| Pubbl/distr/stampa |
Cham, Switzerland : , : Springer, , [2022]
|
| Descrizione fisica |
1 online resource (271 pages)
|
| Disciplina |
006.31
|
| Collana |
Studies in Big Data
|
| Soggetto topico |
Machine learning
Big data
|
| ISBN |
3-030-75855-9
|
| Formato |
Materiale a stampa  |
| Livello bibliografico |
Monografia |
| Lingua di pubblicazione |
eng
|
| Nota di contenuto |
Intro -- Preface -- Acknowledgment -- Contents -- Contributors -- Acronyms -- Part I Theoretical Foundation of Deep Learning Theory and Analysis -- A Study on Discrete Action Sequences Using Deep Emotional Intelligence -- 1 Introduction -- 2 Emotion Recognition from Static Action Sequences -- 3 Feature Descriptions -- 3.1 Block Based Intensity Value Feature Extraction -- 3.2 Different Bin Level HoG Feature Extraction -- 3.3 K Nearest Neighbor -- 3.4 Random Forest Classifier -- 3.5 Support Vector Machine -- 3.6 GEMEP Dataset -- 4 Performance Evaluations -- 4.1 Performance Measure of BBIV Feature with KNN, RF and SVM -- 4.2 Performance Measure of DBLHOG Feature with KNN, RF and SVM -- 5 Deep Emotional Intelligence -- 5.1 Non-Deep Learning Based Methods -- 5.2 Deep Learning Based Model -- 6 Conclusion -- References -- A Novel Noise Removal Technique Influenced by Deep Convolutional Autoencoders on Mammograms -- 1 Introduction -- 2 Related Works -- 3 Preliminaries of Convolutional Autoencoder -- 3.1 Denoise Model for Mammogram Images -- 3.2 Denoise Autoencoder for Noisy Mammogram -- 4 Research Methodology -- 4.1 Denoising Autoencoder with Batch Normalization and Dropout -- 4.2 Training -- 4.3 Loss Function -- 5 Result and Discussion -- 5.1 Experimental Setup and Validation -- 5.2 Metrics and Baseline Method -- 5.3 Analysis of Different Parameters -- 5.4 Subjective Analysis -- 5.5 Objective Analysis -- 5.6 Discussion -- 6 Conclusion and Future Extension -- References -- A High Security Framework Through Human Brain Using Algo Mixture Model Deep Learning Algorithm -- 1 Introduction -- 2 Machine Learning Trends -- 2.1 Current Trends of Machine Intelligence in Real World -- 3 Related Research Works -- 3.1 Deep Learning Algorithms -- 4 Proposed Design -- 4.1 Proposed Algorithm -- 5 Eperimental Results and Discussions -- 5.1 Performance Measures.
5.2 Result Analysis -- 6 Conclusion and Further Research Directions -- References -- Knowledge Framework for Deep Learning: Congenital Heart Disease -- 1 Introduction -- 2 Materials and Methods -- 3 Coronary Heart Disease Knowledge Framework -- 3.1 Pre-processing -- 3.2 Data Transformation -- 3.3 Classifier -- 4 Accuracy Measures -- 4.1 Training Data and Cross Validation -- 4.2 Predictive Data Mining and Knowledge Discovery -- 4.3 Decision Making and Future Policies -- 5 Experimental Results -- 6 Conclusion and Discussion -- References -- Part II Computing System and Machine Learning -- Automatic Image Segmentation by Ranking Based SVM in Convolutional Neural Network on Diabetic Fundus Image -- 1 Introduction -- 2 Related Work -- 3 Background Preliminaries -- 3.1 Ranking SVM -- 3.2 Convolutional Neural Network -- 3.3 Dropout, ReLU, and Batch Normalization -- 3.4 Framework of the SVM with CNNs -- 4 Designed RSVM with CNNs Segmentation Method -- 4.1 CNNs Architecture for Segmentation -- 4.2 Initialization -- 4.3 Training -- 4.4 Test Phase -- 4.5 Post-processing -- 5 Experimental Results and Analysis -- 5.1 Benchmark Data -- 5.2 Performance Metrics -- 5.3 Segmentation Results Comparison and Analysis -- 6 Conclusion -- References -- Deep Learning in Healthcare -- 1 Introduction -- 2 Machine Learning -- 2.1 Supervised Learning -- 2.2 Unsupervised Learning -- 2.3 Reinforcement Learning -- 2.4 Semi Supervised and Active Learning -- 3 Deep Learning -- 3.1 Deep Generative Models -- 4 Deep Learning in Healthcare -- 4.1 Deep Learning in Cancer Detection -- 4.2 Deep Learning in Alzeihmer's Detection -- 4.3 Deep Learning in Parkinson's Detection -- 4.4 Deep Learning in Diabetes Detection -- 5 Conclusions -- References -- On the Study of Machine Learning Algorithms Towards Healthcare Applications -- 1 Introduction -- 2 Machine Learning.
