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Proceedings of Data Analytics and Management : ICDAM 2023, Volume 4
| Proceedings of Data Analytics and Management : ICDAM 2023, Volume 4 |
| Autore | Swaroop Abhishek |
| Edizione | [1st ed.] |
| Pubbl/distr/stampa | Singapore : , : Springer Singapore Pte. Limited, , 2024 |
| Descrizione fisica | 1 online resource (550 pages) |
| Disciplina | 005.7565 |
| Altri autori (Persone) |
PolkowskiZdzislaw
CorreiaSérgio Duarte VirdeeBal |
| Collana | Lecture Notes in Networks and Systems Series |
| ISBN | 981-9965-53-5 |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Intro -- ICDAM 2023 Steering Committee Members -- Preface -- Contents -- Editors and Contributors -- Deep Spectral Feature Representations Via Attention-Based Neural Network Architectures for Accented Malayalam Speech-A Low-Resourced Language -- 1 Introduction -- 2 Related Work -- 3 Methodology -- 3.1 Data Collection -- 3.2 Data Preprocessing -- 3.3 Accented Model Construction -- 3.4 Conclusion -- References -- Improving Tree-Based Convolutional Neural Network Model for Image Classification -- 1 Introduction -- 1.1 Contribution of the Research Work -- 2 Literature Review -- 2.1 Previous Work -- 2.2 Contribution -- 3 Methodology -- 3.1 Overview -- 3.2 Dataset -- 3.3 1D Convolutions and Strides -- 3.4 Removal of Max Pooling -- 3.5 Leaky ReLU -- 3.6 Model Architecture -- 4 Results and Conclusion -- References -- Smartphone Malware Detection Based on Enhanced Correlation-Based Feature Selection on Permissions -- 1 Introduction -- 1.1 Motivation -- 1.2 Contributions -- 2 Related Work -- 3 Proposed Methodology -- 3.1 Datasets -- 3.2 Feature Extraction -- 3.3 Feature-Feature Correlation with ENMRS -- 3.4 Feature-Class Correlation Measure: crRelevance -- 3.5 Proposed Feature Selection Technique: ECFS -- 3.6 Machine Learning Techniques Used -- 4 Results and Discussion -- 4.1 n 1 equals 0.1n1=0.1 and n 1 equals 0.9n1=0.9 -- 4.2 n 1 equals 0.2n1=0.2 and n 2 equals 0.8n2=0.8 -- 4.3 n 1 equals 0.3n1=0.3 and n 2 equals 0.7n2=0.7 -- 4.4 n 1 equals 0.4n1=0.4 and n 2 equals 0.6n2=0.6 -- 4.5 n 1 equals 0.5n1=0.5 and n 2 equals 0.5n2=0.5 -- 4.6 n 1 equals 0.6n1=0.6 and n 2 equals 0.4n2=0.4 -- 4.7 n 1 equals 0.7n1=0.7 and n 2 equals 0.3n2=0.3 -- 4.8 n 1 equals 0.8n1=0.8 and n 2 equals 0.2n2=0.2 -- 4.9 n 1 equals 0.9n1=0.9 and n 2 equals 0.1n2=0.1 -- 5 Conclusion -- References -- Fake News Detection Using Ensemble Learning Models -- 1 Introduction -- 2 Related Works.
