fowler defines a pattern as follows:
“
Patterns display relationships and interactions between classes or objects. Well-designed object-oriented systems have various design patterns integrated into them. Design patterns can be highly useful for developers when they are used in the right manner and place. Thus, developers avoid recreating methods previously refined by others. The pattern approach was initially presented in 1994 by four authors—namely, Erich Gama, Richard Helm, Ralph Johnson, and John Vlissides—called the Gang of Four (GOF) in 1994 [85]. According to the authors, there are three types of design patterns:
Creational patterns provide an object creation mechanism to create the necessary objects based on predetermined conditions. They allow the system to call appropriate object and add flexibility to the system when objects are created. Some creational design patterns are factory method, abstract factory, builder, and singleton.
Structural patterns focus on the composition of classes and objects to allow the establishment of larger software groups. Some of the structural design patterns are adapter, bridge, composite, and decorator.
Behavioral patterns determine common communication patterns between objects and how multiple classes behave when performing a task. Some behavioral design patterns are command, interpreter, iterator, observer, and visitor.
Many design pattern studies exist in the literature. Table 4 shows some design pattern mining studies related to machine learning and data mining. This table contains the aim of the study, mining task, year, and design patterns selected by the study, input data, dataset, and results of the studies.
Ref. | Year | Task | Objective | Algorithms | EL | Selected design patterns | Input data | Dataset | Evaluation metrics and results |
---|---|---|---|---|---|---|---|---|---|
[86] | 2012 | Text classification | Two-phase method: 1—text classification to 2—learning design patterns | NB, KNN, DT, SVM | — | 46 security patterns, 34 Douglass patterns, 23 GoF patterns | Documents | Security, Douglass, GoF | PR, recall, EWM PR = 0.62, recall = 0.75 |
[87] | 2013 | Regression | An approach is to find a valid instance of a DP or not | ANN | — | Adapter, command, composite, decorator, observer, and proxy | Set of candidate classes | JHotDraw 5.1 open-source application | 10 fold CV, PR, recall |
[88] | 2014 | Graph mining | Sub-graph mining-based approach | CloseGraph | — | — | Java source code | Open-source project:YARI, Zest, JUnit, JFreeChart, ArgoUML | No any empirical comparison |
[89] | 2015 | Classification/clustering | MARPLE-DPD is developed to classify instances whether it is a bad or good instance | SVM, DT, RF, K-means, ZeroR, OneR, NB, JRip, CLOPE. | — | Classification for singleton and adapter Classification and clustering for composite, decorator, and factory method | — | 10 open-source software systems DPExample, QuickUML 2001, Lexi v0.1.1 alpha, JRefactory v2.6.24, Netbeans v1.0.x, JUnit v3.7, JHotDraw v5.1, MapperXML v1.9.7, Nutch v0.4, PMD v1.8 | 10-fold CV, ACC, F-score, AUC ACC > =85% |
[90] | 2015 | Regression | A new method (SVM-PHGS) is proposed | Simple Logistic, C4.5, KNN, SVM, SVM-PHGS | — | Adapter, builder, composite, factory method, iterator, observer | Source code | P-mart repository | PR, recall, F-score, FP PR = 0.81, recall =0.81, F-score = 0.81, FP = 0.038 |
[91] | 2016 | Classification | Design pattern recognition using ML algorithms. | LRNN, DT | — | Abstract factory, adapter patterns | Source code | Dataset with 67 OO metrics, extracted by JBuilder tool | 5-fold CV, ACC, PR, recall, F-score ACC = 100% by LRNN |
[92] | 2016 | Classification | Three aspects: design patterns, software metrics, and supervised learning methods | Layer Recurrent Neural Network (LRNN) | RF | Abstract factory, adapter, bridge, singleton, and template method | Source code | Dataset with 67 OO metrics, extracted by JBuilder tool | PR, recall, F-score F-score = 100% by LRNN and RF ACC = 100% by RF |
[93] | 2017 | Classification | 1. Creation of metrics-oriented dataset 2. Detection of software design patterns | ANN, SVM | RF | Abstract factory, adapter, bridge, composite, and Template | Source code | Metrics extracted from source codes (JHotDraw, QuickUML, and Junit) | 5-fold and 10-fold CV, PR, recall, F-score ANN, SVM, and RF yielded to 100% PR for JHotDraw |
[94] | 2017 | Classification | Detection of design motifs based on a set of directed semantic graphs | Strong graph simulation, graph matching | — | All three groups: creational, structural, behavioral | UML class diagrams | — | PR, recall High accuracy by the proposed method |
[95] | 2017 | Text categorization | Selection of more appropriate design patterns | Fuzzy c-means | Ensemble-IG | Various design patterns | Problem definitions of design patterns | DP, GoF, Douglass, Security | F-score |
[96] | 2018 | Classification | Finding design pattern and smell pairs which coexist in the code | J48 | — | Used patterns: adapter, bridge, Template, singleton | Source code | Eclipse plugin Web of Patterns The tool selected for code smell detection is iPlasma | PR, recall, F-score, PRC, ROC Singleton pattern shows no presence of bad smells |
Data mining and machine learning studies on the subject “design pattern mining.”
In design pattern mining, detecting the design pattern is a frequent study objective. To do so, studies have used machine learning algorithms [87, 89, 90, 91], ensemble learning [95], deep learning [97], graph theory [94], and text mining [86, 95].
In study [91], the training dataset consists of 67 object-oriented (OO) metrics extracted by using the JBuilder tool. The authors used LRNN and decision tree techniques for pattern detection. Alhusain et al. [87] generated training datasets from existing pattern detection tools. The ANN algorithm was selected for pattern instances. Chihada et al. [90] created training data from pattern instances using 45 OO metrics. The authors utilized SVM for classifying patterns accurately. Another metrics-oriented dataset was developed by Dwivedi et al. [93]. To evaluate the results, the authors benefited from three open-source software systems (JHotDraw, QuickUML, and JUnit) and applied three classifiers, SVM, ANN, and RF. The advantage of using random forest is that it does not require linear features and can manage high-dimensional spaces.
To evaluate methods and to find patterns, open-source software projects such as JHotDraw, Junit, and MapperXML have been generally preferred by researchers. For example, Zanoni et al. [89] developed a tool called MARPLE-DPD by combining graph matching and machine learning techniques. Then, to obtain five design patterns, instances were collected from 10 open-source software projects, as shown in Table 4.
Design patterns and code smells are related issues: Code smell refers to symptoms in code, and if there are code smells in a software, its design pattern is not well constructed. Therefore, Kaur and Singh [96] checked whether design pattern and smell pairs appear together in a code by using J48 Decision Tree. Their obtained results showed that the singleton pattern had no presence of bad smells.
According to the studies summarized in the table, the most frequently used patterns are abstract factory and adapter. It has recently been observed that studies on ensemble learning in this field are increasing.
One of the SE tasks most often used to improve the quality of a software system is refactoring, which Martin Fowler has described as “a technique for restructuring an existing body of code, altering its internal structure without changing its external behavior” [98]. It improves readability and maintainability of the source code and decreases complexity of a software system. Some of the refactoring types are: Add Parameter, Replace Parameter, Extract method, and Inline method [99].
Code smell and refactoring are closely related to each other: Code smells represent problems due to bad design and can be fixed during refactoring. The main challenge is to obtain which part of the code needs refactoring.
Some of data mining studies related to software refactoring are presented in Table 5. Some studies focus on historical data to predict refactoring [100] or to obtain both refactoring and software defects [101] using different data mining algorithms such as LMT, Rip, and J48. Results suggest that when refactoring increases, the number of software defects decreases, and thus refactoring has a positive effect on software quality.
Ref. | Year | Task | Objective | Algorithms | EL | Dataset | Evaluation metrics and results |
---|---|---|---|---|---|---|---|
[100] | 2007 | Regression | Stages: (1) data understanding, (2) preprocessing, (3) ML, (4) post-processing, (5) analysis of the results | J48, LMT, Rip, NNge | — | ArgoUML, Spring Framework | 10-fold CV, PR, recall, F-score PR and recall are 0.8 for ArgoUML |
[101] | 2008 | Classification | Finding the relationship between refactoring and defects | C4.5, LMT, Rip, NNge | — | ArgoUML, JBoss Cache, Liferay Portal, Spring Framework, XDoclet | PR, recall, F-score |
[102] | 2014 | Regression | Propose GA-based learning for software refactoring based on ANN | GA, ANN | — | Xerces-J, JFreeChart, GanttProject, AntApache, JHotDraw, and Rhino. | Wilcoxon test with a 99% confidence level (α = 0.01) |
[103] | 2015 | Regression | Removing defects with time series in a multi-objective approach | Multi-objective algorithm, based on NSGA-II, ARIMA | FindBugs, JFreeChart, Hibernate, Pixelitor, and JDI-Ford | Wilcoxon rank sum test with a 99% confidence level (α < 1%) | |
[104] | 2016 | Web mining/clustering | Unsupervised learning approach to detect refactoring opportunities in service-oriented applications | PAM, K-means, COBWEB, X-Means | — | Two datasets of WSDL documents | COBWEB and K-means max. 83.33% and 0%, inter-cluster COBWEB and K-means min. 33.33% and 66.66% intra-cluster |
[105] | 2017 | Clustering | A novel algorithm (HASP) for software refactoring at the package level | Hierarchical clustering algorithm | — | Three open-source case studies | Modularization Quality and Evaluation Metric Function |
[99] | 2017 | Classification | A technique to predict refactoring at class level | PCA, SMOTE LS-SVM, RBF | — | From tera- PROMISE Repository seven open-source software systems | 10-fold CV, AUC, and ROC curves RBF kernel outperforms linear and polynomial kernel The mean value of AUC for LS-SVM RBF kernel is 0.96 |
[106] | 2017 | Classification | Exploring the impact of clone refactoring (CR) on the test code size | LR, KNN, NB | RF | data collected from an open-source Java software system (ANT) | PR, recall, accuracy, F-score kNN and RF outperform NB ACC (fitting (98%), LOOCV (95%), and 10 FCV (95%)) |
[107] | 2017 | — | Finding refactoring opportunities in source code | J48, BayesNet, SVM, LR | RF | Ant, ArgoUML, jEdit, jFreeChart, Mylyn | 10-fold CV, PR, recall 86–97% PR and 71–98% recall for proposed tech |
[108] | 2018 | Classification | A learning-based approach (CREC) to extract refactored and non-refactored clone groups from repositories | C4.5, SMO, NB. | RF, Adaboost | Axis2, Eclipse.jdt.core, Elastic Search, JFreeChart, JRuby, and Lucene | PR, recall, F-score F-score = 83% in the within-project F-score = 76% in the cross-project |
[109] | 2018 | Clustering | Combination of the use of multi-objective and unsupervised learning to decrease developer’s effort | GMM, EM | — | ArgoUML, JHotDraw, GanttProject, UTest, Apache Ant, Azureus | One-way ANOVA with a 95% confidence level (α = 5%) |
Data mining and machine learning studies on the subject “refactoring.”
