Solar panel fault diagnosis based on the intelligentrecursive method by Saadat Boulanouar, Fengal Boualem

“…These faults are diagnosed and located by implementing an algorithm based on the measured values ​​of the solar panel using an intelligent recursive least squares approach. …”
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Rail surface identification and adhesion control based on dynamic adhesion characteristics by Wen Liu, Hongfeng Qi, Jingchun Huang, Yiyuan Chen, Sheng He, Haoxiang Feng

“…Subsequently, the trained model is integrated into the vehicle simulation model for real-time identification of current rail surface conditions, and the output value from this module is fed into the subsequent optimal adhesion search algorithm. …”
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Estimation of High Spatial Resolution CO₂ Concentration in China from 2010 to 2022 Based on Multi-Source Carbon Satellite Data by Shanzhao Cai, Heng Dong, Bo Zhang, Huan Huang

“…To address this, this study effectively integrates XCO₂ data retrieved from the GOSAT and OCO-2 satellites using atmospheric profile adjustment and spatial grid integration techniques. …”
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PM2.5 concentration 7-day prediction in the Beijing–Tianjin–Hebei region using a novel stacking framework by Xintong Gao, Xiaohong Wang, Fuping Li, Wenhao Jiang, Meng Zhe, Jiaxing Sun, Ao Zhang, Linlin Jiao

“…The findings of this study demonstrated that the integration of the LSTM-RF model with the fusion-based Stacking algorithm led to a substantial enhancement in the accuracy of PM2.5 predictions. …”
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Applications of artificial intelligence in neurosurgical education: a scoping review by Hector Julio Piñera-Castro, Christian Borges-García

“…This evolution promises to elevate training quality while preserving the indispensable value of hands-on experience in neurosurgical practice.…”
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Path planning method for maritime dynamic target search based on improved GBNN by Zhaozhen Jiang, Xuehai Sun, Wenlon Wang, Shuzeng Zhou, Qiang Li, Lianglong Da

“…Moreover, the update rule of the activity value of the neural network is reconfigured. The influence of the peak of the dynamic target distribution probability on the activity value of the neuron is regarded as the external incentive element. …”
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Machine learning prediction of metabolic dysfunction-associated fatty liver disease risk in American adults using body composition: explainable analysis based on SHapley Additive e... by Yan Hong, Xinrong Chen, Ling Wang, Fan Zhang, ZiYing Zeng, Weining Xie

“…VAT had the highest SHAP value, underscoring its central role in MAFLD pathogenesis.ConclusionThis study demonstrates the effectiveness of integrating body composition features with machine learning techniques for MAFLD risk prediction. …”
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Deep Learning-Based Sound Source Localization: A Review by Kunbo Xu, Zekai Zong, Dongjun Liu, Ran Wang, Liang Yu

“…This work provides both theoretical foundations and technical references for algorithm selection and practical implementation in complex real-world scenarios.…”
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Multi-modal denoised data-driven milling chatter detection using an optimized hybrid neural network architecture by Haining Gao, Haoyu Wang, Hongdan Shen, Shule Xing, Yong Yang, Yinlin Wang, Wenfu Liu, Lei Yu, Mazhar Ali, Imran Ali Khan

“…A data denoising model combining Complementary Ensemble Empirical Mode Decomposition (CEEMD) and Singular Value Decomposition (SVD) is established. The Ivy algorithm is employed to optimize the hyperparameters of CEEMD-SVD. …”
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Application of random forest in big data completion by Zheng WANG, Hua REN, Yanping FANG

“…Relying on the country's existing log retention system, template library design data integrity, authentication could not meet the quality requirements of the data, using the random forest algorithm, the same data with or related data was found, data was completed and data quality was improved, and the template library was extended by optimization of feedback. …”
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Beyond personas. Machine Learning to personalise the project by Niccolò Casiddu, Francesco Burlando, Isabella Nevoso, Claudia Porfirione, Annapaola Vacanti

“…These archetypal models of ‘human’ are questioned in favour of a data processing approach, that better responds to the needs of the projects contextualised in our hyperconnected society. The core value of this approach is the ability to adapt, based on algorithms capable of matching the product to the activity of each user. …”
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Hybrid Metaheuristic Model for Optimal Economic Load Dispatch in Renewable Hybrid Energy System by D. Blandina Miracle, R. K. Viral, P. M. Tiwari, Mohit Bansal

