Prediction of barite scale formation and inhibition in hydrocarbon reservoirs using AI modeling: Focus on different optimization algorithms by Ouafa Belkacem, Ahmed Rezrazi, Kamel Aizi, Lokmane Abdelouahed, Maamar Laidi, Abdelhafid Touil, Leila Cherifi, Salah Hanini

“…Innovative intelligent models, including Random Forest (RF), k-nearest Neighbors (KNN), Extreme Learning Machine (ELM), Support Vector Regression (SVR), Decision Trees (DT), and Multilayer Perceptron (MLP), were developed and optimized for this purpose. …”
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Integrating Bioengineering and Machine Learning: A Multi-Algorithm Approach to Enhance Agricultural Sustainability and Resource Efficiency by Senthil G.A., Prabha R., Asha R.M., Suganthi S.U., Sridevi S.

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A Multi-Algorithm Machine Learning Model for Predicting the Risk of Preterm Birth in Patients with Early-Onset Preeclampsia by Xu Y, Zu Y, Zhang Y, Liang Z, Xu X, Yan J

“…A Stacking ensemble model was constructed, and SHapley Additive exPlanations (SHAP) was used for feature interpretation.Results: The area under the receiver operating characteristic curve (AUROC) for predicting preterm birth in EOPE patients using Logistic Regression, Gaussian Naive Bayes, Extreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine, Support Vector Machine (SVM), Gradient Boosting Decision Tree (GBDT), Multi-Layer Perceptron, and Elastic Net were 0.763, 0.712, 0.821, 0.832, 0.821, 0.842, 0.784, and 0.763, respectively. …”
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Transforming Wind Data into Insights: A Comparative Study of Stochastic and Machine Learning Models in Wind Speed Forecasting by Türker Tuğrul, Sertaç Oruç, Mehmet Ali Hınıs

“…This parameter is of interest to both researchers interested in climate change and researchers working on issues related to energy production. Based on this, in this study, prospective analyses were made with various machine learning algorithms, the long-short term memory (LSTM), the artificial neural network (ANN), and the support vector machine (SVM) algorithms, and one of the stochastic methods, the seasonal autoregressive integrated moving average (SARIMA), using the monthly wind data obtained from Bodo. …”
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A Correlative Analysis of Modern Logistics Industry to Developing Economy Using the VAR Model: A Case of Pakistan by Salman Hanif, Dong Mu, Saranjam Baig, Khalid Mehmood Alam

“…Accordingly, we exemplify our analysis by employing the vector autoregression (VAR) model to the most updated time series data of investment in the logistics industry and the economic growth of Pakistan from 1990 to 2018. …”
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Explainable artificial intelligence-machine learning models to estimate overall scores in tertiary preparatory general science course by Sujan Ghimire, Shahab Abdulla, Lionel P. Joseph, Salvin Prasad, Angela Murphy, Aruna Devi, Prabal Datta Barua, Ravinesh C. Deo, Rajendra Acharya, Zaher Mundher Yaseen

“…Additionally, Local Interpretable Model-agnostic Explanations (LIME) and SHapley Additive explanations (SHAP) techniques are employed to elucidate the inner workings of these prediction models. …”
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A machine learning-based approach for constructing a 3D apparent geological model using multi-resistivity data by Jordi Mahardika Puntu, Ping-Yu Chang, Haiyina Hasbia Amania, Ding-Jiun Lin, M. Syahdan Akbar Suryantara, Jui-Pin Tsai, Hwa-Lung Yu, Liang-Cheng Chang, Jun-Ru Zeng, Lingerew Nebere Kassie

“…A key contribution of this work is the rigorous harmonization of these data sets, ensuring consistent resistivity values across different methods before constructing the 3D resistivity model, addressing a gap in previous studies that typically handled these data sets separately, either building models individually or comparing results side-by-side without fully integrating the data. …”
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A hybrid approach to time series forecasting: Integrating ARIMA and prophet for improved accuracy by Sherly A, Mary Subaja Christo, Jesi V Elizabeth

“…A key aspect of this work involves evaluating the performance of these models using RMSE (Root Mean Squared Error) and MAE (Mean Absolute Error. …”
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RoBERTaNET: Enhanced RoBERTa Transformer Based Model for Cyberbullying Detection With GloVe Features by Arwa A. Jamjoom, Hanen Karamti, Muhammad Umer, Shtwai Alsubai, Tai-Hoon Kim, Imran Ashraf

