Explainable machine learning models for identifying mild cognitive impairment in older patients with chronic pain by Xiaoang Zhang, Yuping Liao, Daying Zhang, Weichen Liu, Zhijian Wang, Yaxin Jin, Shushu Chen, Jianmei Wei

“…Data collected included patients’ general information, cognitive function, pain level, depression, and sleep quality. …”
Get full text

A Deep-Learning-Based Detection Method for Small Target Tomato Pests in Insect Traps by Song Wang, Daqing Chen, Jianxia Xiang, Cong Zhang

“…Finally, the SCYLLA-IoU (SIoU) loss function is introduced, and its components are redefined to incorporate direction information, which enhances the model’s learning ability and convergence performance. …”
Get full text

Hippocampus- and neocortex-specific deletion of Aeg-1 causes learning memory impairment and depression in mice by Ya-he Wang, Ning Zhou, Pan-pan Wan, Xin-tong Li, Chun-yang Yu, Jinjiang Chou, Zong-yi Feng, Lian-xiang Zhang, Juan-juan Li, Bao-cong Yu, Zhen-ning Tang, Kun-mei Liu, Le Guo

“…In conclusion, our findings suggest that Aeg-1 deficiency in the hippocampus and neocortex leads to learning and memory impairments and depression in mice, which is mediated by the abnormalities of neuronal morphology and the impaired synaptic functions.…”
Get full text

The future of spatial epidemiology in the AI era: enhancing machine learning approaches with explicit spatial structure by Nima Kianfar, Benn Sartorius, Colleen L. Lau, Robert Bergquist, Behzad Kiani

“…Research in spatial epidemiology relies on both conventional approaches and Machine- Learning (ML) algorithms to explore geographic patterns of diseases and identify influential factors (Pfeiffer & Stevens, 2015). …”
Get full text

Analysis of Gas Pipeline Failure Factors Based on the Novel Bayesian Network by Machine Learning Optimization by Shuangqing Chen, Shun Zhou, Zhe Xu, Yongbin Liu, Bing Guan, Xiaoyu Jiang, Wencheng Li

“…The two-way inference function of Bayesian network is used to obtain the failure probability of gas pipeline and the posterior probability of root node. …”
Get full text

Novel Unsupervised Cluster Reinforcement Q-Learning in Minimizing Energy Consumption of Federated Edge Cloud by Guruh Fajar Shidik, Oki Setiono, Edi Jaya Kusuma, L. Budi Handoko, Pulung Nurtantio Andono, Mohd Faizal Abdollah

“…The method enhances energy efficiency and workload balance by incorporating a modified reward function in Q-Learning. Experimental evaluations demonstrate that UCRL-FEC reduces energy consumption (EC) up to 1.07%, supporting reductions in both operational costs and greenhouse gas emissions, which is critical for large-scale cloud environments. …”
Get full text

Lessons to be learned from real life data based on 98 gout patients using benzbromarone by Ioana Hotea, Matthijs Janssen, Tim L. Jansen

Get full text

Joint Distributed Computation Offloading and Radio Resource Slicing Based on Reinforcement Learning in Vehicular Networks by Khaled A. Alaghbari, Heng-Siong Lim, Charilaos C. Zarakovitis, N. M. Abdul Latiff, Sharifah Hafizah Syed Ariffin, Su Fong Chien

“…We introduce both fixed and adaptive low-complexity mechanisms to allocate resources of the cloud server, formulating the reward function of the Q-learning method to achieve efficient offloading decisions. …”
Get full text

Stressors and Coping Strategies as Perceived among Nursing Students during Related Learning Experience (RLE) by Geraldine Sabate Ridad, Haniya Angintaopan, Princess Haniefa Mae Ayunan, Saipoden Manalocon

“…As stress is inevitable in nursing students’ Related Learning Experience (RLE), recognizing the stressors that affect their quality of RLE is necessary. …”
Get full text

Load Optimization for Connected Modern Buildings Using Deep Hybrid Machine Learning in Island Mode by Seyed Morteza Moghimi, Thomas Aaron Gulliver, Ilamparithi Thirumarai Chelvan, Hossen Teimoorinia

“…Operating in island mode, CSGBs can function independently of the grid, providing resilience during power outages and reducing reliance on external energy sources. …”
Get full text

Learning‐based tracking control of AUV: Mixed policy improvement and game‐based disturbance rejection by Jun Ye, Hongbo Gao, Manjiang Hu, Yougang Bian, Qingjia Cui, Xiaohui Qin, Rongjun Ding

