Barely three months into the new year and we are happy to announce a monumental milestone reached - 150 million downloads.
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This achievement solidifies IntechOpen’s place as a pioneer in Open Access publishing and the home to some of the most relevant scientific research available through Open Access.
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We are so proud to have worked with so many bright minds throughout the years who have helped us spread knowledge through the power of Open Access and we look forward to continuing to support some of the greatest thinkers of our day.
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Thank you for making IntechOpen your place of learning, sharing, and discovery, and here’s to 150 million more!
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\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:"10470",leadTitle:null,fullTitle:"Current Perspectives on Viral Disease Outbreaks - Epidemiology, Detection and Control",title:"Current Perspectives on Viral Disease Outbreaks",subtitle:"Epidemiology, Detection and Control",reviewType:"peer-reviewed",abstract:"The COVID-19 pandemic has reminded the world that infectious diseases are still important. The last 40 years have experienced the emergence of new or resurging viral diseases such as AIDS, ebola, MERS, SARS, Zika, and others. These diseases display diverse epidemiologies ranging from sexual transmission to vector-borne transmission (or both, in the case of Zika). This book provides an overview of recent developments in the detection, monitoring, treatment, and control of several viral diseases that have caused recent epidemics or pandemics.",isbn:"978-1-83881-911-8",printIsbn:"978-1-83881-910-1",pdfIsbn:"978-1-83881-915-6",doi:"10.5772/intechopen.92485",price:119,priceEur:129,priceUsd:155,slug:"current-perspectives-on-viral-disease-outbreaks-epidemiology-detection-and-control",numberOfPages:286,isOpenForSubmission:!1,isInWos:null,isInBkci:!1,hash:"c14777459bf484d53004a121bf8c761e",bookSignature:"David Claborn",publishedDate:"January 12th 2022",coverURL:"https://cdn.intechopen.com/books/images_new/10470.jpg",numberOfDownloads:2900,numberOfWosCitations:0,numberOfCrossrefCitations:1,numberOfCrossrefCitationsByBook:0,numberOfDimensionsCitations:2,numberOfDimensionsCitationsByBook:0,hasAltmetrics:1,numberOfTotalCitations:3,isAvailableForWebshopOrdering:!0,dateEndFirstStepPublish:"June 1st 2020",dateEndSecondStepPublish:"September 15th 2020",dateEndThirdStepPublish:"November 14th 2020",dateEndFourthStepPublish:"February 2nd 2021",dateEndFifthStepPublish:"April 3rd 2021",currentStepOfPublishingProcess:5,indexedIn:"1,2,3,4,5,6",editedByType:"Edited by",kuFlag:!1,featuredMarkup:null,editors:[{id:"169536",title:"Dr.",name:"David",middleName:null,surname:"Claborn",slug:"david-claborn",fullName:"David Claborn",profilePictureURL:"https://mts.intechopen.com/storage/users/169536/images/system/169536.jpeg",biography:"David Claborn is a Professor of Public Health at Missouri State University (MSU), USA, where he teaches courses in environmental health, infectious disease, and international health. He obtained a DrPH from the Uniformed Services University of the Health Sciences, Bethesda, Maryland, with his dissertation on the re-emergence of malaria in South Korea. He has been the director of the Master of Public Health program at MSU since 2013. Prior to his academic career, Dr. Claborn served in the US Navy for 20 years, retiring at the rank of Commander in 2008. His work as a medical entomologist has taken him to several international settings including Japan, Australia, South Korea, Italy, and, during Operation Desert Storm, Saudi Arabia.",institutionString:null,position:null,outsideEditionCount:0,totalCites:0,totalAuthoredChapters:"5",totalChapterViews:"0",totalEditedBooks:"6",institution:{name:"Missouri State University",institutionURL:null,country:{name:"United States of America"}}}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,coeditorOne:null,coeditorTwo:null,coeditorThree:null,coeditorFour:null,coeditorFive:null,topics:[{id:"1129",title:"Epidemiology",slug:"epidemiology"}],chapters:[{id:"76945",title:"Convalescent Plasma Immunotherapy - A Possible Mitigation Strategy for SARS-CoV-2 Pandemic",doi:"10.5772/intechopen.98254",slug:"convalescent-plasma-immunotherapy-a-possible-mitigation-strategy-for-sars-cov-2-pandemic",totalDownloads:147,totalCrossrefCites:0,totalDimensionsCites:0,hasAltmetrics:0,abstract:"Recently, a newly emerged severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) caused a pandemic coronavirus disease (COVID-19). More than 10 million confirmed cases and 503,867 associated deaths of SARS-CoV-2 have been reported worldwide to WHO in the end of July 2020. According to WHO guidelines, there is no effective therapy available for treating devastating SARS-CoV-2. Consequently, lack of evidence for appropriate treatment and vaccines has led to the re-emergence of convalescent plasma (CP) immunotherapy. Herein, we discuss the historical perspectives of CP against SARS-CoV, MERS-CoV, H1N1 pandemic and mainly the clinical outcomes of COVID-19 patients with respect to neutralizing antibodies (nAbs). A brief possible clinical protocol for CP transfusion with its adverse effects and limitation were also highlighted. It is concluded that, CP transfusion with high neutralizing antibody titer administered in early course of disease significantly improved clinical outcomes in COVID-19 patients by reducing morbidity and mortality. Thus, CP immunotherapy is considered as noteworthy candidate to be further re-evaluated as a most suitable therapeutic option against SARS-CoV-2 pandemic.",signatures:"Rajendran Manikandan, Mithilesh Singh, Vishal Chander, Gaurav Kumar Sharma, Suresh Bindu and Murali Dinesh",downloadPdfUrl:"/chapter/pdf-download/76945",previewPdfUrl:"/chapter/pdf-preview/76945",authors:[{id:"343879",title:"Dr.",name:"Mithilesh",surname:"Singh",slug:"mithilesh-singh",fullName:"Mithilesh Singh"},{id:"344642",title:"Dr.",name:"Rajendran",surname:"Manikandan",slug:"rajendran-manikandan",fullName:"Rajendran Manikandan"},{id:"344643",title:"Dr.",name:"Vishal",surname:"Chander",slug:"vishal-chander",fullName:"Vishal Chander"},{id:"344644",title:"Dr.",name:"Gaurav",surname:"Sharma",slug:"gaurav-sharma",fullName:"Gaurav Sharma"},{id:"347522",title:"Dr.",name:"Suresh",surname:"Bindu",slug:"suresh-bindu",fullName:"Suresh Bindu"},{id:"347525",title:"Dr.",name:"Murali",surname:"Dinesh",slug:"murali-dinesh",fullName:"Murali Dinesh"}],corrections:null},{id:"79434",title:"Recombinant Human Interferon-Gamma: Prospects for the Treatment of Chronic Epstein-Barr Viral Infection",doi:"10.5772/intechopen.101325",slug:"recombinant-human-interferon-gamma-prospects-for-the-treatment-of-chronic-epstein-barr-viral-infecti",totalDownloads:89,totalCrossrefCites:0,totalDimensionsCites:0,hasAltmetrics:1,abstract:"Infection of Epstein-Barr virus (EBV) is about 90% among people over the age of 40. The EBV causes a chronic infection that is characterized by chronic or recurrent symptoms and persists for a long time. Recombinant interferon-gamma (IFN-γ) has high clinical and antiviral efficacy in the treatment of herpesvirus infections. 110 patients with chronic EBV infection were examined. The patients were divided into three groups for different treatment regimens: Group 1—IFN-γ therapy (15 injections of Ingaron i/m, 500,000 IU every other day); Group 2—valaciclovir (Valtrex 500 mg × 2 times/day, orally for 2 months); Group 3—valganciclovir (Valcyte 450 mg × 2 times/day, orally for 2 months) and IFN-γ (10–20 injections of Ingaron i/m, 500,000 IU every other day). The best results were obtained in group 3–73.07% negative PCR. In this group, the combination of valganciclovir + IFN-γ was different. We showed that the efficacy of therapy in patients with chronic EBV is determined by the duration of INF-γ administration. We also determined spontaneous and induced production of IFN-α and -γ cytokines in serum and in lymphocyte culture. We demonstrated that in patients with an initially low level of induced IFN-γ, the production of this cytokine significantly increased in three months after the end of therapy.",signatures:"Irina A. Rakityanskaya and Tatiana S. Ryabova",downloadPdfUrl:"/chapter/pdf-download/79434",previewPdfUrl:"/chapter/pdf-preview/79434",authors:[{id:"357403",title:"Prof.",name:"Irina A.",surname:"Rakityanskaya",slug:"irina-a.-rakityanskaya",fullName:"Irina A. Rakityanskaya"},{id:"357408",title:"Dr.",name:"Tatiana S.",surname:"Ryabova",slug:"tatiana-s.-ryabova",fullName:"Tatiana S. Ryabova"}],corrections:null},{id:"75202",title:"A Narrative Review of the Measles Outbreak in North America and Globally",doi:"10.5772/intechopen.95921",slug:"a-narrative-review-of-the-measles-outbreak-in-north-america-and-globally",totalDownloads:216,totalCrossrefCites:0,totalDimensionsCites:0,hasAltmetrics:0,abstract:"In the early twenty-first century, measles was completely eradicated in the United States of America (USA) and almost eliminated in Canada. This was greatly due to most of the population being vaccinated against the virus. In 2018 and 2019, the USA and Canada experienced a rapidly developing measles virus outbreak due to growing debates about vaccine efficacy and side effects. Therefore, some people refused to vaccinate their children against measles, as well as many other life-threatening preventable diseases. This led to a major measles outbreak and health concern in the USA, Canada, and globally. Some countries including the Democratic Republic of the Congo (DRC) reported a significant number of cases and casualties resulting from measles, mainly due to the lack of funding for vaccines, as well as inadequate vaccination coverage in certain socio-demographic areas. People traveling from these countries can easily transmit the disease, though there has been a steep decline in cases since the travel ban due to coronavirus disease-2019 (COVID-19). The number of unvaccinated children currently in the USA and Canada has quadrupled since 2001. Over the past couple of years, most of the measles cases have been diagnosed in those who either did not receive the measles vaccine or complete the recommended doses of the vaccine. This paper reviews the measles outbreak, in recent years, among unvaccinated individuals in the USA, Canada, and globally.",signatures:"Adekunle Sanyaolu, Chuku Okorie, Aleksandra Marinkovic, Kareem Hamdy, Stephanie Prakash, Risha Patidar, Abu Fahad Abbasi, Priyank Desai, Abdul Jan and Jasmine Mangat",downloadPdfUrl:"/chapter/pdf-download/75202",previewPdfUrl:"/chapter/pdf-preview/75202",authors:[{id:"324992",title:"Dr.",name:"Adekunle",surname:"Sanyaolu",slug:"adekunle-sanyaolu",fullName:"Adekunle Sanyaolu"},{id:"329146",title:"Dr.",name:"Chuku",surname:"Okorie",slug:"chuku-okorie",fullName:"Chuku Okorie"},{id:"329147",title:"Dr.",name:"Aleksandra",surname:"Marinkovic",slug:"aleksandra-marinkovic",fullName:"Aleksandra Marinkovic"},{id:"329149",title:"Dr.",name:"Abu",surname:"Abbasi",slug:"abu-abbasi",fullName:"Abu Abbasi"},{id:"329150",title:"Dr.",name:"Stephanie",surname:"Prakash",slug:"stephanie-prakash",fullName:"Stephanie Prakash"},{id:"329153",title:"Dr.",name:"Jasmine",surname:"Mangat",slug:"jasmine-mangat",fullName:"Jasmine Mangat"},{id:"345833",title:"Dr.",name:"Kareem",surname:"Hamdy",slug:"kareem-hamdy",fullName:"Kareem Hamdy"},{id:"345834",title:"Dr.",name:"Risha",surname:"Patidar",slug:"risha-patidar",fullName:"Risha Patidar"},{id:"345835",title:"Dr.",name:"Priyank",surname:"Desai",slug:"priyank-desai",fullName:"Priyank Desai"},{id:"345836",title:"Dr.",name:"Abdul",surname:"Jan",slug:"abdul-jan",fullName:"Abdul Jan"}],corrections:null},{id:"76433",title:"Severe Acute Respiratory Syndromes and Coronaviruses (SARS-CoV, MERS-CoV, and SARS-CoV-2)",doi:"10.5772/intechopen.97564",slug:"severe-acute-respiratory-syndromes-and-coronaviruses-sars-cov-mers-cov-and-sars-cov-2-",totalDownloads:182,totalCrossrefCites:0,totalDimensionsCites:0,hasAltmetrics:0,abstract:"The current SARS-CoV-2 (coronavirus) outbreak has reached pandemic proportions with a large global imprint. In December 2019, COVID-19 was first reported in Wuhan, Hubei Province, China and has continued largely unabated. The SARS-CoV-2 (coronavirus) is much talked about currently; however, it is worth noting that there are several different coronaviruses known to man, with most of them being responsible for causing illness in animals. Seven (7) types of coronaviruses are identified as causing illnesses in humans. Of the seven human coronavirus infections, four involve mild upper respiratory tract complaints that produce slight symptoms of the common cold. Conversely, the other three human coronavirus infections present more severe consequences as recently demonstrated by the SARS-CoV-2. These deadly outbreaks of pneumonia can have consequences that are far-reaching and are global in nature. SARS-CoV was the first new viral pandemic of the 21st century. It had its beginnings in southern China during November 2002 having started mysteriously; It was contained in 2004 after having spread to five continents and thirty-three countries, infecting approximately 8000 people. MERS-CoV the virus that the causes Middle East respiratory syndrome (MERS) was first identified in 2012 in Saudi Arabia and Jordan and has since registered roughly 2,220 confirmed cases and 790 deaths.",signatures:"Bradley Fevrier",downloadPdfUrl:"/chapter/pdf-download/76433",previewPdfUrl:"/chapter/pdf-preview/76433",authors:[{id:"337426",title:"Assistant