| 1. |
- Kabir, Md Alamgir, et al.
(författare)
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Cross-Version Software Defect Prediction Considering Concept Drift and Chronological Splitting
- 2023
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Ingår i: Symmetry. - : Multidisciplinary Digital Publishing Institute (MDPI). - 2073-8994. ; 15:10
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Tidskriftsartikel (refereegranskat)abstract
- Concept drift (CD) refers to a phenomenon where the data distribution within datasets changes over time, and this can have adverse effects on the performance of prediction models in software engineering (SE), including those used for tasks like cost estimation and defect prediction. Detecting CD in SE datasets is difficult, but important, because it identifies the need for retraining prediction models and in turn improves their performance. If the concept drift is caused by symmetric changes in the data distribution, the model adaptation process might need to account for this symmetry to maintain accurate predictions. This paper explores the impact of CD within the context of cross-version defect prediction (CVDP), aiming to enhance the reliability of prediction performance and to make the data more symmetric. A concept drift detection (CDD) approach is further proposed to identify data distributions that change over software versions. The proposed CDD framework consists of three stages: (i) data pre-processing for CD detection; (ii) notification of CD by triggering one of the three flags (i.e., CD, warning, and control); and (iii) providing guidance on when to update an existing model. Several experiments on 30 versions of seven software projects reveal the value of the proposed CDD. Some of the key findings of the proposed work include: (i) An exponential increase in the error-rate across different software versions is associated with CD. (ii) A moving-window approach to train defect prediction models on chronologically ordered defect data results in better CD detection than using all historical data with a large effect size (Formula presented.).
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| 2. |
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| 3. |
- Schoenrock, A., et al.
(författare)
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Efficient prediction of human protein-protein interactions at a global scale
- 2014
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Ingår i: Bmc Bioinformatics. - : Springer Science and Business Media LLC. - 1471-2105. ; 15
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Tidskriftsartikel (refereegranskat)abstract
- Background: Our knowledge of global protein-protein interaction (PPI) networks in complex organisms such as humans is hindered by technical limitations of current methods. Results: On the basis of short co-occurring polypeptide regions, we developed a tool called MP-PIPE capable of predicting a global human PPI network within 3 months. With a recall of 23% at a precision of 82.1%, we predicted 172,132 putative PPIs. We demonstrate the usefulness of these predictions through a range of experiments. Conclusions: The speed and accuracy associated with MP-PIPE can make this a potential tool to study individual human PPI networks (from genomic sequences alone) for personalized medicine.
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| 4. |
- Kaushik, M. M., et al.
(författare)
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The Effects of Class Rebalancing Techniques on Ensemble Classifiers on Credit Card Fraud Detection : An Empirical Study
- 2023
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Ingår i: AIP Conference Proceedings. - : American Institute of Physics Inc.. - 9780735447332
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Konferensbidrag (refereegranskat)abstract
- Millions of dollars in financial fraud losses can be minimized, and in some cases, completely avoided by implementing the appropriate fraud prediction model. Combining a suitable rebalancing strategy with data mining techniques on a large dataset can enhance the prediction model for credit card fraud. The objective of this study is to investigate the impact of sampling techniques on ensemble classifiers for constructing credit card default prediction models. To decide which combination of rebalancing technique and ensemble classifier works best on skewed datasets for credit card fraud detection, in this paper, we investigate and assess the performance of no sampling, random under sampling, Tomek link removal, random oversampling, SMOTE, and a combination of SMOTE and Tomek link removal using ensemble classifiers including XGBoost, LightGBM, and Random Forest. For evaluating the best combination of rebalancing technique and ensemble classifier, we have used precision, recall, f1 score, mcc, PR-AUC curve and ROCAUC curve as evaluation metrics. Based on overall evaluation matrics Random Forest, XGBoost perform best when paired with Tomek link removal, and LightGBM performs best when paired with random oversampling. All evaluation metrics of our empirical study indicate that Tomek link removal with Random Forest works best among all the different combinations of rebalancing techniques and ensemble classifiers for predicting fraudulent credit card transactions.