3 Proposed Methodology -- 3.1 Dataset Description and Data Preprocessing -- 3.2 Feature Extraction -- 3.3 Algorithms -- 3.4 Evaluation Metrics -- 3.5 Web Application -- 4 Results and Discussion -- 5 Conclusion -- References -- Ensemble Approach for Suggestion Mining Using Deep Recurrent Convolutional Networks -- 1 Introduction -- 2 Related Work -- 3 Proposed Architecture -- 4 Experiments -- 4.1 Dataset and Pre-processing -- 4.2 Experimental Setup -- 5 Results and Discussion -- 6 Limitations -- 7 Conclusion -- References -- A CNN-Based Self-attentive Approach to Knowledge Tracing -- 1 Introduction -- 2 Related Works -- 3 Proposed Method -- 3.1 Architecture for the Proposed Model -- 4 Experimentations -- 4.1 Dataset -- 4.2 Evaluation Methodology -- 5 Results and Discussion -- 6 Conclusion and Future Work -- References -- LIPFCM: Linear Interpolation-Based Possibilistic Fuzzy C-Means Clustering Imputation Method for Handling Incomplete Data -- 1 Introduction -- 2 Related Work -- 3 Proposed Methodology -- 4 Experimental Framework -- 4.1 Dataset Description -- 4.2 Missing Value Simulation -- 4.3 Evaluation Criteria -- 5 Experimental Results and Discussion -- 6 Conclusion and Future Work -- References -- Experimental Analysis of Two-Wheeler Headlight Illuminance Data from the Perspective of Traffic Safety -- 1 Introduction -- 2 Literature Review -- 3 Methodology -- 3.1 Methodology -- 3.2 Experimental Setup -- 4 Results and Discussion -- 5 Conclusion and Future Scope -- References -- Detecto: The Phishing Website Detection -- 1 Introduction -- 2 Literature Review -- 2.1 Methodologies for Phishing Website Detection -- 2.2 Dataset -- 2.3 Feature Extraction -- 2.4 Machine Learning Algorithm -- 3 Result -- 4 Limitation -- 5 Conclusion -- References -- Synergizing Voice Cloning and ChatGPT for Multimodal Conversational Interfaces -- 1 Introduction. 2 Related Works -- 3 The Proposed System -- 3.1 Voice-Enabled ChatGPT -- 3.2 Voice Cloning Model -- 4 Methodology -- 4.1 Voice-Enabled ChatGPT -- 5 Voice Cloning -- 6 Result and Discussion -- 7 Conclusion -- 8 Future Scope -- References -- A Combined PCA-CNN Method for Enhanced Machinery Fault Diagnosis Through Fused Spectrogram Analysis -- 1 Introduction -- 2 Proposed Methodology -- 2.1 Dataset -- 2.2 Data Preprocessing -- 2.3 Fusion -- 2.4 CNN -- 3 Results and Discussion -- 4 Conclusion -- References -- FPGA-Based Design of Chaotic Systems with Quadratic Nonlinearities -- 1 Introduction -- 2 Design Methodology -- 2.1 Mathematical Representation -- 3 Results -- 3.1 Synthesis Results -- 3.2 Simulation Results -- 4 Conclusion -- References -- A Comprehensive Survey on Replay Strategies for Object Detection -- 1 Introduction -- 2 Object Detectors -- 3 Continual Learning and Catastrophic Forgetting -- 4 Replay Strategies for Object Detection -- 5 Conclusions -- References -- Investigation of Statistical and Machine Learning Models for COVID-19 Prediction -- 1 Introduction -- 2 Related Work -- 3 Methodology -- 3.1 Data Collection -- 3.2 Data Preprocessing -- 3.3 Performance Metrics -- 4 Algorithms Used -- 4.1 Statistical Model -- 4.2 Machine Learning Algorithms -- 5 Result Analysis -- 6 Conclusion -- References -- SONAR-Based Sound Waves' Utilization for Rocks' and Mines' Detection Using Logistic Regression -- 1 Introduction -- 2 Literature Review -- 3 Proposed Work -- 4 Implementation Analysis -- 5 Conclusion and Future Scope -- References -- A Sampling-Based Logistic Regression Model for Credit Card Fraud Estimation -- 1 Introduction -- 2 Literature Review -- 3 Proposed Model -- 4 Result Analysis and Discussion -- 5 Conclusion and Future Scope -- References -- iFlow: Powering Lightweight Cross-Platform Data Pipelines -- 1 Introduction. 