Data Management, Analytics and Innovation : Proceedings of ICDMAI 2024, Volume 2 / / edited by Neha Sharma, Amol C. Goje, Amlan Chakrabarti, Alfred M. Bruckstein

• Pubblicazione: Singapore : , : Springer Nature Singapore : , : Imprint : Springer, , 2024
• Edizione: 1st ed. 2024.
• Descrizione fisica: 1 online resource (459 pages)
• Disciplina: 005.74
• Soggetto topico: Computational intelligence; Engineering - Data processing; Big data; Computational Intelligence; Data Engineering; Big Data
• Persona (resp. second.): SharmaNeha
• Nota di bibliografia: Includes bibliographical references and index.
• Nota di contenuto: Intro -- Preface -- Contents -- Editors and Contributors -- Comprehensive Survey of Nonverbal Emotion Recognition Techniques -- 1 Introduction -- 2 Applications Based on Understanding Nonverbal Emotion -- 3 Machine/Deep Learning Methods for Recognition of Nonverbal Emotion -- 3.1 Facial Expressions Recognition Machine/deep Learning Methods -- 3.2 Hand Gestures Recognition Machine/Deep Learning Methods -- 3.3 Body Language Recognition Machine/Deep Learning Methods -- 4 Findings -- 5 Conclusion -- References -- A Two-Stage CNN Based Satellite Image Analysis Framework for Estimating Building-Count in Residential Built-Up Area -- 1 Introduction -- 2 Review of the Relevant Research Work -- 3 Background Study -- 3.1 Mask R-CNN -- 3.2 Regression Using CNN -- 4 Proposed Methodology -- 4.1 Overview of Proposed Methodology -- 4.2 Mask R-CNN Top-Down Approach for Segmentation of Built Up Area -- 4.3 CNN Based Regression Model to Estimate Building-Count Within Segmented Built-Up Area -- 5 Experimental Evaluation of the Proposed Framework -- 5.1 Dataset Used -- 5.2 Experimental Setup -- 5.3 Experimental Evaluation Metric -- 5.4 Experimental Results and Discussion -- 6 Conclusion -- References -- Forecast of Energy Demand Using Temporal Fusion Transformer -- 1 Introduction -- 2 Survey of Literature -- 3 Proposed Work -- 3.1 Data Collection and Preprocessing -- 3.2 TFT Model Architecture -- 3.3 Training and Validation -- 4 Results -- 4.1 Forecasts -- 4.2 Interpreting the Seasonality -- 4.3 Detecting Some Accidental or Extreme Events -- 4.4 Ranking the Features -- 5 Conclusion -- References -- Mental Health Prediction Using Artificial Intelligence -- 1 Introduction -- 2 Literature Survey -- 3 Design -- 4 Methodology -- 5 Results -- 6 Future Directions and Limitations -- 7 Conclusion -- References. ; VGGish Deep Learning Model: Audio Feature Extraction and Analysis -- 1 Introduction -- 1.1 Feature Extraction -- 1.2 Dataset -- 2 Related Work -- 3 Proposed System -- 3.1 Preprocessing -- 3.2 Feature Extraction -- 3.3 Feature Concatenation and Selection -- 3.4 Classification -- 3.5 Output -- 4 Proposed Algorithm -- 4.1 Initialization -- 5 Results -- 6 Conclusion -- References -- Stacking Ensemble-Based Approach for Sarcasm Identification with Multiple Contextual Word Embeddings -- 1 Introduction -- 2 Related Work -- 3 Materials and Methods -- 3.1 Preprocessing -- 3.2 Contextual Word Embeddings -- 3.3 Proposed Model -- 4 Materials and Methods -- 4.1 Dataset -- 4.2 Experimental Setup -- 4.3 Results and Analysis -- 5 Conclusion -- References -- Trigger-Based Pothole Detection, and Warning System with RQ and PHR Mapping -- 1 Introduction -- 2 Related Work and Comparative Study -- 3 Methodology -- 4 Flowcharts -- 5 Result and Discussions -- 6 Conclusion -- References -- Blending Motion Capture and 3D Human Reconstruction Techniques for Enhanced Character Animation -- 1 Introduction -- 2 Literature Review -- 3 Methodology -- 3.1 Technologies Used for 3D Model Building -- 3.2 Technology Used for MoCap -- 3.3 Integration of the Technologies Used -- 3.4 