AI-Based Prediction of Visual Performance in Rhythmic Gymnasts Using Eye-Tracking Data and Decision Tree Models by Ricardo Bernardez-Vilaboa, F. Javier Povedano-Montero, José Ramon Trillo, Alicia Ruiz-Pomeda, Gema Martínez-Florentín, Juan E. Cedrún-Sánchez

“…Background/Objective: This study aims to evaluate the predictive performance of three supervised machine learning algorithms—decision tree (DT), support vector machine (SVM), and k-nearest neighbors (KNN) in forecasting key visual skills relevant to rhythmic gymnastics. …”
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An automatic approach to detect skin cancer utilizing active infrared thermography by Ricardo F. Soto, Sebastián E. Godoy

“…Our previous research showed that spatio-temporal features can be extracted from suspicious lesions to accurately determine malignancy, which was applied in a distance-based classifier.In this study, we build on that foundation by introducing a set of novel spatial and temporal features that enhance classification accuracy and can be integrated into any machine learning approach. These features were implemented in a support-vector machine classifier to detect malignancy. …”
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Optimizing ML models for cybercrime detection: balancing performance, energy consumption, and carbon footprint through multi-objective optimization by Romil Rawat

“…The performance of models such as LSTM (Long short-term memory) and SVM (Support Vector Machine) is assessed in terms of F-measure, with computational environments (Google Colab vs. personal laptop) compared for sustainability. …”
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A Survey on Data Mining for Data-Driven Industrial Assets Maintenance by Eduardo Coronel, Benjamín Barán, Pedro Gardel

“…The survey also highlights the most frequently referenced data mining algorithms, such as the proportional hazard model, expert systems, support vector machines, random forest, autoencoder, and convolutional neural networks. …”
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Random Forest-Based Prediction of the Optimal Solid Ink Density in Offset Lithography by Laihu Peng, Hao Fan, Yubao Qi, Jianqiang Li

“…A comparison between the proposed prediction model and several mainstream machine-learning algorithms indicates that the Random Forest model performs superiorly in both the coefficient of determination (R²) and the mean squared error (MSE). …”
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Dyslexia Analysis and Diagnosis Based on Eye Movement by R. Vaitheeshwari, Chih-Hsuan Chen, Chia-Ru Chung, Hsuan-Yu Yang, Shih-Ching Yeh, Eric Hsiao-Kuang Wu, Mukul Kumar

“…This data extracts features such as eye movement metrics, word vectors, and saliency maps. We introduce a novel fusion model that integrates various machine learning algorithms to objectively and automatically assess dyslexia using physiological data derived from user interactions. …”
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State of charge estimation of lithium-ion batteries in an electric vehicle using hybrid metaheuristic - deep neural networks models by Zuriani Mustaffa, Mohd Herwan Sulaiman, Jeremiah Isuwa

“…Additionally, the TLBO-DNNs approach outperformed standalone models, including the autoregressive integrated moving average (ARIMA) model (MAE: 14.3301, RMSE: 7.0697) and support vector machines (MAE: 6.0065, RMSE: 8.0360). …”
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Atmospheric Modeling for Wildfire Prediction by Fathima Nuzla Ismail, Brendon J. Woodford, Sherlock A. Licorish

“…Our study focuses on developing wildfire prediction models using one-class classification algorithms. These include Support Vector Machine, Isolation Forest, AutoEncoder, Variational AutoEncoder, Deep Support Vector Data Description, and Adversarially Learned Anomaly Detection. …”
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An extreme forecast index-driven runoff prediction approach using stacking ensemble learning by Zhiyuan Leng, Lu Chen, Binlin Yang, Siming Li, Bin Yi

“…EFI is introduced as an input into four machine learning models (Support Vector Regression, Multi-layer Perceptron, Gradient Boosting Decision Tree, and Ridge Regression) for runoff prediction with lead times of 24 h, 48 h, and 72 h. …”
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A Novel Classification of Uncertain Stream Data using Ant Colony Optimization Based on Radial Basis Function by Tahsin Ali Mohammed Amin, Sabah Robitan Mahmood, Rebar Dara Mohammed, Pshtiwan Jabar Karim

“…Finally, we evaluate our proposed method against some of the most popular ML methods, including a k-nearest neighbor, support vector machine, random forest, decision tree, logistic regression, and extreme gradient boosting (Xgboost). …”
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Artificial Intelligence in Breast Reconstruction: A Narrative Review by Andrei Iulian Rugină, Andreea Ungureanu, Carmen Giuglea, Silviu Adrian Marinescu

