Details of datasets.
\\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
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Problems with scheduling comprise both a set of resources and a set of a consumers. As such, managing scheduling problems involves managing the use of resources by several consumers. This book presents some new applications and trends related to task and data scheduling. In particular, chapters focus on data science, big data, high-performance computing, and Cloud computing environments. In addition, this book presents novel algorithms and literature reviews that will guide current and new researchers who work with load balancing, scheduling, and allocation problems.",isbn:"978-1-78985-054-3",printIsbn:"978-1-78985-053-6",pdfIsbn:"978-1-83962-169-7",doi:"10.5772/intechopen.80171",price:119,priceEur:129,priceUsd:155,slug:"scheduling-problems-new-applications-and-trends",numberOfPages:154,isOpenForSubmission:!1,isInWos:null,isInBkci:!1,hash:"c700a7e3dbf7482d7d82a1e6639b7f32",bookSignature:"Rodrigo da Rosa Righi",publishedDate:"July 8th 2020",coverURL:"https://cdn.intechopen.com/books/images_new/8754.jpg",numberOfDownloads:5503,numberOfWosCitations:0,numberOfCrossrefCitations:7,numberOfCrossrefCitationsByBook:0,numberOfDimensionsCitations:8,numberOfDimensionsCitationsByBook:0,hasAltmetrics:0,numberOfTotalCitations:15,isAvailableForWebshopOrdering:!0,dateEndFirstStepPublish:"January 7th 2019",dateEndSecondStepPublish:"March 7th 2019",dateEndThirdStepPublish:"May 6th 2019",dateEndFourthStepPublish:"July 25th 2019",dateEndFifthStepPublish:"September 23rd 2019",currentStepOfPublishingProcess:5,indexedIn:"1,2,3,4,5,6,7",editedByType:"Edited by",kuFlag:!1,featuredMarkup:null,editors:[{id:"69889",title:"Prof.",name:"Rodrigo",middleName:null,surname:"da Rosa Righi",slug:"rodrigo-da-rosa-righi",fullName:"Rodrigo da Rosa Righi",profilePictureURL:"https://mts.intechopen.com/storage/users/69889/images/system/69889.jpg",biography:"Prof. Dr. Rodrigo da Rosa Righi is professor and researcher at University of Vale do Rio dos Sinos (Unisinos), Brazil. Rodrigo is a member of the Applied Computing Graduate Program at the same university, where he advises graduate and undergraduate students. He obtained his Ph.D. in Computer Science from the Federal University of Rio Grande do Sul (UFRGS), Brazil, in 2009. During his doctorate, Rodrigo performed a sandwich stage at Technische Universitaet Berlin, Germany. In 2013, Rodrigo was visiting professor for six months at KAIST (Korea Advanced Institute of Science and Technology), South Korea. Currently, his research interests include performance analysis, process scheduling and migration, load balancing, and resource provisioning on Cluster, Cloud, and Fog environments. He is also a senior member at IEEE and ACM. More details about Rodrigo's research can be found on his homepage: professor.unisinos.br/rrrighi",institutionString:"Universidade do Vale do Rio dos Sinos",position:null,outsideEditionCount:0,totalCites:0,totalAuthoredChapters:"3",totalChapterViews:"0",totalEditedBooks:"2",institution:{name:"Universidade do Vale do Rio dos Sinos",institutionURL:null,country:{name:"Brazil"}}}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,coeditorOne:null,coeditorTwo:null,coeditorThree:null,coeditorFour:null,coeditorFive:null,topics:[{id:"596",title:"Computational Algorithm",slug:"computational-algorithm"}],chapters:[{id:"67033",title:"Global Optimization Using Local Search Approach for Course Scheduling Problem",doi:"10.5772/intechopen.86228",slug:"global-optimization-using-local-search-approach-for-course-scheduling-problem",totalDownloads:820,totalCrossrefCites:0,totalDimensionsCites:1,hasAltmetrics:0,abstract:"Course scheduling problem is a combinatorial optimization problem which is defined over a finite discrete problem whose candidate solution structure is expressed as a finite sequence of course events scheduled in available time and space resources. This problem is considered as non-deterministic polynomial complete problem which is hard to solve. Many solution methods have been studied in the past for solving the course scheduling problem, namely from the most traditional approach such as graph coloring technique; the local search family such as hill-climbing search, taboo search, and simulated annealing technique; and various population-based metaheuristic methods such as evolutionary algorithm, genetic algorithm, and swarm optimization. This article will discuss these various probabilistic optimization methods in order to gain the global optimal solution. Furthermore, inclusion of a local search in the population-based algorithm to improve the global solution will be explained rigorously.",signatures:"Ade Jamal",downloadPdfUrl:"/chapter/pdf-download/67033",previewPdfUrl:"/chapter/pdf-preview/67033",authors:[{id:"292309",title:"Dr.",name:"Ade",surname:"Jamal",slug:"ade-jamal",fullName:"Ade Jamal"}],corrections:null},{id:"67906",title:"Real-Time Scheduling Method for Middleware of Industrial Automation Devices",doi:"10.5772/intechopen.86769",slug:"real-time-scheduling-method-for-middleware-of-industrial-automation-devices",totalDownloads:759,totalCrossrefCites:1,totalDimensionsCites:1,hasAltmetrics:0,abstract:"In this study, a real-time scheduling algorithm, which supports periodic and sporadic executions with event handling, is proposed for the middleware of industrial automation devices or controllers, such as industrial robots and programmable logic controllers. When sensors and embedded controllers are included in control loops having different control periods, they should transmit their data periodically to the controllers and actuators; otherwise, fatal failure of the system including the devices could occur. The proposed scheduling algorithm manages modules, namely, the thread type (or .so type) and process type (or .exe type), for periodic execution, sporadic execution, and non-real-time execution. The program structures for the thread-type and process-type modules that can make the proposed algorithm manage the modules efficiently are suggested; then, they are applied in periodic and sporadic executions. For sporadic executions, the occurrences of events are first examined to invoke the execution modules corresponding to the events. The proposed scheduling algorithm is implemented using the Xenomai real-time operating system (OS) and Linux, and it is validated through several examples.",signatures:"Hong Seong Park",downloadPdfUrl:"/chapter/pdf-download/67906",previewPdfUrl:"/chapter/pdf-preview/67906",authors:[{id:"110782",title:"Prof.",name:"Hong Seong",surname:"Park",slug:"hong-seong-park",fullName:"Hong Seong Park"}],corrections:null},{id:"68147",title:"Intelligent Workload Scheduling in Distributed Computing Environment for Balance between Energy Efficiency and Performance",doi:"10.5772/intechopen.86874",slug:"intelligent-workload-scheduling-in-distributed-computing-environment-for-balance-between-energy-effi",totalDownloads:730,totalCrossrefCites:0,totalDimensionsCites:0,hasAltmetrics:0,abstract:"Global