{"product_id":"mathematical-modeling-for-computer-applications-hardback-9781394248407","title":"Mathematical Modeling for Computer Applications (Hardback) 9781394248407","description":"\u003cfont face=\"Georgia\"\u003e\r\n\u003cp\u003e\u003cfont size=\"6\"\u003eMathematical Modeling for Computer Applications\u003c\/font\u003e\u003cbr\u003e\r\n\r\n\r\n\r\n\r\n\r\n\u003c\/p\u003e\n\u003cp\u003e\u003cfont size=\"4\"\u003eBiswadip Basu Mallik (Edited by), Mallik (Author), M. Niranjanamurthy (Edited by), Sharmistha Ghosh (Edited by), Valentina Emilia Balas (Edited by), Krishanu Deyasi (Edited by), Santanu Das (Edited by)\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e9781394248407, Wiley\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003eHardback, published 19 September 2024\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e576 pages\u003cbr\u003e25 x 15 x 1.5 cm, 1.089 kg\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\r\n\r\n\r\n\u003cp align=\"justify\"\u003e\u003cstrong\u003e\u003cfont size=\"3\"\u003e\u003cp\u003e\u003ci\u003e\u003cb\u003eMathematical Modeling for Computer Applications\u003c\/b\u003e\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003eThe mathematical sciences are part of nearly all aspects of everyday life. The discipline has underpinned such beneficial modern capabilities as internet searches, medical imaging, computer animation, numerical weather predictions, and all types of digital communications. This outstanding new volume in the series, \"Mathematics and Computer Science,\" examines the current state of mathematical science and explores the changes needed for the discipline to be in a strong position and able to maximize its contributions.\u003c\/p\u003e \u003cp\u003eBesides covering important practical applications for the areas where mathematics and computer science intersect, the editors of this new volume recommend that training for future generations of mathematical scientists should be re-assessed in light of the increasingly cross-disciplinary nature of the mathematical sciences. In addition, because of the valuable interplay between ideas and people from all parts of the mathematical sciences, this group of curated papers emphasizes that universities and governments need to continue to invest in the full spectrum of the mathematical sciences in order for the whole enterprise to continue to flourish long-term. Emphasis on important developments in applied mathematics and modeling, analysis and its applications, applied algebra and its applications, geometry and its applications, algebraic statistics and its applications as well as algebraic topology and its applications are covered. Whether for the veteran engineer, new hire, or student, this is a must-have volume for any library.\u003c\/p\u003e\u003c\/font\u003e\u003c\/strong\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e\u003cp\u003ePreface xxi\u003c\/p\u003e \u003cp\u003e\u003cb\u003e1 Fermatean Fuzzy Entropy Measure with Application in Decision Making Using COPRAS Approach 1\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eMansi Bhatia, H. D. Arora, Anjali Naithani and Vijay Kumar\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e1.1 Introduction 2\u003c\/p\u003e \u003cp\u003e1.2 Preliminaries 4\u003c\/p\u003e \u003cp\u003e1.3 Novel Fermatean Entropy