Intelligent Systems and Sustainable Computing : Proceedings of ICISSC 2022

• Autore: Reddy V. Sivakumar
• Pubblicazione: Singapore : , : Springer, , 2023; ©2023
• Edizione: 1st ed.
• Descrizione fisica: 1 online resource (562 pages)
• Disciplina: 006.3
• Altri autori: PrasadV. Kamakshi WangJiacun Rao DasariNaga Mallikarjuna
• Nota di contenuto: Intro -- Conference Committee -- Preface -- Contents -- About the Editors -- 1 Air Pollution Detection and Prediction Using Moving Average in Indian Cities -- 1.1 Introduction -- 1.1.1 Major Air Pollutants -- 1.1.2 The Most Polluted Cities in India -- 1.2 Related Work -- 1.3 Proposed Model -- 1.3.1 AQI Calculation -- 1.4 Conclusion -- References -- 2 Implementation of ResNet-50 with the Skip Connection Principle in Transfer Learning Models for Lung Disease Prediction -- 2.1 Introduction -- 2.1.1 Related Works -- 2.2 Proposed Methods -- 2.2.1 Describe the Dataset -- 2.2.2 Technique for Preprocessing -- 2.2.3 Transfer Learning -- 2.3 Result and Discussion -- 2.4 Conclusion and Future Work -- References -- 3 Performance Analysis of American Sign Language Using Wavelet Transform and CNN -- 3.1 Introduction and Background Study -- 3.2 Methodology and Implementation -- 3.2.1 Database Creation and Image Preprocessing -- 3.2.2 Segmentation and Cropping -- 3.2.3 Feature Extraction and Classification -- 3.3 Results and Discussion -- 3.3.1 Using Wavelet Transform and Feed-Forward Backpropagation Neural Network-Based Classifier -- 3.3.2 Using CNN Classifier -- 3.4 Conclusion -- References -- 4 Image Transmission in Underwater Through Li-Fi -- 4.1 Introduction -- 4.2 Literature Survey -- 4.3 System Design and Implementation -- 4.3.1 System Design -- 4.3.2 Implementation -- 4.4 Results and Discussions -- 4.5 Conclusions and Future Scope -- 4.5.1 Conclusion -- 4.5.2 Future Scope -- References -- 5 Leaf Disease Identification with Multi-label Classification of Various Plants Using Dense CNN Model -- 5.1 Introduction -- 5.2 Literature Survey -- 5.3 Methodology -- 5.3.1 Data Collection -- 5.3.2 Preprocessing -- 5.3.3 Classification: CNN -- 5.4 Experimental Settings -- 5.5 Model Performance -- 5.6 Experimental Results -- 5.7 Comparative Results. ; 5.8 Conclusion and Future Work -- References -- 6 Enhancing the MANET AODV Forecast of a Broken Link with LBP -- 6.1 Introduction -- 6.1.1 AODV -- 6.1.2 Route Maintenance and Discovery in AODV -- 6.1.3 Link Breakage in MANET -- 6.2 Proposed Solution for Predicting Link Breakages -- 6.2.1 Proposed Architecture -- 6.2.2 Proposed Flowchart -- 6.2.3 Procedure (LBP Algorithm) -- 6.2.4 Methodology -- 6.3 Simulation, Result and Result Analysis -- 6.3.1 Simulation Scenario and Model -- 6.3.2 Simulation Environment -- 6.3.3 Evaluation Metrics -- 6.4 Conclusion -- References -- 7 Educational Innovation Using Augmented Reality: Systematic Literature Review -- 7.1 Introduction -- 7.2 Research Method -- 7.3 Results and Discussion -- 7.3.1 Throughout the Year Publications -- 7.3.2 Multilevel Analysis -- 7.3.3 Geography-Related Contexts -- 7.4 Conclusions -- References -- 8 A Current Survey Trends on Child Safety Devices Using IoT -- 8.1 Introduction -- 8.2 Child Safety Related Factor -- 8.2.1 Location Tracking -- 8.2.2 Notification -- 8.2.3 Sensors -- 8.2.4 Video/Image Capturing -- 8.2.5 Voice Recognition -- 8.3 Child