Principles of Big Graph: In-depth Insight, Volume 128 in the Advances in Computer series, highlights new advances in the field with this new volume presenting interesting chapters on a variety of topics, including CESDAM: Centered subgraph data matrix for large graph representation, Bivariate, cluster and suitability analysis of NoSQL Solutions for big graph applications, An empirical investigation on Big Graph using deep learning, Analyzing correlation between quality and accuracy of graph clustering, geneBF: Filtering protein-coded gene graph data using bloom filter, Processing large graphs with an alternative representation, MapReduce based convolutional graph neural networks: A comprehensive review. Fast exact triangle counting in large graphs using SIMD acceleration, A comprehensive investigation on attack graphs, Qubit representation of a binary tree and its operations in quantum computation, Modified ML-KNN: Role of similarity measures and nearest neighbor configuration in multi label text classification on big social network graph data, Big graph based online learning through social networks, Community detection in large-scale real-world networks, Power rank: An interactive web page ranking algorithm, GA based energy efficient modelling of a wireless sensor network, The major challenges of big graph and their solutions: A review, and An investigation on socio-cyber crime graph. Provides an update on the issues and challenges faced by current researchers Updates on future research agendas Includes advanced topics for intensive research for researchers

This book discusses smart computing techniques which offer an effective solution for investigating and modeling the stochastic behavior of renewable energy generation, operation of grid-connected renewable energy systems, and smart decision-making among alternatives. It also discusses applications of soft computing techniques to make an intelligent decision for optimum use of suitable alternatives which gives an upper hand compared to conventional systems. It includes upgradation of the existing system by embedding of machine intelligence. The authors present combination of use of neutral networks, fuzzy systems, and genetic algorithms which are illustrated in several applications including forecasting, security, verification, diagnostics of a specific fault, efficiency optimization, etc. Smart energy systems integrate a holistic approach in diverse sectors including electricity, thermal comfort, power industry, transportation. It allows affordable and sustainable solutions to solve the future energy demands with suitable alternatives. Thus, contributions regarding integration of the machine intelligence with the energy system, for efficient collection and effective utilization of the available energy sources, are useful for further advanced studies.

This book includes selected peer-reviewed papers presented at the International Conference on Modeling, Simulation and Optimization (CoMSO 2021), organized by National Institute of Technology, Silchar, Assam, India, during December 16–18, 2021. The book covers topics of modeling, simulation and optimization, including computational modeling and simulation, system modeling and simulation, device/VLSI modeling and simulation, control theory and applications, modeling and simulation of energy systems and optimization. The book disseminates various models of diverse systems and includes solutions of emerging challenges of diverse scientific fields.

In "The Big Data Analytics Course," readers are introduced to the world of big data and its significance in today's digital age. The book covers a wide range of topics, starting with an understanding of big data and its challenges. It then delves into data collection methods and storage technologies, emphasizing data quality and governance. The next section focuses on data processing and analysis, including techniques for preprocessing, analysis, and visualization. Readers are also introduced to popular big data technologies like Hadoop, Spark, and NoSQL databases. The book then explores the application of machine learning in big data, covering both supervised and unsupervised learning. Real-world applications of big data analytics are discussed, including its use in healthcare, finance, and e-commerce. The book also addresses data security and privacy concerns, emphasizing the importance of ethical use and considerations like bias, transparency, and accountability. Other topics covered include data mining and predictive analytics, scalable computing, data governance and management, business intelligence and decision support, IoT and big data, big data in social media, and advanced topics like text analytics, graph analytics, and deep learning for big data. Overall, "The Big Data Analytics Course" provides a comprehensive guide for understanding and utilizing big data analytics in various industries, emphasizing the importance of data-driven decision making and responsible use of data.

This book discusses the evolution of future-generation technologies through the Internet of things, bringing together all the related technologies on a single platform to offer valuable insights for undergraduate and postgraduate students, researchers, academics and industry practitioners. The book uses data, network engineering and intelligent decision- support system-by-design principles to design a reliable IoT-enabled ecosystem and to implement cyber-physical pervasive infrastructure solutions. It takes readers on a journey that begins with understanding the insight paradigm of IoT-enabled technologies and how it can be applied. It walks readers through engaging with real-time challenges and building a safe infrastructure for IoT-based, future-generation technologies. The book helps researchers and practitioners to understand the design architecture through IoT and the state of the art in IoT countermeasures. It also highlights the differences between heterogeneous platforms in IoT-enabled infrastructure and traditional ad hoc or infrastructural networks, and provides a comprehensive discussion on functional frameworks for IoT, object identification, IoT domain model, RFID technology, wearable sensors, WBAN, IoT semantics, knowledge extraction, and security and privacy issues in IoT-based ecosystems. Written by leading international experts, it explores IoT-enabled insight paradigms, which are utilized for the future benefit of humans. It also includes references to numerous works. Divided into stand-alone chapters, this highly readable book is intended for specialists, researchers, graduate students, designers, experts, and engineers involved in research on healthcare-related issues.

This book presents new and innovative current discoveries in social networking which contribute enough knowledge to the research community. The book includes chapters presenting research advances in social network analysis and issues emerged with diverse social media data. The book also presents applications of the theoretical algorithms and network models to analyze real-world large-scale social networks and the data emanating from them as well as characterize the topology and behavior of these networks. Furthermore, the book covers extremely debated topics, surveys, future trends, issues, and challenges.