Comparison of active machine learning and passive learning.
\r\n\t
",isbn:"978-1-80356-357-2",printIsbn:"978-1-80356-356-5",pdfIsbn:"978-1-80356-358-9",doi:null,price:0,priceEur:0,priceUsd:0,slug:null,numberOfPages:0,isOpenForSubmission:!0,isSalesforceBook:!1,hash:"3aba1eb3600a8c9ff880c628f70b3298",bookSignature:"Ph.D. Delfín Ortega-Sánchez",publishedDate:null,coverURL:"https://cdn.intechopen.com/books/images_new/11481.jpg",keywords:"Integrated Curriculum, Transdisciplinarity, Integrated Active Learning, Educational Programs, Contemporary Social Problems, Critical Thinking, Creative Thinking, Social Thinking, Agenda 2030, Sustainable Development Goals, Educational Paradigm, Social Reality",numberOfDownloads:null,numberOfWosCitations:0,numberOfCrossrefCitations:null,numberOfDimensionsCitations:null,numberOfTotalCitations:null,isAvailableForWebshopOrdering:!0,dateEndFirstStepPublish:"February 18th 2022",dateEndSecondStepPublish:"March 18th 2022",dateEndThirdStepPublish:"May 17th 2022",dateEndFourthStepPublish:"August 5th 2022",dateEndFifthStepPublish:"October 4th 2022",remainingDaysToSecondStep:"2 months",secondStepPassed:!0,currentStepOfPublishingProcess:3,editedByType:null,kuFlag:!1,biosketch:"Internationally recognized researcher in the field of historical and social science education. Author of more than 100 publications, awarded three Doctorate degrees and the National End of Degree Award, granted by the Ministry of Education to the best academic records of Bachelor's degrees in Spain. Dr. Ortega-Sánchez has been Vice-Rector for Social Responsibility, Culture, and Sports at the University of Burgos since 2021.",coeditorOneBiosketch:null,coeditorTwoBiosketch:null,coeditorThreeBiosketch:null,coeditorFourBiosketch:null,coeditorFiveBiosketch:null,editors:[{id:"302925",title:"Ph.D.",name:"Delfín",middleName:null,surname:"Ortega-Sánchez",slug:"delfin-ortega-sanchez",fullName:"Delfín Ortega-Sánchez",profilePictureURL:"https://mts.intechopen.com/storage/users/302925/images/system/302925.jpg",biography:"I hold a PhD in Didactics of Social Sciences from the Autonomous University of Barcelona, a PhD in Educational Sciences from the University of Burgos, and a PhD in History from the University of Extremadura. 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I am currently a Social Sciences teacher and researcher at University of Burgos (Spain).",institutionString:"University of Burgos",position:null,outsideEditionCount:0,totalCites:0,totalAuthoredChapters:"1",totalChapterViews:"0",totalEditedBooks:"0",institution:{name:"University of Burgos",institutionURL:null,country:{name:"Spain"}}}],coeditorOne:null,coeditorTwo:null,coeditorThree:null,coeditorFour:null,coeditorFive:null,topics:[{id:"23",title:"Social Sciences",slug:"social-sciences"}],chapters:null,productType:{id:"1",title:"Edited Volume",chapterContentType:"chapter",authoredCaption:"Edited by"},personalPublishingAssistant:{id:"429339",firstName:"Jelena",lastName:"Vrdoljak",middleName:null,title:"Ms.",imageUrl:"https://mts.intechopen.com/storage/users/429339/images/20012_n.jpg",email:"jelena.v@intechopen.com",biography:"As an Author Service Manager, my responsibilities include monitoring and facilitating all publishing activities for authors and editors. 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Machine learning (ML) is a prominent area of computer science that evolved from the study of pattern recognition and computational learning theory in artificial intelligence (AI) [1]. ML is algorithms that are able to learn from data, identify patterns in observed data, and build models that make predictions about unseen data. Algorithm or model enables computer (machine) to learn from data. Over the past decades, learning algorithms have found widespread applications in numerous areas such as computer vision, object recognition, web search, natural language processing, emotion recognition, etc. The performance of different ML algorithms strongly depends on the size and structure of dataset of the domain.
\nSupervised machine learning learns from the available data (experience), which is given in the form of training data (instances). The knowledge induced from the data can then be used for descriptive or predictive purposes. Supervised learning problems can be categorized as either classification or regression, depending on the output label of the data [2]. Classification problems assign a discrete class label to input instance, while regression problems have continuous numeric values. Classification is a function that assigns a new object (or instance) as belonging to one of the predefined classes [1]. The goal of classification is to accurately predict the target class for each instance in the data.
\nFigure 1 shows the workflow in supervised learning. We can see that there are two different steps: training and prediction. During training, a feature extractor is used to convert each input (training data instance) to a feature set. These feature sets with labels are fed into the learning algorithm to generate a classifier model. During prediction, the same feature extractor is used to convert unseen inputs to feature sets that are then fed into the model, which generates predicted labels [3, 4].
\nThe architecture of a supervised classification.
ML with many other disciplines of AI are gaining popularity, and they have been used in numerous fields and industries, including finance, healthcare, education, and psychology. Since learning is an important aspect of intelligent behavior, ML can be used commonly for data analysis in psychology and cognitive science. Recently, ML methods have been investigated in experimental psychology and human categorization.
\nIn ML, active learning refers to an approach that selects the queries (instances) for labeling from a large pool of unlabeled data [5, 6]. In most cases, an active learning algorithm outperformed random sampling method and reduced number of instances that necessary to achieve similar performance. Active learning is often used for problems where it is difficult (expensive and/or time-consuming) to obtain labeled training data [7, 8].
\nResearchers have shown that active learning is beneficial in many domains [9], including education [10], machine learning [5], remote sensing [11], and cognitive science [12].
\nSemi-supervised learning (or SSL) has attracted a highly considerable amount of interest in ML. SSL techniques allow classifiers or learners to learn from labeled and unlabeled data at the same time [13, 14]. Typically, they are used when we have a small-size labeled dataset with a large-size unlabeled dataset. Figure 2 intuitively shows the difference between supervised and semi-supervised learning. Actually most real-world learning scenarios are SSL. During the last two decades, SSL methods such as active learning, co-training, and co-testing have significantly improved learning performance in various applications.
\nSupervised vs. semi-supervised learning.
When a machine learning model is trained, learning is performed on a random subset of all the available sets of labeled training data. We will refer to this mode of learning as passive learning (PL). In PL mode of learning, the classifier (learner) does not participate interactively with the teacher [13]. A passive learner receives a random dataset from the world and then produces a classifier or model. Thus, PL is more straightforward and easier to implement.
\nActive machine learning (AML) is a popular research area in ML [5, 8, 13]. It allows selection of the most informative instances in training dataset of the domain for manual labeling. AML aims to produce a highly accurate classifier using as few labeled instances as possible, thereby minimizing the cost of obtaining labeled data [5]. With this assumption, AML is a specialized version of SSL, and they aim to reduce manual labeling workload. We provide a comparison of AML and PL inTable 1 [5, 13]. Although most research in AL has tended to focus on binary classification problems and achieve high classification accuracy, some studies have addressed multicategory classification [15]. In AML, the classifier is initially trained on a small set of instances (labeled pool
PL | \nAML | \n
---|---|
No control over training instances | \nSelects training instances from a pool of unlabeled data (queries) | \n
Large number of required training instances | \nRelatively small | \n
Examine the entire training data before inducing a classifier (batch process) | \nLearner sees one or a subset instances at a time (iterative process) | \n
One classifier induced | \nMany | \n
Simple stopping criteria | \nComplex | \n
Comparison of active machine learning and passive learning.
Pool-based AML cycle.
AML is an important technique in machine learning, because labeled data is often more difficult and expensive to obtain than unlabeled data [17]. For example, if one is classifying web pages into categories based on the content, labeled data would likely be collected by hand, while unlabeled data could be found from the Internet automatically. Multiple studies proposed several AML algorithms and applied them to many applications. They have shown that when AML is used, ML models require significantly less training data and can still perform well without loss of accuracy [4, 8].
\nTypically, AML approaches select a single unlabeled instance, which is the most informative at that iteration, and then retrain the classifier. The training process in this case is hard and time-consuming; further, repeated retraining is inefficient. Thus, a batch mode AML strategy [9, 10] that selects multiple instances each time is more appropriate under these circumstances.
\nPool-based method is the most prominent technique used in AML, and most research work of AML is pool based in recent years as unlabeled data has become easier to collect [5]. Pool-based AML assumes that the model has access to the entire set of unlabeled data at selection time.
\nThere are a number of AML query selection strategies, which have been presented by Settles [5]: (1) Uncertainty sampling is the simplest and the most commonly used strategy. Uncertainty sampling focuses on selecting the instance that the classifier is most uncertain about to label. This strategy can be divided into two categories: maximum entropy of the estimated label and minimum margin (distance of an instance to the decision boundary). (2) Expected error reduction, which aims to query instance that minimizes the expected error of the classifier. (3) Query by Committee (QBC) in which the most informative instance is the one that a committee of classifiers finds most disagreement. Bagging and boosting are used to generate committees of classifiers from the same dataset. They aim to combine a set of weak classifiers to create a single strong classifier. While bagging creates each base classifier independently, boosting allows these classifiers to influence each other during training process [18]. Boosting is an iterative process that initially assigns equal weight to each of the training samples; then the weights are modified based on the error rate of individual classifier.
\nActually human learns concepts in similar way of SSL, from a limited number of labeled data (e.g., parental labeling of objects) to a large amount of unlabeled data (e.g., observation of objects without naming in real life) [14]. In the ML scenario, it is easy to obtain the predictions of the classifier, and it is usually expensive to obtain the actual labels for instances.
