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[null,"1.","Record Nr.",null,"UNINA9910864192403321"]
[null,null,"Autore",null,"Satheeskumaran S"]
[null,null,"Titolo",null,"Intelligent Computing for Sustainable Development : First International Conference, ICICSD 2023, Hyderabad, India, August 25-26, 2023, Revised Selected Papers, Part II"]
[null,null,"Pubbl/distr/stampa",null,"Cham : , : Springer, , 2024"]
[{"$nulls":4},"©2024"]
[null,null,"ISBN",null,"3-031-61298-1"]
[null,null,"Edizione",null,"[1st ed.]"]
[null,null,"Descrizione fisica",null,"1 online resource (227 pages)"]
[null,null,"Collana",null,"Communications in Computer and Information Science Series ; ; v.2122"]
[null,null,"Lingua di pubblicazione",null,"Inglese"]
[null,null,"Formato",null,"Materiale a stampa"]
[{"$nulls":3},"Livello bibliografico","Monografia"]
[null,null,"Nota di contenuto",null,"Intro -- Preface -- Organization -- Contents - Part II -- Contents - Part I -- A Cognitive Architecture Based Conversation Agent Technology for Secure Communication -- 1 Introduction -- 2 Literature Review -- 3 Evaluation Based on Reputation and Risk Degree -- 4 Information Retrieval for Buyer and Suppler -- 5 Intercession Process -- 6 Negotiation Process -- 7 Conclusion -- References -- Darwinian Lion Swarm Optimization-Based Extreme Learning Machine with Adaptive Weighted Smote for Heart Disease Prediction -- 1 Introduction -- 2 Related Works -- 3 Methods -- 3.1 Data Pre-processing -- 3.2 Feature Selection Using OOA -- 3.3 CVD Prediction Using DLSO-ELM -- 4 Performance Evaluation -- 5 Conclusion -- References -- Alzheimer's Disease Detection Using Convolution Neural Networks -- 1 Introduction -- 2 Literature Review -- 2.1 Issues Identified -- 3 Proposed System -- 4 Result and Discussion -- 4.1 Result Analysis -- 5 Conclusion -- References -- A Comparison Study of Cyberbullying Detection Using Various Machine Learning Algorithms -- 1 Introduction -- 2 Literature Survey -- 3 Methodology -- 3.1 Proposed System -- 3.2 Architecture -- 3.3 Modules -- 3.4 UML Diagrams -- 4 Performance Analysis -- 5 Conclusion -- 6 Future Work -- References -- State of the Art Analysis of Word Sense Disambiguation -- 1 Introduction -- 2 Related Work -- 3 Main Approaches and Datasets Used for WSD -- 3.1 Dictionary-Based or Knowledge-Based Approach -- 3.2 Machine Learning Approach -- 4 Evaluation of the Survey -- 4.1 Supervised Approach -- 4.2 Unsupervised Approach -- 4.3 Knowledge-Based Approach -- 4.4 "]
[{"$nulls":4},"Semi-supervised Approach -- 5 Conclusion and Future Work -- References -- Oral Cancer Classification Using GLRLM Combined with Fuzzy Cognitive Map and Support Vector Machines from Dental Radiograph Images -- 1 Introduction -- 2 Related Works -- 3 Methodology."]
[{"$nulls":4},"3.1 Image Preprocessing -- 3.2 Image Segmentation -- 3.3 Gray Level Run Length Matrix -- 3.4 Fuzzy Cognitive Map -- 3.5 Support Vector Machine -- 4 Results and Discussions -- 5 Conclusion -- References -- Machine Learning Based Delta Sigma Modulator Using Memristor for Neuromorphic Computing -- 1 Introduction -- 2 Memristor for Artificial Neural Network -- 3 Two Stage CMOS Op-Amp Using Memristor -- 3.1 Subtractor -- 3.2 Integrator -- 3.3 Comparator -- 3.4 D Flip-Flop -- 3.5 1-Bit DAC -- 4 Sigma Delta ADC -- 5 Conclusion -- References -- An Effective Framework for the Background Removal of Tomato Leaf Disease Using Residual Transformer Network -- 1 Introduction -- 2 Literature Survey -- 2.1 Related Works -- 2.2 Problem Statement -- 3 Tomato Leaf Disease Classification with Deep Learning Using Background Removal -- 3.1 Dataset Information -- 3.2 Developed Model -- 3.3 Median Blur-Based Image Pre-processing -- 4 The Concept of Background Removal in Tomato Leaf Disease Classification Using Deep Learning -- 4.1 Background Removal Using FCN -- 4.2 Residual Transformer Network-Based Disease Classification -- 5 Result and Discussion -- 5.1 Experimental Setup -- 5.2 Performance Metrics -- 5.3 Resultant Background Removed Images -- 5.4 Analysis of Initiated Model with Conventional Classifiers -- 5.5 Performance Analysis on the Recommended Model -- 6 Conclusion -- References -- An Intelligent Ensemble Architecture to Accurately Predict Housing Price for Smart Cities -- 1 Introduction -- 2 The Literature Review -- 3 Methodology -- 3.1 Research Data Flow Diagram -- 3.2 Data Source and Selection -- 3.3 Data Preprocessing -- 3.4 Model Selection -- 4 Results and Discussions -- 5 Conclusion -- References -- Detection of Leaf Blight Disease in Sorghum Using Convolutional Neural Network -- 1 Introduction -- 2 Research Design and Model Development."]
