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14th International Conference on Information, Intelligence, Systems and Applications, IISA 2023

作者： Vasileiou, Maria Papageorgiou, George Tjortjis, Christos School of Science and Technology International Hellenic University The Data Mining and Analytics Research Group Thessaloniki Greece

ISBN: (纸本)9798350318067

This paper examines an efficient Machine Learning (ML) strategy for software defect detection, focusing on identifying defective and non-defective software files and investigating the possibility of using exclusively structural code metrics. The real-life dataset, from the source code of twenty-six N otepad++ releases generated with the LocMetrics tool, was used to classify files as defective or non-defective. Seven ML models, namely Decision Tree, Support Vector Machine, Logistic Regression, Naive Bayes, k-Nearest Neighbors, Random Forest, and Extra Trees, were applied and benchmarked using accuracy, precision, recall, F1-score and their macro/weighted metrics. All models were fine-tuned with Randomized Grid Search, trained using to-fold Cross Validation, and evaluated in two steps: test data evaluation and extra unseen data validation. Results revealed that the Extra Trees model outperformed others with an accuracy of 82.09% and retained its dominance in validation and evaluation procedures. Lastly, the most important predictors were the type of source codes (presence of C++), BLOC, and SLOC-L, illustrating that the programming language, the clarity, and readability of the code (denoted by the number of blank lines), and the logical source lines of code play crucial roles in determining the likelihood of software defects. © 2023 IEEE.

关键词： Decision trees

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DepBoost-TransNet: Boosted Transformer Network for Depression Classification 2nd

DepBoost-TransNet: Boosted Transformer Network for Depressi...

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2nd International Conference on Speech and Language Technologies for Low-Resource Languages, SPELLL 2023

作者： Anil Rahood, Pratik Kumaresan, Prasanna Kumar Chakravarthi, Bharathi Raja School of Computer Science University of Galway Galway Ireland Insight SFI Research Centre for Data Analytics University of Galway Galway Ireland

ISBN: (纸本)9783031584947

Depression presents a significant global mental health challenge affecting countless individuals across the globe. Early detection is of paramount importance, yet conventional methods, such as self-reporting and professional evaluations, possess inherent limitations and subjectivity. With the proliferation of social media and the abundance of user-generated content, novel approaches employing transformer-based models and text-mining techniques hold the potential to reveal signs of depression within social media text. This study delves into the efficacy of transformer-based models and natural language processing techniques in recognizing depressive symptoms within social media text. Social media serves as a distinct window into mental well-being, and this investigation explores the utilization of advanced transformers to tackle this issue. Results indicate that transformers trained on imbalanced datasets outperform those employing data augmentation techniques, like SMOTE, due to their proficiency in discerning intricate patterns and sensitivity to synthetic data. Furthermore, the ensemble model, which amalgamates predictions from multiple transformer models, surpasses individual models, underscoring the potential of harnessing the diverse strengths of these models for enhanced depression classification. Despite certain limitations and privacy considerations, leveraging social media data for early depression detection shows promise in improving the well-being of affected individuals through timely intervention and treatment. Subsequent research endeavors should prioritize hyperparameter optimization and the acquisition of more balanced datasets to further enhance the accuracy and dependability of depression diagnosis via the analysis of social media text. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Machine learning

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To Train, or Not to Train: Exploring Foundation Models for TBAD Segmentation 26

To Train, or Not to Train: Exploring Foundation Models for T...

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26th Irish Machine Vision and Image Processing Conference, IMVIP 2024

作者： Abaid, Ayman Ullah, Ihsan School of Computer Science University of Galway Galway Ireland Insight SFI Research Centre for Data Analytics University of Galway Galway Ireland

ISBN: (纸本)9781837242672

Recent advancements in biomedical image analysis have been significantly influenced by vision foundation models (FMs) originally designed for general computer vision tasks. These models, such as the Segment Anything Model (SAM) have quickly transitioned into applications in medical image processing. This study evaluates three cutting-edge segmentation FMs using Type B Aortic Dissection (TBAD) CTA images. Our experiments critically assess their strengths and weaknesses compared to traditional methods, emphasizing their potential to enhance efficiency in medical image analysis. However, our findings also indicate that these FMs have not surpassed traditional segmentation models in terms of performance, suggesting limitations in their adaptability for medical image segmentation tasks straight out of the box. © This is an open access article published by the IET under the Creative Commons Attribution License (http://***/licenses/by/3.0/)

关键词： Dissection

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Multilayer Network Analysis of Brain Signals for Detecting Alzheimer’s Disease 12th

Multilayer Network Analysis of Brain Signals for Detecting...

