Physics Informed Neural Networks-Based AC Optimal Power Flow Under High RES Penetration by Fahad Saleh Al-Ismail

“…This paper proposes a physics-informed neural network (PINN) model that performs complex power system tasks, such as optimal power flow, in less computation time and with higher accuracy, using less data to support high RES penetration. …”
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PROGRAM COMPLEX FOR MODELING OF THE DETAILS HARDENING PROCESS by S. P. Kundas, B. A. Tonconogov, Ja. S. Levashkevich, A. V. Lemzikov, I. A. Gishkeluk, P. S. Garchenko, I. S. Gauhshtein

“…In the article there is presented the program complex ThermoSim, consisting of preprocessor, processor and postprocessor and intended for modeling (analysis) of thermalphysic processes and characteristics of details of instrument-making and machine-building, diagnostics and optimization of technological processes of heat treatment and details constructions without using the destructive control methods.…”
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Global burden of non-melanoma skin cancers among older adults: a comprehensive analysis using machine learning approaches by Yumeng Pan, Bo Tang, Yingwu Guo, Yuzhou Cai, Yu-Ye Li

“…To forecast future disease burden from 2022 to 2050, eight machine learning models (Prophet, ARIMA, TBATS, ElasticNet, ETS, VAR, HoltWinters, and Theta) were applied, and the most optimal predictive models for BCC and SCC were selected based on performance metrics. …”
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Exploring fecal microbiota signatures associated with immune response and antibiotic impact in NSCLC: insights from metagenomic and machine learning approaches by Wenjie Han, Wenjie Han, Yuhang Zhou, Yuhang Zhou, Yiwen Wang, Yiwen Wang, Xiaolin Liu, Tao Sun, Tao Sun, Junnan Xu, Junnan Xu, Junnan Xu

“…We performed taxonomic and functional characterization of gut microbiota (GM) differentiating responders from non-responders, delineated microbiome dynamics during treatment, and assessed the impact of antibiotics on key microbial taxa. Among eight machine learning algorithms evaluated, the optimal model was selected to construct a predictive framework for immunotherapy response.ResultsMicrobial α-diversity was significantly elevated in responders compared to non-responders, with antibiotic administration further amplifying this difference—most notably at the species level. …”
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Urban Recreational Space Heat-Emotion Distribution and Matching Pattern Based on Machine Learning: A Case Study of Guangzhou City by Wang Fuyuan, Zhang Zhiyu, Xie Yuanjing, Yang Xinyi, Sun Miao

“…Using Guangzhou as a case study, this study harnesses big data sourced from Ma Feng Wo and Ctrip. Based on machine learning techniques, it discerns recreational emotions and synergizes Geographic Information System (GIS) spatial analysis with heat-emotion matching analysis. …”
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Identification of dominant instability modes in power systems based on spatial‐temporal feature mining and TSOA optimization by Miao Yu, Jianqun Sun, Shuoshuo Tian, Shouzhi Zhang, Jingjing Wei, Yixiao Wu

“…Finally, the transit search optimization algorithm optimizes the global model parameters, further increasing the accuracy of the model. …”
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UAV-assisted MEC offloading strategy with peak AOI boundary optimization: A method based on DDQN by Zhixiong Chen, Jiawei Yang, Zhenyu Zhou

“…Finally, an unmanned aerial vehicle-assisted multi-level offloading model with cache is designed, in which the peak AoI violation probability and energy consumption provide the optimization goals. …”
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RETRACTED ARTICLE: Multiclass skin lesion classification using deep learning networks optimal information fusion by Muhammad Attique Khan, Ameer Hamza, Mohammad Shabaz, Seifeine Kadry, Saddaf Rubab, Muhammad Abdullah Bilal, Muhammad Naeem Akbar, Suresh Manic Kesavan

“…This paper proposes a computerized method for multiclass lesion classification using a fusion of optimal deep-learning model features. The dataset used in this work, ISIC2018, is imbalanced; therefore, augmentation is performed based on a few mathematical operations. …”
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Ambulance route optimization in a mobile ambulance dispatch system using deep neural network (DNN) by C. Selvan, Basha H. Anwar, Soumyalatha Naveen, Shaik Thasleem Bhanu

“…For real-time route optimization, a convolutional neural network (CNN)-based deep learning model is used to adjust ambulance routes based on current traffic and road conditions, achieving an accuracy of 99.15%. …”
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A Deep Learning-Based Framework for Bearing RUL Prediction to Optimize Laser Shock Peening Remanufacturing by Yuchen Liang, Yuqi Wang, Anping Li, Chengyi Gu, Jie Tang, Xianjuan Pang