Prof.",name:"Bradley",surname:"Fevrier",slug:"bradley-fevrier",fullName:"Bradley Fevrier"}],corrections:null},{id:"75478",title:"Chikungunya Neurological Manifestations: A Systematic Literature Review",doi:"10.5772/intechopen.95525",slug:"chikungunya-neurological-manifestations-a-systematic-literature-review",totalDownloads:90,totalCrossrefCites:0,totalDimensionsCites:0,hasAltmetrics:0,abstract:"Although the most common Chikungunya (do not capitalize the disease unless it is named after a proper noun such as Zika, Ebola or Carrion’s Disease) manifestations are osteoarticular, those which bring the most morbidity and mortality are neurological, where thorough mapping through studies with a methodological outline have not yet been well structured. Therefore, the objective was to review the literature to identify neurological manifestations of CHIKV. We used the Virtual Health Library (VHL) and PubMed with the following descriptors: #1 “Chikungunya” [MeSH]; #2 “neurological manifestations” [MeSH] and their equivalents in the Portuguese language, selecting literature published between July 2007 to January 2018. From the 180 studies that were found, 30 were selected. Findings were divided into two subcategories: “Chikungunya: Typical Neurological Manifestations” and “Chikungunya: Severe Neurological Manifestations”. The studies show that headaches were characterized as the most common symptom in adult patients affected by CHIKV, followed by meningeal involvement. Meningeal involvement is also a more serious clinical scenario associated with encephalitis, convulsions, polyneuropathies such as Guillain-Barré syndrome and death. CHIKV is a public health problem for many reasons including its chronic potential complications. Given the neurological symptoms, this disease is concerning in age extremes, for patients with comorbidities and for patients with more than one viral infection by arboviruses, in whom the most severe neurological manifestations are more common.",signatures:"Jucier Gonçalves Júnior, Marília de Oliveira Bringel, Leonardo Rodrigues de Morais, Luiz Fernando de Castro Malinverno, Giselle Vasconcelos Liberato, Myllena Maria de Morais Pereira, Modesto Leite Rolim Neto, Leonardo de Oliveira Cavalcante and Estelita Lima Cândido",downloadPdfUrl:"/chapter/pdf-download/75478",previewPdfUrl:"/chapter/pdf-preview/75478",authors:[{id:"325332",title:"Dr.",name:"Modesto Leite",surname:"Neto",slug:"modesto-leite-neto",fullName:"Modesto Leite Neto"},{id:"325872",title:"Dr.",name:"Jucier Gonçalves",surname:"Junior",slug:"jucier-goncalves-junior",fullName:"Jucier Gonçalves Junior"},{id:"325873",title:"Dr.",name:"Marília De Oliveira",surname:"Bringel",slug:"marilia-de-oliveira-bringel",fullName:"Marília De Oliveira Bringel"},{id:"325983",title:"Dr.",name:"Leonardo Rodrigues De",surname:"Morais",slug:"leonardo-rodrigues-de-morais",fullName:"Leonardo Rodrigues De Morais"},{id:"325984",title:"Dr.",name:"Luiz Fernando De Castro",surname:"Malinverno",slug:"luiz-fernando-de-castro-malinverno",fullName:"Luiz Fernando De Castro Malinverno"},{id:"325985",title:"Dr.",name:"Giselle Vasconcelos",surname:"Liberato",slug:"giselle-vasconcelos-liberato",fullName:"Giselle Vasconcelos Liberato"},{id:"325986",title:"Dr.",name:"Myllena Maria De Morais",surname:"Pereira",slug:"myllena-maria-de-morais-pereira",fullName:"Myllena Maria De Morais Pereira"},{id:"325990",title:"Dr.",name:"Leonardo De Oliveira",surname:"Cavalcante",slug:"leonardo-de-oliveira-cavalcante",fullName:"Leonardo De Oliveira Cavalcante"},{id:"325992",title:"Dr.",name:"Estelita Lima",surname:"Cândido",slug:"estelita-lima-candido",fullName:"Estelita Lima Cândido"}],corrections:null},{id:"78821",title:"Herpesviridae and microRNAs",doi:"10.5772/intechopen.100370",slug:"herpesviridae-and-micrornas",totalDownloads:126,totalCrossrefCites:0,totalDimensionsCites:0,hasAltmetrics:0,abstract:"MicroRNAs (miRNAs), first discovered in the year 1993 in the nematode C. elegans, are small, approximately 22-nucleotide-long, non-coding RNAs that regulate gene expression. Cellular miRNAs have been implicated in the control of many biological processes, and their dysregulation is associated with different diseases. They can be significantly up/downregulated upon infection or disease, serving as excellent biomarkers and therapeutic targets. Several human DNA viruses, including many herpesviruses, have now been reported to encode viral miRNAs. There are a variety of possible interactions and mechanisms of viral microRNAs (vmiRNAs) which are yet to be remains obscure. Viral miRNAs can function as orthologs of cellular miRNAs and regulate their expression. Additionally, viruses have also developed vmiRNA mechanisms to avoid being targeted by the host miRNAs. Herpes Simplex Viruses (HSV-1 & HSV-2) cause genital and oral herpes, establishing lifelong latent infections in their hosts, and it is one of the most prevalent sexually transmitted infections (STIs) worldwide. vmiRNAs play essential roles in Herpesvirus biology. In this chapter, we will discuss the current knowledge about miRNAs and their role in different stages of Herpesvirus infection. It will also elaborate the biomarkers, therapeutic potential of these molecules, and the prospective areas of future research.",signatures:"Anwesha Banerjee and Anupam Mukherjee",downloadPdfUrl:"/chapter/pdf-download/78821",previewPdfUrl:"/chapter/pdf-preview/78821",authors:[{id:"357335",title:"Dr.",name:"Anupam",surname:"Mukherjee",slug:"anupam-mukherjee",fullName:"Anupam Mukherjee"},{id:"416431",title:"Ms.",name:"Anwesha",surname:"Banerjee",slug:"anwesha-banerjee",fullName:"Anwesha Banerjee"}],corrections:null},{id:"77367",title:"Immunological and Virological Failure among Individuals on Highly-Active Antiretroviral Therapy",doi:"10.5772/intechopen.98593",slug:"immunological-and-virological-failure-among-individuals-on-highly-active-antiretroviral-therapy",totalDownloads:105,totalCrossrefCites:0,totalDimensionsCites:0,hasAltmetrics:0,abstract:"Initiation of antiretroviral treatment decreased HIV related mortality and morbidity. Virological failure (a condition defined when the plasma viral load of HIV infected individuals greater than 1000 RNA copies/ml based on two consecutive viral load measurements with adherence support) have an increased risk of clinical progression to acquired immune deficiency syndrome (AIDS) and death. Nowadays, combination of highly active antiretroviral therapy is recommended to decrease the likelihood of drug resistance. However, there is emergence of drug resistance and treatment failure during treatment. Hence, managing and detecting antiretroviral treatment response is important to monitor the effectiveness of medication and possible drug switching for treatment regimens. Additionally, mechanisms of drug resistance and factors associated with immunological and virological treatment failure should be addressed.",signatures:"Hadush Negash, Brhane Berhe and Miglas Welay",downloadPdfUrl:"/chapter/pdf-download/77367",previewPdfUrl:"/chapter/pdf-preview/77367",authors:[{id:"329709",title:"M.Sc.",name:"Hadush",surname:"Negash",slug:"hadush-negash",fullName:"Hadush Negash"},{id:"329713",title:"MSc.",name:"Miglas",surname:"Welay",slug:"miglas-welay",fullName:"Miglas Welay"},{id:"329715",title:"MSc.",name:"Brhane",surname:"Berhe",slug:"brhane-berhe",fullName:"Brhane Berhe"}],corrections:null},{id:"74915",title:"Trained Immunity-Based Vaccines: A Ready-to-Act Strategy to Tackle Viral Outbreaks",doi:"10.5772/intechopen.95765",slug:"trained-immunity-based-vaccines-a-ready-to-act-strategy-to-tackle-viral-outbreaks",totalDownloads:340,totalCrossrefCites:0,totalDimensionsCites:1,hasAltmetrics:0,abstract:"Viral outbreaks have become significant threats to global human public health. New emerging viruses, pathogen mutations, and even the progressive loss of efficacy in some existing vaccines are behind this problem, which is amplified by the rapid virus spread given the ease of current mobility. Taking into account that these outbreaks arise in the absence of conventional effective vaccines, alternative approaches based on trained (innate) immunity are being considered. This immunity is dependent on a functional reprogramming of innate immune cells, leading to an enhanced nonspecific response towards different pathogens, including viruses. Trained immunity-based vaccines (TIbVs), defined as vaccine formulations containing trained immunity inducers, could be used during viral outbreaks to confer non-specific protection but also to enhance adaptive specific immune responses. In this chapter, we aim to illustrate how TIbVs could tackle the above-mentioned situations derived from viral outbreaks, reviewing the potential of available TIbVs in such urgent situations with a special mention to COVID-19.",signatures:"Laura Conejero, Paula Saz-Leal and José Luis Subiza",downloadPdfUrl:"/chapter/pdf-download/74915",previewPdfUrl:"/chapter/pdf-preview/74915",authors:[{id:"330515",title:"Dr.",name:"José Luis",surname:"Subiza",slug:"jose-luis-subiza",fullName:"José Luis Subiza"},{id:"330517",title:"Dr.",name:"Laura",surname:"Conejero",slug:"laura-conejero",fullName:"Laura Conejero"},{id:"330518",title:"Dr.",name:"Paula",surname:"Saz-Leal",slug:"paula-saz-leal",fullName:"Paula Saz-Leal"}],corrections:null},{id:"74486",title:"The MONITOR Ecosystem: A Digital Health Intervention for the Early Detection, Control, Follow-Up, and Management of COVID-19 in Mexico",doi:"10.5772/intechopen.95260",slug:"the-monitor-ecosystem-a-digital-health-intervention-for-the-early-detection-control-follow-up-and-ma",totalDownloads:245,totalCrossrefCites:0,totalDimensionsCites:0,hasAltmetrics:0,abstract:"In fighting infectious disease outbreaks, a basic epidemiological principle is to detect cases quickly and to isolate each case, to interrupt transmission. This principle has been the cornerstone of the Carso Group (CG) COVID Protocol, a systematic blueprint for the reopening of operations of workplaces in the context of ongoing disease transmission in Mexico. The CG comprises over 50 companies with approximately 180,000 employees engaged in economic activities including telecommunications, retail, construction, banking, mining, and manufacturing, among others. To cope with the COVID-19 pandemic within the CG, the Carlos Slim Foundation designed, developed and implemented MONITOR, a digital health ecosystem comprising a mobile phone application, web portal, and analytics platform, to assess the infection risk of each employee, follow-up their health status, and detect early symptoms of COVID-19. MONITOR provides daily notifications for any suspected cases and activates a COVID-19 testing request and follow-up of results. This intervention helps rapidly identify and isolate suspected cases, as well as follow-up of work and family contacts, to prevent further outbreaks. Use of MONITOR has thus enabled containment of COVID-19 in workplaces and safe return to work. MONITOR is an example of the implementation of public health practices in workplaces and can serve as the basis for larger deployment in population-wide settings.",signatures:"Miguel Betancourt-Cravioto, Jorge Falcón-Lezama, Fernando Rojas-Estrella, Rodrigo Saucedo-Martínez and Roberto Tapia-Conyer",downloadPdfUrl:"/chapter/pdf-download/74486",previewPdfUrl:"/chapter/pdf-preview/74486",authors:[{id:"198398",title:"Dr.",name:"Miguel",surname:"Betancourt-Cravioto",slug:"miguel-betancourt-cravioto",fullName:"Miguel Betancourt-Cravioto"},{id:"198399",title:"Dr.",name:"Jorge",surname:"Falcón-Lezama",slug:"jorge-falcon-lezama",fullName:"Jorge Falcón-Lezama"},{id:"198658",title:"Dr.",name:"Roberto",surname:"Tapia-Conyer",slug:"roberto-tapia-conyer",fullName:"Roberto Tapia-Conyer"},{id:"340621",title:"Dr.",name:"Rodrigo",surname:"Saucedo-Martínez",slug:"rodrigo-saucedo-martinez",fullName:"Rodrigo Saucedo-Martínez"},{id:"340822",title:"Dr.",name:"Fernando",surname:"Rojas-Estrella",slug:"fernando-rojas-estrella",fullName:"Fernando Rojas-Estrella"}],corrections:null},{id:"75084",title:"The One-Health Approach to Infectious Disease Outbreaks Control",doi:"10.5772/intechopen.95759",slug:"the-one-health-approach-to-infectious-disease-outbreaks-control",totalDownloads:208,totalCrossrefCites:0,totalDimensionsCites:0,hasAltmetrics:1,abstract:"Close contact between people, animals, plants, and their shared environment provides more disease transmission opportunities. Host characteristics, environmental conditions, and habitat disruption can provide new opportunities for disease to occur. These changes may lead to the spread of existing and new diseases. Bacteria, viruses, fungi, protozoans, sporozoans, worms, and others cause infectious diseases. Some of these diseases may be prone to explosive outbreaks and may constitute deadly epidemic threats that could rapidly reach pandemic proportions. Drugs and vaccines can successfully control many infectious diseases; however, this is challenged by the lack of facilities and resources. In all parts of the world, infectious disease is an essential constraint to increased human, animal, and environmental interactions. Identifying hot-spot and interventions for prevention while considering the heterogeneity of target diseases to places, population time, or situation is essential. Therefore, successful infectious disease control measures must be based on understanding disease transmission pathways, strengthening surveillance systems, and intervention. Application of the One Health method is a responsive approach to