Optimization of sports injury treatment through artificial intelligence: Methods for effective prevention, diagnosis and rehabilitation by Kimi Milić Marko, Sinanović Šćepan, Jestrović Vladimir

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Anatomical Parameter-driven Volumetric Modulated Arc Therapy Optimization in Left-sided Breast Cancer: A Machine Learning Framework for Lung Dose Prediction by Mukesh Kumar Zope, Deepali Patil, Rishi Raj, Seema Devi, Richa Madhawi

“…Purpose: The aim of this research is to assess different volumetric modulated arc therapy (VMAT) methods employed in the radiotherapy treatment of left-sided breast cancer, as well as to develop a predictive model for lung doses by leveraging machine learning techniques. …”
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Optimizing photovoltaic performance: Data-driven maximum power point prediction via advanced regression models by Maissa Farhat, Azzeddine Dekhane, Abdelhak Djellad, Maen Takruri, Aws Al-Qaisi, Oscar Barambones

“…These findings underscore the potential of machine learning techniques in optimizing PV system performance. …”
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Performance prediction and optimization of a high-efficiency tessellated diamond fractal MIMO antenna for terahertz 6G communication using machine learning approaches by Kamal Hossain Nahin, Jamal Hossain Nirob, Akil Ahmad Taki, Md Ashraful Haque, Narinderjit Singh Sawaran Singh, Liton Chandra Paul, Reem Ibrahim Alkanhel, Hanaa A. Abdallah, Abdelhamied A. Ateya, Ahmed A. Abd El-Latif

“…Leveraging a meta learner-based stacked generalization ensemble strategy, this study integrates classical machine learning techniques with an optimized multi-feature stacked ensemble to predict antenna properties with greater accuracy. …”
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Optimized classification of dental implants using convolutional neural networks and pre-trained models with preprocessed data by Reza Ahmadi Lashaki, Zahra Raeisi, Nasim Razavi, Mehdi Goodarzi, Hossein Najafzadeh

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Blockchain enabled deep learning model with modified coati optimization for sustainable healthcare disease detection and classification by Heba G. Mohamed, Fadwa Alrowais, Fahd N. Al-Wesabi, Mesfer Al Duhayyim, Anwer Mustafa Hilal, Abdelwahed Motwakel

“…For the optimal subset of features, the spotted hyena optimization algorithm (SHOA) model is used. …”
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An optimized ensemble ML-WQI model for reliable water quality prediction by minimizing the eclipsing and ambiguity issues by Ashifur Rahman, M. M. Mahbubul Syeed, Md. Rajaul Karim, Kaniz Fatema, Razib Hayat Khan, Mohammad Faisal Uddin

“…In addressing these, recently, data-driven approaches through the integration of machine learning or deep learning (ML/DL) techniques are notably applied to develop improved WQI models. …”
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Optimizing soybean variety selection for the Pan-African Trial network using factor analytic models and envirotyping by Maurício S. Araújo, João P. S. Pavan, André A. Stella, Bruno F. Fregonezi, Natally F. Lima, Erica P. Leles, Michelle F. Santos, Peter Goldsmith, Godfree Chigeza, Brian W. Diers, José B. Pinheiro

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Prediction on Permeability Coefficient of Continuously Graded Coarse-Grained Soils: A Data-Driven Machine Learning Method by Jinhua Wang, Haibin Ding, Lingxiao Guan, Yulin Wang

“…In this study, 64 coarse-grained soil (CGS) samples were designed using a negative exponential gradation equation (NEGE), and computational fluid dynamics–discrete element method (CFD-DEM) coupled seepage simulations were conducted to generate a permeability coefficient (k) dataset comprising 256 entries under varying porosity and gradation conditions. Three machine learning models—a neural network model (BPNN), a regression model (GPR), and a tree-based model (RF)—were employed to predict <i>k</i>, with hyperparameters optimized via particle swarm optimization (PSO) and four-fold cross-validation applied to improve generalization. …”
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Smart deep learning model for enhanced IoT intrusion detection by Faisal S. Alsubaei

