QSAR Models for Predicting the Antioxidant Potential of Chemical Substances by Sofia Ghironi, Edoardo Luca Viganò, Gianluca Selvestrel, Emilio Benfenati

“…Gradient Boosting and eXtreme Gradient Boosting also achieved promising results with R² values of 0.76 and 0.75, respectively. Given these results, we developed an integrated method that not only outperformed the individual models, achieving an R² of 0.78 on the external test set, but also provided valuable insights into the range of predicted values.…”
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State of Health Prediction for Lithium-Ion Batteries Based on Gated Temporal Network Assisted by Improved Grasshopper Optimization by Xiankun Wei, Silun Peng, Mingli Mo

“…Accurate SOH prediction provides a reliable reference for lithium-ion battery maintenance. However, novel algorithms are still needed because few studies have considered the correlations between monitored parameters in Euclidean space and non-Euclidean space at different time points. …”
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Assessment of Landslide Susceptibility Based on the Two-Layer Stacking Model—A Case Study of Jiacha County, China by Zhihan Wang, Tao Wen, Ningsheng Chen, Ruixuan Tang

“…These landslide conditioning factors were integrated into a total of 4660 Stacking ensemble learning models, randomly combined by 10 base-algorithms, including AdaBoost, Decision Tree (DT), Gradient Boosting Decision Tree (GBDT), k-Nearest Neighbors (kNNs), LightGBM, Multilayer Perceptron (MLP), Random Forest (RF), Ridge Regression, Support Vector Machine (SVM), and XGBoost. …”
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Enhancing SQL Learning: Gamified tutorials and flipped classroom synergy by Sandor Kiraly, Tamas Balla, Dora Kiraly, Geoffrey Vaughan

“…These results underscore the value of integrating interactive, student-centered approaches in early SQL education.…”
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HAF-YOLO: Dynamic Feature Aggregation Network for Object Detection in Remote-Sensing Images by Pengfei Zhang, Jian Liu, Jianqiang Zhang, Yiping Liu, Jiahao Shi

“…The growing use of remote-sensing technologies has placed greater demands on object-detection algorithms, which still face challenges. This study proposes a hierarchical adaptive feature aggregation network (HAF-YOLO) to improve detection precision in remote-sensing images. …”
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Improving Sharpness-Aware Minimization Using Label Smoothing and Adaptive Adversarial Cross-Entropy Loss by Tanapat Ratchatorn, Masayuki Tanaka

“…Recent advances in learning algorithms have identified loss surface sharpness as an effective metric for reducing the generalization gap. …”
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Archetype Identification and Energy Consumption Prediction for Old Residential Buildings Based on Multi-Source Datasets by Chengliang Fan, Rude Liu, Yundan Liao

“…Furthermore, XGBoost and Random Forest machine learning algorithms were used to predict city-scale old residential building energy consumption. …”
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HMSTNet: A Deep Learning Multimodal Approach for Personalized English Literature Recommendations by Xiaopeng Wang, Xue Liang

“…This research serves to unite conventional recommendation algorithms with English literature reading requirements. …”
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Assessing Climate Change Impacts on Cropland and Greenhouse Gas Emissions Using Remote Sensing and Machine Learning by Nehir Uyar, Azize Uyar

“…Specifically, GBT and RF achieved the highest R² value (0.71, 0.59) and the lowest error metrics in modeling emissions, whereas SVM performed poorly across all cases. …”
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Analyzing the Impact of Geoenvironmental Factors on the Spatiotemporal Dynamics of Forest Cover via Random Forest by Hendaf N. Habeeb, Yaseen T. Mustafa

“…This study investigates the spatiotemporal dynamics of forest cover in the Duhok District in the Kurdistan Region of Iraq over a decade (2013–2023), emphasizing the impact of geoenvironmental factors via Random Forest algorithms and Landsat data. This research integrates datasets including fractional vegetation cover (FVC), groundwater levels, climate data, topography, and soil moisture data, offering a comprehensive analysis of the factors influencing forest cover. …”
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Exploring Brain Activity in Different Mental Cognitive Workloads by Sahar Oftadeh Balani, Ali Fawzi Al-Hussainy, Alhan Abd Al-Hassan Shalal, Mohammed Ubaid, Zinab Aluquaily, Jaafar Alamoori, Saeid Motevalli

“…These changes in connectivity and complexity facilitate the integration of multiple cognitive processes essential for effective arithmetic problem-solving. …”
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Development of a host-signature-based machine learning model to diagnose bacterial and viral infections in febrile children by Fang Bai, Zelong Gong, Dong Cui, Xiaomei Zhang, Wenteng Hong, Yi Gao, Kai Lin, Weijie Chen, Lu Li, Juan Huang, Biying Zheng, Junfa Xu, Na Xiao