infectious disease control. Much of the One-Health based approach to managing an infectious disease has been utilized with a promising effect on controlling current outbreaks. More deliberate efforts should encourage understanding of disease determinants to analyze infectious disease issues through a One-Health lens. Only through the extensive participation of all related field stakeholders can One-Health truly reach its potential to mitigate infectious disease outbreaks. This chapter reviews utilization of the One Health approach to infectious disease outbreak control.",signatures:"Sima Ernest Rugarabamu",downloadPdfUrl:"/chapter/pdf-download/75084",previewPdfUrl:"/chapter/pdf-preview/75084",authors:[{id:"322766",title:"Dr.",name:"Sima",surname:"Ernest Rugarabamu",slug:"sima-ernest-rugarabamu",fullName:"Sima Ernest Rugarabamu"}],corrections:null},{id:"75644",title:"Human Cultural Dimensions and Behavior during COVID-19 Can Lead to Policy Resistance and Economic Losses: A Perspective from Game Theory Analysis",doi:"10.5772/intechopen.96689",slug:"human-cultural-dimensions-and-behavior-during-covid-19-can-lead-to-policy-resistance-and-economic-lo",totalDownloads:255,totalCrossrefCites:1,totalDimensionsCites:1,hasAltmetrics:0,abstract:"The recent COVID-19 pandemic has caused significant societal impacts. Besides loss of life there were large additional costs incurred by every country including the treatment of patients and costs to implement response plans. The pandemic resulted in major economic disruptions and stalled growth worldwide due to travel bans, lockdowns, social distancing, and non-essential business closures. Public health officials in almost every country implemented and encouraged Nonpharmaceutical Interventions (NPIs) such as contact tracing, social distancing, masks, and isolation. Human behavioral decision-making concerning social isolation was a major hindrance to the success in curbing the pandemic worldwide. In many developing countries individuals’ choices were motivated by the competing risk of losing jobs, and daily income. In this chapter we focus on human behavior concerning social isolation in the context of decision-making during the pandemic. We developed a conceptual framework and deterministic model that integrated evolutionary game theory within our disease transmission model. We illustrate scenarios numerically simulating the model. This study highlights the idea that human behavior is an important component in successful disease control strategies. Economic resilience, especially in low-income countries, can improve public understanding and uptake of NPIs.",signatures:"Tamer Oraby, Michael G. Tyshenko and Samit Bhattacharyya",downloadPdfUrl:"/chapter/pdf-download/75644",previewPdfUrl:"/chapter/pdf-preview/75644",authors:[{id:"208850",title:"Dr.",name:"Tamer",surname:"Oraby",slug:"tamer-oraby",fullName:"Tamer Oraby"},{id:"329860",title:"Associate Prof.",name:"Samit",surname:"Bhattacharyya",slug:"samit-bhattacharyya",fullName:"Samit Bhattacharyya"},{id:"344430",title:"Prof.",name:"Michael G.",surname:"Tyshenko",slug:"michael-g.-tyshenko",fullName:"Michael G. Tyshenko"}],corrections:null},{id:"74377",title:"Antiphospholipid Antibodies in Patients with COVID-19",doi:"10.5772/intechopen.95261",slug:"antiphospholipid-antibodies-in-patients-with-covid-19",totalDownloads:199,totalCrossrefCites:0,totalDimensionsCites:0,hasAltmetrics:0,abstract:"Patients infected with COVID-19 are at higher risk of thrombosis, suggesting an important role of COVID-19 induced coagulopathy. Abnormal coagulation parameters such as elevation in D-dimer are found in patients, with frequent thrombotic events ranging from peripheral ischemia, pulmonary thromboembolism to disseminated intravascular coagulation. Recently, the role of antiphospholipid antibodies (aPL) in the pathophysiology of COVID-19 have been questioned but it remains unclear whether they contribute to coagulopathy. We aim to evaluate the presence of aPL, including LAC, aCL (IgG, IgM), aβ2GPI (IgG, IgM) in a cohort of patients with SARS-CoV-2, study clinical associations and discuss the relevance. The relevance of aPLs in patients with COVID-19 is yet to be determined. Inflammation is closely associated to thrombosis and the presence of inflammatory mediators in COVID-19 infection can lead to thrombosis. Further studies are needed before to determine the role of aPL in COVID-19 patients and their relationship with thrombosis. The presence of aPL should be carefully interpreted as it is important to evaluate the persistence of aPL positivity in patients infected with COVID-19.",signatures:"Cristiana Isabel Sieiro Santos",downloadPdfUrl:"/chapter/pdf-download/74377",previewPdfUrl:"/chapter/pdf-preview/74377",authors:[{id:"329604",title:"Dr.",name:"Cristiana",surname:"Isabel Sieiro Santos",slug:"cristiana-isabel-sieiro-santos",fullName:"Cristiana Isabel Sieiro Santos"}],corrections:null},{id:"77453",title:"The Power of Computational Intelligence Methods in the Containment of COVID-19 Pandemic from Detection to Recovery",doi:"10.5772/intechopen.98931",slug:"the-power-of-computational-intelligence-methods-in-the-containment-of-covid-19-pandemic-from-detecti",totalDownloads:107,totalCrossrefCites:0,totalDimensionsCites:0,hasAltmetrics:0,abstract:"The coronavirus disease (SARS-CoV-2) pandemic has caused unprecedented economic crises, and changes in our lifestyle to different things that we have not experienced before in this century, which cause by movement restriction order by the authority to halt the spread of the disease around the globe. Researchers around the globe applied computational intelligence methods in numerous fields which exhibits a successful story. The computational intelligence methods play an important role in dealing with coronavirus pandemics. This research will focus on the use of computational intelligence methods in understanding the infection, accelerating drugs and treatments research, detecting, diagnosis, and predicting the virus, surveillance, and contact tracing to prevent or slow the virus from the spread, monitoring the recovery of the infected individuals. This study points out promising CI techniques utilized as an adjunct along with the current methods used in containments of COVID-19. It is imagined that this study will give CI researchers and the wider community an outline of the current status of CI applications and motivate CI researchers in harnessing CI technique possibilities in the battle against COVID-19.",signatures:"Abdullahi Isa and Barka Piyinkir Ndahi",downloadPdfUrl:"/chapter/pdf-download/77453",previewPdfUrl:"/chapter/pdf-preview/77453",authors:[{id:"347240",title:"Mr.",name:"Abdullahi",surname:"Isa",slug:"abdullahi-isa",fullName:"Abdullahi Isa"},{id:"419071",title:"M.Sc.",name:"Barka",surname:"Ndahi",slug:"barka-ndahi",fullName:"Barka Ndahi"}],corrections:null},{id:"76319",title:"Docking-Based Screening of Cell-Penetrating Peptides with Antiviral Features and Ebola Virus Proteins as a Drug Discovery Approach to Develop a Treatment for Ebola Virus Disease",doi:"10.5772/intechopen.97222",slug:"docking-based-screening-of-cell-penetrating-peptides-with-antiviral-features-and-ebola-virus-protein",totalDownloads:248,totalCrossrefCites:0,totalDimensionsCites:0,hasAltmetrics:0,abstract:"Ebola drug discovery continues to be challenging as yet. Proteins of the virus should be targeted at the relevant biologically active site for drug or inhibitor binding to be effective. In this regard, by considering the important role of Ebola virus proteins in the viral mechanisms of this viral disease, the Ebola proteins are selected as our drug targets in this study. The discovery of novel therapeutic molecules or peptides will be highly expensive; therefore, we attempted to identify possible antigens of EBOV proteins by conducting docking-based screening of cell penetrating peptides (CPPs) that have antiviral potential features utilizing Hex software version 8.0.0. The E-value scores obtained in this research were very much higher than the previously reported docking studies. CPPs that possess suitable interaction with the targets would be specified as promising candidates for further in vitro and in vivo examination aimed at developing new drugs for Ebola infection treatment.",signatures:"Ehsan Raoufi, Bahar Bahramimeimandi, Mahsa Darestanifarahani, Fatemeh Hosseini, Mohammad Salehi-Shadkami, Hossein Raoufi and Reza Afzalipour",downloadPdfUrl:"/chapter/pdf-download/76319",previewPdfUrl:"/chapter/pdf-preview/76319",authors:[{id:"210529",title:"Ph.D. Student",name:"Ehsan",surname:"Raoufi",slug:"ehsan-raoufi",fullName:"Ehsan Raoufi"},{id:"347331",title:"BSc.",name:"Hossein",surname:"Raoufi",slug:"hossein-raoufi",fullName:"Hossein Raoufi"},{id:"347332",title:"Dr.",name:"Reza",surname:"Afzalipour",slug:"reza-afzalipour",fullName:"Reza Afzalipour"},{id:"347333",title:"MSc.",name:"Bahar",surname:"Bahramimeimandi",slug:"bahar-bahramimeimandi",fullName:"Bahar Bahramimeimandi"},{id:"347334",title:"MSc.",name:"Fatemeh",surname:"Hosseini",slug:"fatemeh-hosseini",fullName:"Fatemeh Hosseini"},{id:"347335",title:"Mr.",name:"Mohammad",surname:"Salehi-Shadkami",slug:"mohammad-salehi-shadkami",fullName:"Mohammad Salehi-Shadkami"},{id:"347336",title:"MSc.",name:"Mahsa",surname:"Darestani Farahani",slug:"mahsa-darestani-farahani",fullName:"Mahsa Darestani Farahani"}],corrections:null},{id:"74041",title:"Survey for a Vector of Zika Virus and Two Other Mosquito Species in Four Ecoregions of Missouri: An A Posteriori Analysis",doi:"10.5772/intechopen.94499",slug:"survey-for-a-vector-of-zika-virus-and-two-other-mosquito-species-in-four-ecoregions-of-missouri-an-e",totalDownloads:177,totalCrossrefCites:0,totalDimensionsCites:0,hasAltmetrics:0,abstract:"In 2015, Zika emerged as a vector-borne disease in the Americas, causing a variety of health issues ranging from Guillain-Barre syndrome in adults to microcephaly in newborns. Following the documentation of mosquito-borne transmission of the disease in the southern United States, the Missouri Department of Health and Senior Services contracted with researchers at Missouri State University to complete a survey of possible mosquito vectors of the Zika in the state. The primary vector of the disease, Aedes aegypti, had been reported from Missouri in previous surveys from several decades ago, but a comprehensive survey of the state mosquitoes and never been completed. Researchers focused on mosquitoes that spend the immature stages in artificial containers because this is descriptive of the most important Zika vectors. The large survey over three years provided an opportunity for post hoc analysis of mosquito occurrence data across a variety of ecoregions inside the state, documenting changes in the vector populations as a result of invasive species. The survey also allowed an analysis of different trapping techniques for important species in the state. The results are reported in this chapter along with a discussion of the potential impact on human health of changes to the mosquito population.",signatures:"David M. Claborn, Sapana Subedi Chowi, Matthew Flint and Clement Acheampong",downloadPdfUrl:"/chapter/pdf-download/74041",previewPdfUrl:"/chapter/pdf-preview/74041",authors:[{id:"169536",title:"Dr.",name:"David",surname:"Claborn",slug:"david-claborn",fullName:"David Claborn"},{id:"328843",title:"Ms.",name:"Sapana",surname:"Chowi",slug:"sapana-chowi",fullName:"Sapana Chowi"},{id:"328844",title:"Mr.",name:"Matthew",surname:"Flint",slug:"matthew-flint",fullName:"Matthew Flint"},{id:"328845",title:"Mr.",name:"Clement",surname:"Acheampong",slug:"clement-acheampong",fullName:"Clement Acheampong"}],corrections:null},{id:"75586",title:"Frog Virology: Biosafety in an Experimental Farm",doi:"10.5772/intechopen.96605",slug:"frog-virology-biosafety-in-an-experimental-farm",totalDownloads:170,totalCrossrefCites:0,totalDimensionsCites:0,hasAltmetrics:0,abstract:"Understanding and detecting diseases of amphibians has become vitally important in conservation and ecological studies and prevent and biosecurity a determinant priority in experimental farms, mainly when related with academic and research activities. Ranavirus belongs to the family Iridoviridae, and causes an emergent infectious disease that affects different species, especially fish, reptiles and amphibians, with a significant contribution to the decline of the population. In amphibian systems, Ranaviruses transmission can occur between vertebrate classes through direct contact, by scavenging or through virus particles persisting in the environment. Subclinical infected individuals may serve as reservoirs in the most susceptible anura species. Humans play a significant role in this emergent disease and biosecurity measures are determinant to prevent the introduction of these viruses, either in commercial or experimental farms. A Biosafety Plan is a fundamental tool in the Ranaviruses prevention and include educational and training programs, relevant to the mission of a Higher Education Institution.",signatures:"Cláudia Luís Martins Batista, Alexandre Nuno Vaz Batista de Vieira e Brito and Júlio César Oliveira Lopes",downloadPdfUrl:"/chapter/pdf-download/75586",previewPdfUrl:"/chapter/pdf-preview/75586",authors:[{id:"339241",title:"Prof.",name:"Nuno",surname:"Brito",slug:"nuno-brito",fullName:"Nuno Brito"},{id:"340071",title:"Prof.",name:"Julio",surname:"Lopes",slug:"julio-lopes",fullName:"Julio Lopes"},{id:"340218",title:"BSc.",name:"Cláudia",surname:"Batista",slug:"claudia-batista",fullName:"Cláudia Batista"}],corrections:null}],productType:{id:"1",title:"Edited Volume",chapterContentType:"chapter",authoredCaption:"Edited by"},subseries:null,tags:[{id:"54",label:"covid-19 book program"}]},relatedBooks:[{type:"book",id:"3842",title:"Leishmaniasis",subtitle:"Trends in Epidemiology, Diagnosis and Treatment",isOpenForSubmission:!1,hash:"861f3ca84eede677ba6cd863093d62f8",slug:"leishmaniasis-trends-in-epidemiology-diagnosis-and-treatment",bookSignature:"David M. 