\nIn the real world, learners are not provided with labeled category information with every object they encounter (like in supervised category learning tasks), nor do they receive only unlabeled information (like in unsupervised category learning tasks). People use labeled (with feedback) and unlabeled (no feedback) information when learning categories. In supervised learning, individuals learn the categories by correcting their performance based on the feedback they receive. The feedback people receive about categories may be either true or false. However, unsupervised learning category gives no feedback (information) about the category of an object. The individual learns from his/her experiences with different category objects, without receiving any feedback. Actually, humans categorize real-world categories most similarly to an SSL technique.
\nGibson et al. [19] have used the equivalences between models found in human categorization and machine learning research to explain how the SSL techniques can be applied to human learning. In human AML, participants are usually first shown a small number of labeled instances, followed by a large set of unlabeled instances [20]. A set of experiments conducted by Gibson et al.[19] showed that SSL models are useful for explaining human behavior when they used both labeled and unlabeled data.
\nUnlike machines, a human learner gets tired as she/he answers questions, so finding out whether she/he knows a concept or not (i.e., getting the labels) is usually an expensive task. Therefore, using AML for human learning may help it become more efficient and effective and hence reduce the cost of teaching [21]. It might be costlier to teach an example to a human than it is to teach a computer program.
\nAlthough AML has been studied in different domains, such as video annotation and web page classification, its applications to human learning have been studied very little. There are a few empirical studies on the applications of AML to the human learning domain. The first such study was by [22], which showed that humans can use unlabeled data in addition to labeled data in categorization tasks. The authors in [23] proved by empirical evidence that human category learning is influenced by unlabeled data in a supervised categorization task, but they did not explain how individual can select these unlabeled examples.
\nThere are a few studies on the applications of AML to the human category learning domain. Castro et al. [12] investigated what they refer to as “human active learning.” They tried to answer a research question “Can machine learning be used to enhance human learning?” in the context of human category learning. We consider [16] as the most interesting study in human AML field. Castro et al. showed that humans learn faster with greater performance when they can actively select the instances from a pool of unlabeled data instead of random sampling and their performance is nearly optimal. However, they did not address how humans choose the next best instance. Moreover, they conducted their experiments by humans in a simple binary classification task, not in a real-life situation. Participants were presented with artificial novel 3D shapes (stimuli) that varied along a single, continuous dimension (spiky to smooth) and were given feedback as to which category the stimulus belonged to (see Figure 4). The task for each participant was to find out the precise egg shape (category boundary) for which eggs that were any spikier would hatch into snakes, while eggs that were any smoother would hatch into birds. Authors compared the performance of three distinct conditions for each participant. In the active learning condition, the participant could choose specific observations to test their beliefs based on her previous queries and their noisy labels, whereas, in the PL condition, the sequence of data was generated randomly by the experiment. They also included a “Machine-Yoked” condition where participants saw sequences of observations created by active learners but did not have control over the sequence.
\nExample stimuli used in the experiment, with corresponding values.
The closest research to investigation by Castro et al. is [24] and [25] using a slightly modified procedure. They have successfully shown that learners benefit from the selection in category learning. Gureckis and Markant [25] concluded that AML can be superior because it allows humans to use their prior experience and current hypotheses to select the most helpful instances (e.g., asking a question about something that is especially confusing).
\nThe work [24] by the same authors examined the interaction of self-directed information selection and category learning. Self-directed learning in humans can be inspired by “active learning” research in the ML literature [25, 26]. In this study, participants learned about two categories of “antennas” that varied along two dimensions (circles that differed in size and the orientation of a central line segment) and received one of the two television stations (CH1 or CH2). They compared active learning (or self-directed learning) condition, in which participants designed stimuli to learn about, with passive condition in which instances were generated from predefined distributions. Their results showed that for simple one-dimensional rules, active learners acquired the correct category rule faster than passive learners. Also, the AML advantage only held for the less complex, rule-based category.
\nSim et al. [27] showed that school-age children learn more effectively when they are allowed to make decisions about what information they wish to gather than others who could only observe samples that were randomly generated for them. This results lead to the conclusion that children are capable of learning from the data they generate by themselves even at an early age. This result also suggests that the children’s information gathering was informed by uncertainty and previous feedback, leading them to sample items that were near the true category boundary. This result was successfully replicated in [24]. Adams and Kachergis proved the effectiveness of AML for preschoolers, and they use an informative sampling strategy in an active category learning task. The authors suggest that children’s performance in the AML task is related to their early math and preliteracy skills.
\nKachergis et al. [28] in their paper investigated whether AML is better than PL in a cross-situational word learning context. They also investigated the strategies and found that most learners use immediate repetition to disambiguate pairings.
\nResearches in computer science on computationally efficient AML have inspired new theoretical approaches to inquiry behavior in humans.
\nMost of the empirical studies, which addressed human HAML, focus on learning two-class problems on a one-dimensional input space [15, 29], but there are obstacles to generalize the model to multiclass classification problems [19]. The same is true for a single-modality (visual) object recognition. It is important to investigate the generalization of human AML to multidimensional objects (stimuli), such as auditory stimuli, high-dimensional stimuli, real-world stimuli, other demographic groups, etc.
\nExperimental studies on the human AML show that it is sensitive to noise and humans are not as good as machines in selecting queries from an unlabeled dataset of artificial visual stimuli [21]. Thus, human AML performance declines with higher noise levels. Humans perform relatively well in at least some noise settings, suggesting that they took the experiment seriously [30].
\nZhu et al. [22] showed that humans are sensitive to the distributional structure of the subsequent unlabeled experience. Gibson et al. [19] investigated the effects of the distributions of unlabeled instances (stimuli) to human learner in two experiments, and they also investigated the effect of the order of the unlabeled items that participants encountered in an experiment. They concluded that human categorization is sensitive to both the distribution and ordering of unlabeled instances [19].
\nThe small number of participants limits the generalization of the findings to other humans. In many studies that investigated human AML, small group of people participated in the experiments. In addition, a small number of objects used in the investigations lead to a similar limitation, because a limited number of teaching and test instances reduce the reliability of the results.
\nPeople are sensitive to the value of both labeled and unlabeled stimuli, and this depends on the structure of the concept being learned [24]. Markant and Gureckis [31] showed that the effectiveness of AML might interact with the particular structure of the target categories. Two types of category structures were used in that study: rule-based (RB), in which the decision rule is defined as a criterion along a single dimension, and information integration, in which the decision rule is a function of at least two dimensions.
\nIn human learning, people often learn by asking rich and interesting questions, which more directly target the concepts in a learning task. For example, a child might ask “Do all dogs have long tails?” or “What is the difference between cats and dogs?” [32]. The main challenge for AML method is to predict which question a human will ask from the given context. A number of recent studies have discussed this challenge [33, 34, 35]. Rothe et al. in [33] proposed a model that predicts what questions human learners will ask and can creatively generate novel questions that did not exist in the training data. Their work in [34] showed that human can accurately evaluate question quality by using the Bayesian ideal observers. In the most recent review [35], authors highlight and discuss nine challenges about the psychology of human inquiry.
\nOne goal of recent researches is to incorporate ML models with behavioral models to teach students and to investigate whether they can benefit from ML techniques to learn better.
\nRadwan et al. [21] were the first to attempt to use AML for teaching students with autism spectrum disorders (ASD). Students with ASD cannot learn in the same way as most people, and they need special treatment to learn a concept or an object. One of the difficulties faced by people with ASD is the recognition of categories. Radwan et al. [21] proposed a novel batch-mode pool-based AML framework for teaching students with ASD and compare the effectiveness of PL vs. AML on teaching object recognition for those students. AML approach presented to the student the most informative teaching set of objects based on the uncertainty sampling strategy. In this framework, a student plays the role of the classifier and does not have a probabilistic model. So, the uncertainty is computed in the context of the child’s responses to measure informativeness for all objects. If an object’s uncertainty is high, it implies that the student does not have sufficient knowledge to classify the object, and then adding this object into the training set can improve the child’s recognition ability.
\nFor this purpose, a web- and touch-based application was developed and presented on a tablet PC. Objects from everyday lives of children were grouped based on their categories and four difficulty levels L1–L4; see Figure 5. Picture stimuli of target objects were colored images, and they were collected using image search engines, in particular Google and Bing. The teaching procedure was based on applied behavioral analysis (ABA) principles. Five students with mild to moderate levels of ASD participated in the experiment. An alternating treatment design of single subject research methods was used to compare the effects of AML and PL.
\nA sample of images from the dataset used for teaching and test. Columns L1–L4 show different difficulty levels.
The results indicate that AML was more effective than PL for four out of the five students. Consequently, students can learn faster and are able to reach a learning criterion with fewer teaching trials [21]. AML approach was generally more effective in terms of accuracy. The statistical results demonstrated that there was a statistically significant difference in accuracy level between the means of PL and AML. The AML approach and procedures provide two features that helped to reduce repetition in learning environment: (1) minimizing the number of teaching trials required for training and (2) determining mastery criterion for levels. When a participant reached mastery criterion, the application no longer assesses this level in the following phases.
\nThe applications of AML in human categorization have become increasingly common in recent years. Humans and machines seem to benefit from AML in similar ways [24, 30, 36]. In AML setting, a learning machine is able to query an oracle in order to obtain the most informative instances that are expected to improve performance [37]. However, humans ask far richer and more sophisticated questions. In this review, we present and discuss benefits and challenges of using AML for human categorization and concept learning. More research is needed to address several limitations of human AL.
\nSome desirable properties of medical fibers include non-toxicity strength ability, biocompatibility, biodegradability, good absorbability, softness and freedom from additives and contaminates. The textile material and scientification technics has used generally in medical, surgical application like strength, flexibility, comfort and antimicrobial performances. The basically medical material products are made to multifilament and monofilament yarn, these are made by knitted, nonwoven, woven, braided fabrics and composite structures [1]. The term medical textile literally means textile used for medical purposes. Newsday around the world in textile industries are more growing part of the medical sectors and hygiene products. Medical textiles represent one of the maximum dynamic studies fields` features of technical textiles and its variety of applications. They constitute systems designed and done for a scientific application (intra body/greater body, implantable and non-implantable) textiles utilized in organic structures to estimate, treat, growth or regenerate a tissue, organ or characteristic of the body (plaster, dressings, bandages, strain garments) [2].