digital transformation requires more productive large-scale distributed systems. Such systems should meet lots of requirements, such as high availability, low latency and reliability. However, new challenges become more and more important nowadays. One of them is energy efficiency of large-scale computing systems. Many service providers prefer to use cheap commodity servers in their distributed infrastructure, which makes the problem of energy efficiency even harder because of hardware inhomogeneity. In this chapter an approach to finding balance between performance and energy efficiency requirements within inhomogeneous distributed computing environment is proposed. The main idea of the proposed approach is to use each node’s individual energy consumption models in order to generate distributed system scaling patterns based on the statistical daily workload and then adjust these patterns to match the current workload while using energy-aware Power Consumption and Performance Balance (PCPB) scheduling algorithm. An approach is tested using Matlab modeling. As a result of applying the proposed approach, large-scale distributed computing systems save energy while maintaining a fairly high level of performance and meeting the requirements of the service-level agreement (SLA).",signatures:"Larysa Globa, Oleksandr Stryzhak, Nataliia Gvozdetska and Volodymyr Prokopets",downloadPdfUrl:"/chapter/pdf-download/68147",previewPdfUrl:"/chapter/pdf-preview/68147",authors:[{id:"105085",title:"Prof.",name:"Larysa",surname:"Globa",slug:"larysa-globa",fullName:"Larysa Globa"},{id:"219896",title:"Prof.",name:"Alexander",surname:"Koval",slug:"alexander-koval",fullName:"Alexander Koval"},{id:"296047",title:"Ms.",name:"Nataliia",surname:"Gvozdetska",slug:"nataliia-gvozdetska",fullName:"Nataliia Gvozdetska"},{id:"296048",title:"Mr.",name:"Volodymyr",surname:"Prokopets",slug:"volodymyr-prokopets",fullName:"Volodymyr Prokopets"}],corrections:null},{id:"70290",title:"Approximation for Scheduling on Parallel Machines with Fixed Jobs or Unavailability Periods",doi:"10.5772/intechopen.89694",slug:"approximation-for-scheduling-on-parallel-machines-with-fixed-jobs-or-unavailability-periods",totalDownloads:600,totalCrossrefCites:1,totalDimensionsCites:1,hasAltmetrics:0,abstract:"We survey results that address the problem of non-preemptive scheduling on parallel machines with fixed jobs or unavailability periods with the purpose of minimizing the maximum completion time. We consider both identical and uniform processors, and also address the special case of scheduling on nonsimultaneous parallel machines, which may start processing at different times. The discussed results include polynomial-time approximation algorithms that achieve the best possible worst-case approximation bound of 1.5 in the class of polynomial algorithms unless P = NP for scheduling on identical processors with at most one fixed job on each machine and on uniform machines with at most one fixed job on each machine. The presented heuristics have similarities with the LPT algorithm or the MULTIFIT algorithm and they are fast and easy to implement. For scheduling on nonsimultaneous machines, experiments suggest that they would perform well in practice. We also include references to the relevant work in this area that contains more complex algorithms. We then discuss the main methods of argument used in the approximation bound proofs for the simple heuristics, and comment upon current challenges in this area by describing aspects of related practical problems from the automotive industry.",signatures:"Liliana Grigoriu",downloadPdfUrl:"/chapter/pdf-download/70290",previewPdfUrl:"/chapter/pdf-preview/70290",authors:[{id:"293390",title:"Dr.",name:"Liliana",surname:"Grigoriu",slug:"liliana-grigoriu",fullName:"Liliana Grigoriu"}],corrections:null},{id:"71826",title:"An Empirical Survey on Load Balancing: A Nature-Inspired Approach",doi:"10.5772/intechopen.87002",slug:"an-empirical-survey-on-load-balancing-a-nature-inspired-approach",totalDownloads:608,totalCrossrefCites:0,totalDimensionsCites:0,hasAltmetrics:0,abstract:"Since the dawn of humanity man tried to mimic several animals and their behavior be it in the age of hunting of while designing the aero plane. Human brain holds a significant amount of power in observing the species around him and trying to incorporate their behavior in several walks of life. This mimicking has helped human to evolve into beings which we are now. Some typical examples include navigation systems, designing several gadgets like aero planes, boats, etc. These days these inspirations are several, and their inspiration is being utilized in several fields like operations, supply-chain management, machine learning and several other fields. The similar kind of approach has been discussed in this paper where we tried to analyze different phenomenon in nature and how different algorithms were designed from these and how these can ultimately be used to solve different issues in cloud balancing. Essential component of cloud computing is load balancer which holds a crucial role of task allocation in virtual machines and several kinds of algorithms were developed on different ways of task allocation procedures each holding its significance here we tried to find the optimal resource allocation in terms of task allocation and rather than approaching through traditional methods we tried to solve this issue by using soft computing techniques. Specifically, nature-inspired algorithms as it hold the key to unlocking massive potential regarding research and problem-solving approach. The central idea of this paper is to connect different optimization techniques to load balancer and how could we make a hybrid algorithm to serve the purpose. We also discussed several different types of algorithms each bearing its roots from different natural procedures. All the algorithms in this paper can be broadly tabulated into three different types SO (Swarm optimization techniques), GO (Genetic-based algorithms), PO (Physics-based algorithms).",signatures:"Surya Teja Marella and Thummuru Gunasekhar",downloadPdfUrl:"/chapter/pdf-download/71826",previewPdfUrl:"/chapter/pdf-preview/71826",authors:[{id:"297632",title:"Mr.",name:"Surya Teja",surname:"Marella",slug:"surya-teja-marella",fullName:"Surya Teja Marella"},{id:"298899",title:"Dr.",name:"Thummuru",surname:"Gunasekhar",slug:"thummuru-gunasekhar",fullName:"Thummuru Gunasekhar"}],corrections:null},{id:"72346",title:"Looking at Data Science through the Lens of Scheduling and Load Balancing",doi:"10.5772/intechopen.92578",slug:"looking-at-data-science-through-the-lens-of-scheduling-and-load-balancing",totalDownloads:655,totalCrossrefCites:0,totalDimensionsCites:0,hasAltmetrics:0,abstract:"The growth in data generated by private and public organizations leads to several opportunities to obtain valuable knowledge. In this scenario, data science becomes pertinent to define a structured methodology to extract valuable knowledge from raw data. It encompasses a heterogeneous group of techniques that challenge the implementation of a single platform capable of incorporating all the available resources. Thus, it is necessary to formulate a data science workflow based on different tools to extract knowledge from massive datasets. In this context, high-performance computing (HPC) provides the infrastructure required to optimize the processing time of data science workflows, which become a collection of tasks that must be efficiently scheduled to provide results in acceptable time intervals. While few studies explore the use of HPC for data science tasks, in the best of our knowledge, none conducts an in-depth analysis of scheduling and load balancing on such workflows. In this context, this chapter proposes an analysis of scheduling and load balancing from the perspective of data science scenarios. It presents concepts, environments, and tools to summarize the theoretical background required to define, assign, and execute data science workflows. 