Identifying trade-offs and synergies among land use functions using an XGBoost-SHAP model: A case study of Kunming, China by Kun Li, Junsan Zhao, Yongping Li, Yilin Lin

“…Then, an interpretable machine learning model (XGBoost-SHAP) was utilized to provide an intuitive explanation of the nonlinear response mechanism of LUF trade-offs/synergies. …”
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Enhancing Visual Perception in Sports Environments: A Virtual Reality and Machine Learning Approach by Taiyang Wang, Peng Luo, Sihan Xia

“…Furthermore, this study identifies the best-performing machine learning model for predicting sports perception, which is subsequently integrated with a genetic algorithm to optimize environmental design thresholds. …”
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Quantitative dynamics of neural uncertainty in sensory processing and decision-making during discriminative learning by Soonho Shin, Joonsu Oh, Sun Kwang Kim, Yong-Seok Lee, Sang Jeong Kim

“…We confirmed that uncertainty decreases as learning progresses and increases with interruptions in learning. …”
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Learning-based locomotion control fusing multimodal perception for a bipedal humanoid robot by Chao Ji, Diyuan Liu, Wei Gao, Shiwu Zhang

“…The expert policies of different terrains to meet the requirements of gait aesthetics are trained through reinforcement learning, and these expert policies are distilled into student through policy distillation. …”
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Intelligent Manufacturing in Wine Barrel Production: Deep Learning-Based Wood Stave Classification by Frank A. Ricardo, Martxel Eizaguirre, Desmond K. Moru, Diego Borro

“…Several techniques using classical image processing and deep learning have been developed to detect tree-ring boundaries, but they often struggle with woods exhibiting heterogeneity and texture irregularities. (2) Methods: This study proposes a hybrid approach combining classical computer vision techniques for preprocessing with deep learning algorithms for classification, designed for continuous automated processing. …”
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Linker-GPT: design of Antibody-drug conjugates linkers with molecular generators and reinforcement learning by An Su, Yanlin Luo, Chengwei Zhang, Hongliang Duan

“…The model integrates transfer learning from large-scale molecular datasets and reinforcement learning (RL) to iteratively refine molecular properties such as drug-likeness and synthetic accessibility. …”
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Trust Region Policy Learning for Adaptive Drug Infusion with Communication Networks in Hypertensive Patients by Mai The Vu, Seong Han Kim, Ha Le Nhu Ngoc Thanh, Majid Roohi, Tuan Hai Nguyen

“…Secondly, a model-free deep reinforcement learning (MF-DRL) algorithm is integrated into the NBC to adjust dynamically the coefficients of the controller. …”
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Exploring the Controlling Factors of Watershed Streamflow Variability Using Hydrological and Machine Learning Models by Bingbing Ding, Xinxiao Yu, Guodong Jia

“…This study demonstrated the potential of integrating hydrological models with machine learning by constructing two machine learning methods, Extreme Gradient Boosting (XGBoost) and Random Forest (RF), based on the input and output data from the Soil and Water Assessment Tool (SWAT) and comparing their streamflow simulation performances. …”
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Advanced machine learning models for the prediction of ceramic tiles’ properties during the firing stage by V. Vasic, Milica, Awoyera, Paul O., Fadugba, Oladlu George, Barisic, Ivana, Nettinger Grubeša, Ivanka

“…This study employs advanced machine learning (ML) models to accurately predict these properties by capturing their complex nonlinear relationships. …”
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Advancements in Frank’s sign Identification using deep learning on 3D brain MRI by Sungman Jo, Jun Sung Kim, Min Jeong Kwon, Jieun Park, Jeong Lan Kim, Jin Hyeong Jhoo, Eosu Kim, Leonard Sunwoo, Jae Hyoung Kim, Ji Won Han, Ki Woong Kim

“…Four deep learning architectures were evaluated for FS segmentation on a dataset of 400 brain MRI scans. …”
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A Machine Learning Algorithm to Predict Medical Device Recall by the Food and Drug Administration by Victor Barbosa Slivinskis, Isabela Agi Maluli, Joshua Seth Broder

“…Our objective was to evaluate the sensitivity, specificity, and accuracy of a machine learning (ML) algorithm using publicly available data to predict medical device recalls by the FDA. …”
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Mapping China Aquaculture Ponds: Integrating a New Aquaculture Index With Machine Learning by JianChun Chen, Chen Lin, Kun Xue, Ke Song, ZhiGang Cao, RongHua Ma, DanHua Ma, YiJun Tong

“…However, existing methods for large‐scale extraction of AP face challenges, such as difficulty in transferring segmentation thresholds and confusion with similar land features, which limits the accurate determination of their spatial distribution. …”
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A Machine Learning-Based Guide for Repeated Laboratory Testing in Pediatric Emergency Departments by Adi Shuchami, Teddy Lazebnik, Shai Ashkenazi, Avner Herman Cohen, Yael Reichenberg, Vered Shkalim Zemer

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Cooperate or Not Cooperate: Transfer Learning With Multi-Armed Bandit for Spatial Reuse in Wi-Fi by Pedro Enrique Iturria-Rivera, Marcel Chenier, Bernard Herscovici, Burak Kantarci, Melike Erol-Kantarci

“…Under dynamic scenarios, transfer learning mitigates service drops for at least 60% of the total users.…”
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Multicenter Evaluation of Machine-Learning Continuous Pulse Rate Algorithm on Wrist-Worn Device by Weixuan Chen, Rafael Cordero, Jessie Lever Taylor, Domenico R. Pangallo, Rosalind W. Picard, Marisa Cruz, Giulia Regalia

“…The primary acceptance threshold was an accuracy root-mean-square (ARMS) ≤3 beats per minute (bpm) or 5 bpm under no-motion and motion conditions, respectively. …”
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A Representation-Learning-Based Graph and Generative Network for Hyperspectral Small Target Detection by Yunsong Li, Jiaping Zhong, Weiying Xie, Paolo Gamba

“…To address these issues, this work proposes a representation-learning-based graph and generative network for hyperspectral small target detection. …”
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Predicting the Likelihood of Operational Risk Occurrence in the Banking Industry Using Machine Learning Algorithms by Hamed Naderi, Mohammad Ali Rastegar Sorkhe, Bakhtiar Ostadi, Mehrdad Kargari

“…In another study, Akbari and Yazdanian (2023) applied machine learning algorithms to determine optimal thresholds for operational loss severity data, classifying the data and estimating the capital required to cover operational risk by integrating severity and frequency distribution functions with Monte Carlo simulation. …”
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Unveiling PFAS hazard in European surface waters using an interpretable machine-learning model by Li Zhao, Jian Chen, Jiaqi Wen, Yangjie Li, Yingjie Zhang, Qunyue Wu, Gang Yu

“…Importantly, we determined a threshold distance (4.1–4.9 km) from PFAS point sources, below which PFAS hazards in surface waters could be elevated. …”
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A comparison of statistical methods for deriving occupancy estimates from machine learning outputs by Lydia K. D. Katsis, Tessa A. Rhinehart, Elizabeth Dorgay, Emma E. Sanchez, Jake L. Snaddon, C. Patrick Doncaster, Justin Kitzes

“…Abstract The combination of autonomous recording units (ARUs) and machine learning enables scalable biodiversity monitoring. …”
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Understanding forest insect outbreak dynamics: a comparative analysis of machine learning techniques by Roberto Molowny-Horas, Saeed Harati-Asl, Liliana Perez

“…All calculations were carried out for different mountain pine beetle map sets and time differences, and we employed up to seven performance metrics (six threshold-dependent and one threshold-independent) and four error metrics to assess goodness of prediction. …”
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