Info
- Utilizzare la checkbox di selezione a fianco di ciascun documento per attivare le funzionalità di stampa, invio email, download nei formati disponibili del (i) record.
Soft computing in data science : 6th international conference, SCDS 2021, virtual event, November 2-3, 2021 : proceedings / / Azlinah Mohamed [and three others] editors
| Soft computing in data science : 6th international conference, SCDS 2021, virtual event, November 2-3, 2021 : proceedings / / Azlinah Mohamed [and three others] editors |
| Pubbl/distr/stampa | Singapore : , : Springer, , [2021] |
| Descrizione fisica | 1 online resource (450 pages) |
| Disciplina | 006.3 |
| Collana | Communications in Computer and Information Science |
| Soggetto topico |
Soft computing
Data mining Evolutionary computation |
| ISBN | 981-16-7334-9 |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Intro -- Preface -- Organization -- Contents -- AI Techniques and Applications -- Comparison Performance of Long Short-Term Memory and Convolution Neural Network Variants on Online Learning Tweet Sentiment Analysis -- 1 Introduction -- 2 Proposed Methodology -- 2.1 Data Collection -- 2.2 Data Pre-processing -- 2.3 Sentiment Labelling -- 2.4 Sentiment Visualization -- 2.5 Dataset Normalization -- 2.6 Online Learning Dataset Analysis -- 2.7 Splitting Dataset -- 2.8 Deep Learning Model Design -- 2.9 Deep Learning Model Evaluation -- 3 Results -- 3.1 Pre-processed Datasets -- 3.2 Results from Experiment 1: 64 Batch Size and 10 Epoch -- 3.3 Results from Experiment 2: 64 Batch Size and 30 Epochs -- 3.4 Results from Experiment 3: 32 Batch Size and 30 Epoch -- 3.5 Results from Experiment 4: 32 Batch Size and 30 Epochs with Random Oversampling -- 3.6 Results from Experiment 5: 64 Batch Size 64 and 50 Epoch with Random Oversampling -- 4 Summary of the Results and Analysis -- 5 Conclusion -- References -- Performance Analysis of Hybrid Architectures of Deep Learning for Indonesian Sentiment Analysis -- 1 Introduction -- 2 Materials and Method -- 2.1 Dataset -- 2.2 Preprocessing -- 2.3 Convolutional Neural Network (CNN) -- 2.4 Long Short-Term Memory (LSTM) -- 2.5 Gated Recurrent Unit (GRU) -- 2.6 RNN-CNN -- 2.7 CNN-RNN -- 3 Result and Discussion -- 3.1 Experimental Setup -- 3.2 Results -- 4 Conclusion -- References -- Machine Learning Based Biosignals Mental Stress Detection -- 1 Introduction -- 2 Literature Review on Stress and Its Effect -- 2.1 Concept of Stress -- 2.2 Stressors Maintaining and Integrity -- 2.3 Stress Effects on Emotion and Mental Health -- 2.4 Mental Stress Impact on Health -- 2.5 Biosignals Connected to Predict Acute Mental Stress -- 2.6 Machine Learning Approach to Detect Mental Stress -- 3 Data Collection -- 4 Methodology.