“…This research deals with the challenge of automatically identifying cyberbullying in tweets from a publicly available cyberbullying dataset. This research work employs robustly optimized bidirectional encoder representations from the transformers approach (RoBERTa), utilizing global vectors for word representation (GloVe) word embedding features. …”
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Application of an Inertial Sensor Unit for Position Estimation and Motion Control of the Lower-Extremity Powered Exoskeleton by G. R. Saypulaev, M. R. Saypulaev, I. V. Merkuryev, B. I. Adamov, R. B. Garcia

“…A mathematical model of measurements of these sensors was described. …”
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Methods for Extracting Fractional Vegetation Cover from Differentiated Scenarios Based on Unmanned Aerial Vehicle Imagery by Changning Sun, Yonggang Ma, Heng Pan, Qingxue Wang, Jiali Guo, Na Li, Hong Ran

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Comparison of the Combined Shielding Effectiveness Based on Experimental Studies of Overhead Power Lines Magnetic Field 3D Model by Bovdui I.V.

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Enhancing gender equity in resume job matching via debiasing-assisted deep generative model and gender-weighted sampling by Swati Tyagi, Anuj, Wei Qian, Jiaheng Xie, Rick Andrews

“…The goal of this study is to (1) mitigate bias at the level of word embeddings via a debiasing-assisted deep generative modeling approach, thereby fostering more equitable and gender-fair vector representations; (2) evaluate the resultant impact on the fairness of job classification; (3) explore the implementation of a gender-weighted sampling technique to achieve a more balanced representation of genders across various job categories when such an imbalance exists. …”
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Combining Generalized Linear Autoregressive Moving Average and Bootstrap Models for Analyzing Time Series of Respiratory Diseases and Air Pollutants by Ana Julia Alves Camara, Valdério Anselmo Reisen, Glaura Conceicao Franco, Pascal Bondon

“…The generalized linear autoregressive moving-average model (GLARMA) has been used in epidemiology to evaluate the impact of pollutants on health. …”
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Characterizing Pairwise Social Relationships Quantitatively: Interest-Oriented Mobility Modeling for Human Contacts in Delay Tolerant Networks by Jiaxu Chen, Yazhe Tang, Chengchen Hu, Guijuan Wang

“…Human mobility modeling has increasingly drawn the attention of researchers working on wireless mobile networks such as delay tolerant networks (DTNs) in the last few years. …”
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Machine Learning Performance Analysis for Bagging System Improvement: Key Factors, Model Optimization, and Loss Reduction in the Fertilizer Industry by Ari Primantara, Udisubakti Ciptomulyono, Berlian Al Kindhi

“…Among the models, RFR achieved the highest predictive accuracy (R² = 0.9638, RMSE = 0.0496, MAE = 0.0338). …”
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Automated Detection of Gastrointestinal Diseases Using Resnet50*-Based Explainable Deep Feature Engineering Model with Endoscopy Images by Veysel Yusuf Cambay, Prabal Datta Barua, Abdul Hafeez Baig, Sengul Dogan, Mehmet Baygin, Turker Tuncer, U. R. Acharya

“…This work aims to develop a novel convolutional neural network (CNN) named ResNet50* to detect various gastrointestinal diseases using a new ResNet50*-based deep feature engineering model with endoscopy images. …”
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Color-Based Lifetime Estimation of LEDs Using Spectral Power Distribution Prediction Through Analytical and Machine Learning Models by J. Lokesh, Savitha G. Kini, M. G. Mahesha, Anjan N. Padmasali

“…Both analytical and machine learning (ML) models are employed for SPD prediction, with the support vector machine demonstrating superior performance. …”
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Evaluating microstructural and machine learning predictive models for friction drilling of sustainable snail shell reinforced aluminium matrix composites by Rajesh Jesudoss Hynes Navasingh, R. Sankaranarayanan, Priyanka Mishra, Angela Jennifa Sujana J, Jebasingh Jeremiah Rajesh, Jana Petru

“…Random Forest (RF), Multilayer Perceptron (MLP), Gaussian Process Regression (GPR) and Support Vector Machine (SVM) models were employed for the prediction of distinct output responses.…”
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Explainability enhanced liver disease diagnosis technique using tree selection and stacking ensemble-based random forest model by Mohammad Mamun, Safiul Haque Chowdhury, Muhammad Minoar Hossain, M.R. Khatun, Sadiq Iqbal

“…A combination of preprocessing techniques, including feature optimization methods such as Forward Feature Selection (FFS), Backward Feature Selection (BFS), and Recursive Feature Elimination (RFE), is applied to enhance data quality. After that, ML models, namely Support Vector Machines (SVM), Naive Bayes (NB), Random Forest (RF), K-nearest neighbors (KNN), Decision Trees (DT), and a novel Tree Selection and Stacking Ensemble-based RF (TSRF), are assessed in the dataset to diagnose LD. …”
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