Summary of the morphological findings in different conditions affecting gastrointestinal barrier.
\\n\\n
Released this past November, the list is based on data collected from the Web of Science and highlights some of the world’s most influential scientific minds by naming the researchers whose publications over the previous decade have included a high number of Highly Cited Papers placing them among the top 1% most-cited.
\\n\\nWe wish to congratulate all of the researchers named and especially our authors on this amazing accomplishment! We are happy and proud to share in their success!
Note: Edited in March 2021
\\n"}]',published:!0,mainMedia:{caption:"Highly Cited",originalUrl:"/media/original/117"}},components:[{type:"htmlEditorComponent",content:'IntechOpen is proud to announce that 191 of our authors have made the Clarivate™ Highly Cited Researchers List for 2020, ranking them among the top 1% most-cited.
\n\nThroughout the years, the list has named a total of 261 IntechOpen authors as Highly Cited. Of those researchers, 69 have been featured on the list multiple times.
\n\n\n\nReleased this past November, the list is based on data collected from the Web of Science and highlights some of the world’s most influential scientific minds by naming the researchers whose publications over the previous decade have included a high number of Highly Cited Papers placing them among the top 1% most-cited.
\n\nWe wish to congratulate all of the researchers named and especially our authors on this amazing accomplishment! We are happy and proud to share in their success!
Note: Edited in March 2021
\n'}],latestNews:[{slug:"webinar-introduction-to-open-science-wednesday-18-may-1-pm-cest-20220518",title:"Webinar: Introduction to Open Science | Wednesday 18 May, 1 PM CEST"},{slug:"step-in-the-right-direction-intechopen-launches-a-portfolio-of-open-science-journals-20220414",title:"Step in the Right Direction: IntechOpen Launches a Portfolio of Open Science Journals"},{slug:"let-s-meet-at-london-book-fair-5-7-april-2022-olympia-london-20220321",title:"Let’s meet at London Book Fair, 5-7 April 2022, Olympia London"},{slug:"50-books-published-as-part-of-intechopen-and-knowledge-unlatched-ku-collaboration-20220316",title:"50 Books published as part of IntechOpen and Knowledge Unlatched (KU) Collaboration"},{slug:"intechopen-joins-the-united-nations-sustainable-development-goals-publishers-compact-20221702",title:"IntechOpen joins the United Nations Sustainable Development Goals Publishers Compact"},{slug:"intechopen-signs-exclusive-representation-agreement-with-lsr-libros-servicios-y-representaciones-s-a-de-c-v-20211123",title:"IntechOpen Signs Exclusive Representation Agreement with LSR Libros Servicios y Representaciones S.A. de C.V"},{slug:"intechopen-expands-partnership-with-research4life-20211110",title:"IntechOpen Expands Partnership with Research4Life"},{slug:"introducing-intechopen-book-series-a-new-publishing-format-for-oa-books-20210915",title:"Introducing IntechOpen Book Series - A New Publishing Format for OA Books"}]},book:{item:{type:"book",id:"7432",leadTitle:null,fullTitle:"Nanowires - Synthesis, Properties and Applications",title:"Nanowires",subtitle:"Synthesis, Properties and Applications",reviewType:"peer-reviewed",abstract:"Nanowires are attracting wide scientific interest due to the unique properties associated with their one-dimensional geometry. Developments in the understanding of the fundamental principles of the nanowire growth mechanisms and mastering functionalization provide tools to control crystal structure, morphology, and the interactions at the material interface, and create characteristics that are superior to those of planar geometries. This book provides a comprehensive overview of the most important developments in the field of nanowires, starting from their synthesis, discussing properties, and finalizing with nanowire applications. The book consists of two parts: the first is devoted to the synthesis of nanowires and characterization, and the second investigates the properties of nanowires and their applications in future devices.",isbn:"978-1-78985-906-5",printIsbn:"978-1-78985-905-8",pdfIsbn:"978-1-83962-127-7",doi:"10.5772/intechopen.75337",price:119,priceEur:129,priceUsd:155,slug:"nanowires-synthesis-properties-and-applications",numberOfPages:120,isOpenForSubmission:!1,isInWos:1,isInBkci:!1,hash:"459e88b91c31f4788eadbeb0ebe36b0b",bookSignature:"Simas Rackauskas",publishedDate:"April 10th 2019",coverURL:"https://cdn.intechopen.com/books/images_new/7432.jpg",numberOfDownloads:5697,numberOfWosCitations:3,numberOfCrossrefCitations:3,numberOfCrossrefCitationsByBook:2,numberOfDimensionsCitations:5,numberOfDimensionsCitationsByBook:2,hasAltmetrics:1,numberOfTotalCitations:11,isAvailableForWebshopOrdering:!0,dateEndFirstStepPublish:"March 15th 2018",dateEndSecondStepPublish:"April 5th 2018",dateEndThirdStepPublish:"June 4th 2018",dateEndFourthStepPublish:"August 23rd 2018",dateEndFifthStepPublish:"October 22nd 2018",currentStepOfPublishingProcess:5,indexedIn:"1,2,3,4,5,6,7",editedByType:"Edited by",kuFlag:!1,featuredMarkup:null,editors:[{id:"195783",title:"Dr.",name:"Simas",middleName:null,surname:"Rackauskas",slug:"simas-rackauskas",fullName:"Simas Rackauskas",profilePictureURL:"https://mts.intechopen.com/storage/users/195783/images/system/195783.jpg",biography:"Simas Rackauskas defended his PhD in Physics at Aalto University, Finland in 2011. He was a Marie Curie Fellow in University of Turin (Italy). He also held post-doctoral positions in Aalto University (Finland) and State University of Campinas (Brazil). He had fellowships in Swiss Federal Institute of Technology in Lausanne (EPFL, Switzerland), Technical University of Denmark (DTU) and University of Nagoya (Japan). His research interests are mainly focused on non-catalytic growth of metal oxide nanowires, application in sensing, carbon nanomaterials and flexible electronics.",institutionString:"Kaunas University of Technology",position:null,outsideEditionCount:0,totalCites:0,totalAuthoredChapters:"1",totalChapterViews:"0",totalEditedBooks:"2",institution:{name:"University of Turin",institutionURL:null,country:{name:"Italy"}}}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,coeditorOne:null,coeditorTwo:null,coeditorThree:null,coeditorFour:null,coeditorFive:null,topics:[{id:"208",title:"Material Science",slug:"nanotechnology-and-nanomaterials-material-science"}],chapters:[{id:"63601",title:"Nanowires of Fe/MgO/Fe Encapsulated in Carbon Nanotubes",doi:"10.5772/intechopen.79819",slug:"nanowires-of-fe-mgo-fe-encapsulated-in-carbon-nanotubes",totalDownloads:878,totalCrossrefCites:1,totalDimensionsCites:1,hasAltmetrics:0,abstract:"Nanowires of tunneling magnetoresistance (TMR) were synthesized using magnetron DC/RF sputtering by filling Fe/MgO/Fe inside vertically grown and substrate-supported carbon nanotubes. Nanocolumns of Fe/MgO/Fe TMR were synthesized using glancing angle deposition. The magnetic properties of nanowires, nanocolumns and planar nanometric thin films of Fe/MgO/Fe showed similarities including twofold magnetic symmetry. Nanowires of Fe/MgO/Fe showed improved magnetic properties, in particular its coercive field, which is 754% higher than planar thin films of Fe/MgO/Fe. A macroscopic phenomenon that can be explained only by quantum mechanics is TMR, where electrical current can flow across a nanometric thin insulator layer between two electrodes when an external magnetic field is applied parallel to the trilayer system. Coherence in the TMR effect is paramount to make spintronic devices. Nanowires possess shape anisotropy, which can play an important role in coherence.",signatures:"Dereje Seifu",downloadPdfUrl:"/chapter/pdf-download/63601",previewPdfUrl:"/chapter/pdf-preview/63601",authors:[{id:"252636",title:"Dr.",name:"Dereje",surname:"Seifu",slug:"dereje-seifu",fullName:"Dereje Seifu"}],corrections:null},{id:"62093",title:"Diamond Nanowire Synthesis, Properties and Applications",doi:"10.5772/intechopen.78794",slug:"diamond-nanowire-synthesis-properties-and-applications",totalDownloads:1080,totalCrossrefCites:1,totalDimensionsCites:2,hasAltmetrics:1,abstract:"Due to the superior hardness and Young’s modulus, biocompatibility, optical and fluorescence nanodiamond seems to be outstanding among carbon nanomaterials. In this footpath, the development of diamond nanowires (DNWs) is known to be a significantly innovative field due to their diverse applications such as sensors, semiconductors, and electrochemical utilities. Compared to carbon nanotubes, DNWs theoretically have energetic and mechanically viable structures. However, DNW synthesis in a reproducible way is still a challenging task. In fact, most of the DNWs can be successfully synthesized by chemical vapor deposition (CVD) and reactive-ion etching (RIE) techniques. By contrast, solution-based DNW synthesis has also emerged recently. A detailed study on DNW structures may help the emerging researchers to direct toward diverse applications. In this chapter, we comprehensively presented the up-to-date applications of DNWs along with their synthesis, structures and properties.",signatures:"Muthaiah Shellaiah and Kien Wen Sun",downloadPdfUrl:"/chapter/pdf-download/62093",previewPdfUrl:"/chapter/pdf-preview/62093",authors:[{id:"251482",title:"Prof.",name:"Kien Wen",surname:"Sun",slug:"kien-wen-sun",fullName:"Kien Wen Sun"},{id:"251485",title:"Dr.",name:"Muthaiah",surname:"Shellaiah",slug:"muthaiah-shellaiah",fullName:"Muthaiah Shellaiah"}],corrections:null},{id:"63599",title:"Analysis of Electrochemical and Structurally Enhanced LiMn2O4 Nanowire Cathode System",doi:"10.5772/intechopen.80077",slug:"analysis-of-electrochemical-and-structurally-enhanced-limn2o4-nanowire-cathode-system",totalDownloads:1007,totalCrossrefCites:1,totalDimensionsCites:1,hasAltmetrics:0,abstract:"The performance of the battery cathode depends on the electrode microstructure and morphology, as well as the inherent electrochemical properties of the cathode materials. The spinel LiMn2O4 is the most promising candidate as a cathode material because of its low cost and nontoxicity compared with commercial LiCoO2. However, there is still a challenge to synthesize high-quality single-crystal nanostructured cathode materials. Nanowires offer advantages of a large surface to volume ratio, efficient electron conducting pathways and facile strain relaxation. To enhance the activity and stability, flexible spinel nanowires are synthesized, via α-MnO2 nanowire precursor method. Ultrathin LiMn2O4 nanowires with cubic spinel structure were synthesized by using a solvothermal reaction to produce α-MnO2 nanowire followed by solid-state lithiation. LiMn2O4 nanowires have diameters less than 10 nm and lengths of several micrometers. The LiMn2O4 nanowires are used as stabilizing support during the electrochemical redox processes. The unique nanoporous material effectively accommodates structural transformation during Li+ ion insertion and effectively reduces Li+ diffusion distances, reducing the volumetric changes and lattice stresses during charge and discharge. Galvanostatic battery testing showed that LiMn2O4 nanowires delivered 146 mAh/g in a large potential window. The electrochemical and spectrochemical interrogation techniques demonstrated that LiMn2O4 nanowires are promising cathode materials for lithium ion batteries as apposed to LiMn2O4 powders.",signatures:"Natasha Ross, Shane Willenberg and Emmanuel Iwuoha",downloadPdfUrl:"/chapter/pdf-download/63599",previewPdfUrl:"/chapter/pdf-preview/63599",authors:[{id:"178638",title:"Dr.",name:"Natasha",surname:"Ross",slug:"natasha-ross",fullName:"Natasha Ross"}],corrections:null},{id:"63785",title:"ZnO nanowires growth direction and parameters affecting their surface morphology",doi:"10.5772/intechopen.80538",slug:"zno-nanowires-growth-direction-and-parameters-affecting-their-surface-morphology",totalDownloads:130,totalCrossrefCites:0,totalDimensionsCites:0,hasAltmetrics:0,abstract:"ZnO nanowires (or nanorods) have been widely studied due to their unique material properties and remarkable performance in electronics, optics, and photonics. This chapter presents a review of the current research of ZnO nanowires (or nanorods) synthesized by hydrothermal method. We discussed the mechanism of its nucleation and growth taking the effect of different parameters on its growth direction and their final morphology into account. A mixture of zinc nitrate and hexamine as precursor is the most popular. We reported the effect of precursor type and concentration, pH of the growth solution, bath temperature, substrate type and seeded layer, and duration time.",signatures:"Shrok Allami",downloadPdfUrl:"/chapter/pdf-download/63785",previewPdfUrl:"/chapter/pdf-preview/63785",authors:[null],corrections:null},{id:"62628",title:"Nanowires for Room-Temperature Mid-Infrared Emission",doi:"10.5772/intechopen.79463",slug:"nanowires-for-room-temperature-mid-infrared-emission",totalDownloads:965,totalCrossrefCites:0,totalDimensionsCites:0,hasAltmetrics:0,abstract:"InAs-based nanowires hold a promise to offer transformational technologies for infrared photonic applications. Site-controlled InAs nanowire growth on low-cost Si substrates offers the practical integration advantages that silicon photonics benefits from. This includes the realisation of cheap photonic circuitries, light emitters and detectors that are otherwise expensive to realise with III/V material-based substrates. This chapter details the growth development of advanced faceted multi-quantum well structures within InAs nanowires using molecular beam epitaxy. We review the crystal structure for the faceted quantum wells along with an analysis of their optical emission characteristics which shows quantum confinement and localisation of the carriers on the quantum well nanostructure. This enables tuning of the emission wavelength and enhanced emission intensity up to the technologically important room-temperature operation point.",signatures:"Aiyeshah Alhodaib, Yasir J. Noori, Anthony Krier and Andrew R.J.\nMarshall",downloadPdfUrl:"/chapter/pdf-download/62628",previewPdfUrl:"/chapter/pdf-preview/62628",authors:[{id:"17149",title:"Dr.",name:"Andrew R.J.",surname:"Marshall",slug:"andrew-r.j.-marshall",fullName:"Andrew R.J. Marshall"},{id:"224728",title:"Dr.",name:"Yasir",surname:"Noori",slug:"yasir-noori",fullName:"Yasir Noori"},{id:"253350",title:"Dr.",name:"Aiyeshah",surname:"Alhodaib",slug:"aiyeshah-alhodaib",fullName:"Aiyeshah Alhodaib"},{id:"261501",title:"Prof.",name:"Anthony",surname:"Krier",slug:"anthony-krier",fullName:"Anthony Krier"}],corrections:null},{id:"63643",title:"Parasitic Capacitances on Scaling Lateral Nanowire",doi:"10.5772/intechopen.81099",slug:"parasitic-capacitances-on-scaling-lateral-nanowire",totalDownloads:871,totalCrossrefCites:0,totalDimensionsCites:1,hasAltmetrics:0,abstract:"The gate-all-around silicon nanowire transistor (GAA-NW) has manifested itself as one of the most fortunate candidates for advanced node integrated circuits (ICs). As the GAA transistor has stronger gate control, better scalability, as well as improved transport properties, the device has been considered as a potential alternative for scaling beyond FinFET. In recent publications, the basic feature and scalability of nanowire have been widely explored primarily focusing on intrinsic device characteristics. Although the GAA-NW has superior gate control compared to other architectures, the device is surrounded by huge vertical gate metal line and S/D contact metal lines. The presence of this vast metal line forms a strong parasitic capacitance. While scaling down sub-7 nm node dimensions, these capacitances influence strongly on the overall device performances. In this chapter, we have discussed the effects of various parasitic capacitances on scaling the device dimensions as well as their performances at high-frequency operations. TCAD-based compact model was used to study the impacts of scaling GAA-NW’s dimensions on power performance and area gain perspective (PPA).",signatures:"Uttam Kumar Das and Tarun Kanti Bhattacharyya",downloadPdfUrl:"/chapter/pdf-download/63643",previewPdfUrl:"/chapter/pdf-preview/63643",authors:[{id:"71655",title:"Dr.",name:"Tarun K.",surname:"Bhattacharyya",slug:"tarun-k.