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Kemper",coverURL:"https://cdn.intechopen.com/books/images_new/10558.jpg",editedByType:"Edited by",editors:[{id:"276287",title:"Dr.",name:"Kotsedi Daniel",surname:"Monyeki",slug:"kotsedi-daniel-monyeki",fullName:"Kotsedi Daniel Monyeki"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}}],ofsBooks:[]},correction:{item:{id:"65200",slug:"corrigendum-to-evaluation-of-psoriasis-patients",title:"Corrigendum to: Evaluation of Psoriasis Patients",doi:null,correctionPDFUrl:"https://cdn.intechopen.com/pdfs/65200.pdf",downloadPdfUrl:"/chapter/pdf-download/65200",previewPdfUrl:"/chapter/pdf-preview/65200",totalDownloads:null,totalCrossrefCites:null,bibtexUrl:"/chapter/bibtex/65200",risUrl:"/chapter/ris/65200",chapter:{id:"63332",slug:"evaluation-of-psoriasis-patients",signatures:"Meda Sandra Orasan, Iulia Ioana Roman and Andrei Coneac",dateSubmitted:"April 17th 2018",dateReviewed:"June 26th 2018",datePrePublished:"November 5th 2018",datePublished:"July 17th 2019",book:{id:"7045",title:"Tailored Treatments in Psoriatic Patients",subtitle:null,fullTitle:"Tailored Treatments in Psoriatic Patients",slug:"tailored-treatments-in-psoriatic-patients",publishedDate:"July 17th 2019",bookSignature:"Shahin Aghaei",coverURL:"https://cdn.intechopen.com/books/images_new/7045.jpg",licenceType:"CC BY 3.0",editedByType:"Edited by",editors:[{id:"64024",title:"Associate Prof.",name:"Shahin",middleName:null,surname:"Aghaei",slug:"shahin-aghaei",fullName:"Shahin Aghaei"}],productType:{id:"1",title:"Edited Volume",chapterContentType:"chapter",authoredCaption:"Edited by"}},authors:[{id:"202125",title:"Dr.",name:"Meda",middleName:"Sandra",surname:"Orasan",fullName:"Meda Orasan",slug:"meda-orasan",email:"meda2002m@yahoo.com",position:null,institution:{name:"Iuliu Hațieganu University of Medicine and Pharmacy",institutionURL:null,country:{name:"Romania"}}},{id:"205669",title:"Dr.",name:"Andrei",middleName:null,surname:"Coneac",fullName:"Andrei Coneac",slug:"andrei-coneac",email:"andrei.coneac@gmail.com",position:null,institution:null},{id:"255002",title:"Dr.",name:"Iulia Ioana",middleName:null,surname:"Roman",fullName:"Iulia Ioana Roman",slug:"iulia-ioana-roman",email:"iuliaroman09@gmail.com",position:null,institution:null}]}},chapter:{id:"63332",slug:"evaluation-of-psoriasis-patients",signatures:"Meda Sandra Orasan, Iulia Ioana Roman and Andrei Coneac",dateSubmitted:"April 17th 2018",dateReviewed:"June 26th 2018",datePrePublished:"November 5th 2018",datePublished:"July 17th 2019",book:{id:"7045",title:"Tailored Treatments in Psoriatic Patients",subtitle:null,fullTitle:"Tailored Treatments in Psoriatic Patients",slug:"tailored-treatments-in-psoriatic-patients",publishedDate:"July 17th 2019",bookSignature:"Shahin Aghaei",coverURL:"https://cdn.intechopen.com/books/images_new/7045.jpg",licenceType:"CC BY 3.0",editedByType:"Edited by",editors:[{id:"64024",title:"Associate Prof.",name:"Shahin",middleName:null,surname:"Aghaei",slug:"shahin-aghaei",fullName:"Shahin Aghaei"}],productType:{id:"1",title:"Edited Volume",chapterContentType:"chapter",authoredCaption:"Edited by"}},authors:[{id:"202125",title:"Dr.",name:"Meda",middleName:"Sandra",surname:"Orasan",fullName:"Meda Orasan",slug:"meda-orasan",email:"meda2002m@yahoo.com",position:null,institution:{name:"Iuliu Hațieganu University of Medicine and Pharmacy",institutionURL:null,country:{name:"Romania"}}},{id:"205669",title:"Dr.",name:"Andrei",middleName:null,surname:"Coneac",fullName:"Andrei Coneac",slug:"andrei-coneac",email:"andrei.coneac@gmail.com",position:null,institution:null},{id:"255002",title:"Dr.",name:"Iulia Ioana",middleName:null,surname:"Roman",fullName:"Iulia Ioana Roman",slug:"iulia-ioana-roman",email:"iuliaroman09@gmail.com",position:null,institution:null}]},book:{id:"7045",title:"Tailored Treatments in Psoriatic Patients",subtitle:null,fullTitle:"Tailored Treatments in Psoriatic Patients",slug:"tailored-treatments-in-psoriatic-patients",publishedDate:"July 17th 2019",bookSignature:"Shahin Aghaei",coverURL:"https://cdn.intechopen.com/books/images_new/7045.jpg",licenceType:"CC BY 3.0",editedByType:"Edited by",editors:[{id:"64024",title:"Associate Prof.",name:"Shahin",middleName:null,surname:"Aghaei",slug:"shahin-aghaei",fullName:"Shahin Aghaei"}],productType:{id:"1",title:"Edited Volume",chapterContentType:"chapter",authoredCaption:"Edited by"}}},ofsBook:{item:{type:"book",id:"12331",leadTitle:null,title:"Selenium and Human Health",subtitle:null,reviewType:"peer-reviewed",abstract:"This book will be a self-contained collection of scholarly papers targeting an audience of practicing researchers, academics, PhD students and other scientists. 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1. Introduction
In the last decade, industrial manufacturing such as healthcare, smart grids based on Cyber-Physical Internet of Thing Systems (CPIoTS) has been widespread [1]. In this context, IIoT network, which is characterized by the unified network physical layer, the QoS constraints, the autonomous connection requirements, is considered one of the key issues. The rapid increase in data amounts with diverse QoS requirements [2] brings several challenges in order to meet the complex requirements, as well as resource and QoS requirements with high data rate and low latency. In fact, the advanced 5G technology has a significant potential to provide IIoT QoS satisfactory [3]. With their architectural approaches which are founded on a unified physical layer, addressing the diverging performance requirements in terms scalability and availability still a hot challenges topic. Today’s drastic digital transformations empowered by emerging technology like Edge Computing, Software Defined Networking (SDN), Network Function Virtualization (NFV), and LoRaWAN can bring smart services for network candidates [4]. Network slicing (NS) is the key solution that provides smart service’s connectivity with diverse QoS requirements. Using deep RL at each LoRa agent in the environment. Each agent considered to be a Deep Q-learning (DQL) brain interacts with the environment to find the best action on their parameters that brings the best reward. In addition, it introduces the FL approach to provide better RL based action on each agent, to maximize QoS, and hence throughput revenue. However, NS provides the network availability as a service following the slice instances exploiting NFV and SDN [5]. In this context, a Mini Batch GD and GMM framework is proposed in [7] to provides radio resources for the virtual slice member. In addition, a LoRa network slicing technique based on Maximum Likelihood Estimation proposed, in [6], to allocate network resources in inter and intra mode. Meanwhile, recently supervised learning approach-based resource allocation is also proposed to manage network resources, but due to the training data unavailability or the high computational training process, are not appropriate for large-scale network and cannot satisfy dynamic slices requirements.
RL technique can improve efficient resource management by interacting with the environment, in which Q-learning is the widely used. The RL agent learns the association between taken action and the received feedback in terms of reward. It follows a policy, which is updated according to the maximized revenue via several action series. Therefore, high-quality policies building in a centralized network architecture faces a major challenge, especially when the space of state features is restricted. To deal with these issues, Federated Learning (FL) has been suggested as a decentralized tool for machine learning, which is designed to be a global learning system. In this context, the aim of this work is to propose a deep federated reinforcement learning (DFRL), to equip the slice member with the required channel resources, by tuning LoRa TP and SF parameters [8].
The leftover of this chapter is organized in six sections. Section 2 presents the related work of this chapter. In Section 3, we give a brief overview on the Industrial IoT, the federated learning, and the network slicing. We highlight, in Section 5, the proposed slicing architecture and the system model. After that, in Section 6, the relation between wireless sensor network (WSN) and the IIoT is well highlighted. Next, the proposed slicing resource reservation-based DFRL framework is presented in Section 6. Section 7 evaluates the simulation results. Finally, Section 8 concludes the chapter.
2. Related works
Recently, several articles have investigated the many challenges typical of the network slicing approach. In particular, in [9] the authors propose an online auction algorithm to realize a resource allocation framework, capable of guaranteeing the diversity of services to users and high levels of social welfare. Differently, the work in [10] deals with a new resource allocation framework to automatically and automatically size the capacity and size of network slices. In this chapter, resource partitioning is done based on both available network bandwidth and LoRa configurations parameters resulting in an optimal trade-off between traffic and network aspects. The authors of [11] focus on the design and implementation of a dynamic slicing sharing system to ensure minimum user throughput requirements. Therefore, it addresses three sub-issues: admission control, resource allocation, and user abandonment issues. The contextualization of the placement of VNFs to the network slicing problem is presented in [12], where, in particular, the topological information of the network is exploited to provide an appropriate deployment of functions, with regard to different service classes. Moreover, the placement problem of VNFs has also been addressed in [13], which considers the function decomposition and the sub-functions sharing, a profitable heuristic algorithm is proposed based on Linear integer programming formulation of the VNF placement problem. Further, in the work [14], a formulation of mixed integer linear program is exploited to process the number identification of VNFs to use, which aims to meet specific service requirements. To solve the placing VNFs problem in a federated cloud, the coalition formation game is proposed in [14]. Alternatively, a Pareto analysis of the VNF placement problem is the subject of [15]. In [16], a network partitioning policy is developed to take into account the social well-being and the supplier profit of the network. Finally, special cases for the VNF placement problem are discussed and analyzed in [17].
The problem of bandwidth slicing in software-defined networks is studied in [18], where price spikes are exploited to indicate the presence of traffic spikes and network congestion. This work provides a time-based price analysis combined with a Stackelberg game, in which the gain of SP Internet is the gain of income. In a different way, the work cited in [19] studies the correlation between the network slices size and the resource pricing strategy. In addition, an algorithm to vary the prices is proposed by the authors in [19], with the aim of maximizing both the customer profit and the SP. Although FL has not been used in the field of network slicing research, FL has recently reached attention and several papers have presented its use, the methods cited in [20, 21] being prime examples of’such a branch of literature. In [20], a new aggregation data scheme for wireless computation is proposed. The strategy exploits the signal overlay property of the wireless channels. Differently, articles [22, 20] focus on maximizing the number of devices involved in the aggregation process, also taking into account the minimization of aggregation error. Thus, it contextualize the FL in an MEC system and apply the distributed gradient descent method to identify the best compromise between local updates and global aggregations, aiming to minimize the loss function, in taking into account certain resource constraints. Likewise, the article in [21] analyzes the MEC environment and presents the application of hybrid filtering on stacked encoders to predict fluctuation in file popularity in the content caching problem. Moreover, the article cited [23] modifies the proposed, federated averaging algorithm with the stochastic gradient descent algorithm, to train the data in a distributed way, thus reducing communication costs. The multitasking learning problem is studied in the work cited in [24], authors proposed a new Mocha contextual optimization approach that used in combination with the FL system. The work cited in [25] analyzed the End-to-end delay in a blockchain framework, in which an FL blockchain structure is developed to perform a distributed consensus strategy. In order to improve the transmission and computational costs in a hybrid IoT-MEC network, authors in [26] proposed to use the FL powered by the multiple deep reinforcement learning agents. In addition, ultra-dense scenarios are also considered in [27], where an approach based on the technique of deep learning of short-term long-term memory is applied to forecast local network traffic in order to avoid congestion.