Measure 5\u003c\/p\u003e \u003cp\u003e1.4 Application of Entropy Measure Through COPRAS 7\u003c\/p\u003e \u003cp\u003e1.5 Comparative Analysis 12\u003c\/p\u003e \u003cp\u003e1.6 Conclusion 13\u003c\/p\u003e \u003cp\u003eReferences 13\u003c\/p\u003e \u003cp\u003e\u003cb\u003e2 Some Properties of Cartesian and Lexicographic Products of Soft Graphs 17\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eJinta Jose, Bobin George and Rajesh K. Thumbakara\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e2.1 Introduction 17\u003c\/p\u003e \u003cp\u003e2.2 Soft Graphs 18\u003c\/p\u003e \u003cp\u003e2.3 Some Properties of Cartesian and Restricted Cartesian Products of Soft Graphs 20\u003c\/p\u003e \u003cp\u003e2.4 Some Properties of Lexicographic and Restricted Lexicographic Products of Soft Graphs 23\u003c\/p\u003e \u003cp\u003e2.5 Conclusion 26\u003c\/p\u003e \u003cp\u003eReferences 26\u003c\/p\u003e \u003cp\u003e\u003cb\u003e3 Advancements in Enhancing Car Object Detection in Complex and Adverse Environmental Conditions Through Deep Learning Techniques 29\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eRejuwan Shamim, Biswadip Basu Mallik and Trapty Agarwal\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e3.1 Introduction 30\u003c\/p\u003e \u003cp\u003e3.2 Literature Review 33\u003c\/p\u003e \u003cp\u003e3.3 Methodology 35\u003c\/p\u003e \u003cp\u003e3.4 Result 44\u003c\/p\u003e \u003cp\u003e3.5 Discussion 49\u003c\/p\u003e \u003cp\u003e3.6 Conclusion 54\u003c\/p\u003e \u003cp\u003eReferences 57\u003c\/p\u003e \u003cp\u003e\u003cb\u003e4 Approximation by Durrmeyer Type Operators Using Polya Distribution 61\u003cbr\u003e \u003c\/b\u003e\u003ci\u003ePrerna Sharma and Diwaker Sharma\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e4.1 Introduction 61\u003c\/p\u003e \u003cp\u003e4.2 Basic Outcomes 63\u003c\/p\u003e \u003cp\u003e4.3 Direct Results 65\u003c\/p\u003e \u003cp\u003e4.4 Discussion 73\u003c\/p\u003e \u003cp\u003eDisclaimer 73\u003c\/p\u003e \u003cp\u003eReferences 73\u003c\/p\u003e \u003cp\u003e\u003cb\u003e5 Solution of Pollutant Dispersion in Porous Medium Under Linear Sorption Using Finite Element Method 75\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eRashmi Radha, Tapan Paul, Rakesh Kumar Singh, Nav Kumar Mahato and Mritunjay Kumar Singh\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e5.1 Introduction 76\u003c\/p\u003e \u003cp\u003e5.2 Mathematical Formulation 77\u003c\/p\u003e \u003cp\u003e5.3 Numerical Derivation of the Proposed Model Problem by FEM Method 79\u003c\/p\u003e \u003cp\u003e5.4 Analytical Derivation of the Proposed Model Equation 81\u003c\/p\u003e \u003cp\u003e5.5 Results and Discussion 83\u003c\/p\u003e \u003cp\u003e5.6 Conclusion 87\u003c\/p\u003e \u003cp\u003eReferences 88\u003c\/p\u003e \u003cp\u003e\u003cb\u003e6 A Comparative Analysis of Fuzzy and Neutrosophic Database Models in Handling Imprecise Queries 91\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eDoyel Sarkar and Sharmistha Ghosh\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e6.1 Introduction 92\u003c\/p\u003e \u003cp\u003e6.2 Basic Definitions 93\u003c\/p\u003e \u003cp\u003e6.3 Processing Imprecise Query using Fuzzy and Neutrosophic Sets 94\u003c\/p\u003e \u003cp\u003e6.4 Results and Discussion 98\u003c\/p\u003e \u003cp\u003e6.5 Concluding