4.1 Filtering and Preprocessing -- 4.2 Feature Extraction -- 4.3 Modeling -- 5 Result -- 6 Conclusion -- References -- Sentences Prediction Based on Automatic Lip-Reading Detection with Deep Learning Convolutional Neural Networks Using Video-Based Features -- 1 Introduction -- 2 Literature Review -- 3 Pre-processing and Methodology -- 4 Results and Discussion -- 5 Conclusion -- References -- Unsupervised Learning Approach for Evaluating the Impact of COVID-19 on Economic Growth in Indonesia -- 1 Introduction -- 2 Unsupervised Learning -- 2.1 Biplot Analysis -- 2.2 K-Means Clustering -- 2.3 K-Medoids Clustering -- 2.4 Self-Organizing Map (SOM) -- 2.5 Silhouette Coefficient -- 2.6 Gross Regional Domestic Product (GRDP) -- 3 Data and Methodology -- 4 Result and Discussion -- 4.1 Descriptive Statistic -- 4.2 Biplot Analysis -- 4.3 K-Medoids Clustering -- 4.4 K-Means Clustering -- 4.5 Self-Organizing Map (SOM) -- 4.6 Hybrid SOM - K-MeansClustering -- 4.7 Comparison Unsupervised Learning Method -- 5 Conclusion and Future Research -- References -- Rainfall Prediction in Flood Prone Area Using Deep Learning Approach -- 1 Introduction -- 2 Methodology -- 2.1 Study Area and Data Preparation -- 2.2 Model Development -- 2.3 Model Evaluation -- 3 Result and Discussion -- 3.1 Preliminary Studies -- 3.2 Performance Evaluation -- 3.3 Model Comparison and Discussion -- 4 Conclusion and Recommendation -- 4.1 Conclusion -- 4.2 Recommendation -- References -- Auto-DL: A Platform to Generate Deep Learning Models -- 1 Introduction -- 2 Related Work -- 3 Methodology Applied -- 4 Auto-DL Platform -- 4.1 React Application (Frontend) -- 4.2 Django Application (Backend) -- 4.3 MongoDB Instance (Database) -- 5 Testing and Result Analysis -- 5.1 Testing Model and Suite -- 5.2 About Datasets -- 5.3 Platform Usability Report -- 5.4 Comparative Study with Other Products. 5.5 Generated Code Evaluation -- 5.6 Stress Testing the Auto-DL Platform -- 6 Conclusion -- 7 Future Scope -- References -- A Smart Predictive Maintenance Scheme for Classifying Diagnostic and Prognostic Statuses -- 1 Introduction -- 2 Related Studies -- 3 Method -- 3.1 Notations and Problems -- 3.2 Machine State Annotation -- 3.3 Machine State Model Construction -- 3.4 Future Machine State Prediction -- 4 Discussions -- 4.1 Data Information and Experiment Setups -- 4.2 State Annotation Analysis -- 4.3 Diagnostic and Prognostic Engine State Models Analysis -- 4.4 Testing Model Performances Analysis -- 5 Conclusion -- References -- Data Analytics and Technologies -- Optimal Portfolio Construction of Islamic Financing Instrument in Malaysia -- 1 Introduction -- 1.1 Equity Financing Instruments: Profit Sharing Contracts -- 1.2 Debt Financing Instruments -- 2 Literature Review -- 3 Methodology -- 3.1 Capital Asset Pricing Model -- 3.2 Construction of Optimal Portfolio -- 3.3 Portfolio Return and Risk -- 4 Results and Discussion -- 4.1 A Composition of Islamic Financing Instrument (IFI) -- 4.2 Comparing Mean Return, Variance and Risk of IFI -- 4.3 Capital Asset Pricing Model of IFI -- 4.4 Excess Return to Beta Ratio and Ranking Procedure -- 4.5 Selecting Instrument from Cut-Off Rate -- 4.6 Proportion of Each Selected Instruments -- 4.7 Portfolio Return and Risk -- 5 Conclusion -- References -- Analytics-Based on Classification and Clustering Methods for Local Community Empowerment in Indonesia -- 1 Introduction -- 2 Methodology -- 2.1 Classification -- 2.2 Clustering -- 3 Data Set -- 4 Result and Discussion -- 4.1 Classification (Supervised Learning) -- 4.2 Clustering (Unsupervised Learning) -- 5 Conclusion -- References -- Two-Step Estimation for Modeling the Earthquake Occurrences in Sumatra by Neyman-Scott Cox Point Processes -- 1 Introduction. 