Mathematical Models Using Artificial Intelligence For Surveillance Systems

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Mathematical Models Using Artificial Intelligence for Surveillance Systems

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Author : Padmesh Tripathi
Publisher : John Wiley & Sons
ISBN 13 : 1394200587
Total Pages : 373 pages
Book Rating : 4.3/5 (942 download)

Book Synopsis Mathematical Models Using Artificial Intelligence for Surveillance Systems by : Padmesh Tripathi

Download or read book Mathematical Models Using Artificial Intelligence for Surveillance Systems written by Padmesh Tripathi and published by John Wiley & Sons. This book was released on 2024-09-18 with total page 373 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book gives comprehensive insights into the application of AI, machine learning, and deep learning in developing efficient and optimal surveillance systems for both indoor and outdoor environments, addressing the evolving security challenges in public and private spaces. Mathematical Models Using Artificial Intelligence for Surveillance Systems aims to collect and publish basic principles, algorithms, protocols, developing trends, and security challenges and their solutions for various indoor and outdoor surveillance applications using artificial intelligence (AI). The book addresses how AI technologies such as machine learning (ML), deep learning (DL), sensors, and other wireless devices could play a vital role in assisting various security agencies. Security and safety are the major concerns for public and private places in every country. Some places need indoor surveillance, some need outdoor surveillance, and, in some places, both are needed. The goal of this book is to provide an efficient and optimal surveillance system using AI, ML, and DL-based image processing. The blend of machine vision technology and AI provides a more efficient surveillance system compared to traditional systems. Leading scholars and industry practitioners are expected to make significant contributions to the chapters. Their deep conversations and knowledge, which are based on references and research, will result in a wonderful book and a valuable source of information.

Automated Multi-Camera Surveillance

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Author : Omar Javed
Publisher : Springer Science & Business Media
ISBN 13 : 0387788816
Total Pages : 107 pages
Book Rating : 4.3/5 (877 download)

Book Synopsis Automated Multi-Camera Surveillance by : Omar Javed

Download or read book Automated Multi-Camera Surveillance written by Omar Javed and published by Springer Science & Business Media. This book was released on 2008-12-16 with total page 107 pages. Available in PDF, EPUB and Kindle. Book excerpt: The recent development of intelligent surveillance systems has captured the interest of both academic research labs and industry. Automated Multi-Camera Surveillance addresses monitoring of people and vehicles, and detection of threatening objects and events in a variety of scenarios. In this book, techniques for development of an automated multi-camera surveillance system are discussed and proposed. The state-of-the-art in the automated surveillance systems is reviewed as well. Detailed explanation of sub-components of surveillance systems are provided, and enhancements to each of these components are proposed. The authors identify important challenges that such a system must address, and propose solutions. Development of a specific surveillance system called “KNIGHT” is described, along with the authors’ experience using it. This book enables the reader to understand the mathematical models and algorithms underlying automated surveillance as well as the benefits and limitations of using such methods.

Artificial Intelligence

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Author :
Publisher : Elsevier
ISBN 13 : 0443137641
Total Pages : 260 pages
Book Rating : 4.4/5 (431 download)

Book Synopsis Artificial Intelligence by :

Download or read book Artificial Intelligence written by and published by Elsevier. This book was released on 2023-09-01 with total page 260 pages. Available in PDF, EPUB and Kindle. Book excerpt: Artificial Intelligence, Volume 49 in the Handbook of Statistics series, highlights new advances in the field, with this new volume presenting interesting chapters on a variety of timely topics. Chapters in this new release include AI Teacher-Student based Adaptive Structural Deep Learning Model and Its Estimating Uncertainty of Image Data, Machine-derived Intelligence: Computations Beyond the Null Hypothesis, Object oriented basis of artificial intelligence methodologies I in Judicial Systems in India, Artificial Intelligence in Systems Biology, Machine-Learning in Geometry and Physics, Innovation and Machine Learning: Crowdsourcing Open-Source Natural Language Processing (NLP) Algorithms to Advance Public Health Surveillance, and more. Other chapters cover Learning and identity testing of Markov chains, Data privacy for machine learning and statistics, and The interface between AI and Mathematics. Provides the authority and expertise of leading contributors from an international board of authors Presents the latest release in the Handbook of Statistics series Includes the latest information on Artificial Intelligence

Adversarial Machine Learning

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Author : Aneesh Sreevallabh Chivukula
Publisher : Springer Nature
ISBN 13 : 3030997723
Total Pages : 316 pages
Book Rating : 4.0/5 (39 download)

