Advances in Data-Driven Computing and Intelligent Systems : Selected Papers from ADCIS 2023, Volume 1

• Autore: Das Swagatam
• Pubblicazione: Singapore : , : Springer, , 2024; ©2024
• Edizione: 1st ed.
• Descrizione fisica: 1 online resource (553 pages)
• Altri autori: SahaSnehanshu Coello CoelloCarlos A BansalJagdish C
• Nota di contenuto: Intro -- Preface -- Contents -- Editors and Contributors -- Influences of Specimen and Fiber Sizes on the Direct Tensile Resistance of Ultra-High-Performance Fiber-Reinforced Concretes -- 1 Introduction -- 2 Experimental Program -- 2.1 Materials -- 2.2 Test Setup -- 3 Test Results and Discussion -- 3.1 Effects of Specimen Size on the Tensile Resistance of UHPFRCs -- 3.2 Influences of Fiber Size on the Tensile Performance of UHPFRC -- 4 Conclusion -- References -- Conceptual Model for Data Collection and Processing in a Smart Medical Ward -- 1 Introduction -- 2 Related Work -- 3 Conceptual Model -- 4 Simulation -- 5 Conclusion -- References -- Parts-of-Speech Tagger in Assamese Using LSTM and Bi-LSTM -- 1 Introduction -- 2 Literature Review -- 2.1 International Language -- 2.2 National Language -- 3 Approaches Used -- 3.1 Long Short-Term Memory -- 3.2 Bidirectional Long Short-Term Memory -- 4 Methodology -- 4.1 Tagset -- 4.2 Preprocessing -- 4.3 Assamese Corpus -- 4.4 Training and Testing -- 5 Experimental Result -- 6 Performance Analysis -- 7 Conclusion and Future Work -- References -- Detection of Explicit Lyrics in Hindi Music Using Different Machine Learning Algorithms -- 1 Introduction -- 2 Related Work -- 2.1 Study of International Music -- 2.2 Study of Hindi Music -- 3 Data -- 3.1 Data Collection -- 3.2 Data Description -- 4 Methodology -- 4.1 Data Preprocessing -- 4.2 Proposed Approaches -- 4.3 Training and Testing -- 5 Experimental Results -- 5.1 Evaluation of the Proposed Models in Hindi Lyric Detection -- 5.2 Detection of User Input Hindi Lyrics for Explicitness -- 6 Conclusion -- References -- Does the Resilience Learning Game Foster Workforce Open Innovation and Sustainability Attributes? Empirical Evidence from Greek Food Industry -- 1 Introduction -- 2 Related Work -- 3 Method -- 3.1 Research Design -- 3.2 Description of the Game. ; 3.3 Data Analysis and Results -- 4 Discussion -- 5 Conclusion -- References -- Seizure Detection by Analyzing EEG Signals Using Deep Learning Networks -- 1 Introduction -- 2 Literature Review -- 3 Proposed Methodology -- 3.1 Dataset Description -- 3.2 The DLN-SD: A Proposed Model -- 4 Results and Discussions -- 5 Conclusion and Future Scope of the Work -- References -- Enhancing Intelligent Video Surveillance: Deep Learning Approaches for Human Anomalous Behavior Recognition -- 1 Introduction -- 2 Related Works -- 3 Methodology -- 4 Results and Discussion -- 5 Conclusion -- References -- GujFormer: A Vision Transformer-Based Architecture for Gujarati Handwritten Character Recognition -- 1 Introduction -- 2 Literature Survey -- 3 Materials and Models -- 3.1 Vision Transformer (ViT) -- 3.2 Encoder and Decoder -- 4 Methodology -- 4.1 Patch Embedding -- 4.2 Multihead Self-Attention -- 4.3 Classification -- 5 Experiments and Result -- 5.1 Dataset and Data Augmentation -- 5.2 Simulation Details -- 5.3 Results and Analysis -- 6 Conclusion -- References -- Prediction of Soil Properties for Agriculture Using Ensemble Learning