11277nam 2200541 450 991058578740332120230106025533.03-031-12638-6(MiAaPQ)EBC7052887(Au-PeEL)EBL7052887(CKB)24286235700041(PPN)263898407(EXLCZ)992428623570004120230106d2022 uy 0engurcnu||||||||txtrdacontentcrdamediacrrdacarrierAdvances in computing and data sciences 6th International Conference, ICACDS 2022, Kurnool, India, April 22-23, 2022, Revised selected papers. Part I /Mayank Singh [and four others] editorsCham, Switzerland :Springer,[2022]©20221 online resource (448 pages)Communications in computer and information science ;1613Print version: Singh, Mayank Advances in Computing and Data Sciences Cham : Springer International Publishing AG,c2022 9783031126376 Includes bibliographical references and index.Intro -- Preface -- Organization -- Contents - Part I -- Contents - Part II -- Hardware Description Language Enhancements for High Level Synthesis of Hardware Accelerators -- 1 Introduction -- 2 Literature Review -- 3 Methodology -- 3.1 Syntax Identification and Code Extraction -- 3.2 Syntax Transformation -- 3.3 Code Replacement -- 4 Implementation -- 4.1 Keyword Extractor -- 4.2 Argument Extractor -- 4.3 Syntax Transformation -- 5 Results -- 6 Conclusion and Future Work -- References -- CrDrcnn: Design and Development of Crow Optimization-Based Deep Recurrent Neural Network for Software Defect Prediction -- 1 Introduction -- 2 Related Works -- 2.1 Challenges -- 2.2 Feature Selection Using Wrapper Algorithm -- 3 Proposed Software Defect Prediction Model Using the Optimized Deep NN Classifier: -- 3.1 Software Defect Prediction Using Proposed Crow Optimization-Based DNN for Software Defect Prediction -- 4 Results and Discussion -- 4.1 Experimental Setup: -- 4.2 Performance Metrics -- 4.3 Comparative Analysis -- 4.4 Comparative Discussion -- 5 Conclusion -- References -- Text Sentiment Analysis Using the Bald Eagle-Based Bidirectional Long Short-Term Memory -- 1 Introduction -- 2 Motivation -- 3 Proposed Model for Sentiment Analysis Using the Textual Data -- 3.1 Data Pre-processing: -- 3.2 Feature Extraction -- 3.3 Textual Sentimental Analysis Using the Proposed Bald Eagle-Based Deep BiLSTM Classifier -- 3.4 Architecture of BiLSTM Classifier -- 4 Results and Discussion -- 4.1 Dataset Description -- 4.2 Parameter Metrics -- 4.3 Experimental Setup -- 4.4 Comparative Methods -- 4.5 Comparative Analysis of Bald Eagle Based BiLSTM Classifier -- 5 Conclusion -- References -- Comparison of Multiple Machine Learning Approaches and Sentiment Analysis in Detection of Spam -- 1 Introduction -- 2 Literature Review -- 3 Proposed Methodology -- 4 Result Analysis.5 Conclusion -- References -- A Voice Assisted Chatbot Framework for Real-Time Implementation in Medical Care -- 1 Introduction -- 2 Background and Related Works -- 3 Methodology -- 4 Algorithm and Results -- 5 Conclusion -- References -- Robust Vehicle Detection for Highway Monitoring Using Histogram of Oriented Gradients and Reduced Support Vector Machine -- 1 Introduction -- 2 Related Work -- 3 Proposed Methodology -- 3.1 Feature Extraction -- 3.2 Detection -- 3.3 Classification -- 4 Results and Discussion -- 5 Conclusion -- References -- A Secure Framework Based on Nature-Inspired Optimization for Vehicle Routing -- 1 Introduction -- 2 State of the Art -- 3 Proposed Framework -- 3.1 Registration Process -- 3.2 Communication Process -- 3.3 Proposed Algorithm for Vehicle Routing -- 4 Performance Evaluation -- 5 Conclusion -- References -- Detection of Bangla Hate