“…As a result, the proposed approach accelerates the learning speed compared to data‐driven methods, concurrently also enhancing the tracking performance in comparison to model‐based control methods. …”
Get full text

Reinforcement Q-Learning-Based Adaptive Encryption Model for Cyberthreat Mitigation in Wireless Sensor Networks by Sreeja Balachandran Nair Premakumari, Gopikrishnan Sundaram, Marco Rivera, Patrick Wheeler, Ricardo E. Pérez Guzmán

“…A Hybrid Policy Derivation Algorithm is introduced to balance encryption complexity and computational overhead by dynamically switching between these learning models. The proposed system is formulated as a Markov Decision Process (MDP), where encryption level selection is driven by a reward function that optimizes the trade-off between energy efficiency and security robustness. …”
Get full text

Large Language Model Enhanced Particle Swarm Optimization for Hyperparameter Tuning for Deep Learning Models by Saad Hameed, Basheer Qolomany, Samir Brahim Belhaouari, Mohamed Abdallah, Junaid Qadir, Ala Al-Fuqaha

“…Determining the ideal architecture for deep learning models, such as the number of layers and neurons, is a difficult and resource-intensive process that frequently relies on human tuning or computationally costly optimization approaches. …”
Get full text

Multi-Objective Automated Machine Learning for Inversion of Mesoscopic Parameters in Discrete Element Contact Models by Xu Ao, Shengpeng Hao, Yuyu Zhang, Wenyu Xu

“…Although conventional machine learning techniques have been applied to invert the contact model parameters, they are hampered by the difficulty of selecting the optimal hyperparameters and, in some cases, insufficient data, which limits both the predictive accuracy and robustness. …”
Get full text

Integrating Fitness Training in Oncologic Care: Lessons Learned from a Large Telemedicine Trial by Karen S. Schaepe, PhD, Jeffrey R. Basford, MD, PhD, Andrea L. Cheville, MD, MSCE

Get full text

Integrating transcriptomics and hybrid machine learning enables high-accuracy diagnostic modeling for nasopharyngeal carcinoma by Hehe Wang, Junge Zhang, Peng Cheng, Lujie Yu, Chunlin Li, Yaowen Wang

“…Immune infiltration patterns and functional enrichment were analyzed using CIBERSORT and GSEA/GSVA, respectively. …”
Get full text

Research on short-term power load forecasting based on deep reinforcement learning with multiple intelligences by Tianyun Luo, Dunlin Zhu, Jinming Liu, Sheng Yang, Jinglong He, Yuan Fu

“…In this paper, we analyze the multi-intelligence application architecture in power load forecasting, and analyze the function of each intelligent unit applied to short-term power load forecasting; based on clarifying the interaction relationship of each intelligent unit in short-term power load forecasting, we model short-term power load forecasting as a distributed and partially observable Markov decision-making process, which is suitable for multi-intelligence deep reinforcement learning; based on the MATD3 algorithm, a centralized training-distributed execution framework is used to train multiple intelligences within the model to achieve short-term power load forecasting. …”
Get full text

Identification and Immunological Characterization of Cuproptosis Related Genes in Preeclampsia Using Bioinformatics Analysis and Machine Learning by Tiantian Yu, Guiying Wang, Xia Xu, Jianying Yan

“…The five genes that ranked highest in the RF machine learning model were considered to be predictor genes. …”
Get full text

End-to-End Mandarin Speech Reconstruction Based on Ultrasound Tongue Images Using Deep Learning by Fengji Li, Fei Shen, Ding Ma, Jie Zhou, Shaochuan Zhang, Li Wang, Fan Fan, Tao Liu, Xiaohong Chen, Tomoki Toda, Haijun Niu

“…The loss of speech function following a laryngectomy usually leads to severe physiological and psychological distress for laryngectomees. …”
Get full text

Gait-based Parkinson’s disease diagnosis and severity classification using force sensors and machine learning by Navita, Pooja Mittal, Yogesh Kumar Sharma, Anjani Kumar Rai, Sarita Simaiya, Umesh Kumar Lilhore, Vimal Kumar

“…Abstract A dual-stage model for classifying Parkinson’s disease severity, through a detailed analysis of Gait signals using force sensors and machine learning approaches, is proposed in this study. Parkinson’s disease is the primary neurodegenerative disorder that results in a gradual reduction in motor function. …”
Get full text