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[null,"1.","Record Nr.",null,"UNINA9910874674803321"]
[null,null,"Autore",null,"Pant Millie"]
[null,null,"Titolo",null,"Proceedings of the 12th International Conference on Soft Computing for Problem Solving : SocProS 2023, Volume 1 / / edited by Millie Pant, Kusum Deep, Atulya Nagar"]
[null,null,"Pubbl/distr/stampa",null,"Singapore : , : Springer Nature Singapore : , : Imprint : Springer, , 2024"]
[null,null,"ISBN",null,"9789819731800"]
[null,null,"Edizione",null,"[1st ed. 2024.]"]
[null,null,"Descrizione fisica",null,"1 online resource (942 pages)"]
[null,null,"Collana",null,"Lecture Notes in Networks and Systems, , 2367-3389 ; ; 994"]
[null,null,"Disciplina",null,"006.3"]
[null,null,"Soggetti",null,"Computational intelligence"]
[{"$nulls":4},"Artificial intelligence"]
[{"$nulls":4},"Algorithms"]
[{"$nulls":4},"Computational Intelligence"]
[{"$nulls":4},"Artificial Intelligence"]
[null,null,"Lingua di pubblicazione",null,"Inglese"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,null,"Nota di contenuto",null,"Intro -- Preface -- Contents -- Editors and Contributors -- MIM-ViT: Deepfake Detection Using Masked Image Modelling and Vision Transformer -- 1 Introduction -- 2 Related Work -- 2.1 Deepfake Generation -- 2.2 Deepfake Detection -- 2.3 Research Gaps in Existing Work -- 3 Proposed Architecture -- 3.1 Dataset -- 3.2 Preprocessing -- 3.3 Face Quality Testing -- 3.4 Model -- 4 Experimental Setup -- 5 Results and Discussion -- 5.1 Performance Metrics -- 5.2 Experiments -- 6 Conclusion and Future Scope -- References -- A Study on Generalized Hough Transform for Detecting Fuzzy Lines -- 1 Introduction -- 2 Preliminaries -- 2.1 Classical Hough Transform -- 3 Fuzzy Hough Transform -- 3.1 Generalized Version of Fuzzy Hough Transform -- 3.2 Fuzzy Line Detection Using FHT -- 4 Similarity Measure Between Two Fuzzy Lines -- 4.1 Distance Measure Between Two Fuzzy Lines -- 5 Experimental Results -- 6 Conclusion -- References -- 'KSK' Algorithm for Optimizing DCS Performance Using 'R' -- 1 Introduction -- 2 Literature Review -- 3 Objective -- 4 Technique -- 5 Flowchart of Algorithm -- 6 Implementation -- 7 Comparison -- 8 Conclusion -- References -- A Knee-Based Multi-objective Optimization for Gait Cycle of 25-DOF NAO Humanoid Robot -- 1 Introduction -- 2 Past Studies -- 3 Knee-Based Optimization Methodology -- 3.1 Angle-Based Focus -- 3.2 Utility-Based Focus -- 4 "]
[{"$nulls":4},"Problem Definition -- 5 Multi-Objective Optimization Formulation -- 6 Results and Discussion -- 7 Conclusions -- References -- Estimating Severity for Knee Osteoarthritis Radiographs Using Deep Learning and Machine Learning Algorithms -- 1 Introduction -- 2 Literature Review -- 3 Methods and Materials Used -- 3.1 Dataset Used -- 3.2 Dataset Pre-processing -- 3.3 Extracting Relevant Features -- 3.4 Classification -- 3.5 Investigating Parameters -- 4 Experimental Analysis -- 5 Conclusion -- References."]
