LEADER 10708nam 22004693 450 001 9910838282603321 005 20240223080249.0 010 $a981-9979-54-4 035 $a(MiAaPQ)EBC31172450 035 $a(Au-PeEL)EBL31172450 035 $a(EXLCZ)9930464532100041 100 $a20240223d2024 uy 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aComputational Intelligence in Machine Learning $eProceedings of the 2nd International Conference ICCIML 2022 205 $a1st ed. 210 1$aSingapore :$cSpringer,$d2024. 210 4$d©2024. 215 $a1 online resource (686 pages) 225 1 $aLecture Notes in Electrical Engineering Series ;$vv.1106 311 $a981-9979-53-6 327 $aIntro -- Contents -- About the Editors -- A Deep Learning Method for Autism Spectrum Disorder -- 1 Introduction -- 2 Transfer Learning -- 3 Experiments -- 3.1 Data Set -- 3.2 VGG-16 -- 4 Results and Discussion -- 5 Conclusion -- References -- Cyber-Attacks and Anomaly Detection in Networking Based on Deep Learning-A Survey -- 1 Introduction -- 2 Taxonomy on Anomaly Detection of Cyberattacks in Networking -- 2.1 Machine Learning -- 2.2 Supervised Learning -- 2.3 Unsupervised Learning -- 2.4 Semi-supervised Learning -- 3 Literature -- 4 Comparative Analysis -- 5 Problem Statement -- 6 Conclusion -- References -- AI-Enabled Analysis of Climate Change on Agriculture and Yield Prediction for Coastal Area -- 1 Introduction -- 2 Literature Survey -- 3 Implementation -- 3.1 LSTM Model -- 3.2 Random Forest Model -- 3.3 Predicted Temperature and Actual Temperature -- 3.4 Shows the Baseline Predicted Example -- 3.5 Shows the Prediction Weather -- 3.6 Shows the Alternate Crop -- 4 Result Analysis -- 5 Conclusion and Future Scope -- References -- Deep Learning Methods for Predicting Severity for Diabetic Retinopathy on Retinal Fundus Images -- 1 Introduction -- 2 DR Features -- 3 Literature Review -- 4 Proposed Methodology -- 5 Conclusion -- References -- Hate Text Finder Using Logistic Regression -- 1 Introduction -- 2 Related Work -- 3 Proposed System -- 4 Working of Proposed System -- 5 Results -- 6 Conclusion -- 7 Future Scope -- References -- Automated Revealing and Warning System for Pits and Blockades on Roads to Assist Carters -- 1 Introduction -- 2 Pothole and Obstacle Detection Working Model -- 3 Implementation of Pothole and Obstacle Detection -- 3.1 Stage 1: When an Obstacle is Detected -- 3.2 Stage 2: When the Working Model Detects Potholes -- 4 Conclusion -- References. 327 $aGreen Data Center Power Flow Management with Renewable Energy Sources and Interlinking Converter -- 1 Introduction -- 2 Uninterruptible Power Supply -- 3 Proposed Framework for Green Data Center -- 4 Simulation Result -- 5 Conclusion -- References -- Design of Grid-Connected Battery Storage Wave Energy and PV Hybrid Renewable Power Generation -- 1 Introduction -- 2 Problem Formulation -- 2.1 Scenario 1: Rising Voltage Profile -- 2.2 Scenario 2: Backward Power Flow -- 2.3 Scenario 3: An Increase in the Fault Current's Size -- 2.4 Scenario 4: The Traditional Method is to Estimate the EG's Penetration Limitations -- 3 Proposed Solutions -- References -- Power Quality Enhancement with PSO-Based Optimisation of PI-Based Controller for Active Power Filter -- 1 Introduction -- 2 Power Quality -- 2.1 Power Quality Problems -- 2.2 The Benefits of Power Quality -- 3 PSO -- 4 Optimisation of PI Controller By Using PSO -- 5 Conclusion -- References -- Monitoring and Control of Motor Drive Parameters Using