Book Synopsis Adversarial Machine Learning by : Aneesh Sreevallabh Chivukula

Download or read book Adversarial Machine Learning written by Aneesh Sreevallabh Chivukula and published by Springer Nature. This book was released on 2023-03-06 with total page 316 pages. Available in PDF, EPUB and Kindle. Book excerpt: A critical challenge in deep learning is the vulnerability of deep learning networks to security attacks from intelligent cyber adversaries. Even innocuous perturbations to the training data can be used to manipulate the behaviour of deep networks in unintended ways. In this book, we review the latest developments in adversarial attack technologies in computer vision; natural language processing; and cybersecurity with regard to multidimensional, textual and image data, sequence data, and temporal data. In turn, we assess the robustness properties of deep learning networks to produce a taxonomy of adversarial examples that characterises the security of learning systems using game theoretical adversarial deep learning algorithms. The state-of-the-art in adversarial perturbation-based privacy protection mechanisms is also reviewed. We propose new adversary types for game theoretical objectives in non-stationary computational learning environments. Proper quantification of the hypothesis set in the decision problems of our research leads to various functional problems, oracular problems, sampling tasks, and optimization problems. We also address the defence mechanisms currently available for deep learning models deployed in real-world environments. The learning theories used in these defence mechanisms concern data representations, feature manipulations, misclassifications costs, sensitivity landscapes, distributional robustness, and complexity classes of the adversarial deep learning algorithms and their applications. In closing, we propose future research directions in adversarial deep learning applications for resilient learning system design and review formalized learning assumptions concerning the attack surfaces and robustness characteristics of artificial intelligence applications so as to deconstruct the contemporary adversarial deep learning designs. Given its scope, the book will be of interest to Adversarial Machine Learning practitioners and Adversarial Artificial Intelligence researchers whose work involves the design and application of Adversarial Deep Learning.

Research Directions in Computational Mechanics

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Author : National Research Council
Publisher : National Academies Press
ISBN 13 : 0309046483
Total Pages : 145 pages
Book Rating : 4.3/5 (9 download)

Book Synopsis Research Directions in Computational Mechanics by : National Research Council

Download or read book Research Directions in Computational Mechanics written by National Research Council and published by National Academies Press. This book was released on 1991-02-01 with total page 145 pages. Available in PDF, EPUB and Kindle. Book excerpt: Computational mechanics is a scientific discipline that marries physics, computers, and mathematics to emulate natural physical phenomena. It is a technology that allows scientists to study and predict the performance of various productsâ€"important for research and development in the industrialized world. This book describes current trends and future research directions in computational mechanics in areas where gaps exist in current knowledge and where major advances are crucial to continued technological developments in the United States.

Deep Learning for Unmanned Systems

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Author : Anis Koubaa
Publisher : Springer Nature
ISBN 13 : 3030779394
Total Pages : 731 pages
Book Rating : 4.0/5 (37 download)

Book Synopsis Deep Learning for Unmanned Systems by : Anis Koubaa

Download or read book Deep Learning for Unmanned Systems written by Anis Koubaa and published by Springer Nature. This book was released on 2021-10-01 with total page 731 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is used at the graduate or advanced undergraduate level and many others. Manned and unmanned ground, aerial and marine vehicles enable many promising and revolutionary civilian and military applications that will change our life in the near future. These applications include, but are not limited to, surveillance, search and rescue, environment monitoring, infrastructure monitoring, self-driving cars, contactless last-mile delivery vehicles, autonomous ships, precision agriculture and transmission line inspection to name just a few. These vehicles will benefit from advances of deep learning as a subfield of machine learning able to endow these vehicles with different capability such as perception, situation awareness, planning and intelligent control. Deep learning models also have the ability to generate actionable insights into the complex structures of large data sets. In recent years, deep learning research has received an increasing amount of attention from researchers in academia, government laboratories and industry. These research activities have borne some fruit in tackling some of the challenging problems of manned and unmanned ground, aerial and marine vehicles that are still open. Moreover, deep learning methods have been recently actively developed in other areas of machine learning, including reinforcement training and transfer/meta-learning, whereas standard, deep learning methods such as recent neural network (RNN) and coevolutionary neural networks (CNN). The book is primarily meant for researchers from academia and industry, who are working on in the research areas such as engineering, control engineering, robotics, mechatronics, biomedical engineering, mechanical engineering and computer science. The book chapters deal with the recent research problems in the areas of reinforcement learning-based control of UAVs and deep learning for unmanned aerial systems (UAS) The book chapters present various techniques of deep learning for robotic applications. The book chapters contain a good literature survey with a long list of references. The book chapters are well written with a good exposition of the research problem, methodology, block diagrams and mathematical techniques. The book chapters are lucidly illustrated with numerical examples and simulations. The book chapters discuss details of applications and future research areas.