Absorbency, high flexibility, softness, high strength, non-toxicity and biocompatibility of textile materials are the key factors which has fuelled the growth of the textiles for its use in implantable, non-implantable, extracorporeal and hygienic products1. Although the natural way to replace a defective body part is the transplantation method, however owing to a number of incentives counting availability this is not always possible thus implantable textiles in the form of fiber and fabric are used in effective repair to the body. Sutures, soft tissue implants, orthopedic and cardiovascular grafting are the implantable textiles which has helped medical science in achieving unparalleled success in recent times [3, 4]. Non-implantable substances are utilized in outside packages, which can also additionally or might not keep in touch with the skin. The substances used must be nonallergenic, anti-cancer, anti-bacterial, permeable to air have a very good capacity to take in liquids, excessive capillarity and wettability, permit moisture shipping and feature the capacity to be sterilized. The foremost packages of those substances confer with wound care and bandages. These materials can be classified into two separated and specialization areas of application. Implantable materials: sutures or wound closure, vascular grafts, artificial ligaments, artificial joints. Non-implantable materials: wound dressing, bandages, plasters, pressure garments, orthopedic belts etc.
These are used for replacing diseased organ or tissue within the body. These replacements must be non-toxic and biocompatible. The implants are normally used for replacing arteries, heart valves, joints etc. Two types of fibers are used for implantable textile.
These are the fibers which are degraded by biological conditions within 2–3 months and mostly used inside the body. These include collagen, alginate, polyactide, polyglycolide, polyamine and some polyurethane [5, 6].
These are the fibers which are not degraded by biological condition for a long time and mostly used for external purposes. These include polytetrafluoroethylene (PTFE), polyester, polypropylene, carbon and others.
Factors which are important for implantable textiles are:
Biocompatibility and biostability
The properties of polyester will influence the success of implantation in terms of biodegradability (Tables 1 and 2).
Parameters | Fiber type | Fabric type |
---|---|---|
Cardiovascular implants, vascular grafts, heart valves | Polyester, PTFE | Knitted, woven |
Artificial tendons, artificial ligaments, artificial skin, artificial lumen, eye contact lenses etc. | PTFE, polyester, polyamide, silk, carbon, etc. | Woven, braided |
Sutures thread | Collagen, polylactide, polyglycolide, polyester, polyamide, PTFE, polypropylene, polyethylene | Mono-filament, braided |
Orthopedic implants, artificial joints, artificial bones etc. | Silicone, polyacetal, polyethylene, polysulphone, carbon, polyester, glass, ceramic | Composite |
Application | Implant |
---|---|
Abdominal wall, hernia | Meshes, patches |
Blood vessel | Tubular prostheses (woven, knitted, nonwoven), stents, stent graft coatings |
Dura | Patches (nonwoven) |
Heart | Patches, occluder, suturing ring of valves |
Osteosynthesis | Fiber reinforced devices, cords for fixation |
Tendon/ligament | Reinforcement |
Trachea, esophagus | Prostheses |
Suture is a generic term for all materials used to bring the served body tissue together and to hold these tissues in their normal position until healing takes place. Sutures are threads that are used as the way of repairing damaged tissues, cut vessels and surgical incisions by uniting the basic edges of the wounds in their required sites. It provides the necessary strength and a temporary barrier to prevent the unwanted infection. The key qualities stimulating the suture design are universal applicability, easy to handle, no kinks, coiling, twisting, or levitating, biocompatibility, inertness, uniformity in tensile strength in terms of suture type and size, frictionless surface to glide through tissue high friction for secure knotting, sterilizable without composition changes, complete absorption i.e. no residue after healing. A suture is a thread that both approximated and maintains tissues until the natural healing process has provided a sufficient level of wound strength or compresses blood vessels in order to stop bleeding. Sutures for wound closure are either monofilament or multifilament threads twisted, spun together or braided. They can also be dyed, undyed, coated or uncoated [7]. Patients’ safety is major factor for application of a suture. An incision into the lung would need to be closed using a suture with a high elasticity level, slow degradation rate and high tensile strength level. So, a surgery is never successful if the wound is not sutured or closed in a proper manner as to promote healing in a timely and safe fashion also if the suture of a rough morphology (e.g. braided) the tissue will swell more and more susceptible to infection than if a smooth suture (e.g. monofilament) is used [8].
The classification of the sutures may be done as follows into two types depending on their nature and structure:
Assimilated type of sutures is intended to be absorbed by the body i.e. to be broken down in the body and a second surgery for their removal is not desired. e.g. catgut, collagen and poly glycolic acid. Catgut is one of the most commonly used materials for the manufacture of sutures and is extracted from the ox bone. Being highly absorbable it can also be implanted in the human body even in the case of an infection however its strength deteriorates to half after a week in the body, regardless of the fact that 3 weeks are required for the recovery of an incision after surgery [9].
Non-assimilated types of sutures are considered to be implanted for long term and need to be removed latter. (E.g. cotton, silk, polyester, polyamide and polyethylene.) Cotton sutures necessitate meticulous aseptic technique during use. The main benefit of such sutures is that they are not irritant and the shortcoming is that it is the weakest suture material. Despite the possession of necessary physical form, compatibility and mechanical properties, the very slow biodegradation of the silk filament and the need for the surgical removal is the main draw back in many applications (Figure 1) [10].
Nylon monofilament suture [
The different types of suture include monofilament suture, a braided suture, a pseudo monofilament suture and a twisted strand suture each having its own positive and negative points. Monofilament sutures are made of a single filament of polyester, polyamide, polypropylene or polydioxanone and offer smooth suture drag and low tissue drag. Using such sutures, it is easy to make or place a knot in the depth of the body although the security and the flexibility of the knot are low. In braided type of sutures 8–16 polyester, polyamide or silk monofilaments are braided and coated with a lubricant to increase the flexibility and handle of the sutures. A pseudo monofilament sutures have a core of several twisted materials coated with an extrusion of the same material. It offers low tissue drag, good knottability, low knot security and fair flexibility.
The basically are used sutures in the surgical operation and other injuries. The basically are used suture thread length to tie blood vessels or sew tissues part of body. The many types of suture threads are used as absorbable performance characteristics. All this absorbable intelligent materials technique in sutures are very good working and this is doing better performance in medical sectors. This types all material are used biodegradable and biocompatible polymer. The generally many types of absorbable suture are used made from synthetic polymers.
The soft tissues are utilization in biomedical materials application like artificial tender, artificial corners and artificial prosthness etc. There are two main thrust of tissue engineering research. They are (i) the in vivo route and (ii) the in vitro approach. The objective of in vivo route is to initiate tissue engineering therapies inside the body for the repair and regeneration of damaged or diseased tissue. This approach can be successful for blood cell and nerve regeneration (both peripherial and spiral cord), skin repair, remodeling of defective bone, cornea and retina and for repairing damaged myocardium (heart muscle) following a myocardial infarction (heart attack). Not all diseases and injuries can be controlled by in vivo therapies. For example use in complex tissue cultures for the production of enzymes, drug and growth factors and for toxicological and pharmacological assays. It is depending on the medical sectors application. Ligament implants are carried out to provide autologous transplant reinforcement in construction or to cure the functional residual instabilities. These implants are either made by the braiding process or by the special flat knitting process and high tenacity polyethylene terephthalate or high tenacity polypropylene multifilament are used in making the implants for the artificial ligaments (Figure 2) [11].
Woven ligament structure [
Hard tissue compatible materials must have excellent mechanical properties compatible to hard tissue. Textile structural composites are used for implants. Typical characteristics of polymer related to hard tissue replacement are good processability, chemical stability and biocompatibility. Applications include artificial bone, bone cement and artificial joints. The current practice is to combine bioactive ceramics with polymers or metals to improve interfacial properties. Fiber reinforced composite material may be designed with the required high structure strength and biocompatibility properties needed for these application and are now replacing metal implants for artificial joints and bones.
Orthopedics is a branch of medicine that deals with disorders with the bones, joints and associated muscles. Orthopedic implants generally serve two purposes, as hard tissue to replace bones and joints, and as fixation plates to stabilize fractured bones. The first orthopedic implants were mainly metal structures. Fracture fixation devices include, spinal fixation devices, fracture plates, wires, pins and screws, adhesives while joint replacement hip, knee, elbow, wrist and finger (Figure 3).
Hip bone implants [
The fiber types used for orthopedic implants include polyacetal, polypropylene, and silicone. Composite structures composed of poly (d, l-lactide urethane) and reinforced with polyglycolic acid have excellent physical properties. This sensor principle is designed to allow for a relative strain resolution as small as 10-4–10-5.
Due to a steadily growing number of patients and considerable diagnostic and therapeutic advances, vascular diseases are becoming more and more important in general and clinical practices thus the vascular grafts are the need of the hour. Vascular grafts are used in surgery to replace damaged thick arteries or veins. The implantation of synthetic and biological grafts in the circulatory system yield several types of complications ranging from infection to wall rupture. Dilation, suture line failure, structural defects (holes, perforations, rents, and slits), bleeding and infection are some of the main problems caused due to the failure of the grafts. Textile structures are usually the materials used for arterial replacement; however, they do not always meet all the requirements. Gel weave is a true zero-porosity twill woven polyester graft. It is manufactured using an advanced technique of weaving fully texturized polyester on modern looms (Figure 4) [12].
Knitted structure for a cardiovascular implant [
The most important aspects of an arterial graft include porosity, compliance, and biodegradability and the design considerations for the graft are selection of the right type of polymer, the type of the yarn, fabric and the crimping. Polyester (e.g. Dacron) or PTFE (e.g. Teflon) and polyurethane are the most commonly. Commercial prostheses contain either single- or two-ply yarns. On one hand these yarns usually have a round cross-section and on the other hand trilobal yarns have been used for the reason it provides the advantage of offering a large surface area making the preclott easier and faster, but they are more prone to fatigue and mechanical damage [13, 14].
