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Proceedings of ELM-2015 Volume 1 by Jiuwen Cao,Kezhi Mao,Jonathan Wu,Amaury Lendasse Pdf
This book contains some selected papers from the International Conference on Extreme Learning Machine 2015, which was held in Hangzhou, China, December 15-17, 2015. This conference brought together researchers and engineers to share and exchange R&D experience on both theoretical studies and practical applications of the Extreme Learning Machine (ELM) technique and brain learning. This book covers theories, algorithms ad applications of ELM. It gives readers a glance of the most recent advances of ELM.
Proceedings of ELM-2015 Volume 2 by Jiuwen Cao,Kezhi Mao,Jonathan Wu,Amaury Lendasse Pdf
This book contains some selected papers from the International Conference on Extreme Learning Machine 2015, which was held in Hangzhou, China, December 15-17, 2015. This conference brought together researchers and engineers to share and exchange R&D experience on both theoretical studies and practical applications of the Extreme Learning Machine (ELM) technique and brain learning. This book covers theories, algorithms ad applications of ELM. It gives readers a glance of the most recent advances of ELM.
Proceedings of ELM-2014 Volume 1 by Jiuwen Cao,Kezhi Mao,Erik Cambria,Zhihong Man,Kar-Ann Toh Pdf
This book contains some selected papers from the International Conference on Extreme Learning Machine 2014, which was held in Singapore, December 8-10, 2014. This conference brought together the researchers and practitioners of Extreme Learning Machine (ELM) from a variety of fields to promote research and development of “learning without iterative tuning”. The book covers theories, algorithms and applications of ELM. It gives the readers a glance of the most recent advances of ELM.
Proceedings of ELM 2018 by Jiuwen Cao,Chi Man Vong,Yoan Miche,Amaury Lendasse Pdf
This book contains some selected papers from the International Conference on Extreme Learning Machine 2018, which was held in Singapore, November 21–23, 2018. This conference provided a forum for academics, researchers and engineers to share and exchange R&D experience on both theoretical studies and practical applications of the ELM technique and brain learning. Extreme Learning Machines (ELM) aims to enable pervasive learning and pervasive intelligence. As advocated by ELM theories, it is exciting to see the convergence of machine learning and biological learning from the long-term point of view. ELM may be one of the fundamental “learning particles” filling the gaps between machine learning and biological learning (of which activation functions are even unknown). ELM represents a suite of (machine and biological) learning techniques in which hidden neurons need not be tuned: inherited from their ancestors or randomly generated. ELM learning theories show that effective learning algorithms can be derived based on randomly generated hidden neurons (biological neurons, artificial neurons, wavelets, Fourier series, etc.) as long as they are nonlinear piecewise continuous, independent of training data and application environments. Increasingly, evidence from neuroscience suggests that similar principles apply in biological learning systems. ELM theories and algorithms argue that “random hidden neurons” capture an essential aspect of biological learning mechanisms as well as the intuitive sense that the efficiency of biological learning need not rely on computing power of neurons. ELM theories thus hint at possible reasons why the brain is more intelligent and effective than current computers. The main theme of ELM2018 is Hierarchical ELM, AI for IoT, Synergy of Machine Learning and Biological Learning. This book covers theories, algorithms and applications of ELM. It gives readers a glance at the most recent advances of ELM.