Modelling and Simulation for Autonomous Systems

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Author : Jan Mazal
Publisher : Springer Nature
ISBN 13 : 3030707407
Total Pages : 315 pages
Book Rating : 4.0/5 (37 download)

Book Synopsis Modelling and Simulation for Autonomous Systems by : Jan Mazal

Download or read book Modelling and Simulation for Autonomous Systems written by Jan Mazal and published by Springer Nature. This book was released on 2021-03-04 with total page 315 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the thoroughly refereed post-conference proceedings of the 7th International Conference on Modelling and Simulation for Autonomous Systems, MESAS 2020, held in Prague, Czech Republic, in October 2020.* The 19 full papers included in the volume were carefully reviewed and selected from 26 submissions. They are organized in the following topical sections: future challenges of advanced M&S technology; M&S of intelligent systems – R&D and application; and AxS/AI in context of future warfare and security environment. *The conference was held virtually.

Adversarial Machine Learning

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Author : Yevgeniy Vorobeychik
Publisher : Springer
ISBN 13 : 9783031004520
Total Pages : 152 pages
Book Rating : 4.0/5 (45 download)

Book Synopsis Adversarial Machine Learning by : Yevgeniy Vorobeychik

Download or read book Adversarial Machine Learning written by Yevgeniy Vorobeychik and published by Springer. This book was released on 2018-08-08 with total page 152 pages. Available in PDF, EPUB and Kindle. Book excerpt: The increasing abundance of large high-quality datasets, combined with significant technical advances over the last several decades have made machine learning into a major tool employed across a broad array of tasks including vision, language, finance, and security. However, success has been accompanied with important new challenges: many applications of machine learning are adversarial in nature. Some are adversarial because they are safety critical, such as autonomous driving. An adversary in these applications can be a malicious party aimed at causing congestion or accidents, or may even model unusual situations that expose vulnerabilities in the prediction engine. Other applications are adversarial because their task and/or the data they use are. For example, an important class of problems in security involves detection, such as malware, spam, and intrusion detection. The use of machine learning for detecting malicious entities creates an incentive among adversaries to evade detection by changing their behavior or the content of malicius objects they develop. The field of adversarial machine learning has emerged to study vulnerabilities of machine learning approaches in adversarial settings and to develop techniques to make learning robust to adversarial manipulation. This book provides a technical overview of this field. After reviewing machine learning concepts and approaches, as well as common use cases of these in adversarial settings, we present a general categorization of attacks on machine learning. We then address two major categories of attacks and associated defenses: decision-time attacks, in which an adversary changes the nature of instances seen by a learned model at the time of prediction in order to cause errors, and poisoning or training time attacks, in which the actual training dataset is maliciously modified. In our final chapter devoted to technical content, we discuss recent techniques for attacks on deep learning, as well as approaches for improving robustness of deep neural networks. We conclude with a discussion of several important issues in the area of adversarial learning that in our view warrant further research. Given the increasing interest in the area of adversarial machine learning, we hope this book provides readers with the tools necessary to successfully engage in research and practice of machine learning in adversarial settings.

Concepts and Real-Time Applications of Deep Learning

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Author : Smriti Srivastava
Publisher : Springer Nature
ISBN 13 : 3030761673
Total Pages : 212 pages
Book Rating : 4.0/5 (37 download)

Book Synopsis Concepts and Real-Time Applications of Deep Learning by : Smriti Srivastava

Download or read book Concepts and Real-Time Applications of Deep Learning written by Smriti Srivastava and published by Springer Nature. This book was released on 2021-09-23 with total page 212 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides readers with a comprehensive and recent exposition in deep learning and its multidisciplinary applications, with a concentration on advances of deep learning architectures. The book discusses various artificial intelligence (AI) techniques based on deep learning architecture with applications in natural language processing, semantic knowledge, forecasting and many more. The authors shed light on various applications that can benefit from the use of deep learning in pattern recognition, person re-identification in surveillance videos, action recognition in videos, image and video captioning. The book also highlights how deep learning concepts can be interwoven with more modern concepts to yield applications in multidisciplinary fields. Presents a comprehensive look at deep learning and its multidisciplinary applications, concentrating on advances of deep learning architectures; Includes a survey of deep learning problems and solutions, identifying the main open issues, innovations and latest technologies; Shows industrial deep learning in practice with examples/cases, efforts, challenges, and strategic approaches.