These are the materials which are used for external applications on the body and may or may not make contact with skin. This includes:
Wound care
Plasters
Orthopedic belts
Wadding
Protective eye pads
Absorbent, wicking performance, non-toxic, breathability, soft, elasticity, non-allergic, ability to be sterilize etc (Table 3).
Application | Fiber types | Fabric structure |
---|---|---|
Absorbent pads | Cotton, viscose, lyocell | Non-woven |
Wound contact layer | Alginate fiber, chitosan, silk, lyocell, cotton, viscose | Non-woven, woven knitted |
Base layer | Viscose, lyocell, plastic film | Non-woven, woven |
Simple non-elastic and non-elastic bandages | Cotton, viscose, lyocell, polyamide fiber, elastomeric fiber yarns | Non-woven, woven |
High–support bandages | Cotton, viscose, lyocell, elastomeric fiber yarns | Non-woven, woven knitted |
Compression bandages | Cotton, viscose, lyocell, elastomeric fiber yarns | Non-woven, woven knitted |
Orthopedic bandages | Cotton, viscose, lyocell, PET, PP, polyurethane form | Woven, knitted |
Plaster | Cotton, viscose, lyocell, PET, PP, glass fiber | Non-woven, woven knitted |
Gauges | Alginate fiber, chitosan, lyocell, cotton, viscose | Non-woven, woven knitted |
Wadding | Viscose, cotton linter, wood pulp | Nonwoven |
Lint | Cotton | Woven |
Different types of dressings are available for a variety of medical and surgical applications.
Functions of wound dressings:
Protection against infection
Absorb blood and exudate
Promote healing
To keep the wound smooth and pliable
Medication to the wound
The wound contact layer should prevent adherence of the dressing to the wound and be easily removed without disturbing new tissue growth. Gauge and paraffin coated gauge are the most common dressings used. Most gauges are made from cotton in the form of a loose plain weave. The burns and skin graft sites must have their dressing changed frequency. When the dressing is removed, it is not only painful, but it can also destroy the regenerating tissues. This can delay the healing process because scarring and reopen the wounds for possible bacteria entrance. The paraffin coated gauge which is usually multilayered is a little easier to remove than dry gauge. Gauge may be impregnated with plaster sterilization is required. Finishing agents such as wetting agents and optical whiteners are not added to gauge fabrics because of the possibility of irritation and possible carcinogenic effects [15, 16].
Nonwoven fabrics can be used for the following advantages:
Better sterilization
Smooth and lint free (allows for a lesser change for debris to be left in the wound)
Can be made softer and more absorbent by latex or thermal calendering
For port operative dressing, sophisticated nonwoven structure is possible. Nonwoven fabrics made of atelocollagen filaments are used as wound dressing for burns.
Polypropylene fabric/carbonized rayon fabric would transmit liquid to the absorbent material and enable to keep the skin dry.
Wound dressings act as physical barrier for wounds and are found to have some distinguished Properties like fluid control, odor management, and microbial control and wound healing acceleration (Table 4).
Types | Properties |
---|---|
Passive products | Traditional dressing that provide cover over the wound |
Interactive products | Polymeric film to permeable to oxygen but not bacteria |
Bioactive products | Dressing that deliver substance active in healing, e.g. alginate, chitogan |
The basically raw material for the product of this fiber is alginic acid, an emulsion attained from the marine brown algae. It possesses a variety of parcels, including the capability to stabilize thick suspense, to form film layers, and to turn into gels. When the dressing made of this fiber is applied to crack, the rear ion exchange take place and this fiber is placed on the crack in dry state and begin to absorb the exudates.
It is a supple, non-woven dressing made from high quality calcium alginate fiber with excellent gel forming properties A Sorbalgon dressing absorbs approximately 10 ml exudates per gram dry weight (Figure 5).
Sorbalgon wound dressing [
Thin film has very superior absorbent properties and outer surface thin film give better comfort behavior. This thin layer film has basically working of the easily absorb body fluid and proper safe keep it to the dressing leakage and wound maceration.
Acticoat dressing is give better protection against fungal infection performance as compared to traditional antimicrobial dressing materials. This dressing is better kill rate and more effective fungal species.
The bandage has generally essential properties should be like breathable, stretchable, non-slip, non-stick to more comfort help during injuries time of human body. Bandages are designed to perform a whole variety of specific functions depending upon the final medical requirement. The basically bandages are used in injuries and wound place to keep it dressing. Such bandages are in form of light-weight knitted fabrics or open-weave woven fabrics, made from either cotton or viscose. Their primary function is to hold the healing wound dressings firmly in place. They themselves do not have healing functions to play [17, 18].
Orthopedic cushion bandages are used under plaster casts and compression bandages to prove padding and prevent discomfort.
Different types of bandages can be classified.
It provides necessary support to circumscribe movement and speed up the mending process Compression tapes are used for the treatment and forestallment of deep tone thrombosis, leg ulceration, and swollen modes and are designed to ply a needed quantum of contraction on the leg when applied at a constant pressure. Compression tapes are classified by the quantum of contraction they can play at the ankle and include extra-high, high, moderate, and light contraction and can be either woven and contain cotton and elastomeric yarns or underpinning and weft knitted in both tubular or completely-fashioned forms.
Compression hosiery can be used as an alternative to compression bandaging for the treatment of active ulcers.
Compression hosiery is classified according to the pressure level applied at the ankle.
Compression hosiery is made from a number of different fibers including nylon, cotton yarn and elastane.
A cloth girth saturated with cataplasm of Paris is dipped into water and also wrapped around the broken branch thereby creating an establishment- fitting yet fluently removed flake in the shape of a tube or cylinder. This type of operation of cataplasm in the form of a broken branch is generally known as an orthopedic cast. The modern plaster fabric is made from spun bonded nonwovens of cotton, viscose, polyester or glass fiber (Figure 6) [19].
Orthopedic bandages [
Pressure garments play a vital role in the proper healing of wounds and reduce the effects of scaring, but for the garments to perform their job properly, they need to be in good condition. The continuous wearing of pressure garments prevents the thickening, buckling, and nodular formations seen in hypertrophic scars [20].
Medical textiles have visible speedy improvement over the previous couple of decades. Nowadays, new biodegradable fibers have enabled the improvement of novel sorts of implants, and contemporary-day fabric machines can produce third-dimensional spacer fabric that supply advanced overall performance over conventional fabric materials.
These and lots of different advances have made clinical textiles a crucial detail in contemporary-day ailment management, and they are turning into increasingly critical with the growing quantity of aged humans with inside the populations of evolved countries.
The more significance of medical textiles in human life, healthful residing and development is immense.
The improvement of latest technology and new gadgets will assist sufferers to conquer the hardships that they used to go through with inside the past.
There are many extra unknown regions of medical textiles; we must do studies on the ones issues. We must pay extra interest to the manufacturing of healthful and nice clinical fabric materials. In addition to technology, we want to hold a watch at the rate of our products.
Through this it is going to be viable to supply nice whole and easily to be had contemporary-day medical textiles.
Textile substances preserve to serve a critical feature with inside the improvement of number clinical and surgical products.