While automated refactoring does not always give the desired result, manual refactoring is time-consuming. Therefore, one study [109] proposed a clustering-based recommendation tool by combining multi-objective search and unsupervised learning algorithm to reduce the number of refactoring options. At the same time, the number of refactoring that should be selected is decreasing with the help of the developer’s feedback.
Since many SE studies that apply data mining approaches exist in the literature, this article presents only a few of them. However, Figure 4 shows the current number of papers obtained from the Scopus search engine for each year from 2010 to 2019 by using queries in the title/abstract/keywords field. We extracted publications in 2020 since this year has not completed yet. Queries included (“data mining” OR “machine learning”) with (“defect prediction” OR “defect detection” OR “bug prediction” OR “bug detection”) for defect prediction, (“effort estimation” OR “effort prediction” OR “cost estimation”) for effort estimation, (“vulnerab*” AND “software” OR “vulnerability analysis”) for vulnerability analysis, and (“software” AND “refactoring”) for refactoring. As seen in the figure, the number of studies using data mining in SE tasks, especially defect prediction and vulnerability analysis, has increased rapidly. The most stable area in the studies is design pattern mining.
Number of publications of data mining studies for SE tasks from Scopus search by their years.
Figure 5 shows the publications studied in classification, clustering, text mining, and association rule mining as a percentage of the total number of papers obtained by a Scopus query for each SE task. For example, in defect prediction, the number of studies is 339 in the field of classification, 64 in clustering, 8 in text mining, and 25 in the field of association rule mining. As can be seen from the pie charts, while clustering is a popular DM technique in refactoring, no study related to text mining is found in this field. In other SE tasks, the preferred technique is classification, and the second is clustering.
Number of publications of data mining studies for SE tasks from Scopus search by their topics.
Defect prediction generally compares learning algorithms in terms of whether they find defects correctly using classification algorithms. Besides this approach, in some studies, clustering algorithms were used to select futures [110] or to compare supervised and unsupervised methods [27]. In the text mining area, to extract features from scripts, TF-IDF techniques were generally used [111, 112]. Although many different algorithms have been used in defect prediction, the most popular ones are NB, MLP, and RBF.
Figure 6 shows the number of document types (conference paper, book chapter, article, book) published between the years of 2010 and 2019. It is clearly seen that conference papers and articles are the most preferred research study type. It is clearly seen that there is no review article about data mining studies in design pattern mining.
The number of publications in terms of document type between 2010 and 2019.
Table 6 shows popular repositories that contain various datasets and their descriptions, which tasks they are used for, and hyperlinks to download. For example, the PMART repository includes source files of java projects, and the PROMISE repository has different datasets with software metrics such as cyclomatic complexity, design complexity, and lines of code. Since these repositories contain many datasets, no detailed information about them has been provided in this article.
Repository | Topic | Description | Web link |
---|---|---|---|
Nasa MDP | Defect Pred. | NASA’s Metrics Data Program | https://github.com/opensciences/opensciences.github.io/tree/master/repo/defect/mccabehalsted/_posts |
Android Git | Defect Pred. | Android version bug reports | https://android.googlesource.com/ |
PROMISE | Defect Pred. Effort Est. | It includes 20 datasets for defect prediction and cost estimation | http://promise.site.uottawa.ca/SERepository/datasets-page.html |
Software Defect Pred. Data | Defect Pred. | It includes software metrics, # of defects, etc. Eclipse JDT: Eclipse PDE: | http://www.seiplab.riteh.uniri.hr/?page_id=834&lang=en |
PMART | Design pattern mining | It has 22 patterns 9 Projects, 139 ins. Format: XML Manually detected and validated | http://www.ptidej.net/tools/designpatterns/ |
Description of popular repositories used in studies.
Refactoring can be applied at different levels; study [105] predicted refactoring at package level using hierarchical clustering, and another study [99] applied class-level refactoring using LS-SVM as learning algorithm, SMOTE for handling refactoring, and PCA for feature extraction.
Data mining techniques have been applied successfully in many different domains. In software engineering, to improve the quality of a product, it is highly critical to find existing deficits such as bugs, defects, code smells, and vulnerabilities in the early phases of SDLC. Therefore, many data mining studies in the past decade have aimed to deal with such problems. The present paper aims to provide information about previous studies in the field of software engineering. This survey shows how classification, clustering, text mining, and association rule mining can be applied in five SE tasks: defect prediction, effort estimation, vulnerability analysis, design pattern mining, and refactoring. It clearly shows that classification is the most used DM technique. Therefore, new studies can focus on clustering on SE tasks.
LMT | logistic model trees |
Rip | repeated incremental pruning |
NNge | nearest neighbor generalization |
PCA | principal component analysis |
PAM | partitioning around medoids |
LS-SVM | least-squares support vector machines |
MAE | mean absolute error |
RBF | radial basis function |
RUS | random undersampling |
SMO | sequential minimal optimization |
GMM | Gaussian mixture model |
EM | expectation maximizaion |
LR | logistic regression |
SMB | SMOTEBoost |
RUS-bal | balanced version of random undersampling |
THM | threshold-moving |
BNC | AdaBoost.NC |
RF | random forest |
RBF | radial basis function |
CC | correlation coefficient |
ROC | receiver operating characteristic |
BayesNet | Bayesian network |
SMOTE | synthetic minority over-sampling technique |
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His studies in robotics lead him not only to a PhD degree but also inspired him to co-found and build the International Journal of Advanced Robotic Systems - world's first Open Access journal in the field of robotics.",institutionString:null,institution:{name:"TU Wien",country:{name:"Austria"}}},{id:"441",title:"Ph.D.",name:"Jaekyu",middleName:null,surname:"Park",slug:"jaekyu-park",fullName:"Jaekyu Park",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/441/images/1881_n.jpg",biography:null,institutionString:null,institution:{name:"LG Corporation (South Korea)",country:{name:"Korea, South"}}},{id:"465",title:"Dr",name:"Christian",middleName:null,surname:"Martens",slug:"christian-martens",fullName:"Christian Martens",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:null},{id:"479",title:"Dr.",name:"Valentina",middleName:null,surname:"Colla",slug:"valentina-colla",fullName:"Valentina Colla",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/479/images/358_n.jpg",biography:null,institutionString:null,institution:{name:"Sant'Anna School of Advanced Studies",country:{name:"Italy"}}},{id:"494",title:"PhD",name:"Loris",middleName:null,surname:"Nanni",slug:"loris-nanni",fullName:"Loris Nanni",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/494/images/system/494.jpg",biography:"Loris Nanni received his Master Degree cum laude on June-2002 from the University of Bologna, and the April 26th 2006 he received his Ph.D. in Computer Engineering at DEIS, University of Bologna. On September, 29th 2006 he has won a post PhD fellowship from the university of Bologna (from October 2006 to October 2008), at the competitive examination he was ranked first in the industrial engineering area. He extensively served as referee for several international journals. He is author/coauthor of more than 100 research papers. He has been involved in some projects supported by MURST and European Community. His research interests include pattern recognition, bioinformatics, and biometric systems (fingerprint classification and recognition, signature verification, face recognition).",institutionString:null,institution:null},{id:"496",title:"Dr.",name:"Carlos",middleName:null,surname:"Leon",slug:"carlos-leon",fullName:"Carlos Leon",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"University of Seville",country:{name:"Spain"}}},{id:"512",title:"Dr.",name:"Dayang",middleName:null,surname:"Jawawi",slug:"dayang-jawawi",fullName:"Dayang Jawawi",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"University of Technology Malaysia",country:{name:"Malaysia"}}},{id:"528",title:"Dr.",name:"Kresimir",middleName:null,surname:"Delac",slug:"kresimir-delac",fullName:"Kresimir Delac",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/528/images/system/528.jpg",biography:"K. Delac received his B.Sc.E.E. degree in 2003 and is currentlypursuing a Ph.D. degree at the University of Zagreb, Faculty of Electrical Engineering andComputing. His current research interests are digital image analysis, pattern recognition andbiometrics.",institutionString:null,institution:{name:"University of Zagreb",country:{name:"Croatia"}}},{id:"557",title:"Dr.",name:"Andon",middleName:"Venelinov",surname:"Topalov",slug:"andon-topalov",fullName:"Andon Topalov",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/557/images/1927_n.jpg",biography:"Dr. Andon V. Topalov received the MSc degree in Control Engineering from the Faculty of Information Systems, Technologies, and Automation at Moscow State University of Civil Engineering (MGGU) in 1979. He then received his PhD degree in Control Engineering from the Department of Automation and Remote Control at Moscow State Mining University (MGSU), Moscow, in 1984. From 1985 to 1986, he was a Research Fellow in the Research Institute for Electronic Equipment, ZZU AD, Plovdiv, Bulgaria. In 1986, he joined the Department of Control Systems, Technical University of Sofia at the Plovdiv campus, where he is presently a Full Professor. He has held long-term visiting Professor/Scholar positions at various institutions in South Korea, Turkey, Mexico, Greece, Belgium, UK, and Germany. And he has coauthored one book and authored or coauthored more than 80 research papers in conference proceedings and journals. 