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Cloud computing is distinguished from traditional computing paradigms by its scalability, adjustable costs, accessibility, reliability, and on-demand pay-as-you-go services. As cloud computing is serving millions of users simultaneously, it must have the ability to meet all users requests with high performance and guarantee of quality of service (QoS). Therefore, we need to implement an appropriate task scheduling algorithm to fairly and efficiently meet these requests. Task scheduling problem is the one of the most critical issues in cloud computing environment because cloud performance depends mainly on it. There are various types of scheduling algorithms; some of them are static scheduling algorithms that are considered suitable for small or medium scale cloud computing; and dynamic scheduling algorithms that are considered suitable for large scale cloud computing environments. In this research, we attempt to show the most popular three static task scheduling algorithms performance there are: first come first service (FCFS), short job first scheduling (SJF), MAX-MIN. 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However, diagnosis of these conditions that is sufficiently accurate for most of the clinical conditions can usually be established from complete family history (pedigree analysis) and a complete clinical and hematological examination of the patient and their family members.
Generally, doctors everywhere in the world diagnose thalassemia using blood tests which include a complete blood count and special tests for hemoglobin abnormalities. Initially, the primary screening of the thalassemia depends on the complete blood count (CBC), detection of carriers is done by hematological tests with red cell indices and microcytic hypochromic with mild anemia. The high performance liquid chromatography (HPLC) or capillary zone electrophoresis is used for the identification of qualitative and quantitative assessment of hemoglobin (Hb) components. HbA2 test is the most significant identification test for β thalassemia minor, but it can be vary by the presence of defective δ-thalassemia. In earlier days various molecular techniques e.g. amplification refractory mutation specific-polymerase chain reaction ((ARMS-PCR), GAP PCR have been used for the detection of mutations in β- and α-thalassemia, which may help in the prenatal diagnosis of thalassemia hemoglobinopathy in a limited time. Recently the evolution of Next- Generation Sequencing (NGS) has taken an important place for the diagnosis of thalassemia as a confirmatory diagnostic test. The NGS has been introduced for the characterization of both α- and β-thalassemia genes. It gives an accurate diagnosis of thalassemia, although NGS predicts much higher carrier frequencies. The molecular analysis is fundamental to foresee the severe blood transfusion-dependent thalassemia cases to the mild or no blood transfusion. The prenatal diagnosis based on DNA by amniocentesis and chorionic villus sampling (CVS) is equivalent required to detect the genetic abnormalities of the foetus as per expertise. Now a day’s NIPT (Noninvasive prenatal testing) is a technique to identify the genetic abnormalities of the foetus. This testing examines the small remains of foetus DNA that are circulating in a pregnant mother’s blood. An appropriate lab conclusion is urgent for describing the various types of thalassemia hemoglobinopathy with a significant association for prevention and treatment.
This chapter will explain all of these tests, the information of which will be useful for those who are working and interested in the diagnosis of thalassemia hemoglobinopathy.
In many diagnostic labs, the diagnostic strategies have been established for the diagnosis of thalassemia from the simple PCR to NGS for the detection of common, less common and rare thalassemia mutations [1]. Although there are lots of PCR technologies available in the laboratory but most of the diagnostics labs are using the simple and robust technique on allele- specific oligonucleotide hybridization or allele-specific priming, e.g. reverse dot-blotting or ARMS-PCR for the identification of the beta-thalassemia carrier [2]. This approach helps in identifying the common and less common mutations in 90% of cases. The rare mutations will be identifying by secondary screening. The mutation which remains unidentified after these two screenings will be then characterized by DNA sequencing [3]. DNA sequencing is a technique used to identify the specific arrangement of nucleotide bases (A, C, G, and T) in a DNA. The DNA carries the information a cell needs to collect protein and RNA molecule. DNA grouping data is imperative to researchers examining the elements of qualities. DNA sequence information is mandatory to researchers examining the functions of genes [4].
However worldwide laboratories, it has been noticed that a result of the migration of the natives with different ethnicity has led to increasing the variety of hemoglobinopathy and thalassemia mutations that need to be identified. Because of migration the genetic makeup of the population has mixed, these Hb variations are presently seen everywhere. Thus, the compound heterozygous conditions (Hb D–β thalassemia, Hb E–β thalassemia and Hb Sβ thalassemia) are seen in various places [5]. Molecular indicative research centers in such nations should have the specialized skill, equipment, and diagnostic approach to distinguish an enormous variety of mutations rapidly for prenatal diagnosis, and these labs use DNA sequencing as the principle evaluating strategy for the diagnosis of β-thalassemia point mutations.
The
Flow chart explain the diagnosis of thalassemia from primary to secondary screening.
The government of the country should make various screening and diagnosis mandatory for the eradication of thalassemia. They are indicated as under mentioned [6]. The country Greece has followed this rule now Greece is a thalassemia free country.
