{"product_id":"integrating-metaheuristics-in-computer-vision-for-real-world-optimization-problems-hardback-9781394230921","title":"Integrating Metaheuristics in Computer Vision for Real-World Optimization Problems (Hardback) 9781394230921","description":"\u003cfont face=\"Georgia\"\u003e\r\n\u003cp\u003e\u003cfont size=\"6\"\u003eIntegrating Metaheuristics in Computer Vision for Real-World Optimization Problems\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\"\u003eShubham Mahajan (Edited by), M Mahajan (Author), Kapil Joshi (Edited by), Amit Kant Pandit (Edited by), Nitish Pathak (Edited by)\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e9781394230921, Wiley\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003eHardback, published 13 August 2024\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e368 pages\u003cbr\u003e25.4 x 17.8 x 2.3 cm, 0.975 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\u003cb\u003eA comprehensive book providing high-quality research addressing challenges in theoretical and application aspects of soft computing and machine learning in image processing and computer vision.\u003c\/b\u003e \u003c\/p\u003e\n\u003cp\u003eResearchers are working to create new algorithms that combine the methods provided by CI approaches to solve the problems of image processing and computer vision such as image size, noise, illumination, and security. The 19 chapters in this book examine computational intelligence (CI) approaches as alternative solutions for automatic computer vision and image processing systems in a wide range of applications, using machine learning and soft computing. \u003c\/p\u003e\n\u003cp\u003eApplications highlighted in the book include: \u003c\/p\u003e\n\u003cul\u003e\n\u003cli\u003ediagnostic and therapeutic techniques for ischemic stroke, object detection, tracking face detection and recognition;\u003c\/li\u003e \u003cli\u003ecomputational-based strategies for drug repositioning and improving performance with feature selection, extraction, and learning;\u003c\/li\u003e \u003cli\u003emethods capable of retrieving photometric and geometric transformed images;\u003c\/li\u003e \u003cli\u003econcepts of trading the cryptocurrency market based on smart price action strategies; comparative evaluation and prediction of exoplanets using machine learning methods; the risk of using failure rate with the help of MTTF and MTBF to calculate reliability; a detailed description of various techniques using edge detection algorithms;\u003c\/li\u003e \u003cli\u003emachine learning in smart houses; the strengths and limitations of swarm intelligence and computation; how to use bidirectional LSTM for heart arrhythmia detection;\u003c\/li\u003e \u003cli\u003ea comprehensive study of content-based image-retrieval techniques for feature extraction;\u003c\/li\u003e \u003cli\u003emachine learning approaches to understanding angiogenesis;\u003c\/li\u003e \u003cli\u003ehandwritten image enhancement based on neutroscopic-fuzzy.\u003c\/li\u003e\n\u003c\/ul\u003e \u003cp\u003e\u003cb\u003eAudience\u003c\/b\u003e \u003c\/p\u003e\n\u003cp\u003eThe book has been designed for researchers, engineers, graduate, and post-graduate students wanting to learn more about the theoretical and application aspects of soft computing and machine learning in image processing and computer vision.