A practical guide for nephrologist peer reviewers: evaluating artificial intelligence and machine learning research in nephrology by Yanni Wang, Wisit Cheungpasitporn, Hatem Ali, Jianbo Qing, Charat Thongprayoon, Wisit Kaewput, Karim M. Soliman, Zhengxing Huang, Min Yang, Zhongheng Zhang

“…Artificial intelligence (AI) and machine learning (ML) are transforming nephrology by enhancing diagnosis, risk prediction, and treatment optimization for conditions such as acute kidney injury (AKI) and chronic kidney disease (CKD). …”
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A Mitochondria‐Related Signature in Diffuse Large B‐Cell Lymphoma: Prognosis, Immune and Therapeutic Features by Zhen‐Zhong Zhou, Jia‐Chen Lu, Song‐Bin Guo, Xiao‐Peng Tian, Hai‐Long Li, Hui Zhou, Wei‐Juan Huang

“…Conclusions We established a novel prognostic mitochondria‐related signature by machine learning algorithm, which also demonstrated outstanding predictive value in tumor microenvironment and responses to therapies.…”
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Comparing the potential of tree-based and area-based forest height metrics for aboveground biomass estimation in complex forest landscapes by Weiyan Liu, Yuling Chen, Haitao Yang, Guangcai Xu, Shiyu Yan, Lingjun Li, Ling Chen, Qinghua Guo

“…Two modeling approaches—parametric mixed-effects models (MEM) and non-parametric machine learning (ML) algorithms—were applied to evaluate predictive accuracy. …”
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Radiomics analysis of thoracic vertebral bone marrow microenvironment changes before bone metastasis of breast cancer based on chest CT by Hao-Nan Zhu, Yi-Fan Guo, YingMin Lin, Zhi-Chao Sun, Xi Zhu, YuanZhe Li

“…Multiple machine learning algorithms were utilized to construct various radiomics models for predicting the risk of bone metastasis, and the model with optimal performance was integrated with clinical features to develop a nomogram. …”
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In-Memory Versus Disk-Based Computing with Random Forest for Stock Analysis: A Comparative Study by Chitra Joshi, Chitrakant Banchorr, Omkaresh Kulkarni, Kirti Wanjale

“…The effectiveness of these frameworks plays a crucial role in determining data processing speed, model training efficiency and predictive accuracy. As data become increasingly large, diverse and fast-moving, conventional processing systems often fall short of the performance required for modern analytics.Objective: This research seeks to thoroughly assess the performance of two prominent big data processing frameworks-Apache Spark (in-memory computing) and MapReduce (disk-based computing)-with a focus on applying random forest algorithms to predict stock prices. …”
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Virtual Communicative Review of Children’s Art Schools of Irkutsk Region by Dvorovenko Olga

“…The accounts with high engagement rates became involved in social media recommendation algorithms.…”
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An Introduction to the Laplace-Sumudu-Elzaki Transformation and Its Applications in Mathematical Physics by Qike Li, Wei Wang

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A Hybrid Particle Swarm Optimizer for Curriculum Sequencing Problem by Xianjie Peng, Xiaonan Sun, Zhen He

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Modern aspects of diagnostics and treatment of <i>H. pylori</i> infection from standpoint of stomach cancer prophylaxis by A. A. Sheptulin, T. L. Lapina

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Machine Learning Applied to Improve Prevention of, Response to, and Understanding of Violence Against Women by Mariana Carolyn Cruz-Mendoza, Roberto Angel Melendez-Armenta, Juana Canul-Reich, Julio Muñoz-Benítez

“…The methodology integrates Random Forest (RF) and Gradient Boosting Classifier (GBC) algorithms to classify IPV cases by leveraging historical data for predictive analysis. …”
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Improving thyroid disorder diagnosis via innovative stacking ensemble learning model by Ayesha Hassan, Shabana Ramzan, Ali Raza, Muhammad Munwar Iqbal, Aseel Smerat, Norma Latif Fitriyani, Muhammad Syafrudin, Seung Won Lee

“…Five advanced machine learning (ML) algorithms, logistic regression, support vector machine, decision tree, random forest, and gradient boosting are employed to develop predictive models. …”
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A digital twin-enabled fog-edge-assisted IoAT framework for Oryza Sativa disease identification and classification by Goluguri N.V. Rajareddy, Kaushik Mishra, Satish Kumar Satti, Gurpreet Singh Chhabra, Kshira Sagar Sahoo, Amir H. Gandomi

