An optimal control method considering degradation and economy based on mutual learn salp swarm algorithm of an islanded zero‐carbon DC microgrid by Ying Han, Yujing Hou, Luoyi Li, Weifeng Meng, Qi Li, Weirong Chen

“…In order to realize the economic operation, operation cost and degradation cost of battery and hydrogen system are considered as the objective function first. Then, salp swarm algorithm based on mutual learn strategy is introduced to obtain optimal economy power allocation results in real‐time with higher convergence speed and increased accuracy. …”
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Ensemble machine learning-based pre-trained annotation approach for scRNA-seq data using gradient boosting with genetic optimizer by Osama Elnahas, Waleed M. Ead, Yushan Qiu, Jian Lu

“…We propose an ensemble machine learning-based pre-trained annotation framework that integrates gradient boosting and genetic optimization for robust feature selection. …”
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Comparison of Different Machine Learning Methodologies for Predicting the Non‐Specific Treatment Response in Placebo Controlled Major Depressive Disorder Clinical Trials by Roberto Gomeni, Françoise Bressolle‐Gomeni

“…Treatment effect in randomized, placebo‐controlled trials, is usually estimated by the mean baseline adjusted difference of treatment response in active and placebo arms and is function of treatment‐specific and non‐specific effects. …”
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Data Mining of 24-Hour Cumulative Precipitation Data in Iran Using Machine Learning: Multilayer Perceptron Neural Network and Decision Tree by mozaffar faraji, Majid Rezaii Banafsheh Daragh, Behroz sarisarraf, Ali Mohammad Khorshid Dust

“…Method: A daily precipitation dataset D was collected from Iranian stations and prepared using normalization. Two machine learning models including MLP with activation function σ and decision tree with entropy criterion were implemented. …”
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Optimization and modeling of sulfur removal from liquid fuel using carbon-based adsorbents through synergistic application of RSM and machine learning by Karim Maghfour Sarkarabad, Mohsen Shayanmehr, Ahad Ghaemi

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Detection and recognition of foreign objects in Pu-erh Sun-dried green tea using an improved YOLOv8 based on deep learning. by Houqiao Wang, Xiaoxue Guo, Shihao Zhang, Gongming Li, Qiang Zhao, Zejun Wang

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A Decision-Making Model for Autonomous Vehicles Considering Pedestrian’s Time Pressure Based on Game Theory and Reinforcement Learning by Qing-Feng Lin, Heng-Yu Xue, Yang Lyu, Qing-Kun Li, Ju-Shang Ou

“…Then, leveraging game theory, we established a pedestrian crossing decision-making model considering pedestrian heterogeneity evoked by time pressure (TP). A reward function was developed to enhance driving performance by combining safety, efficiency, and comfort. …”
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From prediction to practice: mitigating bias and data shift in machine-learning models for chemotherapy-induced organ dysfunction across unseen cancers by Heather Shaw, Pinkie Chambers, Matthew Watson, Luke Steventon, James Harmsworth King, Angelo Ercia, Noura Al Moubayed

“…Objectives Routine monitoring of renal and hepatic function during chemotherapy ensures that treatment-related organ damage has not occurred and clearance of subsequent treatment is not hindered; however, frequency and timing are not optimal. …”
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Predicting Protein Interactions Using a Deep Learning Method-Stacked Sparse Autoencoder Combined with a Probabilistic Classification Vector Machine by Yanbin Wang, Zhuhong You, Liping Li, Li Cheng, Xi Zhou, Libo Zhang, Xiao Li, Tonghai Jiang

“…Protein-protein interactions (PPIs), as an important molecular process within cells, are of pivotal importance in the biochemical function of cells. Although high-throughput experimental techniques have matured, enabling researchers to detect large amounts of PPIs, it has unavoidable disadvantages, such as having a high cost and being time consuming. …”
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Few-Shot Unsupervised Domain Adaptation Based on Refined Bi-Directional Prototypical Contrastive Learning for Cross-Scene Hyperspectral Image Classification by Xuebin Tang, Hanyi Shi, Chunchao Li, Cheng Jiang, Xiaoxiong Zhang, Lingbin Zeng, Xiaolei Zhou

“…To facilitate prototype contrastive learning, we employ a Siamese-style distance metric loss function to aggregate intra-class features while increasing the discrepancy of inter-class features. …”
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A Hybrid Three-Staged, Short-Term Wind-Power Prediction Method Based on SDAE-SVR Deep Learning and BA Optimization by Ruiqin Duan, Xiaosheng Peng, Cong Li, Zimin Yang, Yan Jiang, Xiufeng Li, Shuangquan Liu