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Moreover, the analysis of sex chromosomes is crucial for a proper classification of disorders of sex development (DSD). For many years, chromosome studies mainly addressed the livestock species, while recently, increasing interest in such analysis in companion animals is observed. New molecular and cytogenetic tools and techniques have given opportunities for a precise identification of chromosome mutations. Among them, fluorescence in situ hybridization, besides chromosome banding, has become a gold standard. In this chapter, recent advances in the cytogenetic diagnosis of cattle, pigs, horses, dogs and cats are presented.",book:{id:"5105",slug:"insights-from-animal-reproduction",title:"Insights from Animal Reproduction",fullTitle:"Insights from Animal Reproduction"},signatures:"Izabela Szczerbal and Marek Switonski",authors:[{id:"177030",title:"Prof.",name:"Marek",middleName:null,surname:"Switonski",slug:"marek-switonski",fullName:"Marek Switonski"},{id:"177045",title:"Dr.",name:"Izabela",middleName:null,surname:"Szczerbal",slug:"izabela-szczerbal",fullName:"Izabela Szczerbal"}]},{id:"49857",doi:"10.5772/62207",title:"Germ Cell Determinant Transmission, Segregation, and Function in the Zebrafish Embryo",slug:"germ-cell-determinant-transmission-segregation-and-function-in-the-zebrafish-embryo",totalDownloads:2295,totalCrossrefCites:4,totalDimensionsCites:11,abstract:"Animals specify primordial germ cells (PGCs) in two alternate modes: preformation and epigenesis. Epigenesis relies on signal transduction from the surrounding tissues to instruct a group of cells to acquire PGC identity. Preformation, thought to be the more derived PGC specification mode, is instead based on the maternal inheritance of germ cell-determining factors. We use the zebrafish as a model system, in which PGCs are specified through maternal inheritance of germ plasm, to study this process in vertebrates. In zebrafish, maternally inherited germ plasm ribonucleoparticles (RNPs) have co-opted the cytoskeletal machinery to reach progressive levels of multimerization, resulting in the formation of four large masses of aggregated germ plasm RNPs. At later stages, germ plasm masses continue to use components of the cell division machinery, such as the spindles, centrosomes, and/or subcellular organelles to segregate asymmetrically during cell division and subsequently induce germ cell fate. This chapter discusses the current knowledge of germ cell specification focusing on the zebrafish as a model system. We also provide a comparative analysis of the mechanism for germ plasm RNP segregation in zebrafish versus other known vertebrate systems of germ cell preformation, such as in amphibian and avian models.",book:{id:"5105",slug:"insights-from-animal-reproduction",title:"Insights from Animal Reproduction",fullTitle:"Insights from Animal Reproduction"},signatures:"Celeste Eno and Francisco Pelegri",authors:[{id:"177209",title:"Prof.",name:"Francisco",middleName:null,surname:"Pelegri",slug:"francisco-pelegri",fullName:"Francisco Pelegri"}]},{id:"62171",doi:"10.5772/intechopen.79106",title:"Intraoviductal Instillation of a Solution as an Effective Route for Manipulating Preimplantation Mammalian Embryos in vivo",slug:"intraoviductal-instillation-of-a-solution-as-an-effective-route-for-manipulating-preimplantation-mam",totalDownloads:1138,totalCrossrefCites:8,totalDimensionsCites:9,abstract:"Preimplantation embryos of mammals are enclosed by a translucent layer called zona pellucida (ZP), which is composed of glycoproteins. ZP is important for protecting against infection by virus and bacteria, and to prevent attachment of embryos to the oviductal epithelia. Due to the presence of ZP, it has been difficult to transfect preimplantation embryos existing within the oviductal lumen, with exogenous nucleic acids, such as DNA and mRNA. However, intraoviductal instillation of nucleic acids, and subsequent in vivo electroporation in pregnant females, enables transfection of these embryos, leading to the production of gene-modified animals. This new method for production of genetically modified animals does not require any ex vivo handling of embryos, which has been essential for traditional transgenesis. In this article, we describe recent advances in the in vivo transfection of preimplantation mammalian embryos, and also the possibility of simple transfection of these embryos through intraoviductal instillation of a solution, alone.",book:{id:"7233",slug:"new-insights-into-theriogenology",title:"New Insights into Theriogenology",fullTitle:"New Insights into Theriogenology"},signatures:"Masahiro Sato, Masato Ohtsuka and Shingo Nakamura",authors:[{id:"177440",title:"Dr.",name:"Masato",middleName:null,surname:"Ohtsuka",slug:"masato-ohtsuka",fullName:"Masato Ohtsuka"},{id:"177444",title:"Dr.",name:"Shingo",middleName:null,surname:"Nakamura",slug:"shingo-nakamura",fullName:"Shingo Nakamura"},{id:"245795",title:"Prof.",name:"Masahiro",middleName:null,surname:"Sato",slug:"masahiro-sato",fullName:"Masahiro Sato"}]},{id:"63404",doi:"10.5772/intechopen.80229",title:"Subclinical Endometritis in Dairy Cattle",slug:"subclinical-endometritis-in-dairy-cattle",totalDownloads:1824,totalCrossrefCites:2,totalDimensionsCites:6,abstract:"Subclinical endometritis is recognized as a cause of poor reproductive performance in dairy cows. Inflammation of the endometrium persisting after postpartum uterine involution has been related with prolonged calving-conception intervals and low fertility in dairy cows. The subclinical nature of this condition makes it necessary in the use of endometrial cytology or biopsy for diagnosing it. There are some controversies among authors in relation to the postpartum period from which a physiological endometrial inflammation should be considered a pathological subclinical endometritis. Therefore, depending on the sampling period after calving, different studies establish a different degree of polymorphonuclear leukocyte infiltration as cutoff point to diagnose subclinical endometritis. Controversies also exist regarding the pathogenesis of the disease and its consequences on the fertility of dairy cattle. The aim of this chapter was to review the current knowledge on this uterine pathology.",book:{id:"7233",slug:"new-insights-into-theriogenology",title:"New Insights into Theriogenology",fullTitle:"New Insights into Theriogenology"},signatures:"Luis Angel Quintela Arias, Marcos Vigo Fernández, Juan José\nBecerra González, Mónica Barrio López, Pedro José García Herradón\nand Ana Isabel Peña Martínez",authors:[{id:"243272",title:"Prof.",name:"Luis Angel",middleName:null,surname:"Quintela Arias",slug:"luis-angel-quintela-arias",fullName:"Luis Angel Quintela Arias"},{id:"243886",title:"Prof.",name:"Ana Isabel",middleName:null,surname:"Peña Martínez",slug:"ana-isabel-pena-martinez",fullName:"Ana Isabel Peña Martínez"},{id:"243887",title:"Prof.",name:"Pedro",middleName:null,surname:"García Herradón",slug:"pedro-garcia-herradon",fullName:"Pedro García Herradón"},{id:"243888",title:"Prof.",name:"Juan José",middleName:null,surname:"Becerra González",slug:"juan-jose-becerra-gonzalez",fullName:"Juan José Becerra González"},{id:"256852",title:"Dr.",name:"Mónica",middleName:null,surname:"Barrio López",slug:"monica-barrio-lopez",fullName:"Mónica Barrio López"},{id:"256854",title:"Dr.",name:"Marcos",middleName:null,surname:"Vigo Fernández",slug:"marcos-vigo-fernandez",fullName:"Marcos Vigo Fernández"}]},{id:"50061",doi:"10.5772/62470",title:"Sperm Motility Regulatory Proteins: A Tool to Enhance Sperm Quality",slug:"sperm-motility-regulatory-proteins-a-tool-to-enhance-sperm-quality",totalDownloads:2143,totalCrossrefCites:2,totalDimensionsCites:6,abstract:"Sperm forward motility is an essential parameter in mammalian fertilization. Studies from our laboratory have identified and characterized a few unique sperm motility regulatory proteins/glycoproteins from the male reproductive fluids and mammalian blood serum. The purified sperm motility-initiating protein (MIP) from caprine epididymal plasma as well as the forward motility-stimulating factor (FMSF) and motility-stimulating protein (MSP) from buffalo and goat serum, respectively, have high efficacy to initiate or increase motility in nonmotile or less motile sperm. Antibody of sperm motility inhibitory factor (MIF-II) has the high potential to enhance sperm vertical velocity and forward motility by increasing intracellular cyclic adenosine monophosphate (cAMP) level. The appearance and disappearance of D-galactose–specific lectin and its receptor along the epididymis has been reported to be involved in motility regulation in spermatozoa. A novel synthetic cryopreservation method and role of lipid to protect membrane damage during cryopreservation have been demonstrated. Motility-promoting proteins may be extremely useful for improving cattle breeding and breeding of endangered species, thereby helping in enhanced production of animal products as well as in the conservation of animals. Isolated proteins and developed cryopreservation technology may also be beneficial in human infertility clinics to increase the chance of fertilization.",book:{id:"5105",slug:"insights-from-animal-reproduction",title:"Insights from Animal Reproduction",fullTitle:"Insights from Animal Reproduction"},signatures:"Sandhya R. Dungdung, Arpita Bhoumik, Sudipta Saha, Prasanta\nGhosh, Kaushik Das, Sandipan Mukherjee, Debjani Nath, Jitamanyu\nChakrabarty, Chanakyanath Kundu, Bijay Shankar Jaiswal, Mahitosh\nMandal, Arunima Maiti, Saswati Banerjee, Madhumita\nRoychowdhury, Debleena Ray, Debdas Bhattacharyya and Gopal C.