\u003c\/p\u003e\u003c\/font\u003e\u003c\/strong\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e\u003cp\u003ePreface xv\u003c\/p\u003e \u003cp\u003e\u003cb\u003e1 Advancement in Diagnostic and Therapeutic Techniques for Ischemic Stroke 1\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eMukul Jain, Divya Patil, Shubham Gupta and Shubham Mahajan\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e1.1 Introduction 2\u003c\/p\u003e \u003cp\u003e1.2 Diagnostic Tools of Ischemic Stroke 4\u003c\/p\u003e \u003cp\u003e1.3 Artificial Intelligence–Based Diagnostic Tools 7\u003c\/p\u003e \u003cp\u003e1.4 Blood-Based Protein Biomarker for Stroke 8\u003c\/p\u003e \u003cp\u003e1.5 Markers for Endothelial Damage 8\u003c\/p\u003e \u003cp\u003e1.6 Markers of Brain Injury 9\u003c\/p\u003e \u003cp\u003e1.7 Therapeutic Advances in Ischemic Stroke 9\u003c\/p\u003e \u003cp\u003e1.8 Nanoparticles 11\u003c\/p\u003e \u003cp\u003e1.9 Conclusion 13\u003c\/p\u003e \u003cp\u003e\u003cb\u003e2 Object Detection and Tracking Face Detection and Recognition 25\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eVarsha K. Patil, Pawan Nawade, Rudra Nagarkar and Paresh Kadale\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e2.1 Introduction 25\u003c\/p\u003e \u003cp\u003e2.2 Motivation 30\u003c\/p\u003e \u003cp\u003e2.3 The Basics of Computer Vision 31\u003c\/p\u003e \u003cp\u003e2.4 Face Detection 34\u003c\/p\u003e \u003cp\u003e2.5 Facial Expression 38\u003c\/p\u003e \u003cp\u003e2.6 Object Detection 41\u003c\/p\u003e \u003cp\u003e2.7 Face Detection and Identification in Practical Situations 44\u003c\/p\u003e \u003cp\u003e2.8 Future Direction in Object Detection and Tracking 47\u003c\/p\u003e \u003cp\u003e2.9 Conclusion 52\u003c\/p\u003e \u003cp\u003e\u003cb\u003e3 Printing Organs with 3D Technology 55\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eShaik Aminabee\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e3.1 Introduction 55\u003c\/p\u003e \u003cp\u003e3.2 Bioprinting in Three Dimensions (3D) 56\u003c\/p\u003e \u003cp\u003e3.3 3D Printing Types 57\u003c\/p\u003e \u003cp\u003e3.4 Applications for 3D Printing in Cells 60\u003c\/p\u003e \u003cp\u003e3.5 New Developments 65\u003c\/p\u003e \u003cp\u003e3.6 Progress in India 66\u003c\/p\u003e \u003cp\u003e3.7 Limitation 67\u003c\/p\u003e \u003cp\u003e3.8 A Future Point of View 67\u003c\/p\u003e \u003cp\u003e3.9 Conclusion 68\u003c\/p\u003e \u003cp\u003e\u003cb\u003e4 Comparative Evaluation of Machine Learning Algorithms for Bank Fraud Detection 71\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eKiran Jot Singh, Divneet Singh Kapoor, Kunal Ranjan Singh, Chirag Kalucha, Gatik Alagh, Khushal Thakur and Anshul Sharma\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e4.1 Introduction 71\u003c\/p\u003e \u003cp\u003e4.2 Proposed Framework 73\u003c\/p\u003e \u003cp\u003e4.3 Results 74\u003c\/p\u003e \u003cp\u003e4.4 Concluding Remarks and Future Scope 77\u003c\/p\u003e \u003cp\u003e\u003cb\u003e5 An Overview of Computational-Based Strategies for Drug Repositioning 81\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eShalu Verma, Nidhi Nainwal, Alka Singh, Gauree Kukreti and Kiran Dobhal\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e5.1 Introduction 81\u003c\/p\u003e \u003cp\u003e5.2 Drug Repositioning 82\u003c\/p\u003e \u003cp\u003e5.3 Challenges and Opportunities for Drug Repurposing 93\u003c\/p\u003e \u003cp\u003e5.4 Conclusion 94\u003c\/p\u003e \u003cp\u003e\u003cb\u003e6 Improving Performance With Feature Selection, Extraction, and Learning 99\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eVarsha K. Patil, Vrinda Shinde, Ritika Singh and Vipul Singh\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e6.1 Introduction 99\u003c\/p\u003e \u003cp\u003e6.2 Feature Selection 100\u003c\/p\u003e \u003cp\u003e6.3 Feature Extraction 110\u003c\/p\u003e \u003cp\u003e6.4 Feature Learning 115\u003c\/p\u003e \u003cp\u003e6.5 Future Research and Development 123\u003c\/p\u003e \u003cp\u003e6.6 Future Scope 124\u003c\/p\u003e \u003cp\u003e6.7 Conclusion 125\u003c\/p\u003e \u003cp\u003e\u003cb\u003e7 Fusion of Phase and Local Features for CBIR 129\u003c\/b\u003e\u003cbr\u003e\u003ci\u003ePooja Sharma\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e7.1 Introduction 129\u003c\/p\u003e \u003cp\u003e7.2 Overview