-bhattacharyya",fullName:"Tarun K. Bhattacharyya"},{id:"250951",title:"Ph.D. Student",name:"Uttam",surname:"Kumar Das",slug:"uttam-kumar-das",fullName:"Uttam Kumar Das"}],corrections:null},{id:"63290",title:"Magnetoresistance and Structural Characterization of Electrospun La1−xSrxMnO3 Nanowire Networks",doi:"10.5772/intechopen.80451",slug:"magnetoresistance-and-structural-characterization-of-electrospun-la1-xsrxmno3-nanowire-networks",totalDownloads:766,totalCrossrefCites:0,totalDimensionsCites:0,hasAltmetrics:0,abstract:"Nanowire network fabrics of La1−xSrxMnO3 (LSMO) with different doping levels x = 0.2, 0.3, and 0.4 were fabricated by means of electrospinning. The resulting nanowires are up to 100 μm long with a mean diameter of about 230 nm. The nanowires form a nonwoven fabric-like arrangement, allowing to attach electric contacts for magnetoresistance (MR) measurements. The resistance in applied magnetic fields and the MR effect were measured in the temperature range 2 K < T < 300 K in magnetic fields up to 10 T applied perpendicular to the sample surface. An MR ratio of about 70% is obtained for x = 0.2 at 10 T applied field and T = 20 Kr. The highest low-field MR of 5.2% (0.1 T) is obtained for the sample with x = 0.2. Magnetization measurements reveal the soft magnetic character of the samples. A thorough analysis of the microstructure of these nanowire networks is performed including scanning electron microscopy (SEM) and transmission electron microscopy (TEM).",signatures:"Xian Lin Zeng, Thomas Karwoth, Anjela Koblischka-Veneva, Michael\nR. 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Ross and Leonardo G. Montilla",dateSubmitted:"November 6th 2018",dateReviewed:"December 12th 2018",datePrePublished:"February 5th 2019",datePublished:"September 4th 2019",book:{id:"8633",title:"Novel Diagnostic Methods in Ophthalmology",subtitle:null,fullTitle:"Novel Diagnostic Methods in Ophthalmology",slug:"novel-diagnostic-methods-in-ophthalmology",publishedDate:"September 4th 2019",bookSignature:"Anna Nowinska",coverURL:"https://cdn.intechopen.com/books/images_new/8633.jpg",licenceType:"CC BY 3.0",editedByType:"Edited by",editors:[{id:"261466",title:"Dr.",name:"Anna",middleName:"Karolina",surname:"Nowińska",slug:"anna-nowinska",fullName:"Anna Nowińska"}],productType:{id:"1",title:"Edited Volume",chapterContentType:"chapter",authoredCaption:"Edited by"}},authors:[{id:"274007",title:"Prof.",name:"Mandi D.",middleName:null,surname:"Conway",fullName:"Mandi D. Conway",slug:"mandi-d.-conway",email:"mconway1@yahoo.com",position:null,institution:null},{id:"283754",title:"Dr.",name:"Robin",middleName:"Demi",surname:"Ross",fullName:"Robin Ross",slug:"robin-ross",email:"robindross@email.arizona.edu",position:null,institution:null},{id:"284051",title:"BSc.",name:"David",middleName:null,surname:"Rosen",fullName:"David Rosen",slug:"david-rosen",email:"davidrosen@email.arizona.edu",position:null,institution:null},{id:"284377",title:"BSc.",name:"Leonardo",middleName:null,surname:"Montilla",fullName:"Leonardo Montilla",slug:"leonardo-montilla",email:"funrunner13@gmail.com",position:null,institution:null},{id:"284378",title:"MSc.",name:"Charles",middleName:null,surname:"Ingram",fullName:"Charles Ingram",slug:"charles-ingram",email:"cingram@optics.arizona.edu",position:null,institution:null}]},book:{id:"8633",title:"Novel Diagnostic Methods in Ophthalmology",subtitle:null,fullTitle:"Novel Diagnostic Methods in Ophthalmology",slug:"novel-diagnostic-methods-in-ophthalmology",publishedDate:"September 4th 2019",bookSignature:"Anna Nowinska",coverURL:"https://cdn.intechopen.com/books/images_new/8633.jpg",licenceType:"CC BY 3.0",editedByType:"Edited by",editors:[{id:"261466",title:"Dr.",name:"Anna",middleName:"Karolina",surname:"Nowińska",slug:"anna-nowinska",fullName:"Anna Nowińska"}],productType:{id:"1",title:"Edited Volume",chapterContentType:"chapter",authoredCaption:"Edited by"}}},ofsBook:{item:{type:"book",id:"11476",leadTitle:null,title:"Globalization and Sustainability - Recent Advances, New Perspectives and Emerging Issues",subtitle:null,reviewType:"peer-reviewed",abstract:"\r\n\tThe debate on globalization and sustainability issues has gained momentum in the last few years, thus shedding unprecedented light upon their interrelatedness, as well as their cross-cultural dimensions. They range from the trade-off between global and local aspects to the urban-rural polarization, from global health security to international migration flows, and from cultural globalization to glocalization of technocultures, just to mention a few topics in relation to globalization. Turning to sustainability, it comes naturally to evoke the 2030 Agenda as a strategic to-do list, which leads to focus on its Sustainable Development Goals and associated targets. The areas to be further explored include – but are not limited to – sustainable growth, tourism, and food systems.
\r\n\r\n\t
\r\n\tWithin this scenario, special attention needs to be devoted to financial implications, due to their pervasiveness. Nobody would question the key role that finance plays to complement the real sphere of the economy and that has increasingly attracted both academics and practitioners. As a result, traditional pillars – such as financial markets, products, and institutions – have evolved significantly, with financial innovation fueling further progress over time. The global side of the coin features – among others – financially connected markets, international financial exchanges, and financial conglomerates that provide valuable opportunities in terms of international corporate finance. On the other side, recent advances have involved a wider recourse to ESG factors, allowed forward steps towards a more inclusive financial system, and have made digital finance a must, rather than an option, even though much remains to be accomplished, for instance, to facilitate access to formal financial channels in many underdeveloped regions.
\r\n\t
\r\n\tThis book aims to examine emerging trends, new perspectives, and empirical applications that deal with globalization and sustainability. The goal is to provide a comprehensive overview of these important concepts as valuable support to successfully meet the challenges and take on the opportunities ahead. At the same time, drawing upon empirical evidence can contribute to bridging the gap between theory and practice, which also fits within the scope of this book.
Beside its main function of digestion and absorption, intestinal mucosa acts as an important barrier to toxic and harmful materials and protects an individual from different antigenic and inflammatory reactions. The intestinal barrier is composed of a mucin layer covering the cells, enterocytes and the apical junctional complex in between the cells [1].
The epithelium of the small intestine is characterized by villi & crypts. Villi are folds of the epithelium into the lumen with a core of lamina propria. Villi are tallest in the jejunum and shortest in ileum. The lamina propria core contains white blood cells, lacteals, a rich fenestrated capillary network, nerves; and scattered smooth muscle cells.
The villus epithelium is composed of enterocytes and goblet cells. Enterocytes are columnar absorptive cells and has an apical striated border of microvilli. Goblet cells secrete mucin to provide a protective coating. Only a few goblet cells are present in the the upper small intestine, more in the ileum.
The apical junctional complex consists of a network of tight junction proteins and the adherens junction [2]. They are anchored in the cell via the filamentous actin cytoskeleton [3]. Zonula occludens proteins (ZO-1, ZO-2 and ZO-3) are important intracellular tight junction proteins, linking the cell cytoskeleton to the transmembrane TJ proteins such as claudins, occludin and junctional adhesion molecules (JAM). Whereas occludin and JAM have a regulatory role, transmembrane protein claudins, abundantly present between adjacent healthy intestinal epithelial cells, are mainly responsible for the intestinal barrier function [4].
Crypts are folds of the epithelium that invaginate down into the lamina propria. Many of the cells in the crypt serve as precursors for enterocytes or goblet cells of the villi. Paneth cells are situated at the base of the crypts. Enteroendocrine cells are scattered through the small intestinal crypts.
Microvilli are folds of the apical plasma membrane of each enterocyte with a core of actin cytoskeleton. A thick glycoprotein coat, the glycocalyx covers the microvilli. The glycocalyx contains hydrolytic enzymes such as enteropeptidase, dipeptidases and disaccharidases.
Lymphoid cells are found throughout the GI tract lamina propria, submucosa and even the epithelium itself and are known as gut associated lymphoid tissue or GALT. Aggregated nodules are found in the jejunum and ileum, with more prominent ones in the ileum and known as Peyer’s patches.
At the site of Peyer’s patches, the overlying villi are frequently absent. The epithelium covering a patch is called the follicle associated epithelium (FAE). The FAE is composed of specialized cells called "M" cells. M cells pinocytose a representative sample of intraluminal antigens and transcytose them across to intraepithelial antigen presenter cells.
Recently, disruption of the above mentioned structures during several physiological or pathological conditions has been reported, which were associated with impaired intestinal barrier function and lead to the passage of intraluminal solutes into the systemic circulation [5,6,7].
Investigations showed that the changes of intestinal barrier function were mainly due to the relaxation of the tight-junction between intestinal epithelial cells [8]. Some studies proposed that factors causing alterations in gut microbiota, hormones secreted by the enterocytes and changes of related enzymatic system caused damage of intestinal barrier, and the enteric bacteria and endotoxin reinforced the damage [9].
Histopathological data of the gastrointestinal barrier of human being is scarce. This chapter mainly focuses on the research findings of the morphological changes of the gut during intestinal barrier dysfunction in laboratory animals. Contents of the chapter will help the researchers interested in gastrointestinal morphological changes.
Discussion about the factors and type of morphological changes of the gastrointestinal barrier is provided below. For easy understanding, discussion is done under several headings.
Recently, Milićević Z and his team analyzed the effects of gender and ageing on the histoquantitative parameters of healthy jejunal and ileal mucosa. Computer-aided morphometric analysis of 24 jejunal and 25 ileal biopsy samples collected during routine endoscopy of healthy individuals with family history of intestinal malignancy was done. Jejunal mucosal thickness was significantly reduced in elderly subjects above 60 years of age (p<0.05), especially in elderly females compared to the adults (p<0.05). Jejunal villi were significantly wider in adults than in the elderly subjects (p<0.05), whereas ileal villi were significantly wider in elderly compared to adult subjects (p<0.01) and in male compared to female subjects (p<0.05). Other histoquantitative parameters eg. mucosa epithelium height, crypt numerical density, villous height, crypts and villous perimeter, diameter and epithelium height of jejunal and ileal mucosa were not different significantly in their observation [10].
Adequate nutrition is necessary for the normal cell division and cell migration from the crypt to the villi thus in maintaining the gastrointestinal barrier. Diet with protein and vitamin restriction (75% protein and 50% vitamin restrictions) but not food restriction to weanling, Wistar/NIN male rats for 20 weeks significantly increased intestinal cell apoptosis observed by morphometry, Annexin V binding, M30 CytoDeath assay, and DNA fragmentation [11].
Providing a diet moderately deficient in protein, fat, and minerals to C57BL/6 mice from the 10 day of their life for 6 weeks resulted in decreased villous height and crypt depth in the jejunum of the undernourished weanlings, stained by haematoxylin and eosin (H&E) and observed under light microscope, increased claudin-3 expression, decreased epithelial cell proliferation measured by immunohistochemistry eg. MTS and bromodeoxyuridine assays and increased epithelial cell apoptosis as measured by annexin and 7-amino-actinomycin D staining. All these changes were associated with decreased transmucosal resistance and increased permeability to FITC-dextran indicating intestinal barrier dysfunction [12].
Cancer cachexia is also reported to induce alterations to some of the morphological parameters of the small intestine. Light microscopic observation of the H&E stained intestinal specimen of tumor-bearing mice was reported to have lower villus height and contour length than in healthy mice (
Various kinds of psychological stresses such as chronic water avoidance stress (WAS) in rats and mice which mimics chronic depression in human being has been reported to compromise small intestinal mucosal structure and hamper intestinal barrier function. H&E and Periodic acid schiff (PAS) stain of the microtome sections of the small intestinal segments of the Sprague Dawley rats subjected to water avoidance stress for 10 days showed that villus height (p<.005), crypt depth (p<0.00), number of goblet cells in villus (p<.0.00) and crypt (p<.0.03) decreased significantly in the jejunum as compared to the control. Ileum also had atrophy but villus height and the number of goblet cells in the villi did not differ significantly. Number of polymorphonuclear neutrophil infiltration was significantly higher in stress group as compared to control (p<0.00) [6].
Light microscope findings of PAS stain of control (A) and WAS (B) rats of the mucosal layer of jejunum (× 100).
Microtome sections of the rat distal colon subjected to WAS stained with H&E showed inflammatory cell infiltration in the lamina propria of stressed rats fed with standard diet that was not observed in non-stressed rats [14].
Noise-induced stress (15 min of white noise at 90 dB daily for 3 wks) has been reported to disrupt the intestinal barrier of laboratory rats. Light microscopic observation of the ileum stained by H&E showed significantly more degranulated mast cells (mean+SE, 3.95+0.8 vs 0.35+ 0.29, respectively) and eosinophils (mean+SE 9.46+0.44 vs 4.58+0.38) per villus section adjacent to the Peyer patches in noise exposed rats than in quiet rats. The mean width of villus laminar propria was significantly greater in noise exposed rats than in quiet rats, suggesting edema. Mucosal epithelial cells of noise rats were often separated, sometimes detaching from the basement membrane, whereas those of quiet rats were intact. Recovery rats who were kept in quiet room for a further 3 weeks after the initial 3 weeks of noise exposure, showed no reduction in mast cell degranulation or mean width of villus lamina propria, but there were increased numbers of secreting goblet cells in the villi adjacent to Peyer patches and some recovery of epithelial integrity [15].
High fat diet consumption and obesity has recently been identified to be associated with compromised tight junction integrity of the enterocytes. An altered distribution such as substantial decreased staining of occludin and discontinuous signals for ZO-1, in the intestinal epithelium of leptin-deficient obese
Medium chain triglycerides (MCTs) enhance cell proliferation of the intestinal epithelium and mucous secretion from goblet cells in the small intestine. Number of goblet cells measured by periodic acid Schiff and alcian blue stain was significantly higher in rats given MCTs as compared to rats given corn oil (5 g/kg per day) or chow for 2 weeks. Proliferating cells on the villi and the crypts in the small intestine, detected by immunohistochemistry using monoclonal mouse anti rat Ki-67 antigen and the apoptotic cells detected by polyclonal rabbit anti single stranded DNA antibody were also significantly greater in rats given MCTs than rats given corn oil or normal rats. Both proliferative and apoptotic index were significantly increased in rats receiving MCTs.
