Powdery mildew resistance prediction in Barley (Hordeum Vulgare L) with emphasis on machine learning approaches by Farveh Vahidpour, Hossein Sabouri, Fakhtak Taliei, Sayed Javad Sajadi, Saeed Yarahmadi, Hossein Hosseini Moghaddam

“…The Bayesian algorithm was utilized to optimize the parameters of the machine-learning models. …”
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MACHINE LEARNING-BASED CLASSIFICATION OF HBV AND HCV-RELATED HEPATOCELLULAR CARCINOMA USING GENOMIC BIOMARKERS by Sami Akbulut, Zeynep Küçükakçalı, Cemil Çolak

“…Accuracy, balanced accuracy, sensitivity, specificity, the positive predictive value, the negative predictive value, and F1 score performance metrics were evaluated for a model performance. Results: With the feature selection approach, 17 genes were chosen, and modeling was done using these input variables. …”
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Machine learning in big data: A performance benchmarking study of Flink-ML and Spark MLlib by Messaoud MEZATI, Ines AOURIA

“…Flink-ML is designed for real-time, event-driven ML applications and provides native support for streaming-based model training and inference. In contrast, Spark MLlib is optimized for batch processing and micro-batch streaming, making it more suitable for traditional machine learning workflows. …”
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Evaluating the strength of industrial wastesbased concrete reinforced with steel fiber using advanced machine learning by Kennedy C. Onyelowe, Viroon Kamchoom, Ahmed M. Ebid, Shadi Hanandeh, Susana Monserrat Zurita Polo, Vilma Fernanda Noboa Silva, Rodney Orlando Santillán Murillo, Rolando Fabián Zabala Vizuete, Paul Awoyera, Siva Avudaiappan

“…Advanced machine learning techniques were applied to model the compressive strength (Cs) of the steel fiber reinforced concrete such as “Semi-supervised classifier (Kstar)”, “M5 classifier (M5Rules), “Elastic net classifier (ElasticNet), “Correlated Nystrom Views (XNV)”, and “Decision Table (DT)”. …”
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Dynamic Optimization of Recurrent Networks for Wind Speed Prediction on Edge Devices by Laeeq Aslam, Runmin Zou, Ebrahim Shahzad Awan, Sayyed Shahid Hussain, Muhammad Asim, Samia Allaoua Chelloug, Mohammed A. ELAffendi

“…Server-dependent machine learning models, commonly deployed in wind farms, prove infeasible for domestic systems due to high costs and energy demands. …”
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Machine Learning-Powered Segmentation of Forage Crops in RGB Imagery Through Artificial Sward Images by Hugo Moreno, Christian Rueda-Ayala, Victor Rueda-Ayala, Angela Ribeiro, Carlos Ranz, Dionisio Andújar

“…Unsupervised and supervised ML models, including a hybrid approach combining Gaussian Mixture Model (GMM) and Nearest Centroid Classifier (NCC), were applied for pixel-wise segmentation and classification. …”
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Optimizing encrypted search in the cloud using autoencoder-based query approximation by Mahmoud Mohamed, Khaled Alosman

“…Recent work has explored using machine learning models like autoencoders to optimize similarity search under encryption. …”
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A Weighted Bayesian Kernel Machine Regression Approach for Predicting the Growth of Indoor-Cultured Abalone by Seung-Won Seo, Gyumin Choi, Ho-Jin Jung, Mi-Jin Choi, Young-Dae Oh, Hyun-Seok Jang, Han-Kyu Lim, Seongil Jo

“…The proposed method employs a weighted Bayesian kernel machine regression model, integrating Gaussian processes with a spike-and-slab prior to identify influential variables. …”
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An FPGA Prototype for Parkinson’s Disease Detection Using Machine Learning on Voice Signal by Mujeev Khan, Abdul Moiz, Gani Nawaz Khan, Mohd Wajid, Mohammed Usman, Jabir Ali

“…This paper proposes an efficient machine learning model for PD detection using voice-based features, which offer a non-invasive, cost-effective, and accessible alternative to complex imaging methods. …”
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Prediction of Body Mass of Dairy Cattle Using Machine Learning Algorithms Applied to Morphological Characteristics by Franck Morais de Oliveira, Patrícia Ferreira Ponciano Ferraz, Gabriel Araújo e Silva Ferraz, Marcos Neves Pereira, Matteo Barbari, Giuseppe Rossi

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Heavy metal biomarkers and their impact on hearing loss risk: a machine learning framework analysis by Ali Nabavi, Mohammad Kashkooli, Sara Sadat Nabavizadeh, Farimah Safari

