Comparison of different literatures with respect to methicillin- and vancomycin-resistant pattern of Staphylococcus aureus in different countries.
\\n\\n
Dr. Pletser’s experience includes 30 years of working with the European Space Agency as a Senior Physicist/Engineer and coordinating their parabolic flight campaigns, and he is the Guinness World Record holder for the most number of aircraft flown (12) in parabolas, personally logging more than 7,300 parabolas.
\\n\\nSeeing the 5,000th book published makes us at the same time proud, happy, humble, and grateful. This is a great opportunity to stop and celebrate what we have done so far, but is also an opportunity to engage even more, grow, and succeed. It wouldn't be possible to get here without the synergy of team members’ hard work and authors and editors who devote time and their expertise into Open Access book publishing with us.
\\n\\nOver these years, we have gone from pioneering the scientific Open Access book publishing field to being the world’s largest Open Access book publisher. Nonetheless, our vision has remained the same: to meet the challenges of making relevant knowledge available to the worldwide community under the Open Access model.
\\n\\nWe are excited about the present, and we look forward to sharing many more successes in the future.
\\n\\nThank you all for being part of the journey. 5,000 times thank you!
\\n\\nNow with 5,000 titles available Open Access, which one will you read next?
\\n\\nRead, share and download for free: https://www.intechopen.com/books
\\n\\n\\n\\n
\\n"}]',published:!0,mainMedia:null},components:[{type:"htmlEditorComponent",content:'
Preparation of Space Experiments edited by international leading expert Dr. Vladimir Pletser, Director of Space Training Operations at Blue Abyss is the 5,000th Open Access book published by IntechOpen and our milestone publication!
\n\n"This book presents some of the current trends in space microgravity research. The eleven chapters introduce various facets of space research in physical sciences, human physiology and technology developed using the microgravity environment not only to improve our fundamental understanding in these domains but also to adapt this new knowledge for application on earth." says the editor. Listen what else Dr. Pletser has to say...
\n\n\n\nDr. Pletser’s experience includes 30 years of working with the European Space Agency as a Senior Physicist/Engineer and coordinating their parabolic flight campaigns, and he is the Guinness World Record holder for the most number of aircraft flown (12) in parabolas, personally logging more than 7,300 parabolas.
\n\nSeeing the 5,000th book published makes us at the same time proud, happy, humble, and grateful. This is a great opportunity to stop and celebrate what we have done so far, but is also an opportunity to engage even more, grow, and succeed. It wouldn't be possible to get here without the synergy of team members’ hard work and authors and editors who devote time and their expertise into Open Access book publishing with us.
\n\nOver these years, we have gone from pioneering the scientific Open Access book publishing field to being the world’s largest Open Access book publisher. Nonetheless, our vision has remained the same: to meet the challenges of making relevant knowledge available to the worldwide community under the Open Access model.
\n\nWe are excited about the present, and we look forward to sharing many more successes in the future.
\n\nThank you all for being part of the journey. 5,000 times thank you!
\n\nNow with 5,000 titles available Open Access, which one will you read next?
\n\nRead, share and download for free: https://www.intechopen.com/books
\n\n\n\n
\n'}],latestNews:[{slug:"intechopen-authors-included-in-the-highly-cited-researchers-list-for-2020-20210121",title:"IntechOpen Authors Included in the Highly Cited Researchers List for 2020"},{slug:"intechopen-maintains-position-as-the-world-s-largest-oa-book-publisher-20201218",title:"IntechOpen Maintains Position as the World’s Largest OA Book Publisher"},{slug:"all-intechopen-books-available-on-perlego-20201215",title:"All IntechOpen Books Available on Perlego"},{slug:"oiv-awards-recognizes-intechopen-s-editors-20201127",title:"OIV Awards Recognizes IntechOpen's Editors"},{slug:"intechopen-joins-crossref-s-initiative-for-open-abstracts-i4oa-to-boost-the-discovery-of-research-20201005",title:"IntechOpen joins Crossref's Initiative for Open Abstracts (I4OA) to Boost the Discovery of Research"},{slug:"intechopen-hits-milestone-5-000-open-access-books-published-20200908",title:"IntechOpen hits milestone: 5,000 Open Access books published!"},{slug:"intechopen-books-hosted-on-the-mathworks-book-program-20200819",title:"IntechOpen Books Hosted on the MathWorks Book Program"},{slug:"intechopen-s-chapter-awarded-the-guenther-von-pannewitz-preis-2020-20200715",title:"IntechOpen's Chapter Awarded the Günther-von-Pannewitz-Preis 2020"}]},book:{item:{type:"book",id:"3329",leadTitle:null,fullTitle:"Liver Biopsy - Indications, Procedures, Results",title:"Liver Biopsy",subtitle:"Indications, Procedures, Results",reviewType:"peer-reviewed",abstract:"Liver biopsy is a procedure that involves obtaining a small piece of liver tissue, which is then analyzed in the laboratory. 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Pavement",doi:null,correctionPDFUrl:"https://cdn.intechopen.com/pdfs/71364.pdf",downloadPdfUrl:"/chapter/pdf-download/71364",previewPdfUrl:"/chapter/pdf-preview/71364",totalDownloads:null,totalCrossrefCites:null,bibtexUrl:"/chapter/bibtex/71364",risUrl:"/chapter/ris/71364",chapter:{id:"71109",slug:"the-mechanism-of-misalignment-of-saw-cutting-crack-of-concrete-pavement",signatures:"Chatarina Niken",dateSubmitted:"October 18th 2019",dateReviewed:"January 11th 2020",datePrePublished:"February 14th 2020",datePublished:"September 23rd 2020",book:{id:"7615",title:"Fracture Mechanics Applications",subtitle:null,fullTitle:"Fracture Mechanics Applications",slug:"fracture-mechanics-applications",publishedDate:"September 23rd 2020",bookSignature:"Hayri Baytan Ozmen and H. Ersen Balcioglu",coverURL:"https://cdn.intechopen.com/books/images_new/7615.jpg",licenceType:"CC BY 3.0",editedByType:"Edited by",editors:[{id:"198122",title:"Dr.",name:"Hayri Baytan",middleName:null,surname:"Ozmen",slug:"hayri-baytan-ozmen",fullName:"Hayri Baytan Ozmen"}],productType:{id:"1",title:"Edited Volume",chapterContentType:"chapter",authoredCaption:"Edited by"}},authors:[{id:"313776",title:"Dr.",name:"Chatarina",middleName:null,surname:"Niken",fullName:"Chatarina Niken",slug:"chatarina-niken",email:"chatarinaniken@yahoo.com",position:null,institution:null}]}},chapter:{id:"71109",slug:"the-mechanism-of-misalignment-of-saw-cutting-crack-of-concrete-pavement",signatures:"Chatarina Niken",dateSubmitted:"October 18th 2019",dateReviewed:"January 11th 2020",datePrePublished:"February 14th 2020",datePublished:"September 23rd 2020",book:{id:"7615",title:"Fracture Mechanics Applications",subtitle:null,fullTitle:"Fracture Mechanics Applications",slug:"fracture-mechanics-applications",publishedDate:"September 23rd 2020",bookSignature:"Hayri Baytan Ozmen and H. Ersen Balcioglu",coverURL:"https://cdn.intechopen.com/books/images_new/7615.jpg",licenceType:"CC BY 3.0",editedByType:"Edited by",editors:[{id:"198122",title:"Dr.",name:"Hayri Baytan",middleName:null,surname:"Ozmen",slug:"hayri-baytan-ozmen",fullName:"Hayri Baytan Ozmen"}],productType:{id:"1",title:"Edited Volume",chapterContentType:"chapter",authoredCaption:"Edited by"}},authors:[{id:"313776",title:"Dr.",name:"Chatarina",middleName:null,surname:"Niken",fullName:"Chatarina Niken",slug:"chatarina-niken",email:"chatarinaniken@yahoo.com",position:null,institution:null}]},book:{id:"7615",title:"Fracture Mechanics Applications",subtitle:null,fullTitle:"Fracture Mechanics Applications",slug:"fracture-mechanics-applications",publishedDate:"September 23rd 2020",bookSignature:"Hayri Baytan Ozmen and H. Ersen Balcioglu",coverURL:"https://cdn.intechopen.com/books/images_new/7615.jpg",licenceType:"CC BY 3.0",editedByType:"Edited by",editors:[{id:"198122",title:"Dr.",name:"Hayri Baytan",middleName:null,surname:"Ozmen",slug:"hayri-baytan-ozmen",fullName:"Hayri Baytan Ozmen"}],productType:{id:"1",title:"Edited Volume",chapterContentType:"chapter",authoredCaption:"Edited by"}}},ofsBook:{item:{type:"book",id:"9841",leadTitle:null,title:"Agroforestry - Small Landholder’s Tool for Climate Change Resiliency and Mitigation",subtitle:null,reviewType:"peer-reviewed",abstract:"
\r\n\tFarming of agricultural crops and livestock integrated with tree components is known as agroforestry. It is practiced by farming communities of all over world since time immemorial, but its appreciation as a science is practically four decades old. The scientific and systematic research approach is applicable in land, water, and energy utilization, in order to improve the overall production and productivity of food gain. Apart from the above mentioned, agroforestry is also a helpful tool for climate change impact mitigation through location-specific agroforestry models and enhancing the economic status and livelihood of the related farming communities. In the present day context, agroforestry acts a viable option for carbon capture and agroecosystems health restoration with the involvement of nitrogen-fixing plants, at the same time providing multiple products. With this background, agroforestry land use should become an important strategy option for policy makers which should be considered for climate change mitigation actions applied to climate-smart farming and sustainable development. However, agroforestry research carried out until today is unfortunately not on hand for the farming communities who are the backbone and pioneer in community development, particularly the ones in developing countries. In this view, the volume “Agroforestry - Small Landholder’s Tool for Climate Change Resiliency and Mitigation” is proposed.