Safety Research Works with the Contribution and Limitations -- 8.4 Conclusion -- References -- 9 Intelligent Children Safety and Security Wearable Shield Using IoT -- 9.1 Introduction -- 9.2 Proposed Design -- 9.3 Results and Discussion -- 9.4 Conclusion -- References -- 10 Software Defects Prediction Using Machine Learning Algorithms -- 10.1 Introduction -- 10.2 Literature Review -- 10.3 Methodology -- 10.3.1 Artificial Neural Network -- 10.3.2 Random Forest -- 10.3.3 Random Tree -- 10.3.4 Linear Regression -- 10.3.5 Gaussian Processes -- 10.3.6 Decision Table -- 10.3.7 SMOreg -- 10.3.8 M5P -- 10.4 Performance Evaluation Measures -- 10.4.1 Accuracy -- 10.4.2 Precession -- 10.4.3 Recall -- 10.4.4 F Measure -- 10.4.5 Cross Validation. ; 10.5 Experimental Results and Analysis -- 10.6 Conclusion -- References -- 11 Farmers Market-Agricultural Marketing and Management System to Connect Farmers to Retailers -- 11.1 Introduction -- 11.1.1 Objectives -- 11.2 Problems in Current/Existing Models -- 11.3 Proposed System -- 11.3.1 Motivation for Proposed System -- 11.4 Implementation -- 11.4.1 Farmer Module -- 11.4.2 Buyer Module -- 11.4.3 Admin Module -- 11.4.4 Farmers Dashboard -- 11.4.5 Buyer Dashboard -- 11.4.6 Admin Dashboard -- 11.5 Conclusion and Future Scope -- References -- 12 Driver Drowsiness Detection System Based on Behavioral Method, Biological Method and Vehicular Feature-Based Method-A Review -- 12.1 Introduction -- 12.2 Behavioral Method -- 12.3 Biological Method -- 12.4 Vehicular Features-Based Method -- 12.5 Comparison and Discussion -- 12.6 Conclusion -- References -- 13 A Virtual Machine Protection Framework Against Compromised Hypervisor in Cloud Computing -- 13.1 Introduction -- 13.2 Related Work -- 13.3 Methodology -- 13.3.1 The Proposed Framework -- 13.3.2 Hypervisor Stability and Vulnerability Evaluation -- 13.3.3 Proposed Algorithms -- 13.4 Results and Discussion -- 13.4.1 Security Analysis -- 13.4.2 Performance Evaluation -- 13.5 Conclusion and Future Work -- References -- 14 SVM Versus KNN: Prediction of Best Image Classifier -- 14.1 Introduction -- 14.2 Related Works -- 14.3 Basic Concepts -- 14.3.1 Support Vector Machines (SVM) -- 14.3.2 K-Nearest Neighbors (KNN) -- 14.4 Implementation -- 14.5 Results -- 14.5.1 Support Vector Machines (SVM) -- 14.5.2 K-Nearest Neighbors (KNN) -- 14.6 Conclusion -- References -- 15 Developing a SVM Model of Big Data Analytics for Healthcare Recommendation System -- 15.1 Introduction -- 15.2 Literature Survey -- 15.3 SVM Model of Big Data Analytics for Healthcare Recommendation System -- 15.4 Result Analysis. ; 15.4.1 Comparison Between Methods -- 15.5 Conclusion -- References -- 16 Machine Learning Approach Towards the Breast Cancer Detection with Microwave Imaging -- 16.1 Introduction -- 16.2 Design of Proposed Model in HFSS -- 16.2.1 Basic Antenna Modelling -- 16.2.2 Basic Female Breast Structure Modelling -- 16.3 Simulation of Proposed Model in HFSS -- 16.3.1 Working Procedure of the Proposed Model -- 16.3.2 Dataset Collection for the Classification -- 16.4 Result Analysis -- 16.5 Conclusion -- References -- 17 Initial Intrusion Detection in Advanced Persistent Threats (APT's) Using Machine Learning -- 17.1 Introduction -- 17.2 Background Knowledge -- 17.3 Related Work -- 17.4 Proposed Method -- 17.4.1 Datasets -- 17.5 