2 Study Area and Data Description -- 3 Methodology -- 3.1 Neyman-Scott Cox Processes (NSCP) -- 3.2 Parameter Estimation -- 3.3 Model Assessment -- 4 Result -- 4.1 Spatial Trend and Clustering Detection -- 4.2 Model Comparison -- 4.3 Model Interpretation and Prediction -- 5 Conclusion -- References -- Construction of Optimal Stock Market Portfolios Using Outlier Detection Algorithm -- 1 Introduction -- 2 Related Work -- 2.1 Utilising Data Mining Methods -- 2.2 Utilising MVPO and Sharpe Ratio -- 3 Methodology -- 3.1 Preparing Stock Data -- 3.2 Preparing Proxy Data -- 3.3 Identifying Outliers -- 3.4 Measuring Performance of the Stock Portfolios Using MVPO -- 3.5 Assessing the Stock Portfolios Using the Sharpe Ratio -- 4 Results and Discussion -- 5 Conclusion -- References -- Simulating the Upcoming Trend of Malaysia's Unemployment Rate Using Multiple Linear Regression -- 1 Introduction -- 2 Literature Review -- 2.1 Overview and Definition of Unemployment -- 2.2 Issues of Unemployment -- 2.3 Contributing Factors of Unemployment -- 2.4 Multiple Linear Regression -- 2.5 Monte Carlo Simulation -- 3 Methodology -- 3.1 Multiple Linear Regression -- 3.2 Simulation of the Upcoming Trend of Malaysia's Unemployment Rate -- 4 Result -- 4.1 Multiple Linear Regression -- 4.2 Building the Regression Model -- 4.3 Simulation of Malaysia's Upcoming Trend of Unemployment Rate -- 5 Conclusion -- References -- Time Series Forecasting Using a Hybrid Prophet and Long Short-Term Memory Model -- 1 Introduction -- 2 Literature Review -- 2.1 Time Series Forecasting Model -- 2.2 Hybrid Methodology -- 3 Hybrid Methodology Framework -- 3.1 Linear Prediction -- 3.2 Nonlinear Prediction -- 3.3 Hybrid Time Series Forecasting Model -- 3.4 Data -- 3.5 Performance Metric -- 4 Empirical Results -- 4.1 Prophet Decomposition Result -- 4.2 Hybrid Algorithm -- 5 Conclusions -- References. Financial Analytics on Malaysia's Equity Fund Performance and Its Timing Liquidity -- 1 Introduction -- 1.1 Mutual Fund in Malaysia -- 2 Methodology -- 2.1 Return and Traditional Financial Ratios -- 2.2 Capital Asset Pricing Model -- 2.3 Liquidity Measures, Trading Volume and Turnover -- 2.4 Multiple Linear Regression -- 3 Analysis and Findings -- 3.1 Performance of Equity Funds Versus Market Return -- 3.2 Performance of Equity Funds Versus Market Return: A Month Before and After the 14thGeneral Election (GE14) -- 3.3 Relationship Between Expected Return of CAPM and Liquidity Timing -- 4 Conclusion -- References -- An Autoregressive Distributed Lag (ARDL) Analysis of the Relationships Between Employees Provident Fund's Wealth and Its Determinants -- 1 Introduction -- 2 Methodology -- 3 Analysis and Findings -- 3.1 Time Series Plots -- 3.2 Unit Root Test -- 3.3 Testing for the Presence of Co-integration -- 3.4 Unit Root Test -- 4 Conclusion -- References -- Data Mining and Image Processing -- Iris Segmentation Based on an Adaptive Initial Contour and Partly-Normalization -- 1 Introduction -- 2 Methodology -- 2.1 Pre-processing -- 2.2 Adaptive Initial Contour (AIC) -- 2.3 Partly-Normalization -- 3 Results and Discussion -- 3.1 Pre-test on Iris Images -- 3.2 Adaptive Initial Contour (AIC) -- 3.3 Iris Segmentation on Blurry Iris Images -- 3.4 Computational Time -- 4 Conclusion -- References -- Identifying the Important Demographic and Financial Factors Related to the Mortality Rate of COVID-19 with Data Mining Techniques -- 1 Introduction -- 2 Literature Review -- 2.1 Related Work -- 2.2 Feature Selection -- 2.3 Supervised Machine Learning -- 3 Methodology -- 3.1 Data Collection -- 3.2 Data Set Pre-processing -- 3.3 Creating Target Variable -- 3.4 Feature Selection Methods -- 3.5 Supervised Machine Learning Models -- 3.6 Evaluation. 3.7 Data Visualization. |
| Record Nr. | UNINA-9910508476703321 |
| Singapore : , : Springer, , [2021] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Soft computing in data science : 6th international conference, SCDS 2021, virtual event, November 2-3, 2021 : proceedings / / Azlinah Mohamed [and three others] editors
| Soft computing in data science : 6th international conference, SCDS 2021, virtual event, November 2-3, 2021 : proceedings / / Azlinah Mohamed [and three others] editors |