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12th International Conference on Computational Advances in Bio and Medical sciences, ICCABS 2023

作者： Nguyen, Sean M. Basiri, Mohammad Amin Khanmohammadi, Sina School of Computer Science University of Oklahoma NormanOK73019 United States Data Science and Analytics Institute University of Oklahoma NormanOK73019 United States

ISBN: (纸本)9783031827679

Human neuroimaging datasets provide rich multi-scale spatiotemporal information about the state of the brain. Most current methods, such as spectral analysis, focus on a single facet of these datasets and do not take full advantage of the inherent spatiotemporal information. Here, we consider a multilayer cross-frequency functional connectivity analysis to capture the complex spatiotemporal features of neural datasets at multiple scales and show that such features could potentially provide a better description of the neural activity. We demonstrate the effectiveness of this approach by applying the proposed method to capture disruptions of cross-frequency brain connections in Alzheimer’s patients. More specifically, we compared the multi-scale features extracted from electroencephalogram (EEG) data with traditional features in a machine learning framework to distinguish Alzheimer’s patients from control subjects. Our results show that such multi-scale features improve the prediction accuracy when compared to traditional feature extraction methods in EEG analysis. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Multilayer neural networks

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Investigating the Impact of Encoder Architectures and Batch Size on Depth Estimation through Semantic Consistency 26

Investigating the Impact of Encoder Architectures and Batch ...

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26th Irish Machine Vision and Image Processing Conference, IMVIP 2024

作者： Nosheen, Iqra Iqbal, Talha Ullah, Ihsan Ennis, Cathy Madden, Michael G. School of Computer Science University of Galway Ireland Insight SFI Research Centre for Data Analytics University of Galway Ireland School of Computer Science Technological University Dublin Ireland

ISBN: (纸本)9781837242672

Traditional methods for depth estimation rely on supervised learning with resource-intensive LiDAR data. Virtual synthetic datasets provide a cost-effective alternative, but bridging the domain gap between synthetic and real-world data remains a significant challenge. In existing work, this gap is addressed through domain adaptation techniques, aligning the feature distributions of synthetic (source) and real-world (target) domains. Our study explores the efficacy of different encoder architectures (ResNet variants with 35, 50, 101, 101-with-attention, and 152 convolution layers) and two batch sizes (2 and 4) for the depth estimation task. Our experiments show that ResNet101 without and with attention mechanisms provide the best performance across 2 and 4 batch sizes, respectively, compared to the other models. Conversely, the deeper architecture considered, ResNet152, shows the lowest performance, indicating that increasing the network depth does not necessarily lead to improved results for depth estimation tasks. This study's findings provide valuable insights for developing more effective depth estimation algorithms, and it suggests future directions in hyperparameter optimization and semantic consistency modeling. © This is an open access article published by the IET under the Creative Commons Attribution License (http://***/licenses/by/3.0/)

关键词： Semantics

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A Transmission and Transformation Fault Detection Algorithm Based on Improved YOLOv5

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Computers, Materials & Continua 2023年第9期76卷 2997-3011页

作者： Xinliang Tang Xiaotong Ru Jingfang Su Gabriel Adonis School of Information Science and Engineering Hebei University of Science and TechnologyShijiazhuang050000China Department of Computer Science and Information Systems Birkbeck Institute for Data AnalyticsLondonB100ABWC1E 7HXUK

On the transmission line,the invasion of foreign objects such as kites,plastic bags,and balloons and the damage to electronic components are common transmission line *** these faults is of great significance for the safe operation of power ***,a YOLOv5 target detection method based on a deep convolution neural network is *** this paper,Mobilenetv2 is used to replace Cross Stage Partial(CSP)-Darknet53 as the *** structure uses depth-wise separable convolution to reduce the amount of calculation and parameters;improve the detection *** the same time,to compensate for the detection accuracy,the Squeeze-and-Excitation Networks(SENet)attention model is fused into the algorithm framework and a new detection scale suitable for small targets is added to improve the significance of the fault target area in the *** pictures of foreign matters such as kites,plastic bags,balloons,and insulator defects of transmission lines,and sort theminto a data *** experimental results on datasets show that themean Accuracy Precision(mAP)and recall rate of the algorithm can reach 92.1%and 92.4%,*** the same time,by comparison,the detection accuracy of the proposed algorithm is higher than that of other methods.

关键词： Transmission line YOLOv5 multi-scale integration SENet

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Counterfactual Explanation Through Constraint Relaxation 36

Counterfactual Explanation Through Constraint Relaxation

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36th IEEE International Conference on Tools with Artificial Intelligence, ICTAI 2024

作者： Gupta, Sharmi Dev O'Sullivan, Barry Quesada, Luis Insight Centre for Data Analytics SFI Centre for Research Training in Artificial Intelligence School of Computer Science IT University College Cork Cork Ireland

ISBN: (纸本)9798331527235

Interactive constraint systems often suffer from infeasibility (no solution) due to conflicting user constraints. A common approach to recover feasibility is to eliminate the constraints that cause the conflicts in the system. This approach allows the system to provide an explanation as: 'if the user is willing to drop some of their constraints, there exists a solution'. However, this form of explanation might not be very informative. A counterfactual explanation is a type of explanation that can provide a basis for the user to recover feasibility by helping them understand what changes can be applied to their existing constraints rather than removing them. We propose an iterative method based on conflict detection and maximal relaxations in over-constrained constraint satisfaction problems to help compute a counterfactual explanation. We have evaluated our approach using well known instances that occur in industrial applications and demonstrated the relevance of multi-point relaxations. © 2024 IEEE.