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Eeg-based detection of epileptic seizures in patients with disabilities using a novel attention-driven deep learning framework with SHAP interpretability by Tawfeeq Shawly, Ahmed A. Alsheikhy

“…The proposed system employs Fourier Transform for feature extraction, utilizes Principal Component Analysis (PCA) for reducing dimensionality, and applies an optimized stochastic gradient descent approach with the Adam optimizer to enhance the learning process. We articulate the mathematical characteristics of feature selection driven by NAM, delineate the convergence attributes of the loss function, and present measures of explainability through Shapley Additive Explanations (SHAP). …”
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Enhanced AUV Autonomy Through Fused Energy-Optimized Path Planning and Deep Reinforcement Learning for Integrated Navigation and Dynamic Obstacle Detection by Kaijie Zhang, Yuchen Ye, Kaihao Chen, Zao Li, Kangshun Li

“…The global path intelligence of MEO-BIT* is dynamically informed and refined by the DQN’s learned perception. This allows the DQN to make effective decisions to avoid moving obstacles. …”
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Smoke and Fire-You Only Look Once: A Lightweight Deep Learning Model for Video Smoke and Flame Detection in Natural Scenes by Chenmeng Zhao, Like Zhao, Ka Zhang, Yinghua Ren, Hui Chen, Yehua Sheng

“…Owing to the demand for smoke and flame detection in natural scenes, this paper proposes a lightweight deep learning model, SF-YOLO (Smoke and Fire-YOLO), for video smoke and flame detection in such environments. …”
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Spatiotemporal Analysis and Anomalous Trends of Asia AOD (2001–2024): Insights from a Deep Learning Fusion Model and EOF Decomposition by Yu Ding, Wenjia Ni, Jiaxin Dong, Jie Yang, Shiyao Meng, Siwei Li

“…To overcome these challenges, this study employs the deep learning model TabNet, incorporating Digital Elevation Model (DEM) data and ERA5 meteorological variables, to fuse MERRA-2 AOD with MODIS MAIAC AOD observations. …”
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A machine learning-based predictive model for the occurrence of lower extremity deep vein thrombosis after laparoscopic surgery in abdominal surgery by Su-Zhen Yang, Ming-Hui Peng, Quan Lin, Shi-Wei Guan, Kai-Lun Zhang, Hai-Bo Yu

“…Background & aimsDeep vein thrombosis, a common complication after laparoscopic surgery, can negatively affect patients' limb motor function and even seriously threaten their lives. Therefore, it is crucial to accurately identify patients at high risk of lower extremity deep vein thrombosis. …”
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OA-MEN: a fusion deep learning approach for enhanced accuracy in knee osteoarthritis detection and classification using X-Ray imaging by Xiaolu Ren, Xiaolu Ren, Lingxuan Hou, Shan Liu, Peng Wu, Siming Liang, Haitian Fu, Chengquan Li, Ting Li, Yongjing Cheng

“…BackgroundKnee osteoarthritis (KOA) constitutes the prevailing manifestation of arthritis. Radiographs function as a common modality for primary screening; however, traditional X-ray evaluation of osteoarthritis confronts challenges such as reduced sensitivity, subjective interpretation, and heightened misdiagnosis rates. …”
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Multi-Objective Scheduling for Green Flexible Assembly Job-Shop System via Multi-Agent Deep Reinforcement Learning With Game Theory by Xiao Wang, Zhongyuan Liang, Peisi Zhong, Dongmin Li, Hongqi Li, Mei Liu

“…The processing state feature data that uses a deep convolutional neural network to fit the value function is extracted from three matrices including the processing time matrix, task assignment Boolean matrix, and an adjacency matrix. …”
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Double-diffusive magnetoconvection in a tilted porous parallelogrammic domain with discrete heated-cooled segments: Leveraging machine learning and CFD approach by P. Ravindra, M. Sankar, Suresh Rasappan, Wardah Abdullah Al Majrafi, Pugalarasu Rajan, S. Sanal Kumar

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Machine learning-driven prediction of risk factors for postoperative re-fractures in elderly OVCF patients with underlying diseases: model development and validation by Bao Qi, Kai Kong, Qingquan Wu, Lu Zhang, Wei Wei, Chunyang Meng, Hong Wang, Hong Wang, Qingwei Li, Qingwei Li

“…Our findings highlight the critical need for integrated management of spinal deformity, mental health, and renal function in this vulnerable population. This ML framework offers a paradigm shift in personalized risk stratification and postoperative care.…”
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