",isbn:"978-1-83962-730-9",printIsbn:"978-1-83962-729-3",pdfIsbn:"978-1-83962-731-6",doi:null,price:0,priceEur:0,priceUsd:0,slug:null,numberOfPages:0,isOpenForSubmission:!1,hash:"ec5444e2a12dcd63ab9e7246d93a63ab",bookSignature:"Dr. Gopal Shukla, Dr. Sumit Chakravarty, Dr. Pankaj Panwar and Dr. Jahangeer A. Bhat",publishedDate:null,coverURL:"https://cdn.intechopen.com/books/images_new/9841.jpg",keywords:"Agroforestry, Food Production, Medicinal Plants, Livestock, Climate Change, Soil Improvement, Moisture Conservation, Carbon Sequestration, Water Conservation, GIS and Remote Sensing, Region-Specific Models, Sustainable Development",numberOfDownloads:null,numberOfWosCitations:0,numberOfCrossrefCitations:null,numberOfDimensionsCitations:null,numberOfTotalCitations:null,isAvailableForWebshopOrdering:!0,dateEndFirstStepPublish:"August 27th 2020",dateEndSecondStepPublish:"September 24th 2020",dateEndThirdStepPublish:"November 23rd 2020",dateEndFourthStepPublish:"February 11th 2021",dateEndFifthStepPublish:"April 12th 2021",remainingDaysToSecondStep:"4 months",secondStepPassed:!0,currentStepOfPublishingProcess:4,editedByType:null,kuFlag:!1,biosketch:"As an expert in forestry ecology and conservation, Dr. Shukla is affiliated with the North Bengal Agricultural University and was engaged in NAIP, NICRA, and SERB projects.",coeditorOneBiosketch:"Dr. Chakravarty has a wide experience in forestry training, research and development and is currently working as a Professor in Uttar Banga Krishi Viswavidyalaya, India.",coeditorTwoBiosketch:"Dr. Pankaj Panwar is the Principal Scientist in Forestry and Agroforestry with research experience in both temperate and dry subtropical and humid subtropical climates. He has over 80 publications including 7 books.",coeditorThreeBiosketch:"Dr. Bhat has been instrumental for developing HE and TVET streams of forestry and allied programs and he has worked closely in the area of accreditation with the Fiji Higher Education Commission and forestry stakeholders.",coeditorFourBiosketch:null,coeditorFiveBiosketch:null,editors:[{id:"101105",title:"Dr.",name:"Gopal",middleName:null,surname:"Shukla",slug:"gopal-shukla",fullName:"Gopal Shukla",profilePictureURL:"https://mts.intechopen.com/storage/users/101105/images/system/101105.jpg",biography:"Dr. Gopal Shukla is currently working as a Assistant Professor of Forestry at Uttar Banga Krishi Viswavidyalaya, Pundibari, Cooch Behar, West Bengal, India. He holds M.Sc and Ph.D. degrees in forestry from Uttar Banga Krishi Viswavidyalaya. Prior to joining the university job he has worked under NAIP, NICRA and SERB projects. The focus of his research and development work is forest ecology and conservation. He is currently engaged in forestry training and development, especially in the aspects of forestry, agroforestry, medicinal plants, and climate change.",institutionString:"North Bengal Agricultural University",position:null,outsideEditionCount:0,totalCites:0,totalAuthoredChapters:"1",totalChapterViews:"0",totalEditedBooks:"2",institution:{name:"North Bengal Agricultural University",institutionURL:null,country:{name:"India"}}}],coeditorOne:{id:"94999",title:"Dr.",name:"Sumit",middleName:null,surname:"Chakravarty",slug:"sumit-chakravarty",fullName:"Sumit Chakravarty",profilePictureURL:"https://mts.intechopen.com/storage/users/94999/images/system/94999.jpg",biography:"Dr. Sumit Chakravarty has a wide experience in forestry training, research, and development. He is currently working as a Professor at Uttar Banga Krishi Viswavidyalaya, Pundibari, Cooch Behar, West Bengal, India. He holds a M.Sc. degree in forestry and Ph.D. degree in agronomy from Punjab Agricultural University, Ludhiana, India. He has conducted research on several aspects of forestry, agroforestry, medicinal plants, and climate change. He has trained many students in these fields. 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Parmar University of Horticulture and Forestry, Solan (Himachal Pradesh). Dr. Panwar started his career in 2002 as an Assistant Professor (Forestry) in Uttar Banga Krishi Viswavidyalaya, West Bengal. Later he joined as a Senior Scientist (Forestry) at ICAR Research Complex for Eastern Region, Ranchi Centre in 2008 and served there up to 2009. He is presently working as a Principal Scientist (Forestry/Agroforestry) at ICAR-Indian Institute of Soil and Water Conservation, Research Centre- Chandigarh since 2014. He has to his credit 80 research publications along with seven books, few among those are “Handbook on Practical Forestry”, “Practical Manual on Plantation Forestry”, “Practical Manual on Agroforestry” and “Agroforestry Systems and Practices”. He has also published eight technical bulleting/brochures and one policy paper. He is a life member of Indian Society of Agroforestry, Range Management and Agroforestry, Indian Association of Soil and Water Conservationists and Indian Society of Hill Agriculture. At present Dr. Panwar is working on tree-crop interactions in agroforestry and in developing land use technologies for Shivalik Himalayas. He has handled projects on Agroforestry, climate change, bamboo and jatropha from external funding agencies like the Ministry of Rural Development, DST and DBT.",institutionString:"Indian Institute of Soil and Water Conservation",position:null,outsideEditionCount:0,totalCites:0,totalAuthoredChapters:"0",totalChapterViews:"0",totalEditedBooks:"0",institution:{name:"Indian Institute of Soil and Water Conservation",institutionURL:null,country:{name:"India"}}},coeditorThree:{id:"329967",title:"Dr.",name:"Jahangeer A.",middleName:null,surname:"Bhat",slug:"jahangeer-a.-bhat",fullName:"Jahangeer A. Bhat",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0033Y000030zCfhQAE/Profile_Picture_1599199812252",biography:"Presently a faculty member at Rani LakshmiBai Central Agricultural University, India. Before joining the present University, Dr. Jahangeer was the Head of Department of Forestry at College of Agriculture, Fisheries and Forestry, Fiji National University, Republic of Fiji Islands. Dr. Jahangeer has worked as counselor, mentor and coordinator for forestry academic programmes. He has been instrumental for developing HE and TVET streams of forestry and allied programmes and he worked closely in the area of accreditation with the Fiji Higher Education Commission and forestry stakeholders. Before joining Fiji National University, Dr. Jahangeerhas worked for HNB Garhwal University and has eleven years of research and eight years of teaching experience with a publication record of more than 50, which includes research articles, review papers, conference papers and books with national and international repute. Dr.Jahangeer is reviewing research articles for a number of scientific journals and has handled research projects in his capacity as PI and Co Pi. 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From chapter submission and review, to approval and revision, copyediting and design, until final publication, I work closely with authors and editors to ensure a simple and easy publishing process. I maintain constant and effective communication with authors, editors and reviewers, which allows for a level of personal support that enables contributors to fully commit and concentrate on the chapters they are writing, editing, or reviewing. I assist authors in the preparation of their full chapter submissions and track important deadlines and ensure they are met. I help to coordinate internal processes such as linguistic review, and monitor the technical aspects of the process. As an ASM I am also involved in the acquisition of editors. Whether that be identifying an exceptional author and proposing an editorship collaboration, or contacting researchers who would like the opportunity to work with IntechOpen, I establish and help manage author and editor acquisition and contact."}},relatedBooks:[{type:"book",id:"5539",title:"Forest Ecology and Conservation",subtitle:null,isOpenForSubmission:!1,hash:"6bd160f6d1da73fc253dfe6c4df7c095",slug:"forest-ecology-and-conservation",bookSignature:"Sumit Chakravarty and Gopal Shukla",coverURL:"https://cdn.intechopen.com/books/images_new/5539.jpg",editedByType:"Edited by",editors:[{id:"101105",title:"Dr.",name:"Gopal",surname:"Shukla",slug:"gopal-shukla",fullName:"Gopal Shukla"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"6264",title:"Forest Biomass and Carbon",subtitle:null,isOpenForSubmission:!1,hash:"964f96c9209ff2a3eaf3c5c6a54d81c3",slug:"forest-biomass-and-carbon",bookSignature:"Gopal Shukla and Sumit Chakravarty",coverURL:"https://cdn.intechopen.com/books/images_new/6264.jpg",editedByType:"Edited by",editors:[{id:"101105",title:"Dr.",name:"Gopal",surname:"Shukla",slug:"gopal-shukla",fullName:"Gopal Shukla"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"1591",title:"Infrared Spectroscopy",subtitle:"Materials Science, Engineering and Technology",isOpenForSubmission:!1,hash:"99b4b7b71a8caeb693ed762b40b017f4",slug:"infrared-spectroscopy-materials-science-engineering-and-technology",bookSignature:"Theophile Theophanides",coverURL:"https://cdn.intechopen.com/books/images_new/1591.jpg",editedByType:"Edited by",editors:[{id:"37194",title:"Dr.",name:"Theophanides",surname:"Theophile",slug:"theophanides-theophile",fullName:"Theophanides Theophile"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"3092",title:"Anopheles mosquitoes",subtitle:"New insights into malaria vectors",isOpenForSubmission:!1,hash:"c9e622485316d5e296288bf24d2b0d64",slug:"anopheles-mosquitoes-new-insights-into-malaria-vectors",bookSignature:"Sylvie