This chapter aims to address the problem of network slicing using deep federated RL at each LoRa agent in the environment. Each agent considered to be a Deep Q-learning (DQL) brain interacts with the environment to find the best action on their parameters that brings the best reward. In addition, it introduces the FL approach to provide better RL-based action on each agent, to maximize QoS, and hence throughput revenue.
3. Overview on industrial IoT, federated learning and network slicing
The development and evolution of modern information and communication technologies lead us to the Fourth Industrial Revolution, in which the Industrial Internet of Things (IIoT) is supposed to be one of the key aspects to realize the industry 4.0. With an unprecedented increase in the number of Internet of Things (IoT) devices and emerging applications, a large amount of traffic is created every day. Such an increase represents a heavy load on the Internet network and also requires significant investments for the upgrade of the infrastructure. However, with the development of big data analysis and artificial intelligence (AI) techniques such as deep learning (DL) and machine learning (ML), the data collected can be effectively exploited for many purposes. From a communication point of view, the last few years have seen the emergence of AI applications in various fields. For example, ML is used to study efficient antenna selection in multi-antenna wireless systems [28], DL is used to handle the computational offload problem in IoT systems with edge computing [29], and Deep reinforcement learning (DRL) is used to optimize resource allocation issues at the edge of the network, such as traffic classification, edge caching, network security, and data offload [30]. However, conventional AI models generally require central processing of the data collected from all users on the network, i.e. users have to upload their own data to a central server to train the learning model. However, a key concern with central learning is data privacy, i.e., some users want to keep track of their local data and do not want to transmit their local data to the central server. Training the learning model centrally requires a central cloud with extremely powerful compute and storage capabilities. Meanwhile, recent advancements in computer hardware and the proliferation of smart devices in our daily lives have shown that every IoT device can be equipped with reasonable levels of compute and storage, which is closely comparable to a desktop computer there was. is 10 years old [31]. Therefore, the standard ML model is not easily applicable to large scale IoT networks and cannot exploit the availability of distributed computing. This requires a new learning model that leaves training data distributed across individual IoT devices instead of being centralized.
Motivated by this problem, Google invented the concept of federated learning (FL) for on-device learning and data privacy preservation [32]. Using the FL approach, each IoT device can train its model based on locally collected data. Local data from IoT devices does not need to be sent to the centralized cloud. The centralized cloud only needs to collect the updated local training model from individual users. Due to its characteristics, FL has been adopted in many applications, for example FL for improving Google keyboard suggestions [33], FL for healthcare [34], and FL for smart city detection [35]. To illustrate the concept of FL, an overview of FL in IoT systems is shown in Figure 1. In general, each IoT device has its own set of data and the aggregation server can either be located at the edge of the network or in a virtual cloud in the remote cloud computing system [36]. Each FL model has its own advantages and disadvantages, depending on various factors. For example, FL with the server at the edge of the network is suitable for applications requiring low latency, location awareness and contextual information on the network while cloud-based FL is suitable for applications with IoT devices massive over multiple regions and computing power requirements/storage capacities.
Figure 1.
An overview of FL in IoT systems.
Recently, the integration of DL models with IoT and edge devices has become more popular, which provides real-time analytics with limited resources [60]. Thus, Federated DL (FDL) allows Industry 4.0 companies to integrate DL into IoT devices and provides a secure framework using FL, as shown in Figure 2. DL is computationally expensive, which requires resources and an expensive framework. Thus, the decentralization of DL models is a multidimensional problem that requires a framework of new technologies to integrate DL with advanced computing and the IIoT. The main goal of FDL is to provide the IIoT with advanced capabilities using optimized DL that would turn Industry 4.0 factories into smart factories. Some of the parameters required to create the FDL model in IIoT are the FDL model, FDL networking, FDL security, and FDL optimization.
Figure 2.
Federated DL in IIoT.
An FDL model can be implemented on both client side and server side. On the client side, private networks are defined, the DL model of which is tuned and optimized from the general model present in the cloud. Optimized and fitted models are then deployed on the client side, where the model is trained with data generated locally from the end device. Finally, the final device contains a highly quantized and compressed FDL model. On the server side, the model in the cloud is continuously updated by differentially integrating the gradients of each private network. Each local DL network in turn is responsible for continuously uploading and uploading the currently updated gradients to the cloud model. Thus, a distributed selective stochastic gradient descent approach is presented in [37] which can be applied in the cloud model to frequently update the local private model. The first decentralized model called”Model chain” uses blockchain technology [38, 39] to allow the preservation of confidentiality in the transfer of data. In addition to this, asynchronous stochastic gradient descent can also be used when a single model can be trained in parallel among all devices, aggregated and processed.
Regarding FDL communication and networking, is that the main benefit of using FDL is to run DL models in IoT devices and involve the model in the decision making process. This type of decentralized DL process improves the robustness, operational efficiency and reliability of IoT devices. FDL provides two types of communication, namely intra-communication channel and inter-communication channel. Train transmits data between all levels of the framework. FDL communicates between the IoT and the cloud tier where the cloud-optimized model is deployed on the end device. However, security and confidentiality must be maintained in the FDL during communication. In inter communication channels, the components of each layer communicate with each other in three different ways, such as cloud, edge, and end device. The main objective of FDL is to minimize intra-communication and to maximize inter-communication, which would greatly reduce the cost of communication. By the way, to maintain privacy and security, FDL builds DL models that do not expose information about the data to the cloud. Security issue on the server side includes sharing of DL models on the cloud that leads to confidentiality and security risk. Security issue on the client side is done by encrypting the data during the training process before sending it to the cloud server. Some mechanisms and homomorphic encryption technique controls the amount of data to be shared on the cloud. Since peripherals have limited memory and computational requirements, DL models must be optimized so that they can be deployed to IoT or peripherals efficiently. In terms of hardware optimization, the GPU provides low-power computation that reduces computation time. The FPGA and Google’s Tensor Processing Unit [40] are other DL devices that enhance DL network processing. In terms of memory optimization, algorithms such as shared memory allocation algorithms for DL models can be used. Dynamic scheduling [41] is one of the main processes used to improve performance on a cloud server.
4. Wireless sensor networks and its relation to industrial internet of things
4.1 Relation between IIoT and IWSN
At the heart of the IIoT are the WSNs, that include of multi-functional nodes, low-cost, along with sensing, have both communication and processing capabilities. In order to communicate wirelessly over short distances, these little, inexpensive sensor nodes have built-in transceivers and processors. They are densely exploited in an area of interest to collect sensory data, by coordinating and collaboratively exchanging information by training ad hoc wireless networks. Due to the small size and the batteries use, Sensor nodes are limited in processing, communication, and power. A unique feature of WSNs is their network processing attribute, whereby sensor nodes do not send raw sense data directly to the gateway but merge it locally to make it more consistent and save significant communication costs. Their application field is multiple and they are now ubiquitous components of intelligent environments, due to their unique attributes. Their various area of application covers home, surveillance, military, smart city, patient health monitoring, automation, etc. WSNs are used in telehealth applications in patient healthcare monitoring scenarios. As example, to monitor patients with chronic diseases and regularly check their various parameters such as heart rate, blood sugar and send this information wirelessly to a doctor remotely for further diagnosis. In order to help the elderly and disabled in their daily tasks, the WSNs are also used.
Indeed, they have seen major deployments in a diversity of applications, including agriculture, industrial process automation and control, transportation, and supply chain management over the past decades. Due to their ubiquitous presence and considering the potential benefits of these networks, such as simple deployment, cheap installation cost, no cabling cost, less complexity and mobility, they are increasingly used. in IIoT applications, which gave rise to IWSNs. WSNs can be used in an IIoT environment such as automation and control, process monitoring, and safety and emergency applications. In automation and process control applications, several tasks may require active nodes named actuators, which have the capability to act autonomously on the physical environment based on the detected measurements. For example, in the automation and control of feedback-based chemical processes, sensors measure temperature; if the temperature crosses a certain threshold value, they inform the actuators to reduce the temperature to a desired value so that the process remains in a stable state. Such applications place strict constraints on low latency and reliability because the sensor measurements must reach the actuator in a timely and reliable manner in order for the valve control action to be performed on time [42].
Today’s sensor nodes have more processing power, longer battery life and memory, due to recent technological improvements compared to the first resource-constrained sensor nodes. This allowed them to be used in IIoT applications and resulted in IWSN. IWSN makes processes independent and autonomous, especially in difficult areas, to get actuation and control information, sensory. Sensor nodes in the WSN field detect process variables (e.g. temperature, pressure, etc.) and pass them to the well or gateway. The sink then passes it to the process controller whose job is to control the process variable under some required value. The receiver is responsible for the sensor network management and is controlled and managed by the host application management. The Network and Security Manager is responsible for entire network monitoring and ensuring security against attacks. Therefore, WSN has the potential to improve production processes and quality of products without compromising the IIoT QoS. Actuation and control, and sensing, are also imperative in majority industrial applications. In these applications, the sensors detect the data and the actuators act on the data based on certain control decisions made by the process controller.
4.2 Federated learning implementation challenges in IIoT and IWSN
To implement FL’s full potential in IIoT and IWSN, there are still several fundamental challenges that need to be addressed. In this section, we describe the challenges followed by very promising opportunities to meet those challenges.
4.2.1 Limited computational resources
Indeed, the FL deployment on IIoT and IWSN networks relies deeply on the computational resources and memory of edge devices. Consequently, people often focus only on the IoT devices capabilities to gather data while ignoring their limited memory and compute resources, which makes it hard for most IoT and sensor devices to finish local computation with massive data or sophisticated models. In order to address this challenge, lightweight AI techniques have been explored, which can be implemented in resource-constrained FL-IoT and WSN environments, such as improved resource management approaches to accelerate FL training on devices.
4.2.2 Device heterogeneity
In the multi-device settings, participants under the FL framework have various system resources, such as compute and memory resources. As the trend in machine learning is for larger and deeper models, the hardware heterogeneity within the IIoT and IWSN systems pose several challenges for the FL structure. They could easily train large models as devices with powerful memory and computing resources while other devices with limited resources could only train smaller models. Reader speed will also vary across devices, even for the same model size, which can trigger the problem of asynchronous communication discussed above. Due to the availability of the resources, an FL framework for IIoT and IWSN should provide a graceful adaptation of data and compute load on diverse devices.
4.2.3 Limited networking bandwidth
Communication overload is considered to be one of the main challenges in FL-based IIoT and IWSN environments. Currently, most IoT and WSN devices communicate using wireless networks that have a much lower bandwidth than the wired network bandwidth. As more and more devices join the system, the communication problem arises when the clients have different resource allocations. The limited network bandwidth not only makes the communication between clients and the server inefficient, but also triggers the presence of late clients, which fail to share their local update with the server during the communication cycle. To meet this challenge, some key ideas can be used, such as decentralized training, data compression and participant selection.
4.2.4 Adversarial attack and defense
The IoT devices prevalence also poses an attractive target in the real-world deployment for adversaries seeking to launch attacks, such as identity theft, phishing, and distributed denial of service (DDoS). Many IoT and ISN devices do not have the compute resources to do so, although these attacks can be easily defended by installing security patches. It is critical for the IIoT andd IWSN systems to detect the malicious or broken IoT devices that will ruin the model training with limited resource. To address this challenge, one of the promising directions is to implement a lightweight security protocol in the IIoT andd IWSN systems for the detection of broken and malicious devices.
4.2.5 Expected future solutions
Undeniably, the IIoT and IWSN ecosystems continue to evolve at a breakneck pace, exceeding all growth expectations and ubiquity barriers. From sensor to cloud, this giant network keeps breaking technological bounds in several domains, and wireless sensor nodes are expected to be predominant as the number of IoT devices grows toward the trillions to connect the unconnected world and things. However, their future in the IIoT and IWSN ecosystems still seems foggy, where several challenges, such as device’s connectivity, artificial intelligence (AI) at the edge, security and privacy concerns, growing energy needs, the right technologies to be used and keep pulling in opposite directions. To address these issues, which are caused by the complexity and variability of the environment, advanced computing related technologies are widely applied. However, the edge computing is limited by cost, volume, power consumption, and other conditions, so the capacity of edge computing cannot be fully exploited. So that edge computing fully exploits its characteristics of flexible management, federated and collaborative execution and heterogeneous environment, the reconfigurable real-time computer system based on FPGA SoC is strongly recommended. The system, as depicted in Figure 3 can be built in real time as needed, by the characteristics of the FPGA SoC, including its reconfigurability, partial and total and precise clock control.
Figure 3.
Reconfigurable edge computing system based on FPGA SoC.
A multi-threaded huge number, computing requirements, and parallel heterogeneous data processing are persistently proposed in many environments of manufacturing. However, depending on the multi-environment’s requirements with different multiple tasks and several scenes, a single algorithm can no longer face the requirements so that numerous complicated tasks require the algorithm to be reconfigured and replaced. Without a doubt, the FPGA employment gratifies this multitude of requirements. However, it can rebuild the logic of the chip by means of configuration and reconfiguration of the resources inside the chip to form hardware with different functions by means of software. Therefore, in addition to the programmability and flexibility of the software, the FPGA also exhibits high throughput, low power, and low latency characteristics. In addition, due to its rich In-Output, FPGA SoCs are also very relevant for use On-chip protocols applications and interface conversion. The main benefits from employing FPGA for the edge computing are as follows:
A constant throughput can be provided by the FPGA with a constant load size-based application, so that can integrate multiple service requests from several sensors in the IoT.
