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Bio-inspired computing for information retrieval applications / / D. P. Acharjya and Anirban Mitra
| Bio-inspired computing for information retrieval applications / / D. P. Acharjya and Anirban Mitra |
| Autore | Acharjya D. P. |
| Pubbl/distr/stampa | Hershey, Pennsylvania : , : IGI Global, , 2017 |
| Descrizione fisica | 1 online resource (411 pages) |
| Disciplina | 005.74 |
| Collana | Advances in Knowledge Acquisition, Transfer, and Management (AKATM) Book Series |
| Soggetto topico |
Natural computation
Information storage and retrieval systems Querying (Computer science) Database searching |
| Soggetto genere / forma | Electronic books. |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910164874403321 |
Acharjya D. P.
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| Hershey, Pennsylvania : , : IGI Global, , 2017 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Deep Learning in Data Analytics : Recent Techniques, Practices and Applications
| Deep Learning in Data Analytics : Recent Techniques, Practices and Applications |
| Autore | Acharjya Debi Prasanna |
| Pubbl/distr/stampa | Cham : , : Springer International Publishing AG, , 2021 |
| Descrizione fisica | 1 online resource (271 pages) |
| Altri autori (Persone) |
MitraAnirban
ZamanNoor |
| Collana | Studies in Big Data Ser. |
| Soggetto genere / forma | Electronic books. |
| 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. |
| Altri titoli varianti | Deep Learning in Data Analytics |
| Record Nr. | UNINA-9910497109903321 |
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Acharjya Debi Prasanna
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| Cham : , : Springer International Publishing AG, , 2021 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Deep learning in data analytics : recent techniques, practices and applications / / Debi Prasanna Acharjya, Anirban Mitra, Noor Zaman, editors
| Deep learning in data analytics : recent techniques, practices and applications / / Debi Prasanna Acharjya, Anirban Mitra, Noor Zaman, editors |
| 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. |
| Record Nr. | UNINA-9910523003903321 |
| Cham, Switzerland : , : Springer, , [2022] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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The Tumor Microenvironment of High Grade Serous Ovarian Cancer / Kenneth P. Nephew, Anirban Mitra, M. Sharon Stack, Joanna E. Burdette
| The Tumor Microenvironment of High Grade Serous Ovarian Cancer / Kenneth P. Nephew, Anirban Mitra, M. Sharon Stack, Joanna E. Burdette |
| Autore | Nephew Kenneth P |
| Pubbl/distr/stampa | MDPI - Multidisciplinary Digital Publishing Institute, 2019 |
| Descrizione fisica | 1 electronic resource (434 p.) |
| Soggetto topico | Biology, life sciences |
| Soggetto non controllato |
ovarian cancer
transcriptomic stroma immune cells epigenetics recurrence immunotherapies chemoresistance fibroblasts metastasis genomic tumor microenvironment cancer stem cells |
| ISBN |
9783038975557
3038975559 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910346663003321 |
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Nephew Kenneth P
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| MDPI - Multidisciplinary Digital Publishing Institute, 2019 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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