of the Proposed System 132\u003c\/p\u003e \u003cp\u003e7.3 Proposed Hybrid-Shape Descriptors 132\u003c\/p\u003e \u003cp\u003e7.4 Similarity Measurement 137\u003c\/p\u003e \u003cp\u003e7.5 Experimental Study and Performance Evaluation 139\u003c\/p\u003e \u003cp\u003e7.6 Conclusions 147\u003c\/p\u003e \u003cp\u003e\u003cb\u003e8 Trading Bot for Cryptocurrency Market Based on Smart Price Action Strategies 151\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eDivneet Singh Kapoor, Kiran Jot Singh, Anshoom Jain, Rhythm Chauhan, Khushal Thakur and Anshul Sharma\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e8.1 Introduction 151\u003c\/p\u003e \u003cp\u003e8.2 Background 154\u003c\/p\u003e \u003cp\u003e8.3 Proposed Framework 156\u003c\/p\u003e \u003cp\u003e8.4 Results 158\u003c\/p\u003e \u003cp\u003e8.5 Conclusion and Future Scope 161\u003c\/p\u003e \u003cp\u003e\u003cb\u003e9 Comparative Evaluation and Prediction of Exoplanets Using Machine Learning Methods 163\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eDivneet Singh Kapoor, Kiran Jot Singh, Ashirvad Singh, Benarji Mulakala, Karan Singh, Prashant, Ramanjeet Singh and Shubham Mahajan\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e9.1 Introduction 164\u003c\/p\u003e \u003cp\u003e9.2 Background 167\u003c\/p\u003e \u003cp\u003e9.3 Proposed Framework 169\u003c\/p\u003e \u003cp\u003e9.4 Results 171\u003c\/p\u003e \u003cp\u003e9.5 Conclusion and Future Scope 182\u003c\/p\u003e \u003cp\u003e\u003cb\u003e10 The Risk of Using Failure Rate With the Help of MTTF and MTBF to Calculate Reliability 185\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eHarpreet Kaur and Shiv Kumar Sharma\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e10.1 Introduction 185\u003c\/p\u003e \u003cp\u003e10.2 Failure 186\u003c\/p\u003e \u003cp\u003e10.3 Conclusion 191\u003c\/p\u003e \u003cp\u003e\u003cb\u003e11 A Detailed Description on Various Techniques of Edge Detection Algorithms 193\u003c\/b\u003e\u003cbr\u003e\u003ci\u003ePritha A. and G. Fathima\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e11.1 Introduction 193\u003c\/p\u003e \u003cp\u003e11.2 Edge Detection Techniques 194\u003c\/p\u003e \u003cp\u003e11.3 Experimental Results 203\u003c\/p\u003e \u003cp\u003e11.4 Comparative Results 203\u003c\/p\u003e \u003cp\u003e11.5 Conclusion 203\u003c\/p\u003e \u003cp\u003e11.6 Future Work 204\u003c\/p\u003e \u003cp\u003e\u003cb\u003e12 Advancement of ML in Smart House 207\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eGokula Udhayan V., K. Mahaeshwari and N. Vinoth Kumar\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e12.1 Objective 207\u003c\/p\u003e \u003cp\u003e12.2 Introduction 207\u003c\/p\u003e \u003cp\u003e12.3 Smart House System With IoT 208\u003c\/p\u003e \u003cp\u003e12.4 Future Scope 223\u003c\/p\u003e \u003cp\u003e12.5 Conclusion 223\u003c\/p\u003e \u003cp\u003e\u003cb\u003e13 Multi-Robot Navigation: A Biologically Inspired Framework 225\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eImran Mir and Faiza Gul\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e13.1 Introduction 225\u003c\/p\u003e \u003cp\u003e13.2 Optimization Algorithms 226\u003c\/p\u003e \u003cp\u003e13.3 Algorithms and Self-Organization 236\u003c\/p\u003e \u003cp\u003e13.4 Future Research Directions 238\u003c\/p\u003e \u003cp\u003e13.5 Conclusion 239\u003c\/p\u003e \u003cp\u003e\u003cb\u003e14 Bidirectional LSTM for Heart Arrhythmia Detection 243\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eNikhil M. Agrawal, H. D. Bhanu Cheitanya, Abhishek Kumar Rai and Shubham Mahajan\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e14.1 Introduction 243\u003c\/p\u003e \u003cp\u003e14.2 About the Dataset 245\u003c\/p\u003e \u003cp\u003e14.3 Flow of the Model 246\u003c\/p\u003e \u003cp\u003e14.4 Results 248\u003c\/p\u003e \u003cp\u003e14.5 