| Pubbl/distr/stampa | Singapore : , : Springer, , [2021] |
| Descrizione fisica | 1 online resource (450 pages) |
| Disciplina | 006.3 |
| Collana | Communications in Computer and Information Science |
| Soggetto topico |
Soft computing
Data mining Evolutionary computation |
| ISBN | 981-16-7334-9 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Intro -- Preface -- Organization -- Contents -- AI Techniques and Applications -- Comparison Performance of Long Short-Term Memory and Convolution Neural Network Variants on Online Learning Tweet Sentiment Analysis -- 1 Introduction -- 2 Proposed Methodology -- 2.1 Data Collection -- 2.2 Data Pre-processing -- 2.3 Sentiment Labelling -- 2.4 Sentiment Visualization -- 2.5 Dataset Normalization -- 2.6 Online Learning Dataset Analysis -- 2.7 Splitting Dataset -- 2.8 Deep Learning Model Design -- 2.9 Deep Learning Model Evaluation -- 3 Results -- 3.1 Pre-processed Datasets -- 3.2 Results from Experiment 1: 64 Batch Size and 10 Epoch -- 3.3 Results from Experiment 2: 64 Batch Size and 30 Epochs -- 3.4 Results from Experiment 3: 32 Batch Size and 30 Epoch -- 3.5 Results from Experiment 4: 32 Batch Size and 30 Epochs with Random Oversampling -- 3.6 Results from Experiment 5: 64 Batch Size 64 and 50 Epoch with Random Oversampling -- 4 Summary of the Results and Analysis -- 5 Conclusion -- References -- Performance Analysis of Hybrid Architectures of Deep Learning for Indonesian Sentiment Analysis -- 1 Introduction -- 2 Materials and Method -- 2.1 Dataset -- 2.2 Preprocessing -- 2.3 Convolutional Neural Network (CNN) -- 2.4 Long Short-Term Memory (LSTM) -- 2.5 Gated Recurrent Unit (GRU) -- 2.6 RNN-CNN -- 2.7 CNN-RNN -- 3 Result and Discussion -- 3.1 Experimental Setup -- 3.2 Results -- 4 Conclusion -- References -- Machine Learning Based Biosignals Mental Stress Detection -- 1 Introduction -- 2 Literature Review on Stress and Its Effect -- 2.1 Concept of Stress -- 2.2 Stressors Maintaining and Integrity -- 2.3 Stress Effects on Emotion and Mental Health -- 2.4 Mental Stress Impact on Health -- 2.5 Biosignals Connected to Predict Acute Mental Stress -- 2.6 Machine Learning Approach to Detect Mental Stress -- 3 Data Collection -- 4 Methodology.
4.1 Filtering and Preprocessing -- 4.2 Feature Extraction -- 4.3 Modeling -- 5 Result -- 6 Conclusion -- References -- Sentences Prediction Based on Automatic Lip-Reading Detection with Deep Learning Convolutional Neural Networks Using Video-Based Features -- 1 Introduction -- 2 Literature Review -- 3 Pre-processing and Methodology -- 4 Results and Discussion -- 5 Conclusion -- References -- Unsupervised Learning Approach for Evaluating the Impact of COVID-19 on Economic Growth in Indonesia -- 1 Introduction -- 2 Unsupervised Learning -- 2.1 Biplot Analysis -- 2.2 K-Means Clustering -- 2.3 K-Medoids Clustering -- 2.4 Self-Organizing Map (SOM) -- 2.5 Silhouette Coefficient -- 2.6 Gross Regional Domestic Product (GRDP) -- 3 Data and Methodology -- 4 Result and Discussion -- 4.1 Descriptive Statistic -- 4.2 Biplot Analysis -- 4.3 K-Medoids Clustering -- 4.4 K-Means Clustering -- 4.5 Self-Organizing Map (SOM) -- 4.6 Hybrid SOM - K-MeansClustering -- 4.7 Comparison Unsupervised Learning Method -- 5 Conclusion and Future Research -- References -- Rainfall Prediction in Flood Prone Area Using Deep Learning Approach -- 1 Introduction -- 2 Methodology -- 2.1 Study Area and Data Preparation -- 2.2 Model Development -- 2.3 Model Evaluation -- 3 Result and Discussion -- 3.1 Preliminary Studies -- 3.2 Performance Evaluation -- 3.3 Model Comparison and Discussion -- 4 Conclusion and Recommendation -- 4.1 Conclusion -- 4.2 Recommendation -- References -- Auto-DL: A Platform to Generate Deep Learning Models -- 1 Introduction -- 2 Related Work -- 3 Methodology Applied -- 4 Auto-DL Platform -- 4.1 React Application (Frontend) -- 4.2 Django Application (Backend) -- 4.3 MongoDB Instance (Database) -- 5 Testing and Result Analysis -- 5.1 Testing Model and Suite -- 5.2 About Datasets -- 5.3 Platform Usability Report -- 5.4 Comparative Study with Other Products. 