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Multi-Strategy Learning Environment : Proceedings of ICMSLE 2024 / / edited by Vrince Vimal, Isidoros Perikos, Amrit Mukherjee, Vincenzo Piuri
| Multi-Strategy Learning Environment : Proceedings of ICMSLE 2024 / / edited by Vrince Vimal, Isidoros Perikos, Amrit Mukherjee, Vincenzo Piuri |
| Edizione | [1st ed. 2024.] |
| Pubbl/distr/stampa | Singapore : , : Springer Nature Singapore : , : Imprint : Springer, , 2024 |
| Descrizione fisica | 1 online resource (710 pages) |
| Disciplina | 006.31 |
| Collana | Algorithms for Intelligent Systems |
| Soggetto topico |
Computational intelligence
Artificial intelligence Computer vision Natural language processing (Computer science) Game theory Computational Intelligence Artificial Intelligence Computer Vision Natural Language Processing (NLP) Game Theory |
| ISBN | 981-9714-88-5 |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Intro -- Preface -- Contents -- About the Editors -- 1 AI Powered Chat Assistant for Trauma Detection from Text and Voice Conversations with a Direct Doctor Connection -- 1 Introduction -- 2 Related Works -- 2.1 Emotional Intelligence in Communication: Research Challenges, Literature and Recent Achievements -- 2.2 Chatbot Mobile Isolation App for Depression -- 2.3 Mental Health Chatbot that Uses NLP and AI to Deliver Behavioral Insights and Remote Healthcare -- 2.4 Mental Health Support Chatbot Using NLP -- 2.5 Dost-Chatbot as Mental Health Assistant -- 2.6 Application of Cognitive Behavioral Therapy in Psychiatry: A Review -- 2.7 Revivify: Depression Research and Management Using Automated Tweets and Chatbots -- 2.8 Proposed Chatbot: Thinking and Problem-Solving Experience -- 2.9 Psykh, the Chatbot Using the Rasa Open Source Framework, to be Your Therapist and Stress Reliever -- 2.10 Identify Depression in a Person Using Speech Signals by Extracting Energy and Situations -- 3 Methodology -- 3.1 Module Description -- 4 Implementation -- 4.1 Implementation of Machine Learning Models -- 4.2 NLP Model Development -- 5 Result and Discussion -- 6 Conclusion -- References -- 2 An Intelligent Car Locating System Based on Arduino for a Massive Parking Place -- 1 Introduction -- 2 Motivation -- 3 Problem Statement -- 4 Project Scope -- 5 Objectives -- 6 Related Study -- 7 Comparison with Other System -- 8 Background Tools and Technology -- 8.1 Software Tools -- 8.2 Arduino Simulator -- 8.3 Hardware Tools -- 9 PCB Design of Intelligent Car Locating System for a Massive Parking Place -- 10 Proposed Model of Smart Car Parking System -- 11 Work Flow Diagram of Smart Car Locating System -- 12 Final Circuit of Intelligent Car Locating System for a Massive Parking Place -- 13 Implementation.
13.1 Final Output of Intelligent Car Locating System for a Massive Parking Place -- 14 Result and Discussion -- 15 Testing and Evaluation -- 15.1 Performance Analysis of IoT-Based Intelligent Car Parking System for Large Parking Lot Using Arduino -- 16 Contribution -- 17 Conclusion -- 18 Future Recommendation -- References -- 3 Electricity Load Forecasting Using LSTM for Household Usage -- 1 Introduction -- 2 Literature Review -- 3 LSTM for Electricity Load Forecasting Methodology -- 3.1 Building Machine-Learning Model -- 3.2 Training ML Model -- 4 Results and Discussions -- 4.1 Electricity Load Prediction Over Different Periods -- 4.2 Error Analysis for Electricity Load Prediction -- 4.3 Comparison with Conventional Methods -- 5 Conclusion -- References -- 4 A Cybersecurity Classification Model for Detecting Cyberattacks -- 1 Introduction -- 2 Literature Survey -- 2.1 Training with Support Vector Machine (SVM) for Cyberattack Detection -- 2.2 Normalization for Removing Noise from Given Datasets -- 2.3 Autoencoders with Cybersecurity -- 3 Dataset Description -- 4 Experimental Results -- 5 Conclusion -- References -- 5 Implementation of Baumann Skin Type Indicator Using Machine Learning -- 1 Introduction -- 2 Related Work -- 3 Brief Overview of the Fundamental Dichotomies of BSTI -- 4 Proposed Architecture -- 4.1 Machine Learning Model Architecture -- 4.2 Dataset -- 5 Experiments and Results -- 5.1 Results of the InceptionV3 Model -- 5.2 Comparison with Other Deep Learning Techniques -- 6 Conclusion and Future Work -- References -- 6 An On-demand Data Delivery and Secured Platform in Cloud Computing -- 1 Introduction -- 1.1 Methodology -- 2 Literature Survey -- 3 On-demand Data Delivery and Secured Platform (ODDSP) -- 3.1 Quality of Service (QoS) -- 4 Advanced Encryption Standard (AES) for Cloud Data -- 4.1 Performance Metrics. 