Enhancing predictive maintenance in automotive industry: addressing class imbalance using advanced machine learning techniques by Yashashree Mahale, Shrikrishna Kolhar, Anjali S. More

“…The on-board diagnostic dataset utilized has only 16.3% of the failure data, and to address this, 3 key approaches were explored: [i] synthetic minority oversampling technique (SMOTE), [ii] cost-sensitive learning, [iii] ensemble methods. …”
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Artificial intelligence in electroencephalography analysis for epilepsy diagnosis and management by Chenxi Wang, Chenxi Wang, Xinyue Yuan, Wei Jing

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Joint Lagrangian Relaxation and Dynamic Programming Approach for a Multi-Period Multi-Product Location-Inventory Problem by Claudio Araya-Sassi, German Paredes-Belmar, Gabriel Gutierrez-Jarpa, Ricardo Gatica Escobar

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Construction of Analogy Indicator System and Machine-Learning-Based Optimization of Analogy Methods for Oilfield Development Projects by Muzhen Zhang, Zhanxiang Lei, Chengyun Yan, Baoquan Zeng, Fei Huang, Tailai Qu, Bin Wang, Li Fu

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Dynamic Evacuation Shelter Allocation in Response to Human Mobility: A Case Study of Taipei City by Chang-Hung Shih, Cheng-Yun Wu, Shu-Ping Tseng, Yi-Lin Huang, Rong-Pu Jhuang, Yi-Chung Chen, Tien-Yi Yang, Wei-Ting Chen

“…Dynamic updates on the shelter capacities make it possible for citizens to make informed decisions during air raid emergencies.…”
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Artificial intelligence technology in ophthalmology public health: current applications and future directions by ShuYuan Chen, Wen Bai, Wen Bai

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Robotic irrigation complex for crop cultivation in irrigated areas by M. G. Zagoruiko, D.  A. Soloviev, N. F. Ryzhko, S. N. Ryzhko

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Interchangeability of hunts as a factor of sustainability of hunting use by S. P. Matveytchuk, I. A. Grebnev

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CONTROL RESEARCH AND AUTOMATION OF STOCHASTIC DEVIATIONS IN ORGANISATIONAL MANAGEMENT PRODUCTION PROCESS SYSTEMS by R. B. Aghajanyan, D. O. Baizhanova, M. V. Markosyan

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Using a Camera System for the In-Situ Assessment of Cordon Dieback due to Grapevine Trunk Diseases by Julie Tang, Olivia Yem, Finn Russell, Cameron A. Stewart, Kangying Lin, Hiranya Jayakody, Matthew R. Ayres, Mark R. Sosnowski, Mark Whitty, Paul R. Petrie

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Predicting the Remaining Useful Life of an Aircraft Engine Using a Stacked Sparse Autoencoder with Multilayer Self-Learning by Jian Ma, Hua Su, Wan-lin Zhao, Bin Liu

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Small-Signal Stability Analysis and Parameters Optimization of Virtual Synchronous Generator for Low-Inertia Power System by Alaa Altawallbeh, Abdulrahman Alassi, Nader Meskin, Mohammed A. Al-Hitmi, Ahmed M. Massoud

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The Evolution of Service Ecosystems Based on the Lotka–Volterra Model by Binbin Shi, Yu Li, Tingting Liang, Xixi Sun, Liquan Cui, Haonan Zhang, Yuyu Yin

“…In addition, an agent-based computational experiment is designed to integrate adversarial games for decision-making and genetic algorithms for service evolution. …”
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Modeling and validation of wearable sensor-based gait parameters in Parkinson’s disease patients with cognitive impairment by Guo Hong, Guo Hong, Fengju Mao, Fengju Mao, Mingming Zhang, Fei Zhang, Fei Zhang, Xiangcheng Wang, Kang Ren, Kang Ren, Zhonglue Chen, Zhonglue Chen, Xiaoguang Luo, Xiaoguang Luo

“…The logistic regression model demonstrated superior predictive performance (test set AUC: 0.957), outperforming other machine learning algorithms. SHAP analysis revealed that Step Length, UPDRS-III score, Duration of PD, and Peak angular velocity during steering were the most influential predictors in the logistic regression model. …”
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Interactive Mitigation of Biases in Machine Learning Models for Undergraduate Student Admissions by Kelly Van Busum, Shiaofen Fang

“…Because these issues are intrinsically subjective and context-dependent, creating trustworthy software requires human input and feedback. (1) Introduction: This work introduces an interactive method for mitigating the bias introduced by machine learning models by allowing the user to adjust bias and fairness metrics iteratively to make the model more fair in the context of undergraduate student admissions. (2) Related Work: The social implications of bias in AI systems used in education are nuanced and can affect university reputation and student retention rates motivating a need for the development of fair AI systems. (3) Methods and Dataset: Admissions data over six years from a large urban research university was used to create AI models to predict admissions decisions. …”
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Sensor-Generated In Situ Data Management for Smart Grids: Dynamic Optimization Driven by Double Deep Q-Network with Prioritized Experience Replay by Peiying Zhang, Siyi Li, Dandan Li, Qingyang Ding, Lei Shi

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Machine learning combined with multi-omics to identify immune-related LncRNA signature as biomarkers for predicting breast cancer prognosis by Yuxing Liu, Jintao Chen, Daifeng Yang, Chenming Liu, Chunhui Tang, Shanshan Cai, Yingxuan Huang

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Leveraging machine learning for data-driven building energy rate prediction by Nasim Eslamirad, Mehdi Golamnia, Payam Sajadi, Francesco Pilla

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Urban tourism management based on artificial neural networks analysis and data mining by Xin Cui

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CHANGING STATUS OF GLOBAL COVID-19 OUTBREAK IN THE WORLD AND IN TURKEY AND CLUSTERING ANALYSIS by Elif Kartal, Mehmet Erdal Balaban, Bülent Bayraktar

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