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rapidly expanding, creating visible environmental and social challenges. The generation of waste is one of the central concerns in urban agglomerations, particularly in the global South, where inadequacies, absences and weaknesses shape the local waste management system. Uneven geographic development has created obvious spaces of exclusion and neglect. In response, informal and organized waste pickers engage in selective waste collection and recycling, serving their community and the environment. These contributions are still mostly unrecognized and unaccounted for. This chapter begins with emphasizing the challenges of urban growth, consumption, poverty and waste. In the global South, every day millions of informal waste pickers reclaim recyclables from household waste to earn their living. In doing so they make an important contribution to reducing the carbon footprint of cities, recovering resources, improving environmental conditions and health creating jobs and income among the poor, particularly in low-income residential areas. This chapter discusses the organization of these initiatives into networks and examines the challenges and benefits of such practices that promote grassroots resilience and contribute to reducing both the adverse impacts of cities on climate and environmental change (UN sustainable development target # 11.6) as well as urban poverty (Goal # 8).",book:{id:"6396",slug:"urban-agglomeration",title:"Urban Agglomeration",fullTitle:"Urban Agglomeration"},signatures:"Jutta Gutberlet",authors:[{id:"188532",title:"Prof.",name:"Jutta",middleName:null,surname:"Gutberlet",slug:"jutta-gutberlet",fullName:"Jutta Gutberlet"}]},{id:"58196",doi:"10.5772/intechopen.72191",title:"The Multivariated Effect of City Cooperation in Land Use Planning and Decision-Making Processes: A European Analysis",slug:"the-multivariated-effect-of-city-cooperation-in-land-use-planning-and-decision-making-processes-a-eu",totalDownloads:1049,totalCrossrefCites:7,totalDimensionsCites:17,abstract:"Spatial and urban planning processes regarding border cooperation have reached unprecedented levels in recent decades, not only due to their potential for territorial integration, i.e., infrastructure construction and planning activities worldwide. Bearing in mind the European project, for a united and strong network of nations, this scenario is more evident in European territories. In this regard, through multivariated analyzes of city cooperation on European border areas, it is possible to identify the factors that influence the territorial success and also a sustainable regional development and even their effects over the urban agglomerations. From the identified factors, the study pointed out one that is common to all cases: connectivity-movement between cities.",book:{id:"6396",slug:"urban-agglomeration",title:"Urban Agglomeration",fullTitle:"Urban Agglomeration"},signatures:"Luís Carlos Loures, Rui Alexandre Castanho, José Manuel Naranjo\nGómez, Ana Vulevic, José Cabezas and Luis Fernández-Pozo",authors:[{id:"108118",title:"Dr.",name:"Luis",middleName:null,surname:"Loures",slug:"luis-loures",fullName:"Luis Loures"},{id:"215340",title:"Dr.",name:"Ana",middleName:null,surname:"Vulevic",slug:"ana-vulevic",fullName:"Ana Vulevic"},{id:"215341",title:"Prof.",name:"José",middleName:null,surname:"Cabezas Fernández",slug:"jose-cabezas-fernandez",fullName:"José Cabezas Fernández"},{id:"215342",title:"Prof.",name:"José Manuel",middleName:null,surname:"Naranjo Gómez",slug:"jose-manuel-naranjo-gomez",fullName:"José Manuel Naranjo Gómez"},{id:"222742",title:"Dr.",name:"Luis",middleName:null,surname:"Fernández-Pozo",slug:"luis-fernandez-pozo",fullName:"Luis Fernández-Pozo"},{id:"290571",title:"Dr.",name:"Rui Alexandre",middleName:null,surname:"Castanho",slug:"rui-alexandre-castanho",fullName:"Rui Alexandre Castanho"}]},{id:"59481",doi:"10.5772/intechopen.73524",title:"Characteristics of Urban Agglomerations in Different Continents: History, Patterns, Dynamics, Drivers and Trends",slug:"characteristics-of-urban-agglomerations-in-different-continents-history-patterns-dynamics-drivers-an",totalDownloads:2780,totalCrossrefCites:6,totalDimensionsCites:14,abstract:"Urban agglomerations show different development patterns and stages. Here, we describe, discuss and compare urban agglomerations in different continents. The introduction section gives a general overview of specific issues of urban agglomerations. Different characteristics in Europe, Asia and America are discussed as experienced by the article’s co-authors, living in or working for urban agglomerations in these continents. First, the history of urbanization and agglomeration evolvement is described, then patterns, functional structures and relations, drivers as well as social and demographic characteristics are discussed (e.g. migration, aging, household structure, housing patterns, workplaces, etc.). Transportation infrastructure (roads, public transport systems) is also addressed as trigger for spatial dynamics causing certain effects (floor space, office and apartment rents releasing urban sprawl or hyper-densification), as well as gentrification. Further topics are urban governance and its impact on agglomeration development. Recent state and future trends will be debated, if important. A conclusion section summarizes the comparison of state, dynamics, drivers and trends.",book:{id:"6396",slug:"urban-agglomeration",title:"Urban Agglomeration",fullTitle:"Urban Agglomeration"},signatures:"Wolfgang Loibl, Ghazal Etminan, Ernst Gebetsroither-Geringer,\nHans-Martin Neumann and Santiago Sanchez-Guzman",authors:[{id:"160720",title:"Dr.",name:"Ernst",middleName:null,surname:"Gebetsroither",slug:"ernst-gebetsroither",fullName:"Ernst Gebetsroither"},{id:"167419",title:"Dr.",name:"Wolfgang",middleName:null,surname:"Loibl",slug:"wolfgang-loibl",fullName:"Wolfgang Loibl"},{id:"214919",title:"MSc.",name:"Ghazal",middleName:null,surname:"Etminan",slug:"ghazal-etminan",fullName:"Ghazal Etminan"},{id:"214920",title:"MSc.",name:"Hans-Martin",middleName:null,surname:"Neumann",slug:"hans-martin-neumann",fullName:"Hans-Martin Neumann"},{id:"214923",title:"MSc.",name:"Santiago",middleName:null,surname:"Sanchez-Guzman",slug:"santiago-sanchez-guzman",fullName:"Santiago Sanchez-Guzman"}]},{id:"46448",doi:"10.5772/58225",title:"Mapping of Ground Deformations with Interferometric Stacking Techniques",slug:"mapping-of-ground-deformations-with-interferometric-stacking-techniques",totalDownloads:3484,totalCrossrefCites:8,totalDimensionsCites:13,abstract:null,book:{id:"3727",slug:"land-applications-of-radar-remote-sensing",title:"Land Applications of Radar Remote Sensing",fullTitle:"Land Applications of Radar Remote Sensing"},signatures:"Paolo Pasquali, Alessio Cantone, Paolo Riccardi, Marco Defilippi,\nFumitaka Ogushi, Stefano Gagliano and Masayuki Tamura",authors:[{id:"168247",title:"Dr.",name:"Paolo",middleName:null,surname:"Pasquali",slug:"paolo-pasquali",fullName:"Paolo Pasquali"},{id:"168811",title:"Mr.",name:"Paolo",middleName:null,surname:"Riccardi",slug:"paolo-riccardi",fullName:"Paolo Riccardi"},{id:"168812",title:"Mr.",name:"Alessio",middleName:null,surname:"Cantone",slug:"alessio-cantone",fullName:"Alessio Cantone"},{id:"168813",title:"Mr.",name:"Marco",middleName:null,surname:"Defilippi",slug:"marco-defilippi",fullName:"Marco Defilippi"},{id:"168814",title:"Mr.",name:"Fumitaka",middleName:null,surname:"Ogushi",slug:"fumitaka-ogushi",fullName:"Fumitaka Ogushi"},{id:"168815",title:"Mr.",name:"Stefano",middleName:null,surname:"Gagliano",slug:"stefano-gagliano",fullName:"Stefano Gagliano"},{id:"170671",title:"Prof.",name:"Masayuki",middleName:null,surname:"Tamura",slug:"masayuki-tamura",fullName:"Masayuki Tamura"}]},{id:"46357",doi:"10.5772/58220",title:"Large Scale Mapping of Forests and Land Cover with Synthetic Aperture Radar Data",slug:"large-scale-mapping-of-forests-and-land-cover-with-synthetic-aperture-radar-data",totalDownloads:4076,totalCrossrefCites:5,totalDimensionsCites:11,abstract:null,book:{id:"3727",slug:"land-applications-of-radar-remote-sensing",title:"Land Applications of Radar Remote Sensing",fullTitle:"Land Applications of Radar Remote Sensing"},signatures:"Josef Kellndorfer, Oliver Cartus, Jesse Bishop, Wayne Walker and\nFrancesco Holecz",authors:[{id:"168830",title:"Dr.",name:"Francesco",middleName:null,surname:"Holecz",slug:"francesco-holecz",fullName:"Francesco Holecz"},{id:"170758",title:"Dr.",name:"Josef",middleName:null,surname:"Kellndorfer",slug:"josef-kellndorfer",fullName:"Josef Kellndorfer"}]}],mostDownloadedChaptersLast30Days:[{id:"57824",title:"Waste in the City: Challenges and Opportunities for Urban Agglomerations",slug:"waste-in-the-city-challenges-and-opportunities-for-urban-agglomerations",totalDownloads:2821,totalCrossrefCites:11,totalDimensionsCites:26,abstract:"Worldwide cities are rapidly expanding, creating visible environmental and social challenges. The generation of waste is one of the central concerns in urban agglomerations, particularly in the global South, where inadequacies, absences and weaknesses shape the local waste management system. Uneven geographic development has created obvious spaces of exclusion and neglect. In response, informal and organized waste pickers engage in selective waste collection and recycling, serving their community and the environment. These contributions are still mostly unrecognized and unaccounted for. This chapter begins with emphasizing the challenges of urban growth, consumption, poverty and waste. In the global South, every day millions of informal waste pickers reclaim recyclables from household waste to earn their living. In doing so they make an important contribution to reducing the carbon footprint of cities, recovering resources, improving environmental conditions and health creating jobs and income among the poor, particularly in low-income residential areas. This chapter discusses the organization of these initiatives into networks and examines the challenges and benefits of such practices that promote grassroots resilience and contribute to reducing both the adverse impacts of cities on climate and environmental change (UN sustainable development target # 11.6) as well as urban poverty (Goal # 8).",book:{id:"6396",slug:"urban-agglomeration",title:"Urban Agglomeration",fullTitle:"Urban Agglomeration"},signatures:"Jutta Gutberlet",authors:[{id:"188532",title:"Prof.",name:"Jutta",middleName:null,surname:"Gutberlet",slug:"jutta-gutberlet",fullName:"Jutta Gutberlet"}]},{id:"59481",title:"Characteristics of Urban Agglomerations in Different Continents: History, Patterns, Dynamics, Drivers and