\nMajumder",authors:[{id:"50052",title:"Dr.",name:"Mahitosh",middleName:null,surname:"Mandal",slug:"mahitosh-mandal",fullName:"Mahitosh Mandal"},{id:"177044",title:"Dr.",name:"Sandhya",middleName:null,surname:"Dungdung",slug:"sandhya-dungdung",fullName:"Sandhya Dungdung"},{id:"177920",title:"Dr.",name:"Arpita",middleName:null,surname:"Bhoumik",slug:"arpita-bhoumik",fullName:"Arpita Bhoumik"},{id:"177921",title:"Dr.",name:"Sudipta",middleName:null,surname:"Saha",slug:"sudipta-saha",fullName:"Sudipta Saha"},{id:"177922",title:"MSc.",name:"Prasanta",middleName:null,surname:"Ghosh",slug:"prasanta-ghosh",fullName:"Prasanta Ghosh"},{id:"177923",title:"Dr.",name:"Kaushik",middleName:null,surname:"Das",slug:"kaushik-das",fullName:"Kaushik Das"},{id:"177924",title:"MSc.",name:"Sandipan",middleName:null,surname:"Mukherjee",slug:"sandipan-mukherjee",fullName:"Sandipan Mukherjee"},{id:"177925",title:"Dr.",name:"Debjani",middleName:null,surname:"Nath",slug:"debjani-nath",fullName:"Debjani Nath"},{id:"177927",title:"Dr.",name:"Jitamanyu",middleName:null,surname:"Chakrabarty",slug:"jitamanyu-chakrabarty",fullName:"Jitamanyu Chakrabarty"},{id:"177928",title:"Dr.",name:"Chanakyanath",middleName:null,surname:"Kundu",slug:"chanakyanath-kundu",fullName:"Chanakyanath Kundu"},{id:"177929",title:"Dr.",name:"Bijay Shankar",middleName:null,surname:"Jaiswal",slug:"bijay-shankar-jaiswal",fullName:"Bijay Shankar Jaiswal"},{id:"177930",title:"Dr.",name:"Arunima",middleName:null,surname:"Maiti",slug:"arunima-maiti",fullName:"Arunima Maiti"},{id:"177931",title:"Dr.",name:"Saswati",middleName:null,surname:"Banerjee",slug:"saswati-banerjee",fullName:"Saswati Banerjee"},{id:"177932",title:"Dr.",name:"Madhumita",middleName:null,surname:"Roychowdhury",slug:"madhumita-roychowdhury",fullName:"Madhumita Roychowdhury"},{id:"177933",title:"MSc.",name:"Debleena",middleName:null,surname:"Ray",slug:"debleena-ray",fullName:"Debleena Ray"},{id:"177934",title:"Dr.",name:"Debdas",middleName:null,surname:"Bhattacharyya",slug:"debdas-bhattacharyya",fullName:"Debdas Bhattacharyya"},{id:"177935",title:"Dr.",name:"Gopal Chandra",middleName:null,surname:"Majumder",slug:"gopal-chandra-majumder",fullName:"Gopal Chandra Majumder"}]}],mostDownloadedChaptersLast30Days:[{id:"49736",title:"Chromosome Abnormalities in Domestic Animals as Causes of Disorders of Sex Development or Impaired Fertility",slug:"chromosome-abnormalities-in-domestic-animals-as-causes-of-disorders-of-sex-development-or-impaired-f",totalDownloads:4164,totalCrossrefCites:8,totalDimensionsCites:20,abstract:"Cytogenetic evaluation is an important step in the diagnosis of infertile or sterile animals. Moreover, the analysis of sex chromosomes is crucial for a proper classification of disorders of sex development (DSD). For many years, chromosome studies mainly addressed the livestock species, while recently, increasing interest in such analysis in companion animals is observed. New molecular and cytogenetic tools and techniques have given opportunities for a precise identification of chromosome mutations. Among them, fluorescence in situ hybridization, besides chromosome banding, has become a gold standard. In this chapter, recent advances in the cytogenetic diagnosis of cattle, pigs, horses, dogs and cats are presented.",book:{id:"5105",slug:"insights-from-animal-reproduction",title:"Insights from Animal Reproduction",fullTitle:"Insights from Animal Reproduction"},signatures:"Izabela Szczerbal and Marek Switonski",authors:[{id:"177030",title:"Prof.",name:"Marek",middleName:null,surname:"Switonski",slug:"marek-switonski",fullName:"Marek Switonski"},{id:"177045",title:"Dr.",name:"Izabela",middleName:null,surname:"Szczerbal",slug:"izabela-szczerbal",fullName:"Izabela Szczerbal"}]},{id:"50144",title:"Proliferative Endometrial Lesions Hidden behind the Feline Pyometra",slug:"proliferative-endometrial-lesions-hidden-behind-the-feline-pyometra",totalDownloads:2500,totalCrossrefCites:0,totalDimensionsCites:4,abstract:"The literature refers to pyometra as the most important pathology in the feline uterus, which is often associated with cystic endometrial disease (cystic endometrial hyperplasia/pyometra complex or CEH-Pyo). The etiology of pyometra is complex and probably multifactorial, but hormonal influences are suggested to play an important role in the pathogenesis. Progestagen-based contraceptives may be risk factors for the CEH-Pyo syndrome, for endometrial adenocarcinoma and also to mammary tumors in this species.",book:{id:"5105",slug:"insights-from-animal-reproduction",title:"Insights from Animal Reproduction",fullTitle:"Insights from Animal Reproduction"},signatures:"Maria dos Anjos Pires, Hugo Vilhena, Sónia Miranda, Miguel\nTavares Pereira, Fernanda Seixas and Ana Laura Saraiva",authors:[{id:"41065",title:"Dr.",name:"Sónia",middleName:null,surname:"Miranda",slug:"sonia-miranda",fullName:"Sónia Miranda"},{id:"161556",title:"Dr.",name:"Maria Dos Anjos",middleName:null,surname:"Pires",slug:"maria-dos-anjos-pires",fullName:"Maria Dos Anjos Pires"},{id:"179547",title:"MSc.",name:"Hugo",middleName:null,surname:"Vilhena",slug:"hugo-vilhena",fullName:"Hugo Vilhena"},{id:"179548",title:"MSc.",name:"Miguel",middleName:null,surname:"Tavares Pereira",slug:"miguel-tavares-pereira",fullName:"Miguel Tavares Pereira"},{id:"179549",title:"Prof.",name:"Fernanda",middleName:null,surname:"Seixas",slug:"fernanda-seixas",fullName:"Fernanda Seixas"},{id:"179550",title:"Prof.",name:"Ana Laura",middleName:null,surname:"Saraiva",slug:"ana-laura-saraiva",fullName:"Ana Laura Saraiva"}]},{id:"49944",title:"The Use of Reproductive Technologies to Produce Transgenic Goats",slug:"the-use-of-reproductive-technologies-to-produce-transgenic-goats",totalDownloads:2444,totalCrossrefCites:2,totalDimensionsCites:3,abstract:"Recombinant DNA technology has revolutionized the production of therapeutic proteins. Thus, genes of a great number of human proteins have already been identified and cloned. The use of farm animals as bioreactors may be the better choice to produce recombinant therapeutic proteins. For this activity, the term “pharming” was created, referring to the use of genetic engineering to obtain a transgenic or genetically modified animal. Considering the advantages and disadvantages of livestock species, goats appear as a very good model. In addition, the first human commercially approved biological drug (antithrombin (AT)) was produced from the milk of transgenic goats. The aim of this chapter is to present various reproductive technologies used to obtain transgenic goats secreting recombinant proteins in milk. Initially, this chapter presents the methods for embryo production (in vivo and in vitro) to realize the DNA microinjection in pronuclear embryos. Thus, the techniques of superovulation of donors (in vivo embryo production) and ovarian stimulation for oocyte recovery (in vitro embryo production) are described. Also, the methods for DNA microinjection and embryo transfer are detailed in this chapter. Finally, this chapter describes the reproductive procedures used for obtaining transgenic goats by cloning.",book:{id:"5105",slug:"insights-from-animal-reproduction",title:"Insights from Animal Reproduction",fullTitle:"Insights from Animal Reproduction"},signatures:"Vicente J. F. Freitas, Luciana M. Melo, Dárcio I.A. Teixeira, Maajid H.