[{"$nulls":4},"Knee-Osteoarthritis Detection Using Deep Learning -- 1 Introduction -- 2 Literature Review -- 3 Proposed Model -- 4 Methodology -- 4.1 Image Preprocessing -- 4.2 Application of CNN Algorithm -- 4.3 Dataset -- 4.4 Training -- 5 Results -- 6 Implementation of Online Tool -- 7 Benefits -- 8 Conclusion and Future Scope -- References -- Hybrid Method for Named Entity Recognition in Kumauni Language Using Machine Learning -- 1 Introduction -- 1.1 NER and Its Approaches -- 1.2 Applications of Named Entity Recognition -- 2 Review of Literature -- 3 Background Study -- 4 Problem Formulation -- 5 Research Objectives -- 6 Research Methodology -- 6.1 CRF -- 6.2 CNN -- 6.3 Bi-LSTM -- 7 Proposed Methodology -- 8 Results and Discussion -- 8.1 Dataset Description -- 8.2 Performance Measure -- 9 Results and Discussion -- 10 Comparative Analysis -- 11 Conclusion and Future Work -- References -- Implementation of Basic Mathematical Operations on Openpower-ISA  of Libresoc -- 1 Introduction -- 2 Literature Survey -- 3 Methodology and Implementation -- 4 Results and Discussions -- 4.1 Implementation for Addition Operation -- 4.2 Implementation for Subtraction Operation  on the Decoder Test Cases of Openpower-ISA -- 4.3 Implementation for Multiplication Operation -- 4.4 Implementation for Division Operation -- 5 Conclusion -- 6 Future Scope -- References -- Machine Learning-Based Node Localization in IoT-Assisted WSN: An Initial Framework for Real-Time Applications -- 1 Introduction -- 1.1 Main Contributions -- 2 Related Work -- 3 Localization in IoT -- 4 Machine Learning-Based Localization in IoT Context -- 5 Proposed Framework for ML-Based Localization in IoT-Assisted WSN -- 5.1 Offline Phase -- 5.2 Model Selection and Training -- 5.3 Online Phase -- 5.4 Node Localization -- 6 Conclusion -- 7 Future Scope -- References."]
[{"$nulls":4},"Implementing Blockchain Technology in Healthcare: An Overview, Key Requirements, and Challenges -- 1 Introduction -- 2 Literature Review -- 3 Proposed Model -- 4 Future Scope -- 5 Conclusion -- References -- Path Planning for Autonomous Ground Vehicles by Applying Modified Harris Hawks Optimization Technique -- 1 Introduction -- 2 Problem Description and System Modeling -- 3 Modified Harris Hawks Optimization (MHHO) Algorithm -- 4 Simulation Results and Discussions -- 4.1 Performance of the Modified HHO Optimization Algorithm -- 4.2 Performance of MHHO Optimization Algorithm in Path Planning Algorithms -- 5 Conclusion and Future Scope -- References -- Glaucoma Classification Using Improved Pretrained Model -- 1 Introduction -- 2 Related Work -- 3 Proposed Methodology -- 3.1 Phase 1: RIM-1 DL Dataset -- 3.2 Phase 2: Preprocessing -- 3.3 Phase 3: Transfer Learning -- 3.4 Phase 4: Hybrid Model Development -- 4 Results and Discussion -- 5 Conclusion -- References -- Performance Optimization of a Waste Heat-Operated Tri-generation Cycle Under Different Energy Situations -- 1 Introduction -- 2 System Description and Performance Evaluation -- 2.1 Description of the Cycle -- 2.2 Assumptions Required During Simulation -- 2.3 Performance Evaluation of the Cycle -- 3 Optimization Strategy Used in the Study -- 3.1 Dragonfly Optimization Algorithm -- 3.2 Flowchart of the Optimization Strategy -- 4 Results -- 4.1 Calculation of Suitable "]
[{"$nulls":4},"Range of GF, PF, and SF -- 4.2 Optimized Results for Residential or Goods Storage Facilities -- 5 Conclusion -- References -- Organizational Supply Chain Risk Assessment Using Machine Learning and Backpropagation Neural Network -- 1 Introduction -- 2 Literature Review -- 3 Research and Analysis on the Model Construction for Supply Chain Risk Assessment -- 3.1 Research Methodology -- 3.2 Backpropagation Neural Network Model."]