Trends",slug:"characteristics-of-urban-agglomerations-in-different-continents-history-patterns-dynamics-drivers-an",totalDownloads:2780,totalCrossrefCites:6,totalDimensionsCites:14,abstract:"Urban agglomerations show different development patterns and stages. Here, we describe, discuss and compare urban agglomerations in different continents. The introduction section gives a general overview of specific issues of urban agglomerations. Different characteristics in Europe, Asia and America are discussed as experienced by the article’s co-authors, living in or working for urban agglomerations in these continents. First, the history of urbanization and agglomeration evolvement is described, then patterns, functional structures and relations, drivers as well as social and demographic characteristics are discussed (e.g. migration, aging, household structure, housing patterns, workplaces, etc.). Transportation infrastructure (roads, public transport systems) is also addressed as trigger for spatial dynamics causing certain effects (floor space, office and apartment rents releasing urban sprawl or hyper-densification), as well as gentrification. Further topics are urban governance and its impact on agglomeration development. Recent state and future trends will be debated, if important. A conclusion section summarizes the comparison of state, dynamics, drivers and trends.",book:{id:"6396",slug:"urban-agglomeration",title:"Urban Agglomeration",fullTitle:"Urban Agglomeration"},signatures:"Wolfgang Loibl, Ghazal Etminan, Ernst Gebetsroither-Geringer,\nHans-Martin Neumann and Santiago Sanchez-Guzman",authors:[{id:"160720",title:"Dr.",name:"Ernst",middleName:null,surname:"Gebetsroither",slug:"ernst-gebetsroither",fullName:"Ernst Gebetsroither"},{id:"167419",title:"Dr.",name:"Wolfgang",middleName:null,surname:"Loibl",slug:"wolfgang-loibl",fullName:"Wolfgang Loibl"},{id:"214919",title:"MSc.",name:"Ghazal",middleName:null,surname:"Etminan",slug:"ghazal-etminan",fullName:"Ghazal Etminan"},{id:"214920",title:"MSc.",name:"Hans-Martin",middleName:null,surname:"Neumann",slug:"hans-martin-neumann",fullName:"Hans-Martin Neumann"},{id:"214923",title:"MSc.",name:"Santiago",middleName:null,surname:"Sanchez-Guzman",slug:"santiago-sanchez-guzman",fullName:"Santiago Sanchez-Guzman"}]},{id:"58027",title:"Do Degradation of Urban Greenery and Increasing Land Prices Often Come along with Urbanization?",slug:"do-degradation-of-urban-greenery-and-increasing-land-prices-often-come-along-with-urbanization-",totalDownloads:1051,totalCrossrefCites:0,totalDimensionsCites:0,abstract:"In the wake of urbanization, driven by a variety of individual and socio-economic merits, human’s basic residential needs and standard of living may be compromised in the urban areas, as the population agglomerates. However, the knowledge of the associations of urbanization with urban greenery and residential land prices is still in the pursuing process. This empirical research aims to contribute whether the degradation of essential living conditions is a trade-off for the pursued urban life. Hence, Taiwan is selected as the case to analyze the associated relations primarily between 1976 and 2016. The research methods involve descriptive statistics, the panel data analysis, and the cluster analysis. The panel data analysis demonstrates that degraded urban greenery and increasing residential land prices came along with the urbanization in Taiwan between 2001 and 2016. Policy implications include rethinking of the building coverage rate for renewed buildings for more plant-friendly ground, the adoption of building setback policy for more accessible mid-air mini-parks, and avoiding residential units as an investment commodity.",book:{id:"6396",slug:"urban-agglomeration",title:"Urban Agglomeration",fullTitle:"Urban Agglomeration"},signatures:"Yu-hsin Tsai, Jhong-yun Guan and Yu-Hsin Huang",authors:[{id:"215320",title:"Prof.",name:"Yu-Hsin",middleName:null,surname:"Tsai",slug:"yu-hsin-tsai",fullName:"Yu-Hsin Tsai"},{id:"224006",title:"Ms.",name:"Jhong-Yun",middleName:null,surname:"Guan",slug:"jhong-yun-guan",fullName:"Jhong-Yun Guan"},{id:"224007",title:"Ms.",name:"Yu-Hsin",middleName:null,surname:"Huang",slug:"yu-hsin-huang",fullName:"Yu-Hsin Huang"}]},{id:"58922",title:"Promotion of Smart Community Strategy in Vietnam’s Binh Duong Province",slug:"promotion-of-smart-community-strategy-in-vietnam-s-binh-duong-province",totalDownloads:1117,totalCrossrefCites:0,totalDimensionsCites:0,abstract:"Vietnam’s Binh Duong Province is located in southeastern Vietnam, immediately to the north of Ho Chi Minh City (HCMC). The province constitutes the southern economic zone of Vietnam, and many Japanese companies—mainly manufacturing enterprises, including small and medium enterprises—are investing there. Becamex IDC Corp (a Vietnamese state-owned developer) and Tokyu Corporation (a Japanese company) established Becamex Tokyu, a joint venture company, in March 2012. As a smart community strategy, Becamex Tokyu is promoting Tokyu Binh Duong Garden City, which is a development plan integrating new urban development. The Becamex Tokyu Bus, which is a wholly owned subsidiary of Becamex Tokyu, operates the new transportation system KAZE Shuttle. In addition, Binh Duong Province has developed a smart community strategy centered on energy establishment in urban development, information and communications technology infrastructure development and utilization, the elimination of physical waste in infrastructure construction, and smart traffic. In this chapter, by using his field work as a basis, the author examines the current status of, and issues faced by, development of the smart community strategy in Binh Duong Province in Vietnam.",book:{id:"6396",slug:"urban-agglomeration",title:"Urban Agglomeration",fullTitle:"Urban Agglomeration"},signatures:"Tetsuro Saisho",authors:[{id:"211195",title:"Dr.",name:"Tetsuro",middleName:null,surname:"Saisho",slug:"tetsuro-saisho",fullName:"Tetsuro Saisho"}]},{id:"46357",title:"Large Scale Mapping of Forests and Land Cover with Synthetic Aperture Radar Data",slug:"large-scale-mapping-of-forests-and-land-cover-with-synthetic-aperture-radar-data",totalDownloads:4076,totalCrossrefCites:5,totalDimensionsCites:11,abstract:null,book:{id:"3727",slug:"land-applications-of-radar-remote-sensing",title:"Land Applications of Radar Remote Sensing",fullTitle:"Land Applications of Radar Remote Sensing"},signatures:"Josef Kellndorfer, Oliver Cartus, Jesse Bishop, Wayne Walker and\nFrancesco Holecz",authors:[{id:"168830",title:"Dr.",name:"Francesco",middleName:null,surname:"Holecz",slug:"francesco-holecz",fullName:"Francesco Holecz"},{id:"170758",title:"Dr.",name:"Josef",middleName:null,surname:"Kellndorfer",slug:"josef-kellndorfer",fullName:"Josef Kellndorfer"}]}],onlineFirstChaptersFilter:{topicId:"636",limit:6,offset:0},onlineFirstChaptersCollection:[],onlineFirstChaptersTotal:0},preDownload:{success:null,errors:{}},subscriptionForm:{success:null,errors:{}},aboutIntechopen:{},privacyPolicy:{},peerReviewing:{},howOpenAccessPublishingWithIntechopenWorks:{},sponsorshipBooks:{sponsorshipBooks:[],offset:8,limit:8,total:0},allSeries:{pteSeriesList:[{id:"14",title:"Artificial Intelligence",numberOfPublishedBooks:8,numberOfPublishedChapters:87,numberOfOpenTopics:6,numberOfUpcomingTopics:0,issn:"2633-1403",doi:"10.5772/intechopen.79920",isOpenForSubmission:!0},{id:"7",title:"Biomedical Engineering",numberOfPublishedBooks:12,numberOfPublishedChapters:98,numberOfOpenTopics:3,numberOfUpcomingTopics:0,issn:"2631-5343",doi:"10.5772/intechopen.71985",isOpenForSubmission:!0}],lsSeriesList:[{id:"11",title:"Biochemistry",numberOfPublishedBooks:27,numberOfPublishedChapters:285,numberOfOpenTopics:4,numberOfUpcomingTopics:0,issn:"2632-0983",doi:"10.5772/intechopen.72877",isOpenForSubmission:!0},{id:"25",title:"Environmental Sciences",numberOfPublishedBooks:1,numberOfPublishedChapters:9,numberOfOpenTopics:4,numberOfUpcomingTopics:0,issn:"2754-6713",doi:"10.5772/intechopen.100362",isOpenForSubmission:!0},{id:"10",title:"Physiology",numberOfPublishedBooks:11,numberOfPublishedChapters:139,numberOfOpenTopics:4,numberOfUpcomingTopics:0,issn:"2631-8261",doi:"10.5772/intechopen.72796",isOpenForSubmission:!0}],hsSeriesList:[{id:"3",title:"Dentistry",numberOfPublishedBooks:8,numberOfPublishedChapters:129,numberOfOpenTopics:0,numberOfUpcomingTopics:2,issn:"2631-6218",doi:"10.5772/intechopen.71199",isOpenForSubmission:!1},{id:"6",title:"Infectious Diseases",numberOfPublishedBooks:13,numberOfPublishedChapters:105,numberOfOpenTopics:3,numberOfUpcomingTopics:1,issn:"2631-6188",doi:"10.5772/intechopen.71852",isOpenForSubmission:!0},{id:"13",title:"Veterinary Medicine and Science",numberOfPublishedBooks:9,numberOfPublishedChapters:101,numberOfOpenTopics:3,numberOfUpcomingTopics:0,issn:"2632-0517",doi:"10.5772/intechopen.73681",isOpenForSubmission:!0}],sshSeriesList:[{id:"22",title:"Business, Management and Economics",numberOfPublishedBooks:1,numberOfPublishedChapters:11,numberOfOpenTopics:2,numberOfUpcomingTopics:1,issn:null,doi:"10.5772/intechopen.100359",isOpenForSubmission:!0},{id:"23",title:"Education and Human Development",numberOfPublishedBooks:0,numberOfPublishedChapters:0,numberOfOpenTopics:2,numberOfUpcomingTopics:0,issn:null,doi:"10.5772/intechopen.100360",isOpenForSubmission:!1},{id:"24",title:"Sustainable Development",numberOfPublishedBooks:0,numberOfPublishedChapters:9,numberOfOpenTopics:4,numberOfUpcomingTopics:1,issn:null,doi:"10.5772/intechopen.100361",isOpenForSubmission:!0}],testimonialsList:[{id:"13",text:"The collaboration with and support of the technical staff of IntechOpen is fantastic. The whole process of submitting an article and editing of the submitted article goes extremely smooth and fast, the number of reads and downloads of chapters is high, and the contributions are also frequently cited.",author:{id:"55578",name:"Antonio",surname:"Jurado-Navas",institutionString:null,profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRisIQAS/Profile_Picture_1626166543950",slug:"antonio-jurado-navas",institution:{id:"720",name:"University of Malaga",country:{id:null,name:"Spain"}}}},{id:"6",text:"It is great to work with the IntechOpen to produce a worthwhile collection of research that also becomes a great educational resource and guide for future research endeavors.",author:{id:"259298",name:"Edward",surname:"Narayan",institutionString:null,profilePictureURL:"https://mts.intechopen.com/storage/users/259298/images/system/259298.jpeg",slug:"edward-narayan",institution:{id:"3",name:"University of Queensland",country:{id:null,name:"Australia"}}}}]},series:{item:{id:"14",title:"Artificial Intelligence",doi:"10.5772/intechopen.79920",issn:"2633-1403",scope:"Artificial