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Machine Learning, Image Processing, Network Security and Data Sciences : 5th International Conference, MIND 2023, Hamirpur, India, December 21-22, 2023, Revised Selected Papers
| Machine Learning, Image Processing, Network Security and Data Sciences : 5th International Conference, MIND 2023, Hamirpur, India, December 21-22, 2023, Revised Selected Papers |
| Autore | Chauhan Naveen |
| Edizione | [1st ed.] |
| Pubbl/distr/stampa | Cham : , : Springer International Publishing AG, , 2024 |
| Descrizione fisica | 1 online resource (372 pages) |
| Altri autori (Persone) |
YadavDivakar
VermaGyanendra K SoniBadal LaraJorge Morato |
| Collana | Communications in Computer and Information Science Series |
| ISBN |
9783031622175
9783031622168 |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
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 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.
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. 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 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. 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. 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 -- 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. COVID-19 Detection from Chest X-Ray Images Using GBM with Comparative Analysis. |
| Record Nr. | UNINA-9910865234703321 |
Chauhan Naveen
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| Cham : , : Springer International Publishing AG, , 2024 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Machine Learning, Image Processing, Network Security and Data Sciences [[electronic resource] ] : Second International Conference, MIND 2020, Silchar, India, July 30 - 31, 2020, Proceedings, Part II / / edited by Arup Bhattacharjee, Samir Kr. Borgohain, Badal Soni, Gyanendra Verma, Xiao-Zhi Gao
| Machine Learning, Image Processing, Network Security and Data Sciences [[electronic resource] ] : Second International Conference, MIND 2020, Silchar, India, July 30 - 31, 2020, Proceedings, Part II / / edited by Arup Bhattacharjee, Samir Kr. Borgohain, Badal Soni, Gyanendra Verma, Xiao-Zhi Gao |
| Edizione | [1st ed. 2020.] |
| Pubbl/distr/stampa | Singapore : , : Springer Singapore : , : Imprint : Springer, , 2020 |
| Descrizione fisica | 1 online resource (665 pages) |
| Disciplina | 006.31 |
| Collana | Communications in Computer and Information Science |
| Soggetto topico |
Artificial intelligence
Computers Optical data processing Computer communication systems Application software Computer security Artificial Intelligence Information Systems and Communication Service Computer Imaging, Vision, Pattern Recognition and Graphics Computer Communication Networks Computer Appl. in Social and Behavioral Sciences Systems and Data Security |
| ISBN | 981-15-6318-7 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNISA-996465346703316 |
| Singapore : , : Springer Singapore : , : Imprint : Springer, , 2020 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
| ||
Machine Learning, Image Processing, Network Security and Data Sciences [[electronic resource] ] : Second International Conference, MIND 2020, Silchar, India, July 30 - 31, 2020, Proceedings, Part I / / edited by Arup Bhattacharjee, Samir Kr. Borgohain, Badal Soni, Gyanendra Verma, Xiao-Zhi Gao
| Machine Learning, Image Processing, Network Security and Data Sciences [[electronic resource] ] : Second International Conference, MIND 2020, Silchar, India, July 30 - 31, 2020, Proceedings, Part I / / edited by Arup Bhattacharjee, Samir Kr. Borgohain, Badal Soni, Gyanendra Verma, Xiao-Zhi Gao |
| Edizione | [1st ed. 2020.] |
| Pubbl/distr/stampa | Singapore : , : Springer Singapore : , : Imprint : Springer, , 2020 |
| Descrizione fisica | 1 online resource (xxiii, 415 pages) |
| Disciplina | 006.31 |
| Collana | Communications in Computer and Information Science |
| Soggetto topico |
Artificial intelligence
Computers Optical data processing Application software Computer organization Artificial Intelligence Information Systems and Communication Service Image Processing and Computer Vision Computer Applications Computer Systems Organization and Communication Networks Computing Milieux |
| ISBN | 981-15-6315-2 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNISA-996465343803316 |
| Singapore : , : Springer Singapore : , : Imprint : Springer, , 2020 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
| ||
Machine Learning, Image Processing, Network Security and Data Sciences : Second International Conference, MIND 2020, Silchar, India, July 30 - 31, 2020, Proceedings, Part I / / edited by Arup Bhattacharjee, Samir Kr. Borgohain, Badal Soni, Gyanendra Verma, Xiao-Zhi Gao
| Machine Learning, Image Processing, Network Security and Data Sciences : Second International Conference, MIND 2020, Silchar, India, July 30 - 31, 2020, Proceedings, Part I / / edited by Arup Bhattacharjee, Samir Kr. Borgohain, Badal Soni, Gyanendra Verma, Xiao-Zhi Gao |
| Edizione | [1st ed. 2020.] |
| Pubbl/distr/stampa | Singapore : , : Springer Singapore : , : Imprint : Springer, , 2020 |
| Descrizione fisica | 1 online resource (xxiii, 415 pages) |
| Disciplina | 006.31 |
| Collana | Communications in Computer and Information Science |
| Soggetto topico |
Artificial intelligence
Computers Optical data processing Application software Computer organization Artificial Intelligence Information Systems and Communication Service Image Processing and Computer Vision Computer Applications Computer Systems Organization and Communication Networks Computing Milieux |
| ISBN | 981-15-6315-2 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910410033603321 |
| Singapore : , : Springer Singapore : , : Imprint : Springer, , 2020 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Machine Learning, Image Processing, Network Security and Data Sciences : Second International Conference, MIND 2020, Silchar, India, July 30 - 31, 2020, Proceedings, Part II / / edited by Arup Bhattacharjee, Samir Kr. Borgohain, Badal Soni, Gyanendra Verma, Xiao-Zhi Gao
| Machine Learning, Image Processing, Network Security and Data Sciences : Second International Conference, MIND 2020, Silchar, India, July 30 - 31, 2020, Proceedings, Part II / / edited by Arup Bhattacharjee, Samir Kr. Borgohain, Badal Soni, Gyanendra Verma, Xiao-Zhi Gao |
| Edizione | [1st ed. 2020.] |
| Pubbl/distr/stampa | Singapore : , : Springer Singapore : , : Imprint : Springer, , 2020 |
| Descrizione fisica | 1 online resource (665 pages) |
| Disciplina | 006.31 |
| Collana | Communications in Computer and Information Science |
| Soggetto topico |
Artificial intelligence
Computers Optical data processing Computer networks Application software Computer security Artificial Intelligence Information Systems and Communication Service Computer Imaging, Vision, Pattern Recognition and Graphics Computer Communication Networks Computer Appl. in Social and Behavioral Sciences Systems and Data Security |
| ISBN | 981-15-6318-7 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910410033503321 |
| Singapore : , : Springer Singapore : , : Imprint : Springer, , 2020 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||