Premarriage screening
Antenatal screening
Preconception screening
Neonatal screening
Preoperative/pre-anesthesia screening
Genetic counseling
Premarriage screening should be implemented to detect β thalassemia carriers and hemoglobinopathies such as sickle cell trait. In developing country, it is not frequently acceptable because of social stigma and reasons in the general public. The premarriage screening should be possible in universities and colleges, schools, or public places such as theater, shopping mall etc. Earlier it was performed in where the predominance of thalassemia is high. But nowadays due to the mixing up of the gene pool this screening is recommended in all colleges where students are of marriageable age. Our lab has performed this screening in the Tharu tribal area of the Kheri Lakhimpur, Uttar Pradesh, India. We have collected more than 700 samples from the college and carrier screening of sickle cell disease has been performed. It is a belt of HBS [7].
In antenatal screening irrespective of gestational age of all pregnant women should be screened for carrier status of thalassemia and hemoglobinopathies. The spouse of the affected female should be test for mutation such as α-, β thalassemia and hemoglobinopathies (HbS trait, Hb E trait, Hb D trait etc.). The Prenatal diagnosis have to be encouraged if the foetus is in danger for the having thalassemia mutations e.g. α-, β thalassemia and hemoglobinopathies (HbS trait, Hb E trait, Hb D trait etc.). If the couple found positive for thalassemia during antenatal screening they can decide for prenatal diagnosis and subsequent pregnancy [8, 9, 10, 11].
While troublesome circumstances are in the developing country like India, this ought to be done but mostly females frequently do not enroll in antenatal centres before 12 weeks of gestation. A similar methodology with respect to antenatal screening ought to be followed. The preconception screening is important for all couples coming to IVF in infertility clinics. If the female is a thalassemia carrier then her husband or sperm donor should be screened and vice versa [8, 9, 10, 11].
Infant screening is mainly suggested in haemoglobinopathies such as sickle cell diseases, prevalent in tribal and urban populations. If possible, neonatal screening has to be implemented universally where infants are at high-risk for homozygous β-thalassemia and all instances of HbS–β thalassemia. This methodology will miss a couple of instances of sickle β-thalassemia when the mother is a β-thalassemia carrier and the father is a carrier of HbS. All babies with significant hemoglobinopathy should be re-tested using molecular technology to confirm the diagnosis within three months after birth [8, 9, 10, 11].
The primary screening of thalassemia is based on (MCV, MCH) values, Hb A2 levels, and F levels by automated blood analyzer, Hb electrophoresis and HPLC, respectively. The secondary screening encompasses further hematological studies to detect suspected variant such as sickle solubility, iron levels and heat instability tests. The
The variations from the typical hematological phenotypes of β-thalassemia carrier include:
Decrease level of MCV and MCH with marginal or normal levels of Hb A2 when person must consider α -thalassemia, heterozygosity for mild β-thalassemia mutations, iron deficiency, heterozygosity for eγδβ-thalassemia.
Normal/Borderline MCV and MCH levels with higher Hb A2 when person must deliberate co-inheritance of alpha and beta-thalassemia.
Normal Hb A2 with Normal or decreased red cell indices but raised Hb F level when person should consider heterozygous δβ -thalassemia or HPFH.
The deranged hematological and biochemical value after primary and secondary screening is confirmed by molecular analysis.
As per the International Committee for Standardization in Hematology (ICSH) in 1978, has recommended three kinds of lab tests for the diagnosis of thalassemia and hemoglobinopathies [15]. In that rule, the screening research facility ought to have the option to perform the alkaline electrophoresis. The reference lab approved by ICSH needed to perform challenging tests like globin electrophoresis and citrate agar electrophoresis. It is necessary to have manual strides all through hemoglobin investigation from reagent availability, electrophoresis, and information examination, and accordingly, the experience of the research centre proficient was a key to fruitful recognizable proof. Late improvement of lab strategies and expanded information on hemoglobinopathy and thalassemia has determined the distribution of refreshed rules [16]. The British Committee for Standards in Hematology suggests possible recognizable proof of hemoglobins on at least two procedures and gets conclusive ID as that dependent on DNA examination, protein sequencing or mass spectrometry.
In early long stretches of finding cellulose acetic acid derivation electrophoresis is an agent custom electrophoresis strategy. It gives distinguishing proof of Hb A, F, S/G/D, C/E, and H and different variations [16].
HPLC of various types of haemoglobinopathies and thalassemia; (a) Normal hemoglobin (Hb); (b) β-thalassemia trait with A2 fraction on 5.8%; (c) and (d) compound heterozygous HbS and β-thalassemia trait (Nigam et al., 2020).
HPLC is useful for the finding of β-thalassemia carrier since that HbA2 can be correctly quantitated [16]. Similar other HPLC methods, a wary control of insightful conditions like segment temperature, stream rate, and backup conditions are important.
There are various different molecular techniques available to identify the globin chain gene mutations. These molecular techniques can be clustered for detection by mutation types such as structural variations (Gene deletion, duplication, or triplication) and alteration in gene sequences (Insertion, substitution, or short insertion/deletions) [18, 19]. Over 90% of α-thalassemia patients are caused by gene deletion. Approximate 10% of the α-thalassemia cases are due to the alteration in the gene sequence such as single nucleotide insertion, deletion or substitution [20]. The α-globin gene group encode identical protein which consists of exceptionally homologous genes as well as 2 HBA genes. The gene deletions in α-thalassemia are mainly cause due to imbalanced crossing over between these homologous regions during meiosis [21]. The most well-known deletion of 3.7 kb and 4.2 kb has been reported [22]. More than 90% of β-thalassemia cases are caused by the alteration in the gene sequence as compared to α-thalassemia. Approximately, 280 gene sequence alterations are related with β-thalassemia in which some mutations are caused by the deletion of gene including the HBB gene [22].
Gap polymerase chain reaction (PCR) technique is mainly used for the detection of deletion. The southern blotting may be used for unknown deletions with the help of using probes. The MLPA (multiplex ligation-dependent probe amplification) technique can identify both known and unknown deletions. It is commonly used in diagnostics labs for its highly sensitive and is easy to use. Common mutation or alteration in gene sequences can be detected by using techniques such as amplification refractory mutation specific (ARMS) PCR, allele-specific PCR, denaturing gradient gel electrophoresis, reverse dot blotting, DGGE, SSCP, HRM (High resolution melting), sequencing technology and microarray in a cost-effective manner.