Deep Learning for Biometrics

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Author : Bir Bhanu
Publisher : Springer
ISBN 13 : 3319616579
Total Pages : 329 pages
Book Rating : 4.3/5 (196 download)

Book Synopsis Deep Learning for Biometrics by : Bir Bhanu

Download or read book Deep Learning for Biometrics written by Bir Bhanu and published by Springer. This book was released on 2017-08-01 with total page 329 pages. Available in PDF, EPUB and Kindle. Book excerpt: This timely text/reference presents a broad overview of advanced deep learning architectures for learning effective feature representation for perceptual and biometrics-related tasks. The text offers a showcase of cutting-edge research on the use of convolutional neural networks (CNN) in face, iris, fingerprint, and vascular biometric systems, in addition to surveillance systems that use soft biometrics. Issues of biometrics security are also examined. Topics and features: addresses the application of deep learning to enhance the performance of biometrics identification across a wide range of different biometrics modalities; revisits deep learning for face biometrics, offering insights from neuroimaging, and provides comparison with popular CNN-based architectures for face recognition; examines deep learning for state-of-the-art latent fingerprint and finger-vein recognition, as well as iris recognition; discusses deep learning for soft biometrics, including approaches for gesture-based identification, gender classification, and tattoo recognition; investigates deep learning for biometrics security, covering biometrics template protection methods, and liveness detection to protect against fake biometrics samples; presents contributions from a global selection of pre-eminent experts in the field representing academia, industry and government laboratories. Providing both an accessible introduction to the practical applications of deep learning in biometrics, and a comprehensive coverage of the entire spectrum of biometric modalities, this authoritative volume will be of great interest to all researchers, practitioners and students involved in related areas of computer vision, pattern recognition and machine learning.

Quantitative Measure for Discrete Event Supervisory Control

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Author : Asok Ray
Publisher : Springer
ISBN 13 : 9781441918512
Total Pages : 0 pages
Book Rating : 4.9/5 (185 download)

Book Synopsis Quantitative Measure for Discrete Event Supervisory Control by : Asok Ray

Download or read book Quantitative Measure for Discrete Event Supervisory Control written by Asok Ray and published by Springer. This book was released on 2011-09-29 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Supervisory Control Theory (SCT) provides a tool to model and control human-engineered complex systems, such as computer networks, World Wide Web, identification and spread of malicious executables, and command, control, communication, and information systems. Although there are some excellent monographs and books on SCT to control and diagnose discrete-event systems, there is a need for a research monograph that provides a coherent quantitative treatment of SCT theory for decision and control of complex systems. This new monograph will assimilate many new concepts that have been recently reported or are in the process of being reported in open literature. The major objectives here are to present a) a quantitative approach, supported by a formal theory, for discrete-event decision and control of human-engineered complex systems; and b) a set of applications to emerging technological areas such as control of software systems, malicious executables, and complex engineering systems. The monograph will provide the necessary background materials in automata theory and languages for supervisory control. It will introduce a new paradigm of language measure to quantitatively compare the performance of different automata models of a physical system. A novel feature of this approach is to generate discrete-event robust optimal decision and control algorithms for both military and commercial systems.

Advances in Hybridization of Intelligent Methods

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Author : Ioannis Hatzilygeroudis
Publisher : Springer
ISBN 13 : 9783319667898
Total Pages : 0 pages
Book Rating : 4.6/5 (678 download)

Book Synopsis Advances in Hybridization of Intelligent Methods by : Ioannis Hatzilygeroudis

Download or read book Advances in Hybridization of Intelligent Methods written by Ioannis Hatzilygeroudis and published by Springer. This book was released on 2017-10-23 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents recent research on the hybridization of intelligent methods, which refers to combining methods to solve complex problems. It discusses hybrid approaches covering different areas of intelligent methods and technologies, such as neural networks, swarm intelligence, machine learning, reinforcement learning, deep learning, agent-based approaches, knowledge-based system and image processing. The book includes extended and revised versions of invited papers presented at the 6th International Workshop on Combinations of Intelligent Methods and Applications (CIMA 2016), held in The Hague, Holland, in August 2016. The book is intended for researchers and practitioners from academia and industry interested in using hybrid methods for solving complex problems.