2 Literature Review -- 3 Proposed Methodology -- 4 Result Analysis and Discussion -- 5 Conclusion and Future Work -- References -- Developing a Deep Learning Model to Classify Cancerous and Non-cancerous Lung Nodules -- 1 Introduction -- 2 Related Work -- 3 Proposed Method -- 3.1 Dataset -- 3.2 Model Architecture -- 4 Results and Discussion -- 5 Conclusion -- References -- Concrete Crack Detection Using Thermograms and Neural Network -- 1 Introduction -- 1.1 Related Work -- 2 Experiment Design -- 2.1 Simulation Dataset Creation -- 2.2 Camera and Concrete Blocks Specifications -- 2.3 Compression-Exposed Concrete Data Collection -- 2.4 Simulation Dataset Model -- 2.5 Laboratory Dataset Model -- 3 Results and Analysis -- 3.1 Simulation Dataset Model Results -- 3.2 Laboratory Dataset Model Results -- 3.3 The Challenges of Using Thermal Images -- 4 Conclusion -- References -- Wind Power Prediction in Mediterranean Coastal Cities Using Multi-layer Perceptron Neural Network -- 1 Introduction -- 2 Material and Method -- 2.1 Study Area and Dataset -- 2.2 MLPNN Model -- 2.3 Statistical Indices (SI) -- 3 Results and Discussions -- 4 Conclusions -- References -- Next Generation Intelligent IoT Use Case in Smart Manufacturing -- 1 Introduction -- 2 Literature Review -- 2.1 Research Objectives of This Study -- 2.2 Research Methodology -- 3 Next Generation Technology Development -- 4 Defining '4*S Model' -- 4.1 Conceptualization of '4*S Model' -- 5 Challenges in Smart Manufacturing -- 6 Advantages in Smart Manufacturing -- 6.1 Direct Cost Savings -- 6.2 Indirect Cost Savings -- 7 Limitations of This Study -- 8 Conclusion -- References -- Forecasting Financial Success App: Unveiling the Potential of Random Forest in Machine Learning-Based Investment Prediction -- 1 Introduction -- 2 Literature Review -- 3 Concept -- 3.1 Financial Investment -- 3.2 Machine Learning. 4 Methodology -- 5 Results -- 6 Discussions -- 7 Limitations -- 8 Conclusion and Future Scope -- References -- Integration of Blockchain-Enabled SBT and QR Code Technology for Secure Verification of Digital Documents -- 1 Introduction -- 2 Literature Review -- 3 Methodology -- 4 Performance Analysis -- 4.1 Time -- 4.2 Scalability -- 4.3 Authentication and Security -- 4.4 Automation -- 5 Conclusion and Future Scope -- References -- Time Series Forecasting of NSE Stocks Using Machine Learning Models (ARIMA, Facebook Prophet, and Stacked LSTM) -- 1 Introduction -- 2 Literature Review -- 3 Dataset Description -- 4 Data Preparation -- 5 Assessment Metric -- 6 Models -- 6.1 ARIMA -- 6.2 Facebook Prophet -- 6.3 LSTM -- 7 Proposed Methodology -- 8 Observations and Results -- 9 Result Analysis -- 10 Limitations -- 11 Conclusion -- 12 Social Impact -- 13 Future Scope -- References -- Analysis of Monkey Pox (MPox) Detection Using UNETs and VGG16 Weights -- 1 Introduction -- 2 Previous Works -- 3 Methodology -- 3.1 VGG16 -- 3.2 CNN -- 3.3 Custom CNN -- 3.4 UNET -- 4 Implementation Analysis -- 4.1 Data Preprocessing -- 4.2 Extracting Features -- 4.3 Measures of Performance -- 5 Discussion -- 6 Conclusion -- 7 Future Enhancement -- References -- Role of Robotic Process Automation in Enhancing Customer Satisfaction in E-commerce Through E-mail Automation -- 1 Introduction -- 2 Review of Literature -- 3 The E-learning Environment -- 4 Need for Robotic Process Automation -- 5 RPA Implementation -- 5.1 Payment Management -- 5.2 Moodle Account Creation -- 5.3 E-mail Automation -- 6 Discussions -- 7 Conclusion -- References -- Gene Family Classification Using Machine Learning: A Comparative Analysis -- 1 Introduction -- 1.1 Problem Statement -- 1.2 Machine Learning in Bioinformatics -- 1.3 Motivation -- 2 Literature Survey -- 3 Proposed Work -- 3.1 Architecture. 3.2 Implementation of Machine Learning Algorithms. |
| Record Nr. | UNINA-9910799251203321 |
Swaroop Abhishek
|
||
| Singapore : , : Springer Singapore Pte. Limited, , 2024 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Proceedings of Fifth Doctoral Symposium on Computational Intelligence : DoSCI 2024, Volume 1
