Vai al contenuto principale della pagina
Autore: |
Misra Rajiv
![]() |
Titolo: |
Machine Learning and Big Data Analytics (Proceedings of International Conference on Machine Learning and Big Data Analytics (ICMLBDA) 2021)
![]() |
Pubblicazione: | Cham : , : Springer International Publishing AG, , 2021 |
©2022 | |
Descrizione fisica: | 1 online resource (372 pages) |
Soggetto genere / forma: | Electronic books. |
Altri autori: |
ShyamasundarRudrapatna K
![]() ![]() ![]() |
Nota di contenuto: | Intro -- Preface -- Organization -- Organizing Committee -- Chairman of Organizing Committee -- Members of Organizing Committee -- Scientific Committee -- Chairperson of Scientific Committee -- Members of Scientific Committee -- Contents -- Analysis on Applying the Capabilities of Deep Learning Based Method for Underwater Fish Species Classification -- 1 Introduction -- 2 Overview of Deep Learning -- 3 Review of Deep Learning Based Fish Classification Models -- 4 Performance Evaluation -- 5 Conclusion and Future Work -- References -- Bug Assignment Through Advanced Linguistic Operations -- 1 Introduction -- 2 Our Work -- 2.1 Data -- 2.2 Data Pre-processing -- 2.3 Classification Models -- 2.4 Similar Issues -- 2.5 Load Balancing -- 3 Accuracy -- 4 Conclusion -- References -- An Empirical Framework for Bangla Word Sense Disambiguation Using Statistical Approach -- 1 Introduction -- 2 Related Work -- 3 Dataset Preparation -- 3.1 Bangla Sense Annotated Corpus -- 4 Proposed BWSD Framework -- 4.1 Ambiguity Checker -- 4.2 Train Classifier -- 4.3 Actual Sense Prediction for Disambiguation -- 5 Experiments -- 5.1 Results -- 5.2 Comparison with Previous Techniques -- 6 Conclusion -- References -- Engagement Analysis of Students in Online Learning Environments -- 1 Introduction -- 2 Related Work -- 3 Proposed Work -- 3.1 Facial Expression Recognition -- 3.2 Gaze Detection -- 3.3 Ensemble Model -- 4 Results -- 4.1 Real-Time Engagement Analysis Capability -- 5 Conclusion -- References -- Static and Dynamic Hand Gesture Recognition for Indian Sign Language -- 1 Introduction -- 2 Related Work -- 3 Dataset and Methods -- 3.1 Description of Data Set -- 3.2 Methods -- 4 Experimental Model -- 4.1 Image Preprocessing -- 4.2 Image Segmentation -- 4.3 Feature Extraction -- 4.4 Classification -- 4.5 Prediction and Output Generation -- 5 Conclusion -- 6 Future Work. |
References -- An Application of Transfer Learning: Fine-Tuning BERT for Spam Email Classification -- 1 Introduction -- 2 Literature Survey -- 2.1 Spam Email Classification -- 2.2 Fine-Tuning BERT for Classification -- 3 BERT Model Architecture -- 4 The Proposed Approach -- 4.1 Data Collection -- 4.2 Document Pre-processing -- 4.3 BERT Tokenizer -- 4.4 Fine-Tuning BERT and Hyper-parameter Setting -- 4.5 BERT as Classifier -- 5 State-Of-The Art Classification Techniques -- 5.1 Logistic Regression -- 5.2 Support Vector Machine (SVM) -- 5.3 Naïve Bayes (NB) -- 5.4 Random Forest -- 5.5 LSTM -- 6 Experiment and Results -- 6.1 Performance Evaluation Measures -- 6.2 Results and Discussion -- 7 Conclusion -- References -- Concurrent Vowel Identification Using the Deep Neural Network -- 1 Introduction -- 2 Methods -- 2.1 Stimuli -- 2.2 Neural Responses from the Auditory-Nerve Model -- 2.3 Deep Neural Network to Predict the Concurrent Vowel Scores -- 3 Results -- 4 General Discussions -- References -- Application of Artificial Intelligence to Predict the Degradation