Forecasting water quality indices using generalized ridge model, regularized weighted kernel ridge model, and optimized multivariate variational mode decomposition by Marjan Kordani, Mohsen Bagheritabar, Iman Ahmadianfar, Arvin Samadi-Koucheksaraee

“…This research developed an optimized multivariate variational mode decomposition (OMVMD) technique, optimized by the Runge-Kutta algorithm (RUN), to decompose the input variables. …”
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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

“…We propose a system model that integrates wearable devices equipped with photoplethysmography (PPG) sensors, transmitting data to a smartphone and then to a cloud server. Two novel algorithms are developed to segment and label features periodically, predicting drowsiness levels based on HRV derived from PPG signals. …”
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The Application of Artificial Digital Models in X-Ray Computed Tomography (CT) of the Core in Solving the Problem of Binarization of the Void Space of Reservoir Rocks by O. A. Melkishev, Y. V. Savitsky, S. V. Galkin

“…The use of the proposed model for predicting boundary values on obtained tomograms demonstrated a high degree of consistency with actual data.…”
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Snow Distribution Patterns Revisited: A Physics‐Based and Machine Learning Hybrid Approach to Snow Distribution Mapping in the Sub‐Arctic by R. L. Crumley, C. L. Bachand, K. E. Bennett

“…We trained a random forest ML algorithm on tens of thousands of snow survey observations from a subarctic study area on the Seward Peninsula, Alaska, collected during peak snow water equivalent (SWE). …”
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Experimental assessment of communication delay's impact on connected automated vehicle speed volatility and energy consumption by Wan Li, Jackeline Rios-Torres, Boyu Wang, Zulqarnain H. Khattak

“…To fill the research gap, this study leverages the facilities at America Center of Mobility (ACM) Smart City Test Center to implement and evaluate a CAV merging control algorithm through vehicle-in-the-loop testing. This study aims at achieving three main objectives: (1) develop and implement a CAV merging control strategy in the experimental test bed through vehicle-in-the-loop testing, (2) propose analytical models to quantify the impacts of communication delay on the variability of CAV speed and energy consumption based on field experiment data, and (3) create a predictive model for energy usage considering various CAV attributes and dynamics, e.g., speed, acceleration, yaw rate, and communication delays. …”
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Interpretable machine learning model of effective mass in perovskite oxides with cross-scale features by Changjiao Li, Zhengtao Huang, Hua Hao, Zhonghui Shen, Guanghui Zhao, Ben Xu, Hanxing Liu

“…However, previous works were mainly devoted to algorithm improvement, while the essential multi-scale characteristics are not well addressed. …”
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An Integrative Computational Approach for Identifying Cotton Host Plant MicroRNAs with Potential to Abate CLCuKoV-Bur Infection by Muhammad Aleem Ashraf, Imran Shahid, Judith K. Brown, Naitong Yu

“…Using this suite of robust algorithms, the predicted repertoire of the cotton microRNA-binding landscape was determined for a CLCuKoV-Bur consensus genome sequence. …”
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Detection and Pattern Recognition of Chemical Warfare Agents by MOS-Based MEMS Gas Sensor Array by Mengxue Xu, Xiaochun Hu, Hongpeng Zhang, Ting Miao, Lan Ma, Jing Liang, Yuefeng Zhu, Haiyan Zhu, Zhenxing Cheng, Xuhui Sun

“…Finally, principal component analysis (PCA) and machine learning algorithms were applied for CWA classification. The obtained PCA plots showed significant differences between groups, and the narrow neural network among the machine learning algorithms achieves a prediction accuracy of nearly 100.0%. …”
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Multi-objective autonomous eco-driving strategy: A pathway to future green mobility by He Tong, Liang Chu, Zheng Chen, Yonggang Liu, Yuanjian Zhang, Jincheng Hu

“…The IEDS is a data-driven autonomous driving solution to effectively control vehicle motion and energy management, developed based on refined deep reinforcement learning (DRL) algorithms, integrating safety and efficiency knowledge in autonomous driving through a multi-head deep Q network (DQN) with elaborate rewards for potentially dangerous collisions and fuel consumption. …”
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Natural Defense Mechanisms of the Human Brain against Chronic Ischemia by A. V. Sergeev, S. S. Stepanov, V. A. Akulinin, A. V. Mytsik

