10981nam 22004813 450 991087898290332120240806080308.09789819727162(electronic bk.)9789819727155(MiAaPQ)EBC31579725(Au-PeEL)EBL31579725(CKB)33645287500041(EXLCZ)993364528750004120240806d2024 uy 0engurcnu||||||||txtrdacontentcrdamediacrrdacarrierProceedings of Third International Conference in Mechanical and Energy Technology ICMET 2023, India1st ed.Singapore :Springer,2024.©2024.1 online resource (537 pages)Smart Innovation, Systems and Technologies Series ;v.390Print version: Yadav, Sanjay Proceedings of Third International Conference in Mechanical and Energy Technology Singapore : Springer,c2024 9789819727155 Intro -- Preface -- Contents -- About the Editors -- 1 Performance Analysis of Multiple Leaf Disease Detection in Plants Using CNN Model -- 1.1 Introduction -- 1.2 Related Work -- 1.3 Proposed Work -- 1.3.1 Disease Detection Model -- 1.3.2 Data Collection and Preprocessing: -- 1.4 Testing Data -- 1.4.1 Data Preparation -- 1.4.2 Max-Pooling Layers in CNN -- 1.4.3 Classification -- 1.4.4 Web Application Development -- 1.4.5 Android Development -- 1.5 Testing Module -- 1.6 Results &amp -- Discussion -- 1.7 Conclusion -- References -- 2 Feasibility Assessment of 100 kW Solar Power Plant in a Sub-tropical Region: A Case Study of New Delhi India -- 2.1 Introduction -- 2.2 System Configuration -- 2.3 Techno-Economic Analysis Procedure -- 2.4 Result and Discussion -- 2.5 Conclusion -- References -- 3 A Numerical Investigation on Estimation and Validation of Power Law Constants for TiB2 Reinforced AA2024 Composite Material -- 3.1 Introduction -- 3.2 Determination of Power Law Constants for FEA Analysis -- 3.3 Modeling and Simulation of AA2024/6wt%TiB2 Tensile Behavior -- 3.4 Result and Discussion -- 3.4.1 Plastic Flow Behavior of AA2024/6wt.%TiB2 Composite Material Composition -- 3.4.2 Validation of Power Law Constants -- 3.5 Conclusion -- References -- 4 Power Flow Control of the Grid-Integrated DG System Using Hybrid Aquila Optimizer‑Tangent Search Algorithm -- 4.1 Introduction -- 4.2 Hybrid DG System -- 4.2.1 Current Control Strategy -- 4.3 Hybrid Aquila Optimizer‑tangent Search Algorithm -- 4.4 Simulation Results -- 4.4.1 Scenario 1: Uneven Grid Voltages with an Equal Three Phase Load -- 4.4.2 Scenario 2: Equal Grid Voltages with an Uneven Load -- 4.5 Conclusion -- References -- 5 Design and Analysis of Manual Pallet Stacker by Using FEM Method -- 5.1 Introduction -- 5.2 Modeling and Simulation -- 5.2.1 Boundary Requirements.5.3 Result and Discussion -- 5.4 Conclusions -- 5.5 Future Scope -- References -- 6 Design and Development of an IoT Counter Using ESP8266 for 6 Axis MIG Welding Robot -- 6.1 Introduction -- 6.2 Methodology -- 6.3 Result -- References -- 7 In-Depth Examination of the Mechanical Properties of AA6061/MoS2/SiC Hybrid Composites -- 7.1 Introduction -- 7.2 Material and Methods -- 7.2.1 Aluminium 6061 -- 7.2.2 Silicon Carbide (SiC) -- 7.2.3 Molybdenum Disulphide (MoS2) -- 7.3 Sample Preparation -- 7.4 Results and Discussion -- 7.4.1 Microstructural Analysis -- 7.4.2 Tensile Test -- 7.4.3 Hardness Test -- 7.4.4 Impact Test -- 7.5 Conclusion -- References -- 8 Optimization of Microchannel Heat Sink Shapes to Enhancing Electronic Cooling Efficiency -- 8.1 Introduction -- 8.2 Methodology -- 8.2.1 CAD Model -- 8.2.2 Material Selection -- 8.2.3 Meshing -- 8.3 Result and Discussion -- 8.3.1 Thermal Analysis -- 