4.2 Evaluation Results -- 5 Conclusion -- References -- 7 Real-Time Sign Language Interpreter Using MediaPipe, Dynamic Time Warping, and NLP -- 1 Introduction -- 2 Related Work -- 3 Proposed Methodology -- 3.1 Problem Domain -- 3.2 Problem Definition -- 3.3 Problem Statement -- 3.4 Dataset -- 4 Implementation -- 4.1 MediaPipe Detection/Holistic Model -- 4.2 Extract Landmarks -- 4.3 Draw Landmarks -- 4.4 Models -- 4.5 Dynamic Time Warping (DTW) -- 4.6 Sign Prediction -- 4.7 Phrase Generation -- 5 Result -- 6 Discussion -- 7 Conclusion -- References -- 8 A Support Vector Machine Classifier Approach for Predicting Preeclampsia and Gestational Hypertension -- 1 Introduction -- 2 Literature Review -- 3 Materials and Methods -- 3.1 Description of the Dataset -- 3.2 Data Preprocessing -- 3.3 Importance of Using SVM in ML Experiments -- 3.4 Training and Testing Data -- 3.5 Methodology -- 3.6 Selection of Algorithm -- 3.7 Evaluation of the Schemes' Performance -- 4 Results and Discussion -- 5 Conclusion -- References -- 9 Multilingual Communication: NMT-Based On-Call Speech Translation for Indian Languages -- 1 Introduction -- 2 Literature Review -- 3 Proposed Methodology -- 3.1 Dataset -- 3.2 Algorithms -- 3.3 Architecture -- 3.4 Model Training -- 3.5 Evaluation -- 4 Result -- 5 Conclusion -- 6 Future Work -- References -- 10 Brain Tumor Segmentation and Classification Using Deep Learning -- 1 Introduction -- 2 Literature Survey -- 2.1 Problem Formulation -- 2.2 Research Gap -- 3 Data and Variables -- 3.1 About Dataset -- 3.2 Variables for Segmentation Model: -- 3.3 Variables for Classification Model: -- 4 Methodology and Model Specifications -- 4.1 Segmentation Model -- 4.2 Classification Model -- 5 Empirical Results -- 5.1 Segmentation Model -- 5.2 Classification Model -- 6 Conclusion -- 7 Future Scope -- References. 11 ACO-Optimized DRL Model for Energy-Efficient Resource Allocation in High-Performance Computing -- 1 Introduction -- 2 Literature Review -- 3 Problem Definition -- 4 Methods -- 4.1 Ant Colony Optimization -- 4.2 DRL for Resource Allocation in HPC -- 5 Proposed Model -- 5.1 ACO-Optimized DRL for Resource Allocation in HPC -- 6 Experimental Analysis -- 6.1 Response Time Analysis -- 6.2 Makespan Analysis -- 6.3 Energy Consumption Analysis -- 7 Conclusion and Future Work -- References -- 12 Business Decision-Making Using Hybrid LSTM for Enhanced Operational Efficiency -- 1 Introduction -- 2 Literature Review -- 3 Problem Definition -- 4 Data Collection -- 4.1 Data Preprocessing -- 5 Hybrid Optimized LSTM Model for Sales Prediction -- 5.1 Loss Function -- 6 Experimental Analysis -- 6.1 Model Training -- 6.2 Forecast Analysis -- 6.3 Performance Analysis -- 7 Conclusion and Future Work -- References -- 13 Unveiling New Horizons in Machine Learning, NLP-Driven Framework for Student Learning Behavior -- 1 Introduction -- 2 Literature Review -- 3 Modeling and Analysis of Student Learning Behavior -- 3.1 Role of Machine Learning in Analyzing the Student Behavior -- 4 Techniques for Modeling Student Learning Behavior -- 4.1 Employing Natural Language Processing -- 4.2 Employing Natural Language Processing -- 5 Conclusion and Scope for Future Work -- References -- 14 Mirror Text Classification from Image Using Machine Learning Techniques -- 1 Introduction -- 2 Literature Review -- 3 Methodology -- 3.1 Input and Preprocessing -- 3.2 Clustering and Sub-clustering -- 3.3 Feature Extraction -- 3.4 Training -- 3.5 Mirror Text Identification -- 4 Results and Discussion -- 5 Conclusion -- References -- 15 Using Deep Learning to Identify Types of Lung Diseases from X-Ray Images -- 1 Introduction -- 2 Objective -- 3 Literature Survey -- 4 Proposed System -- 5 Conclusion. References -- 16 A Light-Weight Data Storage and Delivery Platform in Cloud Computing -- 1 Introduction -- 1.1 Methodology -- 2 Literature Survey -- 3 Lempel-Ziv-Markov (LZMA) Compression -- 3.1 RSA-KEM (Key Encapsulation Mechanism) -- 3.2 Symmetric-Key Decryption -- 3.3 Dataset Description -- 3.4 Performance Metrics -- 4 Experimental Results -- 5 Conclusion -- References -- 17 Big Data Analytics Security Issues and Solutions in Healthcare -- 1 Introduction -- 2 The Need for Medical Care Analytics for Big Data -- 3 Different Big Data Analytics Phases -- 3.1 Extracting, Data Cleaning, and Data Collection -- 3.2 Integration and Aggregation of Data -- 3.3 Data Model -- 3.4 Data Delivery, Interpretation, and Feedback -- 4 Role-Based Application Security Principles -- 5 Big Data Lifecycle -- 6 Technologies in Use -- 7 Big Data Security Challenge and Solution -- 8 Conclusion -- References -- 18 Framework for Early-Stage Diabetes Mellitus Risk Prediction Using Hybrid Supervised Learning -- 1 Introduction -- 2 Literature Survey -- 3 Proposed Framework -- 4 Experimental Setup -- 4.1 Evaluation Metrics -- 5 Results and Discussion -- 5.1 Prediction Performance of ML Techniques -- 5.2 Optimization of Prediction Accuracy -- 6 Conclusion -- References -- 19 Brain Tumor Classification in MRI Images: A CNN and U-Net Approach -- 1 Introduction -- 2 Proposed Method -- 2.1 Data Collection -- 2.2 Image Preprocessing -- 2.3 Image Segmentation -- 2.4 Feature Extraction -- 2.5 Convolutional Neural Network (CNN) -- 2.6 U-Net Architecture -- 2.7 Callback Functions -- 3 Result and Discussion -- 3.1 Evaluation Metrics -- 3.2 Experimental Analysis and Result -- 3.3 Prediction of Tumor -- 4 Conclusion -- References -- 20 Cross-Modal Text-to-Video Retrieval Using Deep Learning -- 1 Introduction -- 2 Related Work -- 3 System Architecture -- 4 Methodology. 5 Model Implementation and Output. |
| Record Nr. | UNINA-9910864194303321 |
| Singapore : , : Springer Nature Singapore : , : Imprint : Springer, , 2024 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Production Technology of Underutilized Vegetable Crops / / edited by Savita, Monisha Rawat, and Vrince Vimal
| Production Technology of Underutilized Vegetable Crops / / edited by Savita, Monisha Rawat, and Vrince Vimal |
| Edizione | [1st ed. 2023.] |
| Pubbl/distr/stampa | Cham, Switzerland : , : Springer, , [2023] |
| Descrizione fisica | 1 online resource (XII, 392 p. 1 illus.) |
| Disciplina | 359.8205 |
| Soggetto topico | Vegetables |
| ISBN | 3-031-15385-5 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | 1-Underutilized vegetables introduction and identification -- 2-Production technology of underutilized crops of alliaceae family -- 3-Production technology of underutilized leguminous vegetables -- 4-Production technology of underutilized vegetables of araceae family -- 5-Production technology of underutilized cucurbitaceous -- 6-Production technology of underutilized vegetables of rutaceae family -- 7-Production technology of underutilized vegetables of dioscoreace family -- 8-Production technology of underutilized vegetables of Aizoaceae family -- 9-Production technology of underutilized vegetables of basellaceae family -- 10-Production technology of underutilized vegetables of labitae family -- 11-Production nology of underutilized vegetables of martynaceae family -- 12-Production technology of underutilized vegetables of solanaceae family -- 13-Production technology of underutilized vegetables of apiaceae family -- 14-Production technology of underutilized vegetables of brassicaceae family -- 15-Production technology of underutilized vegetables of chenopodiaceae family -- 16-Production technology of underutilized vegetables of compositae family -- 17-Production technology of underutilized vegetables of euphorbiaceae family -- 18-Production technology of underutilized vegetables of moringaceae family -- 19-Production technology of underutilized vegetables of polygonaceae family -- 20-Production technology of underutilized vegetables of moraceae family -- 21-Production technology of underutilized vegetables of onagraceae family -- 22-Production technology of underutilized vegetables of portulaceae family -- 23-Production technology of underutilized vegetables of cannaceae family -- 24-Production technology of underutilized vegetables of marantaceae family -- 25-Protected cultivation of underutilized vegetables -- 26-Seed production of underutilized vegetables -- 27-Integrated disease management of underutilized vegetables -- 28-Integrated pest management of underutilized vegetables -- 29-Underutelized vegetables are grown in the Hydroponic and aeroponic system -- 30-Sustainable production of underutilized vegetables. |
| Record Nr. | UNINA-9910717424903321 |
| Cham, Switzerland : , : Springer, , [2023] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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