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Neural Information Processing ; 14th International Conference, ICONIP 2007, Kitakyushu, Japan, November 13-16, 2007, Revised Selected Papers, Part II
The 228 revised full papers presented were carefully reviewed and selected from numerous ordinary paper submissions and 15 special organized sessions. The 116 papers of the first volume are organized in topical sections on computational neuroscience, learning and memory, neural network models ,supervised /unsupervised/reinforcement learning, statistical learning algorithms, optimization algorithms, novel algorithms, as well as motor control and vision. The second volume contains 112 contributions related to statistical and pattern recognition algorithms, neuromorphic hardware and implementations, robotics, data mining and knowledge discovery, real world applications, cognitive and hybrid intelligent systems, bioinformatics, neuroinformatics, brain-conputer interfaces, and novel approaches.
Neural Information Processing ; 14th International Conference, ICONIP 2007, Kitakyushu, Japan, November 13-16, 2007, Revised Selected Papers, Part I
The 228 revised full papers presented were carefully reviewed and selected from numerous ordinary paper submissions and 15 special organized sessions. The 116 papers of the first volume are organized in topical sections on computational neuroscience, learning and memory, neural network models, supervised/unsupervised/reinforcement learning, statistical learning algorithms, optimization algorithms, novel algorithms, as well as motor control and vision. The second volume contains 112 contributions related to statistical and pattern recognition algorithms, neuromorphic hardware and implementations, robotics, data mining and knowledge discovery, real world applications, cognitive and hybrid intelligent systems, bioinformatics, neuroinformatics, brain-conputer interfaces, and novel approaches.
Network Classification For Traffic Management : Anomaly detection, feature selection, clustering and classification
Investigates network traffic classification solutions by proposing transport-layer methods to achieve better run and operated enterprise-scale networks. With the massive increase of data and traffic on the Internet within the 5G, IoT and smart cities frameworks, current network classification and analysis techniques are falling short. Novel approaches using machine learning algorithms are needed to cope with and manage real-world network traffic, including supervised, semi-supervised, and unsupervised classification techniques. Accurate and effective classification of network traffic will lead to better quality of service and more secure and manageable networks. Investigates network traffic classification solutions by proposing transport-layer methods to achieve better run and operated enterprise-scale networks. The authors explore novel methods for enhancing network statistics at the transport layer, helping to identify optimal feature selection through a global optimization approach and providing automatic labelling for raw traffic through a SemTra framework to maintain provable privacy on information disclosure properties.
IoT and AI Technologies for Sustainable Living : A Practical Handbook
Brings together all the latest methodologies, tools and techniques related to the Internet of Things and Artificial Intelligence in a single volume to build insight into their use in sustainable living. The areas of application include agriculture, smart farming, healthcare, bioinformatics, self-diagnosis systems, body sensor networks, multimedia mining, and multimedia in forensics and security. Provides a comprehensive discussion of modeling and implementation in water resource optimization, recognizing pest patterns, traffic scheduling, web mining, cyber security and cyber forensics. It will help develop an understanding of the need for AI and IoT to have a sustainable era of human living. The tools covered include genetic algorithms, cloud computing, water resource management, web mining, machine learning, block chaining, learning algorithms, sentimental analysis and Natural Language Processing (NLP).
Introduction to Machine Learning with Applications in Information Security
Provides a classroom-tested introduction to a wide variety of machine learning and deep learning algorithms and techniques, reinforced via realistic applications. The book is accessible and doesn't prove theorems, or dwell on mathematical theory. The goal is to present topics at an intuitive level, with just enough detail to clarify the underlying concepts. The book covers core classic machine learning topics in depth, including Hidden Markov Models (HMM), Support Vector Machines (SVM), and clustering. Additional machine learning topics include k-Nearest Neighbor (k-NN), boosting, Random Forests, and Linear Discriminant Analysis (LDA). The fundamental deep learning topics of backpropagation, Convolutional Neural Networks (CNN), Multilayer Perceptrons (MLP), and Recurrent Neural Networks (RNN) are covered in depth. A broad range of advanced deep learning architectures are also presented, including Long Short-Term Memory (LSTM), Generative Adversarial Networks (GAN), Extreme Learning Machines (ELM), Residual Networks (ResNet), Deep Belief Networks (DBN), Bidirectional Encoder Representations from Transformers (BERT), and Word2Vec.
