Accelerated Bayesian optimization for CNN+LSTM learning rate tuning via precomputed Gaussian process subspaces in soil analysis by Xiaolong Chen, Hongfeng Zhang, Cora Un In Wong, Zhengchun Song

“…This work bridges the gap between computational efficiency and probabilistic robustness, with broader implications for automated machine learning in geoscientific applications.MethodThe key innovation lies in a subspace-accelerated GP surrogate model that precomputes low-rank approximations of covariance matrices offline, thereby decoupling the costly hyperparameter tuning from the online acquisition function evaluations. …”
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Modeling Multivariable Associations and Inter-Eddy Interactions: A Dual-Graph Learning Framework for Mesoscale Eddy Trajectory Forecasting by Yanling Du, Bin Zhang, Jian Wang, Zhenli Qian, Wei Song

“…Finally, a decayed volatility loss function is designed to properly handle the complex and variable data features and improve the forecasting performance. …”
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Lightweight deep learning system for automated bone age assessment in Chinese children: enhancing clinical efficiency and diagnostic accuracy by Pang Hai, Zhang Bin, Liu Kesheng, Li Cong, Xu Fei

“…To address these limitations, this study introduces a novel lightweight two-stage deep learning framework based on the Chinese 05 BAA standard. …”
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A proximal policy optimization based deep reinforcement learning framework for tracking control of a flexible robotic manipulator by Joshi Kumar V, Vinodh Kumar Elumalai

“…This paper puts forward a policy feedback based deep reinforcement learning (DRL) control scheme for a partially observable system by leveraging the potentials of proximal policy optimization (PPO) algorithm and convolutional neural network (CNN). …”
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Six weeks that changed the preterm infant brain: lessons learned from the Family Nurture Intervention randomized controlled trials by Robert J. Ludwig, Robert J. Ludwig, Michael M. Myers, Michael M. Myers, Martha G. Welch, Martha G. Welch

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Encoding local label correlations in multi-instance multi-label learning with an improved multi-objective particle swarm optimization by Xiang Bao, Fei Han, Qinghua Ling

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Impact of Sliding Window Overlap Ratio on EEG-Based ASD Diagnosis Using Brain Hemisphere Energy and Machine Learning by Bashar S. Falih, Mohannad K. Sabir, Ahmet Aydın

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Deep Reinforcement Learning Algorithm with Long Short-Term Memory Network for Optimizing Unmanned Aerial Vehicle Information Transmission by Yufei He, Ruiqi Hu, Kewei Liang, Yonghong Liu, Zhiyuan Zhou

“…In this paper, we propose a novel deep reinforcement learning algorithm that utilizes a hybrid discrete–continuous action space. …”
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Research on Novel Bearing Fault Diagnosis Method Based on Improved Krill Herd Algorithm and Kernel Extreme Learning Machine by Zhijian Wang, Likang Zheng, Junyuan Wang, Wenhua Du

“…Then, it is imported into the kernel extreme learning machine for fault diagnosis. But considering the kernel function parameters σ and the error penalty factor C will affect the classification accuracy of the kernel extreme learning machine, this paper uses the novel krill herd algorithm (NKH) for their optimization. …”
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Enhancing Sniffing Detection in IoT Home Wi-Fi Networks: An Ensemble Learning Approach With Network Monitoring System (NMS) by Hyo Jung Jin, Farshad Rahimi Ghashghaei, Nebrase Elmrabit, Yussuf Ahmed, Mehdi Yousefi

“…Where the wireless network environment can be vulnerable to sniffing vulnerabilities attacks due to the broadcasting function of Wi-Fi network. Wi-Fi access point devices can often be compromised, and critical information is leaked through sniffing attacks. …”
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Role of Artificial Intelligence and Machine Learning to Create Predictors, Enhance Molecular Understanding, and Implement Purposeful Programs for Myocardial Recovery by Frederick M. Lang, Benjamin C. Lee, Dor Lotan, Mert. R. Sabuncu, Veli K. Topkara

