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[null,"1.","Record Nr.",null,"UNISA996547959103316"]
[null,null,"Titolo",null,"Intelligent systems and applications : select proceedings of ICISA 2022 / / edited by Anand J. Kulkarni, Seyedali Mirjalili, and Siba Kumar Udgata"]
[null,null,"Pubbl/distr/stampa",null,"Gateway East, Singapore : , : Springer, , [2023]"]
[{"$nulls":4},"©2023"]
[null,null,"ISBN",null,"981-19-6581-1"]
[null,null,"Descrizione fisica",null,"1 online resource (512 pages)"]
[null,null,"Collana",null,"Lecture Notes in Electrical Engineering ; ; v.959"]
[null,null,"Disciplina",null,"006.3"]
[null,null,"Soggetti",null,"Artificial intelligence"]
[null,null,"Lingua di pubblicazione",null,"Inglese"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,null,null,"Nota di bibliografia","Includes bibliographical references and index."]
[null,null,"Nota di contenuto",null,"Intro -- Preface -- Contents -- About the Editors -- Intelligent Systems in Agriculture -- An Efficient Approach for Plant Leaf Species Identification Based on SVM and SMO and Performance Improvement -- 1 Introduction -- 2 Methodology -- 3 Spider Monkey Optimization Algorithm (SMO) -- 3.1 Population Initiation -- 3.2 Local Leader Phase (LLP) -- 3.3 Global Leader Phase (GLP) -- 4 Support Vector Machine -- 5 Simulation Results -- 6 Conclusion -- References -- Smart IoT-Based Pesticides Recommendation System for Rice Diseases -- 1 Introduction -- 2 Literature Survey -- 3 Methodology -- 3.1 Materials and Methodology -- 4 Results -- 4.1 Evaluation Metrics -- 4.2 Sensors Results and Readings -- 5 Conclusion -- References -- Classification of Tomato Leaf Diseases: A Comparison of Different Optimizers -- 1 Introduction -- 2 Related Work -- 3 Methodology -- 4 Results and Discussion -- 5 Conclusion -- References -- Optimization of Rainfall Intensities Classification Based on Artificial Intelligence Using Recurrent Neural Network -- 1 Introduction -- 1.1 Data and Methodology -- 1.2 MSG Data and Radar Data -- 1.3 Selection of Input Parameters -- 1.4 RNN Model -- 1.5 Choice of RNN Architecture -- 2 Application and Classification Results -- 2.1 Application to a Scene -- 2.2 Application to All Scenes -- 3 Conclusion -- References -- Tomato Plant Leaf Disease Detection Using Inception V3 -- 1 Introduction -- 2 Related Research -- 3 Workflow for Tomato Plant Disease Detection -- 3.1 Inception V3 -- 3.2 Dataset Description -- 3.3 Intermediate Results of Model Training -- 4 Conclusion -- References -- Astute Farm Monitoring Using WSN and AI-A Solution "]
[{"$nulls":4},"for Optimally Monitoring Environmental Conditions -- 1 Introduction -- 2 Literature Review -- 3 Proposed Work -- 3.1 Proposed System -- 3.2 Components Used in Proposed System -- 3.3 System Modules."]
[{"$nulls":4},"4 Result and Discussion -- 4.1 Dataset Description and Experimental Setup -- 4.2 Evaluation Metrics -- 4.3 Result Analysis -- 5 Conclusion -- References -- Intelligent Systems in Internet of Things and Cloud Computing -- A Comprehensive Literature Review of Artificial Intelligent Practices in the Field of Penetration Testing -- 1 Introduction -- 2 Existing Automated Penetration Testing Models -- 3 Results and Discussion -- 4 Future Directions -- 5 Conclusions -- References -- IoT-Cloud-Enabled Smart Framework for Real-World Applications -- 1 Introduction -- 1.1 Motivation for the Proposed Framework -- 2 Related Work -- 3 Proposed IoT-Cloud-Enabled Smart Framework for Real-World Applications -- 3.1 Sensor Connectivity and Network Phase -- 3.2 Gateway and Network Phase -- 3.3 Cloud Computing  Phase -- 3.4 IoT-Cloud Applications  Phase -- 4 Use Case Scenario (Air Quality Monitoring) -- 5 Various Real-World IoT-Cloud Applications -- 5.1 Smart Health Care -- 5.2 Smart Agriculture -- 5.3 Smart Home -- 5.4 Smart Environment Monitoring -- 6 Related Challenges of IoT-Cloud-Enabled Applications -- 6.1 Privacy and Security -- 6.2 Reliability -- 6.3 Scalability -- 6.4 Performance -- 6.5 Heterogeneity -- 6.6 Resource Allocation -- 7 Conclusions and Future Directions -- References -- Empirical Analysis of the Impact of Homomorphic Encryption on Cloud Computing -- 1 Introduction -- 2 Prioritise Risks Insights to Uncover Hidden Threats -- 3 Working Principle -- 3.1 RSA -- 3.2 Elgamal -- 3.3 Paillier -- 3.4 DGHV -- 4 Result with Statistical Analysis -- 5 Conclusion -- References -- Comparative Analysis of Full Training Set and Cross-Validation for Machine Learning Approach to Run Smart Wheelchair -- 1 Introduction -- 2 Background -- 3 Methodology -- 4 Data Analysis and Modeling -- 5 Performance Evaluation Parameter -- 6 Experimental Setup -- 7 Result and Discussion."]
[{"$nulls":4},"8 Conclusion -- References -- Relationship LSTM Network for Prediction in Social Internet of Things -- 1 Introduction -- 2 Related Work -- 3 Problem Statement -- 4 System Model -- 4.1 Problem Formulation -- 5 Proposed Model Design and Algorithm -- 6 Results -- 7 Conclusion and Future Work -- References -- Relationship-Based AES Security Model for Social Internet of Things -- 1 Introduction -- 2 Related Works -- 3 Problem Statement -- 4 System Model -- 4.1 Problem Formulation -- 5 Proposed Security Model Design and Algorithm -- 6 Results and Discussion -- 7 Conclusion and Future Work -- References -- Intelligent Systems in Image Processing and Deep Learning -- Face and Fingerprint Fusion Using Deep Learning -- 1 Introduction -- 2 Literature Survey -- 3 Recent Works -- 4 Methodology -- 4.1 Vggnet -- 4.2 ResNet -- 4.3 Random Forest -- 4.4 Decision Tree -- 4.5 KNN -- 5 Experimental Results -- 5.1 VGG16 -- 5.2 VGG19 -- 5.3 ResNet -- 6 Conclusion -- References -- Artificial Intelligence for Satellite Image Processing: Application to Rainfall Estimation -- 1 Introduction -- 2 Satellite Data -- 3 Application of AI to the Rainfall Estimation -- 3.1 Techniques Based on Machine Learning -- 3.2 Contribution of Deep Learning -- 4 Conclusion -- References -- Cyber Security and Data Security -- A Comprehensive Review of Various Approaches to Intrusion Detection Systems -- 1 Introduction -- 2 Intrusion Detection System (IDS) -- 3 Wireless Intrusion Detection System -- 4 Wireless Intrusion Detection, Prevention, and Attack -- 5 IDS for 802.11 Networks-A Review -- 6 IDS Using Machine Learning (ML) and Deep Learning (DL) -- 7 Experiment -- 8 Conclusion and Future Scope -- References -- Extensive Analysis of Intrusion "]
[{"$nulls":4},"Detection System Using Deep Learning Techniques -- 1 Introduction -- 2 Review of Deep Learning Based IDS Models -- 3 Performance Analysis."]
