Case Studies in Thermal Engineering, 2021 Photovoltaic (PV) hot-spots is a reliability problem in PV modules, where a cell or group of cells heats up significantly, dissipating rather than producing power, and resulting in a loss and further degradation for the PV modules’ performance. Therefore,
Mahmoud Dhimish
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Efficient Optimization of Performance Measures by Classifier Adaptation [PDF]
, 2012 In practical applications, machine learning algorithms are often needed to learn classifiers that optimize domain specific performance measures. Previously, the research has focused on learning the needed classifier in isolation, yet learning nonlinear ...
Li, Nan, Tsang, Ivor W., Zhou, Zhi-Hua
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Adversarial machine learning phases of matter
Quantum Frontiers, 2023 We study the robustness of machine learning approaches to adversarial perturbations, with a focus on supervised learning scenarios. We find that typical phase classifiers based on deep neural networks are extremely vulnerable to adversarial perturbations:
Si Jiang, Sirui Lu, Dong-Ling Deng
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Physical Activity Monitoring and Classification Using Machine Learning Techniques
Life, 2022 Physical activity plays an important role in controlling obesity and maintaining healthy living. It becomes increasingly important during a pandemic due to restrictions on outdoor activities.
Saeed Ali Alsareii +6 more
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Comparison of Machine Learning Classifiers for Reducing Fitness Evaluations of Structural Optimization [PDF]
Journal of Soft Computing in Civil Engineering, 2021 Metaheuristic algorithms have been widely used to solve structural optimization problems. Despite their powerful search capabilities, these algorithms often require a large number of fitness evaluations.
Tran-Hieu Nguyen, Anh-Tuan Vu
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Probability-driven scoring functions in combining linear classifiers [PDF]
Journal of Universal Computer Science, 2022 Although linear classifiers are one of the oldest methods in machine learning, they are still very popular in the machine learning community. This is due to their low computational complexity and robustness to overfitting.
Pawel Trajdos, Robert Burduk
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Machine Learning Diagnosis of Dengue Fever: A Cost-Effective Approach for Early Detection and Treatment [PDF]
Ovidius University Annals: Economic Sciences Series, 2023 This research aims to explore the potential of machine learning algorithms for diagnosing of dengue fever and assess their cost-effectiveness compared to conventional methods.
Hamzat Salami +2 more
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Detection of Dispersed Radio Pulses: A machine learning approach to candidate identification and classification [PDF]
, 2016 Searching for extraterrestrial, transient signals in astronomical data sets is an active area of current research. However, machine learning techniques are lacking in the literature concerning single-pulse detection.
Devine, Thomas +2 more
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LinRegDroid: Detection of Android Malware Using Multiple Linear Regression Models-Based Classifiers
IEEE Access, 2022 In this study, a framework for Android malware detection based on permissions is presented. This framework uses multiple linear regression methods. Application permissions, which are one of the most critical building blocks in the security of the Android
Durmus Ozkan Sahin +2 more
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Classifying topological sector via machine learning [PDF]
Proceedings of 37th International Symposium on Lattice Field Theory — PoS(LATTICE2019), 2020 7 pages, 4 figures, 4 tables, talk presented at the 37th International Symposium on Lattice Field Theory - Lattice 2019, 16-22 June 2019, Wuhan ...
Kitazawa, Masakiyo +2 more
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