Conclusion 248\u003c\/p\u003e \u003cp\u003e\u003cb\u003e15 Study on Content-Based Image Retrieval 253\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eThanga Subha Devi M., R. Suji Pramila and Tibbie Pon Symon\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e15.1 Introduction 254\u003c\/p\u003e \u003cp\u003e15.2 Related Works 256\u003c\/p\u003e \u003cp\u003e15.3 Extraction of Features 261\u003c\/p\u003e \u003cp\u003e15.4 User Interactions for CBIR System 266\u003c\/p\u003e \u003cp\u003e15.5 Conclusions 269\u003c\/p\u003e \u003cp\u003e\u003cb\u003e16 Machine Learning and Angiogenesis in Cancer 273\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eDharambir Kashyap, Riya Sharma, Neelam Goel and Vivek Kumar Garg\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e16.1 Introduction 273\u003c\/p\u003e \u003cp\u003e16.2 History of Angiogenesis Discovery 274\u003c\/p\u003e \u003cp\u003e16.3 Overview of Angiogenesis 274\u003c\/p\u003e \u003cp\u003e16.4 Angiogenesis in Carcinogenesis 275\u003c\/p\u003e \u003cp\u003e16.5 Molecular Mechanisms of Angiogenesis Formation 276\u003c\/p\u003e \u003cp\u003e16.6 Angiogenesis as a Target in Cancer Therapy 276\u003c\/p\u003e \u003cp\u003e16.7 Machine Learning Approaches in Angiogenesis 277\u003c\/p\u003e \u003cp\u003e16.8 Conclusion 278\u003c\/p\u003e \u003cp\u003e\u003cb\u003e17 Handwritten Image Enhancement Based on Neutroscopic-Fuzzy and K-Mean Clustering 283\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eJaspreet Kaur, Divya Gupta, Simarjeet Kaur and Amrinder Singh\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e17.1 Introduction 284\u003c\/p\u003e \u003cp\u003e17.2 Application of Image Processing 286\u003c\/p\u003e \u003cp\u003e17.3 Enhancement of Handwritten Document 287\u003c\/p\u003e \u003cp\u003e17.4 Clustering Techniques 288\u003c\/p\u003e \u003cp\u003e17.5 Performance Parameters 290\u003c\/p\u003e \u003cp\u003e17.6 Results and Discussion 293\u003c\/p\u003e \u003cp\u003e17.7 Conclusion 295\u003c\/p\u003e \u003cp\u003e\u003cb\u003e18 A Texture Classification System Based on an Adaptive Histogram Equalized Shearlet Transform 299\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eK. Gopalakrishnan, V. Karthikeyan and P.T. Vanathi\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e18.1 Introduction 299\u003c\/p\u003e \u003cp\u003e18.2 Literature Survey 303\u003c\/p\u003e \u003cp\u003e18.3 Materials and Methods 305\u003c\/p\u003e \u003cp\u003e18.4 Proposed Methodology 309\u003c\/p\u003e \u003cp\u003e18.5 Result and Discussion 311\u003c\/p\u003e \u003cp\u003e18.6 Conclusion 320\u003c\/p\u003e \u003cp\u003e\u003cb\u003e19 A Thyroid Nodule Detection Using L1-Norm Inception Deep Neural Network 323\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eSaranya G.\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e19.1 Introduction 323\u003c\/p\u003e \u003cp\u003e19.2 Related Work 324\u003c\/p\u003e \u003cp\u003e19.3 Methodology 325\u003c\/p\u003e \u003cp\u003e19.4 Results and Discussion 329\u003c\/p\u003e \u003cp\u003e19.5 Conclusion 336\u003c\/p\u003e \u003cp\u003eReferences 337\u003c\/p\u003e \u003cp\u003eIndex 339\u003c\/p\u003e\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003eSubject Areas: Computer science [\u003ca title=\"See our other books on Computer science\" href=\"https:\/\/freshlyprintedbooks.co.uk\/search?q=%22Computer%20science%20%5BUY%5D%22\"\u003eUY\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":52433221353752,"sku":"9781394230921","price":130.29,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0730\/2037\/5320\/files\/9781394230921.jpg?v=1784852095","url":"https:\/\/freshlyprintedbooks.co.uk\/products\/integrating-metaheuristics-in-computer-vision-for-real-world-optimization-problems-hardback-9781394230921","provider":"Freshly Printed Books","version":"1.0","type":"link"}