| Proceedings of Fifth Doctoral Symposium on Computational Intelligence : DoSCI 2024, Volume 1 |
| Autore | Swaroop Abhishek |
| Edizione | [1st ed.] |
| Pubbl/distr/stampa | Singapore : , : Springer, , 2025 |
| Descrizione fisica | 1 online resource (635 pages) |
| Disciplina | 006.3 |
| Altri autori (Persone) |
KansalVineet
FortinoGiancarlo HassanienAboul Ella |
| Collana | Lecture Notes in Networks and Systems Series |
| ISBN |
9789819763184
9819763185 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910913777203321 |
|
Swaroop Abhishek
|
||
| Singapore : , : Springer, , 2025 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Proceedings of Fifth Doctoral Symposium on Computational Intelligence : DoSCI 2024, Volume 3
| Proceedings of Fifth Doctoral Symposium on Computational Intelligence : DoSCI 2024, Volume 3 |
| Autore | Swaroop Abhishek |
| Edizione | [1st ed.] |
| Pubbl/distr/stampa | Singapore : , : Springer, , 2024 |
| Descrizione fisica | 1 online resource (599 pages) |
| Altri autori (Persone) |
KansalVineet
FortinoGiancarlo HassanienAboul Ella |
| Collana | Lecture Notes in Networks and Systems Series |
| ISBN | 981-9760-36-4 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910896189703321 |
|
Swaroop Abhishek
|
||
| Singapore : , : Springer, , 2024 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Proceedings of Fifth Doctoral Symposium on Computational Intelligence : DoSCI 2024, Volume 4
| Proceedings of Fifth Doctoral Symposium on Computational Intelligence : DoSCI 2024, Volume 4 |
| Autore | Swaroop Abhishek |
| Edizione | [1st ed.] |
| Pubbl/distr/stampa | Singapore : , : Springer, , 2024 |
| Descrizione fisica | 1 online resource (613 pages) |
| Disciplina | 006.3 |
| Altri autori (Persone) |
KansalVineet
FortinoGiancarlo HassanienAboul Ella |
| Collana | Lecture Notes in Networks and Systems Series |
| ISBN | 981-9767-26-1 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Intro -- DoSCI-2024 Steering Committee Members -- Preface -- Contents -- Editors and Contributors -- A Comprehensive Examination of Machine Learning Models in Predicting 16 Personality Traits -- 1 Introduction -- 2 Literature Review -- 3 Dataset -- 4 Implementation -- 5 Result and Discussion -- 6 Conclusion -- References -- A Historical Analysis of Chatbots from Eliza to Google Bard -- 1 Introduction -- 2 History -- 2.1 Early Chatbots (1960-2001) -- 2.2 Late Chatbots (2000-2010) -- 2.3 Modern Chatbots (2020-Present) -- 3 Chatbot Approaches -- 3.1 Rule-Based -- 3.2 Retrieval-Based -- 3.3 Generative-Based Approach -- 3.4 Hybrid-Based Approach -- 4 Large Language Model in ChatGPT and Google Bard -- 4.1 ChatGPT with GPT-3.5 -- 4.2 Google Bard with LaMDA -- 5 Chatbots Programming Languages and Summary of Chatbot Journey -- 6 Conclusion -- References -- A Cutting-Edge Approach to Forest Fire Region Identification Through Deep Learning -- 1 Introduction -- 2 Literature Review -- 3 Methodology -- 3.1 Proposed Approach -- 3.2 Image Pre-processing -- 4 Feature Extraction -- 4.1 Convolutional Neural Network -- 5 Image Classification -- 5.1 Visual Geometry Group-19 or VGG-19 -- 6 Object Detection -- 6.1 You Only Look Once (YOLOv6) -- 7 Spatial Feature Extraction -- 8 Temporal Feature Extraction -- 9 Discussion -- 9.1 Dataset Description -- 10 Results -- 11 Conclusion -- 12 Future Scope -- References -- Brain Tumor MRI Segmentation Using Deep Instance Segmentation with Bioinspired Optimization Algorithm -- 1 Introduction -- 2 Related Works -- 3 Proposed Model -- 4 Results and Discussion -- 5 Conclusion -- References -- Cloud-Based Tool for Node.js Project Generation Using Abstract Syntax Trees -- 1 Introduction -- 2 Basic Concepts and Related Work -- 2.1 Abstract Syntax Trees -- 2.2 Webpack -- 2.3 Babel -- 2.4 Related Research -- 3 Architecture and Design.