\nBhat, Irina A. Serova, Lyudmila E. Andreeva and Oleg L. Serov",authors:[{id:"177122",title:"Dr.",name:"Vicente",middleName:null,surname:"Freitas",slug:"vicente-freitas",fullName:"Vicente Freitas"},{id:"177194",title:"Dr.",name:"Luciana",middleName:null,surname:"Melo",slug:"luciana-melo",fullName:"Luciana Melo"},{id:"177195",title:"Dr.",name:"Dárcio",middleName:null,surname:"Teixeira",slug:"darcio-teixeira",fullName:"Dárcio Teixeira"},{id:"177196",title:"Dr.",name:"Maajid",middleName:null,surname:"Bhat",slug:"maajid-bhat",fullName:"Maajid Bhat"},{id:"185365",title:"Dr.",name:"Irina",middleName:null,surname:"Aleksandrovna SEROVA",slug:"irina-aleksandrovna-serova",fullName:"Irina Aleksandrovna SEROVA"},{id:"185366",title:"Dr.",name:"Lyudmila",middleName:null,surname:"Evgenievna ANDREEVA",slug:"lyudmila-evgenievna-andreeva",fullName:"Lyudmila Evgenievna ANDREEVA"},{id:"185367",title:"Dr.",name:"Oleg",middleName:null,surname:"Leonidovich SEROV",slug:"oleg-leonidovich-serov",fullName:"Oleg Leonidovich SEROV"}]},{id:"63404",title:"Subclinical Endometritis in Dairy Cattle",slug:"subclinical-endometritis-in-dairy-cattle",totalDownloads:1824,totalCrossrefCites:2,totalDimensionsCites:6,abstract:"Subclinical endometritis is recognized as a cause of poor reproductive performance in dairy cows. Inflammation of the endometrium persisting after postpartum uterine involution has been related with prolonged calving-conception intervals and low fertility in dairy cows. The subclinical nature of this condition makes it necessary in the use of endometrial cytology or biopsy for diagnosing it. There are some controversies among authors in relation to the postpartum period from which a physiological endometrial inflammation should be considered a pathological subclinical endometritis. Therefore, depending on the sampling period after calving, different studies establish a different degree of polymorphonuclear leukocyte infiltration as cutoff point to diagnose subclinical endometritis. Controversies also exist regarding the pathogenesis of the disease and its consequences on the fertility of dairy cattle. The aim of this chapter was to review the current knowledge on this uterine pathology.",book:{id:"7233",slug:"new-insights-into-theriogenology",title:"New Insights into Theriogenology",fullTitle:"New Insights into Theriogenology"},signatures:"Luis Angel Quintela Arias, Marcos Vigo Fernández, Juan José\nBecerra González, Mónica Barrio López, Pedro José García Herradón\nand Ana Isabel Peña Martínez",authors:[{id:"243272",title:"Prof.",name:"Luis Angel",middleName:null,surname:"Quintela Arias",slug:"luis-angel-quintela-arias",fullName:"Luis Angel Quintela Arias"},{id:"243886",title:"Prof.",name:"Ana Isabel",middleName:null,surname:"Peña Martínez",slug:"ana-isabel-pena-martinez",fullName:"Ana Isabel Peña Martínez"},{id:"243887",title:"Prof.",name:"Pedro",middleName:null,surname:"García Herradón",slug:"pedro-garcia-herradon",fullName:"Pedro García Herradón"},{id:"243888",title:"Prof.",name:"Juan José",middleName:null,surname:"Becerra González",slug:"juan-jose-becerra-gonzalez",fullName:"Juan José Becerra González"},{id:"256852",title:"Dr.",name:"Mónica",middleName:null,surname:"Barrio López",slug:"monica-barrio-lopez",fullName:"Mónica Barrio López"},{id:"256854",title:"Dr.",name:"Marcos",middleName:null,surname:"Vigo Fernández",slug:"marcos-vigo-fernandez",fullName:"Marcos Vigo Fernández"}]},{id:"79909",title:"Cryopreservation Methods and Frontiers in the Art of Freezing Life in Animal Models",slug:"cryopreservation-methods-and-frontiers-in-the-art-of-freezing-life-in-animal-models",totalDownloads:201,totalCrossrefCites:0,totalDimensionsCites:0,abstract:"The development in cryobiology in animal breeding had revolutionized the field of reproductive medicine. The main objective to preserve animal germplasm stems from variety of reasons such as conservation of endangered animal species, animal diversity, and an increased demand of animal models and/or genetically modified animals for research involving animal and human diseases. Cryopreservation has emerged as promising technique for fertility preservation and assisted reproduction techniques (ART) for production of animal breeds and genetically engineered animal species for research. Slow rate freezing and rapid freezing/vitrification are the two main methods of cryopreservation. Slow freezing is characterized by the phase transition (liquid turning into solid) when reducing the temperature below freezing point. Vitrification, on the other hand, is a phenomenon in which liquid solidifies without the formation of ice crystals, thus the process is referred to as a glass transition or ice-free cryopreservation. The vitrification protocol applies high concentrations of cryoprotective agents (CPA) used to avoid cryoinjury. This chapter provides a brief overview of fundamentals of cryopreservation and established methods adopted in cryopreservation. Strategies involved in cryopreserving germ cells (sperm and egg freezing) are included in this chapter. Last section describes the frontiers and advancement of cryopreservation in some of the important animal models like rodents (mouse and rats) and in few large animals (sheep, cow etc).",book:{id:"10664",slug:"animal-reproduction",title:"Animal Reproduction",fullTitle:"Animal Reproduction"},signatures:"Feda S. Aljaser",authors:[{id:"439435",title:"Dr.",name:"Feda S.",middleName:null,surname:"Aljaser",slug:"feda-s.-aljaser",fullName:"Feda S. Aljaser"}]}],onlineFirstChaptersFilter:{topicId:"1382",limit:6,offset:0},onlineFirstChaptersCollection:[],onlineFirstChaptersTotal:0},preDownload:{success:null,errors:{}},subscriptionForm:{success:null,errors:{}},aboutIntechopen:{},privacyPolicy:{},peerReviewing:{},howOpenAccessPublishingWithIntechopenWorks:{},sponsorshipBooks:{sponsorshipBooks:[],offset:8,limit:8,total:0},allSeries:{pteSeriesList:[{id:"14",title:"Artificial Intelligence",numberOfPublishedBooks:9,numberOfPublishedChapters:89,numberOfOpenTopics:6,numberOfUpcomingTopics:0,issn:"2633-1403",doi:"10.5772/intechopen.79920",isOpenForSubmission:!0},{id:"7",title:"Biomedical Engineering",numberOfPublishedBooks:12,numberOfPublishedChapters:104,numberOfOpenTopics:3,numberOfUpcomingTopics:0,issn:"2631-5343",doi:"10.5772/intechopen.71985",isOpenForSubmission:!0}],lsSeriesList:[{id:"11",title:"Biochemistry",numberOfPublishedBooks:32,numberOfPublishedChapters:318,numberOfOpenTopics:4,numberOfUpcomingTopics:0,issn:"2632-0983",doi:"10.5772/intechopen.72877",isOpenForSubmission:!0},{id:"25",title:"Environmental Sciences",numberOfPublishedBooks:1,numberOfPublishedChapters:12,numberOfOpenTopics:4,numberOfUpcomingTopics:0,issn:"2754-6713",doi:"10.5772/intechopen.100362",isOpenForSubmission:!0},{id:"10",title:"Physiology",numberOfPublishedBooks:11,numberOfPublishedChapters:141,numberOfOpenTopics:4,numberOfUpcomingTopics:0,issn:"2631-8261",doi:"10.5772/intechopen.72796",isOpenForSubmission:!0}],hsSeriesList:[{id:"3",title:"Dentistry",numberOfPublishedBooks:8,numberOfPublishedChapters:129,numberOfOpenTopics:2,numberOfUpcomingTopics:0,issn:"2631-6218",doi:"10.5772/intechopen.71199",isOpenForSubmission:!0},{id:"6",title:"Infectious Diseases",numberOfPublishedBooks:13,numberOfPublishedChapters:113,numberOfOpenTopics:3,numberOfUpcomingTopics:1,issn:"2631-6188",doi:"10.5772/intechopen.71852",isOpenForSubmission:!0},{id:"13",title:"Veterinary Medicine and Science",numberOfPublishedBooks:11,numberOfPublishedChapters:106,numberOfOpenTopics:3,numberOfUpcomingTopics:0,issn:"2632-0517",doi:"10.5772/intechopen.73681",isOpenForSubmission:!0}],sshSeriesList:[{id:"22",title:"Business, Management and Economics",numberOfPublishedBooks:1,numberOfPublishedChapters:19,numberOfOpenTopics:3,numberOfUpcomingTopics:0,issn:"2753-894X",doi:"10.5772/intechopen.100359",isOpenForSubmission:!0},{id:"23",title:"Education and Human Development",numberOfPublishedBooks:0,numberOfPublishedChapters:5,numberOfOpenTopics:1,numberOfUpcomingTopics:1,issn:null,doi:"10.5772/intechopen.100360",isOpenForSubmission:!0},{id:"24",title:"Sustainable Development",numberOfPublishedBooks:0,numberOfPublishedChapters:15,numberOfOpenTopics:5,numberOfUpcomingTopics:0,issn:null,doi:"10.5772/intechopen.100361",isOpenForSubmission:!0}],testimonialsList:[{id:"6",text:"It is great to work with the IntechOpen to produce a worthwhile collection of research that also becomes a great educational resource and guide for future research endeavors.",author:{id:"259298",name:"Edward",surname:"Narayan",institutionString:null,profilePictureURL:"https://mts.intechopen.com/storage/users/259298/images/system/259298.jpeg",slug:"edward-narayan",institution:{id:"3",name:"University of Queensland",country:{id:null,name:"Australia"}}}},{id:"13",text:"The collaboration with and support of the technical staff of IntechOpen is fantastic. The whole process of submitting an article and editing of the submitted article goes extremely smooth and fast, the number of reads and downloads of chapters is high, and the contributions are also frequently cited.",author:{id:"55578",name:"Antonio",surname:"Jurado-Navas",institutionString:null,profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRisIQAS/Profile_Picture_1626166543950",slug:"antonio-jurado-navas",institution:{id:"720",name:"University of Malaga",country:{id:null,name:"Spain"}}}}]},series:{item:{id:"6",title:"Infectious Diseases",doi:"10.5772/intechopen.71852",issn:"2631-6188",scope:"This series will provide a comprehensive overview of recent research trends in various Infectious Diseases (as per the most recent Baltimore classification). Topics will include general overviews of infections, immunopathology, diagnosis, treatment, epidemiology, etiology, and current clinical recommendations for managing infectious diseases. Ongoing issues, recent advances, and future diagnostic approaches and therapeutic strategies will also be discussed. This book series will focus on various aspects and properties of infectious diseases whose deep understanding is essential for safeguarding the human race from losing resources and economies due to pathogens.",coverUrl:"https://cdn.intechopen.com/series/covers/6.jpg",latestPublicationDate:"June 25th, 2022",hasOnlineFirst:!0,numberOfPublishedBooks:13,editor:{id:"131400",title:"Prof.",name:"Alfonso J.",middleName:null,surname:"Rodriguez-Morales",slug:"alfonso-j.