These effects of MCTs might persuade further research on the role of MCTs on the histological changes of the gastrointestinal barrier in patients suffering from inflammatory bowel disease or enterogenous infections [16].
Observation of the Ileal segments of Sprague Dawley rats stained with H&E and PAS staining after seven days of standard total parenteral nutrition (TPN) under light microscope has been associated with villous atrophy, fewer goblet cells, atrophy of Peyer’s patches. Decreased luminal mucus gel was observed by cryostat sections of frozen samples in liquid nitrogen followed by celloidin stabilization and PAS stain in rats receiving TPN [17]. Seven days of Alanyl glutamine supplemented TPN was able to attenuate the changes found in standard TPN observed under light microscope [18].
Enteroaggregative Escherichia coli strains have been associated with persistent diarrhea in several developing countries. Electron microscope observation showed that Enteroaggregative Escherichia coli strains caused total or partial villi destruction, vacuolization of basal cytoplasm of the enterocytes, epithelium detachment, derangement of the structure and epithelial cell extrusion in ileal mucosa. Bacterial aggregates associated with mucus and cellular debris was evident in the intestinal lumen and in the intercellular spaces of the destroyed epithelium, suggesting bacterial invasion which seemed to be secondary to the destruction of the tissue [19].
Duodenal biopsy of Celiac disease patients stained with H&E and studied under light microscope had a raised IEL count (> 20 per 100 enterocytes) and marked villous atrophy [20].
Expression of claudin-2 was distinctly different in active Chron’s disease (CD) and ulcerative colitis (UC) in comparison to its expression pattern in controls. Claudin-2 expression was upregulated along the whole length of intercellular junction (ICJ) in biopsies from patients with active CD and UC in comparison to the biopsies from control patients, where its expression was limited to the uppermost part of ICJ. There was reduced expression of ZO-1 in UC and CD patients. On transmission electron microscopic examination, the pentalaminar structure of tight junction (TJs) was destroyed in patients with CD and UC but no significant change was seen in controls. The redistribution of claudin-2 expression was in accordance with the TJ ultrastructural changes in patients with UC and CD [21].
Endoscopic biopsy of eight insulin dependent diabetes mellitus patients without concomitant celiac disease was devoid of any sign of atrophy or inflammation under light microscope, whereas observation under transmission electron microscope showed remarkable ultra-structural changes in height and thickness of microvilli, space between microvilli and thickness of tight junctions in six out of the eight patients [22].
Electron microscope observation of the samples of the laboratory animals underwent intestinal surgery reported lower expression of occludin. Administration of enteral nutrition (EN) after surgery induced greater expression of occludin in the intestine than in the animals receiving total parenteral nutrition (TPN). Intestinal epithelial tight junction and microvilli were more intact in the animals receiving post surgical EN as compared to those receiving post surgical TPN [23].
Destructive changes such as intense edema of the intestinal wall, mainly in the intestinal lamina propria, as well as blood vessel dilatation and congestion were observed in the intestinal obstruction model of mice. There was also a discrete increase in the cellularity of lamina propria. In addition to that, epithelial reactive changes, superficial erosions, edema, and enlargement of the intestinal villi were observed under light microscope [24].
Ethanol (0, 1, 2.5, 5, 7.5, and 10%) produced a progressive disruption of TJ protein (ZO-1) with separation of ZO-1 proteins from the cellular junctions and formation of large gaps between the adjacent cells of Caco-2 cell line as evidenced by immunofluorescent antibody labeling of ZO-1 proteins [25].
Immunofluorescence analysis of HT-29 cells showed a fragmented and granulous ZO-1 staining, after aspirin treatment. Treating both aspirin and heat-killed Lactobacillus acidophilus strain LB (LaLB) together with the culture supernatant, resulted in fine continuous linear web at cell-cell contacts similarly to control evaluated by immunofluorescence using an anti-ZO-1 antibody [26].
Prolonged subcutaneous injection of Methamphetamine (MA) for 12 weeks decreased the villi height and increased the number of goblet cells significantly as compared to the control group of rats as evidenced by light microscope observation of the H&E and PAS stained samples of the ileum. Withdrawal of MA was able to bring the morphology back to normal [27].
Quantitative analysis of ZO-1 expression in male Wistar rats receiving oral methotraxate (MTX, 15 mg/kg) for 3-5 days showed the absence of significant differences, whereas tyrosine dephosphorylation of ZO-1 was observed. An obvious reduction of ZO-1 immunostaining along the apical membrane of intestinal villi was also observed. These findings suggest that ZO-1 alterations may contribute to the disturbance of TJ barrier in MTX-treated rats, which leads to enhanced intestinal permeability [28].
Paclitaxel, a new chemotherapeutic agent induced apoptosis in 12.5% of jejunum villus cells observed under light microscope after Terminal deoxynucleotidyl transferase-mediated deoxyuridine triphosphate (dUTP)-biotin end labeling assay (TUNEL), which was reduced to 3.8% by granulocyte stimulating factor (G-CSF) treatment. Apoptosis in the control group was 0.6%. Paclitaxel treatment also resulted in villus atrophy observed under light microscope of H&E stained slides, which led to increased intestinal permeability. G-CSF treatment resulted in increased villus height and returned WBC counts to normal levels [29].
Radiation exposure in cancer patients damages the intestinal epithelium and thus can hamper intestinal barrier function [30].
Disruption of the integrity of the intestinal barrier was observed in rat ileum following abdominal X-irradiation, depending on the post irradiation time and the delivered dose. The loss of barrier integrity was characterized by a disorganization of proteins of tight and adherent junctions as evidenced by immunohistochemical analyses of junctional proteins (ZO-1 and beta-catenin) observed by confocal microscope. A disorganization of the localization for ZO-1 and beta-catenin was also observed [31].
Epithelial cell damage was observed in the duodenum, jejunum, ileum and distal colon in paraffin and frozen section at 1, 6, 24, 96h, 1.5 and 3 months after a single dose of 25Gy administered percutaneously to the liver of rats. However, prolonged denudation of the villi together with destruction of the crypt lining was only observed in the ileum, resulting in deficient regeneration. In the colon, changes were minor. Radiation mucositis with granulocyte (MP0+) infiltration was seen from 1 to 24h in the duodenum and jejunum, when ED1+ macrophages, CD3+ T-lymphocytes, and CD34+ hematopoietic precursor cells were recruited, accompanied by an increase in the chemokines MCP-1, MIP-1α, MIP3α and Il-8. In the ileum, early granulocyte infiltration was delayed but continuous. Recruitment of macrophages and lymphocytes was deficient and induction of chemokines such as the adhesion molecules PECAM-1, ICAM-1 was lacking [32].
Thirty percent total body surface area (TBSA) burn resulted in a significant increase in intestinal permeability. Burn injury resulted in a marked decrease in the levels of tight junction proteins occludin and ZO-1 at 6 and 24 h following burn analyzed by immunoblotting and immunohistochemistry and seen under confocal microscope [33].
Gut barrier dysfunction was evident in patients with multiple organ dysfunction (MODS). Breakdown and reorganization of occludin and ZO-1 away from tight junctions was found in all MODS patients analyzed by immunoblotting and immunofluorescence staining [34].
In animal model of cirrhosis of liver, ileal structure was altered by the presence of villous atrophy, lymphangiectasias and submucosal oedema as seen in H&E stained samples under light microscope [35].
Condition | Methods used | Findings | Author |
Gender and age | Endoscopic biopsy, H&E stain, Computer-aided morphometric analysis. | Jejunal mucosal thickness was significantly reduced in elderly subjects above 60 years of age (p<0.05), especially in elderly females compared to the adults (p<0.05). ileal villi were significantly wider in elderly compared to adult subjects (p<0.01) and in male compared to female subjects (p<0.05). | |
Under nutrition Diet with 75% protein and 50% vitamin restrictions to weanling, Wistar/NIN male rats for 20 weeks Diet moderately deficient in protein, fat, and minerals to C57BL/6 mice | Apoptosis observed by morphometry, Annexin V binding. H&E, immunohistochemistry eg. MTS and bromodeoxyuridine assays, annexin and 7-amino-actinomycin D staining. | Significantly increased intestinal cell apoptosis. Decreased villous height and crypt depth in the jejunum. Increased claudin-3 expression, decreased epithelial cell proliferation, increased epithelial cell apoptosis. | 2005 2011 |
Cancer cachexia | Light microscopic observation of the H&E stained intestinal specimen of tumor-bearing mice. | Decreased villus height and contour length than in healthy mice ( Villus width and crypt depth showed substantial evidence of atrophy ( | Susan E 2000 |
Total parenteral nutrition | Ileal segments of rats stained with H&E and PAS staining and observed under light microscope. | Villous atrophy, fewer goblet cells, atrophy of Peyer’s patches. Decreased luminal mucus gel. | Khan J 1999 Iiboshi Y 1994 |
High fat diet | Immunocytochemistry of intestinal cryosections of mice | Altered distribution such as substantial decreased staining of occludin and discontinuous signals for ZO-1 | Suzuki T 2010 |
Medium chain triglycerides (MCTs) | Periodic acid Schiff and alcian blue stain. Immunohistochemistry using monoclonal mouse anti rat Ki-67 antigen, polyclonal rabbit anti single stranded DNA antibody. | Number of goblet cells measured was significantly higher in rats given MCTs as compared to rats given corn oil (5 g/kg per day) or chow for 2 weeks. Proliferating cells on the villi and the crypts in the small intestine, significantly greater in rats given MCTs than rats given corn oil or normal rats. | Iishi K 2009 |
Psychological stress | H&E and periodic acid Schiff stain of the microtome sections of the small intestinal segments of rats under light microscope. Microtome sections of the rat distal colon stained by H&E. Light microscopic observation of the ileum stained by H&E. | Villus height (p<.005), crypt depth (p<0.00), number of goblet cells in villus (p<.0.00) and crypt (p<.0.03) decreased significantly in jejunum as compared to control. Number of polymorphonuclear neutrophil was significantly higher in stress group as compared to control (p<0.00). Inflammatory cell infiltration in the lamina propria of stressed rats. Significantly more degranulated mast cells and eosinophils per villus section adjacent to the Peyer patches in noise exposed rats than in quiet rats. The mean width of villus laminar propria was significantly greater in noise rats than in quiet rats. Mucosal epithelial cells of noise rats were often separated, sometimes detaching from the basement membrane. | Saudi WSW 2009 2012 2006 |
Diabetes mellitus | Transmission electron microscope. | Remarkable ultra-structural changes in height and thickness of microvilli, space between microvilli and thickness of tight junctions. | Secondulfo M 2004 |
Surgery | Electron microscope. | Samples of the laboratory animals underwent intestinal surgery reported lower expression of occludin. | 2009 |
Infection | Electron microscope. | Enteroaggregative Escherichia coli strains caused total or partial villi destruction, vacuolization of basal cytoplasm of the enterocytes, epithelium detachment, derangement of the structure and epithelial cell extrusion in ileal mucosa of laboratory animals. | 2010 |
Inflammatory bowel disease | Hematoxylin-eosin and studied under light microscope. Immunohistochemistry. Electron microscope | Raised IEL count ("/> 20 per 100 enterocytes) and marked villous atrophy of duodenal biopsy. Reduced expression of ZO-1 in UC and CD patients. Pentalaminar structure of tight junction (TJs) was destroyed in patients with CD and UC | 2012 Das P 2012 |
Alcohol | Immunofluorescent antibody labeling of ZO-1 proteins. | Ethanol (0, 1, 2.5, 5, 7.5, and 10%) produced a progressive disruption of TJ protein (ZO-1) with separation of ZO-1 proteins from the cellular junctions and formation of large gaps between the adjacent cells of Caco-2 cell line. | 1999 |
Drugs | Immunofluorescence analysis. H&E and PAS staining of ileum, observed under light microscope. Immunostaining. Light microscope observation after Terminal deoxynucleotidyl transferase-mediated deoxyuridine triphosphate (dUTP)-biotin end labeling assay (TUNEL) and hematoxylin and eosin (H&E) staining. | HT-29 cell line showed a fragmented and granulous ZO-1 staining, after aspirin treatment. Prolonged Methamphatamine abuse in rats decreased the villi height and increased the number of goblet cells significantly as compared to the control group of rats. Reduction of ZO-1 along the apical membrane of intestinal villi was observed in methotraxate treatment in rats. Paclitaxel, a new chemotherapeutic agent induced apoptosis in 12.5% of jejunum villus cells. Apoptosis in the control group was 0.6%. also resulted in villus atrophy which led to increased intestinal permeability. Granulocyte colony-stimulating factor (G-CSF) treatment resulted in increased villus height and returned WBC counts to normal levels. | 2004 Khan J 2008 2010 he of significant differences Zhang C 2011 |
Radiation injury | Immunohistochemical analyses of junctional proteins (ZO-1 and beta-catenin) and confocal microscopy. Light microscope observation. | Disorganization of the proteins of tight and adherent junctions. A disorganization of the localization for ZO-1 and beta-catenin was also observed. Prolonged denudation of the villi together with destruction of the crypt lining observed in the ileum. In the colon, changes were minor. Radiation mucositis with granulocyte (MP0+) infiltration was seen from 1 to 24h in the duodenum and jejunum, when ED1+ macrophages, CD3+ T-lymphocytes, and CD34+ hematopoietic precursor cells were recruited. | 2004 2011 |
Burn | Immunohistochemistry. | Marked decrease in the levels of tight junction proteins occludin and ZO-1 at 6 and 24 h. | 2009 |
Multiple organ dysfunction | Immunoblotting and immunofluorescence staining. | Breakdown and reorganization of occludin and ZO-1 away from tight junctions was found in all MODS patients. | 2010 |
Cirrhosis of liver | Light microscope. | In animal model of cirrhosis of liver, ileal structure was altered by the presence of villous atrophy, lymphangiectasias and submucosal oedema. | V Lorenzo Zúñiga 2006 |
Summary of the morphological findings in different conditions affecting gastrointestinal barrier.
Although still at conceptual level, evidences are persuasive that use of the certain compounds, such as zinc, glutamine, probiotics etc has the potential to attenuate morphological changes by the above factors and might represent a simple device to prevent the occurrence or aggravation of chronic pathologies caused by intestinal barrier dysfunction.
Glutamine is an amino acid important for the growth of enterocytes. Electron microscopy of the intestine in a GLN-deprived infant rat model demonstrated intestinal intercellular junction breakdown [36].
Deprivation of GLN decreased claudin-1, occludin, and ZO-1 protein expression and caused a disappearance of perijunctional claudin-1 and a reduction of occludin but had no effect on ZO-1. Transmission electron microscopy revealed that methionine sulfoximine-treated cells in the absence of GLN formed irregular junctional complexes between the apical lateral margins of adjoining cells. These findings indicate that TJ protein expression and cellular localization in Caco-2 cell monolayers rely on GLN [37].
Trace elements such as zinc may assist in the maintenance of intestinal barrier integrity. Caco-2 cells grown in zinc-deficient media had reduced TEER and altered expression of ZO-1 and occludin ie. localized away from the cell boundaries and less homogenicity as compared with the Caco-2 cells grown in zinc-replete media. This findings were accompanied by disorganization of F-actin filaments [38].
Electron microscopic studies showed that percentage of the disrupted (opened) tight junctions in experimental colitis were reduced by 50% with zinc supplementation [39].