Recently, ARMS PCR technology has been improved for the detection of both normal and altered alleles with internal positive control are detected in a single tube assay [35], referred to as tetra primer ARMS-PCR. Two pairs of primers are used in tetra, ARMS-PCR in which one pair of primers for flanking regions and other pair primers are complementary to different strands. These primers amplify the two different bases that are located in a single position of the globin gene. The different alleles (mutant and wild type) can be detected on an agarose gel based on their sizes (Figure 3). This ARMS PCR technique has been useful in the diagnosis of β-thalassemia mutations. Multiplex ARMS PCR can be screened for more than one mutation a single reaction by multiplexing the ARMS primers attached with a common primer [36].
Gel image shows the multiplex ARMS PCR for five common mutations: [A] IVS 1–5 mutation/normal; [B] IVS 1–5 mutation/normal; [C] Cd 8&9 mutation/normal; [D] Cd 41&42 mutation/normal; and [E] Hb E mutation/normal.
The target genomic DNA containing the mutation is amplified by PCR with the help of specific primers. The PCR products are digested by a specific restriction enzyme. The digested PCR products are separated on the agarose gel. The digested PCR products are separated according to their molecular weight (size). Based on restriction site (presence or absence) is determine the size or pattern of PCR products. This RE analysis is simple, relatively economical, and powerful prompting unequivocal outcomes; the RE-PCR-based technique is a precious molecular diagnostic tool. Be that as it may, it is restricted in its application as just an extent of the alpha-thalassemia, β-thalassemia mutation, and hemoglobin variations, naturally generate restriction sites [26, 39].
The multiplex PCR technique that amplify up to 60 probes by using one pair of primer. The PCR amplicon with novel genomic target and length are fluorescently labeled and identified by capillary electrophoresis. The number of genomic sequence of interest is determined by comparing the peak pattern with reference sample [49].
Since, the varied molecular basis of α-thalassemia and β- thalassemia mutation are uncommon and difficult to detect. In addition to well-established methods, MLPA is known as an effective, simple and unambiguous technique for the identification and classification of deletions and duplications in thalassemia [48, 49, 50].
This is a significant result for the haemoglobinopathies as most cases include carrier testing, subsequently arrangement follows much of the time require checking by eye. There are two diverse sequencing sciences accessible dependent on the Sanger technique [54, 55, 56], dye primer and dye eliminator; they differ from each other in the way wherein the fluorescent level is incorporated during linear cycling. Despite the fact that the dye eliminator is more straightforward to set-up the signal from each nucleotide is less dependable making the dye primer chemistry more suitable for heterozygote detection and it is friendlier with sequence analysis programming. The dye eliminator would have more application in X- linked diseases, for instance, G6PD transformations in which influenced males are hemizygous and will show up as a homozygous change. Taking everything together, the way toward getting a succession from whole blood and the examination may require 4–5 days, with certification using another PCR based test before the change is accounted for. The only drawbacks of using sequencing as a routine investigation technique are the cost and time taking examination compared to PCR. Sequencing is a multistage procedure requiring PCR intensification, cycle sequencing and precipitation before the sequence can be distinguished. After this the sequence ought to be researched and checked and any movements noted. Notwithstanding the grouping examination programming is available it is not 100% compelling at identifying heterozygotes.
Microarray analysis of gene expression has formed into a great tool for the characterization of various pathophysiological processes. The fundamental idea is that RNA isolated from tissue is hybridized to probes for specific genes that are fixed in a grid in small microscopic spots. The microarray is a quick, simple to perform, and precise strategy for concurrent identification of α and β-thalassemias. But, this technique needs should be improved and approved in a bigger number of specimens with hemoglobinopathies before further routine laboratory use [62, 63, 64].
The prenatal diagnosis based on DNA by amniocentesis and CVS sampling is required to detect the genetic abnormalities of the foetus. Now a day’s NIPT is a technique to identify the genetic abnormalities of the foetus. This testing examines the small remains of foetus DNA that are circulating in a pregnant mother’s blood. An appropriate lab conclusion is urgent for describing the various types of thalassemia with a significant association for prevention and treatment. Because of population migration and mixing of the gene pool of different populations in many immigration countries as well as regions the hemoglobiopathies and thalassemia are more prevalent over their [65, 66, 67, 68].
The importance of prenatal diagnosis comes in the diagnostic field as it helps and early diagnoses the growing foetus in the mother wombs for thalassemia and hemoglobinopathies. It plays impartment role in the eradication of these genetic disorders such as β-thalassemia major, sickle cell disease and hemoglobin Bart’s nonimmunehydropsfetalis [69, 70]. The prenatal diagnosis includes the investigation of fetal material from chorionic villi, amniotic liquid, string blood, and fetal DNA in maternal dissemination. In spite of the way that examination of fetal hemoglobin types is successfully performed by means of robotized HPLC, it is assessable through assessment of fetal blood got by cordocentesis and the technique is inclined to mistake because of mixing of sample by maternal tissue [71].
Advances in molecular testing have worked with the assurance of complex thalassemias and hemoglobinopathies saw in ethnically varying population. Comprehensive screening programs highlighted recognizing carriers and offering prenatal diagnosis in pregnancies for thalassemia have been incorporated in Canada and European countries [72, 73]. Different procedures have been implimented to distinguish thalassemia like genotyping assay, genotyping measur next-generation sequencing and mass spectrometry [74, 75, 76]. The methods are as yet testing; consequently, more investigations are expected to create and approve them and eventually lead to proficient, exact and concrete non-invasive prenatal diagnosis of thalassemia and hemoglobinopathies [77].
Low cost techniques | High cost techniques | |
---|---|---|
Hematological and Biochemical Techniques | MCV and MCH | Mass spectrometry |
CBC | ||
RDW | ||
Electrophoresis | ||
HPLC | ||
Molecular Techniques | ASO, RDB, | MLPA |
ARMS PCR | Direct sequence | |
RE PCR/RFLP | Microarray | |
Gap-PCR | HRM | |
DGGE and SSCP |
As there is a saying that prevention is better than cure. The thalassemia and hemoglobinopathies are genetic disorder and due to the migration of population in the different regions and endogamy the new combinations of thalassemia hemoglobinopaty are arising fast. For the eradication and further treatment and management of thalassemia disease an effective diagnostic test at the point of care (POC) is the need of hour.
As thalassemia has been spread worldwide an early diagnosis, economical test, awareness programmes and prenatal screening will be a milestone for the eradication of this genetic disorder and to reduce burden of the health sector of a country subsequently the economics.
