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Innovation on Education and Social Sciences
After a year of a global Covid-19 pandemic, still, we have more questions than answers to the future of education and our social life. It is more important than ever to follow the developments closely in the coming period, pay attention to critical concerns such as inequality, as well as positive signs of transformation and innovation in all aspects of the world of teaching and learning. Expectations on what the future brings will have to be based on solid research rather than short-term perceptions. The proceedings of IJCAH 2021 are an interdisciplinary platform for teachers, researchers, practitioners, and academicians to discuss the latest research findings, concerns, and practical challenges encountered and solutions adopted in the fields of Arts and Humanities. The subject areas within the proceeding are education, language learning, arts, culture, social sciences.
Reimagining Innovation in Education and Social Sciences
Certainly, the pandemic has affected several aspects of life. Several modifications have been made and are now continuing. The number of innovations has expanded substantially, particularly in the fields of education and social sciences. Innovations are produced by educators, scientists, and professionals. These innovations must be distributed to aid the development of society in the sphere of education and beyond. After the eradication of the disease, we shall assist one another in conquering it and then develop and prosper together. This volume contains the works of educators, researchers, practitioners, and academics presenting the most recent research results, issues, and practical diffi...
Proceedings of the 1st International Conference on Neural Networks and Machine Learning 2022 (ICONNSMAL 2022)
This is an open access book. The 1st ICONNSMAL 2022 was held at CGANT Research Group the University of Jember, Jember, East-Java, Indonesia.
Advances in Automation, Signal Processing, Instrumentation, and Control
This book presents the select proceedings of the International Conference on Automation, Signal Processing, Instrumentation and Control (i-CASIC) 2020. The book mainly focuses on emerging technologies in electrical systems, IoT-based instrumentation, advanced industrial automation, and advanced image and signal processing. It also includes studies on the analysis, design and implementation of instrumentation systems, and high-accuracy and energy-efficient controllers. The contents of this book will be useful for beginners, researchers as well as professionals interested in instrumentation and control, and other allied fields.
EEG Signal Processing
Electroencephalography (EEG) is an electrophysiological monitoring method used to record the brain activity in brain-computer interface (BCI) systems. It records the electrical activity of the brain, is typically non-invasive with electrodes placed along the scalp, requires relatively simple and inexpensive equipment, and is easier to use than other methods. EEG-based BCI methods provide modest speed and accuracy which is why multichannel systems and proper signal processing methods are used for feature extraction, feature selection and feature classification to discriminate among several mental tasks. This edited book presents state of the art aspects of EEG signal processing methods, with an emphasis on advanced strategies, case studies, clinical practices and applications such as EEG for meditation, auditory selective attention, sleep apnoea; person authentication; handedness detection, Parkinson's disease, motor imagery, smart air travel support and brain signal classification.
Learning with Support Vector Machines
Support Vectors Machines have become a well established tool within machine learning. They work well in practice and have now been used across a wide range of applications from recognizing hand-written digits, to face identification, text categorisation, bioinformatics, and database marketing. In this book we give an introductory overview of this subject. We start with a simple Support Vector Machine for performing binary classification before considering multi-class classification and learning in the presence of noise. We show that this framework can be extended to many other scenarios such as prediction with real-valued outputs, novelty detection and the handling of complex output structures such as parse trees. Finally, we give an overview of the main types of kernels which are used in practice and how to learn and make predictions from multiple types of input data. Table of Contents: Support Vector Machines for Classification / Kernel-based Models / Learning with Kernels
Pattern Classification
This book provides a unified approach for developing a fuzzy classifier and explains the advantages and disadvantages of different classifiers through extensive performance evaluation of real data sets. It thus offers new learning paradigms for analyzing neural networks and fuzzy systems, while training fuzzy classifiers. Function approximation is also treated and function approximators are compared.
Children with Autism
The authors combine clinical vignettes, research findings, methodological considerations and historical accounts.
Optimal Traffic Control
Despite traffic circles, four-way stop signs, lights regulated by timers or sensors, and other methods, the management of urban intersections remains problematic. Consider that transportation systems have all the features of so-called complex systems: the great number of state and control variables, the presence of uncertainty and indeterminism, th