Retinoic acid (RA), the active form of vitamin A had significant trophic effects in resected and sham-resected rats. Exogenous RA stimulated the adaptive response of the intestine in 70% small bowel resection by 2 weeks, as manifested by a significant increase in crypt depth, villus height, and intestinal surface area of rats. The enlarged crypts and villi were due to adaptive hyperplasia and not to cellular hypertrophy. RA was also trophic in the intestine of control rats that were only subjected to transection and reanastomosis. Villus heights and crypt depths were measured in 20–50 well-oriented hematoxylin-eosin-stained crypt-villus units with the aid of a slide micrometer and Scion Image software. Apoptotic cells were identified by standard morphological changes, including nuclear condensation, perinuclear clearing, and cell shrinkage, and by staining for activated caspase-3 [40].
Arginine is a dibasic amino acid with various metabolic and immunologic effects. Animal models of intestinal obstruction, treated with arginine presented preservation of the tissue structure. The villous epithelium was preserved and only discrete edema and enlargement were present at lamina propria [24].
Light microscope observation of Giemsa staining samples revealed that there was close interaction between luminal bacteria and the apical aspect of surface ileal enterocytes in rats subjected to WAS. Bacterial interactions with ileal enterocytes were not observed in sham stressed animals. Pretreatment with probiotics prevented the bacteria epithelial cell contacts induced by WAS.
TEM confirmed the findings demonstrated with light microscopy. While there were no bacteria adhering to the apical surface of enterocytes in sham stressed rats, multiple bacteria were observed closely adhering and internalised into ileal enterocytes in stressed rats. Electron dense condensation around the internalised bacteria consistent with polymerised actin was observed indicating that enterocytes underwent cytoskeletal rearrangements. Pretreatment with probiotics prevented WAS induced bacteria epithelial cell interactions [41].
Prebiotic treated mice exhibited a decreased hepatic expression of inflammatory and oxidative stress markers. This decreased inflammatory tone was associated with a lower intestinal permeability and improved tight-junction ZO-1 and occludin integrity evidenced by qPCR and immunohistochemistry, compared to controls [9].
Both fibre sources, wheat bran (rich in cellulose and hemicellulose) and pollen from Chinese Masson pine (Pinus massoniana) (rich in lignin) increased villus height of mucosa in jejunum (+10% on average) and ileum (+16% on average) in animal model of 48 weaned piglets [42].
Diet rich in arachidonic and docosahexaenoic acids, galacto- and fructo-oligosaccharides and Lactobacillus paracasei NCC2461 resulted in increased villus length in the small intestine to restore impaired intestinal barrier function and growth after neonatal stress in rats [43].
Quercetin is the most common flavonoid in nature. High amounts of quercetin are found in onions, kale, and apples [44].
Flavonoids, quercetin and myricetin, enhanced barrier function in human intestinal Caco-2 cells. Suzuki and Hara recently reported that a 48-h exposure of quercetin enhanceds the intestinal barrier function through increasing claudin-4 expression in human intestinal Caco-2 monolayers. Quercetin promoted the assembly of TJ proteins, ZO-2, occludin, and claudin-1 and the expression of claudin-4 by inhibiting the PKC
Kaempferol, a natural flavonoid present in fruits, vegetables, and teas, provides beneficial effects for human health. Confocal microscopy showed kaempferol-induced assembly of occludin and claudin-3 occurred at the TJ of Caco -2 cells at 6 h postadministration [46].
Condition | Methods used | Findings | Author |
Deprivation of Glutamine | Electron microscope observation | Decreased claudin-1, occludin, and ZO-1 protein expression and caused a disappearance of perijunctional claudin-1 and a reduction of occludin | Potsic B 2000 |
Zinc supplementation Zinc-deficient media | Electron microscope observation | Percentage of the disrupted (opened) tight junctions in experimental colitis were reduced by 50% with zinc. Caco-2 cells have reduced TEER and altered expression of ZO-1 and occludin ie. localized away from the cell boundaries and less homogenous compared with Caco-2 cells grown in zinc-replete media | Sturniolo GC, 2002 Finamore A 2008 |
Vitamin A | Light microscope, H&E and staining for activated caspase-3 | Significant increase in crypt depth, villus height, and intestinal surface area of 70% bowel resection in rats. | Wang L 2007 |
Arginine | Light microscope | Animal models of intestinal obstruction treated with arginine presented preservation of the tissue structure. The villous epithelium was preserved and only discrete edema and enlargement were present at lamina propria. | 2010 |
Probiotics | Light microscope observation of Giemsa stained sample | Prevented WAS induced close interaction between luminal bacteria and the apical aspect of surface ileal enterocytes in rats. | M Zareie 2007 |
Prebiotics Wheat bran and pollen from Chinese Masson pine | qPCR and immunohistochemistry Light microscope observation of H&E stained samples | Improved tight-junction ZO-1 and occludin Increased villus height of mucosa in jejunum (+10% on average) and ileum (+16% on average) in animal model of 48 weaned piglets | 2009 2008 |
Diet containing arachidonic and docosahexaenoic acids, galacto- and fructo-oligosaccharides and Lactobacillus paracasei NCC2461 | Light microscope observation | Increased villus length in small intestine after neonatal stress in rats. | 2006 |
Quercetin Kaempferol | Confocal microscope observation Confocal microscope observation | Promotes the assembly of TJ proteins, ZO-2, occludin, and claudin-1 and the expression of claudin-4 Assembly of occludin and claudin-3 at the TJ of Caco -2 cells | Suzuki T 2009 Suzuki T 2011 |
Summary of the histological findings of different modulating agents improving gastrointestinal barrier.
Emerging experimental evidences from animal models suggest that altered barrier function is a potential pathway for intestinal and extra intestinal inflammation. Although thousands of research findings are available dealing with gut barrier function during different physiological and pathological conditions, few articles focused on the histolological changes. In this chapter discussion was made in an attempt to provide a generalized idea of morphological changes during several conditions. Future researches are suggested to deal with the effect of different modulating agents on the histolological parameters of intestinal barrier. Therapeutic restoration of barrier function could improve pathophysiology and clinical outcomes of different diseases.
Authors are grateful to Dr. Shyamoli Mostoffa, pathologist and senior lecturer, School of Health Sciences, Universiti Sains Malaysia for her help in interpreting histological data of their research included in this chapter.
Histological images of rat jejunum were taken with permission from the International Medical Journal, Japan.
In the real world, data representation is most often imperfect, in the sense that the data may be either incomplete or redundant. Philosophers, logicians and mathematicians have dealt with this problem for a long time. In recent years, propelled by the advent of the computer, the problem of imperfect knowledge has been becoming an important topic for computer scientists engaged in artificial intelligence research, especially those involved with knowledge discovery from databases, expert systems, and pattern recognition.
\n\t\t\tOur research is focused on rough set as a tool for image processing, or more precisely, for image segmentation. Many techniques for image segmentation have been developed over time. There are clustering, edge detection, region growing and even more advanced techniques that use neural networks. In general, image segmentation techniques can be categorized as supervised or unsupervised. Supervised techniques require previously known truth data for training purposes, while unsupervised techniques have no such requirement.
\n\t\t\tIn this chapter, the classical rough set theory is reviewed in section 2. Particle swarm optimization is then introduced in section 3. The Davies-Bouldin measure for cluster validity is also described in this section. The K-means algorithm is briefly sketched in section 4. Multispectral image classification using rough set theory is discussed in section 5. A hybrid algorithm which combines the K-means algorithm, rough set and particle swarm optimization is given in section 6. Experimental results are shown in section 7. The conclusion and future work then follow.
\n\t\tRough set theory [5] is a mathematical tool that deals with the uncertainty of the data. The theory consists of finite sets, equivalence relations and cardinality concepts. As the theory matures and more applications reap the benefits of the concept, an abundance of related theorems and algorithms are being incorporated to extend rough sets theory.
\n\t\t\tIt was introduced by Pawlak in the early 1980’s and has been argued to overlap with other theories, such as statistics, evidence theory and fuzzy set. Furthermore, rough set is said to complement fuzzy set, a theory introduced by Zadeh in the early period. Rough set and fuzzy set were both introduced to deal with imprecise information however; fuzzy set deals with vagueness, while rough set deals with coarseness. Rough set does not need as much preliminary knowledge about the data where as fuzzy set requires knowledge of the possible values in advance. Basically, when using rough set, the data itself is used to come up with the approximation in order to deal with the imprecision within. It can therefore be considered a self-sufficient discipline.
\n\t\t\tRough set mainly deals with data analysis in table format. The approach is generally to pre-process the data in the table and then to analyze them. Reducts are extracted with an algorithm and finally rules are generated based on the reducts. Rough set does not support analog values in the table attributes; therefore discretization must be performed in advance in order to evaluate the table. The following subsections will use a simple example to illustrate the concept of rough set theory.
\n\t\t\tIn essence, an information system is a set of objects represented in a data table (attribute - value system). Each row contains an object and each column represents a measurable attribute for each object. Formally, an information system is a pair A = (U, A) where U is a non- empty finite set of objects representing the universe and A is a non-empty finite set of attributes such that a: U→Va for every a ∈ A. The set Va is the set of values for a.
\n\t\t\t\tObject Index | \n\t\t\t\t\t\t\tSalary | \n\t\t\t\t\t\t\tAge | \n\t\t\t\t\t\t
u 1\n\t\t\t\t\t\t\t | \n\t\t\t\t\t\t\t80 | \n\t\t\t\t\t\t\t30 | \n\t\t\t\t\t\t
u 2\n\t\t\t\t\t\t\t | \n\t\t\t\t\t\t\t30 | \n\t\t\t\t\t\t\t23 | \n\t\t\t\t\t\t
u 3\n\t\t\t\t\t\t\t | \n\t\t\t\t\t\t\t80 | \n\t\t\t\t\t\t\t40 | \n\t\t\t\t\t\t
u 4\n\t\t\t\t\t\t\t | \n\t\t\t\t\t\t\t50 | \n\t\t\t\t\t\t\t45 | \n\t\t\t\t\t\t
u 5\n\t\t\t\t\t\t\t | \n\t\t\t\t\t\t\t80 | \n\t\t\t\t\t\t\t55 | \n\t\t\t\t\t\t
u 6\n\t\t\t\t\t\t\t | \n\t\t\t\t\t\t\t50 | \n\t\t\t\t\t\t\t45 | \n\t\t\t\t\t\t
u 7\n\t\t\t\t\t\t\t | \n\t\t\t\t\t\t\t30 | \n\t\t\t\t\t\t\t60 | \n\t\t\t\t\t\t
u 8\n\t\t\t\t\t\t\t | \n\t\t\t\t\t\t\t100 | \n\t\t\t\t\t\t\t35 | \n\t\t\t\t\t\t
Shows an information system which is a collection of salary and age attributes.
If an information system has an additional attribute, namely a decision attribute, then it becomes a decision system. The decision attribute is associated with the object classification outcome, and it may depend on several other attributes. Formally, a decision system is a piece of information whose form is A = (U, A ∪ {d}), where d ∈A is the decision attribute. A decision attribute called “Class” has been added as shown in Table 2, where M and E denote Manager and Employee, respectively. The table was modified from the original [12].
\n\t\t\t\tObject Index | \n\t\t\t\t\t\t\tSalary | \n\t\t\t\t\t\t\tAge | \n\t\t\t\t\t\t\tClass | \n\t\t\t\t\t\t
u 1 | \n\t\t\t\t\t\t\t80 | \n\t\t\t\t\t\t\t30 | \n\t\t\t\t\t\t\tM | \n\t\t\t\t\t\t
u 2 | \n\t\t\t\t\t\t\t30 | \n\t\t\t\t\t\t\t23 | \n\t\t\t\t\t\t\tM | \n\t\t\t\t\t\t
u 3 | \n\t\t\t\t\t\t\t80 | \n\t\t\t\t\t\t\t40 | \n\t\t\t\t\t\t\tE | \n\t\t\t\t\t\t
u 4 | \n\t\t\t\t\t\t\t50 | \n\t\t\t\t\t\t\t45 | \n\t\t\t\t\t\t\tM | \n\t\t\t\t\t\t
u 5 | \n\t\t\t\t\t\t\t80 | \n\t\t\t\t\t\t\t55 | \n\t\t\t\t\t\t\tE | \n\t\t\t\t\t\t
u 6 | \n\t\t\t\t\t\t\t50 | \n\t\t\t\t\t\t\t45 | \n\t\t\t\t\t\t\tE | \n\t\t\t\t\t\t
u 7 | \n\t\t\t\t\t\t\t30 | \n\t\t\t\t\t\t\t60 | \n\t\t\t\t\t\t\tM | \n\t\t\t\t\t\t
u 8 | \n\t\t\t\t\t\t\t100 | \n\t\t\t\t\t\t\t35 | \n\t\t\t\t\t\t\tE | \n\t\t\t\t\t\t
A decision system where each row is classified into a class. Salary and Age are the condition attributes, while class is the decision attribute.
Objects in information and decision systems may be indistinguishable from one another based on a set of attributes B that belongs to A (B ⊆ A). A set of objects is indiscernible or equivalent when their attributes are related by an equivalence relation. An equivalence relation is a relation on a set B when it is:
\n\t\t\t\tReflexive (if a R a, then R is reflexive).
Symmetric (if a R b then b R a, then R is symmetric).
Transitive (if a R b and b R c, then a R c, thus R is transitive).
For an information system A = (U, Α), there is an equivalence relation for any of the sets B ⊆ A. The equivalence relation can be formalized as
\n\t\t\t\t\n\t\t\t\t\t\n\t\t\t\t\t
Referring to table 4, the
\n\t\t\t\t\t\n\t\t\t\t\t
It is impossible to write the
Discretization is not directly related to rough set theory. It is simply a preprocessing technique. Discretization is associated with information loss. In general, when it is too coarse (i.e. longer interval), there is too much information loss or noise in the data. However, it is better for the classification capability of unseen objects. When the discretization is more fine (i.e. shorter interval), less noise exists in data, but classification capability of unseen objects may be impaired.
\n\t\t\t\tIn our decision system table, both Salary and Age need to be discretized. The set of possible Salary and Age values, respectively referred to as
Vs = [15, 120)
\n\t\t\t\t\tVa = [18, 65)
\n\t\t\t\tThe lower and upper bounds of the attribute’s interval are extended to cover possible values. For example, the Age attribute is extended to include likely working ages from age 18 through 65.
\n\t\t\t\tThe set of values of
s(U) = {30,50,80,100}
\n\t\t\t\t\ta(U) = {23, 30, 35, 40, 45, 55, 60}
\n\t\t\t\tThe intervals obtained for
[30, 50); [50, 80); [80, 100)
\n\t\t\t\tThe intervals obtained for
[23, 30); [30, 35); [35, 40);
\n\t\t\t\t\t[40, 45); [45, 55); [55, 60).
\n\t\t\t\tBoundary intervals such as [15, 30) and [100, 120) should not be used since one can not discern anything for this data set.
\n\t\t\t\tThe intervals introduce a set of cuts, which are defined as (s, c) where c ∈Vs and (a, c) where c ∈ Va. If the cut is taken based on the mid-point of each interval, the set of cuts P obtained for
(s, 40); (s, 65); (s, 90);
\n\t\t\t\t\t(a, 26.5); (a, 32.5); (a, 37.5);
\n\t\t\t\t\t(a, 42.5); (a, 50); (a, 57.5)
\n\t\t\t\tThe next step is to find the set of minimal cuts that can discern all of the objects that are needed. It turns out that the problem of finding the irreducible set of cuts P in the decision system is NP-complete while the effort to find the optimal set of cuts P in a decision system is NP-hard [5].