The initial hematological, biochemical screening to the advance molecular testing under one roof will not only help to diagnose the thalassemia patients but shall be also helpful in the treatment and management of the disease (Figure 1). The objective of POC for testing thalassemia will be achieved if these quick testing methods like NESTROFT. This has to be in the approachable distance to the patients.
The government should include the thalassemia screening as a mandatory tool for screening. The best example Italy and Cyprus there with the initiation of prenatal diagnosis and screening of the carrier now this is a thalassemia free country.
Likewise in India, the Uttar Pradesh state government has initiated the task by giving free of cost of screening of thalassemia carrier, blood transfusion and iron chelators to several medical colleges. The Indian government has provided the HPLC machines to provide the screening of the carrier of thalassemia hemoglobinopathies. Basically the eradication, treatment and management of thalassemia are joint efforts of government, stake holder, policy makers, pediatrician, pathologist, transfusion medicine and geneticist.
A learning system could be thought as a collection of methods that are brought together in order to create an environment to facilitate different learning processes. The learning systems will provide various types of learning resources and descriptions of procedures for obtaining quality results [1]. The learning systems find their applications in the areas like, image recognition, speech recognition, traffic prediction, e-mail spam and malware filtering, automatic language translation, medical diagnosis, etc. [2].
As the data increases in large volumes in the digital repositories, it has become essential to look for alternative approaches to yield better results in extracting interesting patterns from the repositories. Intelligent learning systems are gaining attention from a wide range of researchers in the recent years in extracting patterns from the data repositories. The learning systems have three kinds of approaches. They are supervised, unsupervised, and semi-supervised learning approaches [3].
The concept of perceptron learning plays a critical role in pattern recognition, which has become a challenging problem in the data science research. In the recent years, perceptron learning algorithms are exhibiting their robust performance in identifying interesting patterns from large data repositories when compared to the traditional supervised learning approaches [4]. A perceptron can be thought as a computational prototype of a neuron. As a supervised learning approach, perceptron learning is used for linear classification of patterns. This learning approach uses the already available labelled data to classify the future data by predicting the class labels.
In the literature, it is studied that many researchers experimented with perceptron learning for identifying interesting patterns from the data. A novel autonomous perceptron model (APM) was proposed to address the issues of complexity of traditional perceptron architectures [4]. APM is a nonlinear supervised learning model, which has the architecture using the computational power of the quantum bits (qubits). The researchers [5], using biophysical perceptron (BP), tried to simulate the pyramidal cells in the brain with a wide variety of active dendritic channels. The BP, here, explores the ability of real neurons with extended non-linear dendritic trees to effectively perform the classification task in identifying interesting patterns from the data. Many researchers have experimented with perceptron learning in a wide variety of ways. However, the perceptron learning suffers several limitations. It works well for linearly separable patterns. Though some researchers experimented for identifying non-linearly separable patterns, the perceptron learning produced best results for binary separation of patterns only [5]. Also that perceptron learning suffers poor performance in case of overlapping patterns, that is, when patterns are not having sharp boundaries.
Fuzzy-based learning, on the other hand, is found to show its ability in performing well for overlapping patterns [6]. As a fuzzy-based learning approach, fuzzy C-means (FCM) is widely used by researchers for pattern recognition. A weighted local fuzzy regression model showed a better efficiency than the least squares regression for non-linear and high-dimensional pattern recognition of transport system in China [7]. The new kernelized fuzzy C-means clustering algorithm [8] uses a kernel-induced distance function as a similarity measure showed improved performance in identifying the patterns when compared to the conventional fuzzy C-means technique. In many research findings, it is observed that the fuzzy-based learning approach was used in a wide variety of ways to achieve better results in extracting non-linear and overlapping patterns.
The present work attempts to experiment with fuzzy perceptron learning, which implements the perceptron learning and fuzzy-based learning techniques in an interfusion manner. In the research literature, we can find a good amount of work related to the combination of fuzzy logic with perceptron learning. The fuzzy neural network (FNN) was proposed for pattern classification, which uses supervised fuzzy clustering and pruning algorithm to determine the precise number of clusters with proper centroids representing the patterns to be recognised [9]. In the fuzzy neural integrated networks [10], the researchers attempted to integrate the concept of fuzzy sets and neural networks to deal with pattern recognition problems. In an enhanced algorithm for fuzzy lattice reasoning (FLR) classifier, a new nonlinear positive valuation function was defined to produce better results for pattern classification [11]. Along with these, however, many other research experiments of fuzzy perceptron learning are supervised learning approaches only. Therefore, the present work focuses on experimenting with effective implementation of some techniques involved in the perceptron and fuzzy-based learning systems for unsupervised learning to identify interesting patterns in large datasets. As part of the present work, five algorithms are developed, two of which are related to perceptron learning, one is the standard fuzzy C-means (FCM) algorithm. The remaining two algorithms are proposed by the present work, which implement the perceptron learning and fuzzy-based learning in an interfusion manner using weights and weighted distances respectively. All the algorithms are implemented using three benchmark datasets. The CPU time, clustering fitness (CF), and sum of squared errors (SSE) are taken into consideration for performance evaluation of the algorithms.
Nowadays, the perceptron learning model can be thought as a more general computational model in identifying interesting patterns in a dataset. It takes an input, aggregates it along with the weights and produces the result. A perceptron is used to learn patterns and relationships in data. Patterns help us knowing about the interesting features around which objects may be grouped in a given population of data.
A perceptron may be configured for a specific application, such as pattern recognition and data classification through some learning process [12]. Perceptrons are information processing devices, which are built from interconnected elementary processing units. These units are called neurons. The perceptrons are robust in exhibiting their ability in distributed representation and computation, learning, generalisation, adaptivity, inherent contextual information processing, and fault tolerance [13].
The perceptron learning uses an iterative weight adjustment for the enhanced retrieval of patterns from a dataset. The iterative process converges to the weights, which produce the patterns that represent the different groups of data objects in the dataset uniquely. While operating for learning on patterns, the perceptrons use weights in connection to every input vector. A weight represents the information used by the perceptron to solve a problem [14].
The perceptron with multiple neurons is shown in Figure 1.
A perceptron with a multiple neurons.