Manguin",coverURL:"https://cdn.intechopen.com/books/images_new/3092.jpg",editedByType:"Edited by",editors:[{id:"50017",title:"Prof.",name:"Sylvie",surname:"Manguin",slug:"sylvie-manguin",fullName:"Sylvie Manguin"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"3161",title:"Frontiers in Guided Wave Optics and Optoelectronics",subtitle:null,isOpenForSubmission:!1,hash:"deb44e9c99f82bbce1083abea743146c",slug:"frontiers-in-guided-wave-optics-and-optoelectronics",bookSignature:"Bishnu Pal",coverURL:"https://cdn.intechopen.com/books/images_new/3161.jpg",editedByType:"Edited by",editors:[{id:"4782",title:"Prof.",name:"Bishnu",surname:"Pal",slug:"bishnu-pal",fullName:"Bishnu Pal"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"72",title:"Ionic Liquids",subtitle:"Theory, Properties, New Approaches",isOpenForSubmission:!1,hash:"d94ffa3cfa10505e3b1d676d46fcd3f5",slug:"ionic-liquids-theory-properties-new-approaches",bookSignature:"Alexander Kokorin",coverURL:"https://cdn.intechopen.com/books/images_new/72.jpg",editedByType:"Edited by",editors:[{id:"19816",title:"Prof.",name:"Alexander",surname:"Kokorin",slug:"alexander-kokorin",fullName:"Alexander Kokorin"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"1373",title:"Ionic Liquids",subtitle:"Applications and Perspectives",isOpenForSubmission:!1,hash:"5e9ae5ae9167cde4b344e499a792c41c",slug:"ionic-liquids-applications-and-perspectives",bookSignature:"Alexander Kokorin",coverURL:"https://cdn.intechopen.com/books/images_new/1373.jpg",editedByType:"Edited by",editors:[{id:"19816",title:"Prof.",name:"Alexander",surname:"Kokorin",slug:"alexander-kokorin",fullName:"Alexander Kokorin"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"57",title:"Physics and Applications of Graphene",subtitle:"Experiments",isOpenForSubmission:!1,hash:"0e6622a71cf4f02f45bfdd5691e1189a",slug:"physics-and-applications-of-graphene-experiments",bookSignature:"Sergey Mikhailov",coverURL:"https://cdn.intechopen.com/books/images_new/57.jpg",editedByType:"Edited by",editors:[{id:"16042",title:"Dr.",name:"Sergey",surname:"Mikhailov",slug:"sergey-mikhailov",fullName:"Sergey Mikhailov"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"371",title:"Abiotic Stress in Plants",subtitle:"Mechanisms and Adaptations",isOpenForSubmission:!1,hash:"588466f487e307619849d72389178a74",slug:"abiotic-stress-in-plants-mechanisms-and-adaptations",bookSignature:"Arun Shanker and B. Venkateswarlu",coverURL:"https://cdn.intechopen.com/books/images_new/371.jpg",editedByType:"Edited by",editors:[{id:"58592",title:"Dr.",name:"Arun",surname:"Shanker",slug:"arun-shanker",fullName:"Arun Shanker"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"878",title:"Phytochemicals",subtitle:"A Global Perspective of Their Role in Nutrition and Health",isOpenForSubmission:!1,hash:"ec77671f63975ef2d16192897deb6835",slug:"phytochemicals-a-global-perspective-of-their-role-in-nutrition-and-health",bookSignature:"Venketeshwer Rao",coverURL:"https://cdn.intechopen.com/books/images_new/878.jpg",editedByType:"Edited by",editors:[{id:"82663",title:"Dr.",name:"Venketeshwer",surname:"Rao",slug:"venketeshwer-rao",fullName:"Venketeshwer Rao"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}}]},chapter:{item:{type:"chapter",id:"60117",title:"Antimicrobial Susceptibility Pattern of Staphylococcus aureus",doi:"10.5772/intechopen.74615",slug:"antimicrobial-susceptibility-pattern-of-staphylococcus-aureus",body:'\nStaphylococcus aureus particularly methicillin-resistant Staphylococcus aureus (MRSA) strains is one of the major causes of community and hospital-acquired bacterial infections. They are also becoming increasingly multidrug resistant and recently developed resistance to vancomycin, which has been used successfully to treat MRSA for many years. In vitro determination of drug resistance patterns of S. aureus is critical for the selection of effective drugs for the treatment of staphylococci infections. The main aim of this review was to determine the prevalence of drug-resistant S. aureus strains from different clinical specimens throughout the world. Different types of research study designs such as cross-sectional and retrospective and laboratory techniques like Kirby Bauer, agar dilution, and E tests were used. The result of each study was narrated with respect to antimicrobial susceptibility pattern of S. aureus to various drugs accordingly.
\nA study on methicillin resistance against S. aureus in Trinidad and Tobago was conducted by Akpaka et al. [1]. Of 1912 S. aureus isolates recovered from different clinical samples, 12.8% were found out to be methicillin (oxacillin) resistant. The highest (86%) of the isolates were obtained from wound swabs and the least from urine (0.4%) specimens. About 85% of methicillin-susceptible S. aureus (MSSA) were sensitive to commonly used antimicrobials in the country. On the other hand, all MRSA isolates were resistant to ceftriaxone, erythromycin, gentamycin, and penicillin but were 100% sensitive to vancomycin, rifampin, and chloramphenicol.
\nSimilar study was carried out by Orrett and Land [2] in Trinidad and Tobago. In this study, 2430 isolates of S. aureus strains recovered from various clinical sources, from hospital and community practices, were analyzed. The prevalence of MRSA varied with the type of clinical sample. The prevalence of MRSA from surgical/burn wound was the highest (60.1%) followed by urine (15.5%) and pus/abscess (6.6%), respectively. The prevalence of MSSA also varied with the type of clinical samples. The major sources of MSSA were surgical/burn wounds, pus/abscess, and upper respiratory tract specimens with rates of 32.9, 17.1, and 14.3%, respectively. Furthermore, 109 (4.5%) S. aureus strains were isolated from sputum, 201(8.3%) from blood, and 95(4%) from eye infection. Clinical specimens each accounting less than 3% of the total include the vagina, ear, and CNS. With regard to the antimicrobial susceptibility profile of the isolates, the greatest prevalence of resistance of MRSA was seen for erythromycin (86.7%) and clindamycin (75.3%). Resistance rates among MSSA were highest for ampicillin (70%).
\nOxacillin-resistant and multidrug-resistant Staphylococcus aureus in Lima, Peru, was studied by Seas et al. [3]. S. aureus isolates were recovered from the blood, sterile body fluids (e.g., cerebrospinal fluid, peritoneal, joint, and pericardial fluids), urine, skin and soft tissue, lungs, abscesses, surgical wound sites, and catheters. Of 103 strains isolated, 70 (68%) were MRSA. In the United States, the prevalence of MRSA in skin and soft tissue infections was conducted by Frazee et al. [4]. Among 137 study subjects, 119 S. aureus isolates were recovered of which MRSA was present in 51% of infection site cultures. Of 119 isolates 89 (75%) were MRSA. All MRSA strains were susceptible to trimethoprim/sulfamethoxazole, 94% to clindamycin, 86% to tetracycline, and 57% to levofloxacin. Similarly, results of this study revealed that the prevalence of MRSA was 59%. Moran et al. [5] conducted MRSA prevalence study in patients with skin and soft tissue infections. In this study a total of 422 patients with skin and soft tissue infections were enrolled. S. aureus was isolated from skin and soft tissue infection in 320 (76%) patients of which 249 (78%) of the S. aureus isolates were MRSA. This study revealed that the isolation rate of MRSA varies with respect to clinical sample. MRSA isolated from abscesses, purulent wounds, and cellulitis with purulent exudates accounted 61, 53, and 47%, respectively.
\nThe prevalence of MRSA across the European countries from 1999 to 2002 was analyzed by Tiemersma et al. [6]. In this study a total of 50,759 S. aureus isolates were collected from 495 hospitals in 26 countries. The prevalence of MRSA varied from 1% in Northern Europe to 40% in Southern and Western Europe. The study also has shown that the prevalence of MRSA increased significantly in countries such as Belgium, Germany, Ireland, the Netherlands, and the United Kingdom, while the prevalence of MRSA showed a decrease in Slovenia. In addition this study revealed that MRSA was more frequently isolated from men than women and patients with blood culture positive for MRSA were older than patients with MSSA.
\nMany studies on the prevalence of MRSA have been conducted in India. A total of 1426 wound swabs were taken from 450 high-risk patients by Vidhani et al. [7] of which S. aureus was isolated from 188 patients (41.8%) and out of which 97 (51.6%) patients were found to be MRSA. A marked difference in antibiotic sensitivity pattern of MRSA and MSSA isolates was reported. According to the results of this study, none of the MRSA isolate was found to be sensitive to penicillin and amoxicillin. However, 6 (5.5%) and 12 (11%) MSSA were sensitive to penicillin and amoxicillin. A total of 85 (77.9%) of MSSA were sensitive to cefotaxime, while only 17 (21.5%) of MRSA were sensitive to this antibiotic. Sensitivity to macrolide group of antibiotics like erythromycin and roxithromycin was seen in 77 (70.6%) of MSSA in comparison to 14 (17.7%) of MRSA. Susceptibility test results of this study further showed that among the aminoglycosides maximum sensitivity of MSSA was seen with amikacin 74 (67.9%), while only 21 (26.6%) of MRSA were sensitive to the same antibiotic. A total of 53 (67%) of MRSA and 76 (69.7%) of MSSA were found to be sensitive to fluoroquinolone group, that is, ofloxacin. All S. aureus isolates (MRSA and MSSA) were found to be uniformly sensitive to vancomycin which is the drug of choice for treating infections caused by MRSA.