Techniques -- 1 Introduction -- 2 Literature Survey -- 3 Overview of Machine Learning -- 3.1 Machine Learning Tasks -- 3.2 Datasets -- 3.3 Preparing the Data -- 3.4 Learning Model -- 4 Results -- 5 Conclusion -- References -- Classification of Organic and Recyclable Waste Using a Deep Learning Approach -- 1 Introduction -- 2 Related Works -- 3 Proposed Methodology -- 3.1 Dataset and Preprocessing -- 3.2 Deep Learning Approach -- 3.3 Transfer Learning Approaches -- 4 Results and Discussion -- 5 Conclusion and Future Scope -- References -- Machine Learning and its Application in Food Safety -- 1 Introduction -- 1.1 Supervised Learning -- 1.2 Unsupervised Learning -- 1.3 Reinforcement Learning -- 2 Relevance of ML in Food Safety. ; 3 Issues Regarding Food Safety -- 4 Recent Technologies Regarding Food Safety -- 4.1 Metal Detector Automatic Testing System -- 4.2 Electronic Sensors -- 4.3 Automatic Monitoring -- 5 Machine Learning Models -- 5.1 Bayesian Networks -- 5.2 Artificial Neural Network (Reinforcement Learning) -- 6 Applications of ML in Food Safety -- 6.1 Smart Traceability -- 6.2 Antimicrobial Resistance Prediction -- 6.3 Antibiotic Resistance Profiles -- 6.4 Detection of Heavy Metals -- 6.5 Detection of Biological Load -- 6.6 Detection of Food Adulteration -- 7 Advantages and Challenges -- 8 Conclusion -- References -- ISO/IEC 27001 Standard: Analytical and Comparative Overview -- 1 Introduction -- 2 Literature Review -- 3 Overview of ISO 27001 -- 3.1 History -- 3.2 Exploring the Key Clauses of ISO 27001 -- 3.3 Annex A-Reference Control Objectives and Controls -- 4 Comparison with Other Frameworks -- 4.1 ISO 27001 Versus NIST CSF -- 4.2 ISO 27001 Versus COBIT -- 5 Conclusion -- References -- Hybrid Deep Learning-Based Potato and Tomato Leaf Disease Classification -- 1 Introduction -- 2 Literature Survey -- 2.1 CNN-Based Crop Disease Classification -- 2.2 LSTM-Based Crop Disease Classification -- 2.3 Hybrid CNN-LSTM-Based Classification -- 3 Proposed Methodology -- 3.1 Preprocessing -- 3.2 Segmentation -- 3.3 Feature Extraction -- 3.4 Leaf Image Disease Classification -- 4 Result -- 4.1 Dataset Collection -- 4.2 Experimental Setup -- 4.3 Performance Analysis -- 5 Discussion -- 5.1 Comparative Analysis -- 5.2 Training Time Analysis -- 6 Conclusion -- References -- Anti-forensic Analysis for Image Splicing Detection Through Advanced Filters -- 1 Introduction -- 2 Related Work -- 3 Deep Learning-Based Image Splicing Detection -- 3.1 Pre-trained ResNet-Based Deep Learning Model -- 3.2 Pre-trained InceptionNet-Based Deep Learning Model -- 4 Filters Used for Anti-forensic. ; 4.1 Weighted Average Filter -- 4.2 Bilateral Blur Filter -- 4.3 Kuwahara Filter -- 5 Implementation and Results -- 5.1 Dataset Description -- 5.2 Experimental Setup -- 5.3 Evaluation Metrics -- 5.4 Performance Evaluation -- 6 Conclusion and Future Scope -- References -- Classification and Prediction of Vibration Natural Frequencies of a Circular Plate Using Chladni Patterns and Deep Learning Techniques -- 1 Introduction -- 2 Methodology -- 3 Experimentation -- 3.1 Experimental Results -- 3.2 Experimental Mode Shapes -- 4 Simulation -- 4.1 Simulation Procedure -- 5 Deep Learning Methodology -- 5.1 Transfer Learning to Identify Natural Frequency -- 5.2 Data Preparation -- 5.3 Choosing Model -- 6 Results and Discussion -- 6.1 