[{"$nulls":4},"2.1 Proposed Model Development -- 3 Research Methodology -- 3.1 Dataset -- 4 Experiment Evaluation and Discussion -- 4.1 Dataset -- 4.2 Training Model -- 4.3 Hyper Parameters -- 4.4 Experimental Results -- 5 Conclusion -- References -- Data Security for Internet of Things (IoT) Using Lightweight Cryptography (LWC) Method -- 1 Introduction -- 2 Related Works -- 3 Proposed Methodology -- 3.1 Light Weight Cryptography (LWC) -- 3.2 Elliptic Curve Cryptography (ECC) -- 3.3 Security Analysis -- 4 Results and Discussion -- 5 Conclusion -- References -- A Hybrid Optimization Driven Deep Residual Network for Sybil Attack Detection and Avoidance in Wireless Sensor Networks -- 1 Introduction -- 2 Proposed ECMVRO Enabling Assault Detection Method Based on DRN -- 2.1 Evaluation of WSN -- 2.2 Cluster Head Selection Using LEACH Protocol -- 2.3 Routing Using FABC -- 2.4 Attack Detection Using Proposed ECMVRO-DRN -- 2.5 Guiding of (DRN) Deep Residual Network -- 2.6 Attack Mitigation with Data Rates -- 2.7 Developed Sybil attack detection Model-Flow Chart -- 3 Simulation Results -- 4 Conclusion -- References -- Feature Engineering Techniques for Stegware Analysis: An Extensive Survey -- 1 Introduction -- 2 Related Study -- 3 Overview of Feature Engineering -- 3.1 Feature Selection Techniques -- 3.2 Feature Extraction Techniques -- 4 Comparative Analysis of Feature Engineering Techniques -- 5 Challenges and Future Directions -- 5.1 Challenges -- 5.2 Scope and Future Directions -- 6 Conclusion -- References -- Text Summarization Using Deep Learning: An Empirical Analysis of Various "]
[{"$nulls":4},"Algorithms -- 1 Introduction -- 2 Related Works -- 3 Model Architecture -- 3.1 Seq2seq Model -- 3.2 Transformers -- 4 Experiment and Result -- 5 Conclusion -- References -- An Improved Detection of Fetal Heart Disease Using Multilayer Perceptron -- 1 Introduction -- 2 Related Work."]
[{"$nulls":4},"3 Dataset -- 3.1 CHD Dataset -- 4 Methodology and Models -- 5 Data Pre-processing -- 5.1 Feature Selection -- 5.2 Image Classification -- 6 Fetal Heart Anatomical Findings -- 7 Performance Metrics Estimation -- 7.1 Accuracy -- 7.2 Precision -- 7.3 Recall -- 7.4 F1-Score -- 8 Results Analysis -- 9 Conclusion -- References -- Application of Deep Learning Techniques for Coronary Artery Disease Detection and Prediction: A Systematic Review -- 1 Introduction -- 2 Deep Learning Architectures and Applications -- 3 Performance Metrics -- 3.1 Accuracy -- 3.2 Sensitivity or P (+/Disease) -- 3.3 Specificity or P (-/Disease) -- 3.4 Prevalence -- 3.5 Positive Predict Value (PPV) -- 3.6 Negative Predict Value (NPV) -- 3.7 Confusion Matrix -- 3.8 ROC Curve -- 3.9 F-Score or F-Measure -- 4 Deep Learning for CAD Prediction -- 5 Conclusion -- References -- Author Index."]