“…In particular, the cost function of POCSO is 6.25%, 6%, 11.7%, 36%, 27%, and 46.42% better than the cost function of whale optimization algorithm (WOA), elephant herd optimization (EHO), moth-flame optimization (MFO), dragonfly algorithm (DA), sealion optimization (SLnO), CSO, and PSO methods, respectively. …”
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NETosis-based prognostic model reveals immune modulation in clear cell renal cell carcinoma using single-cell and bulk RNA sequencing by Zijie Yu, Zihao Xu, Xi Zhang, Wenchuan Shao, Da Zhong, Xinghan Yan, Tingfei Jiang, Yichun Wang, Ninghong Song

“…We developed a prognostic framework for netosis using univariate, Lasso, and multivariate Cox regression analyses. The CIBERSORT algorithm was employed to compute immune infiltration metrics for The Cancer Genome Atlas (TCGA) dataset. …”
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Machine learning-assisted analysis of serum metabolomics and network pharmacology reveals the effective compound from herbal formula against alcoholic liver injury by Jiamu Ma, Peng Wei, Xiao Xu, Ruijuan Dong, Xixi Deng, Feng Zhang, Mengyu Sun, Mingxia Li, Wei Liu, Jianling Yao, Yu Cao, Letian Ying, Yuqing Yang, Yongqi Yang, Xiaopeng Wu, Gaimei She

“…Conclusions Our findings demonstrate the value of a machine learning-assisted approach in identifying the key compound, target and pathway that underpin the efficacy of an herbal formula. …”
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The Machine Learning Models in Major Cardiovascular Adverse Events Prediction Based on Coronary Computed Tomography Angiography: Systematic Review by Yuchen Ma, Mohan Li, Huiqun Wu

“…ObjectiveWe evaluated the diagnostic value of ML models constructed using radiomic features extracted from CCTA in predicting MACEs, and compared the performance of different learning algorithms and models, thereby providing clinical recommendations for the diagnosis, treatment, and prognosis of MACEs. …”
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Agenda setting for health equity assessment through the lenses of social determinants of health using machine learning approach: a framework and preliminary pilot study by Maryam Ramezani, Mohammadreza Mobinizadeh, Ahad Bakhtiari, Hamid R. Rabiee, Maryam Ramezani, Hakimeh Mostafavi, Alireza Olyaeemanesh, Ali Akbar Fazaeli, Alireza Atashi, Saharnaz Sazgarnejad, Efat Mohamadi, Amirhossein Takian

“…Conclusion The findings underscore the significance of employing innovative methods like CRISP-ML(Q) and ML algorithms to enhance health equity. Integrating this platform into health systems can help countries better prioritize and address health inequities. …”
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Exploring the Shared Genetic Characteristics of Peri-Implantitis and Osteoporosis: A Transcriptomic Analysis Perspective by Churen Zhang, Feixiang Wang, Jianguo Ke, Cuihong Lin, Xiaohua Zeng, Qiuyan Yang, Qiaoling Cai

“…Finally, the CIBERSORT core algorithm was used to calculate immune cell infiltration levels. …”
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Adaptive Hybrid PSO-Embedded GA for neuroevolutionary training of multilayer perceptron controllers in VSC-based islanded microgrids by Yared Bekele Beyene, Getachew Biru Worku, Lina Bertling Tjernberg

“…This paper introduces a novel hybrid optimization algorithm, Adaptive Hybrid PSO-Embedded GA (AHPEGA), which dynamically adapts to optimization performance by integrating Particle Swarm Optimization (PSO) and Genetic Algorithms (GA). …”
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Enhancing Extreme Learning Machine Robustness via Residual-Variance-Aware Dynamic Weighting and Broyden–Fletcher–Goldfarb–Shanno Optimization: Application to Metro Crowd Flow Predi... by Lihui Wang, Jianguang Xie

“…Aiming at the robustness problem of the extreme learning machine (ELM) in noisy and nonuniform data scenarios, this paper proposes an improved algorithm (BFGS-URWELM) that integrates uniform residual weighting and Broyden–Fletcher–Goldfarb–Shanno (BFGS) quasi-Newton optimization. …”
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RRBM-YOLO: Research on Efficient and Lightweight Convolutional Neural Networks for Underground Coal Gangue Identification by Yutong Wang, Ziming Kou, Cong Han, Yuchen Qin

“…Coal gangue identification is the primary step in coal flow initial screening, which mainly faces problems such as low identification efficiency, complex algorithms, and high hardware requirements. In response to the above, this article proposes a new “hardware friendly” coal gangue image recognition algorithm, RRBM-YOLO, which is combined with dark light enhancement. …”
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