3.1 Component Identification -- 3.2 System Interaction -- 3.3 DSG Service -- 3.4 Cloud Provider -- 4 Enhancements -- 4.1 Advanced AST Manipulation -- 4.2 AST Analysis and Manipulation -- 4.3 AST Optimization -- 5 Results -- 6 Limitations -- 7 Conclusion -- References -- Complex Network Traffic Prediction Method Under Graph Neural Network -- 1 Introduction -- 2 Dataset -- 3 Graph Neural Network -- 4 Experiment and Results -- 5 Conclusions -- References -- Enhanced Holistically Nested Edge Detection (eHED) Algorithm: A Reliable Edge Detection in Unconstrained Scenarios -- 1 Introduction -- 2 Related Work -- 3 Proposed Methodology -- 3.1 Multi-stream Architecture -- 3.2 Skip-Layer Network Architecture -- 3.3 Multiple Scale Input into a Single Model -- 3.4 Training Distinct Networks Independently -- 3.5 Enhanced Holistically Nested Edge Detection (eHED) -- 4 Formulation -- 5 Experimental Results -- 5.1 Dataset -- 5.2 Histogram of Oriented Gradients -- 5.3 Contour Algorithm -- 5.4 Canny -- 5.5 EHED -- 5.6 Comparison with Canny and eHED -- 6 Conclusion -- 7 Future Research -- References -- Toward Effective Suggestion Mining in Game Reviews: Introducing an Aspect-Based Multiclass Multilevel Dataset -- 1 Introduction -- 2 Related Work -- 3 Dataset -- 3.1 Background -- 3.2 Annotation Scheme -- 3.3 Expert Annotation -- 3.4 Characteristics of Dataset -- 4 Experimental Setup -- 4.1 TCN -- 4.2 LSTM -- 4.3 DRC_Net -- 4.4 GPT-3 -- 4.5 TCN with Machine Learning -- 5 Results -- 5.1 Suggestion Extraction (Superclass) -- 5.2 Evaluation of Sentiments -- 5.3 Evaluation of Subclass -- 5.4 Evaluation of Receiver and Suggestion Type -- 5.5 Evaluation of Aspects -- 6 Conclusion -- References -- Server-Enabled Information Transmission Through Networks Using Federated Learning Approach -- 1 Introduction -- 1.1 Applications of Federated Learning. 2 Federated Learning in Healthcare -- 3 Literature Review -- 4 Proposed Model -- 5 Result Analysis -- 6 Conclusion -- References -- Object Detection for Efficient Lighting: Comparing Models -- 1 Introduction -- 2 Related Works -- 3 System Architecture -- 4 Devices and Methods -- 4.1 Hardware -- 4.2 Software -- 4.3 Methodology -- 5 Analysis -- 5.1 YOLOv5 -- 5.2 Faster RCNN -- 5.3 SSD (Single-Shot Multi-Box Detector) -- 6 Discussion -- 7 Conclusion -- References -- Intelligent Adaptive Control Algorithm Based on Reinforcement Learning in the Field of Robotics -- 1 Background -- 2 Intelligent Adaptive Control Algorithm -- 3 Design of Intelligent Adaptive Control Algorithm in the Field of Robotics -- 3.1 Principles of Adaptive Control -- 3.2 Adaptive Control in Reinforcement Learning -- 4 Experimental Results of Response Time -- 5 Stability Test Results -- 6 Tracking Error Experimental Results -- 7 Conclusions -- References -- Multi-factor Authentication and Data Integrity for WBAN Using Hash-Based Techniques -- 1 Introduction -- 1.1 Motivation -- 1.2 The Main Contributions of the Research Work Are -- 1.3 Structure of the Article -- 2 Extensive Literature Survey -- 3 Research Gaps -- 4 Proposed Hypothesis -- 5 Proposed Methodology -- 5.1 WBAN Creation -- 5.2 Analysis and Update -- 5.3 Prediction and Diagnosis -- 6 Results and Discussion -- 6.1 Formal Security Analysis Using BAN Logic -- 6.2 Other Informal Security Analysis Over -- 6.3 Limitation of the Proposed Work -- 7 Expected Contribution to the Literature -- 8 Conclusion -- References -- Monitoring and Evaluating ECU Communication Data for Vulnerabilities Detection -- 1 Introduction -- 2 Related Works -- 3 Proposed Model -- 3.1 Data Collection -- 3.2 Data Preprocessing -- 3.3 Anomaly Detection and Result Discussion -- 3.4 Evaluation -- 4 Conclusion -- References. Animal Species Classification Using Deep Learning -- 1 Introduction -- 1.1 Need of the Model -- 2 Literature Survey -- 