Large-scale temporal and spatial parallelism is provided by the FPGA with fine granularity, so that ensures a high concurrency and high dependency algorithm with high acceleration performance.
Compared with the processor, the FPGA has the lower power consumption and faster computing speed, which can provide the stability and lower task energy consumption.
5. Network slicing architecture and system model
5.1 Network slicing architecture
The 5G network infrastructure design should focus on attentive consideration of software control, hardware infrastructure, and interconnection between them. In this context, we consider a network slicing architecture consisting of a set of IIoT slices J=1…j, where j represents the slice number. These slices are built on a unified physical infrastructure and share the same network resources. The proposed architecture, which is denoted in Figure 4, consists of three virtual slices. The urgent slice is the UCLE which yields more significance to the QoS and the efficiency. Thereafter, the HCLE slice that donates less importance to the latency. The last one is defined as the LCLE, which has the lowest slice priorities with unsecured QoS. The table denoted in the work [7] represents the slice’s QoS requirements adopted for our architecture. This architecture consists of a set of K=1…k gateways, where k is the number of gateways. Then, gateways take over the task of providing radio resources to the substrate network layer, which contains a set of I=1…i IIoT devices, affected to the slice that meets its QoS demands.
Figure 4.
Deep federated RL-based network slicing architecture.
5.2 Slicing system model
This slice set j∈J is integrated virtually on a Gateways (GWs) set k∈K. However, the physical resources of each GW consist of a set of C=1…c channels, in which each one includes a bandwidth b∈B=1…b. The goal of this work is to provide, for slices member, dynamic channel management based on TP and SF tuning. IN this context, αi∈01 is denoted as the binary value that indicate the admission success of device i∈I to slice j on GW k. Therefore, we define the Throughput ϕi and Delay di models, based on SF and TP parameters for each device ij,k, as in (1), (2), respectively [6].
ϕi=SF.Rc2SF.CR=SF.bj,k2SF.CR,∀i∈Ij,k,E1
di=Liϕi,∀i∈Ij,k,E2
where Rc is the chip rate, bi,j denotes the bandwidth assigned for slice j on LoRa GW k, CR represents the coding rate, and Li is denoted as the packet size. Following the ultimate goal that seek to manage slice’s QoS demands, energy efficiency (EE), given in (3), is considered as the second objective that should be maximized for IIoT devices assigned to each slice on each GW.
maxuEEj,k=∑i∈Ij,kαiϕi¯PjT+Pc,∀i∈Ij,k,E3
however, pit denotes the allocation power for each IIoT device. uEEj,k is the EE metric that provides the efficiency of energy efficiency of each slice. While, Pc denotes the power consumption of the circuit and PjT=∑i∈Ij,kpit is the TP. Finally, we define the multi-objective problem, as in (4), aiming to maximize the slice utility revenues Urmj,k.
maxUrmj,k=∑j,kuQoSj,k+uEEj,k+uRELj,k,∀k∈K,∀j∈J,E4
6. The proposed DFRL for network slicing framework
We assume that agents, by sharing its self models based on the Q-network experiences, collaborate to receive global rewards from the federated orchestrator. While the orchestrator collects these models to builds a global network model that provides an optimal actions, on LoRa parameters, that maximize QoS revenue [43, 44].
The considered network consists of two agents, called as agent α and agent β, that play in Markov environment. In this context, we denote the reply memory for agent α by Dα=sαaαsα′rα and the reply memory for agent β by Dβ=sβaβsβ′rβ. These memories are used to store transitions parameters which will be collected, during interaction, to build an optimal policy (πα∗ and πβ∗). The notations of Q-functions, states, actions, and policy are denoted, respecting to agents α and β, respectively, as Qαsα∈Saα∈Aαπα∗ and Qβsβ∈Saβ∈Aβπβ∗. Thereby, assuming that states spaces (sα and sβ), transitions parameters (Dα and Dβ), and the Q-network functions (Qα and Qβ) are different for the defined agents α and β. Each agent builds its own Q-network (Qα or Qβ), and θ (θα or θβ) parameters. These agents interacts with the DFL model with the aim is to build a global federated model that satisfy dynamic slice’s QoS demands exploiting local agents experiences Qα and Qβ. Therefore, based on the Q-networks models, (5) represents the DFL (based on DNN) Q-network output as Qfθαθβθg.
Qfθαθβθg=DNNQαsαaαθαQβsβaβθβθg,E5
where .. denotes the concatenation symbol and θg denotes the DNN (DFL) parameter shared between agents.
At this stage, the Mean Square Error (MSE), is defined for agents α and β, as a Loss function denoted in formulas (6), (7) [7]. These formulas are used to train the proposed framework, by updating the parameters (θα,θβ,θg), to build federated model that will be able to find, then, an optimal action decision on TP and SF that maximize slice’s QoS rewards.
MSEαtθαθg=EYt−QfαsαtaαtCβθαθg2E6
MSEβtθβθg=EYt−QfβsβtaβtCαθβθg2,E7
while Yt=rts+γmaxa∈AQfαsαtaαtCβθαθg is attributed for agent α only, as a condition to start training. Figure 5 depicts the DFRL framework process.
Figure 5.
Training and testing phases.
7. Experiment results
The proposed framework has been implemented in Python language using TensorFlow-gpu package on Intel Xeon E5-2620 v4 2x 8-Core with 64 GB RAM. Also, the NVIDIA GK110BGL [Tesla K40c] is used to improve speed during the training phase.
We provide, in this section, the mean percentage of Packet Loss Rate (PLR) for IIoT devices, as denoted in Figures 6–8, and compare it to the PLR within MBGD scheme [7].
Figure 6.
PLR of UCLE.
Figure 7.
PLR of HCLE.
Figure 8.
PLR of LCLE.
However, by increasing the devices, PLR will increase subsequently. This return to the data rate, that when increase, the number of successful transmitted packet increase accordingly. While it is not the case when throughput is low. We remark also, in Figures 6–8, that UCLE and HCLE slices have a reduced PLR compared to LCLE. However, this due to the reliability and efficiency constraints dedicated for this slice which is not the case in LCLE slice that consider only the load. Compared with the slice results using the MBGD technique, we could obviously note the efficiency of the proposed federated scheme in supporting dynamic slicing strategy by reducing PLR over than 9%. This improvement return to the shared experience between agents that can improve the action decision on TP and SF to slices.
8. Conclusion
This prospective chapter presents a future outlook on low-end motes in the IIoT and IWSN eras. Following a detailed discussion of the trends and challenges posed by the IIoT and IWSN paradigm to low-end devices, it discusses how modern reconfigurable platforms are the perfect candidate to meet the ever-evolving industrial environments. Indeed, in this chapter, we proposed a federated network slicing based on deep reinforcement learning techniques for channels and bandwidth management based LoRa promising technology that meet IIoT and IWSN network service requirements based on the SDN, NFV, network slicing, and deep reinforcement learning techniques. Each LoRa GW plays an agent role, in the environment, and profits from the learning experience provided by the other coexist agents via the global federated model.
In the case of future studies, this chapter introduced comprehensive review and several research lines, especially one attractive future line is related to the integration of FPGA SoC at the edge to build a smart factory as well as IIoT and IWSN environments with environmentally friendly capabilities and functionalities. In addition, future research is needed to fully embrace cloud services and new ways of connectivity in order to get the full benefits of the new Edge FPGA SoC technology.
Nomenclature
J=1…j
IIoT network slices set
K=1…k
LoRa gateways (agents) set
I=1…i
IIoT devices set associated to each slice
B=1…b
channel bandwidth set
C=1…c
LoRa-GW’s channels set
αi∈01,∀i∈Ij,k
device’s admission and association index to slice
∀i∈Ij,k
device i assigned to slice j on gateway k
TP
transmission power
SF
spreading factor
ϕi,∀i∈Ij,k
throughput of device i
di,∀i∈Ij,k
delay of device i
uQoSj,k
quality of service metric for slice j on GW k
pit,∀i∈Ij,k
the power allocated for each device i
uEEj,k
energy efficiency metric for slice j on GW k
pir
the received power
uRELj,k
reliability metric for slice j on GW k
Urmj,k,∀k∈K,∀j∈J
the global slice utility revenues metric
SATℛ
state, action, transition function, reward
α and β
agent α and agent β
γ
discount factor
θα,θβ
DQL network parameters (weights)
θg
DNN network parameters (weights)
Dα,Dβ
reply memories to store transitions
Abbreviations
IoT
internet of things
IIoT
industrial IoT
IWSN
industrial wireless sensor network
AI
artificial intelligence
ML
machine learning
DL
deep learning
QoS
quality of service
RL
reinforcement learning
DRL
deep reinforcement learning
FL
federated learning
DFL
deep federated learning
DFRL
deep federated reinforcement learning
CPIToS
cyber-physical internet of thing systems
5G
fifth generation network
SDN
software defined network
NFV
network function virtualization
NS
network slicing
DQL
deep Q-learning
GD
gradient descent
GMM
gaussian mixture model
SP
service provide
MEC
mobile edge computing
GPU
graphic processor unit
FPGA
field programmable gate array
UCLE
ultra critical of latency and efficiency
HCLE
high critical of latency and efficiency
LCLE
low critical of latency and efficiency
GW
gateway
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Motivated by the growing computational capacity of the IIoT and the challenges of meeting QoS, federated reinforcement learning (RL) has become a propitious technique that gives out data collection and computation tasks to distributed network agents. This chapter discuss the new federated learning paradigm and then proposes a Deep Federated RL (DFRL) scheme to provide a federated network resource management for future IIoT networks. Toward this goal, the DFRL learns from Multi-Agent local models and provides them the ability to find optimal action decisions on LoRa parameters that satisfy QoS to IIoT virtual slice. 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Network slicing architecture and system model",level:"1"},{id:"sec_12_2",title:"5.1 Network slicing architecture",level:"2"},{id:"sec_13_2",title:"5.2 Slicing system model",level:"2"},{id:"sec_15",title:"6. The proposed DFRL for network slicing framework",level:"1"},{id:"sec_16",title:"7. Experiment results",level:"1"},{id:"sec_17",title:"8. Conclusion",level:"1"},{id:"sec_20",title:"Nomenclature",level:"1"},{id:"sec_20",title:"Abbreviations",level:"1"}],chapterReferences:[{id:"B1",body:'Givehchi O, Landsdorf K, Simoens P, Colombo AW. Interoperability for industrial cyber-physical systems: An approach for legacy systems. IEEE Transactions on Industrial Informatics. 2017;13(6):3370-3378'},{id:"B2",body:'Nordrum, A. Popular Internet of Things. IEEE Spectrum’s Technology Blog [Online]. 2016. 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Benchmarking tpu, gpu, and cpu platforms for deep learning. arXiv preprint arXiv:1907.10701. 2019'},{id:"B41",body:'Cho HD, Engineer PDP, Chung K, Kim T. Benefits of the Big. LITTLE Architecture. EETimes; 2012'},{id:"B42",body:'Liu L, Han G, Xu Z, Shu L, Martinez-Garcia M, Peng B. Predictive boundary tracking based on motion behavior learning for continuous objects in industrial wireless sensor networks. IEEE Transactions on Mobile Computing. 2021. Early Access'},{id:"B43",body:'Haque ME, Baroudi U. Ambient self-powered cluster-based wireless sensor networks for industry 4.0 applications. Soft Computing. 2021;25(3):1859-1884'},{id:"B44",body:'Messaoud S, Bradai A, Ahmed OB, Quang PTA, Atri M, Hossain MS. Deep federated q-learning-based network slicing for industrial IoT. IEEE Transactions on Industrial Informatics. 2020;17(8):5572-5582'}],footnotes:[],contributors:[{corresp:"yes",contributorFullName:"Seifeddine Messaoud",address:"seifeddine.messaoud@fsm.rnu.tn",affiliation:'
Faculty of Sciences, Electronics and Microelectronics Lab., University of Monastir, Tunisia
College of Computer Science, King Khalid University, Saudi Arabia
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The Open Access Publishing Fee (OAPF) is payable only after your book chapter, monograph or journal article is accepted for publication.
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OAPF Publishing Options
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1,400 GBP Chapter - Edited Volume
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850 GBP Chapter - Book Series Topic (Annual Volume)
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10,000 GBP Monograph - Long Form
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4,000 GBP Compacts Monograph - Short Form
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850 GBP Journal Article (Across Portfolio)
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During the launching phase journals do not charge an APC, rather they will be funded by IntechOpen.
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*These prices do not include Value-Added Tax (VAT). Residents of European Union countries need to add VAT based on the specific rate in their country of residence. Institutions and companies registered as VAT taxable entities in their own EU member state will not pay VAT as long as provision of the VAT registration number is made during the application process. This is made possible by the EU reverse charge method.
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Services included are:
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An online manuscript tracking system to facilitate your work
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Personal contact and support throughout the publishing process from your dedicated Author Service Manager
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Assurance that your manuscript meets the highest publishing standards
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English language copyediting and proofreading, including the correction of grammatical, spelling, and other common errors
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XML Typesetting and pagination - web (PDF, HTML) and print files preparation
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Discoverability - electronic citation and linking via DOI
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Permanent and unrestricted online access to your work
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What isn't covered by the Open Access Publishing Fee?
