Predictive Modeling of Yoga's Impact on Venous Clinical Severity Scoring Using Gaussian Process Classification and Advanced Optimization Algorithms by Yazdan Ashgevari, Faranak Kazemi

“…This research explores the impact of yoga on Venous Clinical Severity Score (VCSS) using machine learning techniques. The study employs the Adaptive Opposition Slime Mould Algorithm (AOSM) and Mountain Gazelle Optimizer (MGO) to enhance the predictive capabilities of a Gaussian Process Classification (GPC) model. …”
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Hybrid adaptive method for lane detection of degraded road surface condition by Khaled H. Almotairi

“…This study proposes an adaptive hybrid lane detection method that adopts the advantages of traditional vision-based and machine-learning-based approaches. …”
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Prediction of compressive strength of fiber-reinforced concrete containing silica (SiO2) based on metaheuristic optimization algorithms and machine learning techniques by Hamed Shokrnia, Ashkan KhodabandehLou, Peyman Hamidi, Fedra Ashrafzadeh

“…So, this study integrates the ANFIS (adaptive neuro-fuzzy inference system) and ELM (extreme learning machine) machine learning models with three optimization algorithms, i.e., WCA (water cycle algorithm), PSO (particle swarm optimization), and GWO (grey wolf optimizer) to precisely estimate the CS of fiber-reinforced concrete (FRC) containing SiO2. …”
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Research on Rolling Bearing Fault Diagnosis Method Based on MPE and Multi-Strategy Improved Sparrow Search Algorithm Under Local Mean Decomposition by Haodong Chi, Huiyuan Chen

“…This algorithm integrates an adaptive Levy flight mechanism and dynamic reverse learning. …”
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Personalization of AI-Based Digital Twins to Optimize Adaptation in Industrial Design and Manufacturing—Review by Izabela Rojek, Dariusz Mikołajewski, Ewa Dostatni, Jan Cybulski, Mirosław Kozielski

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YOLO-LSM: A Lightweight UAV Target Detection Algorithm Based on Shallow and Multiscale Information Learning by Chenxing Wu, Changlong Cai, Feng Xiao, Jiahao Wang, Yulin Guo, Longhui Ma

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Adaptive Laguerre Filters for Elimination of Power Line Noise from EMG Signals: Fixed and Fuzzy Step Size Approaches by Mojtaba Malboubi, Seyed Mohammad Kargar, Seyed Ali Mousavi

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Evaluating GRU Algorithm and Double Moving Average for Predicting USDT Prices: A Case Study 2017-2024 by Rahmat Rizky, Munirul ula, Zara Yunizar

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OperonSEQer: A set of machine-learning algorithms with threshold voting for detection of operon pairs using short-read RNA-sequencing data. by Raga Krishnakumar, Anne M Ruffing

“…We present OperonSEQer, a set of machine learning algorithms that uses the statistic and p-value from a non-parametric analysis of variance test (Kruskal-Wallis) to determine the likelihood that two adjacent genes are expressed from the same RNA molecule. …”
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Construction of Clinical Predictive Models for Heart Failure Detection Using Six Different Machine Learning Algorithms: Identification of Key Clinical Prognostic Features by Qu FZ, Ding J, An XF, Peng R, He N, Liu S, Jiang X

“…Following the elimination of features with significant missing values, the remaining features were utilized to construct predictive models employing six machine learning algorithms. The optimal model was selected based on various performance metrics, including the area under the curve (AUC), accuracy, precision, recall, and F1 score. …”
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Technical note: Applicability of physics-based and machine-learning-based algorithms of a geostationary satellite in retrieving the diurnal cycle of cloud base height by M. Wang, M. Min, J. Li, H. Lin, Y. Liang, B. Chen, Z. Yao, N. Xu, M. Zhang

“…<p>Two groups of retrieval algorithms, physics based and machine learning (ML) based, each consisting of two independent approaches, have been developed to retrieve cloud base height (CBH) and its diurnal cycle from Himawari-8 geostationary satellite observations. …”
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A comprehensive machine learning-based models for predicting mixture toxicity of azole fungicides toward algae (Auxenochlorella pyrenoidosa) by Li-Tang Qin, Xue-Fang Tian, Jun-Yao Zhang, Yan-Peng Liang, Hong-Hu Zeng, Ling-Yun Mo