Validation of the Results -- 6.2 Prediction Results for VGG16 Network -- 6.3 Prediction Results for GoogleNet Network -- 7 Comparison of Results from Deep Learning Techniques for Pretrained Network -- 8 Conclusion -- References -- Multi-sensor Data Fusion and Deep Machine Learning Models-Based Mental Stress Detection System -- 1 Introduction -- 2 Related Work -- 3 Experimental Protocol -- 3.1 Placement of IoMT Device and Sensors -- 3.2 Subjects and Study Protocol for Data Acquisition -- 3.3 Dataset Preprocessing and Feature Extraction -- 3.4 Classification Algorithms -- 4 Experiment Results and Discussion -- 5 Conclusion -- References -- Segmentation-Based Transformer Network for Automated Skin Disease Detection -- 1 Introduction -- 2 Review of Literature -- 3 Dataset -- 4 Methodology -- 4.1 Preprocessing -- 4.2 Binary Image Segmentation -- 4.3 Attention Layer -- 4.4 Vision Transformer -- 5 Results -- 6 Conclusions and Future Work -- References -- FASRGAN: Feature Attention Super Resolution Generative Adversarial Network -- 1 Introduction -- 2 Related Works -- 3 Implementation -- 3.1 Dataset -- 3.2 Network Architecture. ; 3.3 Losses -- 4 Results -- 4.1 Quantitative Analysis -- 4.2 Qualitative Analysis -- 5 Conclusion -- References -- Mapping Sentiment: A Geospatial Analysis of Twitter Data in Indian Premier League 2023 -- 1 Introduction -- 2 Related Work -- 3 Methods -- 3.1 Data Collection -- 3.2 Data Preprocessing -- 3.3 Feature Selection -- 3.4 Model Selection -- 3.5 Implementation of Logistic Regression and Linear SVM -- 3.6 Geospatial Mapping -- 3.7 Evaluation Metrics -- 4 Experimental Results and Its Analysis -- 4.1 Sentiment Prediction -- 4.2 Geospatial Analysis -- 5 Conclusion -- References -- The eXtreme Gradient Boosting Method Optimized by Hybridized Sine Cosine Metaheuristics for Ship Vessel Classification -- 1 Introduction -- 2 Background and Related Works -- 2.1 Vessel Classification -- 2.2 XGBoost Overview -- 2.3 Metaheuristics Optimization -- 3 Proposed Method -- 3.1 Original Sine Cosine Algorithm -- 3.2 The Improved Sine Cosine Algorithm -- 4 Experiments and Discussion -- 4.1 Dataset -- 4.2 Experimental Setup -- 4.3 Simulation Results and Discussion -- 5 Conclusion -- References -- A Stacked Model Approach for Machine Learning-Based Traffic Prediction -- 1 Introduction -- 2 Literature Review -- 2.1 Linear Regression -- 2.2 XGBoost -- 3 Proposed Methodology -- 4 Software Implementation -- 4.1 Importing and Splitting Dataset -- 4.2 Creating New Features -- 4.3 Transforming the Training Data -- 4.4 Development of Model and Predictions -- 5 Results and Analysis -- 6 Conclusion -- References -- Deep Reinforcement Learning for Credit Card Fraud Detection -- 1 Introduction -- 2 Literature Survey -- 2.1 Deep RL Approach for Classification Use-Cases -- 3 Research Methodology -- 3.1 Fraud Detection Markov Decision Procedure -- 3.2 Reward Function for Fraud Detection Categorization -- 3.3 DQN-Based Fraud Detection Algorithm -- 4 Results and Discussions. ; 4.1 Comparative Analysis and Evaluation.
• Titolo autorizzato: Advances in Data-Driven Computing and Intelligent Systems
• ISBN: 981-9995-24-8
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Lingua di pubblicazione: Inglese
• Record Nr.: 9910841865203321
• Lo trovi qui: Univ. Federico II
• Serie: Lecture Notes in Networks and Systems Series