[{"$nulls":4},"4 Simulation Result -- 5 Conclusion -- References -- An Approach to Find Critical Path Using Trapezoidal Picture Fuzzy Numbers -- 1 Introduction -- 2 Preliminaries -- 2.1 Trapezoidal Picture Fuzzy Numbers -- 2.2 Operations on Trapezoidal Picture Fuzzy Numbers ch15ddd -- 2.3 Comparison of TPFNs Based on: Expected Values ch15ddd -- 3 Trapezoidal Picture Fuzzy Critical Path Method -- 4 Conclusion and Future Research -- References -- Comparative Analysis of Machine Learning and Deep Learning Algorithms for Automatic Sleep Staging Using EEG Signals -- 1 Introduction -- 2 Literature Review -- 2.1 Machine Learning -- 2.2 Deep Learning -- 2.3 Limitation -- 2.4 Contribution -- 3 Proposed Methodology -- 3.1 Dataset -- 3.2 Pre-processing -- 3.3 Feature Extraction and Selection -- 3.4 Classification Algorithm -- 3.5 Performance Evaluation -- 4 Result Analysis -- 4.1 Machine Learning Evaluation -- 4.2 Deep Learning Evaluation -- 5 Conclusion and Future Work -- References -- Randomized Shuffled Hierarchical Partitioning Technique for Enhancing Efficiency of Swarm Algorithms -- 1 Introduction -- 2 Literature Review -- 2.1 Hierarchical Partitioning -- 2.2 Modified Hierarchical Partitioning -- 2.3 Random Partitioning -- 2.4 Self-adaptive Multi-population Technique with Random Partitioning (SAMPR) -- 3 Proposed Variants -- 3.1 Shuffled Hierarchical Partitioning (SHier) -- 3.2 Randomized Hierarchical Partitioning (RHier) -- 3.3 Randomized Shuffled Hierarchical Partitioning (RSHier) -- 4 Results and Discussion -- 4.1 Comparison Among the Proposed Techniques with HIER and mHIER -- 4.2 Testing the Applicability of RSHier Over Multiple Swarm Algorithms -- 4.3 Comparison Over CEC 2014 Function Set -- 4.4 Studying Diversity and Convergence Improvements -- 5 Conclusion -- References -- A Novel Approach to Solve the Interval-Valued Fermatean Fuzzy Transportation Problem."]
[{"$nulls":4},"1 Introduction -- 2 Preliminaries -- 3 Mathematical Formulation -- 3.1 Interval-Valued Transportation Problem (IVTP) -- 3.2 Equivalent Crisp Transportation Problem Using Order Relation leqRC -- 4 Solution Methodology -- 5 Numerical Example -- 5.1 Discussion -- 6 Conclusion and Future Research Scope -- References -- An Ensemble of PSO and Artificial Electric Field Algorithm for Computationally Expensive Optimization Problems -- 1 Introduction -- 2 Literature Review -- 3 Ensemble of PSO and AEFA -- 3.1 PSO -- 3.2 AEFA -- 3.3 Proposed Algorithm -- 3.4 Time and Space Complexity of the PSAEF Algorithm -- 3.5 Advantages and Disadvantages of the Proposed PSAEF Algorithm -- 4 Results and Discussions -- 5 Component-Wise Comparison -- 6 Conclusion and Future Scope -- References -- Popularity Prediction of Online Social Media Content: A Bibliometric Analysis -- 1 Introduction -- 2 Related Work -- 3 Methodology -- 4 Results -- 4.1 Document and Source Type -- 4.2 Evolution of Publication Over Years -- 4.3 Keyword Analysis -- 4.4 Analysis of Authorship -- 4.5 Analysis of the Author's Main Affiliation -- 4.6 Analysis of the Author's Countries -- 4.7 Citation Analysis -- 4.8 Analysis of Journals -- 5 Conclusion -- References -- Development of an Autonomous Driving Car Prototype Using FPGA -- 1 Introduction -- 2 Literature Survey -- 3 Proposed System -- 3.1 Architecture -- 3.2 Algorithms Used -- 4 Proposed Features -- 4.1 Lane Detection -- 4.2 "]
[{"$nulls":4},"Object Detection -- 4.3 Collision Avoidance -- 5 Result -- 5.1 Testing and Validation -- 6 Suggested Improvements -- 7 Future Scope -- 8 Conclusion -- References -- Custom CDGNet Architecture for Precise Human Part Semantic Segmentation -- 1 Introduction -- 2 Related Work -- 3 Methodology -- 4 Experimental Analysis -- 4.1 Dataset Used -- 4.2 Evaluation Metrics -- 4.3 Quantitative Analysis -- 5 Conclusion and Future Work."]
[{"$nulls":4},"References."]
[null,null,null,"Sommario/riassunto","This book provides an insight into 12th International Conference on Soft Computing for Problem Solving (SocProS 2023), organized by The Department of Applied Mathematics and Scientific Computing, Saharanpur Campus of Indian Institute of Technology, Roorkee, India, in conjunction with Continuing Education Center during 11–13 August 2023. This book presents the latest achievements and innovations in the interdisciplinary areas of soft computing, machine learning, and data science. It covers original research papers in the areas of algorithms (artificial neural network, deep learning, statistical methods, genetic algorithm, and particle swarm optimization) and applications (data mining and clustering, computer vision, medical and health care, finance, data envelopment analysis, business, and forecasting applications). This book is beneficial for young as well as experienced researchers dealing across complex and intricate real-world problems for which finding a solution by traditional methods is a difficult task."]