In Figure 1,
Though the perceptron learning exhibits its robustness in identifying the patterns in the data repositories, it works well for linearly separable patterns, that is, the patterns with sharp boundaries only. However, in the real time world, we may find overlapping patterns, that is, non-linearity in pattern associativity, where data objects may associate with multiple patterns. In such situations, the perceptron learning approach may suffer in identifying the patterns clearly. On the other hand, fuzzy-based learning has its wide applications in identifying patterns in the overlapping scenario. In the present work, two algorithms are implemented for perceptron learning. They are discussed in the following sub-sessions.
This algorithm implements the perceptron learning using weights [12]. With each input data vector, a weight is associated corresponding to each pattern. To generate the initial weights, one iteration of K-means algorithm is performed. The results of K-means iteration are used to compute the weight matrix. This weight matrix will be repeatedly updated in the subsequent iterations. For each input data vector weights are computed corresponding to every pattern. The input data vector is associated with the pattern corresponding to which the weight is maximum. This process is repeated for every iteration. The algorithm terminates when there is no change in the association of data vectors to the patterns. The algorithm for perceptron learning using weights is given below.
Step 1: Determine the number of patterns,
Step 2: Select
Step 3: Perform one iteration of K-means algorithm.
Step 4: Using the results of K-means iteration, compute cluster wise initial weights.
Step 5: Repeat steps 6–8 until the stopping condition.
Step 6: Generate weight matrix, where each element
Here,
Step 7: Assign points to clusters using weights.
Step 8: Update cluster means, that is, refine patterns.
[End of step 5 loop]
Step 9: [End of algorithm]
This algorithm implements the perceptron learning using weighted distances [15]. With each input data vector, a weighted distance is associated corresponding to each pattern. To generate the initial weighted distances, one iteration of K-means algorithm is performed. Using the results of K-means the weight matrix is computed. This weight matrix is used to compute the weighted distances for each input data vector. The data vector is associated with the pattern corresponding to which the weighted distance is minimum. This weight matrix will be repeatedly updated in the subsequent iterations to compute the new weighted distances. This process repeats for every iteration. The algorithm terminates when there is no change in the association of data vectors to the patterns. The algorithm for perceptron learning using weighted distances is given below.
Step 1: Determine the number of patterns,
Step 2: Select
Step 3: Perform one iteration of K-means algorithm.
Step 4: Using the results of K-means iteration, compute cluster wise initial weights.
Step 5: Repeat steps 6–10 until the stopping condition.
Step 6: Generate weight matrix
Step 7: For each data point
Here,
Step 8: For each data point compute the weighted distances as follows:
Step 9: Assign points to clusters using weights.
Step 10: Update cluster means, that is, refine patterns.
[End of step 5 loop]
Step 11: [End of algorithm]
Though the perceptron learning algorithms are experimented widely by many researchers, they exhibit their robustness in identifying linearly separable patterns only.
Fuzzy-based learning is used to handle the concept of partial truth, where the truth value may range between completely true and completely false [16]. It is an approach that allows for multiple possible truth values to be processed through the same data object. In fuzzy-based learning, the data objects are assumed being associated with multiple patterns. For each data object, the degree of association is measured in membership. This membership value may range between 0 and 1 (1 being high similarity and 0 being no similarity with the pattern).
Fuzzy-based learning techniques focus on modelling uncertain and vague information that is found in the real world situations. These techniques deal with the patterns whose boundaries cannot be defined sharply [17, 18]. By fuzzy-based learning, one can know if data objects fully or partially associate with the patterns that are under consideration based on their memberships of association [19]. Among the techniques of fuzzy-based learning, fuzzy C-means (FCM) is the most well-known one as it has the advantage of robustness for obscure information about the patterns [20, 21]. FCM is widely studied and applied in geological shape analysis [22], medical diagnosis [23], automatic target recognition [24], meteorological data [20], pattern recognition, image analysis, image segmentation and image clustering [25, 26, 27], agricultural engineering, astronomy, chemistry [28], detection of polluted sites [29], etc. The following section presents a brief discussion of FCM algorithm.
The fuzzy C-means (FCM) is a technique that uses degree of membership for natural interpretation of patterns recognised [30]. The FCM associates the data vectors among
The FCM algorithm is given below [31]. Here,
Step 1: Determine the number of patterns,
Step 2: Select
Step 3: Perform one iteration of K-means algorithm. Set
Step 4: Using the results of K-means iteration, compute membership matrix
Step 5: Repeat steps 6–9 until the stopping condition.
Step 6: [Refine patterns] Update the mean of
Here,
Step 7: Compute the new membership matrix using:
Step 9: Assign points to clusters using membership degrees. Set
[End of step 5 loop]
Step 10: [End of algorithm]
The fuzzy perceptron learning works in an interfusion manner, where the fuzzy logic is combined with perceptron learning for identifying non-linear and overlapping patterns. Much research work may be found in the literation where fuzzy perceptron learning is experimented in different applications [33, 34]. However, those experiments are confined to supervised learning only. The present work attempts to experiment with fuzzy perceptron learning for unsupervised cases. The present work proposes two algorithms, one is for fuzzy perceptron learning using weights and the other is for fuzzy perceptron learning using weighted distances.
This algorithm implements the perceptron learning using weights and FCM techniques in an interfusion manner. These techniques are performed in alternative iterations until the termination condition. Initially, one iteration of K-means algorithm is performed. Using the results of K-means, initial weights are computed as mentioned in the Section 2.1. Using these weights, weight matrix is generated to perform one iteration of perceptron learning algorithm to associate the input data vectors to the patterns. Using the results of perceptron learning step, membership matrix is computed to perform one iteration of FCM algorithm as mentioned in Section 3.1. The results of FCM step are used to update weight matrix to perform perceptron learning step. In this way the perceptron learning and FCM algorithms are repeated in alternative iterations until termination condition. The algorithm for fuzzy perceptron learning using weights (FPLW) is given below.
Step 1: Determine the number of patterns,
Step 2: Select
Step 3: Perform one iteration of K-means algorithm.
Step 4: Using the results of K-means iteration, compute cluster wise initial weights.
Step-5: Update cluster means
Step 6: Repeat steps 7–13 until the stopping condition.
Step 7: Compute the weight matrix using Eq. (1).
Step 8: Assign points to clusters using weights.
Step 9: If there is no change in cluster assignment then go to step 14.