-rodriguez-morales",fullName:"Alfonso J. Rodriguez-Morales",profilePictureURL:"https://mts.intechopen.com/storage/users/131400/images/system/131400.png",biography:"Dr. Rodriguez-Morales is an expert in tropical and emerging diseases, particularly zoonotic and vector-borne diseases (especially arboviral diseases). He is the president of the Travel Medicine Committee of the Pan-American Infectious Diseases Association (API), as well as the president of the Colombian Association of Infectious Diseases (ACIN). He is a member of the Committee on Tropical Medicine, Zoonoses, and Travel Medicine of ACIN. He is a vice-president of the Latin American Society for Travel Medicine (SLAMVI) and a Member of the Council of the International Society for Infectious Diseases (ISID). Since 2014, he has been recognized as a Senior Researcher, at the Ministry of Science of Colombia. He is a professor at the Faculty of Medicine of the Fundacion Universitaria Autonoma de las Americas, in Pereira, Risaralda, Colombia. He is an External Professor, Master in Research on Tropical Medicine and International Health, Universitat de Barcelona, Spain. He is also a professor at the Master in Clinical Epidemiology and Biostatistics, Universidad Científica del Sur, Lima, Peru. In 2021 he has been awarded the “Raul Isturiz Award” Medal of the API. Also, in 2021, he was awarded with the “Jose Felix Patiño” Asclepius Staff Medal of the Colombian Medical College, due to his scientific contributions to COVID-19 during the pandemic. He is currently the Editor in Chief of the journal Travel Medicine and Infectious Diseases. His Scopus H index is 47 (Google Scholar H index, 68).",institutionString:"Institución Universitaria Visión de las Américas, Colombia",institution:null},editorTwo:null,editorThree:null},subseries:{paginationCount:4,paginationItems:[{id:"3",title:"Bacterial Infectious Diseases",coverUrl:"https://cdn.intechopen.com/series_topics/covers/3.jpg",isOpenForSubmission:!1,editor:null,editorTwo:null,editorThree:null},{id:"4",title:"Fungal Infectious Diseases",coverUrl:"https://cdn.intechopen.com/series_topics/covers/4.jpg",isOpenForSubmission:!0,editor:{id:"174134",title:"Dr.",name:"Yuping",middleName:null,surname:"Ran",slug:"yuping-ran",fullName:"Yuping Ran",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bS9d6QAC/Profile_Picture_1630330675373",biography:"Dr. Yuping Ran, Professor, Department of Dermatology, West China Hospital, Sichuan University, Chengdu, China. Completed the Course Medical Mycology, the Centraalbureau voor Schimmelcultures (CBS), Fungal Biodiversity Centre, Netherlands (2006). International Union of Microbiological Societies (IUMS) Fellow, and International Emerging Infectious Diseases (IEID) Fellow, Centers for Diseases Control and Prevention (CDC), Atlanta, USA. Diploma of Dermatological Scientist, Japanese Society for Investigative Dermatology. Ph.D. of Juntendo University, Japan. Bachelor’s and Master’s degree, Medicine, West China University of Medical Sciences. Chair of Sichuan Medical Association Dermatology Committee. General Secretary of The 19th Annual Meeting of Chinese Society of Dermatology and the Asia Pacific Society for Medical Mycology (2013). In charge of the Annual Medical Mycology Course over 20-years authorized by National Continue Medical Education Committee of China. Member of the board of directors of the Asia-Pacific Society for Medical Mycology (APSMM). Associate editor of Mycopathologia. Vice-chief of the editorial board of Chinses Journal of Mycology, China. Board Member and Chair of Mycology Group of Chinese Society of Dermatology.",institutionString:null,institution:{name:"Sichuan University",institutionURL:null,country:{name:"China"}}},editorTwo:null,editorThree:null},{id:"5",title:"Parasitic Infectious Diseases",coverUrl:"https://cdn.intechopen.com/series_topics/covers/5.jpg",isOpenForSubmission:!0,editor:{id:"67907",title:"Dr.",name:"Amidou",middleName:null,surname:"Samie",slug:"amidou-samie",fullName:"Amidou Samie",profilePictureURL:"https://mts.intechopen.com/storage/users/67907/images/system/67907.jpg",biography:"Dr. Amidou Samie is an Associate Professor of Microbiology at the University of Venda, in South Africa, where he graduated for his PhD in May 2008. He joined the Department of Microbiology the same year and has been giving lectures on topics covering parasitology, immunology, molecular biology and industrial microbiology. He is currently a rated researcher by the National Research Foundation of South Africa at category C2. He has published widely in the field of infectious diseases and has overseen several MSc’s and PhDs. His research activities mostly cover topics on infectious diseases from epidemiology to control. His particular interest lies in the study of intestinal protozoan parasites and opportunistic infections among HIV patients as well as the potential impact of childhood diarrhoea on growth and child development. He also conducts research on water-borne diseases and water quality and is involved in the evaluation of point-of-use water treatment technologies using silver and copper nanoparticles in collaboration with the University of Virginia, USA. He also studies the use of medicinal plants for the control of infectious diseases as well as antimicrobial drug resistance.",institutionString:null,institution:{name:"University of Venda",institutionURL:null,country:{name:"South Africa"}}},editorTwo:null,editorThree:null},{id:"6",title:"Viral Infectious Diseases",coverUrl:"https://cdn.intechopen.com/series_topics/covers/6.jpg",isOpenForSubmission:!0,editor:{id:"158026",title:"Prof.",name:"Shailendra K.",middleName:null,surname:"Saxena",slug:"shailendra-k.-saxena",fullName:"Shailendra K. Saxena",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRET3QAO/Profile_Picture_2022-05-10T10:10:26.jpeg",biography:"Professor Dr. Shailendra K. Saxena is a vice dean and professor at King George's Medical University, Lucknow, India. His research interests involve understanding the molecular mechanisms of host defense during human viral infections and developing new predictive, preventive, and therapeutic strategies for them using Japanese encephalitis virus (JEV), HIV, and emerging viruses as a model via stem cell and cell culture technologies. His research work has been published in various high-impact factor journals (Science, PNAS, Nature Medicine) with a high number of citations. He has received many awards and honors in India and abroad including various Young Scientist Awards, BBSRC India Partnering Award, and Dr. JC Bose National Award of Department of Biotechnology, Min. of Science and Technology, Govt. of India. Dr. Saxena is a fellow of various international societies/academies including the Royal College of Pathologists, United Kingdom; Royal Society of Medicine, London; Royal Society of Biology, United Kingdom; Royal Society of Chemistry, London; and Academy of Translational Medicine Professionals, Austria. He was named a Global Leader in Science by The Scientist. 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He also obtained an MSc in Molecular and Genetic Medicine, and a Ph.D. in Clinical Immunology and Human Genetics from the University of Sheffield, UK. He also completed a short-term fellowship in Pediatric Clinical Immunology and Bone Marrow Transplantation at Newcastle General Hospital, England. Dr. Rezaei is a Full Professor of Immunology and Vice Dean of International Affairs and Research, at the School of Medicine, Tehran University of Medical Sciences, and the co-founder and head of the Research Center for Immunodeficiencies. He is also the founding president of the Universal Scientific Education and Research Network (USERN). Dr. Rezaei has directed more than 100 research projects and has designed and participated in several international collaborative projects. He is an editor, editorial assistant, or editorial board member of more than forty international journals. He has edited more than 50 international books, presented more than 500 lectures/posters in congresses/meetings, and published more than 1,100 scientific papers in international journals.",institutionString:"Tehran University of Medical Sciences",institution:{name:"Tehran University of Medical Sciences",country:{name:"Iran"}}},{id:"180733",title:"Dr.",name:"Jean",middleName:null,surname:"Engohang-Ndong",slug:"jean-engohang-ndong",fullName:"Jean Engohang-Ndong",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/180733/images/system/180733.png",biography:"Dr. Jean Engohang-Ndong was born and raised in Gabon. After obtaining his Associate Degree of Science at the University of Science and Technology of Masuku, Gabon, he continued his education in France where he obtained his BS, MS, and Ph.D. in Medical Microbiology. He worked as a post-doctoral fellow at the Public Health Research Institute (PHRI), Newark, NJ for four years before accepting a three-year faculty position at Brigham Young University-Hawaii. Dr. Engohang-Ndong is a tenured faculty member with the academic rank of Full Professor at Kent State University, Ohio, where he teaches a wide range of biological science courses and pursues his research in medical and environmental microbiology. Recently, he expanded his research interest to epidemiology and biostatistics of chronic diseases in Gabon.",institutionString:"Kent State University",institution:{name:"Kent State University",country:{name:"United States of America"}}},{id:"188773",title:"Prof.",name:"Emmanuel",middleName:null,surname:"Drouet",slug:"emmanuel-drouet",fullName:"Emmanuel Drouet",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/188773/images/system/188773.png",biography:"Emmanuel Drouet, PharmD, is a Professor of Virology at the Faculty of Pharmacy, the University Grenoble-Alpes, France. As a head scientist at the Institute of Structural Biology in Grenoble, Dr. Drouet’s research investigates persisting viruses in humans (RNA and DNA viruses) and the balance with our host immune system. He focuses on these viruses’ effects on humans (both their impact on pathology and their symbiotic relationships in humans). He has an excellent track record in the herpesvirus field, and his group is engaged in clinical research in the field of Epstein-Barr virus diseases. He is the editor of the online Encyclopedia of Environment and he coordinates the Universal Health Coverage education program for the BioHealth Computing Schools of the European Institute of Science.",institutionString:null,institution:{name:"Grenoble Alpes University",country:{name:"France"}}},{id:"131400",title:"Prof.",name:"Alfonso J.",middleName:null,surname:"Rodriguez-Morales",slug:"alfonso-j.