Prediction of anisotropic property of activated metal inert gas welding by employing different supervised machine learning models by Ruturaj U. Kakade, Nitin Khedkar, Amol Dalavi

“…Machine learning models Linear Regression, Random Forest Regression, and Support Vector Regression (SVR) were applied to predict TS based on welding parameters.• The SVR model achieved the best predictive performance, with an R² of 0.8750 and a model accuracy of 96.73 %.• The results confirm the potential of SVR for accurately forecasting TS in A-MIG welded EN10028, facilitating process optimization in pressure applications…”
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Hybrid Gradient Descent Grey Wolf Optimizer for Machine Learning Performance Enhancement by Sri Rossa Aisyah Puteri Baharie, Sugiyarto Surono, Aris Thobirin

“…This study aims to improve diabetes prediction performance using the Support Vector Machine (SVM) model optimized with the Hybrid Gradient Descent Gray Wolf Optimizer (HGD-GWO) method. …”
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Improved Technique in Arabic Handwriting Recognition by Ammar A. Al-Hamadani, Maad Kamal Al-Anni, Gamil R. S. Qaid, Najran Nasser Hamood

“…  Arabic handwriting recognition has significant applications in fields like postal sorting, handwritten text identification, and cheque processing. The process involves several steps: preprocessing, feature extraction, and classification. …”
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Building Fire Location Predictions Based on FDS and Hybrid Modelling by Yanxi Cao, Hongyan Ma, Shun Wang, Yingda Zhang

“…Different scenarios were built to simulate the spatial and temporal distributions of key parameters such as temperature, smoke, and CO concentration during the fire process. Combining convolutional neural networks (CNNs) and support vector machines (SVMs) for prediction, the fire-source location prediction model with temperature, smoke, and CO concentration as feature quantities was constructed, and the hyperparameters affecting the model accuracy and generalisation were optimised by the Crested Porcupine Optimizer (CPO) algorithm. …”
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Enhanced Category-Feature Association Measure by Soran S. Badawi, Ari M. Saeed, Sara A. Ahmed, Diyari A. Hassan

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Prediction of cholesterol level in patients with myocardial infarction based on medical data mining methods by Cemil Colak, Mehmet C. Colak, Necip Ermis, Nevzat Erdil, Ramazan Ozdemir

“…The proposed MLP ANNs modelmight be employed for predicting the level of cholesterol for MI patients in clinical decision support process. …”
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3D path planning for UAV based on A hybrid algorithm of marine predators algorithm with quasi-oppositional learning and differential evolution by Binbin Tu, Fei Wang, Xiaowei Han

“…The greedy selection of test and target vectors further accelerates population convergence. …”
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Geospatial digital mapping of soil organic carbon using machine learning and geostatistical methods in different land uses by Yahya Parvizi, Shahrokh Fatehi

“…Abstract Improper management of soil resources leads to the destruction of soil organic carbon (SOC) stock and, as a result, the reduction of soil quality, as well as accelerating the process of climate change through the release of SOC into the atmosphere. …”
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Deep learning driven methodology for the prediction of mushroom moisture content using a novel LED-based portable hyperspectral imaging system by Kai Yang, Ming Zhao, Dimitrios Argyropoulos

“…For comparison purposes, state-of-the-art machine learning algorithms, i.e., support vector machine regression (SVMR) and partial least squares regression (PLSR) were also investigated for the model development based on five spectra pre-processed methods using two different lighting systems i.e., enhanced light-emitting diode (LED) and tungsten halogen (TH). …”
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Impact-induced energy release of typical HCP metal/PTFE/W reactive materials: Experimental study and predictive modeling via machine learning by Zhenwei Zhang, Weixi Tian, Tianyi Wang, Zhiyuan Liu, Yansong Yang, Chao Ge, Lei Guo, Yuan He, Chuanting Wang, Yong He

“…Additionally, the energy release efficiency of HCP metal/PTFE/W RMs under impact was predicted using the support vector regression (SVR) kernel function model. The datasets of Zr/PTFE/W RMs and Ti/PTFE/W RMs with W contents of 0%, 25%, 50%, and 75% were used as test sets, respectively. …”
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An Identification Method of Polarization Modulation for Ship and Combined Corner Reflector Based on Civil Marine Radar by Di ZHU, Fulai WANG, Chen PANG, Yongzhen LI

“…A support vector machine is then employed to achieve accurate target identification. …”
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Deformation prediction in innovative implant design with machine learning approaches by Mehmet Onur Yağır, Muhammed Fatih Pekşen, Şaduman Şen, Uğur Şen

“…The data obtained were modeled using a finite element analysis system (ANSYS), and instantaneous deformation data were collected during the modeling process. These instantaneous deformation data were included as an additional feature in the ML dataset and used in the analysis processes.In the study, the Kernel Support Vector Machine (Kernel SVM), Kernel Logistic Regression (Kernel LR), and extreme gradient boosting (XGBoost) classification methods were employed to assess the impact of the implant on the jawbone. …”
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Functional literacy of students as a benchmark of modern education by N. A. Aksenova, N. L. Moskovskaya

“…An educated society guarantees the successful functioning of the state, and the educational process of forming functional literacy ensures it.…”
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Enhanced Deep Autoencoder-Based Reinforcement Learning Model with Improved Flamingo Search Policy Selection for Attack Classification by Dharani Kanta Roy, Hemanta Kumar Kalita

“…Initially, the pre-processing is accomplished using null value dropping and standard scaler normalization. …”
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Deep learning application to roughness classification of road surface conditions through an e-scooter’s ride quality by Asher Virin, Lalitphat Khongsomchit, Sakdirat Kaewunruen

“…Three machine learning models—Random Forest Classifier, Extreme Gradient Boosting (XGBoost), and Support Vector Machine (SVM) with k-means clustering—were tested using various hyperparameter tuning, post-processing, and data splitting strategies. …”
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Enhancing the interpretability of electricity consumption forecasting models for mining enterprises using SHapley Additive exPlanations by Pavel V. Matrenin, Alina I. Stepanova

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AI-driven hybrid rehabilitation: synergizing robotics and electrical stimulation for upper-limb recovery after stroke by Ismail Ben Abdallah, Ismail Ben Abdallah, Yassine Bouteraa, Yassine Bouteraa, Ahmed Alotaibi, Ahmed Alotaibi

“…A ROS2-based architecture enables real-time signal processing, adaptive control, and remote supervision by clinicians. …”
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The Effect of CDKN1A on the Expression of Genes Related to Milk Protein and Milk Fat Synthesis in Bovine Mammary Epithelial Cells by Yuanyuan Zhang, Junxi Liang, Kai Zhang, Hong Su, Daqing Wang, Min Zhang, Feifei Zhao, Zhiwei Sun, Zhimin Wu, Guifang Cao, Yong Zhang

“…Its content and composition directly affect the nutritional value, processing characteristics, and economic benefits of dairy products. …”
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Improving Cardiovascular Disease Prediction through Stratified Machine Learning Models and Combined Datasets by Tara Yousif Mawlood, Alla Ahmad Hassan, Rebwar Khalid Muhammed, Aso M. Aladdin, Tarik A. Rashid, Bryar A. Hassan

“…Seven classification algorithms – logistic regression, random forest (RF), support vector machine (SVM), Gaussian naive Bayes (GNB), gradient boosting (GB), K-nearest neighbors, and decision tree (DT) – were employed. …”
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A non-invasive computer-aided personalized diagnosis system for Osteopenia and Osteoporosis by Hadeel Osama El-Sisi, Fatma El-Zahraa Ahmed El-Gamal, Noha Ahmed Hikal

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