Intelligent system algorithms and applications in science and technology
Explores the application of intelligent techniques in various fields of engineering and technology. It addresses diverse topics in such areas as machine learning-based intelligent systems for healthcare, applications of artificial intelligence and the Internet of Things, intelligent data analytics techniques, intelligent network systems and applications, and inequalities and process control systems. The authors explore the full breadth of the field, which encompasses data analysis, image processing, speech processing and recognition, medical science and healthcare monitoring, smart irrigation systems, insurance and banking, robotics and process control, and more.
Intelligent data engineering and automated Learning - IDEAL 2005 ; 6th International Conference, Brisbane, Australia, July 6-8, 2005, Proceedings
Constitutes the refereed proceedings of the 6th International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2005, held in Brisbane, Australia, in July 2005. These papers are organized in topical sections on data mining and knowledge engineering, learning algorithms and systems, bioinformatics, and more.
Intelligent connectivity : AI, IoT, and 5G
Explores the economics and technology of AI, IOT, and 5G integration. Delivers a comprehensive technological and economic analysis of intelligent connectivity and the integration of artificial intelligence, Internet of Things (IoT), and 5G. It covers a broad range of topics, including Machine-to-Machine (M2M) architectures, edge computing, cybersecurity, privacy, risk management, IoT architectures, and more. Will also get access to: A thorough introduction to technology adoption and emerging trends in technology, including business trends and disruptive new applications / Comprehensive explorations of telecommunications transformation and intelligent connectivity, including learning algorithms, machine learning, and deep learning / Practical discussions of the Internet of Things, including its potential for disruption and future trends for technological development / In-depth examinations of 5G wireless technology, including discussions of the first five generations of wireless tech
Intelligent Algorithms for Packing and Cutting Problem
Introduces intelligent solving algorithms for classical packing and cutting problem and their variants / Investigates novel methods, e.g. reinforcement learning algorithms, for rectangular and irregular packing problems / Presents practical engineering application cases in combination of theory and practice / investigates in detail the two-dimensional packing and cutting problems in the field of operations research and management science. It introduces the mathematical models and intelligent solving algorithms for these problems, as well as their engineering applications. Most intelligent methods reported in this book have already been applied in reality, which can provide reference for the engineers. The presented novel methods for the two-dimensional packing problem provide a new way to solve the problem for researchers interested in operations research or computer science. This book also introduces three new variants of packing problems and their solving methods, which offer a different research direction.
Information theory and machine learning
The recent successes of machine learning, especially regarding systems based on deep neural networks, have encouraged further research activities and raised a new set of challenges in understanding and designing complex machine learning algorithms. New applications require learning algorithms to be distributed, have transferable learning results, use computation resources efficiently, convergence quickly on online settings, have performance guarantees, satisfy fairness or privacy constraints, incorporate domain knowledge on model structures, etc. A new wave of developments in statistical learning theory and information theory has set out to address these challenges.
Information extraction : Algorithms and prospects in a retrieval context
The book focuses on content recognition in text. It elaborates on the past and current most successful algorithms and their application in a variety of domains (e.g., news filtering, mining of biomedical text, intelligence gathering, competitive intelligence, legal information searching, and processing of informal text). An important part discusses current statistical and machine learning algorithms for information detection and classification and integrates their results in probabilistic retrieval models. The book also reveals a number of ideas towards an advanced understanding and synthesis of textual content.
Impact of artificial intelligence on organizational transformation
Covers the areas like artificial intelligence & its impact on professions, as well as machine learning algorithms and technologies. The context of applications of AI in business process innovation primarily includes new business models, AI readiness and maturity at the organizational, technological, financial, and cultural levels. The book has extensive details on machine learning and the applications such as robotics, blockchain, Internet of Things. Also discussed are the influence of AI on financial strategies and policies, human skills & values, procurement innovation, production innovation, AI in marketing & sales platforms.
Identification of nonlinear systems using neural networks and polynomial models : A block-oriented approach
The book gives a comparative study of their gradient approximation accuracy, computational complexity, and convergence rates and furthermore presents some new and original methods concerning the model parameter adjusting with gradient-based techniques. "Identification of Nonlinear Systems Using Neural Networks and Polynomal Models".