Remarks 98\u003c\/p\u003e \u003cp\u003eReferences 99\u003c\/p\u003e \u003cp\u003e\u003cb\u003e7 Tweaked Portfolio Estimation Regarding Indian Securities Exchange: An Empirical Study 101\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eAbhijit Biswas and Meghdoot Ghosh\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e7.1 Introduction 102\u003c\/p\u003e \u003cp\u003e7.2 Literature Review in Financial Market 102\u003c\/p\u003e \u003cp\u003e7.3 Method 103\u003c\/p\u003e \u003cp\u003e7.4 Results 107\u003c\/p\u003e \u003cp\u003e7.5 Discussion 116\u003c\/p\u003e \u003cp\u003eReferences 116\u003c\/p\u003e \u003cp\u003e\u003cb\u003e8 Fixed Point Results Related to Graph Theory 117\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eAditya Bhattacharya, Özen Özer, Sonendra Gupta, Ramakant Bhardwaj and Sonam\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e8.1 Introduction 117\u003c\/p\u003e \u003cp\u003e8.2 Preliminaries 118\u003c\/p\u003e \u003cp\u003e8.3 Main Results 119\u003c\/p\u003e \u003cp\u003eReferences 128\u003c\/p\u003e \u003cp\u003e\u003cb\u003e9 Unleashing GPT-3’s Potential in Automatic Text Generation: A Comprehensive Study and Analysis 131\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eRejuwan Shamim and Biswadip Basu Mallik\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e9.1 Introduction 132\u003c\/p\u003e \u003cp\u003e9.2 Literature Review 133\u003c\/p\u003e \u003cp\u003e9.3 Methodology 135\u003c\/p\u003e \u003cp\u003e9.4 Evaluation Metrics 140\u003c\/p\u003e \u003cp\u003e9.5 Experimental Results 143\u003c\/p\u003e \u003cp\u003e9.6 Discussion 147\u003c\/p\u003e \u003cp\u003e9.7 Exploration of the Strengths and Weaknesses of GPT-3 for Automatic Text Generation 148\u003c\/p\u003e \u003cp\u003eConclusion 150\u003c\/p\u003e \u003cp\u003eReferences 151\u003c\/p\u003e \u003cp\u003e\u003cb\u003e10 Optimization Techniques and Their Applications in Science and Engineering 153\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eRavi Kiran Bagadi, Eali Stephen Neal Joshua, T. Pavankumar, S. NagaMallik Raj and Debnath Bhattacharyya\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e10.1 Introduction 154\u003c\/p\u003e \u003cp\u003e10.2 Related Work 159\u003c\/p\u003e \u003cp\u003e10.3 Metaheuristic Optimization Techniques 167\u003c\/p\u003e \u003cp\u003e10.4 Multi-Objective Optimization 172\u003c\/p\u003e \u003cp\u003e10.5 Stochastic Optimization 176\u003c\/p\u003e \u003cp\u003e10.6 Robust Optimization 180\u003c\/p\u003e \u003cp\u003e10.7 Applications of Robust Optimization in Science and Engineering 182\u003c\/p\u003e \u003cp\u003e10.8 Applications of Optimization Techniques in Science and Engineering 183\u003c\/p\u003e \u003cp\u003e10.9 Challenges and Future Directions in Optimization 189\u003c\/p\u003e \u003cp\u003eConclusion 194\u003c\/p\u003e \u003cp\u003eReferences 195\u003c\/p\u003e \u003cp\u003e\u003cb\u003e11 On Sum 3-Equitable Labeling of Some Graphs 197\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eSarang Sadawarte and Sweta Srivastav\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e11.1 Introduction 197\u003c\/p\u003e \u003cp\u003e11.2 Terminology and Notation 198\u003c\/p\u003e \u003cp\u003e11.3 Results 199\u003c\/p\u003e \u003cp\u003e11.4 Conclusions and Perspectives 205\u003c\/p\u003e \u003cp\u003eReferences 205\u003c\/p\u003e \u003cp\u003e\u003cb\u003e12 An Application of Invariant Point Theory in G-Metric Spaces with Special Emphasis on Alpha-Psi Contraction 207\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eSamriddhi Ghosh, Sonam, Deb Sarkar, Poulami Halder and Ramakant Bhardwaj\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e12.1 Introduction 207\u003c\/p\u003e \u003cp\u003e12.2 Elementaries 210\u003c\/p\u003e \u003cp\u003e12.3 Main Result 211\u003c\/p\u003e \u003cp\u003eAcknowledgement 216\u003c\/p\u003e \u003cp\u003eCollision of Interest 216\u003c\/p\u003e \u003cp\u003eReferences 217\u003c\/p\u003e \u003cp\u003e\u003cb\u003e13 Fixed Point Results for Compatible Mapping of Type (α) in Fuzzy Metric Spaces 219\u003cbr\u003e \u003c\/b\u003e\u003ci\u003ePoulami Halder, Samriddhi Ghosh, Ramakant Bhardwaj, Sonam and Satyendra Narayan\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e13.1 Introduction 219\u003c\/p\u003e \u003cp\u003e13.2 Preliminaries 220\u003c\/p\u003e \u003cp\u003e13.3 Compatible Mappings of Type (α) 222\u003c\/p\u003e \u003cp\u003e13.4 Main Results 223\u003c\/p\u003e \u003cp\u003eAcknowledgement 229\u003c\/p\u003e \u003cp\u003eConflict of Interest 229\u003c\/p\u003e \u003cp\u003eReferences 229\u003c\/p\u003e \u003cp\u003e\u003cb\u003e14 Combined Matrices Associated with Soft Digraphs 231\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eBobin George, Jinta Jose and Rajesh K. Thumbakara\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e14.1 Introduction 231\u003c\/p\u003e \u003cp\u003e14.2 Soft Digraphs 232\u003c\/p\u003e \u003cp\u003e14.3 Combined Adjacency Matrix of a Soft Digraph 234\u003c\/p\u003e \u003cp\u003e14.4 Combined Incidence Matrix of a Soft Digraph 236\u003c\/p\u003e \u003cp\u003e14.5 Conclusion 238\u003c\/p\u003e \u003cp\u003eReferences 238\u003c\/p\u003e \u003cp\u003e\u003cb\u003e15 Refining Medical Text Query Responses: Tailoring Hugging Face’s BERT Model for Precise and Swift Medical Question Answering 241\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eRejuwan Shamim, Badria Sulaiman Alfurhood and Biswadip Basu Mallik\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e15.1 Introduction 242\u003c\/p\u003e \u003cp\u003e15.2 Related Work 244\u003c\/p\u003e \u003cp\u003e15.3 Methodology 248\u003c\/p\u003e \u003cp\u003e15.4 Result 253\u003c\/p\u003e \u003cp\u003e15.5 Strengths and Weaknesses of the Fine-Tuned BERT Model 255\u003c\/p\u003e \u003cp\u003e15.6 Applications 257\u003c\/p\u003e \u003cp\u003e15.7 Conclusion 258\u003c\/p\u003e \u003cp\u003eReferences 260\u003c\/p\u003e \u003cp\u003e\u003cb\u003e16 Machine Learning Mathematics: A Study on Concepts of Processing Knowledge 263\u003cbr\u003e \u003c\/b\u003e\u003ci\u003ePrasad Kaviti, Eali Stephen Neal Joshua, N. Venkatram, Dinesh Reddy and Debnath Bhattacharyya\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e16.1 Introduction 264\u003c\/p\u003e \u003cp\u003e16.2 Mathematical Concepts in Machine Learning 264\u003c\/p\u003e \u003cp\u003e16.3 Conclusion 299\u003c\/p\u003e \u003cp\u003eDisclaimer 299\u003c\/p\u003e \u003cp\u003eReferences 299\u003c\/p\u003e \u003cp\u003e\u003cb\u003e17 Nature-Inspired Algorithms to Optimize the Hyper-Parameters of Deep-Learning Networks for Diagnosing Brain Disorders – A Review 301\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eManoj