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[null,"1.","Record Nr.",null,"UNINA9910865234703321"]
[null,null,"Autore",null,"Chauhan Naveen"]
[null,null,"Titolo",null,"Machine Learning, Image Processing, Network Security and Data Sciences : 5th International Conference, MIND 2023, Hamirpur, India, December 21-22, 2023, Revised Selected Papers"]
[null,null,"Pubbl/distr/stampa",null,"Cham : , : Springer International Publishing AG, , 2024"]
[{"$nulls":4},"©2024"]
[null,null,"ISBN",null,"9783031622175"]
[{"$nulls":4},"9783031622168"]
[null,null,"Edizione",null,"[1st ed.]"]
[null,null,"Descrizione fisica",null,"1 online resource (372 pages)"]
[null,null,"Collana",null,"Communications in Computer and Information Science Series ; ; v.2128"]
[null,null,"Lingua di pubblicazione",null,"Inglese"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,null,"Nota di contenuto",null,"Intro -- Preface -- Organization -- Contents -- Machine Learning -- SynText - Data Augmentation Algorithm in NLP to Improve Performance of Emotion Classifiers -- 1 Introduction -- 2 Literature Review -- 2.1 Emotion Taxonomy -- 2.2 Benchmark Datasets -- 2.3 Data Augmentation -- 3 Methodology -- 4 Experimentation -- 5 Conclusion -- 6 Future Scope -- References -- Internet of Medical Things: Empowering Mobility and Health Monitoring with a Smart Walking Stick -- 1 Introduction -- 2 Related Works -- 3 Material and Design -- 4 Material and Methods -- 4.1 Fall Detection and Step Count -- 4.2 Heart Rate and SpO2 Measurement -- 4.3 Smart Home Control -- 4.4 Stopwatch -- 4.5 Weather and Helpline -- 4.6 Stress Monitoring and Wi-Fi Reset -- 5 Results and Discussion -- 6 Conclusion -- References -- MRI Based Spatio-Temporal Model for Alzheimer's Disease Prediction -- 1 Introduction -- 2 Related Work -- 3 Proposed Methodology -- 3.1 Dataset -- 3.2 Spatio-Temporal Model -- 4 Results and Discussion -- 4.1 ConvLSTM -- 4.2 ConvLSTM with Other Spatio-Temporal Model -- 4.3 ConvLSTM with State of the Art -- 5 Conclusion -- References -- Comparative Analysis of Economy-Based Multivariate Oil Price Prediction Using LSTM -- 1 Introduction -- 2 Literature Survey -- 3 Proposed Method -- 3.1 Data Collection -- 3.2 Data Pre-processing and Exploratory Data Analysis (EDA) -- 3.3 Model Training -- 3.4 Model Training -- 4 Results and Discussion -- 5 Conclusion -- References -- Deep Learning Based EV's Charging "]
[{"$nulls":4},"Network Management -- 1 Introduction -- 2 Literature Review -- 2.1 EV Charging Stations -- 2.2 State of Charge (SoC) -- 3 Methodology -- 3.1 Deploy EV Charging Station -- 3.2 Optimal Path to the EV Charging Station -- 3.3 SoC Estimation -- 4 Result -- 5 Conclusions -- References -- Crop Yield Prediction Using Machine Learning Approaches -- 1 Introduction."]