\n\t\t\t\tHowever, there are heuristics that can be used to find the optimal set of cuts P in practical time. One of them is the Maximal Discernability heuristic [1], [5], which is demonstrated here. The algorithm to construct table A* from A is listed in the following steps:
\n\t\t\t\tEach column in table A* is a Boolean variable of the corresponding column in A. If each pair of objects can be discerned by the Boolean variable, then assign value 1, else assign 0.
Choose a column from A* that has a maximal number of 1’s and delete all the rows which contain a 1 in the selected column.
Repeat step 2 and continue until all columns and rows are consumed.
A * | \n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t | \n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t | \n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t | \n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t | \n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t | \n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t | \n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t | \n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t | \n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t | \n\t\t\t\t\t\t\td | \n\t\t\t\t\t\t
u1, u3 | \n\t\t\t\t\t\t\t0 | \n\t\t\t\t\t\t\t0 | \n\t\t\t\t\t\t\t0 | \n\t\t\t\t\t\t\t0 | \n\t\t\t\t\t\t\t1 | \n\t\t\t\t\t\t\t1 | \n\t\t\t\t\t\t\t0 | \n\t\t\t\t\t\t\t0 | \n\t\t\t\t\t\t\t0 | \n\t\t\t\t\t\t\t1 | \n\t\t\t\t\t\t
u1, u5 | \n\t\t\t\t\t\t\t0 | \n\t\t\t\t\t\t\t0 | \n\t\t\t\t\t\t\t0 | \n\t\t\t\t\t\t\t0 | \n\t\t\t\t\t\t\t1 | \n\t\t\t\t\t\t\t1 | \n\t\t\t\t\t\t\t1 | \n\t\t\t\t\t\t\t1 | \n\t\t\t\t\t\t\t0 | \n\t\t\t\t\t\t\t1 | \n\t\t\t\t\t\t
u1, u6 | \n\t\t\t\t\t\t\t0 | \n\t\t\t\t\t\t\t1 | \n\t\t\t\t\t\t\t0 | \n\t\t\t\t\t\t\t0 | \n\t\t\t\t\t\t\t1 | \n\t\t\t\t\t\t\t1 | \n\t\t\t\t\t\t\t1 | \n\t\t\t\t\t\t\t0 | \n\t\t\t\t\t\t\t0 | \n\t\t\t\t\t\t\t1 | \n\t\t\t\t\t\t
u1, u8 | \n\t\t\t\t\t\t\t0 | \n\t\t\t\t\t\t\t0 | \n\t\t\t\t\t\t\t1 | \n\t\t\t\t\t\t\t0 | \n\t\t\t\t\t\t\t1 | \n\t\t\t\t\t\t\t0 | \n\t\t\t\t\t\t\t0 | \n\t\t\t\t\t\t\t0 | \n\t\t\t\t\t\t\t0 | \n\t\t\t\t\t\t\t1 | \n\t\t\t\t\t\t
u2, u3 | \n\t\t\t\t\t\t\t1 | \n\t\t\t\t\t\t\t1 | \n\t\t\t\t\t\t\t0 | \n\t\t\t\t\t\t\t1 | \n\t\t\t\t\t\t\t1 | \n\t\t\t\t\t\t\t1 | \n\t\t\t\t\t\t\t0 | \n\t\t\t\t\t\t\t0 | \n\t\t\t\t\t\t\t0 | \n\t\t\t\t\t\t\t1 | \n\t\t\t\t\t\t
u2, u5 | \n\t\t\t\t\t\t\t1 | \n\t\t\t\t\t\t\t1 | \n\t\t\t\t\t\t\t0 | \n\t\t\t\t\t\t\t1 | \n\t\t\t\t\t\t\t1 | \n\t\t\t\t\t\t\t1 | \n\t\t\t\t\t\t\t1 | \n\t\t\t\t\t\t\t1 | \n\t\t\t\t\t\t\t0 | \n\t\t\t\t\t\t\t1 | \n\t\t\t\t\t\t
u2, u6 | \n\t\t\t\t\t\t\t1 | \n\t\t\t\t\t\t\t0 | \n\t\t\t\t\t\t\t0 | \n\t\t\t\t\t\t\t1 | \n\t\t\t\t\t\t\t1 | \n\t\t\t\t\t\t\t1 | \n\t\t\t\t\t\t\t1 | \n\t\t\t\t\t\t\t0 | \n\t\t\t\t\t\t\t0 | \n\t\t\t\t\t\t\t1 | \n\t\t\t\t\t\t
u2, u8 | \n\t\t\t\t\t\t\t1 | \n\t\t\t\t\t\t\t1 | \n\t\t\t\t\t\t\t1 | \n\t\t\t\t\t\t\t1 | \n\t\t\t\t\t\t\t1 | \n\t\t\t\t\t\t\t0 | \n\t\t\t\t\t\t\t0 | \n\t\t\t\t\t\t\t0 | \n\t\t\t\t\t\t\t0 | \n\t\t\t\t\t\t\t1 | \n\t\t\t\t\t\t
u3, u4 | \n\t\t\t\t\t\t\t0 | \n\t\t\t\t\t\t\t1 | \n\t\t\t\t\t\t\t0 | \n\t\t\t\t\t\t\t0 | \n\t\t\t\t\t\t\t0 | \n\t\t\t\t\t\t\t0 | \n\t\t\t\t\t\t\t1 | \n\t\t\t\t\t\t\t0 | \n\t\t\t\t\t\t\t0 | \n\t\t\t\t\t\t\t1 | \n\t\t\t\t\t\t
u3, u7 | \n\t\t\t\t\t\t\t1 | \n\t\t\t\t\t\t\t1 | \n\t\t\t\t\t\t\t0 | \n\t\t\t\t\t\t\t0 | \n\t\t\t\t\t\t\t0 | \n\t\t\t\t\t\t\t0 | \n\t\t\t\t\t\t\t1 | \n\t\t\t\t\t\t\t1 | \n\t\t\t\t\t\t\t1 | \n\t\t\t\t\t\t\t1 | \n\t\t\t\t\t\t
u4, u5 | \n\t\t\t\t\t\t\t0 | \n\t\t\t\t\t\t\t1 | \n\t\t\t\t\t\t\t0 | \n\t\t\t\t\t\t\t0 | \n\t\t\t\t\t\t\t0 | \n\t\t\t\t\t\t\t0 | \n\t\t\t\t\t\t\t0 | \n\t\t\t\t\t\t\t1 | \n\t\t\t\t\t\t\t0 | \n\t\t\t\t\t\t\t1 | \n\t\t\t\t\t\t
u4, u6 | \n\t\t\t\t\t\t\t0 | \n\t\t\t\t\t\t\t0 | \n\t\t\t\t\t\t\t0 | \n\t\t\t\t\t\t\t0 | \n\t\t\t\t\t\t\t0 | \n\t\t\t\t\t\t\t0 | \n\t\t\t\t\t\t\t0 | \n\t\t\t\t\t\t\t0 | \n\t\t\t\t\t\t\t0 | \n\t\t\t\t\t\t\t1 | \n\t\t\t\t\t\t
u4, u8 | \n\t\t\t\t\t\t\t0 | \n\t\t\t\t\t\t\t1 | \n\t\t\t\t\t\t\t1 | \n\t\t\t\t\t\t\t0 | \n\t\t\t\t\t\t\t0 | \n\t\t\t\t\t\t\t1 | \n\t\t\t\t\t\t\t1 | \n\t\t\t\t\t\t\t0 | \n\t\t\t\t\t\t\t0 | \n\t\t\t\t\t\t\t1 | \n\t\t\t\t\t\t
u5, u7 | \n\t\t\t\t\t\t\t1 | \n\t\t\t\t\t\t\t1 | \n\t\t\t\t\t\t\t0 | \n\t\t\t\t\t\t\t0 | \n\t\t\t\t\t\t\t0 | \n\t\t\t\t\t\t\t0 | \n\t\t\t\t\t\t\t0 | \n\t\t\t\t\t\t\t0 | \n\t\t\t\t\t\t\t1 | \n\t\t\t\t\t\t\t1 | \n\t\t\t\t\t\t
u6, u7 | \n\t\t\t\t\t\t\t1 | \n\t\t\t\t\t\t\t0 | \n\t\t\t\t\t\t\t0 | \n\t\t\t\t\t\t\t0 | \n\t\t\t\t\t\t\t0 | \n\t\t\t\t\t\t\t0 | \n\t\t\t\t\t\t\t0 | \n\t\t\t\t\t\t\t1 | \n\t\t\t\t\t\t\t1 | \n\t\t\t\t\t\t\t1 | \n\t\t\t\t\t\t
u7, u8 | \n\t\t\t\t\t\t\t1 | \n\t\t\t\t\t\t\t1 | \n\t\t\t\t\t\t\t1 | \n\t\t\t\t\t\t\t0 | \n\t\t\t\t\t\t\t0 | \n\t\t\t\t\t\t\t1 | \n\t\t\t\t\t\t\t1 | \n\t\t\t\t\t\t\t1 | \n\t\t\t\t\t\t\t1 | \n\t\t\t\t\t\t\t1 | \n\t\t\t\t\t\t
new | \n\t\t\t\t\t\t\t0 | \n\t\t\t\t\t\t\t0 | \n\t\t\t\t\t\t\t0 | \n\t\t\t\t\t\t\t0 | \n\t\t\t\t\t\t\t0 | \n\t\t\t\t\t\t\t0 | \n\t\t\t\t\t\t\t0 | \n\t\t\t\t\t\t\t0 | \n\t\t\t\t\t\t\t0 | \n\t\t\t\t\t\t\t0 | \n\t\t\t\t\t\t
Decision System A*.
The following example clarifies the process of constructing table A* from A mentioned in step 1 of the algorithm. Each cut previously obtained is assigned a Boolean variable, which in turn is used as a condition attribute in table A*.
\n\t\t\t\tFor example, (s, 40) is assigned Boolean variable\n\t\t\t\t\t
The resulting table A* created from A is shown in Table 3.
\n\t\t\t\tThe optimal cut chosen is (s, 65), (a, 32.5) and (a, 50). These cuts are then used to discretize the decision table 2. The rank can be assigned using the following rules:
\n\t\t\t\tIf s < 65, value 0 is assigned to s, else assign value of 1.
If a < 32.5, value 0 is assigned to a.
If (32.5 ≤ a < 50), value 1 is assigned to a.
If a ≥ 50, value 2 is assigned to a.
A discretized table can be produced by applying the condition to each analog value in the table.
\n\t\t\t\tIndex | \n\t\t\t\t\t\t\tSalary | \n\t\t\t\t\t\t\tAge | \n\t\t\t\t\t\t\tClass | \n\t\t\t\t\t\t
u 1 | \n\t\t\t\t\t\t\t1 | \n\t\t\t\t\t\t\t0 | \n\t\t\t\t\t\t\tM | \n\t\t\t\t\t\t
u 2 | \n\t\t\t\t\t\t\t0 | \n\t\t\t\t\t\t\t0 | \n\t\t\t\t\t\t\tM | \n\t\t\t\t\t\t
u 3 | \n\t\t\t\t\t\t\t1 | \n\t\t\t\t\t\t\t1 | \n\t\t\t\t\t\t\tE | \n\t\t\t\t\t\t
u 4 | \n\t\t\t\t\t\t\t0 | \n\t\t\t\t\t\t\t1 | \n\t\t\t\t\t\t\tM | \n\t\t\t\t\t\t
u 5 | \n\t\t\t\t\t\t\t1 | \n\t\t\t\t\t\t\t2 | \n\t\t\t\t\t\t\tE | \n\t\t\t\t\t\t
u 6 | \n\t\t\t\t\t\t\t0 | \n\t\t\t\t\t\t\t1 | \n\t\t\t\t\t\t\tE | \n\t\t\t\t\t\t
u 7 | \n\t\t\t\t\t\t\t0 | \n\t\t\t\t\t\t\t2 | \n\t\t\t\t\t\t\tM | \n\t\t\t\t\t\t
u 8 | \n\t\t\t\t\t\t\t1 | \n\t\t\t\t\t\t\t1 | \n\t\t\t\t\t\t\tE | \n\t\t\t\t\t\t
Discretized Decision System.
Let U be the non-empty finite set and R be an equivalence relation. The pair A = (U, R) is an approximation space. The equivalence relation R on U leads to a partition of the objects in the universe U. The idea here is to partition the objects that have the same outcome, or in other words, to partition objects that have the same decision attribute. However, this may not always be as easy as stated. There will be objects with the same condition attributes (in the same equivalence class), but different decision attributes. Therefore one can not define every set precisely.
\n\t\t\t\tIn cases where the set can not be defined precisely, it can be approximated. This is where rough set emerges. Let us assume that there is an information system A = (U, Α), a set of attributes B ⊆ A, and a set of objects X ⊆ U. Using the set of attributes B, one can approximate the objects X into:
\n\t\t\t\t1. Lower Approximation: the set of objects that can be classified as member X with certainty. Formally stated as
\n\t\t\t\t2. Upper Approximation: the set of objects that can possibly be classified as a member X. Formally stated as
\n\t\t\t\tBetween the lower and upper approximation, one can define the set of objects that cannot be classified into X decisively. This set is also known as the
There is a coefficient that reflects the accuracy of approximation,
\n\t\t\t\twhere |X| denotes the cardinality of X ≠ ∅. When \n\t\t\t\t\t
For our example, the boundary region would be for object
\n\t\t\t\t\t\n\t\t\t\t\t
In general, the value of \n\t\t\t\t\t
One way to increase computation efficiency is to reduce the size of data by reducing attributes that need to be taken into account. Only attributes that do not contribute to the classification result can be omitted such that the indiscernibility relation remains intact. The set of remaining attributes is the minimal set and is called a reduct.
\n\t\t\t\tAlthough finding the equivalence class is a relatively straightforward computation process, finding reducts with minimal attributes is known to be NP-hard. Fortunately, there are heuristics that allow minimal reducts to be computed in reasonable time.
\n\t\t\tComputing the reducts of an information system A = (U, A) can be started by creating the indiscernibility matrix. This matrix is a symmetric \n\t\t\t\t\t
Each cell in the matrix holds the set of attributes where objects \n\t\t\t\t\t
\n\t\t\t\t\t
\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t
For decision systems, when the decision attribute of objects \n\t\t\t\t\t\t\t
\n\t\t\t\t\t\t\t
\n\t\t\t\t\t\t\t
Based on the discernibility matrix, a discernibility function can be immediately obtained. It is constructed using Boolean expressions from the discernibility matrix, defined as
\n\t\t\t\twhere \n\t\t\t\t\t
\n\t\t\t\t\t
\n\t\t\t\t\t
Once the discernibility function \n\t\t\t\t\t
When applying rough set for supervised learning, we need to construct a set of rules from the training data, such that new or unseen objects can be separated into known classes.
\n\t\t\t\tA basic method for forming the decision rules is begun by finding the reducts of the decision table. Then for each reducts\n\t\t\t\t\t
\n\t\t\t\t\t
\n\t\t\t\t\t
\n\t\t\t\t\t
Rule induction is about deciding which attributes should be included in the predecessor of the rule. Rules obtained can always be minimized, but it will introduce noise and may poorly classify the unseen objects.
\n\t\t\t\tOnce the rules are obtained, they can be used to classify the objects that were unseen before. The basic steps involved can be outlined as follows [1].
