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    1. Data och IT
    2. Affärsapplikationer

    Graph Theory for Computer Science

    AvManikandan Rajagopal,Ramkumar Sivasakthivel

    Inbunden, Engelska, 2025

    2 384 kr

    Beställningsvara. Skickas inom 5-8 vardagar. Fri frakt över 249 kr.

    Beskrivning

    This book is a vital resource for anyone looking to understand the essential role of graph theory as the unifying thread that connects and provides innovative solutions across a wide spectrum of modern computer science disciplines. Graph theory is a traditional mathematical discipline that has evolved as a basic tool for modeling and analyzing the complex relationships between different technological landscapes. Graph theory helps explain the semantic and syntactic relationships in natural language processing, a technology behind many businesses. Disciplinary and industry developments are seeing a major transition towards more interconnected and data-driven decision-making, and the application of graph theory will facilitate this transition. Disciplines such as parallel and distributive computing will gain insights into how graph theory can help with resource optimization and job scheduling, creating considerable change in the design and development of scalable systems. This book provides comprehensive coverage of how graph theory acts as the thread that connects different areas of computer science to create innovative solutions to modern technological problems. Using a multi-faceted approach, the book explores the fundamentals and role of graph theory in molding complex computational processes across a wide spectrum of computer science.

    Produktinformation

    • Utgivningsdatum:2025-11-12
    • Vikt:1 021 g
    • Format:Inbunden
    • Språk:Engelska
    • Antal sidor:576
    • Förlag:John Wiley & Sons Inc
    • ISBN:9781394302598

    Utforska kategorier

    • Affärsapplikationer inom Data och IT

    Mer om författaren

    Manikandan Rajagopal, PhD is an Associate Professor at Christ University with more than a decade of research experience. He has published three textbooks, more than ten book chapters, and 15 journal articles in reputed journals and conferences. His areas of interest include data mining, optimization techniques, semantic mining, and intelligent agents. Ramkumar Sivasakthivel, PhD is an Associate Professor at Christ University with more than 12 years of experience. He has published four textbooks, several papers in international journals and conferences, and has been granted two patents. His fields of interest are biosignal processing, artificial intelligence, human-computer interface, brain-computer interface, and machine vision. Joseph Varghese Kureethara, PhD is a Professor of Mathematics at Christ University with more than 17 years of experience in research and teaching. He has published more than 230 articles in international journals and conferences, co-edited five books, and authored six books. He has also delivered invited talks at over fifty conferences and workshops and serves as a member of several institutions’ boards. Niranjanamurthy M., PhD is an Assistant Professor in the Department of Artificial Intelligence and Machine Learning at the BMS Institute of Technology and Management with more than 13 years of experience. He has published more than 95 articles in various national and international journals and conferences and filed 30 patents. His areas of interest are data science, machine learning, e-commerce, software testing, and software engineering. Biswadip Basu Mallik, PhD is an Associate Professor of Mathematics in the Department of Basic Sciences and Humanities at the Institute of Engineering and Management with more than 22 years of experience. He has published five textbooks, thirteen edited books, five patents, and several research papers and book chapters in various scientific journals. His fields of research work include computational fluid dynamics, mathematical modelling, machine learning, and optimization.