\nAnother study conducted by Rajendra Goud et al. [8] revealed a prevalence 29.76% of community-associated MRSA. All community-associated MRSA were resistant to methicillin and penicillin, while resistance to erythromycin and vancomycin was 65 and 1.12%, respectively, but all MRSA isolates were sensitive to linezolid. A third study conducted by Sharma and Mall [9] found out that out of 200 nasal samples, S. aureus was recovered from 97 patients, and of these, 23 isolates were MRSA. The drug resistance patterns of MRSA isolated from clinical specimens, and carrier screening samples were found to be highly variable. Almost all the MRSA strains (91.3%) screened from nasal samples were resistant to amikacin, 86.95% to kanamycin and cloxacillin, 78.26% to ciprofloxacin, 56.52% to erythromycin, 52.17% to chloramphenicol, and 34.78% to both tetracycline and gentamycin. The production of β-lactamase enzyme in MRSA was found to be 19 (82.6%). Chandrashekhar et al. [10] isolated 312 S. aureus strains of which 177 (56.75%) were found to be MRSA. Susceptibility profile of this study showed that all MRSA were resistant to penicillin, followed by erythromycin (91.5%), ampicillin+ sulbactam (90.4%), amoxicillin (83.6%), norfloxacin (81.4%), cefuroxime (78.5%), and amikacin (25.4%). However, no strains were resistant to vancomycin. Similar study carried out by Kaur et al. [11] revealed that 27 out of 70 (38.6%) S. aureus isolates were MRSA.
\nA number of similar studies were carried out in other Asian countries. A study carried out in Tehran by Vahdani et al. [12] exhibited marked variation in the drug susceptibility of MRSA. The results of this study showed that all the 90 MRSA isolates were resistant to penicillin (100%), ampicillin (92%), and cefotaxime (93%). Vancomycin and chloramphenicol were the most effective antibiotics, and only 7 and 14% of isolates were resistant, respectively. Nitrofurantoin, gentamycin, amikacin, ciprofloxacin, and other cephalosporins like cefepime and cefazolin were better active than penicillin, ampicillin, and cefotaxime. This study showed that 44% of hospital-acquired MRSA strains were resistant to co-trimoxazole. Akhter et al. [13] in Karachi isolated MRSA and determined the drug susceptibility of pattern of both MRSA and MSSA. A total of 87 strains of S. aureus were recovered from various clinical samples by the authors. Of these, 66 (75.8%) strains were recovered from various swabs and 21 (24.13%) from blood. Of the isolates 20 (22.9%) were methicillin resistant. In this group high resistance was found to cloxacillin (100%), co-trimoxazole (95%), erythromycin (70%), and gentamicin (55%), and low resistance was observed to ciprofloxacin (30%). In MSSA 0% resistance was seen to ciprofloxacin and chloromycetin, and high resistance was found to co-trimoxazole (98.5%) and penicillin (73.13%). Both MRSA and MSSA were 100% sensitive to vancomycin. A total of 139 MRSA were isolated by Kaleem et al. [14] in Pakistan. Of this most of the MRSA were isolated from pus samples. As far as their drug susceptibility is considered, all of the isolated MRSA were found to be susceptible to vancomycin and linezolid. Furthermore, 130 isolates (94%) were susceptible to teicoplanin and minocycline, whereas 93% of isolates were sensitive to chloramphenicol and 91% were sensitive to tetracycline. Only 38 and 22% of the isolates were susceptible to fluoroquinolones and macrolides, respectively.
\nA good number of research work on the prevalence, rate of isolation, and drug susceptibility profile of MRSA have been carried out in Africa. A study carried out by Ojulong et al. [15] investigated 188 pus swabs collected from patients with surgical site infections. Out of 54 (28.7%) S. aureus isolates, 17 (31.5%) were found out to be MRSA. Resistance rates of MRSA were found out to be 88.2% for trimethoprim-sulfamethoxazole, 88.2% for erythromycin, 58.8% for gentamycin, 70.6% for ciprofloxacin, and 88.2% for chloramphenicol, and all MRSA isolates were found to be sensitive to vancomycin and clindamycin. A study carried out in Sudan by Alamin et al. [16] recovered 85 S. aureus strains of which 21 (24%) were isolated from nasal cavity, 26 (31%) from skin surface, 22 (26%) from wounds, and 16 (19%) from the throat. Out of 85 isolates, 25 were found out to be MRSA.
\nOkwu et al. [17] in Nigeria examined 120 samples taken from the nose. Of these 22 (18.3%) were found to be positive for S. aureus, and 13 (10.8%) of the isolates were oxacillin resistant. Their studies also depicted that seven (11.7%) MRSA strains were obtained from females, while six (10%) strains were from males. Also, 12 (19.4%) S. aureus and 7 (11.3%) MRSA were isolated from the age group of 9–14 years, while 10 (17.3%) isolated of which 6 (10.3%) were MRSA isolated the age groups of 3–8 years. Furthermore, the isolates were resistant to ampicillin (100%), cloxacillin (100%), penicillin (100%), tetracycline (82%), chloramphenicol (73%), erythromycin (68%), gentamicin (64%), streptomycin (56%), and oxacillin (55%). Another study conducted by Olowe et al. [18] in the same country, Nigeria, depicted that out of 67 S. aureus isolates, 32(47.8%) were resistant to methicillin. High prevalence of MRSA, 13 (19.4%), was isolated from wound, while urine sample had the least, 1(1.5%). High resistance levels (87.5%) were detected against penicillin and tetracycline, while gentamicin and vancomycin recorded the least resistance levels of 62.5 and 6.3%, respectively. The starch paper analysis confirmed the presence of beta-lactamase production in all the isolates tested (100%). Similar study was conducted to detect beta-lactamase production in the same country by Efuntoye et al. [19]; of the 95 isolates tested. A total of 79 (83.2%) were beta-lactamase-producing strains.
\nIn Ethiopia, a retrospective study on the prevalence of MRSA was conducted by Geyid et al. [20]. The results of this study showed that among 249 S. aureus isolates 75 (30.5%) were found out to be MRSA, while 173 (69.5%) were MSSA. With regard to antibiotic susceptibility pattern of the isolates, vancomycin and clindamycin were effective against all S. aureus isolates. The presence of beta-lactamase production was determined in the 355 S. aureus isolates, and 252 (71%) were found to be beta-lactamase producers. Furthermore, 47 (62%) of the MRSA isolates and 140 (81%) of the MSSA isolates were beta-lactamase-positive strains. The sensitivity pattern of all the S. aureus isolates against 11 common drugs indicated that the majority (80%) of the MRSA strains were multidrug resistant, while 4 (8%) were not resistant to any of the drugs tested. A total of 41 (54%) MRSA strains were both beta-lactamase producers and multidrug-resistant isolates. Another study carried out in Felege Hiwot Referral Hospital, Bahir Dar, showed that 55% of S. aureus isolates were MRSA [21].
\nSimilarly, in a study conducted by Dilnessa et al. [22], of 1360 clinical specimens analyzed, S. aureus was recovered from 194 (14.3%). Rate of isolation of S. aureus with regard to clinical specimens was the highest in pus 118 (55.4%). No S. aureus was isolated from CSF and urethral discharge. Out of 194 S. aureus isolates, 34 (17.5%) were found out to be MRSA and the remaining 160 (82.5%) were MSSA. A total of 98 (50.5%) S. aureus isolates were multidrug resistant, and the highest isolates were resistant to penicillin 187 (96.4%) and least resistant for clindamycin 23 (11.9%) and vancomycin 10 (5.1%). MRSA strains were 100% resistant to penicillin G, erythromycin, and trimethoprim-sulfamethoxazole and least resistant to vancomycin 10 (29.4%). Out of 194 S. aureus isolates, 153 (79.0%) were beta-lactamase producers (Table 1).
\nAuthors (publication year) | \nCountry | \nSample size | \nNo. of S. aureus (N/%) | \nMRSA (N/%) | \nMSSA (N/%) | \nVRSA (%) | \nMDRSA (%) | \n
---|---|---|---|---|---|---|---|
Moran et al. (2006) | \nUSA | \n422 | \n320 (76) | \n249 (78.0) | \n71 (22.0) | \n— | \n— | \n
Ojulong et al. (2009) | \nUganda | \n188 | \n54 (28.7) | \n17 (31.5) | \n37 (68.5) | \n0 | \n— | \n
Sharma and Mall (2011) | \nIndia | \n200 | \n97 (48.5) | \n23 (23.7) | \n74 (76.3) | \n— | \n— | \n
Okwu et al. (2012) | \nNigeria | \n120 | \n22 (18.3) | \n13 (59.1) | \n9 (40.9) | \n— | \n100 | \n
Akpaka et al. (2006) | \nSpain | \n— | \n1912 | \n244 (12.8) | \n1668 (87.2) | \n0 | \n— | \n
Geyid et al. (1991) | \nEthiopia | \n17,142 | \n249 (1.4) | \n76 (30.5) | \n173 (69.5) | \n0 | \n80 | \n
Dilnessa et al. (2016) | \nEthiopia | \n1360 | \n194 (14.3) | \n34 (17.5) | \n160 (82.5) | \n5.1 | \n50.5 | \n
Olowe et al. (2012) | \nNigeria | \n— | \n67 | \n32 (47.8) | \n35 (52.2) | \n6.3 | \n100 | \n
Vidhani et al. (2000) | \nIndia | \n450 | \n188 (41.7) | \n97 (51.6) | \n91 (48.4) | \n— | \n79.5 | \n
Alamin et al. (2013) | \nMalaysia | \n— | \n85 | \n25 (29.4) | \n60 (70.6) | \n8 | \n— | \n
Comparison of different literatures with respect to methicillin- and vancomycin-resistant pattern of Staphylococcus aureus in different countries.