\n\t\t\t\tApply the existing rules to the new objects so that it can determine which rules actually are a fit to the new objects.
If none of the rules are matched, then fallback a must be chosen, or the objects would be classified as undefined.
If more than one rule is applicable, then a negotiation among the rules must be performed to decide which one to be used.
For the discernibility function extracted from the decision table 2, we obtain the following sets of decision rules by:
\n\t\t\t\tIf (a < 32.5), then d = M
If (s > 65) and (32.5 ≤ a < 50), then d = E
If (s < 65) and (32.5 ≤ a < 50), then d = M
If (s > 65) and (a ≥ 50), then d = E
If (s < 65) and (32.5 ≤ a < 50), then d = E
If (s < 65) and (a ≥ 50), then d = M
PSO was originally introduced by Kennedy and Eberhart [21]. The algorithm was inspired by a sociological observation of a flock of birds behavior while searching for food. Each member of the flock moves with a direction and speed influence by its own previous state and that of the as a whole flock.
\n\t\t\tPSO consists of a swarm (collection) of particles searching through the solution space. Each particle holds information that can potentially become the solution. Each particle has a position and velocity that are mutually affecting those of other particles. Each particle will adjust its parameter according to the swarm’s best outcome, while still considering its own experience. Therefore, at any instance, the following information is maintained by each particle.
\n\t\t\t\n\t\t\t\t\t\t
\n\t\t\t\t\t\t
\n\t\t\t\t\t\t
\n\t\t\t\t\t\t
The search performed by the swarm is either to maximize or minimize the objective function
The global best position (
After each iteration, the current position (
where
In image classification, the PSO algorithm is used to optimize the objective functions that are mainly to:
\n\t\t\tMinimize the distance between pixels and cluster means for each cluster.
Maximize the distance between clusters.
In unsupervised training, there is no prior knowledge of the number of clusters. Therefore the cluster validity is determined by the objective functions. In the algorithm, the Davies-Bouldin index is used as the means to evaluate the result of each iteration.
\n\t\t\tThe accuracy or validity of the classification results need to be measured using certain criteria. As a prerequisite, a set of objects needs to possess a natural group structure. In our image classification algorithm outlined in section 5, the Davies-Bouldin (DB) index is used as the aid in parameter tuning. Our objective function is to minimize the DB index, since a smaller index value indicates compact and well-separated clusters. The similarity index between two clusters
where
where
where
Let
Then the DB index is defined as [17]
\n\t\t\t\tThe K-means algorithm is one of the simplest and most efficient unsupervised learning algorithms to solve clustering problems in image segmentation. In this algorithm, random cluster means are assigned and repeatedly modified throughout the process in order to minimize the squared error function. Suppose there are \n\t\t\t\t
\n\t\t\t\t
\n\t\t\t\t
\n\t\t\t\t
\n\t\t\t\t
\n\t\t\t\t
Upon the completion of the assignment, each new cluster mean is calculated using
\n\t\t\twhere\n\t\t\t\t
\n\t\t\t\t
\n\t\t\t\t
\n\t\t\t\t
The weakness of K-means is that it is dependent on the initial selection of the cluster means and it may be trapped into locally optimal results. However, running the algorithm repeatedly and randomly selecting different sets of cluster means may offset the problem. In a paper by Hung and Germany [19] it is shown that the local optimal results may also be avoided by assigning the cluster means based on distribution of patterns in histogram of an image.
\n\t\tMulti-spectral images can be analyzed using rough set theory. However, since all the attribute values are analog, the discretization process is required. Multispectral images contain multiple bands, for example the RGB color band.
\n\t\t\tObject Index | \n\t\t\t\t\t\tR | \n\t\t\t\t\t\tG | \n\t\t\t\t\t\tB | \n\t\t\t\t\t\tClass | \n\t\t\t\t\t
u 1 | \n\t\t\t\t\t\t149 | \n\t\t\t\t\t\t148 | \n\t\t\t\t\t\t143 | \n\t\t\t\t\t\t1 | \n\t\t\t\t\t
u 2 | \n\t\t\t\t\t\t154 | \n\t\t\t\t\t\t155 | \n\t\t\t\t\t\t150 | \n\t\t\t\t\t\t1 | \n\t\t\t\t\t
u 3 | \n\t\t\t\t\t\t159 | \n\t\t\t\t\t\t160 | \n\t\t\t\t\t\t155 | \n\t\t\t\t\t\t1 | \n\t\t\t\t\t
u 4 | \n\t\t\t\t\t\t174 | \n\t\t\t\t\t\t171 | \n\t\t\t\t\t\t164 | \n\t\t\t\t\t\t2 | \n\t\t\t\t\t
u 5 | \n\t\t\t\t\t\t164 | \n\t\t\t\t\t\t161 | \n\t\t\t\t\t\t154 | \n\t\t\t\t\t\t2 | \n\t\t\t\t\t
u 6 | \n\t\t\t\t\t\t179 | \n\t\t\t\t\t\t183 | \n\t\t\t\t\t\t186 | \n\t\t\t\t\t\t3 | \n\t\t\t\t\t
u 7 | \n\t\t\t\t\t\t159 | \n\t\t\t\t\t\t165 | \n\t\t\t\t\t\t163 | \n\t\t\t\t\t\t3 | \n\t\t\t\t\t
u 8 | \n\t\t\t\t\t\t178 | \n\t\t\t\t\t\t184 | \n\t\t\t\t\t\t182 | \n\t\t\t\t\t\t3 | \n\t\t\t\t\t
Decision System Table for multi-spectral image.
The values of the condition attributes are obtained from the image data shown in figure 1, while the values of the decision attributes are obtained from \'ground truth\' data..
\n\t\t\tEach object has three condition attributes, Red (R), Green (G) and Blue (B) which are associated with a decision attribute. The decision attributes signify the following:
\n\t\t\tClass 1 represents land
Class 2 represents village
Class 3 represents water.
The value of each attribute ranges from 0 to 255, hence the training data from Table 5 can be expressed as:
\n\t\t\tVR={0, 149, 154, 159, 164, 174, 178, 179, 255}
\n\t\t\tVG={0, 148, 155, 160, 161, 165, 171, 183, 184, 255}
\n\t\t\tVB={0, 143, 150, 154, 155, 163, 164, 182, 186, 255}
\n\t\t\tBased on the above intervals, the following set of cuts are obtained.
\n\t\t\tFor the R attribute:
\n\t\t\t(r, 151.5); (r, 156.5); (r, 161.5); (r, 169); (r,176); (r,178.5)
\n\t\t\tFor the G attribute:
\n\t\t\t(g, 151.5); (g, 157.5); (g, 160.5); (g, 163); (g, 168); (g, 177); (g, 183.5)
\n\t\t\tFor the B attribute:
\n\t\t\t(b, 146.5); (b, 152); (b, 154.4); (b, 159); (b,163.5); (b, 173); (b, 184)
\n\t\t\tThe optimal set of cuts needs to be selected now. There are many ways to perform the selection. For decision table A = (U, A ∪ {d}), a local method can be used as [1]:
\n\t\t\t\n\t\t\t\t
\n\t\t\t\t
\n\t\t\t\t
By applying the algorithm above to the image data as shown in Table 6, the following details are derived. For each cut of the R, G and B attributes, we find the cut that yields the maximum number of pairs. The search gives us (g, 160.5) as the optimal solution which yields 15 pairs.
\n\t\t\tThe cut (g, 160.5) divides the set into two, X1 = {u1, u2, u3} and X2 = {u4, u5, u6, u7, u8}. Notice that X1 actually consists of objects of the same class, so the search ends. The search continues for X2. Three sets of cuts are found from the R, G and B attributes for X2. All of the cuts, (r, 176), (g, 177) and (b, 173) yield the same number of objects (4 pairs). We only need to select one, and the one chosen is (r, 176). Again, this cut divides the set into two, Y1 = {u4, u5, u7} and Y2 = {u6, u8}. Y2 consists of objects of the same class, so the search ends. The search continues for Y1. The cut that can discern the most from Y1 is (r, 161.5).
\n\t\t\tThe cut (r, 161.5) divides Y1 into two sets, Z1 = {u4, u5} and Z2 = {u7}. The search ends since both sets contain objects of the same class.
\n\t\t\tThe set of cuts selected are:
\n\t\t\t(r, 161.5); (r, 176.0)
\n\t\t\t\t(g, 160.5)
\n\t\t\tIt appears that our data set only requires two attributes to be fully discerned. Note that different discretization methods will obtain different results. For example, if a naïve algorithm was used, the B attribute will be considered in generating the cuts.
\n\t\t\tUsing the cuts, a discretized table is subsequently generated. The asterisk in the B column indicates that it is not needed to discern the classes. This, however, will not be the case when the training set grows larger.
\n\t\t\tObject Index | \n\t\t\t\t\t\tR | \n\t\t\t\t\t\tG | \n\t\t\t\t\t\tB | \n\t\t\t\t\t\tClass | \n\t\t\t\t\t
u 1 | \n\t\t\t\t\t\t0 | \n\t\t\t\t\t\t0 | \n\t\t\t\t\t\t* | \n\t\t\t\t\t\t1 | \n\t\t\t\t\t
u 2 | \n\t\t\t\t\t\t0 | \n\t\t\t\t\t\t0 | \n\t\t\t\t\t\t* | \n\t\t\t\t\t\t1 | \n\t\t\t\t\t
u 3 | \n\t\t\t\t\t\t0 | \n\t\t\t\t\t\t0 | \n\t\t\t\t\t\t* | \n\t\t\t\t\t\t1 | \n\t\t\t\t\t
u 4 | \n\t\t\t\t\t\t1 | \n\t\t\t\t\t\t1 | \n\t\t\t\t\t\t* | \n\t\t\t\t\t\t2 | \n\t\t\t\t\t
u 5 | \n\t\t\t\t\t\t1 | \n\t\t\t\t\t\t1 | \n\t\t\t\t\t\t* | \n\t\t\t\t\t\t2 | \n\t\t\t\t\t
u 6 | \n\t\t\t\t\t\t2 | \n\t\t\t\t\t\t1 | \n\t\t\t\t\t\t* | \n\t\t\t\t\t\t3 | \n\t\t\t\t\t
u 7 | \n\t\t\t\t\t\t0 | \n\t\t\t\t\t\t1 | \n\t\t\t\t\t\t* | \n\t\t\t\t\t\t3 | \n\t\t\t\t\t
u 8 | \n\t\t\t\t\t\t2 | \n\t\t\t\t\t\t1 | \n\t\t\t\t\t\t* | \n\t\t\t\t\t\t3 | \n\t\t\t\t\t
Discretized Decision System Table for multispectral image.
Based on Table 6, the following rules are generated:
\n\t\t\tIf (g < 160.5), then d = 1.
If (161.5 ≤ r < 176), then d = 2.
If (r ≥ 176), then d = 3.
If (r < 161.5) and (g ≥ 160.5), then d = 3.
The K-means clustering method is categorized as a hard clustering method. Using K-means to classify images that have obscured or blurred boundaries will not bring a satisfactory result. There are many methods proposed to deal with this. The fuzzy C-means [22] and genetic K-means [23] algorithms are two examples.
\n\t\t\tRough K-means is a recently proposed method that deals with the coarseness of the information. In gray image classification the challenge is on segmenting the blurred boundaries between clusters. Using rough sets theory, an image can be represented as sets of lower and upper approximation. The rough K-means model for our proposed image segmentation algorithm is adapted from [20].
\n\t\t\tEach image pixel can be classified into lower or upper approximations. Following basic rough set properties:
\n\t\t\tA pixel can be part of only one lower approximation
If a pixel is part of a lower approximation, then it is also part of the upper approximation
If a pixel does not belong to any lower approximation, then it belongs to two or more upper approximations.
Applying rough set into K-means requires the formula to include lower and upper approximations. The formula, as shown below, includes the weighing factor \n\t\t\t\t
and
\n\t\t\tIn order to correctly classify a pixel, the following
If
If
To summarize, the following are steps to perform the rough K-means algorithm [26]:
\n\t\t\tInitialize K clusters randomly.
Select
For each cluster, find
Classify the pixel using the
Calculate the new cluster center (mean) using Equation 6.1.
If every cluster converges, then stop. Otherwise, repeat step 3.
The parameters involved are
The proposed algorithm that combines rough K-means and PSO algorithm is outlined as follows [26]:
\n\t\t\tInitialize the mean of each cluster.
Initialize a number of particles where each of the particles is randomly assigned with
Find the minimum pair of distance of
If the difference of the distance
Otherwise, the pixel
Calculate the DB index of each particle. Save the DB index of each particle and compare them with those of other particles. Find the global best index and tune the lower approximation and thresholds of each particle according to the following guidelines.
If the personal best DB index equals the global best DB index, then lower the threshold so that it includes only the pixels that are definitely in the lower approximation.
If the personal best DB index is greater than the global best DB index, then adjust the
Calculate the new mean for each cluster.
Repeat steps 3, 4 and 5 until all particles converge.
To test the effectiveness of the proposed algorithms, multispectral and artificial images were used in our experiments. The original image is processed to obtain the multispectral information. Then the Rough Set Exploration System (RSES) software was used to process the image data [24]. A selected percentage of the image pixels were sampled for training purpose. Finally MATLAB was used to make the results viewable as an image. Experimental results are described in section 7.1. The experiment on the rough K-means algorithm is intended to show the effect of parameter selection on the results of the classification. Experimental results on the algorithm are shown in section 7.2.
\n\t\t\tDue to the size of the table in our training sample (in the range of over 80,000 pixels), we need to resort to the decomposition tree feature of the RSES. This feature allows us to break the table into sections no larger than a predefined size. In this case, a size of 500 samples is selected as the maximum size of each leaf in the decomposition tree. These methods are further elaborated in [3] and [4]. During the decomposition process, the table is also discretized. A local method like the one outlined in Section 2 is chosen as the method for selecting the optimal cuts. Each leaf of the decomposition tree contains a set of rules that was dynamically created. The rules are then used to classify the unseen objects. Using RSES, there are two formats of output that the user can select: confusion matrix or classification results in table format.
\n\t\t\t\tAfter applying the rules to the pixels and obtaining the classification result, the reverse process is done using MATLAB to get the classified image. All pixels, including the unclassified ones, are assigned a specific color for visualization. The original image as shown in Figure 1(a) is a terrain image that has land, water and village. After the classification using rough set theory, the classified result is obtained in Figure 1(b). The confusion matrix with an average accuracy of 0.79 is shown in Table 7.
\n\t\t\t\t(a) An original satellite image, and (b) the classified result using the rough set theory. The color blue represent land, the red represents water and green represents village. Undefined objects are left as black pixels.
\n\t\t\t\t\t\t\t | Classified | \n\t\t\t\t\t\t|||
Actual | \n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t | 1 | \n\t\t\t\t\t\t\t2 | \n\t\t\t\t\t\t\t3 | \n\t\t\t\t\t\t
1 | \n\t\t\t\t\t\t\t181,838 | \n\t\t\t\t\t\t\t2,264 | \n\t\t\t\t\t\t\t1,052 | \n\t\t\t\t\t\t|
2 | \n\t\t\t\t\t\t\t960 | \n\t\t\t\t\t\t\t1,631 | \n\t\t\t\t\t\t\t90 | \n\t\t\t\t\t\t|
3 | \n\t\t\t\t\t\t\t2,083 | \n\t\t\t\t\t\t\t124 | \n\t\t\t\t\t\t\t67,381 | \n\t\t\t\t\t\t|
\n\t\t\t\t\t\t\t | True Pos Rates | \n\t\t\t\t\t\t\t0.98 | \n\t\t\t\t\t\t\t0.41 | \n\t\t\t\t\t\t\t0.98 | \n\t\t\t\t\t\t
Rough set classification accuracy assessment.
