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Computational Intelligence and Mathematical Applications
It is with great pleasure to present the proceedings of the International Conference on Computational Intelligence and Mathematical Applications (ICCIMA 2023), held on 21-22 December 2023, at Panipat Institute of Engineering and Technology, Panipat. This conference brought scholars, researchers, professionals, and intellectuals together from diverse fields to exchange ideas, share insights, and foster collaborations in Optimization, Computational Intelligence and Mathematical Applications. The ICCIMA 2023 served as a platform for contributors to demonstrate their latest findings, discuss emerging trends, and explore innovations to the problems that different disciplines are currently experiencing. The conference’s scope and depth of themes reflect our community’s rich diversity of interests and levels of competence.
Challenges and Opportunities for Deep Learning Applications in Industry 4.0
The competence of deep learning for the automation and manufacturing sector has received astonishing attention in recent times. The manufacturing industry has recently experienced a revolutionary advancement despite several issues. One of the limitations for technical progress is the bottleneck encountered due to the enormous increase in data volume for processing, comprising various formats, semantics, qualities and features. Deep learning enables detection of meaningful features that are difficult to perform using traditional methods. The book takes the reader on a technological voyage of the industry 4.0 space. Chapters highlight recent applications of deep learning and the associated cha...
Big Data Analytics in Intelligent IoT and Cyber-Physical Systems
This book explores the complete system perspective, underlying theories, modeling, and applications of cyber-physical systems (CPS). Considering the interest of researchers and academicians, the editors present this book in a multidimensional perspective covering CPS at breadth. It covers topics ranging from discussion of rudiments of the system and efficient management to recent research challenges and issues. This book is divided into four sections discussing the fundamentals of CPS, engineering-based solutions, its applications, and advanced research challenges. The contents highlight the concept map of CPS including the latest technological interventions, issues, challenges, and the integration of CPS with IoT and big data analytics, modeling solutions, distributed management, efficient energy management, cyber-physical systems research, and education with applications in industrial, agriculture, and medical domains. This book is of immense interest to those in academia and industry.
Emerging Technology Trends in Electronics, Communication and Networking
This book constitutes refereed proceedings of the Fourth International Conference on Emerging Technology Trends in Electronics, Communication and Networking, ET2ECN 2021. The volume covers a wide range of topics, including electronic devices, VLSI design and fabrication, photo electronic systems and applications, integrated optics, embedded systems, wireless communication, optical communication, free-space optics, signal processing, image/audio/video processing, wireless sensor networks, next-generation networks, network security, and many others. The book will serve as a valuable reference resource for academia and researchers across the globe.
Recent Advances in Computational Intelligence
This book comprehensively addresses computational intelligence, including the theories, methodologies and techniques underlying this evolving field, as well as its potential uses in various domains across the entire spectrum of the sciences (the natural sciences, health sciences, engineering, social sciences, and humanities) and in various types of business. Computational intelligence is rapidly spreading into all kinds of products and services. This calls for the adaptation of existing theories, methodologies and techniques – and the development of wholly new ones – to ensure the successful implementation of new intelligent products and services in various domains related to public orga...
All of Us in Our Own Lives
A beautiful story of strangers who shape each other’s lives in fateful ways, All of Us in Our Own Lives delves deeply into the lives of women and men in Nepal and into the world of international aid. Ava Berriden, a Canadian lawyer, quits her corporate job in Toronto to move to Nepal, from where she was adopted as a baby. There she struggles to adapt to her new career in international aid and forge a connection with the country of her birth. Ava’s work brings her into contact with Indira Sharma, who has ambitions of becoming the first Nepali woman director of a NGO; Sapana Karki, a bright young teenager living a small village; and Gyanu, Sapana’s brother, who has returned home from Dubai to settle his sister’s future after their father’s death. Their journeys collide in unexpected ways. All of Us in Our Own Lives is a stunning, keenly observant novel about human interconnectedness, about privilege, and about the ethics of international aid (the earnestness and idealism and yet its cynical, moneyed nature).
Recommender Systems
In this age of information overload, people use a variety of strategies to make choices about what to buy, how to spend their leisure time, and even whom to date. Recommender systems automate some of these strategies with the goal of providing affordable, personal, and high-quality recommendations. This book offers an overview of approaches to developing state-of-the-art recommender systems. The authors present current algorithmic approaches for generating personalized buying proposals, such as collaborative and content-based filtering, as well as more interactive and knowledge-based approaches. They also discuss how to measure the effectiveness of recommender systems and illustrate the methods with practical case studies. The final chapters cover emerging topics such as recommender systems in the social web and consumer buying behavior theory. Suitable for computer science researchers and students interested in getting an overview of the field, this book will also be useful for professionals looking for the right technology to build real-world recommender systems.
Wireless Vehicular Networks for Car Collision Avoidance
Wireless Vehicular Networks for Car Collision Avoidance focuses on the development of the ITS (Intelligent Transportation Systems) in order to minimize vehicular accidents. The book presents and analyses a range of concrete accident scenarios while examining the causes of vehicular collision and proposing countermeasures based on wireless vehicular networks.The book also describes the vehicular network standards and quality of service mechanisms focusing on improving critical dissemination of safety information. With recommendations on techniques and protocols to consider when improving road safety policies in order to minimize crashes and collision risks.
Himalayan Lust
Every year, a group of Isha meditators sets out on a tour of the Himalayas. Accompanying them on this journey is Sadhguru – a spiritual master considered by many to be one of the foremost living yogis on the planet. This is a book for those who stayed behind. It is a chance to make a pilgrimage on the page, travelling through the unpredictable but fascinating terrain of the master’s words. Amalgamating discourses and conversations from several yatras, it is a blend of the specific and the timeless. This book is not just about the Himalayas. And yet, the book would never have happened without the Himalayas. The mountains play a vital role in the text, alternately as context and catalyst, mood and metaphor. Without them, some of the questions in this book would never have been asked. Even if they sometimes seem tangential to the line of enquiry, they remain a powerful subterranean presence, eventually becoming the very bedrock of this book.
Introduction to Statistical Relational Learning
In 'Introduction to Statistical Relational Learning', leading researchers in this emerging area of machine learning describe current formalisms, models, and algorithms that enable effective and robust reasoning about richly structured systems and data.