-rodriguez-morales",fullName:"Alfonso J. Rodriguez-Morales",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/131400/images/system/131400.png",biography:"Dr. Rodriguez-Morales is an expert in tropical and emerging diseases, particularly zoonotic and vector-borne diseases (especially arboviral diseases). He is the president of the Travel Medicine Committee of the Pan-American Infectious Diseases Association (API), as well as the president of the Colombian Association of Infectious Diseases (ACIN). He is a member of the Committee on Tropical Medicine, Zoonoses, and Travel Medicine of ACIN. He is a vice-president of the Latin American Society for Travel Medicine (SLAMVI) and a Member of the Council of the International Society for Infectious Diseases (ISID). Since 2014, he has been recognized as a Senior Researcher, at the Ministry of Science of Colombia. He is a professor at the Faculty of Medicine of the Fundacion Universitaria Autonoma de las Americas, in Pereira, Risaralda, Colombia. He is an External Professor, Master in Research on Tropical Medicine and International Health, Universitat de Barcelona, Spain. He is also a professor at the Master in Clinical Epidemiology and Biostatistics, Universidad Científica del Sur, Lima, Peru. In 2021 he has been awarded the “Raul Isturiz Award” Medal of the API. Also, in 2021, he was awarded with the “Jose Felix Patiño” Asclepius Staff Medal of the Colombian Medical College, due to his scientific contributions to COVID-19 during the pandemic. He is currently the Editor in Chief of the journal Travel Medicine and Infectious Diseases. His Scopus H index is 47 (Google Scholar H index, 68).",institutionString:"Institución Universitaria Visión de las Américas, Colombia",institution:null},{id:"332819",title:"Dr.",name:"Chukwudi Michael",middleName:"Michael",surname:"Egbuche",slug:"chukwudi-michael-egbuche",fullName:"Chukwudi Michael Egbuche",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/332819/images/14624_n.jpg",biography:"I an Dr. Chukwudi Michael Egbuche. I am a Senior Lecturer in the Department of Parasitology and Entomology, Nnamdi Azikiwe University, Awka.",institutionString:null,institution:{name:"Nnamdi Azikiwe University",country:{name:"Nigeria"}}},{id:"284232",title:"Mr.",name:"Nikunj",middleName:"U",surname:"Tandel",slug:"nikunj-tandel",fullName:"Nikunj Tandel",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/284232/images/8275_n.jpg",biography:'Mr. Nikunj Tandel has completed his Master\'s degree in Biotechnology from VIT University, India in the year of 2012. He is having 8 years of research experience especially in the field of malaria epidemiology, immunology, and nanoparticle-based drug delivery system against the infectious diseases, autoimmune disorders and cancer. He has worked for the NIH funded-International Center of Excellence in Malaria Research project "Center for the study of complex malaria in India (CSCMi)" in collaboration with New York University. The preliminary objectives of the study are to understand and develop the evidence-based tools and interventions for the control and prevention of malaria in different sites of the INDIA. Alongside, with the help of next-generation genomics study, the team has studied the antimalarial drug resistance in India. Further, he has extended his research in the development of Humanized mice for the study of liver-stage malaria and identification of molecular marker(s) for the Artemisinin resistance. At present, his research focuses on understanding the role of B cells in the activation of CD8+ T cells in malaria. Received the CSIR-SRF (Senior Research Fellow) award-2018, FIMSA (Federation of Immunological Societies of Asia-Oceania) Travel Bursary award to attend the IUIS-IIS-FIMSA Immunology course-2019',institutionString:"Nirma University",institution:{name:"Nirma University",country:{name:"India"}}},{id:"334383",title:"Ph.D.",name:"Simone",middleName:"Ulrich",surname:"Ulrich Picoli",slug:"simone-ulrich-picoli",fullName:"Simone Ulrich Picoli",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/334383/images/15919_n.jpg",biography:"Graduated in Pharmacy from Universidade Luterana do Brasil (1999), Master in Agricultural and Environmental Microbiology from Federal University of Rio Grande do Sul (2002), Specialization in Clinical Microbiology from Universidade de São Paulo, USP (2007) and PhD in Sciences in Gastroenterology and Hepatology (2012). She is currently an Adjunct Professor at Feevale University in Medicine and Biomedicine courses and a permanent professor of the Academic Master\\'s Degree in Virology. She has experience in the field of Microbiology, with an emphasis on Bacteriology, working mainly on the following topics: bacteriophages, bacterial resistance, clinical microbiology and food microbiology.",institutionString:null,institution:{name:"Universidade Feevale",country:{name:"Brazil"}}},{id:"229220",title:"Dr.",name:"Amjad",middleName:"Islam",surname:"Aqib",slug:"amjad-aqib",fullName:"Amjad Aqib",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/229220/images/system/229220.png",biography:"Dr. Amjad Islam Aqib obtained a DVM and MSc (Hons) from University of Agriculture Faisalabad (UAF), Pakistan, and a PhD from the University of Veterinary and Animal Sciences Lahore, Pakistan. Dr. Aqib joined the Department of Clinical Medicine and Surgery at UAF for one year as an assistant professor where he developed a research laboratory designated for pathogenic bacteria. Since 2018, he has been Assistant Professor/Officer in-charge, Department of Medicine, Manager Research Operations and Development-ORIC, and President One Health Club at Cholistan University of Veterinary and Animal Sciences, Bahawalpur, Pakistan. He has nearly 100 publications to his credit. His research interests include epidemiological patterns and molecular analysis of antimicrobial resistance and modulation and vaccine development against animal pathogens of public health concern.",institutionString:"Cholistan University of Veterinary and Animal Sciences",institution:null},{id:"62900",title:"Prof.",name:"Fethi",middleName:null,surname:"Derbel",slug:"fethi-derbel",fullName:"Fethi Derbel",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/62900/images/system/62900.jpeg",biography:"Professor Fethi Derbel was born in 1960 in Tunisia. He received his medical degree from the Sousse Faculty of Medicine at Sousse, University of Sousse, Tunisia. He completed his surgical residency in General Surgery at the University Hospital Farhat Hached of Sousse and was a member of the Unit of Liver Transplantation in the University of Rennes, France. He then worked in the Department of Surgery at the Sahloul University Hospital in Sousse. Professor Derbel is presently working at the Clinique les Oliviers, Sousse, Tunisia. His hospital activities are mostly concerned with laparoscopic, colorectal, pancreatic, hepatobiliary, and gastric surgery. He is also very interested in hernia surgery and performs ventral hernia repairs and inguinal hernia repairs. He has been a member of the GREPA and Tunisian Hernia Society (THS). During his residency, he managed patients suffering from diabetic foot, and he was very interested in this pathology. For this reason, he decided to coordinate a book project dealing with the diabetic foot. Professor Derbel has published many articles in journals and collaborates intensively with IntechOpen Access Publisher as an editor.",institutionString:"Clinique les Oliviers",institution:null},{id:"300144",title:"Dr.",name:"Meriem",middleName:null,surname:"Braiki",slug:"meriem-braiki",fullName:"Meriem Braiki",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/300144/images/system/300144.jpg",biography:"Dr. Meriem Braiki is a specialist in pediatric surgeon from Tunisia. She was born in 1985. She received her medical degree from the University of Medicine at Sousse, Tunisia. She achieved her surgical residency training periods in Pediatric Surgery departments at University Hospitals in Monastir, Tunis and France.\r\nShe is currently working at the Pediatric surgery department, Sidi Bouzid Hospital, Tunisia. Her hospital activities are mostly concerned with laparoscopic, parietal, urological and digestive surgery. She has published several articles in diffrent journals.",institutionString:"Sidi Bouzid Regional Hospital",institution:null},{id:"229481",title:"Dr.",name:"Erika M.",middleName:"Martins",surname:"de Carvalho",slug:"erika-m.-de-carvalho",fullName:"Erika M. de Carvalho",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/229481/images/6397_n.jpg",biography:null,institutionString:null,institution:{name:"Oswaldo Cruz Foundation",country:{name:"Brazil"}}},{id:"186537",title:"Prof.",name:"Tonay",middleName:null,surname:"Inceboz",slug:"tonay-inceboz",fullName:"Tonay Inceboz",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/186537/images/system/186537.jfif",biography:"I was graduated from Ege University of Medical Faculty (Turkey) in 1988 and completed his Med. PhD degree in Medical Parasitology at the same university. I became an Associate Professor in 2008 and Professor in 2014. I am currently working as a Professor at the Department of Medical Parasitology at Dokuz Eylul University, Izmir, Turkey.