[{"$nulls":4},"2 Related Works -- 3 Proposed Work -- 3.1 Data Collection -- 3.2 Data Preprocessing -- 3.3 Model Selection -- 3.4 Evaluation -- 4 Results -- 5 Conclusion -- 6 Future Work -- References -- Detection and Classification of Waste Materials Using Deep Learning Techniques -- 1 Introduction -- 2 Related Works -- 3 Proposed Model -- 3.1 Data Collection -- 3.2 Pre Processing and Augmentation -- 3.3 Evaluation Metrics -- 3.4 Waste Garbage Detection Algorithms -- 4 Result and Simulation -- 4.1 SSD MobileNet -- 4.2 EfficientDet-D0 -- 4.3 YOLOv7 and YOLOv8 -- 4.4 Comparison of Model -- 5 Conclusion -- References -- A Comparative Analysis of ML Based Approaches for Identifying AQI Level -- 1 Introduction -- 1.1 Arrangement of the Paper -- 2 Related Work -- 3 Materials and Methods -- 3.1 Accumulation of Data and Dataset Description -- 3.2 Pre-processing of Data -- 3.3 Different ML Models -- 4 Results and Discussion -- 4.1 Experimental Result Analysis and Discussion -- 5 Conclusion and Future Scope -- References -- Marker-Based Augmented Reality Application in Education Domain -- 1 Introduction -- 2 AR Approaches -- 3 Related Work -- 4 Proposed Solution -- 4.1 Development Architecture -- 5 Implementation -- 5.1 Building a Raw Mesh on the Marker Image and Marker Detection -- 5.2 Feature Extraction Using Vuforia Image Scanner -- 5.3 Implementing Virtual Buttons with C# Scripting -- 5.4 Creating 3D Models with Blender -- 6 Results -- 7 Conclusion -- References -- Hate Speech Detection Using Machine Learning and Deep Learning Techniques -- 1 Introduction -- 2 Definitions and Taxonomy -- 3 Comprehensive Review of the Literature -- 3.1 Fact-Finding Process -- 3.2 Sources -- 3.3 Study Method Criteria -- 3.4 Research Focus -- 4 Challenges in Defining and Categorizing Hate Speech and Detection with ML/DL -- 4.1 Personalization and Explanation -- 4.2 Evolving Language."]
[{"$nulls":4},"4.3 Legal and Cultural Variations -- 4.4 Subtlety and Micro-aggression -- 4.5 Data Quality and Labeling -- 4.6 Data Imbalance -- 4.7 Multilingual and Multi Modal Content -- 4.8 Evolution of Hate Speech -- 4.9 Adversarial Attacks -- 4.10 Privacy Concerns -- 4.11 Bias and Fairness -- 4.12 Real-Time Detection -- 4.13 Scalability -- 4.14 User Behavior -- 4.15 Legal and Ethical Considerations -- 4.16 Intersectionality -- 4.17 Ambiguity -- 4.18 Freedom of Speech -- 4.19 Digital Evolution -- 4.20 Diverse Expressions -- 5 Hate Speech Detection Datasets -- 6 Machine Learning-Based Approaches -- 6.1 Data Preprocessing -- 6.2 Feature Extraction -- 6.3 Classification Algorithms -- 6.4 Ensemble Methods -- 6.5 Cross-Validation -- 7 Deep Learning-Based Approaches -- 7.1 Convolutional Neural Networks (CNNs) -- 7.2 Recurrent Neural Networks (RNNs) -- 7.3 Transformers -- 8 Evaluation Metrics -- 9 Result and Discussion -- 10 Conclusion -- References -- Phishing Detection Using 1D-CNN and FF-CNN Models Based on URL of the Website -- 1 Introduction -- 2 Literature Survey -- 2.1 Whitelist-Based Techniques -- 2.2 Blacklist-Based Techniques -- 2.3 Content-Based Techniques -- 2.4 URL-Based Techniques -- 3 Proposed Work -- 3.1 1D Convolutional Neural Network (1D-CNN) -- 3.2 FeedForward-Convolutional Neural Network (FF-CNN) -- 4 Dataset and Pre-processing -- 5 Experimentation and Results -- 5.1 Performance Measures -- 5.2 Experiment 1: Comparison of Performance of Proposed 1D-CNN-based Approach on Different "]
[{"$nulls":4},"Datasets -- 5.3 Experiment 2: Comparison of the Performance of Proposed 1D-CNN-based Approach with PCA and Without PCA -- 5.4 Experiment 3: Comparison of Performance of the proposed FF-CNN-based Approach on Different Datasets -- 5.5 Experiment 4: Comparison of Performance of the Proposed 1D-CNN-based Approach and FF-CNN-based Approach."]