MRSA, Methicillin-resistant S. aureus; MSSA, methicillin-susceptible S. aureus; MDRSA, multidrug-resistant S. aureus; VRSA, vancomycin-resistant S. aureus
Factors that could contribute to variations in the prevalence rate of MRSA and vancomycin could be due to differences in the length of study period, number of study sites, sample size, and sample type, and the lab procedures employed can be mentioned. The isolates were multidrug resistant to several combinations of the tested antibiotics. According to Magiorakos et al. [23], MDR is defined as non-susceptibility to at least one agent in three or more antimicrobial categories. Over all drugs such as gentamicin, amoxicillin-clavulanate, clindamycin, cefuroxime, vancomycin, and cephalothin had relatively lower resistance.
\nThe prevalence of S. aureus and MRSA varies appreciably based on the type of clinical samples. Pus is the main source of S. aureus and MRSA than other samples in hospital settings. The prevalence of MRSA stains obtained from different studies varies based on geographical location. Many MRSA strains were multidrug resistant, and a good number of the isolates were also resistant to vancomycin, the drug of choice for treating multidrug-resistant MRSA infections. Reducing this burden by good infection control practices such as strict hand washing, by identifying MRSA carriers, and treating them, the prudent use of antimicrobial agents is recommended. Beta-lactamase production plays a great role for acquisition of MRSA. Physicians should prescribe drugs after the sensitivity pattern of the microbe is known. Additionally, large-scale longitudinal study is needed to determine CA-MRSA and HA-MRSA. Further phenotypic and genotypic studies are needed to establish and clarify the genetic mechanism behind susceptibilities to antibiotics.
\nThe human brain contains billions of neurons which vibrate and generate oscillatory activity. This neural activity of nervous system is studied through brainwaves. These waves are highly complex and can be recorded using the method called electroencephalography (EEG). An epileptic seizure is a symptom due to abnormal and irregular excessive neuronal activity in the brain. The neuronal activity can be recorded by medical tests. Various methods are available for diagnosing brain diseases. Among those methods, the electroencephalogram test is mainly used for diagnosing epilepsy. EEG includes different types of waveforms with different frequency, amplitude, and spatial distribution. The electrical activity of the brain differs due to different stimuli and physiological variables. An EEG test can provide detailed information about the electrical activity of the brain at the testing time. The neurologist recognizes the brain pattern from the EEG test results to diagnose epilepsy. EEG recordings by visual scanning will take time and are inaccurate for detecting epilepsy [1]. Nowadays, the technology of computer-aided diagnosis (CAD) has been used in hospitals; it cannot replace the doctor, but it can assist the professionals to diagnose the disease accurately. The main aim of the CAD systems is to identify the disease in early stages of its development. The CAD supportive tool is developed by using highly complex recognition techniques and machine learning algorithms. The CAD systems are approved by US Food and Drug Administration. They can reduce the false negative rate of recognition of diseases. Recent research studies have identified that the performance of CAD is better in the clinical environment. Establishing CAD systems in medical practice contains some risk and complexity. Sometimes, the interpretation of given data may not yield 100% accurate result. It provides only secondary opinion to the physicians. Especially in epileptic seizure detection, machine learning is very difficult because of understanding the brainwaves. The patterns of brainwaves are completely unique to individuals. Since 1998, CAD tools have been useful for diagnosing disease. It does not mean that they are meant for diagnostic purposes, but the approved CAD system can provide accurate results. Early diagnosis of disease is very important for saving life. Different information can be extracted by using medical image and signal technologies such as X-ray, computed tomography (CT), positron emission tomography (PET), single positron emission computed tomography (SPECT), magnetic resonance imaging (MRI), ultrasound, EEG, electrocardiography (ECG), electromyography (EMG), etc. for diagnosing diseases like cancer and coronary artery, cardiovascular, and neurological disorders. CAD supports accurate diagnosis in early stages of a chronic disease. Soft computing techniques are used in computer-aided diagnosis and computer-aided detection. In the earlier stage of computational approaches, the problem-solving methods were carried out using conventional mathematics and specific analytical models [2].
The traditional way of computing would be less efficient for problem-solving. In the growth of computational science, researchers focus on soft computing in order to overcome the drawbacks of hard computing. Just like artificial intelligence (AI), soft computing method works similar to the human brain. A comparison of hard computing and soft computing is given in Table 1. Soft computing techniques can be applicable in various fields such as signal and image processing, system integration, decision support process and system control, pattern recognition, fault diagnosis, data mining, forecasting applications, robotics, virtual reality, etc. Machine learning, fuzzy logic, evolutionary computation, Bayesian network, and chaos theory techniques are the main components of soft computing. These methods are very useful for automation and necessary for technology development [2]. The following sections of this chapter explain the merits and demerits of soft computing and the procedure for automated epileptic seizure prediction and detection.
Hard computing | Soft computing |
---|---|
There are different types of conventional methods such as Boolean logic, crisp analysis, numerical analysis, deterministic search, analytical model, and binary logic. These conventional methods are also called hard computing techniques | To make something better to handle uncertainty, imprecision, robustness, low solution cost, partial truth, and approximation, the soft computing techniques are introduced |
These techniques commonly use arithmetic, science, and computing | It imitates the model from nature |
Since conventional methods are used from the beginning of computational science, these traditional ways of approach require a lot of computation time | It mimics biological procedures and plays a greater role in the development of computational science and acts efficiently |
The hard computing techniques are inaccurate, inadequate, and unreliable | The soft computing methods can be approached in different ways for finding solution |
The programs that are written by using these techniques are deterministic | The soft computing techniques are developed mainly to get better results for any NP (nondeterministic polynomial)-complete problems |
It involves precise input data and sequential procedures | Unlike hard computing, the inputs are adjusted to optimize the result |
It can produce exact but not an approximate answer | For any given information, the process can give the best result by maximizing the desired benefit and minimizing the undesired one at low solution cost |
Comparison of hard and soft computing.
Machine learning, fuzzy logic, evolutionary computation, and probabilistic ideas are the main components of soft computing. The following sections give detailed descriptions of each component.
Problem-solving is a challenging task for intelligent entities. It has been proved that “a machine can learn new things.” It can adapt to new situations and has an ability to learn from the storage information. Machine learning techniques include artificial neural networks (ANNs), perceptron, and support vector machine (SVM) whereas evolutionary computations include evolutionary algorithms, meta-heuristic and swam intelligence. Just like human brain, a machine is capable of acquiring knowledge from data. It is developed from the field of AI. In order to build intelligent machines, we need machine learning techniques. These techniques deal with huge data in minimum time. There are different types of machine learning methods. They are as follows:
supervised learning;
unsupervised learning; and
reinforcement learning.
Supervised learning technique is used in majority of analyses. In this technique, the system learns from training examples, whereas in unsupervised learning, the system is challenged to discover some patterns directly from the given data. Classification and regression are two different supervised learning problems. The next section gives detailed description about classification using EEG signals for epileptic seizure detection. Regression gives the statistical relationship between two or more variables. An association rule learning problem and clustering problem are major examples explaining unsupervised learning problems. Association rule learning is based on rule-based machine learning method and used to discover the interesting relationship between variables in a huge database whereas clustering method discovers the patterns from the groupings of given data. Reinforcement learning is the third type of machine learning which learns how to behave in an environment merely by interaction. It is a dynamic way of learning. It learns directly and controls the data (no supervisor). Machine learning algorithms have the ability of learning from data and make predictions and classifications for a model based on the sample inputs. ANN is a technique composed of artificial neurons (processing units or elements) and mimics the function of the human brain, whereas SVM is based on associate learning method and performs data classification. It separates the data into corresponding groups using hyperplanes. Perceptron and support vector are very similar linear classifiers. A network with no hidden layers is called a single layer perceptron. Back propagation algorithm and perceptron are second-generation neural networks. Back propagation is a technique used to train the neural network in order to minimize the objective function. It can learn from mistakes. It looks for the minimum value of the error function in weight space. The weight that minimizes the error function is then considered to be a solution for the learning problem. “It is a supervised learning method, and is a generalization of the delta rule or gradient descent” [2].Neural networks can be classified as follows:
single-layer neural network;
multi-layer neural network; and
competitive neural network.