“…By utilizing the transformed value RefValue (i) of these five genes, the RF model achieved an AUC of 0.9917 in training and 0.9517 in testing for diagnosing B/V infection in children. …”
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Machine learning-based prediction of antimicrobial resistance and identification of AMR-related SNPs in Mycobacterium tuberculosis by Yi Xu, Ying Mao, Xiaoting Hua, Yan Jiang, Yi Zou, Zhichao Wang, Zubi Liu, Hongrui Zhang, Lingling Lu, Yunsong Yu

“…By constructing training and test datasets on all SNPs, intersected SNPs, and randomly generated SNPs, we developed a Machine learning (ML) framework using twelve different algorithms. Then, we compared the performances of the various ML models and used the SHapley Additive exPlanations (SHAP) framework to decipher why and how decisions are made within the optimal algorithm. …”
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A machine learning-based approach for constructing a 3D apparent geological model using multi-resistivity data by Jordi Mahardika Puntu, Ping-Yu Chang, Haiyina Hasbia Amania, Ding-Jiun Lin, M. Syahdan Akbar Suryantara, Jui-Pin Tsai, Hwa-Lung Yu, Liang-Cheng Chang, Jun-Ru Zeng, Lingerew Nebere Kassie

“…A key contribution of this work is the rigorous harmonization of these data sets, ensuring consistent resistivity values across different methods before constructing the 3D resistivity model, addressing a gap in previous studies that typically handled these data sets separately, either building models individually or comparing results side-by-side without fully integrating the data. …”
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An inductive learning-based method for predicting drug-gene interactions using a multi-relational drug-disease-gene graph by Jian He, Yanling Wu, Linxi Yuan, Jiangguo Qiu, Menglong Li, Xuemei Pu, Yanzhi Guo

“…Following the extraction of graph features by utilizing graph embedding algorithms, our next step was the retrieval of the attributes of individual gene and drug nodes. …”
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Developing a Critical Imagination for How Researchers can use Artificially Intelligent Tools Reflexively and Responsibly During Qualitative Literature Reviews by Kristen Foley, Caitlan McLean, Reece De Zylva, Gregorius Asa, Jordana Maio, Samantha Batchelor, Gideon Dzando, Ahmad Dimassi

“…Beyond only literature retrieval, some AI tools describe their use-value as being able to appraise and synthesise contrasting evidence within and across papers; work like ‘Spotify’ to cultivate a personalised ecosystem of research articles that evolves via algorithmic learning; and suggest new directions for research based on existing evidence about a topic and an assessment of knowledge gaps. …”
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Improving agricultural commodity allocation and market regulation: a novel hybrid model based on dual decomposition and enhanced BiLSTM for price prediction by Lihua Zhang, Fushun Wang, Fushun Wang, Kejian Wang, Kejian Wang, Zhenxue He, Zhenxue He, Chen Chen, Chen Chen, Jiahao Liu, Jiahao Liu, Chao Wang, Chao Wang, Zhe Wang

“…This paper proposes a sustainable hybrid model SV-PSO-BiLSTM which integrates Seasonal-Trend decomposition procedure based on Loess (STL), Variational Mode Decomposition (VMD), Particle Swarm Optimization (PSO), and Bidirectional Long Short-Term Memory (BiLSTM) neural networks. …”
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Cervical cancer screening uptake and its associated factor in Sub-Sharan Africa: a machine learning approach by Fetlework Gubena Arage, Zinabu Bekele Tadese, Eliyas Addisu Taye, Tigist Kifle Tsegaw, Tsegasilassie Gebremariam Abate, Eyob Akalewold Alemu

“…These algorithms were employed to predict cervical cancer screening uptake. …”
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Leveraging machine learning in nursing: innovations, challenges, and ethical insights by Sophie So Wan Yip, Sheng Ning, Niki Yan Ki Wong, Jeffrey Chan, Kei Shing Ng, Bernadette Oi Ting Kwok, Robert L. Anders, Simon Ching Lam

“…Aim/objectiveThis review aims to provide a comprehensive analysis of the integration of machine learning (ML) (1) in nursing by exploring its implications on patient care, nursing practices, and healthcare delivery. …”
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UAVs’ Flight Dynamics Is All You Need for Wind Speed and Direction Measurement in Air by Sihong Zhu, Tonghui Zhao, Huanji Zhang, Yichao Chen, Dongxu Yang, Yi Liu, Junji Cao

“…The proposed method leverages data from sensors onboard UAV platforms, combined with advanced ML algorithms trained on ground-truth measurements obtained through high-resolution LiDAR systems. …”
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