Step 10: Update cluster means using Eq. (4).
Step 11: Generate membership matrix
Step 12: Assign points to clusters using membership matrix.
Step 13: If there is no change in cluster assignment then go to step 14.
[End of Step 6 loop]
Step 14: [End of Algorithm]
This algorithm implements the perceptron learning using weighted distances and FCM techniques in an interfusion manner. These techniques are performed in alternative iterations until the termination condition. Initially, one iteration of K-means technique is performed. Using the results of K-means, initial weights are computed as mentioned in the Section 2.2. Now, one iteration of perceptron learning algorithm is performed where the weight matrix is generated using the initial weights. Using this weight matrix, weighted distances are computed for every input vector
Step 1: Determine the number of patterns,
Step 2: Select
Step 3: Perform one iteration of K-means algorithm.
Step 4: Using the results of K-means iteration, compute cluster wise initial weights.
Step-5: Update cluster means
Step 6: Repeat steps 7–15 until the stopping condition.
Step 7: Compute the weight matrix using Eq. (1).
Step 8: For each data point compute the Euclidean distance using Eq. (2).
Step 9: For each data point compute weighted distances using Eq. (3).
Step 10: Assign points to clusters using weighted distances.
Step 11: If there is no change in cluster assignment then go to step 16.
Step 12: Update cluster means using Eq. (4).
Step 13: Generate membership matrix
Step 14: Assign points to clusters using membership matrix.
Step 15: If there is no change in cluster assignment then go to step 16.
[End of Step 6 loop]
Step 16: [End of Algorithm]
For performance evaluation of algorithms, CPU time in seconds, sum of squared errors [35] and clustering fitness (CF) [36] are taken into consideration and are calculated for all the algorithms.
The objective of pattern learning is to minimise the intra-cluster sum of squared errors (SSE). The lesser the SSE, the better the goodness of fit is. The SSE for the results of each algorithm is computed using Eq. (6).
Here,
While achieving high intra-cluster similarity, it is also important to achieve well separation of patterns.
So, it is also important to consider inter-cluster similarity while evaluating the performance of the algorithms. For this, the present work, computes the clustering fitness (CF) as a performance criterion, which requires the calculation of both intra-cluster similarity and inter-cluster similarity. The computation of CF also requires the experiential knowledge,
It can be quantified via a function of the reciprocals of intra-cluster radii within each of the resulting clusters. The intra-cluster similarity of a cluster
Here,
It is denoted as
Here,
It can be quantified via a function of the reciprocals of inter-cluster radii of the clustering centroids. The inter-cluster similarity for one of the possible clustering results
Here,
The clustering fitness for one of the possible clustering results
Here,
Experimental work has been carried out on the system with Intel(R) Core(TM) i3-5005 U CPU@2.00GHz processor speed, 4GB RAM, Windows 7 OS (64-bit) and using JDK1.7.0_45. Separate modules are written for each of the above discussed methods to observe the CPU time for clustering any dataset by keeping the cluster seeds same for all methods. I/O operations are eliminated and the CPU time observed is strictly for clustering of the data.
Along with the proposed algorithms FPLW and FPLWD for fuzzy perceptron learning, experiments are also conducted with the algorithms PLW, PLWD and FCM for performance comparison. All the algorithms are executed using the benchmark datasets with varying number of patterns to be recognised. In the present work, Magic Gamma, Letter Recognition and Intrusion datasets are used from UCI ML data repository [37]. All the developed algorithms, PLW, PLWD, FCM, FPLW and FPLWD, are executed using these datasets for varying number of patterns to be recognised (
All the algorithms operate in an iterative manner and terminate when a stopping condition is met. The stopping condition is when there is no change in the pattern associativity of the data vectors. The termination condition is the same for all the algorithms.
Details of the datasets are available in Table 1.
S. No. | Dataset | No. of points | No. of dimensions |
---|---|---|---|
1 | Magic Gamma data | 19,020 | 10 |
2 | Letter Recognition data | 20,000 | 16 |
3 | Intrusion data | 4,94,019 | 35 |
Details of datasets.
The results of all algorithms, using Magic Gamma dataset, with respect to CPU time in seconds, clustering fitness and sum of squared errors are shown in Figures 2–4, respectively.
CPU time of each clustering method (Magic Gamma dataset).
Clustering fitness of each clustering method (Magic Gamma dataset).
SSE of each clustering method (Magic Gamma dataset).
The results of all algorithms, using Letter Recognition dataset, with respect to CPU time in seconds, clustering fitness and sum of squared errors are shown in Figures 5–7, respectively.
CPU time of each clustering method (Letter Recognition dataset).
Clustering fitness of each clustering method (Letter Recognition dataset).
SSE of each clustering method (Letter Recognition dataset).
The results of all algorithms, using Intrusion dataset, with respect to CPU time in seconds, clustering fitness and sum of squared errors are shown in Figures 8–10, respectively.
CPU time of each clustering method (Intrusion dataset).
Clustering fitness of each clustering method (Intrusion dataset).
SSE of each clustering method (Intrusion dataset).
In all the experiments, it is observed that the algorithm FPLW, which implements the perceptron learning using weights and the FCM techniques in an interfusion manner, is showing consistently better performance in terms of clustering fitness (CF) and SSE than the other algorithms.
The present experiment mainly focuses on the study fuzzy perceptron learning for recognising non-linear patterns in the datasets. Many researchers contributed greatly towards fuzzy perceptron learning. However, their experiments are confined to supervised learning only. So, the present work experimented with the fuzzy perceptron learning approaches for unsupervised learning. The work proposes two new algorithms, that is, FPLW and FPLWD. These algorithms are implemented using three benchmark datasets. Along with these algorithms, the algorithms for standard FCM and perceptron learning using weights and weighted distances are also implemented for performance comparison. For all the algorithms the CPU time in seconds, clustering fitness (CF) and sum of squared errors (SSE) are taken into consideration for performance evaluation. All the developed algorithms are experimented with varying number of patterns (
In all the experiments, it is observed that the proposed algorithm for fuzzy perceptron learning using weights (FPLW) is consistently showing better performance with respect to clustering fitness and SSE. Of course, the algorithm FPLW is taking a little more time for its execution than the other algorithms. However, it could be negligible, as the main concern is for clearly recognising the non-linear patterns in the datasets.
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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. 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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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