With the parallelepiped classification algorithm [25], the ordering of the classes affects the final result. The experimental results are shown in Figure 2. First we show the result of classification, where the order of classes are 1, 2, and 3 (respectively land, village and water). It is apparent that the RGB spectral signatures for village and water overlap. Since the order of analysis begins with village (2), most pixels of water (3) were classified as village. The confusion matrix for the results in Figure 2 is shown in Table 8 with an average accuracy of 0.6.
\n\t\t\t\tA classification result of
Ordering of the classification for classes 1, 2, 3 (land, village and water)
\n\t\t\t\t\n\t\t\t\t\t\t\t | Classified | \n\t\t\t\t\t\t|||
Actual | \n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t | 1 | \n\t\t\t\t\t\t\t2 | \n\t\t\t\t\t\t\t3 | \n\t\t\t\t\t\t
\n\t\t\t\t\t\t\t | 1 | \n\t\t\t\t\t\t\t188,043 | \n\t\t\t\t\t\t\t38 | \n\t\t\t\t\t\t\t0 | \n\t\t\t\t\t\t
\n\t\t\t\t\t\t\t | 2 | \n\t\t\t\t\t\t\t2,113 | \n\t\t\t\t\t\t\t1,305 | \n\t\t\t\t\t\t\t0 | \n\t\t\t\t\t\t
\n\t\t\t\t\t\t\t | 3 | \n\t\t\t\t\t\t\t361 | \n\t\t\t\t\t\t\t40,661 | \n\t\t\t\t\t\t\t29,626 | \n\t\t\t\t\t\t
\n\t\t\t\t\t\t\t | True Pos Rates | \n\t\t\t\t\t\t\t0.99 | \n\t\t\t\t\t\t\t0.38 | \n\t\t\t\t\t\t\t0.42 | \n\t\t\t\t\t\t
Parallelepiped classification accuracy assessment.
The classification ordering is class 1, 2, and 3 (land, village, and water)
\n\t\t\t\tThe experiment is repeated for the parallelepiped classifier. The ordering is now started with classes 1, 3 and 2 (respectively land, water and village). Contrary to the result in Figure 2, now most pixels of the village area are classified as water. The confusion matrix for the result in Figure 3 is shown in Table 9 with an average accuracy of 0.72. The increase in the average accuracy, because of the misclassification of village, is mitigated by the number of its pixels overall.
\n\t\t\t\tA classification result of
\n\t\t\t\t\t\t\t | Classified | \n\t\t\t\t\t\t|||
Actual | \n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t | 1 | \n\t\t\t\t\t\t\t3 | \n\t\t\t\t\t\t\t2 | \n\t\t\t\t\t\t
\n\t\t\t\t\t\t\t | 1 | \n\t\t\t\t\t\t\t188,043 | \n\t\t\t\t\t\t\t38 | \n\t\t\t\t\t\t\t0 | \n\t\t\t\t\t\t
\n\t\t\t\t\t\t\t | 3 | \n\t\t\t\t\t\t\t361 | \n\t\t\t\t\t\t\t70,237 | \n\t\t\t\t\t\t\t0 | \n\t\t\t\t\t\t
\n\t\t\t\t\t\t\t | 2 | \n\t\t\t\t\t\t\t2,113 | \n\t\t\t\t\t\t\t689 | \n\t\t\t\t\t\t\t616 | \n\t\t\t\t\t\t
\n\t\t\t\t\t\t\t | True Pos Rates | \n\t\t\t\t\t\t\t0.99 | \n\t\t\t\t\t\t\t0.99 | \n\t\t\t\t\t\t\t0.18 | \n\t\t\t\t\t\t
Parallelepiped classification accuracy assessment.
The classification ordering is class 1, 3, and 2 (land, water, and village)
\n\t\t\t\tThe following experiment requires ground truth data for accuracy assessment. The remote image sensing truth data was obtained from Dr. Su in the National Central University in Taiwan, while the ground truth data for the artificial images were created using custom software written in Java. The decision rules, which are required for classification of the image, are facilitated by RSES [24]. The process for remotely sensed images begins by sampling 30% of the image pixels as training data to create decision rules. The process to create decision rules follows the outline in Section 2. After obtaining the rules with RSES, they are used to classify the image. The image consists of approximately 262,000 pixels. Referring to Figure 4(a), class 1 is land, class 2 village and class 3 water. The average accuracy for the classification is 79%. The confusion matrix is shown in Table 10. The most difficult pixels to classify are the village pixels, as indicated by the small value of its true positive rate. The ground truth data and classified result are shown in Figure (b) and (c).
\n\t\t\t\ta) An original remote sensing image, (b) Ground Truth Data, and (c) Classification result.
\n\t\t\t\t\t\t\t | Classified | \n\t\t\t\t\t\t|||
Actual | \n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t | 1 | \n\t\t\t\t\t\t\t2 | \n\t\t\t\t\t\t\t3 | \n\t\t\t\t\t\t
\n\t\t\t\t\t\t\t | 1 | \n\t\t\t\t\t\t\t181,838 | \n\t\t\t\t\t\t\t2,264 | \n\t\t\t\t\t\t\t1,052 | \n\t\t\t\t\t\t
\n\t\t\t\t\t\t\t | 2 | \n\t\t\t\t\t\t\t960 | \n\t\t\t\t\t\t\t1,631 | \n\t\t\t\t\t\t\t90 | \n\t\t\t\t\t\t
\n\t\t\t\t\t\t\t | 3 | \n\t\t\t\t\t\t\t2,083 | \n\t\t\t\t\t\t\t124 | \n\t\t\t\t\t\t\t67,381 | \n\t\t\t\t\t\t
\n\t\t\t\t\t\t\t | True Positive Rates | \n\t\t\t\t\t\t\t0.98 | \n\t\t\t\t\t\t\t0.41 | \n\t\t\t\t\t\t\t0.98 | \n\t\t\t\t\t\t
Rough set classification accuracy assessment for remote sensing image.
The other experiment is performed on the artificial image that consists of several shapes, namely, a cube, a serpentine, two airbrush shapes and a round shape (Figure 5). Similarly, 30 % of the pixels in the image are used for training. After obtaining the decision rules, the image is classified. The artificial image has a total of 10000 pixels. Referring to Table 11, class 1 is the cube, class 2 is the connector of the airbrush images, class 3 is the airbrush images, class 4 is the round shape and class 5 is the background. Some difficulties occur while trying to obtain the ground truth, due to the inherent limitations of the image processing software. The results however, indicate that class 3, the airbrush shapes, has the most incorrectly identified pixels. The total accuracy is still about 99% as shown in Table 11.
\n\t\t\t\ta) Original artificial shapes (b) Image truth (c) Classification result.
\n\t\t\t\t\t\t\t | Classified | \n\t\t\t\t\t\t|||||
Actual | \n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t | 1 | \n\t\t\t\t\t\t\t2 | \n\t\t\t\t\t\t\t3 | \n\t\t\t\t\t\t\t4 | \n\t\t\t\t\t\t\t5 | \n\t\t\t\t\t\t
\n\t\t\t\t\t\t\t | 1 | \n\t\t\t\t\t\t\t833 | \n\t\t\t\t\t\t\t0 | \n\t\t\t\t\t\t\t0 | \n\t\t\t\t\t\t\t0 | \n\t\t\t\t\t\t\t1 | \n\t\t\t\t\t\t
\n\t\t\t\t\t\t\t | 2 | \n\t\t\t\t\t\t\t5 | \n\t\t\t\t\t\t\t884 | \n\t\t\t\t\t\t\t3 | \n\t\t\t\t\t\t\t7 | \n\t\t\t\t\t\t\t3 | \n\t\t\t\t\t\t
\n\t\t\t\t\t\t\t | 3 | \n\t\t\t\t\t\t\t0 | \n\t\t\t\t\t\t\t13 | \n\t\t\t\t\t\t\t907 | \n\t\t\t\t\t\t\t3 | \n\t\t\t\t\t\t\t2 | \n\t\t\t\t\t\t
\n\t\t\t\t\t\t\t | 4 | \n\t\t\t\t\t\t\t0 | \n\t\t\t\t\t\t\t3 | \n\t\t\t\t\t\t\t0 | \n\t\t\t\t\t\t\t1,434 | \n\t\t\t\t\t\t\t8 | \n\t\t\t\t\t\t
\n\t\t\t\t\t\t\t | 5 | \n\t\t\t\t\t\t\t0 | \n\t\t\t\t\t\t\t0 | \n\t\t\t\t\t\t\t2 | \n\t\t\t\t\t\t\t3 | \n\t\t\t\t\t\t\t5,878 | \n\t\t\t\t\t\t
\n\t\t\t\t\t\t\t | True Positive Rate | \n\t\t\t\t\t\t\t0.99 | \n\t\t\t\t\t\t\t0.98 | \n\t\t\t\t\t\t\t0.99 | \n\t\t\t\t\t\t\t0.99 | \n\t\t\t\t\t\t\t1 | \n\t\t\t\t\t\t
Rough set classification accuracy assessment for artificial shapes.
In the experiment shown in Figure 6.1 (b), the parameter
The experiment was done for several different combinations of
Rough K-Means for remote sensing image. (a) Ground Truth, (b)
Results with similar consistency are obtained for the image in Figure 7. From Figure 7(b) to 7(c), we can see improvement visually. As the parameters change in one direction, the accuracy drops as shown in Figure 7(d). Finally the best value for the experiment is shown in figure 7(f). Looking at those classified results, it may lead us into thinking that increasing the wlower and decreasing the threshold gives a better result. That is not necessarily the case, since doing so means that we are counting on the lower bound more and reducing the threshold value, while at the same time discounting the upper bound. At the extreme, where wlower is almost 1 and threshold is almost zero, roughness is actually removed, and the set becomes crisp. This is also formulated in Equations 6.1, 6.2 and 6.3 earlier.
\n\t\t\t\tRough K-Means for artificial image. (a) Ground Truth, (b)
Experiments using the K-means and rough K-means PSO algorithms are performed. For the comparison, the number of iteration is limited to 50 and the tolerance is set to 0.001. The result shown in Figure 8 is selected from the best outcome of 20 runs of the K-means and rough K-means algorithms. For the rough K-means PSO, 10 particles are used to explore the search space. Comparing the results of the K-means, rough K-means and rough K-means PSO algorithms, it reveals that although the improvement can be made, it is in the order of more or less 5 %. It is not very significant, but we should note that the rough K-means PSO achieve the optimal results independent of initial mean selections.
\n\t\t\t\tComparison of the results. (a) K-Means Accuracy = 56.26 %, (b) Rough K-Means
While running, the algorithm is tuned by keeping track of the DB index and adjusting the PSO particle accordingly to calculate the new mean. Figure 9 shows the DB index tracking for Figure 8(a) and 8(c).
\n\t\t\t\tK-means and rough K-means PSO DB index tracking for
Referring to Figure 9, it is apparent that the K-means algorithm eventually converges and locks into a certain mean value. The rough K-means PSO shows a better capability to search for solutions, because there are about 10 particles to keep track of global best and adjust the velocity towards the best solution in every iteration. Similar results are obtained from the remaining tests performed. The resulting improvement, however, is not as obvious as those shown in the artificial image (Figure 10). Part of the reason is because the artificial image does not have enough roughness. Hence, it is not difficult for K-means to perform well in this case. Figure 11 shows the tracking of the DB index.
\n\t\t\t\tComparison of the results. (a) K-means Accuracy 90.12 %, (b) Rough K-means
The K-means and rough K-means PSO DB index tracking for the artificial image shown in
For the planet image, there is no ground truth data available. However, the visual inspection reveals improvement. Based on the results shown in Figure 12, we can see that the K-means algorithm actually has some difficulty in the segmentation of the blurred or rough boundaries. The rough K-means PSO however, appears to be able to discern the rough boundaries, and therefore comes up with a much more rounded shape for the planet. The outer shape of the planet appears sharper, more rounded and less distorted. Figure 13 shows the DB index tracking of the planet image.
\n\t\t\t\tExperimental results for planet image, (a) K-means, (b) rough K-means
The K-means and rough K-means PSO DB index tracking for the planet image shown in
Image classification and segmentation by applying rough set theory may be approached from two different perspectives: unsupervised or supervised methods. From the experiment, it is generally shown that a supervised classification achieves better results as compared with the unsupervised methods. However, it should be noted that unsupervised classification may be preferred because it requires less prior knowledge. The K-means algorithm can be enhanced by using the rough set theory for image classification, however it has a practical limitation by itself, since the parameters (wlower and the threshold value) are difficult to tune manually. To solve this problem, the PSO is used for tuning these parameters. The algorithm is tested on several and in general its improved noise immunity can be seen in the results especially when the images have rough boundaries or noisy details. For future work, the ant colony optimization and differential evolution algorithms will be explored for tuning the parameters in the rough K-Means algorithm.