8.4 Conclusions -- References -- 9 Empowering Next-Generation Energy Infrastructure Through IoT for Climate Resilience -- 9.1 Introduction -- 9.1.1 Objective of Study -- 9.2 Literature Review -- 9.2.1 Key Components of an Internet of Energy (IoE) for IoT in Energy -- 9.2.2 The "Internet of Things" -- 9.3 Methodology -- 9.3.1 Sensor Technology -- 9.3.2 Actuators -- 9.3.3 Technologies for Communication -- 9.3.4 Data from IoT and Computing -- 9.4 Results and Discussion -- 9.4.1 Energy Generation and IoT -- 9.4.2 City Smarts -- 9.5 Conclusion -- References -- 10 Safeguarding Cryptocurrency Transactions: Leveraging Blockchain and Machine Learning for Enhanced Financial Security -- 10.1 Introduction -- 10.1.1 Objective of Study -- 10.2 Literature Review -- 10.2.1 A Few Fundamentals of Blockchains -- 10.2.2 Machine Learning -- 10.3 Methodology -- 10.3.1 Framework of the Study -- 10.3.2 Dataset -- 10.3.3 K-Mean Clustering Technique.10.3.4 The Methods of Machine Learning that Were Examined -- 10.3.5 Evaluation Technique -- 10.4 Results and Discussion -- 10.5 Conclusion -- References -- 11 Utilizing Machine Learning for Advanced Natural Language Processing and Sentiment Analysis in Social Media Platforms -- 11.1 Introduction -- 11.1.1 Machine Learning for Sentiment Analysis -- 11.1.2 Machine Learning for Natural Language Processing (NLP) -- 11.1.3 Research Objectives -- 11.2 Literature Review -- 11.2.1 Advances in Machine Learning for Analyzing Sentiments -- 11.2.2 Advances in Machine Learning for NLP -- 11.3 Methodology -- 11.3.1 Proposed Approach -- 11.3.2 Supervised Machine Learning Methods for NLP and Sentiment Analysis -- 11.3.3 Unsupervised Machine Learning Methods for NLP and Sentiment Analysis -- 11.4 Results and Discussion -- 11.4.1 Performance Evaluation -- 11.4.2 Analyses of Various Classifiers -- 11.5 Conclusion -- References -- 12 Improving Image Clarity with Artificial Intelligence-Powered Super-Resolution Methods -- 12.1 Introduction -- 12.1.1 Beginning to Super-Resolution Techniques Based on Artificial Intelligence -- 12.1.2 Contributions and Significance -- 12.1.3 Research Objectives -- 12.2 Literature Review -- 12.2.1 Examining Current Developments in AI-Powered Super-Resolution Methodologies -- 12.2.2 Super-Resolution (SR) -- 12.3 Research Methodology -- 12.3.1 Deep Learning Architectures -- 12.3.2 Training Datasets -- 12.3.3 Loss Functions -- 12.3.4 Upscaling Algorithms -- 12.3.5 Generative Adversarial Networks (GANs) -- 12.3.6 Attention Processes -- 12.3.7 Methods of Post-Processing -- 12.3.8 Real-Time Super-Resolution -- 12.4 Results and Discussion -- 12.4.1 Evaluating and Comparing Different Deep Learning Architectures Used in Super-Resolution -- 12.4.2 Visual Illustration of Improved Pictures -- 12.5 Conclusion -- References.13 Revolutionising Tumour Diagnosis: How Clinical Application of Artificial Intelligence and Machine Learning Enhances Accuracy and Efficiency -- 13.1 Introduction -- 13.2 Literature Review -- 13.3 Research Methodology -- 13.4 Results and Discussion -- 13.5 Conclusion -- References -- 14 Harnessing Medical Databases and Data Mining in the Big Data Era: Advancements and Applications in Healthcare -- 14.1 Introduction -- 14.2 Literature Review -- 14.3 Research Methodology -- 14.4 Results -- 14.5 Challenges -- 14.6 Conclusion -- 14.7 Future Scope -- References -- 15 Machine Learning-Powered