The back propagation algorithm works as follows: each neuron has an activation function in the neural network with respect to weights wij defined as:
The sigmoid function with respect to output function is defined as:
Therefore, the error functions of each neuron in the output are defined as:
where dj denotes the jth element of the desired response vector and the sum of the errors in the output layer from all the neurons is defined as:
Since
where η denotes the learning rate parameter. Eqs. (1) and (2) provide the dependency with respect to output as:
Therefore, the weight adjustment of each neuron (from (5) and (8)) is:
Feed forwarding the inputs, calculating the error, and propagating it back to the previous layers are the main steps of an ANN classifier. The error is identified as the difference between the desired response and actual response of the network. Each classifier is based on some learning method. There are different types of learning methods such as error correction learning, memory-based learning, associative learning, neural net learning, genetic learning, etc. SVM is based on the associative learning method. There are many advantages in SVM. The performance of SVM is very competitive with other methods. A drawback is the problem complexity for large sample sizes. Special optimizers are used for optimization. Basically, SVM is a linear classifier that classifies the two different classes (normal and seizure) efficiently. The features of the two classes are categorized by the labels “−1” and “+1.” The features that are extracted from the signal are defined as:
where yi denotes the label related to the pattern xi and n refers to the number of samples. Dot product or the scalar product of linear classifier is defined as:
This Eq. (11) in the function form is:
where wi denotes the weight vector and b refers to the bias. For the case b = 0, the set of vectors in WT(x) = 0 produce a hyperplane through the origin, which divides the features into two classes. The kernel is an algorithm that can produce non-linear decision boundaries. Replacing the normal SVM (linear kernel) dot product with a kernel function defines a Gaussian radial basis function classifier which is expressed as
The variables xi and xj represent the two sample data from the dataset. The default sigma value is one that has been associated with all the attributes in the dataset. The features are separated into two different classes with respect to their feature label. ANN and SVM are supervised learning methods. Both have different working patterns. SVM with kernels is highly suitable for non-linear mapping functions. The classification process is important because a machine has to learn how to classify the data into groups [3].
Machine learning, fuzzy logic, and evolutionary computations can be applicable for any decision-making problems. Unlike Boolean logic, fuzzy logic is an approach that deals with a problem by the level of truth values which lie between 0 and 1. Fuzzy refers to vagueness. The Boolean logic results in true or false for the question (Figure 1) “Is it raining?” but fuzzy logic gives a number in the range from 0 to 1. Here 1.0 represents absolute truth and 0.0 represents absolute false.
Example for fuzzy logic.
This is a logic used for fuzziness. It was introduced in 1965 by Lofti A.Zadeh. Fuzzy classifier is a classifier (algorithm) that uses fuzzy logic for classification and prediction problems. It is based on fuzzy sets (membership functions). The data-driven and trial and error (heuristic) approaches are two different approaches of fuzzy logic. An automated system can be designed using these approaches. Among these approaches, data-driven is most essential for the model to learn and update continuously. Fuzzy logic uses trial and error approach in tuning process for obtaining a satisfactory result. It is a technique that can handle imprecise data and especially analyze crisp/standard data. The data-driven approach is similar to event-driven approach and it is well structured. In classification processes, appropriate features are required to train and test the system. The performance of the system depends on selecting the apt features from the data for modeling the detection system. The heuristic method is not an optimal approach for problem-solving. It gives satisfactory solution. Heuristics, hyper-heuristics, and meta-heuristics are commonly used with machine learning and optimization techniques. Mostly, machine learning techniques are heuristic. Genetic algorithm or any optimization technique can be used to get optimal solution for the given problem. Fuzzy if then rule is the simple form of fuzzy rule based classifier. Fuzzy if-then rule statements are the form of fuzzy logic. Any classifier that uses fuzzy logic is fuzzy rule based classifier. These classifiers are well suited for linear model of classification whereas ANN can predict better on test data. Recently, deep learning has been the popular tool for prediction and detection processes. Fuzzy logic gives multi-value answers, whereas in machine learning, the system learns from data especially with the control or supervisor [2].
Evolutionary computation (EC) is a subdiscipline of AI and soft computing. In computational intelligence, evolutionary algorithms are inspired by biological systems and give optimal solution for problems. Meta-heuristic and swarm intelligence may also yield enough good solutions for any optimization problem. EC is a computational intelligence method involved in a lot of optimization techniques for problem-solving methods. It is a subfield of AI. The algorithms of EC are inspired by biological evolution. These algorithms can give highly optimum solutions for any kind of problems. Ant colony optimization, genetic algorithm (GA), genetic programming, self-organization maps, competitive learning, and swarm intelligence are some examples of EC techniques. Genetic algorithm is a technique used for optimization in problem-solving of various fields. It is derived from the natural genetic systems. It gives accurate results, exhibits robustness, and produces optimal solution for the problem.
In computational intelligence, the application program differs among various problems in various fields. GA starts with the production of the initial chromosome in the population. Chromosomes are binary digits representing the control parameters in the coding of the given problem. Like natural reproduction systems, crossover and mutation processes take place for generating a new population. Fitness calculation is evaluated in successive iterations called generations. After several generations, GA selects the best chromosome using probabilistic transition rules and obtains the optimal or closest optimal solution to the problem. In the automated epileptic seizure detection problem, genetic algorithm is used for feature selection. Selecting relevant features is important for the performance of the system [2].
Both probabilistic ideas and logic are used in probabilistic reasoning in order to handle uncertainty situations. Most of the problems use probability and statistics. “Clean data is greater than more data.” Machine learns from data. Quality of data is important rather than quantity of data. Bayesian analysis is one of the most important approaches for probabilistic reasoning. Unknown information or imperfectness is the situation of uncertainty. Bayesian inference is a statistical inference based on Bayes theorem that can be used for accurate prediction. It is very useful when the available data are insufficient for solving the problem. Data analysis is a procedure of evaluating data that are gathered from various sources. The soft computing techniques play a challenging part in data analysis. For example, data mining techniques are especially used for discovering new information from a huge database, whereas soft computing techniques mimic the process of human brain in order to find effective solutions for any NP-complete problem.
There is a link between data analysis and soft computing. Data may be qualitative or quantitative. Quantitative data can give exact solution for the problem. The data are pre-processed once they have been collected. The raw data are transformed effectively for the purpose of analysis in the pre-processing stage. Any type of data has to be initially pre-processed for analysis. The main principle of data pre-processing is to eliminate the irrelevant and redundant data (noise data) in order to get better detection accuracy of the system. In signal processing, the error is referred to as an artifact or noise. Unwanted information can be removed from the raw data using noise reduction. Different types of algorithms are available for data pre-processing. For example, in the case of EEG signal processing for epileptic seizure detection, artifacts can occur from physiological or mechanical sources. Respiratory, cardiac/pulse, eye movement, and electromyography signals are biological artifacts [4]. These artifacts should be recognized and eliminated for proper diagnosis. More than one variety of artifacts can appear in the recorded EEG. Pre-processing is the first step in classification and diagnostics where the artifacts have to be removed. After pre-processing, the signals are filtered and free from noise. These filtered signals are used for feature extraction process in the next step.
The process that converts the huge samples to a set of features is called feature extraction and feature selection is the process that filters the redundant or irrelevant features. These methods are used to reduce the actual dimension of the given data. Data are important to build a machine learning model. The performance of the classifier depends on the given data. The noise must be removed from data. Classifier cannot separate the noise from data. Pre-processing is the process that is most important for removing noise. Analysis of EEG signals is important to diagnose epilepsy in clinical practice [3]. Fourier transform-based analysis is suitable for stationary signals. Studies have proved that EEG signals change over time and frequency components. Several time-frequency domain-based methods such as short time Fourier transform, discrete wavelet transform (DWT), and multiwavelet transform can be used to decompose the EEG signals [5]. Removing artifacts from the signal especially in biomedical applications is a challenging task, because it creates some signals and disturbs the epilepsy diagnosis. Pre-processing is the process to remove artifacts, and they can be extracted well by a method called independent component analysis (ICA) [6]. In order to reduce the dimension of the raw data and to find optimal solution, feature extraction process with kernel trick is frequently used [7]. Figure 2 explains the EEG signal classification.
EEG signal classification.
In earlier days, reading and interpretations of the EEG signals were very difficult for a neurophysiologist. This drawback has been overcome in the latest computer technology. EEG is a non-stationary signal and is very difficult to understand by an ordinary person. For EEG signal analysis, features are extracted from the EEG vectors and appropriate features are selected for classification. Feature selection is a subset of feature extraction. The irrelevant and redundant features are eliminated for better performance of the system. Feature selection algorithms can be used to select appropriate features. Genetic algorithm is an exact tool for feature selection. It can reduce the computing time and space required to run the algorithms. Filter method, Pearson’s correlation co-efficient, mutual information, wrapper methods, and greedy forward search are some of the methods used to select features for classification. In machine learning, classification is the process of categorizing the data by training the machine with the class label. For example, labels like “Seizure” or “Normal” are used in the case of supervised learning. The clustering technique also known as grouping technique is based on inherence in unsupervised learning and can handle unlabeled data. An algorithm that maps the data into a particular group is called a classifier.
ECG and EEG data are used in seizure detection. Several electronic mobile applications are developed to track seizure information from the patient electronically. The information includes type of seizure, frequency, and duration. The application provides useful data for the epileptologist to treat epilepsy accurately. Already, many applications have been developed and are available on the market. Figure 3 represents the closed-loop warning system for epilepsy.
Warning system in epilepsy.
A new high tech bracelet developed by Netherlands scientists can detect 85% of all severe night time epilepsy seizures. Automated seizure detection methods can overcome some of the difficulties that occur from data collection, patient monitoring, and prediction modeling. Closed-loop system monitors the seizures and can detect, anticipate, and even respond to the real-time information from the patients. These systems have been used in emergency and intensive care settings of medical diagnosis [8].