Proceedings of ELM-2016 by Jiuwen Cao,Erik Cambria,Amaury Lendasse,Yoan Miche,Chi Man Vong Pdf
This book contains some selected papers from the International Conference on Extreme Learning Machine 2016, which was held in Singapore, December 13-15, 2016. This conference will provide a forum for academics, researchers and engineers to share and exchange R&D experience on both theoretical studies and practical applications of the ELM technique and brain learning. Extreme Learning Machines (ELM) aims to break the barriers between the conventional artificial learning techniques and biological learning mechanism. ELM represents a suite of (machine or possibly biological) learning techniques in which hidden neurons need not be tuned. ELM learning theories show that very effective learning algorithms can be derived based on randomly generated hidden neurons (with almost any nonlinear piecewise activation functions), independent of training data and application environments. Increasingly, evidence from neuroscience suggests that similar principles apply in biological learning systems. ELM theories and algorithms argue that “random hidden neurons” capture an essential aspect of biological learning mechanisms as well as the intuitive sense that the efficiency of biological learning need not rely on computing power of neurons. ELM theories thus hint at possible reasons why the brain is more intelligent and effective than current computers. ELM offers significant advantages over conventional neural network learning algorithms such as fast learning speed, ease of implementation, and minimal need for human intervention. ELM also shows potential as a viable alternative technique for large‐scale computing and artificial intelligence. This book covers theories, algorithms ad applications of ELM. It gives readers a glance of the most recent advances of ELM.
Artificial Neural Networks and Machine Learning – ICANN 2017 by Alessandra Lintas,Stefano Rovetta,Paul F.M.J. Verschure,Alessandro E.P. Villa Pdf
The two volume set, LNCS 10613 and 10614, constitutes the proceedings of then 26th International Conference on Artificial Neural Networks, ICANN 2017, held in Alghero, Italy, in September 2017. The 128 full papers included in this volume were carefully reviewed and selected from 270 submissions. They were organized in topical sections named: From Perception to Action; From Neurons to Networks; Brain Imaging; Recurrent Neural Networks; Neuromorphic Hardware; Brain Topology and Dynamics; Neural Networks Meet Natural and Environmental Sciences; Convolutional Neural Networks; Games and Strategy; Representation and Classification; Clustering; Learning from Data Streams and Time Series; Image Processing and Medical Applications; Advances in Machine Learning. There are 63 short paper abstracts that are included in the back matter of the volume.
Examining Multimedia Forensics and Content Integrity by Mahana, Sumit Kumar,Aggarwal, Rajesh Kumar,Singh, Surjit Pdf
Due to the ubiquity of social media and digital information, the use of digital images in today’s digitized marketplace is continuously rising throughout enterprises. Organizations that want to offer their content through the internet confront plenty of security concerns, including copyright violation. Advanced solutions for the security and privacy of digital data are continually being developed, yet there is a lack of current research in this area. Examining Multimedia Forensics and Content Integrity features a collection of innovative research on the approaches and applications of current techniques for the privacy and security of multimedia and their secure transportation. It provides relevant theoretical frameworks and the latest empirical research findings in the area of multimedia forensics and content integrity. Covering topics such as 3D data security, copyright protection, and watermarking, this major reference work is a comprehensive resource for security analysts, programmers, technology developers, IT professionals, students and educators of higher education, librarians, researchers, and academicians.
Recent Advances on Soft Computing and Data Mining by Rozaida Ghazali,Mustafa Mat Deris,Nazri Mohd Nawi,Jemal H. Abawajy Pdf
This book offers a systematic overview of the concepts and practical techniques that readers need to get the most out of their large-scale data mining projects and research studies. It guides them through the data-analytical thinking essential to extract useful information and obtain commercial value from the data. Presenting the outcomes of International Conference on Soft Computing and Data Mining (SCDM-2017), held in Johor, Malaysia on February 6–8, 2018, it provides a well-balanced integration of soft computing and data mining techniques. The two constituents are brought together in various combinations of applications and practices. To thrive in these data-driven ecosystems, researchers, engineers, data analysts, practitioners, and managers must understand the design choice and options of soft computing and data mining techniques, and as such this book is a valuable resource, helping readers solve complex benchmark problems and better appreciate the concepts, tools, and techniques employed.