“…In this study, we applied 12 algorithms, namely, k-nearest neighbor (KNN), kernel k-nearest neighbors (KKNN), support vector machine (SVM), random forest (RF), stochastic gradient boosting (GBM), cubist, bagged multivariate adaptive regression splines (Bagged MARS), eXtreme gradient boosting (XGBoost), boosted generalized linear model (GLMBoost), boosted generalized additive model (GAMBoost), bayesian regularized neural networks (BRNN), and recursive partitioning and regression trees (CART) to build ML models for 225 mixture toxicity of azole fungicides towards Auxenochlorella pyrenoidosa. …”
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Production Capacity Prediction for Tight Gas Reservoirs Based on ADASVRLGBM by MENG Sihai, ZHANG Zhansong, GUO Jianhong, HAN Zihao, ZENG Weijie, LYU Hengyang

“…This paper proposes an innovative production capacity prediction model, ADASVRLGBM, which integrates AdaBoost (Adaptive Boosting), SVR (Support Vector Regression), and LGBM (Light Gradient Boosting Machine) algorithms. …”
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A novel region based neighbors searching classification algorithm for big data by Rajib Kumar Halder, Mohammed Nasir Uddin, Md Ashraf Uddin

“…The K-Nearest Neighbors (KNN) algorithm remains a cornerstone of machine learning due to its intuitive design and effectiveness in classification tasks. …”
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State of the Art in Automated Operational Modal Identification: Algorithms, Applications, and Future Perspectives by Hasan Mostafaei, Mahdi Ghamami

“…This review examines SSI-based algorithms, covering essential components such as system identification, noise mode elimination, stabilization diagram interpretation, and clustering techniques for mode identification. …”
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Random Ensemble MARS: Model Selection in Multivariate Adaptive Regression Splines Using Random Forest Approach by Mehmet Ali Cengiz, Dilek Sabancı

“…Multivariate Adaptive Regression Splines (MARS) is a supervised learning model in machine learning, not obtained by an ensemble learning method. …”
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A state-of-the-art novel approach to predict potato crop coefficient (Kc) by integrating advanced machine learning tools by Saad Javed Cheema, Masoud Karbasi, Gurjit S. Randhawa, Suqi Liu, Travis J. Esau, Kuljeet Singh Grewal, Farhat Abbas, Qamar Uz Zaman, Aitazaz A. Farooque

“…The study was conducted at drainage-type lysimeters placed in the potato field with three types of soils (sandy loam, loamy sand, and loam). A machine learning approach using XGBoost, optimized with the Chaos Game algorithm (CGO-XGBoost), was employed to predict Kc. …”
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An Adaptive SVD-Based Approach to Clutter Suppression for Slow-Moving Targets by Yuhao Hou, Baixiao Chen

“…To address this limitation, this paper proposes an adaptive singular value decomposition (A-SVD) method utilizing support vector machines (SVM). …”
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An Innovative Online Adaptive High-Efficiency Controller for Micro Gas Turbine: Design and Simulation Validation by Rui Yang, Yongbao Liu, Xing He, Zhimeng Liu

“…To evaluate the performance changes of the gas turbine, we applied deep learning techniques to enhance the extreme learning machine (ELM) algorithm, resulting in the development of a high-precision, high-real-time deep extreme learning machine (DL_ELM) prediction model. …”
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Development of machine learning models to predict the risk of fungal infection following flexible ureteroscopy lithotripsy by Haofang Zhang, Changbao Xu, Chenge Hu, Yunlai Xue, Daoke Yao, Yifan Hu, Ankang Wu, Miao Dai, Hang Ye

“…Use LASSO regression to screen clinical features based on the training set. Nine machine learning algorithms, Logistic Regression (LR), k-Nearest Neighbours (KNN), Support Vector Machines (SVM), Random Forest (RF), Categorical Boosting (CatBoost), eXtreme Gradient Boosting (XGBoost), Adaptive Boosting (AdaBoost), Gradient Boosting Machines (GBM), and Neural Network (NNet), were used to construct models. …”
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