of Potential mRNA Vaccines Developed to Treat SARS-CoV-2 -- 1 Introduction -- 2 Current Work -- 3 Methodology -- 3.1 Data Used -- 3.2 Algorithm and Procedure -- 4 The LSTM and GRU Based AI Model -- 4.1 The Proposed Model -- 4.2 Training, Validation and Prediction -- 4.3 Evaluation -- 5 Results and Discussions -- 6 Conclusion -- References -- Explainable AI for Healthcare: A Study for Interpreting Diabetes Prediction -- 1 Introduction -- 2 Artificial Intelligence in Diabetes -- 2.1 Explainable Artificial Intelligence -- 2.2 Diabetes Dataset Description -- 3 Model-Agnostic Methods -- 3.1 Permutation Feature Importance and Feature Interaction -- 3.2 Partial Dependence Plot (PDP), Individual Conditional Expectation (ICE) and Accumulated Local Effects (ALE) Plot. | |
3.3 Local Interpretable Model-Agnostic Explanations (LIME) -- 3.4 Shapley Additive Explanations (SHAP) -- 4 Model Evaluation -- 5 Conclusion -- References -- QR Based Paperless Out-Patient Health and Consultation Records Sharing System -- 1 Introduction -- 2 Related Works -- 3 Motivations -- 4 System Description -- 5 Application Description -- 6 Implementation -- 7 Result Screenshots -- 8 Performance Evaluation and Future Work -- 9 Conclusion -- References -- Searching Pattern in DNA Sequence Using ECC-Diffie-Hellman Exchange Based Hash Function: An Efficient Approach -- 1 Introduction -- 2 Related Work -- 3 Proposed Method -- 4 Results and Discussion -- 5 Conclusion -- References -- Integrated Micro-Video Recommender Based on Hadoop and Web-Scrapper -- 1 Introduction -- 2 Related Work -- 3 Web-Scraper Using Selenium and Beautifulsoup -- 3.1 Selenium Web-Driver -- 3.2 Beautifulsoup -- 3.3 Implementation -- 3.4 Scraper for Search-History Extraction -- 3.5 Scraper for Keyword-Based Search and Extraction of Result -- 3.6 Scraper for Watch-History Extraction -- 4 Proposed Model -- 5 Keyword-Based Recommendation -- 6 Map-Reduce Programming Paradigm -- 7 User Based Collaborative Recommendation Using Map Reduce on Hadoop Platform -- 7.1 Implementation on Hadoop -- 8 Result -- 9 Conclusion -- References -- Comparison of Machine Learning Techniques to Predict Academic Performance of Students -- 1 Introduction -- 2 Literature Review -- 3 Research Model -- 4 Data Collection and Parameters -- 5 Research Methodology -- 5.1 Logistic Regression (LR) -- 5.2 Decision Tree (DT) -- 5.3 Artificial Neural Networks (ANN) -- 5.4 Naïve Bayes (NB) -- 5.5 Validation Methods and Performance Measure Parameters -- 6 Experiment and Result Analysis -- 7 Conclusion -- References -- Misinformation-A Challenge to Medical Sciences: A Systematic Review -- 1 Introduction. | |
2 What is Social Media and Why It is Proving to Be a Cause of Distress in the Field of Medical Sciences? -- 3 Various Methods to Correct Misinformation Related to Medical Sciences -- 4 Limitations of the Research -- 5 Recommendations for the Prevent of Misinformation -- 6 Conclusion -- References -- Comparative Analysis Grey Wolf Optimization Technique & -- Its Diverse Applications in E-Commerce Market Prediction -- 1 Introduction -- 2 Literature Review -- 3 GWO Algorithm -- 4 GWO Algorithm & -- Classification Analysis -- 5 Performance Comparison of Different Applications of GWO Algorithm -- 5.1 In Binary Problems -- 5.2 Dynamic & -- Restricted Problems Having Constraints -- 5.3 In Multi-criterion Problem -- 6 Performance Comparison of Different Techniques of E-commerce Market Prediction -- 7 Conclusion & -- Future Scope -- References -- Applying Extreme Gradient Boosting for Surface EMG Based Sign Language Recognition -- 1 Introduction -- 2 Related Work -- 3 Methodology -- 3.1 Experimental Setup -- 3.2 Dataset -- 3.3 Evaluation Metrics -- 3.4 Algorithm -- 4 Result and Discussion -- 5 Conclusion and Future Work -- References -- Automated Sleep Staging System Based on Ensemble Learning Model Using Single-Channel EEG Signal -- 1 Introduction -- 1.1 Related Work -- 2 Experimental Data -- 3 Methodology -- 3.1 Features Extraction -- 3.2 Feature Selection -- 3.3 Classification -- 4 Experimental Results -- 4.1 Feature Selection Results -- 4.2 Performance of Sleep Staging Using (SG-III) Dataset -- 4.3 Performance of Sleep Staging Using Proposed Ensemble Stacking Algorithm Using Subgroup-III Datasets -- 4.4 Summary of Results -- 5 Conclusion -- References -- Histopathological Image Classification Using Ensemble Transfer Learning -- 1 Introduction -- 2 Methodology -- 2.1 Transfer Learning Method -- 2.2 Ensemble Learning Method. | |
3 Evaluation of Proposed Ensemble Transfer Learning Model -- 3.1 Proposed Ensemble Transfer Learning Method -- 3.2 Evaluation of Proposed Ensemble Transfer Learning Method -- 4 Result -- 5 Conclusion -- References -- A Deep Feature Concatenation Approach for Lung Nodule Classification -- 1 Introduction -- 2 Preliminaries -- 2.1 AlexNet Architecture -- 2.2 ResNet Architecture -- 3 Proposed Work -- 4 Experiment Analysis -- 4.1 Data Description -- 4.2 Data Pre-processing -- 4.3 Data Augmentation -- 4.4 Training -- 4.5 Simulation -- 5 Conclusion -- References -- A Deep Learning Approach for Anomaly-Based Network Intrusion Detection Systems: A Survey and an Objective Comparison -- 1 Introduction -- 2 Theory -- 2.1 Network-Based Intrusion Detection Systems (NIDS) -- 2.2 NSL-KDD Dataset -- 3 Related Work -- 4 Relative Study of Techniques Applied in Network Intrusion Detection System -- 5 Implementation Strategies -- 5.1 Data Preprocessing -- 5.2 Self-taught Learning (STL) -- 6 Conclusion -- References -- Review of Security Aspects of 51 Percent Attack on Blockchain -- 1 Introduction -- 2 Background -- 3 Impacts of 51 percent Attack -- 4 Review of Literature -- 4.1 Analysis of Blockchain Security Aspects -- 4.2 Techniques for 51 percent Attack -- 5 Conclusion -- References -- Transfer Learning Based Convolutional Neural Network (CNN) for Early Diagnosis of Covid19 Disease Using Chest Radiographs -- 1 Introduction -- 2 Related Work -- 3 Methodology -- 3.1 Dataset Used and Pre-processing -- 3.2 Architecture Used -- 3.3 Evaluation Parameters -- 4 Results -- 5 Conclusion -- References -- Review of Advanced Driver Assistance Systems and Their Applications for Collision Avoidance in Urban Driving Scenario -- 1 Introduction -- 2 Background and Need of Research -- 3 Advanced Driver Assistance Systems -- 3.1 Classification of ADAS Systems. | |
3.2 Sensors Used for Navigation Safety. | |
Altri titoli varianti: | Machine Learning and Big Data Analytics |
Titolo autorizzato: | Machine Learning and Big Data Analytics (Proceedings of International Conference on Machine Learning and Big Data Analytics (ICMLBDA) 2021) ![]() |
ISBN: | 3-030-82469-1 |
Formato: | Materiale a stampa ![]() |
Livello bibliografico | Monografia |
Lingua di pubblicazione: | Inglese |
Record Nr.: | 9910502990803321 |
Lo trovi qui: | Univ. Federico II |
Opac: | Controlla la disponibilità qui |