Kumar Sharma, M. Shamim Kaiser and Kanad Ray\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e17.1 Introduction 302\u003c\/p\u003e \u003cp\u003e17.2 Literature Review 303\u003c\/p\u003e \u003cp\u003e17.3 Applications 307\u003c\/p\u003e \u003cp\u003e17.4 Challenges and Future Directions 309\u003c\/p\u003e \u003cp\u003e17.5 Conclusions and Perspectives 312\u003c\/p\u003e \u003cp\u003eReferences 312\u003c\/p\u003e \u003cp\u003e\u003cb\u003e18 YOLOv8 for Anomaly Detection in Surveillance Videos: Advanced Techniques for Identifying and Mitigating Abnormal Events 317\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eRejuwan Shamim, Badria Sulaiman Alfurhood, Trapty Agarwal and Biswadip Basu Mallik\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e18.1 Introduction 318\u003c\/p\u003e \u003cp\u003e18.2 Overview of YOLOv8 and its Advantages in Object Detection 319\u003c\/p\u003e \u003cp\u003e18.3 Related Work 321\u003c\/p\u003e \u003cp\u003e18.4 YOLOv8 Architecture 324\u003c\/p\u003e \u003cp\u003e18.5 Dataset Preparation 327\u003c\/p\u003e \u003cp\u003e18.6 Training YOLOv8 for Anomaly Detection 330\u003c\/p\u003e \u003cp\u003e18.7 Evaluation Metrics 332\u003c\/p\u003e \u003cp\u003e18.8 Results and Discussion 334\u003c\/p\u003e \u003cp\u003e18.9 Conclusion 343\u003c\/p\u003e \u003cp\u003eReferences 346\u003c\/p\u003e \u003cp\u003e\u003cb\u003e19 Linear Stability and Resonance of Oblate Infinitesimal in the Nearby Region of Triangular Equilibrium Points for Triaxial Primaries in the Elliptic Restricted Three Body Problem 351\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eShilpi Dewangan, A. Narayan and Poonam Duggad\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e19.1 Introduction 352\u003c\/p\u003e \u003cp\u003e19.2 Equation of Motion 353\u003c\/p\u003e \u003cp\u003e19.3 Position of Triangular Equilibrium Points 355\u003c\/p\u003e \u003cp\u003e19.4 Normalization at Hamiltonian for Stability of First Order 356\u003c\/p\u003e \u003cp\u003e19.5 Resonance Cases 366\u003c\/p\u003e \u003cp\u003e19.6 Conclusion 368\u003c\/p\u003e \u003cp\u003eReferences 370\u003c\/p\u003e \u003cp\u003e\u003cb\u003e20 Magnetic Nanofluid Flow with Micro‐Organisms and Viscous Dissipation 373\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eShweta Mishra, Sharmistha Ghosh and Hiranmoy Mondal\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003eNomenclature 374\u003c\/p\u003e \u003cp\u003e20.1 Introduction 375\u003c\/p\u003e \u003cp\u003e20.2 Mathematical Exploration 376\u003c\/p\u003e \u003cp\u003e20.3 Similarity Transformation 378\u003c\/p\u003e \u003cp\u003e20.4 Heat-Mass Transferal 379\u003c\/p\u003e \u003cp\u003e20.5 Discussion of Results 380\u003c\/p\u003e \u003cp\u003e20.6 Conclusion 384\u003c\/p\u003e \u003cp\u003eReferences 384\u003c\/p\u003e \u003cp\u003e\u003cb\u003e21 The Application of Deep Learning to the Localization of Brain Tumors 387\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eCharanarur Panem, Srinivasa Rao Gundu, J. Vijaylaxmi and Biswadip Basu Mallik\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e21.1 Introduction 388\u003c\/p\u003e \u003cp\u003e21.2 Proposed Method 389\u003c\/p\u003e \u003cp\u003e21.3 Conclusion 395\u003c\/p\u003e \u003cp\u003eAcknowledgement 395\u003c\/p\u003e \u003cp\u003eReferences 395\u003c\/p\u003e \u003cp\u003e\u003cb\u003e22 