B. Suguna Nanthini [3] had carried out six different analyses for detecting seizures using EEG signals under supervised learning method. The performance of the system in all the analyses is measured by the confusion matrix method. Online available EEG database (Bonn University Database) and real-time data from the EEG center, Coimbatore, India, are used for EEG signal classification analysis. EEG tests taken from 10 normal and seizure subjects for epileptic seizure detection are used in second database. These signals are examined and used for binary classification as well as for validation. Set A (perfectly normal) and Set E (merely seizure) have been chosen from online database. The first three analyses were carried out in the spatial domain and next three analyses were carried out in the frequency (wavelet) domain. In the first analysis [9], gray-level co-occurrence matrix (GLCM) features namely contrast, correlation, energy, and homogeneity are extracted from the EEG vectors. The system is well trained to identify the exact group and tested for classification of data using ANN classifier. The performance of the system is measured by the confusion matrix. The system achieves 85% accuracy. The same problem is examined with an SVM classifier in the second analysis [10]. The classifier achieves 90% accuracy for EEG signal classification. The computational complexity of analyses 1 and 2 are calculated and shown in the following Table 2.
Analysis | Training time (seconds) | Testing time (seconds) | Precision (%) | Miss classification rate (%) |
---|---|---|---|---|
GLCM with ANN | 105 | 0.05 | 100 | 24 |
GLCM with SVM | 64 | 0.02 | 90 | 10 |
Computational complexity of analyses 1 and 2.
When the analyses use ANN and SVM classifiers, the space complexity depends on the number of training samples used in the classification process. In the third analysis [11], eight statistical features are added with GLCM features. The EEG signals are segmented and combinations of normal and seizure signals are used for classification process. In extraction process, eight statistical features and four GLCM features are extracted from each of the segmented signal. An SVM classifier with different kernels is used for seizure detection. The computation complexity of analysis 3 is calculated and presented in the following Table 3. The complexity of the model depends on k-fold cross-validation method. The system executes the same learning algorithm k times. It takes different training sets of size (k−1)/k times the size of the original data. In the execution step, each sample is evaluated (k−1) times. The space complexity of the analysis for RBF kernel is (Number of samples) ^2*(Number of features) and for linear kernel is (Number of samples) * (Number of features). ANN with back propagation algorithm [9] and SVM with linear kernel have achieved almost similar results.
Analysis | Training time (seconds) | Testing time (seconds) |
---|---|---|
GLCM and statistical features with SVM linear kernel | 275 | 245 |
GLCM and statistical features with SVM RBF kernel | 127 | 182 |
GLCM and statistical features with SVM-tuned RBF kernel | 220 | 129 |
Computation complexity of analysis 3.
EEG signals are non-stationery and can be analyzed better through wavelet transform. Different types of wavelets are available to decompose the signal. The challenging part is to select a suitable wavelet and the level of decomposition of the signal. In the fourth analysis [12], statistical features namely mean, median, mode, standard deviation, skewness and kurtosis and four GLCM features are extracted from the EEG signal. The performance of the system is measured to select a suitable classifier for seizure detection. ANN and SVM are two classifiers used in the fourth analysis. The wavelets namely db1, db2, and haar are used for signal decomposition. The signal is decomposed up to level 3.
Statistical and GLCM features are used to examine the EEG signals separately and further they are combined together as an input to the classifier.
Raw EEG data (0–60 Hz) as its subbands (30–60 Hz (cD1), 15–30 Hz (cD2), 8–15 Hz (cD3), and 0–8 Hz (cA3)) are verified and analyzed using all those features.
On comparison of features (statistical, GLCM, and their combination), wavelets (db1,db2, and haar), and classifiers (ANN and SVM), the analysis concluded that the combination of statistical and GLCM features using SVM classifier gives the best outcome.
To extract maximum information from the EEG signal, entropy features are used in the fifth analysis [13]. There are different types of entropies. In this analysis, Shannon, Renyi, and Tsallis entropies are extracted from the EEG signals. On comparison of entropy features, the analysis concluded that Renyi entropy can achieve successful result. Instead of using only statistical features over the wavelet coefficient, this analysis examines the EEG signals through entropy values obtained from different degrees of orders for classification. When comparing with the existing work, this research uses the extended version of Shannon, namely Renyi and Tsallis to extract the maximum information from each EEG signal vector in terms of probability events. In the sixth analysis [14], EEG signals are examined by combining all the features from the previous analysis. Altogether, 16 features from the methods namely GLCM, statistical, and Renyi entropy features are extracted from the raw EEG and its subbands. DWT (db2) is used for decomposition of the signal at level 4.The approximation and detail co-efficient are analyzed individually with 16 and 8 features, respectively. Genetic algorithm is used for selecting 8 appropriate features. The SVM is used as a classifier. Classification is carried out for seizure detection. Accuracies from 16 and 8 dimension features are compared and it is concluded that relevant features can give better accuracy. Moreover, level 4 is enough for decomposing the signal because the lower frequencies namely delta and theta can be obtained at level 4 of decomposition. Mostly, seizures are identified at lower frequencies; so, level 4 is sufficient for decomposition of the EEG signal. Further, the time to execute the algorithm is reduced and it occupies less memory space for the storage of data parameters. The complexity of this EEG signal analysis is calculated and presented in the following Table 4.
Analysis | Training time (seconds) | Testing time (seconds) |
---|---|---|
16 Dimension features with SVM | 0.76 | l 0.0013 |
8 Dimension features with SVM classifier | 0.72 | 0.0011 |
Computational complexity of analysis 6.
Summary and time complexity of the analyses are shown in Tables 5 and 6, respectively. All analyses are carried out in MATLAB environment. From the calculations, the analyses prove that the performance of SVM with significant features is good when compared with ANN using large number of features as the input. The major contributions of these analyses in view of the existing work are as follows:
Two different machine learning algorithms (ANN and SVM) that are based on two different learning methods (error correcting and associative learning) have been examined for seizure detection.
Unique set of features are extracted from the EEG signals for classification.
For optimization, genetic algorithm is used for feature selection and proved that the classifier can perform well with relevant features.
Accuracies are calculated for raw EEG signal and for all decomposed signals
EEG analysis #: | Domain | Feature extraction | Classifier | Conclusion |
---|---|---|---|---|
1 | Spatial domain | GLCM features | ANN | 85% Accuracy |
2 | Spatial domain | GLCM features | SVM | 90% Accuracy |
3 | Spatial domain (EEG segments) | (GLCM and statistical) | SVM (Linear and RBF kernel) | Linear kernel (99.95% accuracy) |
4 | Frequency domain DWT (db1,db2) | GLCM, statistical, and hybrid features | ANN and SVM | Db2 wavelet and hybrid features to SVM classifier are the best outcomes (92.16% accuracy) |
5 | Frequency domain db2 at level 4 | Entropy estimation (Shannon, Renyi, and Tsallis) | SVM | Renyi entropy gives better accuracy (99.9 accuracy) |
6 | Db2 at level 4 | 8 statistical features, 4 GLCM features, 4 Renyi entropy estimation (Genetic algorithm for feature selection) | SVM | Relevant features give successful result with 90% performance accuracy in system validation |
Summary of EEG analyses.
Analysis | Database | LoD | Number of features | Number of features significant | Classifier | Tr time (seconds) | Test Time (seconds) |
---|---|---|---|---|---|---|---|
1 | Bonn University | 3 | 10 | 6 | ANN | 243 | 0.123 |
2 | Bonn University | 3 | 10 | 6 | SVM | 64 | 0.023 |
3 | Bonn University | 4 | 6 | 6 | ANN | 184 | 0.2 |
4 | Bonn University | 4 | 6 | 6 | SVM | 61 | 0.02 |
5 | Real-time data | 4 | 16 | 12 | SVM | 63 | 0.0013 |
6 | Real-time data | 4 | 8 | 4 | SVM | 62 | 0.0011 |
7 | Validation | 4 | 8 | 4 | SVM | — | 0.017 |
4 | 12 | 12 | SVM | — | 0.09 |
Time complexity of EEG analyses.
LoD, Level of decomposition; Tr, Training.
An epileptic seizure is a symptom due to abnormal and excessive irregular neuronal activity in the brain. EEG test is mainly used for diagnosing epilepsy. EEG includes different types of waveforms with different frequency, amplitude, and spatial distribution. Traditional ways of computations would be less efficient for problem-solving. But, soft computing methods can work in an efficient way for discovering solutions from the given data. Components of soft computing are essential for developing automated expert systems. Early diagnosis of disease can save the life of a person. The approved CAD system is able to provide accurate results. Problem-solving is a challenging task for intelligent entities. It has been proved that “a machine can learn new things.” It can adapt to new situations and has an ability to learn from the storage information. Supervised learning technique is used in majority of analyses. Fuzzy logic gives multi-value answers, whereas in machine learning, the system learns from data especially with the control or supervisor. In computational intelligence, evolutionary algorithms are inspired by biological systems and give optimal solution for the problem. “Clean data are greater than more data.” Machine learns from data. Quality of data is important rather than quantity of data. This chapter gave an introduction about the components of soft computing and classification in machine learning. From the review of analyses, this chapter concludes that relevant features and less number of features can make the classifier perform well. Accuracies are compared in all decomposed signals and proved that level 4 of decomposition is enough for EEG signal classification. At level 4, the lower frequencies (delta and theta) can be analyzed perfectly because seizures occur mostly at lower frequencies. Also, from the analyses, it has been proved that the time required and memory space for data parameters are less.
There are no conflicts of interest.
The Internet has irrevocably changed the dynamics of scholarly communication and publishing. Consequently, we find it necessary to indicate, unambiguously, our definition of what we consider to be a published scientific work.