[{"$nulls":4},"4 Challenges and Future Developments -- 5 Conclusion -- References -- Intelligent Systems for Social Welfare I -- A Comprehensive Comparison Between Pre-trained and Custom Trained Object Detection Model for Indian Traffic Scenarios -- 1 Introduction -- 2 Object Detection and YOLO -- 3 Methodology -- 4 Dataset -- 5 Testing -- 6 Evaluation of Results -- 7 Conclusion -- References -- Detection of Outdoor Traffic and Kids Playing Scene for Visually Impaired People -- 1 Introduction and Literature Survey -- 2 Methodology -- 2.1 Dataset and Image Pre-processing -- 2.2 Feature Vector Extraction -- 2.3 Feature Clustering and Dimensionality Reduction -- 2.4 Classification and Recognition of Traffic Jam and Kids Playing Scene -- 3 Result -- 4 Conclusion -- References -- A Real-Time Detection of Indian Traffic Signs for Visually Impaired People -- 1 Introduction -- 2 Methodology -- 2.1 Dataset Collection and Pre-processing -- 2.2 Feature Vector Compilation -- 2.3 Dimensionality Reduction -- 2.4 Classification and Recognition of Traffic Sign -- 3 Result -- 4 Conclusion -- References -- Electronic Travel Aid for Crosswalk Detection for Visually Challenged People -- 1 Introduction -- 2 Methodology -- 3 Performance Evaluation and Results -- 4 Conclusion -- References -- Intelligent Systems for Social Media -- Twitter Sentiment Analysis Using Enhanced BERT -- 1 Introduction -- 2 Literature Survey -- 3 Proposed Enhanced BERT Model -- 4 Experimental Results and Analysis -- 5 Conclusion -- References -- A Study on Sentiment Analysis of Twitter Data in Marathi Language for Measuring Depression -- 1 Introduction -- 2 Related Work -- 3 Proposed Methodology -- 3.1 Collection of Marathi Tweet Data -- 3.2 Preprocessing of Data -- 3.3 Processing of Data -- 3.4 Feature Selection Methods -- 3.5 Feature Extraction -- 3.6 Methods -- 4 Conclusion -- 5 Future Work -- References."]
[{"$nulls":4},"GMM-EM-ACO Model for Congestion Free Routing in Social Internet of Things -- 1 Introduction -- 2 Related Work -- 3 Problem Statement -- 4 System Model -- 4.1 Problem Formulation -- 5 Proposed Model -- 6 Algorithm -- 7 Results -- 8 Conclusion and Future Work -- References -- Natural Language Processing -- Gesture Recognition for American Sign Language Using Pytorch and Convolutional Neural Network -- 1 Introduction -- 2 Literature Review -- 3 Proposed System and Methodology -- 4 Results -- 5 Conclusions and Future Work -- References -- Enhanced Preprocessing Technique for Degraded Printed Marathi Characters -- 1 Introduction -- 2 Related Work -- 3 Data Collection -- 4 Preprocessing and Segmentation -- 5 Assessment Factors for Enhanced Image -- 5.1 Mutual Information -- 5.2 Mean Square Error -- 5.3 Peak Signal to Noise Ratio -- 6 Experimental Results and Discussion -- 7 Conclusion -- References -- Intelligent Systems in Medical and Healthcare Management -- Cough Audio Signal-Based Clinical Emergency Classification of Corona Variant Infected Patients Using Multiclass SVM -- 1 Introduction -- 2 Literature Survey -- 3 Objective -- 4 Experimental Setup for Building the Proposed Model -- 4.1 Audio Features for Classification -- 4.2 Feature Selection -- 5 Identification of Class Labels and Building Training Data -- 5.1 Proposed Model -- 6 Emergency Classification and Prediction Using Multiclass SVM-Implementation -- 7 Results and Evaluation -- 8 Discussion -- 9 Conclusion -- References -- Binary Classification of Mammograms Using Horizontal Visibility Graph -- 1 Introduction -- 2 Related Works -- 3 Methodology -- 3.1 Gist of Datasets -- 3.2 HVG Representation and HIM -- 3.3 Training and "]
[{"$nulls":4},"Performance Analysis -- 3.4 Overview of Implementation -- 4 Results -- 4.1 Results for Image Size 32 times 3232 times32 -- 4.2 Results for Image Size 16 times 1616 times16."]
[{"$nulls":4},"4.3 Computation Time Versus Accuracy."]