Intelligence (AI) is a rapidly developing multidisciplinary research area that aims to solve increasingly complex problems. In today's highly integrated world, AI promises to become a robust and powerful means for obtaining solutions to previously unsolvable problems. This Series is intended for researchers and students alike interested in this fascinating field and its many applications.",coverUrl:"https://cdn.intechopen.com/series/covers/14.jpg",latestPublicationDate:"May 14th, 2022",hasOnlineFirst:!0,numberOfPublishedBooks:8,editor:{id:"218714",title:"Prof.",name:"Andries",middleName:null,surname:"Engelbrecht",slug:"andries-engelbrecht",fullName:"Andries Engelbrecht",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRNR8QAO/Profile_Picture_1622640468300",biography:"Andries Engelbrecht received the Masters and PhD degrees in Computer Science from the University of Stellenbosch, South Africa, in 1994 and 1999 respectively. He is currently appointed as the Voigt Chair in Data Science in the Department of Industrial Engineering, with a joint appointment as Professor in the Computer Science Division, Stellenbosch University. Prior to his appointment at Stellenbosch University, he has been at the University of Pretoria, Department of Computer Science (1998-2018), where he was appointed as South Africa Research Chair in Artifical Intelligence (2007-2018), the head of the Department of Computer Science (2008-2017), and Director of the Institute for Big Data and Data Science (2017-2018). In addition to a number of research articles, he has written two books, Computational Intelligence: An Introduction and Fundamentals of Computational Swarm Intelligence.",institutionString:null,institution:{name:"Stellenbosch University",institutionURL:null,country:{name:"South Africa"}}},editorTwo:null,editorThree:null},subseries:{paginationCount:6,paginationItems:[{id:"22",title:"Applied Intelligence",coverUrl:"https://cdn.intechopen.com/series_topics/covers/22.jpg",isOpenForSubmission:!0,editor:{id:"27170",title:"Prof.",name:"Carlos",middleName:"M.",surname:"Travieso-Gonzalez",slug:"carlos-travieso-gonzalez",fullName:"Carlos Travieso-Gonzalez",profilePictureURL:"https://mts.intechopen.com/storage/users/27170/images/system/27170.jpeg",biography:"Carlos M. Travieso-González received his MSc degree in Telecommunication Engineering at Polytechnic University of Catalonia (UPC), Spain in 1997, and his Ph.D. degree in 2002 at the University of Las Palmas de Gran Canaria (ULPGC-Spain). He is a full professor of signal processing and pattern recognition and is head of the Signals and Communications Department at ULPGC, teaching from 2001 on subjects on signal processing and learning theory. His research lines are biometrics, biomedical signals and images, data mining, classification system, signal and image processing, machine learning, and environmental intelligence. He has researched in 52 international and Spanish research projects, some of them as head researcher. He is co-author of 4 books, co-editor of 27 proceedings books, guest editor for 8 JCR-ISI international journals, and up to 24 book chapters. He has over 450 papers published in international journals and conferences (81 of them indexed on JCR – ISI - Web of Science). He has published seven patents in the Spanish Patent and Trademark Office. He has been a supervisor on 8 Ph.D. theses (11 more are under supervision), and 130 master theses. He is the founder of The IEEE IWOBI conference series and the president of its Steering Committee, as well as the founder of both the InnoEducaTIC and APPIS conference series. He is an evaluator of project proposals for the European Union (H2020), Medical Research Council (MRC, UK), Spanish Government (ANECA, Spain), Research National Agency (ANR, France), DAAD (Germany), Argentinian Government, and the Colombian Institutions. He has been a reviewer in different indexed international journals (<70) and conferences (<250) since 2001. He has been a member of the IASTED Technical Committee on Image Processing from 2007 and a member of the IASTED Technical Committee on Artificial Intelligence and Expert Systems from 2011. \n\nHe has held the general chair position for the following: ACM-APPIS (2020, 2021), IEEE-IWOBI (2019, 2020 and 2020), A PPIS (2018, 2019), IEEE-IWOBI (2014, 2015, 2017, 2018), InnoEducaTIC (2014, 2017), IEEE-INES (2013), NoLISP (2011), JRBP (2012), and IEEE-ICCST (2005)\n\nHe is an associate editor of the Computational Intelligence and Neuroscience Journal (Hindawi – Q2 JCR-ISI). He was vice dean from 2004 to 2010 in the Higher Technical School of Telecommunication Engineers at ULPGC and the vice dean of Graduate and Postgraduate Studies from March 2013 to November 2017. He won the “Catedra Telefonica” Awards in Modality of Knowledge Transfer, 2017, 2018, and 2019 editions, and awards in Modality of COVID Research in 2020.\n\nPublic References:\nResearcher ID http://www.researcherid.com/rid/N-5967-2014\nORCID https://orcid.org/0000-0002-4621-2768 \nScopus Author ID https://www.scopus.com/authid/detail.uri?authorId=6602376272\nScholar Google https://scholar.google.es/citations?user=G1ks9nIAAAAJ&hl=en \nResearchGate https://www.researchgate.net/profile/Carlos_Travieso",institutionString:null,institution:{name:"University of Las Palmas de Gran Canaria",institutionURL:null,country:{name:"Spain"}}},editorTwo:null,editorThree:null},{id:"23",title:"Computational Neuroscience",coverUrl:"https://cdn.intechopen.com/series_topics/covers/23.jpg",isOpenForSubmission:!0,editor:{id:"14004",title:"Dr.",name:"Magnus",middleName:null,surname:"Johnsson",slug:"magnus-johnsson",fullName:"Magnus Johnsson",profilePictureURL:"https://mts.intechopen.com/storage/users/14004/images/system/14004.png",biography:"Dr Magnus Johnsson is a cross-disciplinary scientist, lecturer, scientific editor and AI/machine learning consultant from Sweden. \n\nHe is currently at Malmö University in Sweden, but also held positions at Lund University in Sweden and at Moscow Engineering Physics Institute. \nHe holds editorial positions at several international scientific journals and has served as a scientific editor for books and special journal issues. \nHis research interests are wide and include, but are not limited to, autonomous systems, computer modeling, artificial neural networks, artificial intelligence, cognitive neuroscience, cognitive robotics, cognitive architectures, cognitive aids and the philosophy of mind. \n\nDr. Johnsson has experience from working in the industry and he has a keen interest in the application of neural networks and artificial intelligence to fields like industry, finance, and medicine. \n\nWeb page: www.magnusjohnsson.se",institutionString:null,institution:{name:"Malmö University",institutionURL:null,country:{name:"Sweden"}}},editorTwo:null,editorThree:null},{id:"24",title:"Computer Vision",coverUrl:"https://cdn.intechopen.com/series_topics/covers/24.jpg",isOpenForSubmission:!0,editor:{id:"294154",title:"Prof.",name:"George",middleName:null,surname:"Papakostas",slug:"george-papakostas",fullName:"George Papakostas",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002hYaGbQAK/Profile_Picture_1624519712088",biography:"George A. Papakostas has received a diploma in Electrical and Computer Engineering in 1999 and the M.Sc. and Ph.D. degrees in Electrical and Computer Engineering in 2002 and 2007, respectively, from the Democritus University of Thrace (DUTH), Greece. Dr. Papakostas serves as a Tenured Full Professor at the Department of Computer Science, International Hellenic University, Greece. Dr. Papakostas has 10 years of experience in large-scale systems design as a senior software engineer and technical manager, and 20 years of research experience in the field of Artificial Intelligence. Currently, he is the Head of the “Visual Computing” division of HUman-MAchines INteraction Laboratory (HUMAIN-Lab) and the Director of the MPhil program “Advanced Technologies in Informatics and Computers” hosted by the Department of Computer Science, International Hellenic University. He has (co)authored more than 150 publications in indexed journals, international conferences and book chapters, 1 book (in Greek), 3 edited books, and 5 journal special issues. His publications have more than 2100 citations with h-index 27 (GoogleScholar). His research interests include computer/machine vision, machine learning, pattern recognition, computational intelligence. \nDr. Papakostas served as a reviewer in numerous journals, as a program\ncommittee member in international conferences and he is a member of the IAENG, MIR Labs, EUCogIII, INSTICC and the Technical Chamber of Greece (TEE).",institutionString:null,institution:{name:"International Hellenic University",institutionURL:null,country:{name:"Greece"}}},editorTwo:null,editorThree:null},{id:"25",title:"Evolutionary Computation",coverUrl:"https://cdn.intechopen.com/series_topics/covers/25.jpg",isOpenForSubmission:!0,editor:{id:"136112",title:"Dr.",name:"Sebastian",middleName:null,surname:"Ventura Soto",slug:"sebastian-ventura-soto",fullName:"Sebastian Ventura Soto",profilePictureURL:"https://mts.intechopen.com/storage/users/136112/images/system/136112.png",biography:"Sebastian Ventura is a Spanish researcher, a full professor with the Department of Computer Science and Numerical Analysis, University of Córdoba. Dr Ventura also holds the positions of Affiliated Professor at Virginia Commonwealth University (Richmond, USA) and Distinguished Adjunct Professor at King Abdulaziz University (Jeddah, Saudi Arabia). Additionally, he is deputy director of the Andalusian Research Institute in Data Science and Computational Intelligence (DaSCI) and heads the Knowledge Discovery and Intelligent Systems Research Laboratory. He has published more than ten books and over 300 articles in journals and scientific conferences. Currently, his work has received over 18,000 citations according to Google Scholar, including more than 2200 citations in 2020. In the last five years, he has published more than 60 papers in international journals indexed in the JCR (around 70% of them belonging to first quartile journals) and he has edited some Springer books “Supervised Descriptive Pattern Mining” (2018), “Multiple Instance Learning - Foundations and Algorithms” (2016), and “Pattern Mining with Evolutionary Algorithms” (2016). He has also been involved in more than 20 research projects supported by the Spanish and Andalusian governments and the European Union. He currently belongs to the editorial board of PeerJ Computer Science, Information Fusion and Engineering Applications of Artificial Intelligence journals, being also associate editor of Applied Computational Intelligence