Handbook of Dynamic Data Driven Applications Systems

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Author : Frederica Darema
Publisher : Springer Nature
ISBN 13 : 3031279867
Total Pages : 937 pages
Book Rating : 4.0/5 (312 download)

Book Synopsis Handbook of Dynamic Data Driven Applications Systems by : Frederica Darema

Download or read book Handbook of Dynamic Data Driven Applications Systems written by Frederica Darema and published by Springer Nature. This book was released on 2023-10-16 with total page 937 pages. Available in PDF, EPUB and Kindle. Book excerpt: This Second Volume in the series Handbook of Dynamic Data Driven Applications Systems (DDDAS) expands the scope of the methods and the application areas presented in the first Volume and aims to provide additional and extended content of the increasing set of science and engineering advances for new capabilities enabled through DDDAS. The methods and examples of breakthroughs presented in the book series capture the DDDAS paradigm and its scientific and technological impact and benefits. The DDDAS paradigm and the ensuing DDDAS-based frameworks for systems’ analysis and design have been shown to engender new and advanced capabilities for understanding, analysis, and management of engineered, natural, and societal systems (“applications systems”), and for the commensurate wide set of scientific and engineering fields and applications, as well as foundational areas. The DDDAS book series aims to be a reference source of many of the important research and development efforts conducted under the rubric of DDDAS, and to also inspire the broader communities of researchers and developers about the potential in their respective areas of interest, of the application and the exploitation of the DDDAS paradigm and the ensuing frameworks, through the examples and case studies presented, either within their own field or other fields of study. As in the first volume, the chapters in this book reflect research work conducted over the years starting in the 1990’s to the present. Here, the theory and application content are considered for: Foundational Methods Materials Systems Structural Systems Energy Systems Environmental Systems: Domain Assessment & Adverse Conditions/Wildfires Surveillance Systems Space Awareness Systems Healthcare Systems Decision Support Systems Cyber Security Systems Design of Computer Systems The readers of this book series will benefit from DDDAS theory advances such as object estimation, information fusion, and sensor management. The increased interest in Artificial Intelligence (AI), Machine Learning and Neural Networks (NN) provides opportunities for DDDAS-based methods to show the key role DDDAS plays in enabling AI capabilities; address challenges that ML-alone does not, and also show how ML in combination with DDDAS-based methods can deliver the advanced capabilities sought; likewise, infusion of DDDAS-like approaches in NN-methods strengthens such methods. Moreover, the “DDDAS-based Digital Twin” or “Dynamic Digital Twin”, goes beyond the traditional DT notion where the model and the physical system are viewed side-by-side in a static way, to a paradigm where the model dynamically interacts with the physical system through its instrumentation, (per the DDDAS feed-back control loop between model and instrumentation).

Energy Research Abstracts

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Author :
Publisher :
ISBN 13 :
Total Pages : 762 pages
Book Rating : 4.:/5 (89 download)

Book Synopsis Energy Research Abstracts by :

Download or read book Energy Research Abstracts written by and published by . This book was released on 1988 with total page 762 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Powering the Digital Economy: Opportunities and Risks of Artificial Intelligence in Finance

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Author : El Bachir Boukherouaa
Publisher : International Monetary Fund
ISBN 13 : 1589063953
Total Pages : 35 pages
Book Rating : 4.5/5 (89 download)

Book Synopsis Powering the Digital Economy: Opportunities and Risks of Artificial Intelligence in Finance by : El Bachir Boukherouaa

Download or read book Powering the Digital Economy: Opportunities and Risks of Artificial Intelligence in Finance written by El Bachir Boukherouaa and published by International Monetary Fund. This book was released on 2021-10-22 with total page 35 pages. Available in PDF, EPUB and Kindle. Book excerpt: This paper discusses the impact of the rapid adoption of artificial intelligence (AI) and machine learning (ML) in the financial sector. It highlights the benefits these technologies bring in terms of financial deepening and efficiency, while raising concerns about its potential in widening the digital divide between advanced and developing economies. The paper advances the discussion on the impact of this technology by distilling and categorizing the unique risks that it could pose to the integrity and stability of the financial system, policy challenges, and potential regulatory approaches. The evolving nature of this technology and its application in finance means that the full extent of its strengths and weaknesses is yet to be fully understood. Given the risk of unexpected pitfalls, countries will need to strengthen prudential oversight.

Proceedings

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Author :
Publisher :
ISBN 13 :
Total Pages : 612 pages
Book Rating : 4.3/5 (91 download)

Book Synopsis Proceedings by :

Download or read book Proceedings written by and published by . This book was released on 1990 with total page 612 pages. Available in PDF, EPUB and Kindle. Book excerpt:

International Aerospace Abstracts

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Author :
Publisher :
ISBN 13 :
Total Pages : 980 pages
Book Rating : 4.F/5 ( download)

Book Synopsis International Aerospace Abstracts by :

Download or read book International Aerospace Abstracts written by and published by . This book was released on 1998 with total page 980 pages. Available in PDF, EPUB and Kindle. Book excerpt: