Impaired interhemispheric synchrony in patients with iridocyclitis and classification using machine learning: an fMRI study by Yan Tong, Zhi Wen, Xin Huang

“…BackgroundThis study examined the interhemispheric integration function pattern in patients with iridocyclitis utilizing the voxel-mirrored homotopic connectivity (VMHC) technique. …”
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Exploiting heart rate variability for driver drowsiness detection using wearable sensors and machine learning by Zakwan AlArnaout, Chamseddine Zaki, Yehia Kotb, Mouhammad AlAkkoumi, Nour Mostafa

“…The Support Vector Machine with Radial Basis Function (SVM-RBF) also shows strong generalization performance, with a testing F1-score of 87.15% and the smallest mean change of -3.97%. …”
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Machine learning phase control of filled-aperture coherent beam combining: principle and numerical demonstration by Hongbing Zhou, Rumao Tao, Xi Feng, Haoyu Zhang, Min Li, Xiong Xin, Yuyang Peng, Honghuan Lin, Jianjun Wang, Lixin Yan, Feng Jing

“…Machine learning has already shown promising potential in tiled-aperture coherent beam combining (CBC) to achieve versatile advanced applications. …”
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A novel reinforcement learning framework-based path planning algorithm for unmanned surface vehicle by Jianhui Mou, Bo Shi, Bo Wang, Chengcheng Yu, Yangwei Wang, Fusheng Zhong, Li Zheng, Jian Wang, Junjie Li

“…Second, a comprehensive reward function incorporating artificial potential field (APF) inspiration is designed to guide the USV to reach the target region. …”
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A Deep Learning Approach for Extracting Cyanobacterial Blooms in Eutrophic Lakes From Satellite Imagery by Nan Wang, Zhenyu Tan, Chen Yang, Jinge Ma, Hongtao Duan

“…Deep learning (DL) methods offer enhanced capabilities for CyanoHABs detection by learning complex patterns and adapting to varying conditions. …”
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Bridging the gap: How career development learning mediates higher education and employability outcomes in Nepal by Rewan Kumar Dahal, Binod Ghimire, Dilli Raj Sharma, Dipendra Karki, Surendra Prasad Joshi

“…The results suggest that the higher education institution environment does not significantly improve employability (β = 0.038, p > 0.05). Career development learning influences this association (β = 0.803, p < 0.05), underscoring its essential function in converting educational experiences into competencies and preparedness for the job market. …”
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Predicting the compressive strength of polymer-infused bricks: A machine learning approach with SHAP interpretability by Sathvik Sharath Chandra, Rakesh Kumar, Archudha Arjunasamy, Sakshi Galagali, Adithya Tantri, Sujay Raghavendra Naganna

“…The polymer bricks’ compressive strength was recorded as the output parameter, with cement, fly ash, M sand, PP waste, and age serving as the input parameters. Machine learning models often function as black boxes, thereby providing limited interpretability; however, our approach addresses this limitation by employing the SHapley Additive exPlanations (SHAP) interpretation method. …”
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Research on Behavior Recognition and Online Monitoring System for Liaoning Cashmere Goats Based on Deep Learning by Geng Chen, Zhiyu Yuan, Xinhui Luo, Jinxin Liang, Chunxin Wang

“…This study introduces a deep learning-based behavior recognition and online detection system for Liaoning Cashmere Goats. …”
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TQVGModel: Tomato Quality Visual Grading and Instance Segmentation Deep Learning Model for Complex Scenarios by Peichao Cong, Kun Wang, Ji Liang, Yutao Xu, Tianheng Li, Bin Xue

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Starting driving style recognition of electric city bus based on deep learning and CAN data by Dengfeng Zhao, Zhijun Fu, Chaohui Liu, Junjian Hou, Shesen Dong, Yudong Zhong

“…In this article, a starting driving style recognition method of electric city bus is firstly proposed based on deep learning with in-vehicle Controller Area Network (CAN) bus data. …”
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Incidental mishaps and learning curves during free fibula reconstruction of mandible: a case report by Dharnappa Poojary, K. M. Sandeep, K. P. Shetty, Sameep Shetty, Premalatha Shetty, Joanna Baptist, Ritesh Singh