2.1 Artificial Neural Network -- 2.2 Classification and Clustering -- 3 Healthcare Consortium -- 3.1 Hospital Sector -- 3.2 Pharmaceutical Sector -- 3.3 Personalized Medication -- 3.4 Chemoinformatics -- 3.5 Diagnostic -- 3.6 Drug Discovery -- 3.7 Medical Equipments and Supplies -- 3.8 Medical Insurance -- 3.9 Telemedicine -- 4 Conclusion -- References -- A Predictive Data Analytic Approach to Get Insight of Healthcare Databases -- 1 Introduction -- 2 Data Mining for Congenital Heart Defects -- 3 Material and Method -- 3.1 Data Analytics Approach Using Hierarchical Clustering -- 4 Results and Discussions -- 5 Conclusion -- References -- Part III Deep Learning Algorithms -- A Survey on Deep Learning Methodologies of Recent Applications -- 1 Introduction -- 2 Computer Vision and Pattern Recognition -- 2.1 Automatic Image Colorization with Simultaneous Classification -- 2.2 Pixel Recursive Super Resolution -- 2.3 Real-Time Multi-person Pose Estimation -- 2.4 Generating Image Descriptions -- 2.5 Real-Time Analysis of Behaviors -- 2.6 Iterating Images to Generate New Objects -- 2.7 Real-Time Visual Translation -- 3 Robotics and Automation -- 3.1 Self Driving Cars -- 3.2 Robotics -- 3.3 The OpenAI Universe -- 4 Generation of Sound -- 4.1 Generation of Voice -- 4.2 Restoration of Sound in Videos -- 5 Generation of Art -- 5.1 Image Style Transfer on Famous Paintings -- 5.2 Automatic Generation of Textual Scripts -- 6 Computed Predictions -- 6.1 Deep Neural Networks Dreaming -- 6.2 Predicting Earthquakes -- 7 Conclusion -- References -- An Extensive Study of Privacy Preserving Recommendation System Using Collaborative Filtering -- 1 Introduction -- 2 Recommendation System Foundations -- 2.1 Phases of Recommendation System -- 3 Filtering Techniques Based Recommendation System -- 3.1 Content Based Filtering Recommendation System.
3.2 Collaborative Filtering Recommendation System -- 3.3 Hybrid Filtering Recommendation System -- 4 Security and Privacy Based Collaborative Filtering Techniques in Recommendation System -- 5 Related Work and Original Contribution -- 6 Experimental Result -- 7 Conclusion -- References -- A Comparative Study of Nature-Inspired Algorithm Based Hybrid Neural Network Training Algorithms in Data Classification -- 1 Introduction -- 2 Proposed Method -- 2.1 Back-Propagation Algorithm -- 2.2 Genetic Algorithm Based Neural Network Training -- 2.3 Nature Inspired Algorithm Based Optimization -- 3 Experimental Analysis -- 4 Conclusion -- References -- Anomaly Credit Card Fraud Detection Using Deep Learning -- 1 Introduction -- 2 Machine Learning Techniques -- 3 Credit Card Fraud Detection -- 4 Deep Learning -- 5 Anomaly Credit Card Fraud Detection Model Using Deep Learning -- 5.1 Proposed Model Block Diagram -- 5.2 Implementation of Proposed Model -- 5.3 Result Analysis and Evaluation -- 6 Conclusion and Future Work -- References -- Part IV Applications of Deep Learning Techniques -- Application of Deep Learning for Energy Management in Smart Grid -- 1 Introduction -- 2 Literature Review -- 3 Deep Learning Techniques -- 3.1 Feedforward Neural Network -- 3.2 Recurrent Neural Network -- 3.3 Long Short Term Memory Network -- 3.4 Gated Recurrent Unit Neural Network -- 3.5 Deep Belief Network -- 3.6 Convolutional Neural Network -- 4 Application of Deep Learning Technique in Smart Grid -- 4.1 Deep Learning STLF -- 4.2 Deep Learning on MTLF -- 4.3 Deep Learning on LTLF -- 4.4 Deep Learning in Demand-Side Management for Smart Charging in Smart Vehicle -- 5 Conclusions -- References -- Cost Optimization of Software Quality Assurance -- 1 Introduction -- 2 Related Work -- 2.1 Defect Types -- 2.2 Quality Assurance -- 3 State of Art.
4 Value Finances in Terms of Quality Economics -- 4.1 Various Cases -- 5 Cost Optimization -- 6 Conclusion -- References -- Analytical Approach for Security of Sensitive Business Cloud -- 1 Introduction -- 2 Literature Review -- 3 Security Methodology -- 4 Development of Business Cloud -- 4.1 Use and Convenience -- 4.2 Cost Reduction -- 4.3 Reliability -- 4.4 Security and Privacy -- 5 Conclusion -- References.
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| Record Nr. | UNINA-9910523003903321 |
Info
Deep learning in data analytics : recent techniques, practices and applications / / Debi Prasanna Acharjya, Anirban Mitra, Noor Zaman, editors