Comments and Cyberbullying in Social Media Using NLP and Transformer Models -- 1 Introduction -- 2 Related Works -- 3 Dataset -- 4 Methodology -- 4.1 Preprocessing -- 4.2 Transformer Models -- 5 Result and Analysis -- 6 Conclusion -- References -- A Modified Pyramid Scale Network for Crowd Counting -- 1 Introduction -- 2 Related Works -- 3 The Proposed Method -- 3.1 Modified PSNet -- 3.2 Modified Pyramid Scale Module -- 3.3 Implementation Details -- 4 Experiments and Discussion -- 4.1 Evaluation Metrics -- 4.2 ShanghaiTech dataset -- 4.3 UCFCC50 -- 5 Conclusions and Future Scope -- References -- Driving Impact in Claims Denial Management Using Artificial Intelligence -- 1 Introduction -- 2 Literature Review -- 3 Data Summary -- 4 Methodology -- 4.1 Ground Truth -- 4.2 Data Preparation -- 4.3 Feature Selection -- 4.4 Model Development -- 4.5 Explainability Using SHAP -- 4.6 Joint Probability -- 5 Results and Discussion -- 5.1 Stratification -- 5.2 Strengths -- 6 Conclusion -- References.Identification of Landslide Vulnerability Zones and Triggering Factors Using Deep Neural Networks - An Experimental Analysis -- 1 Introduction -- 1.1 Study Area -- 1.2 Types of Landslides in Kerala -- 2 Related Work -- 3 Proposed Work -- 3.1 Spatial Database -- 3.2 Deep Neural Networks -- 4 Experimental Setup -- 5 Experimental Results -- 6 Conclusion -- References -- Classifying Offensive Speech of Bangla Text and Analysis Using Explainable AI -- 1 Introduction -- 2 Related Work -- 3 Dataset -- 4 Methodology -- 4.1 Convolutional Neural Network (CNN) -- 4.2 Long Short Term Memory (LSTM) -- 4.3 Support Vector Machine (SVM) -- 5 Result and Analysis -- 5.1 Classifiers and Accuracy -- 5.2 Explainable AI -- 6 Conclusion and Future Work -- 6.1 Conclusion -- 6.2 Future Work -- References -- Android Malware Detection Using Hybrid Meta-heuristic Feature Selection and Ensemble Learning Techniques -- 1 Introduction -- 2 Related Work -- 3 The proposed Android Malware Detection using hybrid meta-heuristic feature selection and Ensemble Learning Techniques -- 3.1 Feature Selection Process -- 3.2 Ensemble Learning-based Malware Detection -- 4 Performance Evaluation and Discussion -- 4.1 Without Feature Selection -- 4.2 With Feature Selection -- 5 Conclusion -- References -- Scoring Scheme to Determine the Sensitive Information Level in Surface Web and Dark Web -- 1 Introduction -- 1.1 Personal Information and Sensitive Information -- 1.2 Pastes -- 2 Background -- 2.1 Personal Information -- 2.2 Sensitive Information -- 2.3 Sensitive Personal Information -- 2.4 Types of Sensitive Information -- 3 Implementation -- 4 Results -- 5 Conclusion -- References -- Video Descriptor Using Attention Mechanism -- 1 Introduction -- 2 Literature Survey -- 3 Proposed System -- 3.1 Encoder -- 3.2 Decoder -- 3.3 NLP -- 4 Training Procedure -- 5 Results -- 6 Conclusion -- References.Informative Software Defect Data Generation and Prediction: INF-SMOTE -- 1 Introduction -- 2 Literature Review -- 3 Proposed Method -- 3.1 Elimination Uninformative Samples -- 4 Experiments -- 4.1 Datasets -- 4.2 Evaluation Metric -- 4.3 Experimental Results -- 5 Conclusion and Future Work -- References -- 2D-CNN Model for Classification of Neural Activity Using Task-Based fMRI -- 1 Introduction -- 1.1 Deep Neural Network Framework -- 2 Related Work -- 2.1 Neural Activity -- 2.2 Deep Learning Approaches -- 3 Materials and Methods -- 3.1 Dataset Analysis -- 3.2 Methodology -- 3.3 Functional