“…Despite the incidental mishaps, it was a learning experience for the betterment of the planning of future cases. …”
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Adaptive Neuro-Affective Engagement via Bayesian Feedback Learning in Serious Games for Neurodivergent Children by Diego Resende Faria, Pedro Paulo da Silva Ayrosa

“…A multimodal weighted engagement function continuously updates stimulus–response expectations. …”
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Machine learning enhanced formation pressure prediction using integrated well logging and mud logging by Jiwen Liang, Ming Luo, Wentuo Li, Bo Sun, Chuanliang Yan, Zhongying Han, Yuanfang Cheng

“…Pore pressure has a medium to low correlation with the rotation per minute. Based on machine learning algorithms and a large amount of known data, a machine learning formation pressure model with integrated well logging and mud logging data (IWM) was established. …”
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Failure mechanism-driven multi-adversarial domain transfer learning for rolling bearing fault diagnosis by Zhihui Zhang, Zhidan Zhong, Zhe Li, Wentao Mao, Yunhao Cui

“…To address these challenges, this paper proposes a Failure Mechanism-Driven Multi-Adversarial Domain Transfer Learning algorithm. The core of this method is the deep integration of physical prior knowledge with data-driven models. …”
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Adaptive conflict resolution for IoT transactions: A reinforcement learning-based hybrid validation protocol by Mohammad A. Al Khaldy, Ahmad Nabot, Ahmad al-Qerem, Issam Jebreen, Abdulbasit A. Darem, Asma A. Alhashmi, Mohammad Alauthman, Amjad Aldweesh

“…The RL-based mechanism continuously adapts decision-making by learning from system states, prioritizing transactions, and dynamically resolving conflicts using a reward function that accounts for key performance parameters, including the number of conflicting transactions, cost of aborting transactions, temporal validity constraints, and system resource utilization. …”
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Application of Deep Dictionary Learning and Predefined Filters for Classification of Retinal Optical Coherence Tomography Images by Fariba Shaker, Zahra Baharlouei, Gerlind Plonka, Hossein Rabbani

“…The first method, Simplified Deep Dictionary Learning (S-DDL), is based on deep dictionary learning, where the loss function enforces a sparse feature representation of input image patches before classification. …”
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FFTL-Net: a network for the classification of skin diseases based on feature fusion and transfer learning by Xiaowei Song, Yurong Mei, Zhilei Zhao, Hao Chang, Lina Han, Hui Wang, Xinyi Zhang, Guoqiang Wang

“…Finally, the Softmax-loss function of category equilibrium and the domain adaptive strategy based on the maximum mean difference are established. …”
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MCL-SWT: Mirror Contrastive Learning with Sliding Window Transformer for Subject-Independent EEG Recognition by Qi Mao, Hongke Zhu, Wenyao Yan, Yu Zhao, Xinhong Hei, Jing Luo

“…<b>Methods</b>: We introduce Mirror Contrastive Learning with Sliding Window Transformer (MCL-SWT). …”
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Demand response optimization of gas-electric water heater based on fusing knowledge and reinforcement learning by YANG Xiaokun, YAN Kai, ZHANG Shuo, LIU Yan, YUAN Ruiming, ZHENG Xiaoping

“…Furthermore, we design a DQN (deep Q-learning)-based optimization model with knowledge integration including common features of DQN, the method that optimization knowledge works in reward function and the control mechanism that coordinates the depthand probability of knowledge participation. …”
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Predicting the subclinical carotid atherosclerosis in overweight and obese patients using a machine learning model by D. V. Gavrilov, T. Yu. Kuznetsova, M. A. Druzhilov, I. N. Korsakov, A. V. Gusev

“…The number of final traits for machine learning was 28. When creating the model, 3 Random Forest algorithms, AdaBoostClassifier, KNeighborsClassifier and the Scikit-learn library were used. …”
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