    Innehållsförteckning

    • Preface xxi1 A Comprehensive Study on Pathfinding in Dynamic Graphs Using Automaton and Two-Way Depth-First Search 1Ajayaditya L. and Anitha N.2 Advancing Systemic Risk Assessment in Financial Networks with Neural Networks and Graph Labeling 17Sreena T.D. and Surabhi N.V.3 Advanced Image Segmentation Using Graph Cut Technique 35Ramasubramanian Bhoopalan and Priyadharshini S.4 An Encryption and Decryption of Block Ciphers Using Multipartite Graphs 53A. John Kaspar, Nadar Jenita Mary Masilamani Raja, Tabitha Agnes Mangam and Saravanan V.5 Big Data Analytics—Graph Databases and Insights 69Nishant Wanjari, Aashka Gupta and Reshma Gulwani6 Implementing Various Graph Labeling Techniques to Strengthen Cryptosystem Security 93Shivapriya P., K.N. Meera and Said Broumi7 Graphs in IoT: Network Topology and Connectivity 109Asha Sunilkumar8 Understanding Dependency Graphs in Parallel and Distributed Computing from Concept to Execution 129S. Naganandhini, M. Vijayakumar, K. Gopalakrishnan and T. Nithya9 A Comprehensive Overview on Graph‑Based Modeling of Transactions in Blockchain Technology 161M. Anandaraj, V. Shanmugaveni, K. Ganesh Kumar and T. Saranya10 Graph Databases Unveiling Insights in Big Data Analytics 187V. Balajishanmugam, S. Sumathi, J. Deepika and P. Sindhuja11 Secure Equitability in Chemical Networks 201Annie Alex and V. Sangeetha12 Fuzzy Graph Theory–Enhanced Gradient Boosting Regression with Network Flow Graphs for Effective Inventory Management Amid Shortages 213K. Kalaiarasi and N. Sindhuja13 Graph Unveiling in Image Processing: A Comprehensive Study of Recognition and Segmentation Methods in Medical Images 233M. Indira, M. Midhula and S. Vishnupriya14 From Nodes to Keys: Graph-Based Cryptosystems for Secure Communication 253Meera Saraswathi, Dhanyashree, K. N. Meera and Yuqing Lin15 Graph-Based Representation in Artificial Neural Networks 267K. Swarupa Rani, Boreda Divya, Ravi Uyyala, Ravindra Changala, G. Ganesh Kumar and R. Banu Priya16 Unleashing the Power of Graph Theory in Data Structures 287P. Jayalakshmi and K. Manimekalai17 Digital Payment Satisfaction Analysis Using Graph-Based Factor Analysis Technique 315R. Velmurugan and Reeba, O.B.18 A Statistical Graph–Based Welfare Measure Estimation Provided in the Public Sector Organization 329Roney Rose K. F. and Mathan Kumar. V.19 A Graph Analysis Model for Predicting Stock Market Trends Using Deep Learning 345J. Sudarvel, M. Kalimuthu, Atul Bansal, D. Vishnu Vardhan, T. Rajendran and S. Sridhar20 A Performance Graph-Based Design, Implementation, and Evaluation of Metaverse Technology for Health Education 361T. Nithya, P. Shanmugaprabha, T. Anitha, A. Priya and S. Reshmi21 An Effective Stock Market Price Graph Prediction Model Using Random Forest Algorithm 381Santhosh Nithyananda, R. Sankar Ganesh, Samyuktha P.S., V. Ramadevi, J. Sudarvel and Karthick S.R.22 Forecasting Short-Term Stock Market with Graph Prediction Model and Genetic Algorithm–Based Backpropagation Neural Network 395Santhosh Nithyananda, R. Sankar Ganesh, Samyuktha, P.S., P. Easwaran and Smruthymol J.23 Prediction of Stock Market Prices Using Real-Time Stock Data with Graph Models and Deep Learning 411NadhaSha, B. Ganesh, Ajesh Kumar, P.S., D. Vishnu Vardhan and Smruthymol J.24 Graph-Based Model for Indian Stock Market Trends 431Atul Bansal, K. Jothi, S. Jegadeeswari, M. Kalimuthu, T. Rajendran and S. Sridhar25 Employee Satisfaction Based on Welfare Measures Using Statistical Graphs 449Roney Rose K. F. and Mathan Kumar V.26 A Graph-Based Analysis of Digital Payments and Digital Technologies 475R. Velmurugan and Reeba, O.B.27 Network Analysis of Indian Stock Market at the Onset of Ukraine–Russia War 489Anindita Bhattacharjee and Jaya Mamta Prosad28 Leveraging Graph Theory for Transformative Applications in Computing and Technology 503Niranjanamurthy M., Mayuri K. P., Amitha S. K. and Ranjan Kumar MishraReferences 522About the Editors 525Index 529