“…Existing approaches, however, are usually hampered by the inability to effectively counter the sophisticated and evolving nature of such threats, especially in preprocessing optimization and hyperparameter tuning, which typically adopt conventional machine learning and deep learning models. …”
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NSA-CHG: An Intelligent Prediction Framework for Real-Time TBM Parameter Optimization in Complex Geological Conditions by Youliang Chen, Wencan Guan, Rafig Azzam, Siyu Chen

“…This study proposes an intelligent prediction framework integrating native sparse attention (NSA) with the Chen-Guan (CHG) algorithm to optimize tunnel boring machine (TBM) operations in heterogeneous geological environments. …”
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Predictive modelling of hexagonal boron nitride nanosheets yield through machine and deep learning: An ultrasonic exfoliation parametric evaluation by Jerrin Joy Varughese, Sreekanth M․S․

“…A suite of machine learning regression models including Adaptive Boosting (AdaBoost) Regressor, Random Forest (RF) Regressor, Linear Regressor (LR), and Classification and Regression Tree (CART) Regressor, was employed alongside a deep neural network (DNN) architecture optimized using various algorithms such as Adaptive Moment Estimation (Adam), Root Mean Square Propagation (RMS Prop), Stochastic Gradient Descent (SGD), and Limited-memory Broyden-Fletcher-Goldfarb-Shanno (L-BFGS). …”
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Gully Erosion Susceptibility Prediction Using High-Resolution Data: Evaluation, Comparison, and Improvement of Multiple Machine Learning Models by Heyang Li, Jizhong Jin, Feiyang Dong, Jingyao Zhang, Lei Li, Yucheng Zhang

“…This study employs multiple machine learning models to assess gully erosion susceptibility in this region. …”
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Syntactic complexity recognition and analysis in Chinese-English machine translation: A comparative study based on the BLSTM-CRF model. by Yongli Tian

“…To enhance the recognition and preservation of syntactic complexity in Chinese-English translation, this study proposes an optimized Bidirectional Long Short-Term Memory-Conditional Random Field (BiLSTM-CRF) model. …”
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Development of an HPLC–UV method for quantification of posaconazole in low-volume plasma samples: design of experiments and machine learning models by Fereshteh Bayat, Ali Hashemi Baghi, Zahra Abbasian, Simin Dadashzadeh, Reza Aboofazeli, Azadeh Haeri

“…The results of machine learning models were in line with the results of experimental design. …”
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Mitigating Climate Risk to China's Renewable Energy Generation by Power Plants Redeployment Using a Machine‐Learning Model by Lin Lv, Miaomiao Liu, Yuli Shan, Jinghang Xu, Jianxun Yang, Wen Fang, Zongwei Ma, Jun Bi

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Predicting Postoperative Blood Transfusion in Elderly Patients Undergoing Total Hip and Knee Arthroplasty Using Machine Learning Models by Liang D, Pang Y, Huang J, Che X, Zhou R, Ding X, Wang C, Zhao L, Han Y, Rong X, Li P

“…Accurate transfusion risk prediction is vital for optimizing perioperative blood management. Traditional models often fail to capture complex factor interactions, whereas machine learning enhances predictive accuracy. …”
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Predicting User Purchases From Clickstream Data: A Comparative Analysis of Clickstream Data Representations and Machine Learning Models by A. Aylin Tokuc, Tamer Dag

“…Through comprehensive experimentation, we compared multiple machine learning models, including LightGBM, decision trees, gradient boosting, SVC, and logistic regression, using real-world e-commerce clickstream data. …”
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Predictive modeling of hydrogen production and methane conversion from biomass-derived methane using machine learning and optimisation techniques by Adegboyega Bolu Ehinmowo, Bright Ikechukwu Nwaneri, Joseph Oluwatobi Olaide

“…The predicted methane conversion using the firefly-optimized support vector machine regressor was 72%, with the actual conversion being 68%. …”
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Inflow Prediction for Agricultural Reservoirs Using Disaster Prevention Measurement Data: A Comparison of TANK Model and Machine Learning by Bong-Kuk Lee, Joonyoung Choi, Kyoung Jae Lim, Jeongho Han

“…This superior performance of the RidgeCV model can be attributed to its effective learning of the relationship between inflow data and optimal moving average rainfall, as well as the prevention of overfitting through regularization. …”
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