Diurnal distribution of phytoplankton in large shallow lakes based on time series clustering by Yanhong Chen, Haibin Cai, Yiqing Gong, Kun Lu, Jingqiao Mao, Weiyu Chen, Kang Wang, Huan Gao, Mingming Tian

“…This study demonstrates the potential of employing unsupervised clustering algorithms for identifying the diurnal dynamics of the phytoplankton distribution under complex influences, which can aid in optimizing the management and monitoring of shallow lakes.…”
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A Shared Control Method of Multiobjective Motion Fusion for Surgical Robot by Xilin Xiao, Xiaojian Li, Hangjie Mo, Yudong Shi, Jing Fang, Ling Li, Bo Ouyang, Shanlin Yang

“…Shared control combines human intelligence with autonomous algorithms, demonstrating potential for enhancing the intricate and dynamic operational capabilities of robots. …”
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Artificial Intelligence Models and Tools for the Assessment of Drug–Herb Interactions by Marios Spanakis, Eleftheria Tzamali, Georgios Tzedakis, Chryssalenia Koumpouzi, Matthew Pediaditis, Aristides Tsatsakis, Vangelis Sakkalis

“…Artificial intelligence (AI) has emerged as a powerful tool in medical sciences that is revolutionizing various fields of drug research. AI algorithms can analyze large-scale biological data and identify molecular targets and pathways advancing pharmacological knowledge. …”
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Combination of Remote Sensing and Artificial Intelligence in Fruit Growing: Progress, Challenges, and Potential Applications by Danielle Elis Garcia Furuya, Édson Luis Bolfe, Taya Cristo Parreiras, Jayme Garcia Arnal Barbedo, Thiago Teixeira Santos, Luciano Gebler

“…The analysis included the use of remote sensing (orbital and proximal) imagery and ML/DL algorithms to map crop areas, detect diseases, and monitor crop development, among other analyses. …”
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Artificial intelligence-driven modeling of biodiesel production from fats, oils, and grease (FOG) with process optimization via particle swarm optimization by Badril Azhar, Muhammad Ikhsan Taipabu, Cries Avian, Karthickeyan Viswanathan, Wei Wu, Raymond Lau

“…A ML model evaluation, using various algorithms, identify XGBoost, Extra Trees, Gradient Boosting, LGBM, and Random Forest demonstrate the best performer for predicting process parameters, achieving an R2 value of nearly to 1. …”
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Applying Machine Learning Techniques to Identify Undiagnosed Patients with Exocrine Pancreatic Insufficiency by Bruce Pyenson, Maggie Alston, Jeffrey Gomberg, Feng Han, Nikhil Khandelwal, Motoharu Dei, Monica Son, Jaime Vora

“… # Methods A machine learning algorithm was developed in Scikit-learn, a Python module. …”
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Investigating the impact of fatty acid profiles on biodiesel lubricity using artificial intelligence techniques by Atthaphon Maneedaeng, Attasit Wiangkham, Atthaphon Ariyarit, Anupap Pumpuang, Ekarong Sukjit

“…To further analyze the impact of fatty acid composition on lubricity, an artificial intelligence (AI)-based approach using the Adaptive Boosting (AdaBoost) algorithm was implemented. The AI model effectively predicts wear scar diameter, friction coefficient, and film formation, providing insights into the complex interactions between fatty acid profiles and tribological performance. …”
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Validation of the 18-gene classifier as a prognostic biomarker of distant metastasis in breast cancer. by Skye Hung-Chun Cheng, Tzu-Ting Huang, Yu-Hao Cheng, Tee Benita Kiat Tan, Chen-Fang Horng, Yong Alison Wang, Nicholas Shannon Brian, Li-Sun Shih, Ben-Long Yu

“…The 18-gene scoring algorithm defines scores as: <21, low risk; ≥21, high risk. …”
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Multimodal ultrasound deep learning to detect fibrosis in early chronic kidney disease by Xiachuan Qin, Xiaoling Liu, Linlin Xia, Qi Luo, Chaoxue Zhang

“…The AUC of the multimodal US DL model was significantly better than that of the single-mode DL and clinical models. The DL algorithm developed using multimodal US images can effectively predict early fibrosis in patients with CKD with significantly greater accuracy than single-mode DL or clinical models.…”
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Bioinformatic and clinical experimental assay uncovers resistance and susceptibility mechanisms of human glioblastomas to temozolomide and identifies new combined and individual su... by Alexander Modestov, Marianna Zolotovskaia, Maria Suntsova, Galina Zakharova, Aleksander Seryakov, Ivana Jovcevska, Jernej Mlakar, Elena Poddubskaya, Aleksey Moisseev, Grigory Vykhodtsev, Sergey Roumiantsev, Maksim Sorokin, Victor Tkachev, Aleksander Simonov, Anton Buzdin