\n\nI have given many lectures, presentations in different academic meetings. I have more than 60 articles in peer-reviewed journals, 18 book chapters, 1 book editorship.\n\nMy research interests are Echinococcus granulosus, Echinococcus multilocularis (diagnosis, life cycle, in vitro and in vivo cultivation), and Trichomonas vaginalis (diagnosis, PCR, and in vitro cultivation).",institutionString:"Dokuz Eylül University",institution:{name:"Dokuz Eylül University",country:{name:"Turkey"}}},{id:"71812",title:"Prof.",name:"Hanem Fathy",middleName:"Fathy",surname:"Khater",slug:"hanem-fathy-khater",fullName:"Hanem Fathy Khater",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/71812/images/1167_n.jpg",biography:"Prof. Khater is a Professor of Parasitology at Benha University, Egypt. She studied for her doctoral degree, at the Department of Entomology, College of Agriculture, Food and Natural Resources, University of Missouri, Columbia, USA. She has completed her Ph.D. degrees in Parasitology in Egypt, from where she got the award for “the best scientific Ph.D. dissertation”. She worked at the School of Biological Sciences, Bristol, England, the UK in controlling insects of medical and veterinary importance as a grant from Newton Mosharafa, the British Council. Her research is focused on searching of pesticides against mosquitoes, house flies, lice, green bottle fly, camel nasal botfly, soft and hard ticks, mites, and the diamondback moth as well as control of several parasites using safe and natural materials to avoid drug resistances and environmental contamination.",institutionString:null,institution:{name:"Banha University",country:{name:"Egypt"}}},{id:"99780",title:"Prof.",name:"Omolade",middleName:"Olayinka",surname:"Okwa",slug:"omolade-okwa",fullName:"Omolade Okwa",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/99780/images/system/99780.jpg",biography:"Omolade Olayinka Okwa is presently a Professor of Parasitology at Lagos State University, Nigeria. She has a PhD in Parasitology (1997), an MSc in Cellular Parasitology (1992), and a BSc (Hons) Zoology (1990) all from the University of Ibadan, Nigeria. She teaches parasitology at the undergraduate and postgraduate levels. She was a recipient of a Commonwealth fellowship supported by British Council tenable at the Centre for Entomology and Parasitology (CAEP), Keele University, United Kingdom between 2004 and 2005. She was awarded an Honorary Visiting Research Fellow at the same university from 2005 to 2007. \nShe has been an external examiner to the Department of Veterinary Microbiology and Parasitology, University of Ibadan, MSc programme between 2010 and 2012. She is a member of the Nigerian Society of Experimental Biology (NISEB), Parasitology and Public Health Society of Nigeria (PPSN), Science Association of Nigeria (SAN), Zoological Society of Nigeria (ZSN), and is Vice Chairperson of the Organisation of Women in Science (OWSG), LASU chapter. She served as Head of Department of Zoology and Environmental Biology, Lagos State University from 2007 to 2010 and 2014 to 2016. She is a reviewer for several local and international journals such as Unilag Journal of Science, Libyan Journal of Medicine, Journal of Medicine and Medical Sciences, and Annual Research and Review in Science. \nShe has authored 45 scientific research publications in local and international journals, 8 scientific reviews, 4 books, and 3 book chapters, which includes the books “Malaria Parasites” and “Malaria” which are IntechOpen access publications.",institutionString:"Lagos State University",institution:{name:"Lagos State University",country:{name:"Nigeria"}}},{id:"273100",title:"Dr.",name:"Vijay",middleName:null,surname:"Gayam",slug:"vijay-gayam",fullName:"Vijay Gayam",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/273100/images/system/273100.jpeg",biography:"Dr. Vijay Bhaskar Reddy Gayam is currently practicing as an internist at Interfaith Medical Center in Brooklyn, New York, USA. He is also a Clinical Assistant Professor at the SUNY Downstate University Hospital and Adjunct Professor of Medicine at the American University of Antigua. He is a holder of an M.B.B.S. degree bestowed to him by Osmania Medical College and received his M.D. at Interfaith Medical Center. His career goals thus far have heavily focused on direct patient care, medical education, and clinical research. He currently serves in two leadership capacities; Assistant Program Director of Medicine at Interfaith Medical Center and as a Councilor for the American\r\nFederation for Medical Research. As a true academician and researcher, he has more than 50 papers indexed in international peer-reviewed journals. He has also presented numerous papers in multiple national and international scientific conferences. His areas of research interest include general internal medicine, gastroenterology and hepatology. He serves as an editor, editorial board member and reviewer for multiple international journals. His research on Hepatitis C has been very successful and has led to multiple research awards, including the 'Equity in Prevention and Treatment Award” from the New York Department of Health Viral Hepatitis Symposium (2018) and the 'Presidential Poster Award” awarded to him by the American College of Gastroenterology (2018). He was also awarded 'Outstanding Clinician in General Medicine” by Venus International Foundation for his extensive research expertise and services, perform over and above the standard expected in the advancement of healthcare, patient safety and quality of care.",institutionString:"Interfaith Medical Center",institution:{name:"Interfaith Medical Center",country:{name:"United States of America"}}},{id:"93517",title:"Dr.",name:"Clement",middleName:"Adebajo",surname:"Meseko",slug:"clement-meseko",fullName:"Clement Meseko",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/93517/images/system/93517.jpg",biography:"Dr. Clement Meseko obtained DVM and PhD degree in Veterinary Medicine and Virology respectively. He has worked for over 20 years in both private and public sectors including the academia, contributing to knowledge and control of infectious disease. Through the application of epidemiological skill, classical and molecular virological skills, he investigates viruses of economic and public health importance for the mitigation of the negative impact on people, animal and the environment in the context of Onehealth. \r\nDr. Meseko’s field experience on animal and zoonotic diseases and pathogen dynamics at the human-animal interface over the years shaped his carrier in research and scientific inquiries. He has been part of the investigation of Highly Pathogenic Avian Influenza incursions in sub Saharan Africa and monitors swine Influenza (Pandemic influenza Virus) agro-ecology and potential for interspecies transmission. He has authored and reviewed a number of journal articles and book chapters.",institutionString:"National Veterinary Research Institute",institution:{name:"National Veterinary Research Institute",country:{name:"Nigeria"}}},{id:"158026",title:"Prof.",name:"Shailendra K.",middleName:null,surname:"Saxena",slug:"shailendra-k.-saxena",fullName:"Shailendra K. Saxena",position:null,profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRET3QAO/Profile_Picture_2022-05-10T10:10:26.jpeg",biography:"Professor Dr. Shailendra K. Saxena is a vice dean and professor at King George's Medical University, Lucknow, India. His research interests involve understanding the molecular mechanisms of host defense during human viral infections and developing new predictive, preventive, and therapeutic strategies for them using Japanese encephalitis virus (JEV), HIV, and emerging viruses as a model via stem cell and cell culture technologies. His research work has been published in various high-impact factor journals (Science, PNAS, Nature Medicine) with a high number of citations. He has received many awards and honors in India and abroad including various Young Scientist Awards, BBSRC India Partnering Award, and Dr. JC Bose National Award of Department of Biotechnology, Min. of Science and Technology, Govt. of India. Dr. Saxena is a fellow of various international societies/academies including the Royal College of Pathologists, United Kingdom; Royal Society of Medicine, London; Royal Society of Biology, United Kingdom; Royal Society of Chemistry, London; and Academy of Translational Medicine Professionals, Austria. He was named a Global Leader in Science by The Scientist. He is also an international opinion leader/expert in vaccination for Japanese encephalitis by IPIC (UK).",institutionString:"King George's Medical University",institution:{name:"King George's Medical University",country:{name:"India"}}},{id:"94928",title:"Dr.",name:"Takuo",middleName:null,surname:"Mizukami",slug:"takuo-mizukami",fullName:"Takuo Mizukami",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/94928/images/6402_n.jpg",biography:null,institutionString:null,institution:{name:"National Institute of Infectious Diseases",country:{name:"Japan"}}},{id:"233433",title:"Dr.",name:"Yulia",middleName:null,surname:"Desheva",slug:"yulia-desheva",fullName:"Yulia Desheva",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/233433/images/system/233433.png",biography:"Dr. Yulia Desheva is a leading researcher at the Institute of Experimental Medicine, St. Petersburg, Russia. She is a professor in the Stomatology Faculty, St. Petersburg State University. She has expertise in the development and evaluation of a wide range of live mucosal vaccines against influenza and bacterial complications. 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