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| 5. |
- Elahe, M. F., et al.
(författare)
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Factors Impacting Short-Term Load Forecasting of Charging Station to Electric Vehicle
- 2023
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Ingår i: Electronics. - : MDPI. - 2079-9292. ; 12:1
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Tidskriftsartikel (refereegranskat)abstract
- The rapid growth of electric vehicles (EVs) is likely to endanger the current power system. Forecasting the demand for charging stations is one of the critical issues while mitigating challenges caused by the increased penetration of EVs. Uncovering load-affecting features of the charging station can be beneficial for improving forecasting accuracy. Existing studies mostly forecast electricity demand of charging stations based on load profiling. It is difficult for public EV charging stations to obtain features for load profiling. This paper examines the power demand of two workplace charging stations to address the above-mentioned issue. Eight different types of load-affecting features are discussed in this study without compromising user privacy. We found that the workplace EV charging station exhibits opposite characteristics to the public EV charging station for some factors. Later, the features are used to design the forecasting model. The average accuracy improvement with these features is 42.73% in terms of RMSE. Moreover, the experiments found that summer days are more predictable than winter days. Finally, a state-of-the-art interpretable machine learning technique has been used to identify top contributing features. As the study is conducted on a publicly available dataset and analyzes the root cause of demand change, it can be used as baseline for future research.
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| 8. |
- Haque, Rehnuma, et al.
(författare)
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Wastewater surveillance of SARS-CoV-2 in Bangladesh : Opportunities and challenges
- 2022
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Ingår i: CURRENT OPINION IN ENVIRONMENTAL SCIENCE & HEALTH. - : Elsevier BV. - 2468-5844. ; 27, s. 100334-
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Tidskriftsartikel (refereegranskat)abstract
- The ongoing pandemic of the coronavirus disease 2019 (COVID-19) is a public health crisis of global concern. The progression of the COVID-19 pandemic has been monitored in the first place by testing symptomatic individuals for SARS-CoV-2 virus in the respiratory samples. Concurrently, wastewater carries feces, urine, and sputum that potentially contains SARS-CoV-2 intact virus or partially damaged viral genetic materials excreted by infected individuals. This brings significant opportunities for understanding the infection dynamics by environmental surveillance. It has advantages for the country, especially in densely populated areas where individual clinical testing is difficult. However, there are several challenges including: 1) establishing a sampling plan and schedule that is representative of the various catchment populations 2) development and validation of standardized protocols for the laboratory analysis 3) understanding hydraulic flows and virus transport in complex wastewater drainage systems and 4) collaborative efforts from government agencies, NGOs, public health units and academia.
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| 9. |
- Rehman, Atiq Ur, et al.
(författare)
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Salp Swarm Algorithm for Drift Compensation in E-nose
- 2023
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Ingår i: 2023 15th International Conference on Advanced Computational Intelligence, ICACI 2023. - : Institute of Electrical and Electronics Engineers Inc.. - 9798350321456
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Konferensbidrag (refereegranskat)abstract
- E-nose technology relies on the proper functioning of sensors to identify and discriminate between different chemicals and odors. The long-term reliability of e-nose technology is hindered by the phenomenon of sensor drift. The effect of sensor drift is seen as a random and unpredictable shift in the data domain. This random shift in data deteriorates the performance of machine learning algorithms used in e-nose technology. Swarm intelligence based optimization has been successfully applied in different domains to deal with NP-hard optimization problems. In this paper, a swarm intelligence-based metaheuristic is proposed to deal with the sensors drift issue in e-nose technology. The proposed framework is validated using a benchmark dataset of sensor drift, and a significant improvement is observed in terms of the classification accuracy of different industrial gases. The proposed framework has the following benefits over conventional approaches: (i) there is no need for sensor re-calibration; (ii) there is no need for sensor replacement; (iii) there is no need for target domain data; and (iv) there is no need for domain transformation. Instead, the proposed work relies only on the source domain data and optimizes the feature space to deal with sensor drift. This makes the proposed framework more suitable for real applications of E-nose technology.
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