Constraints of the Proposed System -- 4 Result -- 5 Future Scope -- References -- A Comprehensive Survey of Regression-Based Loss Functions for Time Series Forecasting -- 1 Introduction -- 2 Time Series Data -- 3 Regression Loss Functions -- 3.1 Mean Absolute Error (MAE) -- 3.2 Mean Squared Error (MSE) -- 3.3 Mean Bias Error (MBE) -- 3.4 Relative Absolute Error (RAE) -- 3.5 Relative Squared Error (RSE) -- 3.6 Mean Absolute Percentage Error (MAPE) -- 3.7 Root Mean Squared Error (RMSE) -- 3.8 Mean Squared Logarithmic Error (MSLE) -- 3.9 Root Mean Squared Logarithmic Error (RMSLE). ; 3.10 Normalized Root Mean Squared Error (NRMSE) -- 3.11 Relative Root Mean Squared Error (RRMSE) -- 3.12 Huber Loss -- 3.13 Log-Cosh Loss -- 3.14 Quantile Loss -- 4 Experiments -- 4.1 Datasets -- 4.2 Performance Metrics -- 5 Conclusion -- References -- Diabetic Retinopathy Detection Using Real-World Datasets of Fundus Images -- 1 Introduction -- 1.1 Diabetic Retinopathy -- 1.2 Severity and Stages -- 2 Literature Review -- 2.1 Research Gaps -- 3 The Dataset -- 3.1 Retinal Image Collection -- 4 Related Work -- 5 Methodology -- 5.1 Data Distribution of Retinal Image Collection -- 5.2 Filtering Out Images with Noise -- 5.3 Image Cropping for Removal of Unnecessary Content -- 6 Model Architecture -- 7 Experimental Analysis -- 8 Results and Discussion -- 8.1 Deep Learning Models Overview -- 8.2 Diagnosis & -- Preventative Measures -- 9 Comparative Analysis -- 10 Future Scope -- 11 Conclusion -- References -- Deep Learning for MRI-Based Brain Tumour Identification and Classification -- 1 Introduction -- 1.1 Viewing Brains -- 1.2 PET Scans -- 1.3 CGI -- 1.4 MRI -- 1.5 Diffusion Scaling Imaging -- 2 Literature Survey -- 3 Proposed Method -- 3.1 Pre Processing -- 3.2 Classification -- 3.3 Characterisation -- 3.4 Grouping -- 3.5 Convolution Neural Network -- 4 Results and Discussion -- 5 Conclusion -- References -- Preserving Tamil Brahmi Letters on Ancient Inscriptions: A Novel Preprocessing Technique for Diverse Applications -- 1 Introduction -- 2 Literature Review -- 3 Methodology for Inscription Translation -- 3.1 Image Blurring -- 3.2 Binarization -- 3.3 Edge Detection -- 4 Results and Discussion -- 5 Conclusion -- References -- Analysis of Regular Machine Learning and Ensemble Learning Approaches for Term Insurance Prediction in Banking Data -- 1 Introduction -- 2 Literature Review -- 3 Methodology -- 3.1 Acquisition of Data -- 3.2 Analysis. ; 3.3 Data Preprocessing -- 3.4 Training and Analysis of Models -- 4 Results -- 5 Conclusion -- References -- Platform Independent Satellite Image Processing Using GPGPU -- 1 Introduction -- 2 Literature Survey -- 3 Proposed Methodology -- 3.1 Operating System Portability and Hardware Independence -- 3.2 GPU Detection and Parallel Computing -- 3.3 Change Detection -- 3.4 Algorithms -- 4 Results and Discussions -- 4.1 Evaluation Environment -- 4.2 Evaluation Result -- 5 Conclusion -- 6 Future Scope -- References -- Blending Psychological Models with Modern HCI Techniques to Develop Artificial Emotional Intelligent "Affective" Systems -- 1 Introduction -- 1.1 Understanding Affective Computing -- 1.2 Human Emotions -- 1.3 Paper Organization -- 2 Literature Review -- 3 HCI Techniques for Utilizing Emotion Models -- 3.1 HCI Background -- 3.2 Modern HCI Systems & -- Interaction Modalities -- 4 Blending HCI Approaches with Psychological Models and ML Techniques -- 5 Conclusion -- 5.1 Future Scope -- References -- An Enhanced Deep Learning Method to Generate Synthetic Images with Features That are Comparable to Original Images Using Neural Style Transfer -- 1 Introduction -- 2 Network Architecture -- 2.1 Loss -- 2.2 Content Loss -- 2.3 Style