Hybrid Artificial Intelligent Systems ; 15th International Conference, HAIS 2020, Gijón, Spain, November 11-13, 2020, Proceedings
This book constitutes the refereed proceedings of the 15th International Conference on Hybrid Artificial Intelligent Systems, HAIS 2020, held in Gijón, Spain, in November 2020. The 65 regular papers presented in this book were carefully reviewed and selected from 106 submissions. The papers are grouped into these topics: advanced data processing and visualization techniques; bio-inspired models and optimization; learning algorithms; data mining, knowledge discovery and big data; and hybrid artificial intelligence applications.
Grammatical Inference ; Algorithms and Applications : 9th International Colloquium, ICGI 2008 Saint-Malo, France, September 22-24, 2008 Proceedings
This book constitutes the refereed proceedings of the 9th International Colloquium on Grammatical Inference, ICGI 2008, held in Saint-Malo, France, in September 2008.The 21 revised full papers and 8 revised short papers presented were carefully reviewed and selected from 36 submissions. The topics of the papers presented vary from theoretical results of learning algorithms to innovative applications of grammatical inference, and from learning several interesting classes of formal grammars to applications to natural language processing.
Grammatical Inference : Algorithms and Applications ; 8th International Colloquium, ICGI 2006, Tokyo, Japan, September 20-22, 2006, Proceedings
The topics discussed range from theoretical results of learning algorithms to innovative applications of grammatical inference and from learning several interesting classes of formal grammars to applications to natural language processing.
Fundamentals and Methods of Machine and Deep Learning : Algorithms, Tools, and Applications
provides a practical approach by explaining the concepts of machine learning and deep learning algorithms, evaluation of methodology advances, and algorithm demonstrations with applications. In recent research studies, they are regarded as one of the disruptive technologies that will transform our future life, business, and the global economy. The recent explosion of digital data in a wide variety of domains, including science, engineering, Internet of Things, biomedical, healthcare, and many business sectors, has declared the era of big data, which cannot be analysed by classical statistics but by the more modern, robust machine learning and deep learning techniques. Since machine learning learns from data rather than by programming hard-coded decision rules, an attempt is being made to use machine learning to make computers that are able to solve problems like human experts in the field.
Energy minimization methods in computer vision and pattern recognition ; 6th International Conference, EMMCVPR 2007, Ezhou, China, August 27-29, 2007, Proceedings
Contains critical issues of representation, learning, and inference. Important new themes include pr- abilistic grammars, image parsing, and the use of datasets with ground-truth to act as benchmarks for evaluating algorithms and as a way to train learning algorithms. Other themes include the development of efficient inference algorithms using advanced techniques from statistics, computer science, and applied mathematics. This book makes no distinction between oral and poster papers. It also contiants sections on al- rithms, applications, image parsing, image processing, motion, shape, and thr- dimensional processing.
Deepfake detection
The rise of large language models (LLMs) and the increasing sophistication of deepfake images have made detecting synthetic content a pressing challenge. Several approaches have been proposed to tackle this problem, including statistical analysis, and machine learning algorithms. In this project, A novel zero-shot approach is proposed that utilizes the power of LLMs to detect fake text. The pre-trained LLM is fine-tuned to enhance its ability to differentiate real and fake text. The approach uses the LLM to detect text by analyzing the log probabilities of the text. For detecting fake images, computer vision algorithms and neural networks are used to analyze facial features. The facial region is cropped and preprocessed and the neural network identifies patterns indicative of synthetic content.
Deep learning for computational problems in hardware security : Modeling attacks on strong physically unclonable function circuits
Discusses a broad overview of traditional machine learning methods and state-of-the-art deep learning practices for hardware security applications, in particular the techniques of launching potent "modeling attacks" on Physically Unclonable Function (PUF) circuits, which are promising hardware security primitives. The volume is self-contained and includes a comprehensive background on PUF circuits, and the necessary mathematical foundation of traditional and advanced machine learning techniques such as support vector machines, logistic regression, neural networks, and deep learning. This book can be used as a self-learning resource for researchers and practitioners of hardware security, and will also be suitable for graduate-level courses on hardware security and application of machine learning in hardware security.