“…Demographic, clinical, and heavy metal biomarker data (e.g., blood lead and cadmium levels) were analyzed as features, with hearing loss status—defined as a pure-tone average threshold exceeding 25 dB HL across 500, 1,000, 2000, and 4,000 Hz in the better ear—serving as the binary outcome. Multiple machine learning algorithms, including Random Forest, XGBoost, Gradient Boosting, Logistic Regression, CatBoost, and MLP, were optimized and evaluated. …”
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Investigating the performance of random oversampling and genetic algorithm integration in meteorological drought forecasting with machine learning by Tahsin Baykal, Özlem Terzi, Gülsün Yıldırım, Emine Dilek Taylan

“…To achieve this objective, monthly rainfall data from the Isparta, Eğirdir, Senirkent, Uluborlu, and Yalvaç stations, positioned in Türkiye’s Lakes Region, were utilized to compute the Standardized Precipitation Index (SPI) over 3-, 6-, 9-, and 12- month intervals. Machine learning (ML) models were developed for Isparta drought estimation using SPI values, and the best performance was observed with Extra Tree Regression (ETR) models. …”
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Analyzing High-Speed Rail’s Transformative Impact on Public Transport in Thailand Using Machine Learning by Chinnakrit Banyong, Natthaporn Hantanong, Panuwat Wisutwattanasak, Thanapong Champahom, Kestsirin Theerathitichaipa, Rattanaporn Kasemsri, Manlika Seefong, Vatanavongs Ratanavaraha, Sajjakaj Jomnonkwao

“…The dataset, consisting of 38,400 observations, was analyzed using the CatBoost model and the multinomial logit (MNL) model. CatBoost demonstrated superior predictive performance, achieving an accuracy of 0.853 and an AUC of 0.948, compared to MNL’s accuracy of 0.749 and AUC of 0.879. …”
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Enhanced Forecasting and Assessment of Urban Air Quality by an Automated Machine Learning System: The AI‐Air by Jiayu Yang, Huabing Ke, Sunling Gong, Yaqiang Wang, Lei Zhang, Chunhong Zhou, Jingyue Mo, Yan You

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Parametric Analysis Towards the Design of Micro-Scale Wind Turbines: A Machine Learning Approach by Raneem Mansour, Seifelden Osama, Hazem Ahmed, Mohamed Nasser, Norhan Mahmoud, Amira Elkodama, Amr Ismaiel

“…This work presents a data-based machine learning (ML) approach towards the design of a micro-scale wind turbine. …”
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ONBOARD FUEL PUMP FAULT DIAGNOSIS BASED ON IMPROVED SUPPORT VECTOR MACHINE AND EXPERIMENTAL RESEARCH by LIANG Wei, JING Bo, JIAO XiaoXuan, QIANG XiaoQing, LIU XiaoDong

“…The genetic algorithm is presented to optimize the parameters of SVM. Meanwhile,the fault feature vectors are used to train and validate this classification model. …”
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Optimization of the fermentation process for fructosyltransferase production by Aspergillus niger FS054 by Yingzi Wu, Yuewen Zhang, Xiaoyu Zhong, Huiling Xia, Mingyang Zhou, Wenjin He, Yi Zheng

“…Abstract This study systematically optimized the fermentation process for fructosyltransferase (FTase) production by Aspergillus niger FS054, integrating traditional experimental designs with machine learning approaches. …”
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Encoding-Based Machine Learning Approach for Health Status Classification and Remote Monitoring of Cardiac Patients by Sohaib R. Awad, Faris S. Alghareb

“…The developed ANN classifier and proposed encoding-based ML model are compared to other conventional ML-based models, such as Naive Bayes, SVM, and KNN for model accuracy evaluation. …”
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Social Factors Influencing Healthcare Expenditures: A Machine Learning Perspective on Australia’s Fiscal Challenges by Wei Gu, Zhantian Zhang, Ou Liu

“…By integrating feature importance metrics with SHAP analysis, this study enhances model interpretability and offers actionable insights for policymakers. …”
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Polymer design for solvent separations by integrating simulations, experiments and known physics via machine learning by Janhavi Nistane, Rohan Datta, Young Joo Lee, Harikrishna Sahu, Seung Soon Jang, Ryan Lively, Rampi Ramprasad

“…Traditional experimental and computational methods for determining diffusivity are time- and resource-intensive, while current machine learning (ML) models often lack accuracy outside their training domains. …”
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