Loss -- 3 Results -- 3.1 Comparative Evaluation -- 4 Conclusion -- References -- Improving Sentiment Analysis by Handling Negation on Twitter Data Using Deep Learning Approaches -- 1 Introduction -- 1.1 Contributions -- 1.2 Organization -- 2 Related Work -- 3 Proposed Methodology -- 3.1 WordNet -- 3.2 Preprocessing -- 3.3 Negation Handling -- 3.4 Classification -- 4 Results -- 4.1 Dataset Description -- 4.2 Experimental Results -- 5 Conclusion -- References -- Comparative Analysis of Deep Learning Models for Car Part Image Segmentation -- 1 Introduction -- 2 Related Works -- 3 Dataset Description -- 4 Methodology. ; 4.1 YOLOv8 Segmentation Model -- 4.2 Detectron2 Mask R-CNN Resnet 101 FPN -- 4.3 Detectron 2 Mask R-CNN ResNeXt 101 32×8d FPN -- 5 Experimental Results and Observations -- 6 Conclusion -- References -- Boosting Tiny Object Detection in Complex Backgrounds Through Deep Multi-Instance Learning -- 1 Introduction -- 2 Literature Survey -- 2.1 Multi Instance Metric Learning and Bags -- 3 Methodology -- 3.1 Dataset Preparation -- 3.2 Experimental Design -- 4 Results and Discussion -- 4.1 Experimental Setup -- 5 Conclusion -- References -- Driver Drowsiness Detection System Using YoloV5 -- 1 Introduction -- 2 Literature Review -- 3 Methodology -- 4 Technology Used -- 4.1 Design and Analysis -- 5 Result and Experiment -- 5.1 Design and Analysis -- 5.2 Preprocessing -- 5.3 Performance of the Model -- 5.4 Result and Discussion -- 6 Future Scope -- 7 Conclusion -- References -- Shift of Customer from Unorganised to Organised Sector in Retail: Is Adoption of Technology a Catalyst -- 1 Introduction -- 1.1 Background of the Problem -- 1.2 Research Problem and Relevance -- 2 Theoretical Framework and Hypothesis Development -- 3 Research Methodology -- 4 Result and Analysis -- 5 Findings and Discussions -- 6 Conclusion -- 6.1 Usage and Limitations -- References -- E-CNN-FFE: An Enhanced Convolutional Neural Network for Facial Feature Extraction and Its Comparative Analysis with FaceNet, DeepID, and LBPH Methods -- 1 Introduction -- 2 Literature Review -- 3 Methodology -- 4 Implementation -- 5 Conclusion -- References -- A Graphical Neural Network-Based Chatbot Model for Assisting Cancer Patients with Dietary Assessment in their Survivorship -- 1 Introduction -- 2 Related Work -- 3 Materials and Methods -- 3.1 Material -- 3.2 Software and Hardware Requirements -- 3.3 Method -- 4 Results and Discussion -- 4.1 Time Complexity -- 5 Conclusion -- References. ; Plant Identification and Disease Detection System Using Deep Convolutional Neural Networks.
• Sommario/riassunto: This book presents the latest findings in the areas of data management and smart computing, big data management, artificial intelligence, and data analytics, along with advances in network technologies. The book is a collection of peer-reviewed research papers presented at 8th International Conference on Data Management, Analytics and Innovation (ICDMAI 2024), held during 19–21 January 2024 in Vellore Institute of Technology, Vellore, India. It addresses state-of-the-art topics and discusses challenges and solutions for future development. Gathering original, unpublished contributions by scientists from around the globe, the book is mainly intended for a professional audience of researchers and practitioners in academia and industry. The book is divided into two volumes.
• Titolo autorizzato: Data Management, Analytics and Innovation
• ISBN: 9789819732456; 9789819732449
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Lingua di pubblicazione: Inglese
• Record Nr.: 9910878050703321
• Lo trovi qui: Univ. Federico II
• Serie: Lecture Notes in Networks and Systems, . 2367-3389 ; ; 998