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If your manuscript:
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Exceeds the number of pages defined by the publishing guidelines, an additional fee per page may be required
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If a manuscript requires Heavy Editing or Language Polishing, this will incur additional fees.
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Your Author Service Manager will inform you of any items not covered by the OAPF and provide exact information regarding those additional costs before proceeding.
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Open Access Funding
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To explore funding opportunities and learn more about how you can finance your IntechOpen publication, go to our Open Access Funding page. IntechOpen offers expert assistance to all of its Authors. We can support you in approaching funding bodies and institutions in relation to publishing fees by providing information about compliance with the Open Access policies of your funder or institution. We can also assist with communicating the benefits of Open Access in order to support and strengthen your funding request and provide personal guidance through your application process. You can contact us at funders@intechopen.com for further details or assistance.
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For Authors who are still unable to obtain funding from their institutions or research funding bodies for individual projects, IntechOpen does offer the possibility of applying for a Waiver to offset some or all processing feed. Details regarding our Waiver Policy can be found here.
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Added Value of Publishing with IntechOpen
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Choosing to publish with IntechOpen ensures the following benefits:
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Indexing and listing across major repositories, see details ...
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Long-term archiving
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Visibility on the world's strongest OA platform
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Live Performance Metrics to track readership and the impact of your chapter
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Dissemination and Promotion
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Benefits of Publishing with IntechOpen
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Proven world leader in Open Access book publishing with over 10 years experience
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+5,700 OA books published
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Most competitive prices in the market
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Fully compliant with OA funding requirements
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Optimized processes that assure your research is made available to the scientific community without delay
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Personal support during every step of the publication process
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+184,650 citations in Web of Science databases
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Currently strongest OA platform with over 175 million downloads
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Many critical affairs must be properly treated to obtain the improved operating characteristics. This chapter presents the basic and key technologies of switched-reluctance machine in motor and generator operations. The contents in this chapter include: (1) structures and governing equations of SRM; (2) some commonly used SRM converters; (3) estimation of key parameters and performance evaluation of SRM drive; (4) commutation scheme, current control scheme, and speed control scheme of SRM drive; (5) some commonly used front-end converters and their operation controls for SRM drive; (6) reversible and regenerative braking operation controls for SRM drive; (7) some tuning issues for SRM drive; (8) operation control and some tuning issues of switched-reluctance generators; and (9) experimental application exploration for SRM systems—(a) wind generator and microgrid and (b) EV SRM drive.",book:{id:"8899",slug:"modelling-and-control-of-switched-reluctance-machines",title:"Modelling and Control of Switched Reluctance Machines",fullTitle:"Modelling and Control of Switched Reluctance Machines"},signatures:"Chang-Ming Liaw, Min-Ze Lu, Ping-Hong Jhou and Kuan-Yu Chou",authors:[{id:"37616",title:"Prof.",name:"Chang-Ming",middleName:null,surname:"Liaw",slug:"chang-ming-liaw",fullName:"Chang-Ming Liaw"},{id:"306461",title:"Mr.",name:"Min-Ze",middleName:null,surname:"Lu",slug:"min-ze-lu",fullName:"Min-Ze Lu"},{id:"306463",title:"Mr.",name:"Ping-Hong",middleName:null,surname:"Jhou",slug:"ping-hong-jhou",fullName:"Ping-Hong Jhou"},{id:"306464",title:"Mr.",name:"Kuan-Yu",middleName:null,surname:"Chou",slug:"kuan-yu-chou",fullName:"Kuan-Yu Chou"}]}],onlineFirstChaptersFilter:{topicId:"1",limit:6,offset:0},onlineFirstChaptersCollection:[{id:"82385",title:"Cyclodextrins as Bricks for Tuning Polymer Properties",slug:"cyclodextrins-as-bricks-for-tuning-polymer-properties",totalDownloads:1,totalDimensionsCites:null,doi:"10.5772/intechopen.105688",abstract:"Cyclodextrins are natural cyclic oligosaccharides with a cone shape delimiting a hydrophobic cavity. 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Both natural and synthetic polymers are considered in this chapter.",book:{id:"11901",title:"Cyclodextrins - New Perspectives",coverURL:"https://cdn.intechopen.com/books/images_new/11901.jpg"},signatures:"Ludmila Aricov, Anca Ruxandra Leontieș, Iulia Matei and Gabriela Ioniță"},{id:"82443",title:"Phase Noise in OFDM",slug:"phase-noise-in-ofdm",totalDownloads:0,totalDimensionsCites:0,doi:"10.5772/intechopen.105551",abstract:"Orthogonal frequency division multiplexing (OFDM) technique provides high data rate with high spectral efficiency for operating close to the Shanon capacity bounds. With the advantages of simple channel equalization, robustness against frequency selectivity of the channel, and efficient implementation, this is a widely deployed technique. Orthogonal frequency division multiplexing access (OFDMA), the multiple access technique using OFDM, has the great potential for providing high spectral efficiency due to its integrated space-frequency and multiuser diversity. Besides all the advantages, OFDM/A is very susceptible to transceiver’s impairments such as phase noise (PHN), carrier frequency offset, and in-quadrature phase imbalance effect. Phase noise is the random fluctuation in phase of the sinusoidal waveform used for frequency up/down conversion of baseband signals to/from RF (radio frequency). This occurs due to the inherent imperfections of oscillators used for this purpose. 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The stages of development and structure of electronic testing are presented. The process of forming the base of test tasks is described. The structure of the software is stated. The experience of approbation and application of testing technology is presented. The influence of electronic testing technology on teaching methods is shown. The issues of electronic support of business games are considered. Electronic technologies are considered as a necessary and essential element in the organization and implementation of business games developed at the department. An assessment of the impact of electronic testing and electronic support of business games on the quality of the educational process is given.",book:{id:"11170",title:"Quality Control",coverURL:"https://cdn.intechopen.com/books/images_new/11170.jpg"},signatures:"Valeryi Semenov"},{id:"82348",title:"Biochar Development as a Catalyst and Its Application",slug:"biochar-development-as-a-catalyst-and-its-application",totalDownloads:1,totalDimensionsCites:null,doi:"10.5772/intechopen.105439",abstract:"Biochar is a carbon-rich pyrogenic material that is made from carbon-neutral sources (i.e., biomass). It offers key strategies for carbon capture and storage (CCS) as well as being an environmentally friendly means of soil amendment. The recent recognition of biochar as a versatile media for catalytic applications has prompted preliminary research into biochar’s catalytic capacity and mechanistic practices via various routes. This chapter provides a review of biochar production technologies, biochar’s catalyst development, and its application in various catalytic processes as well as descriptions of the benefits and drawbacks of the various applications currently available. The characteristics of biochar-based catalysts, challenges of effective application of this catalyst system, emerging application, prospects, and future work consideration for effective utilization of biochar-based catalysts were presented.",book:{id:"11537",title:"Biochar - Productive Technologies, Properties and Application",coverURL:"https://cdn.intechopen.com/books/images_new/11537.jpg"},signatures:"Stephen Okiemute Akpasi, Ifeanyi Michael Smarte Anekwe, Jeremiah Adedeji and Sammy Lewis Kiambi"},{id:"82418",title:"Bayesian Networks for Decision Support in Emergency Response: A Model for Missing Person Investigations",slug:"bayesian-networks-for-decision-support-in-emergency-response-a-model-for-missing-person-investigatio",totalDownloads:1,totalDimensionsCites:0,doi:"10.5772/intechopen.105047",abstract:"The successful operation of Emergency services (Police, Fire, Medical Emergency) relies heavily upon Information Systems and particularly Decision Support Systems. Missing person cases consume resources from the already overstretched resources of Police Forces. Such cases predominantly come from at-risk groups such as children in care, people suffering from depression, or elderly people suffering from dementia. 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In this study, the comparative analysis of the variation of the precipitation in relation to the availability of water in the Yautepec and Cuautla rivers in Morelos, Mexico, for the previous period and subsequent period is carried out, to determine the change in the availability of water in the ecosystem. In winter (February), an increase in rainfall on the Yautepec and Cuautla River was observed, where annual seasonal agriculture and Pine and Oyamel forest are the characteristic vegetation. In autumn (October), a decrease in precipitation takes place. The flows in some regions do not coincide with the increase in the percentage of precipitation (Oaxtepec and Las Estacas Stations) and point out the synergistic effect of the human use of the water resource and the effects of climate change. On Ticumán Station, the depletion of the flow only can be associated with the use of the resource by human influence. The modifications caused by alteration of a river’s flow regime and climatic change must be studied through comparative multidisciplinary studies that give to decision-makers the design of environmental flows.",book:{id:"11532",title:"River Basin Management - Under a Changing Climate",coverURL:"https://cdn.intechopen.com/books/images_new/11532.jpg"},signatures:"Rebeca González-Villela, Alfonso Banderas Tarabay and Marco Mijangos Carro"}],onlineFirstChaptersTotal:803},preDownload:{success:null,errors:{}},subscriptionForm:{success:null,errors:{}},aboutIntechopen:{},privacyPolicy:{},peerReviewing:{},howOpenAccessPublishingWithIntechopenWorks:{},sponsorshipBooks:{sponsorshipBooks:[],offset:0,limit:8,total:null},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:105,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:2,numberOfUpcomingTopics:1,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:"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"}}}},{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"}}}}]},series:{item:{id:"14",title:"Artificial Intelligence",doi:"10.5772/intechopen.79920",issn:"2633-1403",scope:"Artificial Intelligence (AI) is a rapidly developing multidisciplinary research area that aims to solve increasingly complex problems. In today's highly integrated world, AI promises to become a robust and powerful means for obtaining solutions to previously unsolvable problems. This Series is intended for researchers and students alike interested in this fascinating field and its many applications.",coverUrl:"https://cdn.intechopen.com/series/covers/14.jpg",latestPublicationDate:"June 11th, 2022",hasOnlineFirst:!0,numberOfPublishedBooks:9,editor:{id:"218714",title:"Prof.",name:"Andries",middleName:null,surname:"Engelbrecht",slug:"andries-engelbrecht",fullName:"Andries Engelbrecht",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRNR8QAO/Profile_Picture_1622640468300",biography:"Andries Engelbrecht received the Masters and PhD degrees in Computer Science from the University of Stellenbosch, South Africa, in 1994 and 1999 respectively. He is currently appointed as the Voigt Chair in Data Science in the Department of Industrial Engineering, with a joint appointment as Professor in the Computer Science Division, Stellenbosch University. Prior to his appointment at Stellenbosch University, he has been at the University of Pretoria, Department of Computer Science (1998-2018), where he was appointed as South Africa Research Chair in Artifical Intelligence (2007-2018), the head of the Department of Computer Science (2008-2017), and Director of the Institute for Big Data and Data Science (2017-2018). 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He is a full professor of signal processing and pattern recognition and is head of the Signals and Communications Department at ULPGC, teaching from 2001 on subjects on signal processing and learning theory. His research lines are biometrics, biomedical signals and images, data mining, classification system, signal and image processing, machine learning, and environmental intelligence. He has researched in 52 international and Spanish research projects, some of them as head researcher. He is co-author of 4 books, co-editor of 27 proceedings books, guest editor for 8 JCR-ISI international journals, and up to 24 book chapters. He has over 450 papers published in international journals and conferences (81 of them indexed on JCR – ISI - Web of Science). He has published seven patents in the Spanish Patent and Trademark Office. He has been a supervisor on 8 Ph.D. theses (11 more are under supervision), and 130 master theses. He is the founder of The IEEE IWOBI conference series and the president of its Steering Committee, as well as the founder of both the InnoEducaTIC and APPIS conference series. He is an evaluator of project proposals for the European Union (H2020), Medical Research Council (MRC, UK), Spanish Government (ANECA, Spain), Research National Agency (ANR, France), DAAD (Germany), Argentinian Government, and the Colombian Institutions. He has been a reviewer in different indexed international journals (<70) and conferences (<250) since 2001. He has been a member of the IASTED Technical Committee on Image Processing from 2007 and a member of the IASTED Technical Committee on Artificial Intelligence and Expert Systems from 2011. \n\nHe has held the general chair position for the following: ACM-APPIS (2020, 2021), IEEE-IWOBI (2019, 2020 and 2020), A PPIS (2018, 2019), IEEE-IWOBI (2014, 2015, 2017, 2018), InnoEducaTIC (2014, 2017), IEEE-INES (2013), NoLISP (2011), JRBP (2012), and IEEE-ICCST (2005)\n\nHe is an associate editor of the Computational Intelligence and Neuroscience Journal (Hindawi – Q2 JCR-ISI). He was vice dean from 2004 to 2010 in the Higher Technical School of Telecommunication Engineers at ULPGC and the vice dean of Graduate and Postgraduate Studies from March 2013 to November 2017. He won the “Catedra Telefonica” Awards in Modality of Knowledge Transfer, 2017, 2018, and 2019 editions, and awards in Modality of COVID Research in 2020.