5.5 Generated Code Evaluation -- 5.6 Stress Testing the Auto-DL Platform -- 6 Conclusion -- 7 Future Scope -- References -- A Smart Predictive Maintenance Scheme for Classifying Diagnostic and Prognostic Statuses -- 1 Introduction -- 2 Related Studies -- 3 Method -- 3.1 Notations and Problems -- 3.2 Machine State Annotation -- 3.3 Machine State Model Construction -- 3.4 Future Machine State Prediction -- 4 Discussions -- 4.1 Data Information and Experiment Setups -- 4.2 State Annotation Analysis -- 4.3 Diagnostic and Prognostic Engine State Models Analysis -- 4.4 Testing Model Performances Analysis -- 5 Conclusion -- References -- Data Analytics and Technologies -- Optimal Portfolio Construction of Islamic Financing Instrument in Malaysia -- 1 Introduction -- 1.1 Equity Financing Instruments: Profit Sharing Contracts -- 1.2 Debt Financing Instruments -- 2 Literature Review -- 3 Methodology -- 3.1 Capital Asset Pricing Model -- 3.2 Construction of Optimal Portfolio -- 3.3 Portfolio Return and Risk -- 4 Results and Discussion -- 4.1 A Composition of Islamic Financing Instrument (IFI) -- 4.2 Comparing Mean Return, Variance and Risk of IFI -- 4.3 Capital Asset Pricing Model of IFI -- 4.4 Excess Return to Beta Ratio and Ranking Procedure -- 4.5 Selecting Instrument from Cut-Off Rate -- 4.6 Proportion of Each Selected Instruments -- 4.7 Portfolio Return and Risk -- 5 Conclusion -- References -- Analytics-Based on Classification and Clustering Methods for Local Community Empowerment in Indonesia -- 1 Introduction -- 2 Methodology -- 2.1 Classification -- 2.2 Clustering -- 3 Data Set -- 4 Result and Discussion -- 4.1 Classification (Supervised Learning) -- 4.2 Clustering (Unsupervised Learning) -- 5 Conclusion -- References -- Two-Step Estimation for Modeling the Earthquake Occurrences in Sumatra by Neyman-Scott Cox Point Processes -- 1 Introduction. 2 Study Area and Data Description -- 3 Methodology -- 3.1 Neyman-Scott Cox Processes (NSCP) -- 3.2 Parameter Estimation -- 3.3 Model Assessment -- 4 Result -- 4.1 Spatial Trend and Clustering Detection -- 4.2 Model Comparison -- 4.3 Model Interpretation and Prediction -- 5 Conclusion -- References -- Construction of Optimal Stock Market Portfolios Using Outlier Detection Algorithm -- 1 Introduction -- 2 Related Work -- 2.1 Utilising Data Mining Methods -- 2.2 Utilising MVPO and Sharpe Ratio -- 3 Methodology -- 3.1 Preparing Stock Data -- 3.2 Preparing Proxy Data -- 3.3 Identifying Outliers -- 3.4 Measuring Performance of the Stock Portfolios Using MVPO -- 3.5 Assessing the Stock Portfolios Using the Sharpe Ratio -- 4 Results and Discussion -- 5 Conclusion -- References -- Simulating the Upcoming Trend of Malaysia's Unemployment Rate Using Multiple Linear Regression -- 1 Introduction -- 2 Literature Review -- 2.1 Overview and Definition of Unemployment -- 2.2 Issues of Unemployment -- 2.3 Contributing Factors of Unemployment -- 2.4 Multiple Linear Regression -- 2.5 Monte Carlo Simulation -- 3 Methodology -- 3.1 Multiple Linear Regression -- 3.2 Simulation of the Upcoming Trend of Malaysia's Unemployment Rate -- 4 Result -- 4.1 Multiple Linear Regression -- 4.2 Building the Regression Model -- 4.3 Simulation of Malaysia's Upcoming Trend of Unemployment Rate -- 5 Conclusion -- References -- Time Series Forecasting Using a Hybrid Prophet and Long Short-Term Memory Model -- 1 Introduction -- 2 Literature Review -- 2.1 Time Series Forecasting Model -- 2.2 Hybrid Methodology -- 3 Hybrid Methodology Framework -- 3.1 Linear Prediction -- 3.2 Nonlinear Prediction -- 3.3 Hybrid Time Series Forecasting Model -- 3.4 Data -- 3.5 Performance Metric -- 4 Empirical Results -- 4.1 Prophet Decomposition Result -- 4.2 Hybrid Algorithm -- 5 Conclusions -- References. Financial Analytics on Malaysia's Equity Fund Performance