关键词： Constraint satisfaction problems

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A Deep Learning Model for Heterogeneous dataset Analysis - Application to Winter Wheat Crop Yield Prediction 8th

A Deep Learning Model for Heterogeneous Dataset Analysis - A...

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8th International Conference on Information, Communication and Computing Technology, ICICCT 2023

作者： Bansal, Yogesh Lillis, David Kechadi, M.-Tahar School of Computer Science University College Dublin Dublin Ireland Insight Centre for Data Analytics Dublin Ireland

ISBN: (纸本)9783031438370

Western countries rely heavily on wheat, and yield prediction is crucial. Time-series deep learning models, such as Long Short Term Memory (LSTM), have already been explored and applied to yield prediction. Existing literature reports that they perform better than traditional Machine Learning (ML) models. However, the existing LSTM cannot handle heterogeneous datasets (a combination of data that varies and remains static with time). In this paper, we propose an efficient deep learning model that can deal with heterogeneous datasets. We developed the system architecture and applied it to the real-world dataset in the digital agriculture area. We showed that it outperformed the existing ML models. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Time series

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Conformal Prediction Techniques for Electricity Price Forecasting 9th

Conformal Prediction Techniques for Electricity Price Forec...

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9th International Workshop on Advanced analytics and Learning on Temporal data, AALTD 2024

作者： O’Connor, Ciaran Prestwich, Steven Visentin, Andrea SFI CRT in Artificial Intelligence School of Computer Science & IT University College Cork Cork Ireland Insight Centre for Data Analytics School of Computer Science & IT University College Cork Cork Ireland

ISBN: (纸本)9783031770654

Integrating the erratic production of renewable energy into the electricity grid poses numerous challenges. One approach to stabilising market prices and reducing energy losses due to curtailments is the deployment of batteries. Efficient electricity arbitrage is crucial to make investments in storage systems financially viable;trading solutions to achieve this rely on price forecasting techniques. This study delves into the application of Conformal Prediction (CP) techniques, including Ensemble Batch Prediction Intervals (EnbPI) and Sequential Predictive Conformal Inference for Time Series (SPCI), for generating probabilistic forecasts in the Irish electricity market. Recent advancements in CP have addressed temporal considerations inherent in time series forecasting, eliminating the need for exchangeability assumptions. Our study demonstrates that despite potential efficiency trade-offs, CP methods consistently yield precise and reliable prediction intervals, ensuring comprehensive coverage. We assess the impact of CP on the financial results of a simulated trading algorithm. Monetary outcomes achieved with EnbPI and SPCI outperform those of both split CP and traditional quantile regression models, highlighting the practical superiority of CP in electricity price forecasting. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Prediction models

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A Machine Learning Approach to Model Counting 36

A Machine Learning Approach to Model Counting

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36th IEEE International Conference on Tools with Artificial Intelligence, ICTAI 2024

作者： Dalla, Marco Visentin, Andrea O'Sullivan, Barry SFI Centre for Research Training in AI School of Computer Science & IT University College Cork Ireland Insight Centre for Data Analytics School of Computer Science & IT University College Cork Ireland

ISBN: (纸本)9798331527235

Model counting (#SAT) is the problem of computing the number of satisfying assignments for a given Boolean formula. It has a significant theoretical and practical interest. Tackling it can be challenging since the number of potential solution grows exponentially with the number of variables. Due to the inherent complexity of the problem, approaches to approximate model counting have been developed as a practical alternative. These methods extract the number of solutions within user-specified tolerance and confidence levels and in a fraction of the time required by exact model counters. However, even these methods require extensive computations, restricting their applicability to relatively small instances. In this paper, we propose a new approximate machine learning model counter that overcome this limitation. Predicting the number of solutions can be seen as a regression problem. We deploy an array of machine learning techniques trained to infer the approximate number of solutions based on statistical features extracted from a SAT propositional formula. Extensive numerical experiments performed on synthetic crafted and benchmark datasets show that learning approaches can provide a good approximation of the number of solutions with a much lower computational time and resource cost than the state-of-the-art approximate and exact model counters, making it possible to approximate the model count of instances previously out of reach. We then investigated the structural factors that lead to a high model count using AI explainability approaches. © 2024 IEEE.

关键词： Adversarial machine learning

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