\n\nPublic References:\nResearcher ID http://www.researcherid.com/rid/N-5967-2014\nORCID https://orcid.org/0000-0002-4621-2768 \nScopus Author ID https://www.scopus.com/authid/detail.uri?authorId=6602376272\nScholar Google https://scholar.google.es/citations?user=G1ks9nIAAAAJ&hl=en \nResearchGate https://www.researchgate.net/profile/Carlos_Travieso",institutionString:null,institution:{name:"University of Las Palmas de Gran Canaria",institutionURL:null,country:{name:"Spain"}}},editorTwo:null,editorThree:null},{id:"23",title:"Computational Neuroscience",coverUrl:"https://cdn.intechopen.com/series_topics/covers/23.jpg",isOpenForSubmission:!0,editor:{id:"14004",title:"Dr.",name:"Magnus",middleName:null,surname:"Johnsson",slug:"magnus-johnsson",fullName:"Magnus Johnsson",profilePictureURL:"https://mts.intechopen.com/storage/users/14004/images/system/14004.png",biography:"Dr Magnus Johnsson is a cross-disciplinary scientist, lecturer, scientific editor and AI/machine learning consultant from Sweden. \n\nHe is currently at Malmö University in Sweden, but also held positions at Lund University in Sweden and at Moscow Engineering Physics Institute. \nHe holds editorial positions at several international scientific journals and has served as a scientific editor for books and special journal issues. \nHis research interests are wide and include, but are not limited to, autonomous systems, computer modeling, artificial neural networks, artificial intelligence, cognitive neuroscience, cognitive robotics, cognitive architectures, cognitive aids and the philosophy of mind. \n\nDr. Johnsson has experience from working in the industry and he has a keen interest in the application of neural networks and artificial intelligence to fields like industry, finance, and medicine. \n\nWeb page: www.magnusjohnsson.se",institutionString:null,institution:{name:"Malmö University",institutionURL:null,country:{name:"Sweden"}}},editorTwo:null,editorThree:null},{id:"24",title:"Computer Vision",coverUrl:"https://cdn.intechopen.com/series_topics/covers/24.jpg",isOpenForSubmission:!0,editor:{id:"294154",title:"Prof.",name:"George",middleName:null,surname:"Papakostas",slug:"george-papakostas",fullName:"George Papakostas",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002hYaGbQAK/Profile_Picture_1624519712088",biography:"George A. Papakostas has received a diploma in Electrical and Computer Engineering in 1999 and the M.Sc. and Ph.D. degrees in Electrical and Computer Engineering in 2002 and 2007, respectively, from the Democritus University of Thrace (DUTH), Greece. Dr. Papakostas serves as a Tenured Full Professor at the Department of Computer Science, International Hellenic University, Greece. Dr. Papakostas has 10 years of experience in large-scale systems design as a senior software engineer and technical manager, and 20 years of research experience in the field of Artificial Intelligence. Currently, he is the Head of the “Visual Computing” division of HUman-MAchines INteraction Laboratory (HUMAIN-Lab) and the Director of the MPhil program “Advanced Technologies in Informatics and Computers” hosted by the Department of Computer Science, International Hellenic University. He has (co)authored more than 150 publications in indexed journals, international conferences and book chapters, 1 book (in Greek), 3 edited books, and 5 journal special issues. His publications have more than 2100 citations with h-index 27 (GoogleScholar). His research interests include computer/machine vision, machine learning, pattern recognition, computational intelligence. \nDr. Papakostas served as a reviewer in numerous journals, as a program\ncommittee member in international conferences and he is a member of the IAENG, MIR Labs, EUCogIII, INSTICC and the Technical Chamber of Greece (TEE).",institutionString:null,institution:{name:"International Hellenic University",institutionURL:null,country:{name:"Greece"}}},editorTwo:null,editorThree:null},{id:"25",title:"Evolutionary Computation",coverUrl:"https://cdn.intechopen.com/series_topics/covers/25.jpg",isOpenForSubmission:!0,editor:{id:"136112",title:"Dr.",name:"Sebastian",middleName:null,surname:"Ventura Soto",slug:"sebastian-ventura-soto",fullName:"Sebastian Ventura Soto",profilePictureURL:"https://mts.intechopen.com/storage/users/136112/images/system/136112.png",biography:"Sebastian Ventura is a Spanish researcher, a full professor with the Department of Computer Science and Numerical Analysis, University of Córdoba. Dr Ventura also holds the positions of Affiliated Professor at Virginia Commonwealth University (Richmond, USA) and Distinguished Adjunct Professor at King Abdulaziz University (Jeddah, Saudi Arabia). Additionally, he is deputy director of the Andalusian Research Institute in Data Science and Computational Intelligence (DaSCI) and heads the Knowledge Discovery and Intelligent Systems Research Laboratory. He has published more than ten books and over 300 articles in journals and scientific conferences. Currently, his work has received over 18,000 citations according to Google Scholar, including more than 2200 citations in 2020. In the last five years, he has published more than 60 papers in international journals indexed in the JCR (around 70% of them belonging to first quartile journals) and he has edited some Springer books “Supervised Descriptive Pattern Mining” (2018), “Multiple Instance Learning - Foundations and Algorithms” (2016), and “Pattern Mining with Evolutionary Algorithms” (2016). He has also been involved in more than 20 research projects supported by the Spanish and Andalusian governments and the European Union. He currently belongs to the editorial board of PeerJ Computer Science, Information Fusion and Engineering Applications of Artificial Intelligence journals, being also associate editor of Applied Computational Intelligence and Soft Computing and IEEE Transactions on Cybernetics. Finally, he is editor-in-chief of Progress in Artificial Intelligence. He is a Senior Member of the IEEE Computer, the IEEE Computational Intelligence, and the IEEE Systems, Man, and Cybernetics Societies, and the Association of Computing Machinery (ACM). Finally, his main research interests include data science, computational intelligence, and their applications.",institutionString:null,institution:{name:"University of Córdoba",institutionURL:null,country:{name:"Spain"}}},editorTwo:null,editorThree:null},{id:"26",title:"Machine Learning and Data Mining",coverUrl:"https://cdn.intechopen.com/series_topics/covers/26.jpg",isOpenForSubmission:!0,editor:{id:"24555",title:"Dr.",name:"Marco Antonio",middleName:null,surname:"Aceves Fernandez",slug:"marco-antonio-aceves-fernandez",fullName:"Marco Antonio Aceves Fernandez",profilePictureURL:"https://mts.intechopen.com/storage/users/24555/images/system/24555.jpg",biography:"Dr. Marco Antonio Aceves Fernandez obtained his B.Sc. (Eng.) in Telematics from the Universidad de Colima, Mexico. He obtained both his M.Sc. and Ph.D. from the University of Liverpool, England, in the field of Intelligent Systems. He is a full professor at the Universidad Autonoma de Queretaro, Mexico, and a member of the National System of Researchers (SNI) since 2009. Dr. Aceves Fernandez has published more than 80 research papers as well as a number of book chapters and congress papers. He has contributed in more than 20 funded research projects, both academic and industrial, in the area of artificial intelligence, ranging from environmental, biomedical, automotive, aviation, consumer, and robotics to other applications. He is also a honorary president at the National Association of Embedded Systems (AMESE), a senior member of the IEEE, and a board member of many institutions. His research interests include intelligent and embedded systems.",institutionString:"Universidad Autonoma de Queretaro",institution:{name:"Autonomous University of Queretaro",institutionURL:null,country:{name:"Mexico"}}},editorTwo:null,editorThree:null},{id:"27",title:"Multi-Agent Systems",coverUrl:"https://cdn.intechopen.com/series_topics/covers/27.jpg",isOpenForSubmission:!0,editor:{id:"148497",title:"Dr.",name:"Mehmet",middleName:"Emin",surname:"Aydin",slug:"mehmet-aydin",fullName:"Mehmet Aydin",profilePictureURL:"https://mts.intechopen.com/storage/users/148497/images/system/148497.jpg",biography:"Dr. Mehmet Emin Aydin is a Senior Lecturer with the Department of Computer Science and Creative Technology, the University of the West of England, Bristol, UK. His research interests include swarm intelligence, parallel and distributed metaheuristics, machine learning, intelligent agents and multi-agent systems, resource planning, scheduling and optimization, combinatorial optimization. Dr. Aydin is currently a Fellow of Higher Education Academy, UK, a member of EPSRC College, a senior member of IEEE and a senior member of ACM. In addition to being a member of advisory committees of many international conferences, he is an Editorial Board Member of various peer-reviewed international journals. He has served as guest editor for a number of special issues of peer-reviewed international journals.",institutionString:null,institution:{name:"University of the West of England",institutionURL:null,country:{name:"United Kingdom"}}},editorTwo:null,editorThree:null}]},overviewPageOFChapters:{paginationCount:19,paginationItems:[{id:"82196",title:"Multi-Features Assisted Age Invariant Face Recognition and Retrieval Using CNN with Scale Invariant Heat Kernel Signature",doi:"10.5772/intechopen.104944",signatures:"Kamarajugadda Kishore Kumar and Movva Pavani",slug:"multi-features-assisted-age-invariant-face-recognition-and-retrieval-using-cnn-with-scale-invariant-",totalDownloads:6,totalCrossrefCites:0,totalDimensionsCites:0,authors:null,book:{title:"Pattern Recognition - New Insights",coverURL:"https://cdn.intechopen.com/books/images_new/11442.jpg",subseries:{id:"26",title:"Machine Learning and Data Mining"}}},{id:"82063",title:"Evaluating Similarities and Differences between Machine Learning and Traditional Statistical Modeling in Healthcare Analytics",doi:"10.5772/intechopen.105116",signatures:"Michele Bennett, Ewa J. 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(Eng.) in Telematics from the Universidad de Colima, Mexico. He obtained both his M.Sc. and Ph.D. from the University of Liverpool, England, in the field of Intelligent Systems. He is a full professor at the Universidad Autonoma de Queretaro, Mexico, and a member of the National System of Researchers (SNI) since 2009. Dr. Aceves Fernandez has published more than 80 research papers as well as a number of book chapters and congress papers. He has contributed in more than 20 funded research projects, both academic and industrial, in the area of artificial intelligence, ranging from environmental, biomedical, automotive, aviation, consumer, and robotics to other applications. He is also a honorary president at the National Association of Embedded Systems (AMESE), a senior member of the IEEE, and a board member of many institutions. His research interests include intelligent and embedded systems.",institutionString:"Universidad Autonoma de Queretaro",institution:{name:"Autonomous University of Queretaro",institutionURL:null,country:{name:"Mexico"}}}]},{type:"book",id:"7726",title:"Swarm Intelligence",subtitle:"Recent Advances, New Perspectives and Applications",coverURL:"https://cdn.intechopen.com/books/images_new/7726.jpg",slug:"swarm-intelligence-recent-advances-new-perspectives-and-applications",publishedDate:"December 4th 2019",editedByType:"Edited by",bookSignature:"Javier Del Ser, Esther Villar and Eneko Osaba",hash:"e7ea7e74ce7a7a8e5359629e07c68d31",volumeInSeries:2,fullTitle:"Swarm Intelligence - Recent Advances, New Perspectives and Applications",editors:[{id:"49813",title:"Dr.",name:"Javier",middleName:null,surname:"Del Ser",slug:"javier-del-ser",fullName:"Javier Del Ser",profilePictureURL:"https://mts.intechopen.com/storage/users/49813/images/system/49813.png",biography:"Prof. Dr. Javier Del Ser received his first PhD in Telecommunication Engineering (Cum Laude) from the University of Navarra, Spain, in 2006, and a second PhD in Computational Intelligence (Summa Cum Laude) from the University of Alcala, Spain, in 2013. He is currently a principal researcher in data analytics and optimisation at TECNALIA (Spain), a visiting fellow at the Basque Center for Applied Mathematics (BCAM) and a part-time lecturer at the University of the Basque Country (UPV/EHU). His research interests gravitate on the use of descriptive, prescriptive and predictive algorithms for data mining and optimization in a diverse range of application fields such as Energy, Transport, Telecommunications, Health and Industry, among others. In these fields he has published more than 240 articles, co-supervised 8 Ph.D. theses, edited 6 books, coauthored 7 patents and participated/led more than 40 research projects. 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He is currently a full professor in\nthe Department of Automation and Applied Informatics at the\nsame university. Dr. Voloşencu is the author of ten books, seven\nbook chapters, and more than 160 papers published in journals\nand conference proceedings. He has also edited twelve books and\nhas twenty-seven patents to his name. He is a manager of research grants, editor in\nchief and member of international journal editorial boards, a former plenary speaker, a member of scientific committees, and chair at international conferences. His\nresearch is in the fields of control systems, control of electric drives, fuzzy control\nsystems, neural network applications, fault detection and diagnosis, sensor network\napplications, monitoring of distributed parameter systems, and power ultrasound\napplications. 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Thus proteomics, an area of research that detects all protein forms expressed in an organism, including splice isoforms and post-translational modifications, is more suitable than genomics for a comprehensive understanding of the biochemical processes that govern life. The most common proteomics applications are currently in the clinical field for the identification, in a variety of biological matrices, of biomarkers for diagnosis and therapeutic intervention of disorders. From the comparison of proteomic profiles of control and disease or different physiological states, which may emerge, changes in protein expression can provide new insights into the roles played by some proteins in human pathologies. Understanding how proteins function and interact with each other is another goal of proteomics that makes this approach even more intriguing. Specialized technology and expertise are required to assess the proteome of any biological sample. Currently, proteomics relies mainly on mass spectrometry (MS) combined with electrophoretic (1 or 2-DE-MS) and/or chromatographic techniques (LC-MS/MS). MS is an excellent tool that has gained popularity in proteomics because of its ability to gather a complex body of information such as cataloging protein expression, identifying protein modification sites, and defining protein interactions. 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