",metaTitle:"Prior Publication Policy",metaDescription:"Prior Publication Policy",metaKeywords:null,canonicalURL:"/page/prior-publication-policy",contentRaw:'[{"type":"htmlEditorComponent","content":"A significant number of working papers, early drafts, and similar work in progress are openly shared online between members of the scientific community. It has become common to announce one’s own research on a personal website or a blog to gather comments and suggestions from other researchers. Such works and online postings are, indeed, published in the sense that they are made publicly available. However, this does not mean that if submitted for publication by IntechOpen they are not original works. We differentiate between reviewed and non-reviewed works when determining whether a work is original and has been published in a scholarly sense or not.
\\n\\nThe significance of Peer Review cannot be overstated when it comes to defining, in our terms, what constitutes a published scientific work. Peer Review is widely considered to be the cornerstone of modern publishing processes and the key value-adding contribution to a scholarly manuscript that a publisher can make.
\\n\\nOther than the issue of originality, research misconduct is another major issue that all publishers have to address. IntechOpen’s Retraction & Correction Policy and various publication ethics guidelines identify both redundant publication and (self)plagiarism to fall within the definition of research misconduct, thus constituting grounds for rejection or the issue of a Retraction if the work has already been published.
\\n\\nIn order to facilitate the tracking of a manuscript’s publishing history and its development from its earliest draft to the manuscript submitted, we encourage Authors to disclose any instances of a manuscript’s prior publication, whether it be through a conference presentation, a newspaper article, a working paper publicly available in a repository or a blog post.
\\n\\nA note to the Academic Editor containing detailed information about a submitted manuscript’s previous public availability is the preferred means of reporting prior publication. This helps us determine if there are any earlier versions of a manuscript that should be disclosed to our readers or if any of those earlier versions should be cited and listed in a manuscript’s references.
\\n\\nSome basic information about the editorial treatment of different varieties of prior publication is laid out below:
\\n\\n1. CONFERENCE PAPERS & PRESENTATIONS
\\n\\nGiven that conference papers and presentations generally pass through some sort of peer or editorial review, we consider them to be published in the accepted scholarly sense, particularly if they are published as a part of conference proceedings.
\\n\\nAll submitted manuscripts originating from a previously published conference paper must contain at least 50% of new original content to be accepted for review and considered for publication.
\\n\\nAuthors are required to report any links their manuscript might have with their earlier conference papers and presentations in a note to the Academic Editor, as well as in the manuscript itself. Additionally, Authors should obtain any necessary permissions from the publisher of their conference paper if copyright transfer occurred during the publishing process. Failure to do so may prevent Us from publishing an otherwise worthy work.
\\n\\n2. NEWSPAPER & MAGAZINE ARTICLES
\\n\\nNewspaper and magazine articles usually do not pass through any extensive peer or editorial review and we do not consider them to be published in the scholarly sense. Articles appearing in newspapers and magazines rarely possess the depth and structure characteristic of scholarly articles.
\\n\\nSubmitted manuscripts stemming from a previous newspaper or magazine article will be accepted for review and considered for publication. However, Authors are strongly advised to report any such publication in an accompanying note to the External Editor.
\\n\\nAs with the conference papers and presentations, Authors should obtain any necessary permissions from the newspaper or magazine that published the work, and indicate that they have done so in a note to the External Editor.
\\n\\n3. GREY LITERATURE
\\n\\nWhite papers, working papers, technical reports and all other forms of papers which fall within the scope of the ‘Luxembourg definition’ of grey literature do not pass through any extensive peer or editorial review and we do not consider them to be published in the scholarly sense.
\\n\\nAlthough such papers are regularly made publicly available via personal websites and institutional repositories, their general purpose is to gather comments and feedback from Authors’ colleagues in order to further improve a manuscript intended for future publication.
\\n\\nWhen submitting their work, Authors are required to disclose the existence of any publicly available earlier drafts in a note to the Academic Editor. In cases where earlier drafts of the submitted version of the manuscript are publicly available, any overlap between the versions will generally not be considered an instance of self-plagiarism.
\\n\\n4. SOCIAL MEDIA, BLOG & MESSAGE BOARD POSTINGS
\\n\\nWe feel that social media, blogs and message boards are generally used with the same intention as grey literature, to formulate ideas for a manuscript and gather early feedback from like-minded researchers in order to improve a particular piece of work before submitting it for publication. Therefore, we do not consider such internet postings to be publication in the scholarly sense.
\\n\\nNevertheless, Authors are encouraged to disclose the existence of any internet postings in which they outline and describe their research or posted passages of their manuscripts in a note to the Academic Editor. Please note that we will not strictly enforce this request in the same way that we would instructions we consider to be part of our conditions of acceptance for publication. We understand that it may be difficult to keep track of all one’s internet postings in which the researcher´s current work might be mentioned.
\\n\\nIn cases where there is any overlap between the Author´s submitted manuscript and related internet postings, we will generally not consider it to be an instance of self-plagiarism. This also holds true for any co-Author as well.
\\n\\nFor more information on this policy please contact permissions@intechopen.com.
\\n\\nPolicy last updated: 2017-03-20
\\n"}]'},components:[{type:"htmlEditorComponent",content:'A significant number of working papers, early drafts, and similar work in progress are openly shared online between members of the scientific community. It has become common to announce one’s own research on a personal website or a blog to gather comments and suggestions from other researchers. Such works and online postings are, indeed, published in the sense that they are made publicly available. However, this does not mean that if submitted for publication by IntechOpen they are not original works. We differentiate between reviewed and non-reviewed works when determining whether a work is original and has been published in a scholarly sense or not.
\n\nThe significance of Peer Review cannot be overstated when it comes to defining, in our terms, what constitutes a published scientific work. Peer Review is widely considered to be the cornerstone of modern publishing processes and the key value-adding contribution to a scholarly manuscript that a publisher can make.
\n\nOther than the issue of originality, research misconduct is another major issue that all publishers have to address. IntechOpen’s Retraction & Correction Policy and various publication ethics guidelines identify both redundant publication and (self)plagiarism to fall within the definition of research misconduct, thus constituting grounds for rejection or the issue of a Retraction if the work has already been published.
\n\nIn order to facilitate the tracking of a manuscript’s publishing history and its development from its earliest draft to the manuscript submitted, we encourage Authors to disclose any instances of a manuscript’s prior publication, whether it be through a conference presentation, a newspaper article, a working paper publicly available in a repository or a blog post.
\n\nA note to the Academic Editor containing detailed information about a submitted manuscript’s previous public availability is the preferred means of reporting prior publication. This helps us determine if there are any earlier versions of a manuscript that should be disclosed to our readers or if any of those earlier versions should be cited and listed in a manuscript’s references.
\n\nSome basic information about the editorial treatment of different varieties of prior publication is laid out below:
\n\n1. CONFERENCE PAPERS & PRESENTATIONS
\n\nGiven that conference papers and presentations generally pass through some sort of peer or editorial review, we consider them to be published in the accepted scholarly sense, particularly if they are published as a part of conference proceedings.
\n\nAll submitted manuscripts originating from a previously published conference paper must contain at least 50% of new original content to be accepted for review and considered for publication.
\n\nAuthors are required to report any links their manuscript might have with their earlier conference papers and presentations in a note to the Academic Editor, as well as in the manuscript itself. Additionally, Authors should obtain any necessary permissions from the publisher of their conference paper if copyright transfer occurred during the publishing process. Failure to do so may prevent Us from publishing an otherwise worthy work.
\n\n2. NEWSPAPER & MAGAZINE ARTICLES
\n\nNewspaper and magazine articles usually do not pass through any extensive peer or editorial review and we do not consider them to be published in the scholarly sense. Articles appearing in newspapers and magazines rarely possess the depth and structure characteristic of scholarly articles.
\n\nSubmitted manuscripts stemming from a previous newspaper or magazine article will be accepted for review and considered for publication. However, Authors are strongly advised to report any such publication in an accompanying note to the External Editor.
\n\nAs with the conference papers and presentations, Authors should obtain any necessary permissions from the newspaper or magazine that published the work, and indicate that they have done so in a note to the External Editor.
\n\n3. GREY LITERATURE
\n\nWhite papers, working papers, technical reports and all other forms of papers which fall within the scope of the ‘Luxembourg definition’ of grey literature do not pass through any extensive peer or editorial review and we do not consider them to be published in the scholarly sense.
\n\nAlthough such papers are regularly made publicly available via personal websites and institutional repositories, their general purpose is to gather comments and feedback from Authors’ colleagues in order to further improve a manuscript intended for future publication.
\n\nWhen submitting their work, Authors are required to disclose the existence of any publicly available earlier drafts in a note to the Academic Editor. In cases where earlier drafts of the submitted version of the manuscript are publicly available, any overlap between the versions will generally not be considered an instance of self-plagiarism.
\n\n4. SOCIAL MEDIA, BLOG & MESSAGE BOARD POSTINGS
\n\nWe feel that social media, blogs and message boards are generally used with the same intention as grey literature, to formulate ideas for a manuscript and gather early feedback from like-minded researchers in order to improve a particular piece of work before submitting it for publication. Therefore, we do not consider such internet postings to be publication in the scholarly sense.
\n\nNevertheless, Authors are encouraged to disclose the existence of any internet postings in which they outline and describe their research or posted passages of their manuscripts in a note to the Academic Editor. Please note that we will not strictly enforce this request in the same way that we would instructions we consider to be part of our conditions of acceptance for publication. We understand that it may be difficult to keep track of all one’s internet postings in which the researcher´s current work might be mentioned.
\n\nIn cases where there is any overlap between the Author´s submitted manuscript and related internet postings, we will generally not consider it to be an instance of self-plagiarism. This also holds true for any co-Author as well.
\n\nFor more information on this policy please contact permissions@intechopen.com.
\n\nPolicy last updated: 2017-03-20
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