“…Morphometric analysis was made using the specially developed algorithms to verify neurons and their elements in the ImageJ 1.46 program. …”
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AAMS-YOLO: enhanced farmland parcel detection for high-resolution remote sensing images by Binyao Wang, Ya’nan Zhou, Weiwei Zhu, Li Feng, Jinke He, Tianjun Wu, Jiancheng Luo, Xin Zhang

“…Extensive experiments on a custom-built dataset validate AAMS-YOLO's effectiveness, demonstrating notable enhancements over the baseline in mAP0.5 (2.6%) and mAP0.5:0.95 (2.2%) and surpassing other state-of-the-art algorithms. The proposed model excels in detecting small and densely overlapping objects through advanced feature fusion and multi-scale processing strategies.…”
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Resampling-driven machine learning models for enhanced high streamflow forecasting by Nureehan Salaeh, Sirimon Pinthong, Warit Wipulanusat, Uruya Weesakul, Jakkarin Weekaew, Quoc Bao Pham, Pakorn Ditthakit

“…These results present a promising framework for high streamflow prediction that can be adapted and applied to other river basins.…”
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Analysis of prognostic factors and nomogram construction for postoperative survival of triple-negative breast cancer by Chenxi Wang, Xiangqian Zhao, Dawei Wang, Jinyun Wu, Jizhen Lin, Weiwei Huang, Yangkun Shen, Qi Chen

“…This study utilized the SEER database to investigate clinicopathologic characteristics and prognostic factors in TNBC patients.MethodsMachine learning algorithms specifically Gradient Boosting Machines (XGBoost) and Random Forest classifiers were applied to develop survival prediction models and identify key prognostic markers.ResultsResults indicated significant predictors of survival, including tumor size, lymph node involvement, and distant metastases. …”
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Use of machine learning in osteoarthritis research: a systematic literature review by Francis Berenbaum, Jérémie Sellam, David Klatzmann, Atul J Butte, Karine Louati, Encarnita Mariotti-Ferrandiz, Marie Binvignat, Valentina Pedoia

“…Twelve articles were related to diagnosis, 7 to prediction, 4 to phenotyping, 12 to severity and 11 to progression. …”
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Enhancing Breast Cancer Diagnosis With Multi-Resolution Vision Transformers and Robust Decision-Making by Margo Sabry, Hossam Magdy Balaha, Khadiga M. Ali, Tayseer Hassan A. Soliman, Dibson Gondim, Mohammed Ghazal, Norah Saleh Alghamdi, Ayman El-Baz

“…Postprocessing techniques, including region-growing and fast-marching level set algorithms, refine whole-slide image (WSI) prediction and postprocessing quality. …”
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Method for Knowledge Transfer via Multi-Task Semi-Supervised Self-Paced by Yao Zhao, Hongying Liu, Huaxian Pan, Zhen Song, Chunting Liu, Anni Wei, Baoshuang Zhang, Wei Lu

“…Experimental results on several benchmark datasets show that our method achieves a performance gain of 3%-15% in classification accuracy compared to baseline algorithms, along with significant advantages in convergence speed.…”
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A simplification method for large-scale urban point clouds considering diversity of terrain object features by Shen Shen, Yonghong Wu, Hongwei Zhang, Jiankai Lu, Hong Fan

“…Comparative experimental results indicate that compared to baseline algorithms that consider geometric features on the test dataset, the proposed method achieves about 16.5-min processing time for 13-million-point dataset, significantly faster than other algorithms. …”
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Explainable light-weight deep learning pipeline for improved drought stress identification by Aswini Kumar Patra, Aswini Kumar Patra, Lingaraj Sahoo

“…Sensor-based imaging data serves as a rich source of information for machine learning and deep learning algorithms, facilitating further analysis that aims to identify drought stress. …”
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An approach to forecasting damage due to unfavorable circumstances associated with indistinguishability of source data by V. F. Zolotukhin, A. V. Matershev, L. A. Podkolzina

“…There is an acute problem of reduction of the likelihood of unwanted events and mitigation of possible damage. …”
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Improved YOLO for long range detection of small drones by Sicheng Zhou, Lei Yang, Huiting Liu, Chongqin Zhou, Jiacheng Liu, Yang Wang, Shuai Zhao, Keyi Wang

“…Additionally, deep learning algorithms often demand substantial computational resources, limiting their use on low-capacity platforms. …”
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