Contextual Information Retrieval using Root Word Stemming in Indian Languages 397\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eChandamita Nath and Bhairab Sarma\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e22.1 Introduction 397\u003c\/p\u003e \u003cp\u003e22.2 Literature Review 399\u003c\/p\u003e \u003cp\u003e22.3 Problem Statement 399\u003c\/p\u003e \u003cp\u003e22.4 Experimental Work 400\u003c\/p\u003e \u003cp\u003e22.5 Conclusion and Future Work 402\u003c\/p\u003e \u003cp\u003eDisclaimer 403\u003c\/p\u003e \u003cp\u003eReferences 404\u003c\/p\u003e \u003cp\u003e\u003cb\u003e23 Recent Advances in Object Detection Based on YOLO-V 4 and Faster RCNN: A Review 405\u003c\/b\u003e\u003cbr\u003e \u003ci\u003eAnwesa Das, Atanu Nandi and Ishani Deb\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e23.1 Introduction 406\u003c\/p\u003e \u003cp\u003e23.2 Literature Review 407\u003c\/p\u003e \u003cp\u003e23.3 Proposed Methodology 409\u003c\/p\u003e \u003cp\u003e23.4 Result and Discussion 413\u003c\/p\u003e \u003cp\u003e23.5 Conclusion 416\u003c\/p\u003e \u003cp\u003eReferences 417\u003c\/p\u003e \u003cp\u003e\u003cb\u003e24 Detection of Leukemia Using Transfer Learning 419\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eBiplab Kanti Das, Chanchal Ghosh, Joydeb Sheet and Himadri Sekhar Dutta\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e24.1 Introduction 420\u003c\/p\u003e \u003cp\u003e24.2 Literature Survey 421\u003c\/p\u003e \u003cp\u003e24.3 Dataset 423\u003c\/p\u003e \u003cp\u003e24.4 Proposed Methodology 424\u003c\/p\u003e \u003cp\u003e24.5 Pre-Processing of Image 425\u003c\/p\u003e \u003cp\u003e24.6 Deep Learning Model Generation 426\u003c\/p\u003e \u003cp\u003e24.7 Classification 430\u003c\/p\u003e \u003cp\u003e24.8 Results and Analysis 430\u003c\/p\u003e \u003cp\u003e24.9 Conclusion and Future Direction 434\u003c\/p\u003e \u003cp\u003eReferences 434\u003c\/p\u003e \u003cp\u003e\u003cb\u003e25 IoMT – An ML-Based Patient Monitoring System with Prediction of Health Condition 437\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eKakali Das, Sagnik Ghosh and Himadri Sekhar Dutta\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e25.1 Introduction 438\u003c\/p\u003e \u003cp\u003e25.2 System Architecture 440\u003c\/p\u003e \u003cp\u003e25.3 Machine Learning Algorithm 443\u003c\/p\u003e \u003cp\u003e25.4 Results and Discussion 444\u003c\/p\u003e \u003cp\u003e25.5 Conclusion 449\u003c\/p\u003e \u003cp\u003eReferences 449\u003c\/p\u003e \u003cp\u003e\u003cb\u003e26 Eulerian Soft Graphs 453\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eJinta Jose, Bobin George and Rajesh K. Thumbakara\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e26.1 Introduction 453\u003c\/p\u003e \u003cp\u003e26.2 Soft Graphs 454\u003c\/p\u003e \u003cp\u003e26.3 Eulerian Soft Graphs 454\u003c\/p\u003e \u003cp\u003e26.4 Conclusion 460\u003c\/p\u003e \u003cp\u003eReferences 461\u003c\/p\u003e \u003cp\u003e\u003cb\u003e27 An Algorithm to Solve an Exponential Diophantine Equation 463\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eSubramani K. and Srinivasa Prasanna\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e27.1 Introduction 464\u003c\/p\u003e \u003cp\u003e27.2 Example 466\u003c\/p\u003e \u003cp\u003e27.3 Conclusion 475\u003c\/p\u003e \u003cp\u003eAcknowledgements 475\u003c\/p\u003e \u003cp\u003eReferences 475\u003c\/p\u003e \u003cp\u003e\u003cb\u003e28 Optimal Number of Emergency Facility and Its Positioning Using Nature-Based Algorithm: A Case of Mumbai City 477\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eK.V. Ajaygopal and Rakesh Verma\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e28.1 Introduction 478\u003c\/p\u003e \u003cp\u003e28.2 Literature Review 479\u003c\/p\u003e \u003cp\u003e28.3 Real-Life Application 484\u003c\/p\u003e \u003cp\u003e28.4 Results and Discussion 487\u003c\/p\u003e \u003cp\u003e28.5 Conclusion 489\u003c\/p\u003e \u003cp\u003eReferences 490\u003c\/p\u003e \u003cp\u003eAppendix 492\u003c\/p\u003e \u003cp\u003e\u003cb\u003e29 Chicken Swarm Optimization Algorithm-Based Propagation Delay Estimation in Transmission Path of UWASN 501\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eA. Kannappan and R.M. Bommi\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e29.1 Introduction 502\u003c\/p\u003e \u003cp\u003e29.2 General Biology Behavior of Chicken Swarm 503\u003c\/p\u003e \u003cp\u003e29.3 Chicken Swarm Pseudocode 506\u003c\/p\u003e \u003cp\u003e29.4 Results and Discussion 507\u003c\/p\u003e \u003cp\u003e29.5 Conclusion 508\u003c\/p\u003e \u003cp\u003eReferences 509\u003c\/p\u003e \u003cp\u003e\u003cb\u003e30 Frequency Analysis of Coreference Resolution 513\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eMridusmita Das and Apurbalal Senapati\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e30.1 Introduction 514\u003c\/p\u003e \u003cp\u003e30.2 Existing Literature 515\u003c\/p\u003e \u003cp\u003e30.3 Resource Building for the English and Assamese Languages 517\u003c\/p\u003e \u003cp\u003e30.4 Description of the Data Sets 517\u003c\/p\u003e \u003cp\u003e30.5 Frequency Analysis of Coreference Relations 518\u003c\/p\u003e \u003cp\u003e30.6 Experiment and Result 520\u003c\/p\u003e \u003cp\u003e30.7 Conclusion 520\u003c\/p\u003e \u003cp\u003eReferences 520\u003c\/p\u003e \u003cp\u003e\u003cb\u003e31 Hate Neologism in Election Context in India 523\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eSujit Das and Apurbalal Senapati\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e31.1 Introduction 524\u003c\/p\u003e \u003cp\u003e31.2 Related Work 525\u003c\/p\u003e \u003cp\u003e31.3 Corpus Creation 526\u003c\/p\u003e \u003cp\u003e31.4 Methodology 528\u003c\/p\u003e \u003cp\u003e31.5 Result 529\u003c\/p\u003e \u003cp\u003e31.6 Conclusion 530\u003c\/p\u003e \u003cp\u003eReferences 530\u003c\/p\u003e \u003cp\u003eIndex 533\u003c\/p\u003e\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003eSubject Areas: Mathematics [\u003ca title=\"See our other books on Mathematics\" href=\"https:\/\/freshlyprintedbooks.co.uk\/search?q=%22Mathematics%20%5BPB%5D%22\"\u003ePB\u003c\/a\u003e]\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\u003c\/font\u003e","brand":"Wiley-Scrivener","offers":[{"title":"Brand New","offer_id":52433233707288,"sku":"9781394248407","price":190.88,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0730\/2037\/5320\/files\/9781394248407.jpg?v=1784852362","url":"https:\/\/freshlyprintedbooks.co.uk\/products\/mathematical-modeling-for-computer-applications-hardback-9781394248407","provider":"Freshly Printed Books","version":"1.0","type":"link"}