[{"$nulls":4},"5.6 Comparison Proposed 1D-CNN-based Approach and FF-CNN-based Approach with Other ML Models -- 6 Conclusion -- References -- Diabetes Prediction Using Machine Learning Classifiers -- 1 Introduction -- 2 Literature Review -- 3 Dataset -- 4 Results and Discussions -- 5 Conclusion -- 6 Future Work -- References -- A Deep Learning Method for Obfuscated Android Malware Detection -- 1 Introduction -- 2 Literature Survey -- 3 Proposed Work -- 3.1 Adversarial Sample Generation -- 3.2 Autoencoder -- 3.3 LSTM Autoencoder -- 3.4 Image Based Autoencoder -- 3.5 Web Application -- 4 Results and Discussions -- 4.1 Dataset Description -- 4.2 LSTM Based Autoencoder -- 4.3 Image-Based Autoencoder -- 5 Result Comparisons -- 5.1 Non-adversarial Training -- 5.2 Adversarial Training -- 6 Conclusion -- References -- Code-Mixed Language Understanding Using BiLSTM-BERT Multi-attention Fusion Mechanism -- 1 Introduction -- 1.1 Contributions -- 2 Related Work -- 3 Proposed Model -- 3.1 Problem Definition -- 3.2 BiLSTM Attention Mechanism for Code-Mixed Intent Classification and Slot Filling -- 3.3 mBERT Code-Mixed Domain Knowledge Adaption -- 3.4 Multi-head Query Attention Mechanism -- 4 Result Analysis -- 4.1 Baseline Methods -- 5 Conclusion -- References -- The Potential of 1D-CNN for EEG Mental Attention State Detection -- 1 Introduction -- 2 Literature Review -- 3 Proposed Methodology -- 3.1 Dataset Selection and Description -- 3.2 Pre-processing -- 3.3 The Application of Machine Learning Models -- 4 Results Discussion -- 5 Conclusions -- References -- Potato Leaf Disease Classification Using Deep Learning Model -- 1 Introduction -- 2 Motivation -- 3 Literature Review -- 4 Problem Statement -- 5 Methodology -- 5.1 Dataset -- 5.2 Data Splitting -- 6 Model Architecture -- 6.1 Convolutional Neural Network -- 7 Results and Discussion -- 7.1 Model Evaluation."]
[{"$nulls":4},"7.2 Model Predictions -- 8 Conclusion -- References -- Breast Cancer Detection: An Evaluation of Machine Learning, Ensemble Learning, and Deep Learning Algorithms -- 1 Introduction -- 2 Related Work -- 3 Methodology -- 3.1 Data Collection -- 3.2 Data Pre-processing -- 3.3 Apply Learning Algorithms -- 3.4 Evaluation Criteria -- 3.5 Training Data and Testing Data -- 4 Result and Discussion -- 4.1 Results of Machine Learning Models -- 4.2 Results of Ensemble Learning Models -- 4.3 Results of Deep Learning Model -- 5 Conclusion -- References -- Advancements in Facial Expression Recognition: A Comprehensive Analysis of Techniques -- 1 Introduction -- 2 Background -- 3 Methods for Facial Expression Recognition -- 3.1 Traditional Methods -- 3.2 Deep Learning Methods -- 3.3 Hybrid Methods -- 4 Models Used for Facial Expression Recognition -- 4.1 Model 1: ResNet-50 -- 4.2 Model 2: FERNet -- 4.3 Model 3: Attentional Convolutional Network -- 5 Performance Metrics and Evaluation -- 5.1 ResNet-50 -- 5.2 FERNet -- 5.3 Attentional Convolutional Network -- 6 Comparative Analysis -- 6.1 Model Architectures -- 6.2 Training Approaches -- 6.3 Computational Efficiency -- 6.4 Robustness and Generalization -- 7 Current Implementations -- 8 Conclusion -- 9 Future Scope -- References -- Image Processing -- Sparse Representation with Residual Learning Model for Medical Image Classification -- 1 Introduction -- 2 Related Work -- 2.1 Dictionary Learning -- 2.2 ResNet -- 3 The Proposed Method -- 3.1 Dictionary Learning and Sparse Representation "]
[{"$nulls":4},"-- 3.2 Residual-CNN Network Features -- 3.3 Dimensionality Reduction with PCA -- 3.4 Deep Neural Network (DNN) for Classification -- 4 Experimental Results -- 4.1 Description of Datasets -- 4.2 System Implementation -- 4.3 Results and Analysis -- 5 Ablation Study -- 6 Conclusion -- References."]
[{"$nulls":4},"COVID-19 Detection from Chest X-Ray Images Using GBM with Comparative Analysis."]