“…Despite the public health burden, medical and device therapies for HF significantly improve clinical outcomes and, in a subset of patients, can cause reversal of abnormalities in cardiac structure and function, termed “myocardial recovery.” By identifying novel patterns in high-dimensional data, artificial intelligence (AI) and machine learning (ML) algorithms can enhance the identification of key predictors and molecular drivers of myocardial recovery. …”
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Optimization of Electric Vehicle Charging and Discharging Strategies Considering Battery Health State: A Safe Reinforcement Learning Approach by Shuifu Gu, Kejun Qian, Yongbiao Yang

“…In response, this paper proposes a safe reinforcement learning–based optimization method for EV charging and discharging strategies, aimed at minimizing charging and discharging costs while accounting for battery SOH. …”
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Predicting cognitive frailty in community-dwelling older adults: a machine learning approach based on multidomain risk factors by Catherine Park, Namhee Kim, Chang Won Won, Miji Kim

“…Among the diverse CF-associated characteristics, the machine learning-based model identified six optimal features (key predictors): motor capacity, education level, physical function limitation, nutritional status, balance confidence, and activities of daily living. …”
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iFQS: An Integrated FCNP-Q-Learning-Based Scheduling Algorithm for On-Demand Charging in Wireless Rechargeable Sensor Networks by Man Gun Ri, Chun Hyok Kim, Se Hun Pak, Chol Min Pong

“…Then, in charging path planning with Q-learning, the BS use these five criteria’s weights to design the reward function and select the most suitable next charging sojourn point. …”
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Adaptive Impact-Time-Control Cooperative Guidance Law for UAVs Under Time-Varying Velocity Based on Reinforcement Learning by Zhenyu Liu, Gang Lei, Yong Xian, Leliang Ren, Shaopeng Li, Daqiao Zhang

“…Firstly, a reinforcement learning framework for the high-speed UAVs’ guidance problem is established. …”
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In-depth and high-throughput spatial proteomics for whole-tissue slice profiling by deep learning-facilitated sparse sampling strategy by Ritian Qin, Jiacheng Ma, Fuchu He, Weijie Qin

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Machine Learning-Enabled Prediction and Optimization of Sulfur Recovery Units: A Step towards Industry 4.0 Integration by Imran Khan, Husnain Saghir, Muhammad Ahsan

“…This study employs a machine learning algorithm to predict sulfur recovery efficiency under uncertain conditions. …”
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Accurate and Data‐Efficient Micro X‐ray Diffraction Phase Identification Using Multitask Learning: Application to Hydrothermal Fluids by Yanfei Li, Juejing Liu, Xiaodong Zhao, Wenjun Liu, Tong Geng, Ang Li, Xin Zhang

“…Herein, the potential of deep learning with a multitask learning (MTL) architecture to overcome these limitations is demonstrated. …”
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A Multi-Task Learning Framework with Enhanced Cross-Level Semantic Consistency for Multi-Level Land Cover Classification by Shilin Tao, Haoyu Fu, Ruiqi Yang, Leiguang Wang

“…A hierarchical loss function is also embedded that explicitly models the semantic dependencies between levels, enhancing semantic consistency across levels. …”
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TriageHD: A Hyper-Dimensional Learning-to-Rank Framework for Dynamic Micro-Segmentation in Zero-Trust Network Security by Ryozo Masukawa, Sanggeon Yun, Sungheon Jeong, Nathaniel D. Bastian, Mohsen Imani

“…TriageHD constructs dynamic scene graphs from time-based network flow data, integrating feature representations extracted via a self-attention-based payload encoder. It employs a learning-to-rank algorithm with an approximated nDCG loss function, incorporating time-aware relevance and graph-aware HDC to prioritize nodes for segregation, thereby mitigating attack propagation. …”
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