and Soft Computing and IEEE Transactions on Cybernetics. Finally, he is editor-in-chief of Progress in Artificial Intelligence. He is a Senior Member of the IEEE Computer, the IEEE Computational Intelligence, and the IEEE Systems, Man, and Cybernetics Societies, and the Association of Computing Machinery (ACM). Finally, his main research interests include data science, computational intelligence, and their applications.",institutionString:null,institution:{name:"University of Córdoba",institutionURL:null,country:{name:"Spain"}}},editorTwo:null,editorThree:null},{id:"26",title:"Machine Learning and Data Mining",coverUrl:"https://cdn.intechopen.com/series_topics/covers/26.jpg",isOpenForSubmission:!0,editor:{id:"24555",title:"Dr.",name:"Marco Antonio",middleName:null,surname:"Aceves Fernandez",slug:"marco-antonio-aceves-fernandez",fullName:"Marco Antonio Aceves Fernandez",profilePictureURL:"https://mts.intechopen.com/storage/users/24555/images/system/24555.jpg",biography:"Dr. Marco Antonio Aceves Fernandez obtained his B.Sc. (Eng.) in Telematics from the Universidad de Colima, Mexico. He obtained both his M.Sc. and Ph.D. from the University of Liverpool, England, in the field of Intelligent Systems. He is a full professor at the Universidad Autonoma de Queretaro, Mexico, and a member of the National System of Researchers (SNI) since 2009. Dr. Aceves Fernandez has published more than 80 research papers as well as a number of book chapters and congress papers. He has contributed in more than 20 funded research projects, both academic and industrial, in the area of artificial intelligence, ranging from environmental, biomedical, automotive, aviation, consumer, and robotics to other applications. He is also a honorary president at the National Association of Embedded Systems (AMESE), a senior member of the IEEE, and a board member of many institutions. His research interests include intelligent and embedded systems.",institutionString:null,institution:{name:"Autonomous University of Queretaro",institutionURL:null,country:{name:"Mexico"}}},editorTwo:null,editorThree:null},{id:"27",title:"Multi-Agent Systems",coverUrl:"https://cdn.intechopen.com/series_topics/covers/27.jpg",isOpenForSubmission:!0,editor:{id:"148497",title:"Dr.",name:"Mehmet",middleName:"Emin",surname:"Aydin",slug:"mehmet-aydin",fullName:"Mehmet Aydin",profilePictureURL:"https://mts.intechopen.com/storage/users/148497/images/system/148497.jpg",biography:"Dr. Mehmet Emin Aydin is a Senior Lecturer with the Department of Computer Science and Creative Technology, the University of the West of England, Bristol, UK. His research interests include swarm intelligence, parallel and distributed metaheuristics, machine learning, intelligent agents and multi-agent systems, resource planning, scheduling and optimization, combinatorial optimization. Dr. Aydin is currently a Fellow of Higher Education Academy, UK, a member of EPSRC College, a senior member of IEEE and a senior member of ACM. In addition to being a member of advisory committees of many international conferences, he is an Editorial Board Member of various peer-reviewed international journals. He has served as guest editor for a number of special issues of peer-reviewed international journals.",institutionString:null,institution:{name:"University of the West of England",institutionURL:null,country:{name:"United Kingdom"}}},editorTwo:null,editorThree:null}]},overviewPageOFChapters:{paginationCount:26,paginationItems:[{id:"81791",title:"Self-Supervised Contrastive Representation Learning in Computer Vision",doi:"10.5772/intechopen.104785",signatures:"Yalin Bastanlar and Semih Orhan",slug:"self-supervised-contrastive-representation-learning-in-computer-vision",totalDownloads:0,totalCrossrefCites:0,totalDimensionsCites:0,authors:null,book:{title:"Pattern Recognition - New Insights",coverURL:"https://cdn.intechopen.com/books/images_new/11442.jpg",subseries:{id:"26",title:"Machine Learning and Data Mining"}}},{id:"79345",title:"Application of Jump Diffusion Models in Insurance Claim Estimation",doi:"10.5772/intechopen.99853",signatures:"Leonard Mushunje, Chiedza Elvina Mashiri, Edina Chandiwana and Maxwell Mashasha",slug:"application-of-jump-diffusion-models-in-insurance-claim-estimation-1",totalDownloads:2,totalCrossrefCites:0,totalDimensionsCites:0,authors:null,book:{title:"Data Clustering",coverURL:"https://cdn.intechopen.com/books/images_new/10820.jpg",subseries:{id:"26",title:"Machine Learning and Data Mining"}}},{id:"81557",title:"Object Tracking Using Adapted Optical Flow",doi:"10.5772/intechopen.102863",signatures:"Ronaldo Ferreira, Joaquim José de Castro Ferreira and António José Ribeiro Neves",slug:"object-tracking-using-adapted-optical-flow",totalDownloads:10,totalCrossrefCites:0,totalDimensionsCites:0,authors:null,book:{title:"Information Extraction and Object Tracking in Digital Video",coverURL:"https://cdn.intechopen.com/books/images_new/10652.jpg",subseries:{id:"24",title:"Computer Vision"}}},{id:"81558",title:"Thresholding Image Techniques for Plant Segmentation",doi:"10.5772/intechopen.104587",signatures:"Miguel Ángel Castillo-Martínez, Francisco Javier Gallegos-Funes, Blanca E. Carvajal-Gámez, Guillermo Urriolagoitia-Sosa and Alberto J. 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(Eng.) in Telematics from the Universidad de Colima, Mexico. He obtained both his M.Sc. and Ph.D. from the University of Liverpool, England, in the field of Intelligent Systems. He is a full professor at the Universidad Autonoma de Queretaro, Mexico, and a member of the National System of Researchers (SNI) since 2009. Dr. Aceves Fernandez has published more than 80 research papers as well as a number of book chapters and congress papers. He has contributed in more than 20 funded research projects, both academic and industrial, in the area of artificial intelligence, ranging from environmental, biomedical, automotive, aviation, consumer, and robotics to other applications. He is also a honorary president at the National Association of Embedded Systems (AMESE), a senior member of the IEEE, and a board member of many institutions. 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He is currently a principal researcher in data analytics and optimisation at TECNALIA (Spain), a visiting fellow at the Basque Center for Applied Mathematics (BCAM) and a part-time lecturer at the University of the Basque Country (UPV/EHU). His research interests gravitate on the use of descriptive, prescriptive and predictive algorithms for data mining and optimization in a diverse range of application fields such as Energy, Transport, Telecommunications, Health and Industry, among others. In these fields he has published more than 240 articles, co-supervised 8 Ph.D. theses, edited 6 books, coauthored 7 patents and participated/led more than 40 research projects. 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David Pan",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bSEI9QAO/Profile_Picture_1623656213532",institutionString:null,institution:{name:"University of Alabama in Huntsville",institutionURL:null,country:{name:"United States of America"}}},{id:"72920",title:"Prof.",name:"Yves",middleName:"Philippe",surname:"Rybarczyk",fullName:"Yves Rybarczyk",profilePictureURL:"https://mts.intechopen.com/storage/users/72920/images/system/72920.jpeg",institutionString:"Dalarna University, Faculty of Data and Information Sciences",institution:{name:"Dalarna University",institutionURL:null,country:{name:"Sweden"}}}]},{id:"27",title:"Multi-Agent Systems",keywords:"Collaborative Intelligence, Learning, Distributed Control System, Swarm Robotics, Decision Science, Software Engineering",scope:"Multi-agent systems are recognised as a state of the art field in Artificial Intelligence studies, which is popular due to the usefulness in facilitation capabilities to handle real-world problem-solving in a distributed fashion. The area covers many techniques that offer solutions to emerging problems in robotics and enterprise-level software systems. Collaborative intelligence is highly and effectively achieved with multi-agent systems. Areas of application include swarms of robots, flocks of UAVs, collaborative software management. Given the level of technological enhancements, the popularity of machine learning in use has opened a new chapter in multi-agent studies alongside the practical challenges and long-lasting collaboration issues in the field. It has increased the urgency and the need for further studies in this field. We welcome chapters presenting research on the many applications of multi-agent studies including, but not limited to, the following key areas: machine learning for multi-agent systems; modeling swarms robots and flocks of UAVs with multi-agent systems; decision science and multi-agent systems; software engineering for and with multi-agent systems; tools and technologies of multi-agent systems.",annualVolume:11423,isOpenForSubmission:!0,coverUrl:"https://cdn.intechopen.com/series_topics/covers/27.jpg",editor:{id:"148497",title:"Dr.",name:"Mehmet",middleName:"Emin",surname:"Aydin",fullName:"Mehmet Aydin",profilePictureURL:"https://mts.intechopen.com/storage/users/148497/images/system/148497.jpg",institutionString:null,institution:{name:"University of the West of England",institutionURL:null,country:{name:"United Kingdom"}}},editorTwo:null,editorThree:null,editorialBoard:[{id:"275140",title:"Dr.",name:"Dinh Hoa",middleName:null,surname:"Nguyen",fullName:"Dinh Hoa Nguyen",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRbnKQAS/Profile_Picture_1622204093453",institutionString:null,institution:{name:"Kyushu University",institutionURL:null,country:{name:"Japan"}}},{id:"20259",title:"Dr.",name:"Hongbin",middleName:null,surname:"Ma",fullName:"Hongbin Ma",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRhDJQA0/Profile_Picture_2022-05-02T08:25:21.jpg",institutionString:null,institution:{name:"Beijing Institute of Technology",institutionURL:null,country:{name:"China"}}},{id:"28640",title:"Prof.",name:"Yasushi",middleName:null,surname:"Kambayashi",fullName:"Yasushi Kambayashi",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002aYOQxQAO/Profile_Picture_1625660525470",institutionString:null,institution:{name:"Nippon Institute of Technology",institutionURL:null,country:{name:"Japan"}}}]}]}},libraryRecommendation:{success:null,errors:{},institutions:[]},route:{name:"profile.detail",path:"/profiles/205069",hash:"",query:{},params:{id:"205069"},fullPath:"/profiles/205069",meta:{},from:{name:null,path:"/",hash:"",query:{},params:{},fullPath:"/",meta:{}}}},function(){var e;(e=document.currentScript||document.scripts[document.scripts.length-1]).parentNode.removeChild(e)}()