3 Methodology and Working -- 3.1 About the Dataset -- 3.2 Preprocessing of Data -- 3.3 (CNN)_ -- 3.4 EffecientNetB3 -- 3.5 Working of the Model -- 4 Result -- 4.1 Confusion Matrix -- 4.2 Accuracy -- 4.3 Precision -- 4.4 Recall -- 4.5 F1-Score -- 5 Conclusion -- 5.1 Model Applied to Input User Image -- 5.2 Implementation of the Model -- 5.3 Outcome -- 6 Recommendation for Future -- 6.1 Application in Veterinary -- 6.2 Zoological Parks -- 6.3 Sanctuaries -- 6.4 Roads Near Forests -- 6.5 Villages in National Parks -- References -- Comparative Analysis of Machine Learning and Deep Learning Classifiers for Crack Classification -- 1 Introduction -- 1.1 Motivation -- 1.2 Our Contribution -- 2 Methodology -- 2.1 Data Collection -- 2.2 Data Augmentation -- 2.3 Classification Models -- 2.4 Testing and Evaluation -- 3 Results -- 3.1 Performance Evaluation of ML Classifiers -- 3.2 Performance Evaluation of Pre-trained Deep Learning Classifiers -- 3.3 Performance Comparison with Existing Similar Models -- 4 Discussion -- 5 Conclusion -- References -- Design of Computer Network Security Defense System Based on Big Data -- 1 Introduction -- 2 Methods of CN SD System Design -- 2.1 Network Data Collection and Storage -- 2.2 Data Preprocessing and Feature Engineering -- 2.3 LSTM Model Construction -- 2.4 Real-Time Data Stream Processing -- 3 CN SD Performance Evaluation -- 3.1 Experimental Environment -- 3.2 Evaluation Indicators -- 4 Results -- 4.1 Threat Detection Accuracy Performance -- 4.2 Real-Time Results -- 5 Conclusions -- References -- Validation of Object-Oriented Metrics Against Standard Properties -- 1 Introduction -- 2 Literature Review -- 3 Proposed Object-oriented Metric Set for Reusability Assessment -- 3.1 Inherited Class Count (ICC). 3.2 Inherited Attribute Count (IAC) -- 3.3 Inherited Method Count (IMC) -- 3.4 Depth of Inheritance Hierarchy (DIH) -- 3.5 Degree of Reusability (DOR) Metric -- 4 Object-Oriented Metric Set Validation -- 4.1 Theoretical Validation -- 4.2 Through Measurement Theory -- 4.3 Through Kaner's Framework -- 5 Result and Discussion -- 6 Conclusion and Future Scope -- References -- A Personalized Health-Care Framework for Health-Care Service -- 1 Introduction -- 2 Machine Intelligence and Personalized Health Care -- 3 Computational Approach for Personalized Spine Care -- 4 Implementation Results -- 5 Conclusion -- References -- Comparative Analysis of Machine-Learning and Deep Learning Algorithms Using Manta Ray Foraging Optimization for the Detection of Hate Speech -- 1 Introduction -- 2 Literature Review -- 3 Dataset Description -- 4 Methodology -- 4.1 Data Collection and Preprocessing -- 4.2 Feature Extraction and Representation -- 4.3 ML Models Used -- 4.4 Manta Ray Foraging Optimization -- 5 Results and Discussion -- 6 Conclusion and Future Work -- References -- Bangladeshi Currency Authentication Checking System Using Convolutional Neural Networks -- 1 Introduction -- 2 Literature Review -- 3 Methodology -- 3.1 Data Collection -- 3.2 Data Preparation -- 3.3 Model Development -- 4 Results and Discussion -- 4.1 Result -- 4.2 Discussion -- 5 Conclusion -- References -- CLASH: A Contrastive Learning Approach for Few-Shot Classification of Histopathological Images -- 1 Introduction -- 2 Related Work -- 3 Proposed Model -- 3.1 Architecture -- 4 Experimental Results -- 4.1 Experiment Setup -- 4.2 Evaluation Metrics -- 4.3 Experiment Data -- 4.4 Performance Evaluation with Histopathology Dataset -- 4.5 Limitation of the Proposed Work -- 5 Conclusion -- References -- Data-Driven Air Quality Prediction with Batch Normalization in Long Short-Term Memory Networks. 1 Introduction. |
| Record Nr. | UNINA-9910903790603321 |
|
Swaroop Abhishek
|
||
| Singapore : , : Springer, , 2024 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||