| An in-Depth Analysis of the Software Features' Impact on the Performance of Deep Learning-Based Software Defect Predictors | 0 | 0.34 | 2022 |
| IntelliDaM: A Machine Learning-Based Framework for Enhancing the Performance of Decision-Making Processes. A Case Study for Educational Data Mining | 0 | 0.34 | 2022 |
| IntelliSwAS: Optimizing deep neural network architectures using a particle swarm-based approach | 0 | 0.34 | 2022 |
| NeXtNow: A Convolutional Deep Learning Model for the Prediction of Weather Radar Data for Nowcasting Purposes | 0 | 0.34 | 2022 |
| DAuGAN: An Approach for Augmenting Time Series Imbalanced Datasets via Latent Space Sampling Using Adversarial Techniques | 0 | 0.34 | 2021 |
| Enhancing the Performance of Image Classification Through Features Automatically Learned from Depth-Maps. | 0 | 0.34 | 2021 |
| AnomalP: An approach for detecting anomalous protein conformations using deep autoencoders | 0 | 0.34 | 2021 |
| Unsupervised learning based mining of academic data sets for students’ performance analysis | 0 | 0.34 | 2020 |
| A comparative study on using unsupervised learning based data analysis techniques for breast cancer detection | 0 | 0.34 | 2020 |
| COMET - A conceptual coupling based metrics suite for software defect prediction. | 0 | 0.34 | 2020 |
| Analysing protein dynamics using machine learning based generative models | 0 | 0.34 | 2020 |
| XNow: A deep learning technique for nowcasting based on radar products’ values prediction | 0 | 0.34 | 2020 |
| RadRAR - A relational association rule mining approach for nowcasting based on predicting radar products' values. | 0 | 0.34 | 2020 |
| CVSimP: An approach for predicting proteins’ structural similarity using one-shot learning | 0 | 0.34 | 2020 |
| Using clustering models for uncovering proteins’ structural similarity | 1 | 0.37 | 2019 |
| Software Defect Prediction Using a Hybrid Model Based on Semantic Features Learned from the Source Code | 0 | 0.34 | 2019 |
| A Study on Applying Relational Association Rule Mining Based Classification for Predicting the Academic Performance of Students | 0 | 0.34 | 2019 |
| DynGRAR - A dynamic approach to mining gradual relational association rules. | 0 | 0.34 | 2019 |
| Analyzing Meteorological Data Using Unsupervised Learning Techniques | 0 | 0.34 | 2019 |
| S PRAR - A novel relational association rule mining classification model applied for academic performance prediction. | 0 | 0.34 | 2019 |
| DynFloR - A Flow Approach for Data Delivery Optimization in Multi-Robot Network Patrolling. | 0 | 0.34 | 2019 |
| A novel concurrent relational association rule mining approach. | 1 | 0.34 | 2019 |
| AutoSimP: An Approach for Predicting Proteins’ Structural Similarities Using an Ensemble of Deep Autoencoders | 1 | 0.37 | 2019 |
| Using self-organizing maps for unsupervised analysis of radar data for nowcasting purposes. | 1 | 0.43 | 2019 |
| An aggregated coupling measure for the analysis of object-oriented software systems. | 0 | 0.34 | 2019 |
| ProteinA: An Approach for Analyzing and Visualizing Protein Conformational Transitions Using Fuzzy and Hard Clustering Techniques | 1 | 0.36 | 2019 |
| Deep Autoencoders For Additional Insight Into Protein Dynamics | 1 | 0.39 | 2018 |
| Using unsupervised learning methods for enhancing protein structure insight. | 1 | 0.39 | 2018 |
| A novel approach for software defect prediction through hybridizing gradual relational association rules with artificial neural networks. | 6 | 0.42 | 2018 |
| A new incremental relational association rules mining approach. | 0 | 0.34 | 2018 |
| An effective approach for determining the class integration test order using reinforcement learning. | 1 | 0.37 | 2018 |
| Analyzing the Impact of Protein Representation on Mining Structural Patterns from Protein Data | 0 | 0.34 | 2018 |
| An Unsupervised Learning Based Conceptual Coupling Measure | 0 | 0.34 | 2017 |
| Identifying Class Integration Test Order Using an Improved Genetic Algorithm-Based Approach. | 0 | 0.34 | 2017 |
| An Improved Approach for Class Test Ordering Optimization using Genetic Algorithms. | 0 | 0.34 | 2017 |
| A Hierarchical Clustering-Based Approach for Software Restructuring at the Package Level | 1 | 0.34 | 2017 |
| A Reinforcement Learning Based Approach to Multiple Sequence Alignment | 0 | 0.34 | 2016 |
| A Novel Approach for Software Defect Prediction Using Fuzzy Decision Trees | 1 | 0.35 | 2016 |
| Machine Learning Based Approaches for Sex Identification in Bioarchaeology | 0 | 0.34 | 2016 |
| Supervised Learning Techniques for Body Mass Estimation in Bioarchaeology | 0 | 0.34 | 2016 |
| A novel approach to adaptive relational association rule mining | 3 | 0.39 | 2015 |
| Detecting software design defects using relational association rule mining | 11 | 0.51 | 2015 |
| Software defect prediction using relational association rule mining | 28 | 0.79 | 2014 |
| A support vector machine model for intelligent selection of data representations | 1 | 0.35 | 2014 |
| Software systems performance improvement by intelligent data structures customization. | 0 | 0.34 | 2014 |
| Text Segmentation Using Roget-Based Weighted Lexical Chains. | 0 | 0.34 | 2013 |
| Intelligent data structures selection using neural networks. | 9 | 0.56 | 2013 |
| Soft Computing Approaches on the Bandwidth Problem | 3 | 0.38 | 2013 |
| Evaluation Measures For Partitioning Based Aspect Mining Techniques | 0 | 0.34 | 2011 |
| A Reinforcement Learning Approach for Solving the Fragment Assembly Problem | 4 | 0.42 | 2011 |