“…Conclusion: In this study, a comprehensive analysis of the expression of 361 DNA repair genes and activation levels of 38 DNA repair pathways revealed 13 potential survival biomarkers with increased prognostic potential compared to MGMT methylation. We algorithmically reconstructed the TMZ sensitivity pathway with strong predictive capacity in GBM.…”
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Efficiently Learning an Encoder That Classifies Token Replacements and Masked Permuted Network-Based BIGRU Attention Classifier for Enhancing Sentiment Classification of Scientific... by Muhammad Inaam Ul Haq, Khalid Mahmood, Qianmu Li, Ashok Kumar Das, Sachin Shetty, Majid Hussain

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Computational hybrid analysis of drug diffusion in three-dimensional domain with the aid of mass transfer and machine learning techniques by Mohammed Alqarni, Ali Alqarni

“…Additionally, $$\:\nu\:$$ -SVR exhibits the lowest RMSE and MAE, showing excellent predictive accuracy compared to KRR and MLR. Overall, our analysis demonstrates the effectiveness of employing tree-based ensemble models coupled with BFO for accurately predicting chemical concentrations in three-dimensional space, with $$\:\nu\:$$ -SVR emerging as the most promising model for this task. …”
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Explainable Artificial Intelligence (XAI) for Flood Susceptibility Assessment in Seoul: Leveraging Evolutionary and Bayesian AutoML Optimization by Kounghoon Nam, Youngkyu Lee, Sungsu Lee, Sungyoon Kim, Shuai Zhang

“…We first employed the Tree-based Pipeline Optimization Tool (TPOT), an evolutionary AutoML algorithm, to construct baseline ensemble models using Gradient Boosting (GB), Random Forest (RF), and XGBoost (XGB). …”
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Toward a conceptual model to improve the user experience of a sustainable and secure intelligent transport system by Abdullah Alsaleh

“…A novel search and management protocol, supported by a tailored algorithm, was developed to enhance resource allocation success rates for vehicles within a defined area of interest. …”
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Rapid and Nondestructive Identification of Origin and Index Component Contents of Tiegun Yam Based on Hyperspectral Imaging and Chemometric Method by Yue Zhang, Yuan Li, Cong Zhou, Junhui Zhou, Tiegui Nan, Jian Yang, Luqi Huang

“…The optimal residual predictive deviation (RPD) values of starch, polysaccharide, and protein prediction models selected in this study were 5.21, 3.21, and 2.94, respectively. …”
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The doctor and patient of tomorrow: exploring the intersection of artificial intelligence, preventive medicine, and ethical challenges in future healthcare by Paulo Santos, Paulo Santos, Isabel Nazaré, Isabel Nazaré

“…AI-driven technologies, including real-time health monitoring and predictive analytics, offer new personalized preventive care possibilities. …”
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Modelling Ergonomic Hazard Risks in Manual Handling: Insights from Ponorogo’s Traditional Industry by Dian Afif Arifah, Ratih Andhika Akbar Rahma, Triana Harmini, Dhiya Irsyad Hafidz

“…This study aims to develop a model to evaluate and predict ergonomic hazards using a neural network algorithm, focusing on the relationship between manual handling postures and musculoskeletal pain in 12 body regions. …”
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Modelling the future of cleaner energy: Explainable artificial intelligence model for green hydrogen production rate estimation by Okorie Ekwe Agwu, Saad Alatefi, Ahmad Alkouh

“…The connection weights algorithm applied to the model enhances its explainability by illustrating the relative contributions of each input variable and their impacts on hydrogen yield. …”
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Classifying detrital zircon U-Pb age distributions using automated machine learning by Jack W. Fekete, Glenn R. Sharman, Xiao Huang

“…Applied to the North American Cordillera dataset, AutoML achieves an ∼0.91 F1 score when predicting between foreland and forearc basin tectonic settings and an ∼0.71 F1 score when predicting subbasins within these settings, outperforming both RF and R2. …”
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Chlorophyll-a in the Chesapeake Bay Estimated by Extra-Trees Machine Learning Modeling by Nikolay P. Nezlin, SeungHyun Son, Salem I. Salem, Michael E. Ondrusek

“…Our approach leverages the Extra-Trees (ET) algorithm, a tree-based ensemble method that offers predictive accuracy comparable to that of other ensemble models, while significantly improving computational efficiency. …”
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