Design and Implementation for Classification of Missing Data in IoT Applications -- 15.1 Introduction -- 15.2 Literature Review -- 15.3 Research Methodology -- 15.4 Results -- 15.5 Conclusion -- 15.6 Challenges -- 15.7 Future Scope -- References -- 16 IoT-Based Security Detection for Cloud Web Applications: Leveraging Internet of Things Approaches -- 16.1 Introduction -- 16.1.1 Research Gap -- 16.2 Literature Review -- 16.3 Research Methodology -- 16.4 Results -- 16.5 Conclusion -- 16.6 Future Scope -- References -- 17 Exploring Ethical Considerations: Privacy and Accountability in Conversational Agents like ChatGPT -- 17.1 Introduction -- 17.2 Research Methodology -- 17.3 Results -- 17.4 Discussion -- 17.5 Conclusion -- 17.6 Challenges -- References -- 18 Navigating Cross-Lingual Natural Language Processing: Challenges, Strategies, and Applications -- 18.1 Introduction -- 18.1.1 Research Objectives -- 18.2 Literature Review -- 18.3 Methodology -- 18.3.1 Research Configuration -- 18.3.2 Assessment Activities -- 18.3.3 Implementation -- 18.4 Results and Discussion -- 18.4.1 Wide Cross-Lingual Transfer -- 18.4.2 Limitations and Difficulties -- 18.5 Conclusion -- References.19 Fostering Understanding: Bridging the Gap Between Black-Box Models and Human Interpretability with Explainable Artificial Intelligence -- 19.1 Introduction -- 19.1.1 Research Objectives -- 19.2 Literature Review -- 19.3 Methodology -- 19.3.1 Data Pre-Processing -- 19.3.2 Classification Assessment -- 19.3.3 Clustering Assessment -- 19.3.4 Explanation Assessment -- 19.4 Results and Discussion -- 19.4.1 Outcomes of Classification -- 19.4.2 Outcomes of Clustering -- 19.4.3 Outcomes of Explanation -- 19.4.4 Discussion -- 19.5 Conclusion -- References -- 20 Semantic Analysis and Machine Learning Techniques for Enhancing Content-Based Image Retrieval -- 20.1 Introduction -- 20.1.1 Research Objectives -- 20.2 Literature Review -- 20.3 Methodology -- 20.3.1 Extraction of Features and LNP -- 20.3.2 Suggested CBIR with ML Techniques -- 20.4 Results and Discussion -- 20.4.1 Color Dataset -- 20.4.2 Texture Dataset -- 20.4.3 Face Dataset -- 20.5 Performance Evaluation -- 20.6 Outcomes -- 20.6.1 Test 1 -- 20.6.2 Test 2 -- 20.7 Conclusion -- References -- 21 Examining ChatGPT's Impact: Challenges, Opportunities, and Future Directions for Enhancing Educational Experiences -- 21.1 Literature Review -- 21.2 Research Methodology -- 21.3 Results -- 21.4 Discussion -- 21.5 Conclusion -- References -- 22 Advancing Beyond Contextual Embeddings: Innovations in Word and Document Representations for Natural Language Processing -- 22.1 Introduction -- 22.2 Literature Review -- 22.3 Research Methodology -- 22.4 Results -- 22.5 Conclusion -- References -- 23 Combatting Cybercrimes: Leveraging Natural Language Processing for Detection in Social Media -- 23.1 Introduction -- 23.2 Literature Review -- 23.3 Research Methodology -- 23.4 Results -- 23.5 Discussion -- 23.6 Conclusion -- References.24 Constructing an Evaluation Framework for English Teaching in Higher Education: Integrating Neural Networks and Natural Language Processing.Smart Innovation, Systems and Technologies SeriesYadav Sanjay1425656Arora P. K1765046Sharma Anuj Kumar1765047Kumar Harish1379206MiAaPQMiAaPQMiAaPQ9910878982903321Proceedings of Third International Conference in Mechanical and Energy Technology4206280UNINA