and Its Timing Liquidity -- 1 Introduction -- 1.1 Mutual Fund in Malaysia -- 2 Methodology -- 2.1 Return and Traditional Financial Ratios -- 2.2 Capital Asset Pricing Model -- 2.3 Liquidity Measures, Trading Volume and Turnover -- 2.4 Multiple Linear Regression -- 3 Analysis and Findings -- 3.1 Performance of Equity Funds Versus Market Return -- 3.2 Performance of Equity Funds Versus Market Return: A Month Before and After the 14thGeneral Election (GE14) -- 3.3 Relationship Between Expected Return of CAPM and Liquidity Timing -- 4 Conclusion -- References -- An Autoregressive Distributed Lag (ARDL) Analysis of the Relationships Between Employees Provident Fund's Wealth and Its Determinants -- 1 Introduction -- 2 Methodology -- 3 Analysis and Findings -- 3.1 Time Series Plots -- 3.2 Unit Root Test -- 3.3 Testing for the Presence of Co-integration -- 3.4 Unit Root Test -- 4 Conclusion -- References -- Data Mining and Image Processing -- Iris Segmentation Based on an Adaptive Initial Contour and Partly-Normalization -- 1 Introduction -- 2 Methodology -- 2.1 Pre-processing -- 2.2 Adaptive Initial Contour (AIC) -- 2.3 Partly-Normalization -- 3 Results and Discussion -- 3.1 Pre-test on Iris Images -- 3.2 Adaptive Initial Contour (AIC) -- 3.3 Iris Segmentation on Blurry Iris Images -- 3.4 Computational Time -- 4 Conclusion -- References -- Identifying the Important Demographic and Financial Factors Related to the Mortality Rate of COVID-19 with Data Mining Techniques -- 1 Introduction -- 2 Literature Review -- 2.1 Related Work -- 2.2 Feature Selection -- 2.3 Supervised Machine Learning -- 3 Methodology -- 3.1 Data Collection -- 3.2 Data Set Pre-processing -- 3.3 Creating Target Variable -- 3.4 Feature Selection Methods -- 3.5 Supervised Machine Learning Models -- 3.6 Evaluation. 3.7 Data Visualization. |
| Record Nr. | UNISA-996464412603316 |
| Singapore : , : Springer, , [2021] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
| ||
Soft Computing in Data Science : 5th International Conference, SCDS 2019, Iizuka, Japan, August 28–29, 2019, Proceedings / / edited by Michael W. Berry, Bee Wah Yap, Azlinah Mohamed, Mario Köppen
| Soft Computing in Data Science : 5th International Conference, SCDS 2019, Iizuka, Japan, August 28–29, 2019, Proceedings / / edited by Michael W. Berry, Bee Wah Yap, Azlinah Mohamed, Mario Köppen |
| Edizione | [1st ed. 2019.] |
| Pubbl/distr/stampa | Singapore : , : Springer Singapore : , : Imprint : Springer, , 2019 |
| Descrizione fisica | 1 online resource (395 pages) |
| Disciplina | 006.3 |
| Collana | Communications in Computer and Information Science |
| Soggetto topico |
Artificial intelligence
Application software Data mining Optical data processing Artificial Intelligence Information Systems Applications (incl. Internet) Data Mining and Knowledge Discovery Image Processing and Computer Vision |
| ISBN | 981-15-0399-0 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Information and Customer Analytics -- Visual Data Science -- Machine and Deep Learning -- Big Data Analytics -- Computational and Artificial Intelligence -- Social Network and Media Analytics. |
| Record Nr. | UNINA-9910350216403321 |
| Singapore : , : Springer Singapore : , : Imprint : Springer, , 2019 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Soft Computing in Data Science : Third International Conference, SCDS 2017, Yogyakarta, Indonesia, November 27–28, 2017, Proceedings / / edited by Azlinah Mohamed, Michael W. Berry, Bee Wah Yap
| Soft Computing in Data Science : Third International Conference, SCDS 2017, Yogyakarta, Indonesia, November 27–28, 2017, Proceedings / / edited by Azlinah Mohamed, Michael W. Berry, Bee Wah Yap |
| Edizione | [1st ed. 2017.] |
| Pubbl/distr/stampa | Singapore : , : Springer Singapore : , : Imprint : Springer, , 2017 |
| Descrizione fisica | 1 online resource (XV, 317 p. 123 illus.) |
| Disciplina | 004 |
| Collana | Communications in Computer and Information Science |