Connectivity Analysis -- 4 Proposed Network Architecture -- 5 Experimental Results -- 5.1 Prediction Accuracy of the Proposed 2D-CNN Model -- 5.2 Evaluation of Model Performance -- 5.3 Classification of Voxel Response fMRI Images -- 6 Conclusion -- References -- Real-Time Multi-task Network for Autonomous Driving -- 1 Introduction -- 2 Related Works -- 3 Methodology -- 3.1 Backbone Network -- 3.2 Neck Network -- 3.3 Detection Head -- 3.4 Segmentation Head -- 3.5 Loss Function -- 4 Experiments -- 4.1 Experiment Settings -- 4.2 Results -- 5 Conclusion -- References -- Cardiovascular Disease Classification Based on Machine Learning Algorithms Using GridSearchCV, Cross Validation and Stacked Ensemble Methods -- 1 Introduction -- 2 Literature Survey -- 3 Research Methodology -- 3.1 Proposed Approach -- 3.2 Cross Validation, Stacked Ensemble, GridSearchCV Method -- 4 Experimental Design -- 4.1 Dataset Description -- 4.2 Data Pre-processing -- 4.3 Baseline Models -- 4.4 Evaluation Classifier Performance -- 4.5 Implementation Specifics -- 5 Experiment Results -- 5.1 Accuracy -- 5.2 AUC, ROC, Precision and Recall -- 5.3 KS Statistics -- 5.4 Cumulative Gain and Lift Curve -- 5.5 Learning Curve and Calibration Curve -- 5.6 Cross Validation Score.6 Conclusion and Future Work -- References -- Human Emotion Recognition from Body Posture with Machine Learning Techniques -- 1 Introduction -- 2 Prior Research -- 3 Proposed Approach -- 4 Emotion Classification Using Machine Learning Models -- 5 Experimental Results and Discussion -- 5.1 GEMEP Dataset -- 5.2 Performance Metrics -- 5.3 Results and Discussion -- 6 Conclusion -- References -- A Body Area Network Approach for Stroke-Related Disease Diagnosis Using Artificial Intelligence with Deep Learning Techniques -- 1 Introduction -- 2 Related Works -- 3 Research Methodology -- 3.1 Data Preprocessing -- 3.2 Proposed Model -- 4 Deep Learning Models -- 4.1 Convolutional Neural Network (CNN) -- 4.2 Radial Basic Function(RBF) -- 4.3 Multilayer Perceptron (MLPs) -- 4.4 Deep Belief Networks (DBNs) -- 5 Experiments -- 5.1 Experimental Setup -- 5.2 Evaluation Metrics -- 5.3 Mathematical Model -- 6 Results and Discussion -- 6.1 Accuracy Results Based on Dataset -- 6.2 Accuracy Results Based on Deep Learning Models -- 6.3 Accuracy Results Based Training Data Set -- 7 Conclusion -- References -- Accelerating the Performance of Sequence Classification Using GPU Based Ensemble Learning with Extreme Gradient Boosting -- 1 Introduction -- 2 Related Work -- 3 Methodology -- 4 Result and Discussion -- References -- Automated Vehicle Number Recognition Scheme Using Neural Networks -- 1 Introduction -- 2 Lıterature Survey -- 3 System Model -- 4 Experimental Results -- 5 Conclusion -- References -- Noise Prediction Using LIDAR 3D Point Data - Determination of Terrain Parameters for Modelling -- 1 Introduction -- 2 Literature Review -- 3 Research Methodology -- 4 Result and Discussion -- 4.1 LIDAR Data Acquisition -- 4.2 Building Extraction -- 4.3 Building Corner Estimation -- 4.4 Path Determination -- 4.5 Formulation for Determination of Terrain parameters.5 Determination of Terrain Parameter and Noise Mapping.Communications in computer and information science ;1613.Artificial intelligenceArtificial intelligenceCongressesComputer scienceArtificial intelligence.Artificial intelligenceComputer science.006.3Singh MayankMiAaPQMiAaPQMiAaPQBOOK9910585787403321Advances in Computing and Data Sciences1961218UNINA