Internet of Things Protocol for Industrial Automation -- 1 Introduction -- 1.1 Automation -- 1.2 Material and Method -- 1.3 Relay and Contactor -- 2 Architecture of SCADA -- 2.1 Human-Machine Interface (HMI) -- 2.2 Internet of Things SCADA System -- 2.3 Data Communication -- 2.4 Data Presentation -- 2.5 Data Acquisition -- 3 Conclusion -- References -- Switching Loss Comparison of a Cascaded Diode-Clamped Inverter with Conventional Multilevel Inverters -- 1 Introduction -- 2 Inverter Topologies -- 2.1 Cascaded H-Bridge Inverter (CHBI)-(9-Level) -- 2.2 Diode-Clamped Multilevel Inverter (DCMLI)-(Five-Level) -- 2.3 Hybrid Inverter-Cascaded Diode-Clamped Inverter (CDCI)-(Nine-Level) -- 3 Switching Loss Calculation -- 4 Simulation and Results -- 4.1 Cascaded H-Bridge Inverter (Nine-Level)-Results -- 4.2 Diode-Clamped Multilevel Inverter (FiveLevel)-Results. 327 $a4.3 Cascaded Diode-Clamped Inverter (Nine-Level)-Results -- 4.4 Comparision of CHBI, DCMLI and CDCI -- 5 Conclusion -- References -- An Energy-Efficient Mechanism Using Blockchain Technology for Machine-Type Communication in LTE Network -- 1 Introduction -- 2 Related Work -- 3 Proposed Cluster-Based Energy-Efficient Cluster Head Using Blockchain -- 3.1 Distance (fdistance) -- 3.2 Delay (fdelay) -- 3.3 Energy (fenergy) -- 3.4 Objective Function -- 3.5 WTL Algorithm for Optimal Cluster Head Selection -- 3.6 Blockchain Technology -- 4 Results and Discussions -- 4.1 Simulation Procedure -- 5 Conclusion -- References -- Comparison of Echo State Network with ANN-Based Forecasting Model for Solar Power Generation Forecasting -- 1 Introduction -- 2 Methodology -- 2.1 Artificial Neural Network -- 2.2 Echo State Network -- 3 Statistical Methods -- 4 Result and Discussion -- 5 Conclusion -- References -- A Grey Wolf Integrated with Jaya Optimization Based Route Selection in IoT Network -- 1 Introduction -- 2 Literature Review -- 3 Proposed Routing Strategy in IoT -- 3.1 Computation of Fitness Value -- 3.2 Optimal Node Selection Using GWJO Model -- 3.3 Route Establishment -- 4 Results -- 4.1 Simulation Set-Up -- 5 Conclusion -- References -- Secure Software Development Life Cycle: An Approach to Reduce the Risks of Cyber Attacks in Cyber Physical Systems and Digital Twins -- 1 Introduction -- 2 Literature Survey -- 2.1 Cyber Physical Systems and Their Attacks? -- 2.2 Advantages of Digital Twin Over Cyber Physical System -- 2.3 Disadvantages of DT -- 2.4 Cyber Digital Twin -- 2.5 How Secure Are CDTs? -- 3 Methodology -- 3.1 Secure Software Development Life Cycle (Proposed Solution) -- 3.2 Why SSDLC and not SDLC? -- 3.3 Detailed Look at the SSDLC -- 4 Conclusion -- References. 327 $aSocial Networks and Time Taken for Adoption of Organic Food Product in Virudhunagar District-An Empirical Study -- 1 Introduction -- 2 Related Works -- 3 Conceptual Framework -- 4 Objectives of the Study -- 5 Area of the Study, Sample Framework and Procedure -- 6 Days to Purchase -- 7 Members of the Social Group -- 8 Reason to Join in the Group -- 9 The Frequency of Participation in the Group -- 10 Information Trust by the Respondents -- 11 Convey Information About New Product -- 12 Cost Spend by the Respondents for Passing the Information -- 13 Like to Spread the Information -- 14 Quantity of Passing of Information -- 15 Hypothesis -- 16 Kruskal-Wallis Test -- 17 Living Place and Adoption of New