Experimental Results and Discussion -- 17.5.1 Random Forest -- 17.5.2 Support Vector Machine (SVM) -- 17.5.3 Multilayer Perceptron (MLP) -- 17.6 Conclusion and Future Direction -- References -- 18 Feature Selection with Binary Differential Evolution for Microarray Datasets -- 18.1 Introduction -- 18.2 Review of Literature -- 18.3 Methodology -- 18.3.1 Datasets Preprocessing -- 18.3.2 Adaptive Scaling Factor -- 18.3.3 Feature Correlation-Based Fitness Function -- 18.4 Experimental Analysis -- 18.4.1 Datasets and Parameters -- 18.4.2 Compared to Conventional Feature Selection Techniques -- 18.4.3 Compared to Sophisticated Feature Selection Techniques -- 18.4.4 Biomarker Analysis -- 18.5 Conclusion -- References -- 19 Survey on Imbalanced Dataset Classification-Machine Learning -- 19.1 Introduction -- 19.2 Problem Statement -- 19.3 Impacts on Classification by Imbalanced Data -- 19.4 Techniques for Classification of Unbalanced Data -- 19.5 Summary -- 19.6 Learning Objectives and Assessment Measures -- 19.7 Conclusion -- References -- 20 AI-Based Smart Farming Technology Using IoT -- 20.1 Introduction. ; 20.1.1 The Various Ways in Which AI Has Contributed in the Agricultural Sector Are as Follows -- 20.2 Issues in Farming -- 20.3 Proposed Solution -- 20.3.1 Methodology and Work Plan -- 20.4 Results and Discussion -- 20.5 Conclusions -- References -- 21 Deep Learning Approach for Auto Counting Complex Plants -- 21.1 Introduction -- 21.2 Collection of Data -- 21.3 Methodology -- 21.3.1 Image Phenotyping System -- 21.4 Experimental Results -- 21.4.1 Regression with Density Maps -- 21.4.2 Regression Using a Classifier -- 21.4.3 Extracting the Foreground -- 21.4.4 Slider Window -- 21.5 Conclusion -- References -- 22 A Survey on Smart Contract Vulnerabilities Including Auditing Tools -- 22.1 Introduction -- 22.2 Smart Contracts and Solidity -- 22.2.1 Smart Contracts -- 22.2.2 Solidity -- 22.3 Vulnerabilities in Smart Contracts -- 22.3.1 Re-entrancy -- 22.3.2 Transaction Origin (tx.origin) -- 22.3.3 Integer Overflow/Underflow -- 22.3.4 Timestamp Dependence -- 22.3.5 Transaction Ordering Dependence -- 22.3.6 Unsafe Delegate Call -- 22.3.7 Insecure Source of Randomness -- 22.4 Smart Contract Auditing Tools -- 22.5 Conclusion -- References -- 23 Breast Cancer Prediction by Levaraging Machine Learning Algorithm and Using Adaptive Voting Ensemble Method -- 23.1 Introduction -- 23.2 Literature Survey -- 23.3 Method of Materials -- 23.4 Result and Discussion -- 23.5 Conclusion and Future Scope -- References -- 24 Vulnerability Classification Based on Fine-Tuned BERT and Deep Neural Network Approaches -- 24.1 Introduction -- 24.2 Literature Review -- 24.3 Implementation -- 24.3.1 Dataset -- 24.3.2 Evaluation Measures -- 24.3.3 Implementation of Proposed Work Flow -- 24.4 Results and Discussion -- 24.4.1 Experimental Setup -- 24.4.2 Result Analysis on Different Machine Learning Approach -- 24.4.3 Result Analysis on Different Deep Learning and Transformer Approaches. ; 24.5 Conclusion and Future Scope.
• Titolo autorizzato: Intelligent Systems and Sustainable Computing
• ISBN: 981-9947-17-0
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Lingua di pubblicazione: Inglese
• Record Nr.: 9910746968203321
• Lo trovi qui: Univ. Federico II
• Serie: Smart Innovation, Systems and Technologies Series