Machine Learning, Big Data, and IoT for Medical Informatics by Pardeep Kumar,Yugal Kumar,Mohamed A. Tawhid Pdf
Machine Learning, Big Data, and IoT for Medical Informatics focuses on the latest techniques adopted in the field of medical informatics. In medical informatics, machine learning, big data, and IOT-based techniques play a significant role in disease diagnosis and its prediction. In the medical field, the structure of data is equally important for accurate predictive analytics due to heterogeneity of data such as ECG data, X-ray data, and image data. Thus, this book focuses on the usability of machine learning, big data, and IOT-based techniques in handling structured and unstructured data. It also emphasizes on the privacy preservation techniques of medical data. This volume can be used as a reference book for scientists, researchers, practitioners, and academicians working in the field of intelligent medical informatics. In addition, it can also be used as a reference book for both undergraduate and graduate courses such as medical informatics, machine learning, big data, and IoT. Explains the uses of CNN, Deep Learning and extreme machine learning concepts for the design and development of predictive diagnostic systems. Includes several privacy preservation techniques for medical data. Presents the integration of Internet of Things with predictive diagnostic systems for disease diagnosis. Offers case studies and applications relating to machine learning, big data, and health care analysis.
International Conference on Innovative Computing and Communications by Aboul Ella Hassanien,Oscar Castillo,Sameer Anand,Ajay Jaiswal Pdf
This book includes high-quality research papers presented at the Sixth International Conference on Innovative Computing and Communication (ICICC 2023), which is held at the Shaheed Sukhdev College of Business Studies, University of Delhi, Delhi, India, on February 17–18, 2023. Introducing the innovative works of scientists, professors, research scholars, students and industrial experts in the field of computing and communication, the book promotes the transformation of fundamental research into institutional and industrialized research and the conversion of applied exploration into real-time applications.
Theory, Methodology, Tools and Applications for Modeling and Simulation of Complex Systems by Lin Zhang,Xiao Song,Yunjie Wu Pdf
This four-volume set (CCIS 643, 644, 645, 646) constitutes the refereed proceedings of the 16th Asia Simulation Conference and the First Autumn Simulation Multi-Conference, AsiaSim / SCS AutumnSim 2016, held in Beijing, China, in October 2016. The 265 revised full papers presented were carefully reviewed and selected from 651 submissions. The papers in this fourth volume of the set are organized in topical sections on Modeling and Simulation Applications; Simulation Software; Social Simulations; Verification, Validation and Accreditation.
Systems Analytics and Integration of Big Omics Data by Gary Hardiman Pdf
A “genotype" is essentially an organism's full hereditary information which is obtained from its parents. A "phenotype" is an organism's actual observed physical and behavioral properties. These may include traits such as morphology, size, height, eye color, metabolism, etc. One of the pressing challenges in computational and systems biology is genotype-to-phenotype prediction. This is challenging given the amount of data generated by modern Omics technologies. This “Big Data” is so large and complex that traditional data processing applications are not up to the task. Challenges arise in collection, analysis, mining, sharing, transfer, visualization, archiving, and integration of these data. In this Special Issue, there is a focus on the systems-level analysis of Omics data, recent developments in gene ontology annotation, and advances in biological pathways and network biology. The integration of Omics data with clinical and biomedical data using machine learning is explored. This Special Issue covers new methodologies in the context of gene–environment interactions, tissue-specific gene expression, and how external factors or host genetics impact the microbiome.
Advances in Multimedia Information Processing – PCM 2017 by Bing Zeng,Qingming Huang,Abdulmotaleb El Saddik,Hongliang Li,Shuqiang Jiang,Xiaopeng Fan Pdf
The two-volume set LNCS 10735 and 10736 constitutes the thoroughly refereed proceedings of the 18th Pacific-Rim Conference on Multimedia, PCM 2017, held in Harbin, China, in September 2017. The 184 full papers presented were carefully reviewed and selected from 264 submissions. The papers are organized in topical sections on: Best Paper Candidate; Video Coding; Image Super-resolution, Debluring, and Dehazing; Person Identity and Emotion; Tracking and Action Recognition; Detection and Classification; Multimedia Signal Reconstruction and Recovery; Text and Line Detection/Recognition; Social Media; 3D and Panoramic Vision; Deep Learning for Signal Processing and Understanding; Large-Scale Multimedia Affective Computing; Sensor-enhanced Multimedia Systems; Content Analysis; Coding, Compression, Transmission, and Processing.