| Soggetto topico |
Artificial intelligence
Data mining Optical data processing Big data Algorithms Artificial Intelligence Data Mining and Knowledge Discovery Computer Imaging, Vision, Pattern Recognition and Graphics Big Data Algorithm Analysis and Problem Complexity |
| ISBN | 981-10-7242-6 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Deep learning and real-time classification -- Image feature classification and extraction -- Classification, clustering, visualization -- Applications of machine learning -- Data visualization -- Fuzzy logic -- Prediction models and e-learning -- Text and sentiment analytics. |
| Record Nr. | UNINA-9910254830703321 |
| Singapore : , : Springer Singapore : , : Imprint : Springer, , 2017 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Soft Computing in Data Science : First International Conference, SCDS 2015, Putrajaya, Malaysia, September 2-3, 2015, Proceedings / / edited by Michael W. Berry, Azlinah Mohamed, Bee Wah Yap
| Soft Computing in Data Science : First International Conference, SCDS 2015, Putrajaya, Malaysia, September 2-3, 2015, Proceedings / / edited by Michael W. Berry, Azlinah Mohamed, Bee Wah Yap |
| Edizione | [1st ed. 2015.] |
| Pubbl/distr/stampa | Singapore : , : Springer Singapore : , : Imprint : Springer, , 2015 |
| Descrizione fisica | 1 online resource (XV, 276 p. 94 illus. in color.) |
| Disciplina | 006.3 |
| Collana | Communications in Computer and Information Science |
| Soggetto topico |
Data mining
Algorithms Artificial intelligence Pattern recognition User interfaces (Computer systems) Data Mining and Knowledge Discovery Algorithm Analysis and Problem Complexity Artificial Intelligence Pattern Recognition User Interfaces and Human Computer Interaction |
| ISBN | 981-287-936-6 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910298963003321 |
| Singapore : , : Springer Singapore : , : Imprint : Springer, , 2015 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Supervised and Unsupervised Learning for Data Science / / edited by Michael W. Berry, Azlinah Mohamed, Bee Wah Yap
| Supervised and Unsupervised Learning for Data Science / / edited by Michael W. Berry, Azlinah Mohamed, Bee Wah Yap |
| Edizione | [1st ed. 2020.] |
| Pubbl/distr/stampa | Cham : , : Springer International Publishing : , : Imprint : Springer, , 2020 |
| Descrizione fisica | 1 online resource (VIII, 187 p. 55 illus., 45 illus. in color.) |
| Disciplina |
621.382
006.31 |
| Collana | Unsupervised and Semi-Supervised Learning |
| Soggetto topico |
Electrical engineering
Signal processing Image processing Speech processing systems Pattern recognition Artificial intelligence Data mining Communications Engineering, Networks Signal, Image and Speech Processing Pattern Recognition Artificial Intelligence Data Mining and Knowledge Discovery |
| ISBN | 3-030-22475-9 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Chapter1: A Systematic Review on Supervised & Unsupervised Machine Learning Algorithms for Data Science -- Chapter2: Overview of One-Pass and Discard-After-Learn Concepts for Classification and Clustering in Streaming Environment with Constraints -- Chapter3: Distributed Single-Source Shortest Path Algorithms with Two Dimensional Graph Layout -- Chapter4: Using Non-Negative Tensor Decomposition for Unsupervised Textual Influence Modeling -- Chapter5: Survival Support Vector Machines: A Simulation Study and Its Health-related Application -- Chapter6: Semantic Unsupervised Learning for Word Sense Disambiguation -- Chapter7: Enhanced Tweet Hybrid Recommender System using Unsupervised Topic Modeling and Matrix Factorization based Neural Network -- Chapter8: New Applications of a Supervised Computational Intelligence (CI) Approach: Case Study in Civil Engineering. |
| Record Nr. | UNINA-9910366591103321 |
| Cham : , : Springer International Publishing : , : Imprint : Springer, , 2020 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||