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MS, Ph.D., is currently with the Department of Research and Evaluation, Kaiser Permanente Southern California. He has both an MS and Ph.D. in Biomedical Engineering. He was previously a research scientist at the University of California Los Angeles (UCLA) and visiting professor and researcher at the University of North Dakota. He is currently working in artificial intelligence and its applications in medical signal processing. In addition, he is using digital signal processing in medical imaging and speech processing. Dr. Asadpour has developed brain-computer interfacing algorithms and has published books, book chapters, and several journal and conference papers in this field and other areas of intelligent signal processing. He has also designed medical devices, including a laser Doppler monitoring system.",institutionString:"Kaiser Permanente Southern California",institution:null},{id:"169608",title:"Prof.",name:"Marian",middleName:null,surname:"Găiceanu",slug:"marian-gaiceanu",fullName:"Marian Găiceanu",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/169608/images/system/169608.png",biography:"Prof. Dr. Marian Gaiceanu graduated from the Naval and Electrical Engineering Faculty, Dunarea de Jos University of Galati, Romania, in 1997. He received a Ph.D. (Magna Cum Laude) in Electrical Engineering in 2002. Since 2017, Dr. Gaiceanu has been a Ph.D. supervisor for students in Electrical Engineering. He has been employed at Dunarea de Jos University of Galati since 1996, where he is currently a professor. Dr. Gaiceanu is a member of the National Council for Attesting Titles, Diplomas and Certificates, an expert of the Executive Agency for Higher Education, Research Funding, and a member of the Senate of the Dunarea de Jos University of Galati. He has been the head of the Integrated Energy Conversion Systems and Advanced Control of Complex Processes Research Center, Romania, since 2016. He has conducted several projects in power converter systems for electrical drives, power quality, PEM and SOFC fuel cell power converters for utilities, electric vehicles, and marine applications with the Department of Regulation and Control, SIEI S.pA. (2002–2004) and the Polytechnic University of Turin, Italy (2002–2004, 2006–2007). He is a member of the Institute of Electrical and Electronics Engineers (IEEE) and cofounder-member of the IEEE Power Electronics Romanian Chapter. He is a guest editor at Energies and an academic book editor for IntechOpen. He is also a member of the editorial boards of the Journal of Electrical Engineering, Electronics, Control and Computer Science and Sustainability. Dr. Gaiceanu has been General Chairman of the IEEE International Symposium on Electrical and Electronics Engineering in the last six editions.",institutionString:'"Dunarea de Jos" University of Galati',institution:{name:'"Dunarea de Jos" University of Galati',country:{name:"Romania"}}},{id:"4519",title:"Prof.",name:"Jaydip",middleName:null,surname:"Sen",slug:"jaydip-sen",fullName:"Jaydip Sen",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/4519/images/system/4519.jpeg",biography:"Jaydip Sen is associated with Praxis Business School, Kolkata, India, as a professor in the Department of Data Science. His research areas include security and privacy issues in computing and communication, intrusion detection systems, machine learning, deep learning, and artificial intelligence in the financial domain. He has more than 200 publications in reputed international journals, refereed conference proceedings, and 20 book chapters in books published by internationally renowned publishing houses, such as Springer, CRC press, IGI Global, etc. Currently, he is serving on the editorial board of the prestigious journal Frontiers in Communications and Networks and in the technical program committees of a number of high-ranked international conferences organized by the IEEE, USA, and the ACM, USA. He has been listed among the top 2% of scientists in the world for the last three consecutive years, 2019 to 2021 as per studies conducted by the Stanford University, USA.",institutionString:"Praxis Business School",institution:null},{id:"320071",title:"Dr.",name:"Sidra",middleName:null,surname:"Mehtab",slug:"sidra-mehtab",fullName:"Sidra Mehtab",position:null,profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0033Y00002v6KHoQAM/Profile_Picture_1584512086360",biography:"Sidra Mehtab has completed her BS with honors in Physics from Calcutta University, India in 2018. She has done MS in Data Science and Analytics from Maulana Abul Kalam Azad University of Technology (MAKAUT), Kolkata, India in 2020. Her research areas include Econometrics, Time Series Analysis, Machine Learning, Deep Learning, Artificial Intelligence, and Computer and Network Security with a particular focus on Cyber Security Analytics. Ms. Mehtab has published seven papers in international conferences and one of her papers has been accepted for publication in a reputable international journal. She has won the best paper awards in two prestigious international conferences – BAICONF 2019, and ICADCML 2021, organized in the Indian Institute of Management, Bangalore, India in December 2019, and SOA University, Bhubaneswar, India in January 2021. Besides, Ms. Mehtab has also published two book chapters in two books. Seven of her book chapters will be published in a volume shortly in 2021 by Cambridge Scholars’ Press, UK. Currently, she is working as the joint editor of two edited volumes on Time Series Analysis and Forecasting to be published in the first half of 2021 by an international house. Currently, she is working as a Data Scientist with an MNC in Delhi, India.",institutionString:"NSHM College of Management and Technology",institution:null},{id:"226240",title:"Dr.",name:"Andri Irfan",middleName:null,surname:"Rifai",slug:"andri-irfan-rifai",fullName:"Andri Irfan Rifai",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/226240/images/7412_n.jpg",biography:"Andri IRFAN is a Senior Lecturer of Civil Engineering and Planning. He completed the PhD at the Universitas Indonesia & Universidade do Minho with Sandwich Program Scholarship from the Directorate General of Higher Education and LPDP scholarship. He has been teaching for more than 19 years and much active to applied his knowledge in the project construction in Indonesia. His research interest ranges from pavement management system to advanced data mining techniques for transportation engineering. He has published more than 50 papers in journals and 2 books.",institutionString:null,institution:{name:"Universitas Internasional Batam",country:{name:"Indonesia"}}},{id:"314576",title:"Dr.",name:"Ibai",middleName:null,surname:"Laña",slug:"ibai-lana",fullName:"Ibai Laña",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/314576/images/system/314576.jpg",biography:"Dr. Ibai Laña works at TECNALIA as a data analyst. He received his Ph.D. in Artificial Intelligence from the University of the Basque Country (UPV/EHU), Spain, in 2018. He is currently a senior researcher at TECNALIA. His research interests fall within the intersection of intelligent transportation systems, machine learning, traffic data analysis, and data science. He has dealt with urban traffic forecasting problems, applying machine learning models and evolutionary algorithms. He has experience in origin-destination matrix estimation or point of interest and trajectory detection. Working with large volumes of data has given him a good command of big data processing tools and NoSQL databases. He has also been a visiting scholar at the Knowledge Engineering and Discovery Research Institute, Auckland University of Technology.",institutionString:"TECNALIA Research & Innovation",institution:{name:"Tecnalia",country:{name:"Spain"}}},{id:"314575",title:"Dr.",name:"Jesus",middleName:null,surname:"L. Lobo",slug:"jesus-l.-lobo",fullName:"Jesus L. Lobo",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/314575/images/system/314575.png",biography:"Dr. Jesús López is currently based in Bilbao (Spain) working at TECNALIA as Artificial Intelligence Research Scientist. In most cases, a project idea or a new research line needs to be investigated to see if it is good enough to take into production or to focus on it. That is exactly what he does, diving into Machine Learning algorithms and technologies to help TECNALIA to decide whether something is great in theory or will actually impact on the product or processes of its projects. So, he is expert at framing experiments, developing hypotheses, and proving whether they’re true or not, in order to investigate fundamental problems with a longer time horizon. He is also able to design and develop PoCs and system prototypes in simulation. He has participated in several national and internacional R&D projects.\n\nAs another relevant part of his everyday research work, he usually publishes his findings in reputed scientific refereed journals and international conferences, occasionally acting as reviewer and Programme Commitee member. Concretely, since 2018 he has published 9 JCR (8 Q1) journal papers, 9 conference papers (e.g. ECML PKDD 2021), and he has co-edited a book. He is also active in popular science writing data science stories for reputed blogs (KDNuggets, TowardsDataScience, Naukas). Besides, he has recently embarked on mentoring programmes as mentor, and has also worked as data science trainer.",institutionString:"TECNALIA Research & Innovation",institution:{name:"Tecnalia",country:{name:"Spain"}}},{id:"103779",title:"Prof.",name:"Yalcin",middleName:null,surname:"Isler",slug:"yalcin-isler",fullName:"Yalcin Isler",position:null,profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRyQ8QAK/Profile_Picture_1628834958734",biography:"Yalcin Isler (1971 - Burdur / Turkey) received the B.Sc. degree in the Department of Electrical and Electronics Engineering from Anadolu University, Eskisehir, Turkey, in 1993, the M.Sc. degree from the Department of Electronics and Communication Engineering, Suleyman Demirel University, Isparta, Turkey, in 1996, the Ph.D. degree from the Department of Electrical and Electronics Engineering, Dokuz Eylul University, Izmir, Turkey, in 2009, and the Competence of Associate Professorship from the Turkish Interuniversity Council in 2019.\n\nHe was Lecturer at Burdur Vocational School in Suleyman Demirel University (1993-2000, Burdur / Turkey), Software Engineer (2000-2002, Izmir / Turkey), Research Assistant in Bulent Ecevit University (2002-2003, Zonguldak / Turkey), Research Assistant in Dokuz Eylul University (2003-2010, Izmir / Turkey), Assistant Professor at the Department of Electrical and Electronics Engineering in Bulent Ecevit University (2010-2012, Zonguldak / Turkey), Assistant Professor at the Department of Biomedical Engineering in Izmir Katip Celebi University (2012-2019, Izmir / Turkey). He is an Associate Professor at the Department of Biomedical Engineering at Izmir Katip Celebi University, Izmir / Turkey, since 2019. In addition to academics, he has also founded Islerya Medical and Information Technologies Company, Izmir / Turkey, since 2017.\n\nHis main research interests cover biomedical signal processing, pattern recognition, medical device design, programming, and embedded systems. He has many scientific papers and participated in several projects in these study fields. He was an IEEE Student Member (2009-2011) and IEEE Member (2011-2014) and has been IEEE Senior Member since 2014.",institutionString:null,institution:{name:"Izmir Kâtip Çelebi University",country:{name:"Turkey"}}},{id:"339677",title:"Dr.",name:"Mrinmoy",middleName:null,surname:"Roy",slug:"mrinmoy-roy",fullName:"Mrinmoy Roy",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/339677/images/16768_n.jpg",biography:"An accomplished Sales & Marketing professional with 12 years of cross-functional experience in well-known organisations such as CIPLA, LUPIN, GLENMARK, ASTRAZENECA across different segment of Sales & Marketing, International Business, Institutional Business, Product Management, Strategic Marketing of HIV, Oncology, Derma, Respiratory, Anti-Diabetic, Nutraceutical & Stomatological Product Portfolio and Generic as well as Chronic Critical Care Portfolio. A First Class MBA in International Business & Strategic Marketing, B.Pharm, D.Pharm, Google Certified Digital Marketing Professional. Qualified PhD Candidate in Operations and Management with special focus on Artificial Intelligence and Machine Learning adoption, analysis and use in Healthcare, Hospital & Pharma Domain. Seasoned with diverse therapy area of Pharmaceutical Sales & Marketing ranging from generating revenue through generating prescriptions, launching new products, and making them big brands with continuous strategy execution at the Physician and Patients level. Moved from Sales to Marketing and Business Development for 3.5 years in South East Asian Market operating from Manila, Philippines. Came back to India and handled and developed Brands such as Gluconorm, Lupisulin, Supracal, Absolut Woman, Hemozink, Fabiflu (For COVID 19), and many more. In my previous assignment I used to develop and execute strategies on Sales & Marketing, Commercialization & Business Development for Institution and Corporate Hospital Business portfolio of Oncology Therapy Area for AstraZeneca Pharma India Ltd. Being a Research Scholar and Student of ‘Operations Research & Management: Artificial Intelligence’ I published several pioneer research papers and book chapters on the same in Internationally reputed journals and Books indexed in Scopus, Springer and Ei Compendex, Google Scholar etc. Currently, I am launching PGDM Pharmaceutical Management Program in IIHMR Bangalore and spearheading the course curriculum and structure of the same. I am interested in Collaboration for Healthcare Innovation, Pharma AI Innovation, Future trend in Marketing and Management with incubation on Healthcare, Healthcare IT startups, AI-ML Modelling and Healthcare Algorithm based training module development. I am also an affiliated member of the Institute of Management Consultant of India, looking forward to Healthcare, Healthcare IT and Innovation, Pharma and Hospital Management Consulting works.",institutionString:null,institution:{name:"Lovely Professional University",country:{name:"India"}}},{id:"1063",title:"Prof.",name:"Constantin",middleName:null,surname:"Volosencu",slug:"constantin-volosencu",fullName:"Constantin Volosencu",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/1063/images/system/1063.png",biography:"Prof. Dr. Constantin Voloşencu graduated as an engineer from\nPolitehnica University of Timișoara, Romania, where he also\nobtained a doctorate degree. 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. He has developed automation equipment for machine tools, spooling\nmachines, high-power ultrasound processes, and more.",institutionString:"Polytechnic University of Timişoara",institution:{name:"Polytechnic University of Timişoara",country:{name:"Romania"}}},{id:"221364",title:"Dr.",name:"Eneko",middleName:null,surname:"Osaba",slug:"eneko-osaba",fullName:"Eneko Osaba",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/221364/images/system/221364.jpg",biography:"Dr. Eneko Osaba works at TECNALIA as a senior researcher. He obtained his Ph.D. in Artificial Intelligence in 2015. He has participated in more than twenty-five local and European research projects, and in the publication of more than 130 papers. He has performed several stays at universities in the United Kingdom, Italy, and Malta. Dr. Osaba has served as a program committee member in more than forty international conferences and participated in organizing activities in more than ten international conferences. He is a member of the editorial board of the International Journal of Artificial Intelligence, Data in Brief, and Journal of Advanced Transportation. He is also a guest editor for the Journal of Computational Science, Neurocomputing, Swarm, and Evolutionary Computation and IEEE ITS Magazine.",institutionString:"TECNALIA Research & Innovation",institution:{name:"Tecnalia",country:{name:"Spain"}}},{id:"275829",title:"Dr.",name:"Esther",middleName:null,surname:"Villar-Rodriguez",slug:"esther-villar-rodriguez",fullName:"Esther Villar-Rodriguez",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/275829/images/system/275829.jpg",biography:"Dr. Esther Villar obtained a Ph.D. in Information and Communication Technologies from the University of Alcalá, Spain, in 2015. She obtained a degree in Computer Science from the University of Deusto, Spain, in 2010, and an MSc in Computer Languages and Systems from the National University of Distance Education, Spain, in 2012. Her areas of interest and knowledge include natural language processing (NLP), detection of impersonation in social networks, semantic web, and machine learning. Dr. Esther Villar made several contributions at conferences and publishing in various journals in those fields. Currently, she is working within the OPTIMA (Optimization Modeling & Analytics) business of TECNALIA’s ICT Division as a data scientist in projects related to the prediction and optimization of management and industrial processes (resource planning, energy efficiency, etc).",institutionString:"TECNALIA Research & Innovation",institution:{name:"Tecnalia",country:{name:"Spain"}}},{id:"49813",title:"Dr.",name:"Javier",middleName:null,surname:"Del Ser",slug:"javier-del-ser",fullName:"Javier Del Ser",position:null,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. He is a Senior Member of the IEEE, and a recipient of the Biscay Talent prize for his academic career.",institutionString:"Tecnalia Research & Innovation",institution:null},{id:"278948",title:"Dr.",name:"Carlos Pedro",middleName:null,surname:"Gonçalves",slug:"carlos-pedro-goncalves",fullName:"Carlos Pedro Gonçalves",position:null,profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRcmyQAC/Profile_Picture_1564224512145",biography:'Carlos Pedro Gonçalves (PhD) is an Associate Professor at Lusophone University of Humanities and Technologies and a researcher on Complexity Sciences, Quantum Technologies, Artificial Intelligence, Strategic Studies, Studies in Intelligence and Security, FinTech and Financial Risk Modeling. He is also a progammer with programming experience in:\n\nA) Quantum Computing using Qiskit Python module and IBM Quantum Experience Platform, with software developed on the simulation of Quantum Artificial Neural Networks and Quantum Cybersecurity;\n\nB) Artificial Intelligence and Machine learning programming in Python;\n\nC) Artificial Intelligence, Multiagent Systems Modeling and System Dynamics Modeling in Netlogo, with models developed in the areas of Chaos Theory, Econophysics, Artificial Intelligence, Classical and Quantum Complex Systems Science, with the Econophysics models having been cited worldwide and incorporated in PhD programs by different Universities.\n\nReceived an Arctic Code Vault Contributor status by GitHub, due to having developed open source software preserved in the \\"Arctic Code Vault\\" for future generations (https://archiveprogram.github.com/arctic-vault/), with the Strategy Analyzer A.I. module for decision making support (based on his PhD thesis, used in his Classes on Decision Making and in Strategic Intelligence Consulting Activities) and QNeural Python Quantum Neural Network simulator also preserved in the \\"Arctic Code Vault\\", for access to these software modules see: https://github.com/cpgoncalves. He is also a peer reviewer with outsanding review status from Elsevier journals, including Physica A, Neurocomputing and Engineering Applications of Artificial Intelligence. 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Currently working as an Assistant Professor in the Department of Mathematics, Institute of Applied Science, Mangalayatan University, Aligarh. She taught so many courses of Mathematics of UG and PG level. Her research Area of Expertise is Functional Analysis & Sequence Spaces. She has been working on Ideal Convergence of double sequence. She has published 17 research papers in National and International Journals including Cogent Mathematics, Filomat, Journal of Intelligent and Fuzzy Systems, Advances in Difference Equations, Journal of Mathematical Analysis, Journal of Mathematical & Computer Science etc. She has also reviewed few research papers for the and international journals. 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