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Construction and Application of Feature Recommendation Model for Remote Sensing Interpretation of Rock Strata Based on Knowledge Graph by Liufeng Tao, Qirui Wu, Miao Tian, Zhong Xie, Jianguo Chen, Yueyu Wu, Qinjun Qiu

“…When combined with Landsat multispectral data and digital elevation model (DEM)-derived terrain parameters, the knowledge-enhanced Random Forest (81.79%) and Support Vector Machine (75.76%) models demonstrated excellent performance in identifying rock-stratigraphic assemblages in the study area. …”
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Leveraging petrophysical and geological constraints for AI-driven predictions of total organic carbon (TOC) and hardness in unconventional reservoir prospects by Nandito Davy, Ammar El-Husseiny, Umair bin Waheed, Korhan Ayranci, Manzar Fawad, Mohamed Mahmoud, Nicholas B. Harris

“…This study examines the impact of incorporating these constraints on prediction accuracy using four manually fine-tuned ML algorithms: Random Forest (RF), Support Vector Regression (SVR), XGBoost (XGB), and Artificial Neural Network (ANN). …”
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Gesture recognition based on flexible solar cells and ultrathin hydrogel film by Rucheng WU, Wenbo DING, Xiaomin XU, Linqi SONG, Weitao XU

“…Daily life involves various gestures, and combining these with smart wearable devices is crucial for improving quality of life.One effective solution to the challenges of gesture recognition and device energy consumption isutilizing the photoelectric conversion characteristics of solar energy-related devices.The data of five commonly used gestures were collected in the research of the combination of flexible solar cells and gesture recognition.Z-Score, low-pass filter, sliding window techniques for signal processing were applied, and successfully achieved 100% predicted accuracy using random forest, support vector machine and neural network algorithms even with small samples which showed that this method had significant advantages in the application of gesture recognition.…”
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An Enhanced Bio-inspired GWO–DE Technique for Efficient Feature Selection in the EEG-RSVP Paradigm by S. Abinayaa, S. S. Sridhar

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Impact of anthropogenic disturbance and climate on bamboo distribution in shifting cultivation landscapes of Northeast India by Muna Tamang, Subrata Nandy, Ritika Srinet, Yamini Bhat, Hitendra Padalia, Arun Jyoti Nath, Ashesh Kumar Das, R. P. Singh

“…Bamboo distribution was mapped using spectral and textural variables from Sentinel-2 imagery (March and November 2022) and topographic data from the Shuttle Radar Topography Mission digital elevation model. Three machine learning classifiers, random forest (RF), support vector machine, and artificial neural network, were evaluated for bamboo classification. …”
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Precise prediction of choke oil rate in critical flow condition via surface data by Qing Wang, Muntadher Abed Hussein, Bhavesh Kanabar, Anupam Yadav, Asha Rajiv, Aman Shankhyan, Sachin Jaidka, Mehul Manu, Issa Mohammed Kadhim, Zainab Jamal Hamoodah, Fadhil Faez, Mohammad Mahtab Alam, Hojjat Abbasi

“…This is the first study that addresses the challenge of accurately predicting oil production rates by utilizing various advanced machine learning methods including Random Forest, convolutional neural network, support vector machine, multilayer perceptron artificial neural network and ridge regression methods. …”
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Breaking Down Walls: A Literature Review on the Power of Artificial Intelligence in Enhancing Universal Language Connection by Sheida Jamalnia, Zahra Karimian

“…The search focused on peer-reviewed articles published in English from January 2020 to June 2024, using keywords like ‘artificial intelligence,’ ‘communication,’ ‘natural language processing,’ and ‘machine translation.’ Studies were selected based on methodological rigor, with data extracted independently by two reviewers and synthesized narratively to identify key themes.Results: AI-driven technologies, including machine translation and NLP tools, significantly enhance multilingual communication across sectors such as business, healthcare, and education. …”
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A Comprehensive Survey on AI in Counter-Terrorism and Cybersecurity: Challenges and Ethical Dimensions by Ioannis Syllaidopoulos, Klimis S. Ntalianis, Ioannis Salmon

“…This paper provides a comprehensive overview of AI methodologies, such as predictive analytics, Natural Language Processing (NLP), and machine learning architectures (e.g., Support Vector Machines – SVM and Long Short-Term Memory – LSTM), and optimization algorithms (e.g., Particle Swarm Optimization – PSO), assessing their effectiveness in security applications. …”
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Graph-based two-level indicator system construction method for smart city information security risk assessment by Li Yang, Kai Zou, Yuxuan Zou

“…For the simulation of risk level prediction, we compared our method with some machine learning algorithms, such as ridge regression, Lasso regression, support vector regression, decision trees, and multi-layer perceptron. …”
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