Proceedings of ELM2019 by Jiuwen Cao,Chi Man Vong,Yoan Miche,Amaury Lendasse Pdf
This book contains some selected papers from the International Conference on Extreme Learning Machine 2019, which was held in Yangzhou, China, December 14–16, 2019. Extreme Learning Machines (ELMs) aim to enable pervasive learning and pervasive intelligence. As advocated by ELM theories, it is exciting to see the convergence of machine learning and biological learning from the long-term point of view. ELM may be one of the fundamental ‘learning particles’ filling the gaps between machine learning and biological learning (of which activation functions are even unknown). ELM represents a suite of (machine and biological) learning techniques in which hidden neurons need not be tuned: inherited from their ancestors or randomly generated. ELM learning theories show that effective learning algorithms can be derived based on randomly generated hidden neurons (biological neurons, artificial neurons, wavelets, Fourier series, etc) as long as they are nonlinear piecewise continuous, independent of training data and application environments. Increasingly, evidence from neuroscience suggests that similar principles apply in biological learning systems. ELM theories and algorithms argue that “random hidden neurons” capture an essential aspect of biological learning mechanisms as well as the intuitive sense that the efficiency of biological learning need not rely on computing power of neurons. ELM theories thus hint at possible reasons why the brain is more intelligent and effective than current computers. The main theme of ELM2019 is Hierarchical ELM, AI for IoT, Synergy of Machine Learning and Biological Learning. This conference provides a forum for academics, researchers and engineers to share and exchange R&D experience on both theoretical studies and practical applications of the ELM technique and brain learning. This book covers theories, algorithms and applications of ELM. It gives readers a glance of the most recent advances of ELM.
Artificial Neural Networks and Machine Learning – ICANN 2018 by Věra Kůrková,Yannis Manolopoulos,Barbara Hammer,Lazaros Iliadis,Ilias Maglogiannis Pdf
This three-volume set LNCS 11139-11141 constitutes the refereed proceedings of the 27th International Conference on Artificial Neural Networks, ICANN 2018, held in Rhodes, Greece, in October 2018. The papers presented in these volumes was carefully reviewed and selected from total of 360 submissions. They are related to the following thematic topics: AI and Bioinformatics, Bayesian and Echo State Networks, Brain Inspired Computing, Chaotic Complex Models, Clustering, Mining, Exploratory Analysis, Coding Architectures, Complex Firing Patterns, Convolutional Neural Networks, Deep Learning (DL), DL in Real Time Systems, DL and Big Data Analytics, DL and Big Data, DL and Forensics, DL and Cybersecurity, DL and Social Networks, Evolving Systems – Optimization, Extreme Learning Machines, From Neurons to Neuromorphism, From Sensation to Perception, From Single Neurons to Networks, Fuzzy Modeling, Hierarchical ANN, Inference and Recognition, Information and Optimization, Interacting with The Brain, Machine Learning (ML), ML for Bio Medical systems, ML and Video-Image Processing, ML and Forensics, ML and Cybersecurity, ML and Social Media, ML in Engineering, Movement and Motion Detection, Multilayer Perceptrons and Kernel Networks, Natural Language, Object and Face Recognition, Recurrent Neural Networks and Reservoir Computing, Reinforcement Learning, Reservoir Computing, Self-Organizing Maps, Spiking Dynamics/Spiking ANN, Support Vector Machines, Swarm Intelligence and Decision-Making, Text Mining, Theoretical Neural Computation, Time Series and Forecasting, Training and Learning.