Product in the Market -- 18 Chi-Square Tests -- 19 Age Group and Information Passing -- 20 Chi-Square Test -- 21 Exponential Smoothing for Applying Roger's Model in Identifying the Adoption of Organic Food Product -- 22 Exponential Growth -- 23 Findings and Discussion -- References -- Usage of Generative Adversarial Network to Improve Text to Image Synthesis -- 1 Introduction -- 2 Literature Survey -- 2.1 Generative Adversarial Networks -- 2.2 Text to Photo-Realistic Image Synthesis with Stacked GAN -- 2.3 Image Generation from Scene Graphs -- 2.4 Fine Grained Text to Image Generation with Attentional Generative Adversarial Networks (Attn GAN) -- 2.5 Realistic Image Synthesis with Stacked Generative Adversarial Networks (Stack GAN++) -- 3 Methodology -- 3.1 Defining Goal -- 3.2 Researching Previous Attempts -- 3.3 Defining Approach -- 3.4 Algorithm -- 4 Experiments and Results -- 4.1 Experiment Setting -- 4.2 Effectiveness of New Modules -- 4.3 Component Analysis of AATM -- 4.4 Component Analysis of SDM -- 4.5 Comparison of KT GAN with Other GAN Models -- 4.6 Visualization -- 5 Conclusion -- References. 327 $aRecurrent Neural Network-Based Solar Power Generation Forecasting Model in Comparison with ANN -- 1 Introduction -- 2 Methodology -- 2.1 Artificial Neural Network (ANN) -- 2.2 Recurrent Neural Network (RNN) -- 3 Statistical Measures -- 4 Result and Discussion -- 5 Conclusion -- References -- Android Malware Detection Using Genetic Algorithm Based Optimized Feature Selection and Machine Learning -- 1 Introduction -- 1.1 A Subsection Sample -- 2 Proposed Method -- 2.1 Supervised Classification (Training Dataset) -- 2.2 Supervised Classification (Test Dataset) -- 2.3 System Design -- 2.4 Use Case Diagram -- 3 Testing and Implementation -- 4 Results and Discussion -- 5 Conclusion -- References -- Mental Health Disorder Predication Using Machine Learning for Online Social Media -- 1 Introduction -- 2 Literature Review -- 3 Methodology -- 3.1 Data Collection -- 3.2 Data Collection -- 3.3 Check Category -- 3.4 Check Wordlist -- 4 Machine Learning Models -- 5 Result and Discussion -- 6 Conclusion -- References -- An Application on Sweeping Machines Detection Using YOLOv5 Custom Object Detection -- 1 Introduction -- 2 Related Work -- 2.1 YOLOv5 -- 2.2 Model Description -- 3 Proposed System -- 3.1 Dataset -- 3.2 Manual Labeling -- 3.3 Augmentation -- 4 Training -- 5 Results -- 6 Command -- 7 Conclusion and Future Work -- References -- Analyze and Detect Lung Disorders Using Machine Learning Approaches-A Systematic Review -- 1 Introduction -- 2 State-of-Art: Overview -- 3 Dataset Availability -- 4 Methodology -- 5 Conclusions and Future Work -- References -- A Study on Predicting Skilled Employees' Using Machine Learning Techniques -- 1 Introduction -- 2 Related Work -- 3 Classifier Construction -- 3.1 Objectives and Problem Definition -- 3.2 Data Collection and Understanding Process -- 3.3 Data Preparation and Pre-processing -- 4 Modeling and Experiments. 327 $a5 Comparative Analysis and Discussion:. 410 0$aLecture Notes in Electrical Engineering Series 700 $aGunjan$b Vinit Kumar$0846445 701 $aKumar$b Amit$0720810 701 $aZurada$b Jacek M$0771206 701 $aSingh$b Sri Niwas$01588990 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910838282603321 996 $aComputational Intelligence in Machine Learning$94132078 997 $aUNINA