“…This study introduces a novel methodology integrating advanced machine learning and optimization techniques to address this challenge. (1) A transformer-attention model was developed to process segmented vibration signals, effectively capturing complex patterns. …”
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Enhancing context awareness in augmented reality environments: a review of opportunities and challenges in object and action recognition through machine learning by Iolanda L. Chamusca, Tiago P. Pagano, Rafael B. Loureiro, Erick G. S. Nascimento, Alex Á. B. Santos, Yiyu Cai, Thiago B. Murari, Ingrid Winkler

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Topological Sequences Connected With Inverse Graphs of Finite Flexible Weak Inverse Property Quasigroups: An Approach From Polynomials to Machine Learning by Faizah D. Alanazi

“…Moreover, they empower models to extrapolate complex real-world data, facilitating tasks like regression, classification, optimized performance, and feature adaptation. …”
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Machine learning-based prediction of preterm birth risk using methylation changes in neonatal cord blood CpG sites by Yuxin Feng, Ying Ni, Wenkai Wang, Fen Guo, Liyu Wang, Fan Zhu, Luyao Zhang, Ying Feng

“…Objective This study aimed to identify differential CpG sites in cord blood and develop predictive machine learning models based on these methylation changes to assess preterm birth risk. …”
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Differentiation between multiple sclerosis and neuromyelitis optic spectrum disorders with multilevel fMRI features: A machine learning analysis by Xiao Liang, Qingwen Zeng, Yanyan Zhu, Yao Wang, Ting He, Lin Wu, Muhua Huang, Fuqing Zhou

“…Notably, similar trends were observed across different brain templates, with models based on RSFC, ALFF, and ReHo yielding optimal performance. …”
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Developing an Equitable Machine Learning–Based Music Intervention for Older Adults At Risk for Alzheimer Disease: Protocol for Algorithm Development and Validation by Chelsea S Brown, Luna Dziewietin, Virginia Partridge, Jennifer Rae Myers

“…Aim 2 is to develop knowledge embedding–based machine learning (ML) models that use music metadata and survey response data to identify optimal therapeutic music components for enhancing engagement and emotional resonance for depression among rural-residing older adults at risk for AD. …”
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Classifying metro drivers’ cognitive distractions during manual operations using machine learning and random forest-recursive feature elimination by Haiyue Liu, Yue Zhou, Chaozhe Jiang

“…Results show that the DT with only one HR-HRV feature extracted from 30-s time-windows and XGBoost with 20 h-HRV features extracted from 60-s time-windows are optimal models for binary and multi-class classification for distractions during driving phase, respectively. …”
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Integrated Technologies for Smart Building Energy Systems Refurbishment: A Case Study in Italy by Lorenzo Villani, Martina Casciola, Davide Astiaso Garcia

“…This study presents an integrated approach for adapting building energy systems using Machine Learning (ML), the Internet of Things (IoT), and Building Information Modeling (BIM) in a hotel retrofit in Italy. …”
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Towards Economic Sustainability: A Comprehensive Review of Artificial Intelligence and Machine Learning Techniques in Improving the Accuracy of Stock Market Movements by Atoosa Rezaei, Iheb Abdellatif, Amjad Umar

“…This study examines the incorporation of artificial intelligence (AI) and machine learning (ML) technologies to address gaps in identifying predictive factors, integrating diverse data sources, and optimizing methodologies. …”
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A Similarity-Based Approach for Diagnosing the Aging of Lithium-Ion Batteries in Second Life Combining Time Series and Machine Learning by Daniela Galatro, Cristina H. Amon

“…Modelling aging in the second life of lithium-ion batteries (LiBs) is challenging due to the complexity of degradation mechanisms that lead to capacity loss and internal resistance increase, as well as uncertainty and variability in the operational and environmental conditions to which the batteries are exposed. …”
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Exploring genomic feature selection: A comparative analysis of GWAS and machine learning algorithms in a large‐scale soybean dataset by Hawlader A. Al‐Mamun, Monica F. Danilevicz, Jacob I. Marsh, Cedric Gondro, David Edwards

“…Utilizing the expansive soybean dataset, we assessed the performance of these methodologies in selecting features that optimize predictive modeling for various phenotypes. …”
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