“…To boost the model's predictive accuracy, the Chaotic Honey Badger Algorithm (CHBA) is employed to optimize the CNN hyperparameters, resulting in an impressive average accuracy of 93.5 %. …”
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The application of suitable sports games for junior high school students based on deep learning and artificial intelligence by Xueyan Ji, Shamsulariffin Bin Samsudin, Muhammad Zarif Bin Hassan, Noor Hamzani Farizan, Yubin Yuan, Wang Chen

“…With the rapid development of artificial intelligence and deep learning technology, a new opportunity is provided for physical education innovation. This study intends to develop a Spatial Temporal-Graph Convolutional Network (ST-GCN) action detection algorithm based on the MediaPipe framework. …”
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Deep reinforcement learning applications and prospects in industrial scenarios by JING TAN, Ligang YANG, Xiaorui LI, Zhaolin YUAN, Yunduan CUI, Chao YAO, Zongjie WANG, Xiaojuan BAN

“…Central to these systems are control algorithms, which enable the automation of operations, optimization of process parameters, and reduction of operational costs. …”
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Physics-informed deep learning model for line-integral diagnostics across fusion devices by Cong Wang, Weizhe Yang, Haiping Wang, Renjie Yang, Jing Li, Zhijun Wang, Yixiong Wei, Xianli Huang, Chenshu Hu, Zhaoyang Liu, Xinyao Yu, Changqing Zou, Zhifeng Zhao

“…The incorporation of the PILF has been shown to correct the model’s predictions, bringing the back-projections closer to the actual inputs and reducing the errors associated with inversion algorithms. …”
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Optimal schedule for extended basic period approach of economic lot scheduling problem by Waldemar Kaczmarczyk

“…Experiments with several problem instances described in the literature confirmed that using the new model within a heuristic algorithm ensures a significant cost reduction for the entire elsp. …”
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A reduced-complexity compressed sensing channel estimation for underwater acoustic channel by Xuan YU, Xuan GENG

“…Aiming at the sparse characteristics of underwater acoustic channels for shallow seas, a reduced-complexity look-ahead backtracking orthogonal matching pursuit (RC-LABOMP) channel estimation algorithm was proposed.Firstly, two types of support sets of orthogonal matching pursuit and subspace pursuit channel estimation algorithms were calculated, and then prior information based on the intersection and union of the two support sets were preprocessed.At last, the preprocessed prior information was used to complete look-ahead backtracking orthogonal matching pursuit channel estimation.The preprocessed prior information leads to the decrease of the iteration number of original LABOMP, and reduction of the atom index range, thus the proposed algorithm can reduce the computational complexity of original LABOMP significantly.In addition, combining the proposed algorithm with the underwater acoustic Turbo equalization system is more suitable for underwater acoustic communication systems.Simulation results show that the proposed algorithm demonstrates high estimation accuracy and low bit error rate performance under both conditions of random channels and underwater acoustic channels.It also reduces the computational complexity of the LABOMP algorithm.Therefore, it is an effective method for shallow seas underwater acoustic channels estimation algorithm.…”
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Measuring the Effectiveness of Carbon-Aware AI Training Strategies in Cloud Instances: A Confirmation Study by Roberto Vergallo, Luca Mainetti

“…Such strategies—natively Cloud-based—use the time resource to postpone or pause the training algorithm when the carbon intensity reaches a threshold. …”
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A reduced-complexity compressed sensing channel estimation for underwater acoustic channel by Xuan YU, Xuan GENG

“…Aiming at the sparse characteristics of underwater acoustic channels for shallow seas, a reduced-complexity look-ahead backtracking orthogonal matching pursuit (RC-LABOMP) channel estimation algorithm was proposed.Firstly, two types of support sets of orthogonal matching pursuit and subspace pursuit channel estimation algorithms were calculated, and then prior information based on the intersection and union of the two support sets were preprocessed.At last, the preprocessed prior information was used to complete look-ahead backtracking orthogonal matching pursuit channel estimation.The preprocessed prior information leads to the decrease of the iteration number of original LABOMP, and reduction of the atom index range, thus the proposed algorithm can reduce the computational complexity of original LABOMP significantly.In addition, combining the proposed algorithm with the underwater acoustic Turbo equalization system is more suitable for underwater acoustic communication systems.Simulation results show that the proposed algorithm demonstrates high estimation accuracy and low bit error rate performance under both conditions of random channels and underwater acoustic channels.It also reduces the computational complexity of the LABOMP algorithm.Therefore, it is an effective method for shallow seas underwater acoustic channels estimation algorithm.…”
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Method of automatic coregistration of digital remote sensing images from different sources by A.N. Borisov, V.V. Myasnikov, V.V. Sergeev

“…To achieve a universal and robust solution in the latter stages, the best-known algorithms were compared: SIFT, SAR-SIFT, RIFT, and the trainable RoMa. …”
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