Machine Learning Techniques for Heart Disease Prediction Using a Multi-Algorithm Approach by Muhammad Kunta Biddinika, Alya Masitha, Herman Herman, Vita Arfiana Nurul Fatimah

“…The novelty of this study lies in the comparative analysis of several algorithms to optimize the heart disease prediction model for clinical use. …”
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Machine learning for detoxification of aflatoxin M1 by Lactococcus lactis probiotic in kashk production by Maryam Jafari, Roshanak Rafiei Nazari, Mohammad Rezaei, Mojtaba Moazzen, Nabi Shariatifar

“…The findings offer valuable insights that highlight the ANN model’s ability to make accurate predictions. Therefore, the detoxification of AFM 1 using probiotics combined with machine learning methods presents a practical, feasible, and simple method for predicting detoxification processes based on various parameters related to the probiotic application in managing aflatoxin in dairy products.…”
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Machine learning based adaptive traffic prediction and control using edge impulse platform by Manoj Tolani, G. E. Saathwik, Ayush Roy, L. A. Ameeth, Dhanush Bharadwaj Rao, Ambar Bajpai, Arun Balodi

“…A Edge-Impulse-based machine learning model is proposed to predict the density and arrival time of the vehicles to the traffic signal. …”
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Effect of Machine Learning on Risk Stratification for Antiretroviral Treatment Failure in People Living with HIV by Zhang W, Ren L, Yang K, Yan J, Yu Q, Qi S, Ruan H, Zhao D, Ruan L

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A feature-level ensemble machine learning approach for attack detection in IoT networks by Firoz Khan, B. S. Sunil Kumar, Sangeeta Sangani

“…To address these limitations, this study proposes a feature-level ensemble machine learning approach called Weight-Optimized Extreme Gradient Boosting (WO-XGB). …”
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A Machine Learning Approach to Generate High-Resolution Maps of Irrigated Olive Groves by Rosa Gutiérrez-Cabrera, Ana M. Tarquis, Javier Borondo

“…Additionally, we compare the dependence of model performance on the length of the NDVI time series (ranging from one to seven seasons), finding that XGBoost requires a shorter time series to achieve optimal results, while KNN with DTW can benefit from longer historical records. …”
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Recent Results on the Use of Artificial Intelligence Techniques Applied to Wireless Power Transfer Systems by Federico Amadei, Michele Quercio, Francesco Riganti Fulginei

“…This article reviews the application of machine learning (ML) techniques in wireless power transfer (WPT) systems, focusing on their role in optimizing system performance, enhancing safety, and improving efficiency. …”
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Machine learning - driven solar forecasting in dust-prone regions for sustainable energy systems by Kadhim Hayawi, Husna Maliakkal, Neethu Venugopal, Thanveer Musthafa Hussain, Gomathi Bhavani Rajagopalan

“…To address this, the study develops machine learning models—including LSTM, GRU, and hybrid LSTM-GRU architectures—that incorporate solar, weather, and dust-related features. …”
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Intelligent Thermal Condition Monitoring for Predictive Maintenance of Gas Turbines Using Machine Learning by Sadiq T. Bunyan, Zeashan Hameed Khan, Luttfi A. Al-Haddad, Hayder Abed Dhahad, Mustafa I. Al-Karkhi, Ahmed Ali Farhan Ogaili, Zainab T. Al-Sharify

“…This study presents an AI-driven approach for thermal condition monitoring and the predictive maintenance of gas turbines using machine learning. An Extreme Gradient Boosting (XGBoost)-based classification model was developed to distinguish between healthy and faulty operating conditions based on thermal load data. …”
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TACO: Adversarial Camouflage Optimization on Trucks to Fool Object Detectors by Adonisz Dimitriu, Tamás Vilmos Michaletzky, Viktor Remeli

“…Adversarial attacks threaten the reliability of machine learning models in critical applications like autonomous vehicles and defense systems. …”
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Detecting Student Engagement in an Online Learning Environment Using a Machine Learning Algorithm by Youssra Bellarhmouch, Hajar Majjate, Adil Jeghal, Hamid Tairi, Nadia Benjelloun

“…Quiz and exam scores were employed to create predictive models for lessons. The performance of the models was evaluated using classic metrics such as precision, recall, and F1-score. …”
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Characterizing the role of early life factors in machine learning-based multimorbidity risk prediction. by Vien Ngoc Dang, Charlotte Cecil, Carmine M Pariante, Jerónimo Hernández-González, Karim Lekadir

“…Moreover, risk prediction models for cardiovascular diseases (CVD) and diabetes, as recommended by current clinical guidelines, typically demonstrate sub-optimal performance in clinically relevant sub-populations where these ELFs may play a substantial role. …”
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Transforming liver transplant allocation with artificial intelligence and machine learning: a systematic review by Lisiane Pruinelli, Kiruthika Balakrishnan, Sisi Ma, Zhigang Li, Anji Wall, Jennifer C. Lai, Jesse D. Schold, Timothy Pruett, Gyorgy Simon

“…Eight studies were led by a Spanish team, focusing on donor-recipient matching and proposing machine learning models to predict post- LT survival. …”
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Novel concept for the healthy population influencing factors by Yuhao Shen, Jichao Wang, Lihua Ma, Huizhe Yan

“…In order to analyze and predict the main elements affecting the well-being of transient population, this study uses advanced machine learning algorithms such as principal component analysis, backpropagation (BP) neural networks, community analysis, random forest models, etc. …”
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Adaptive Energy Management for MATTER-Enabled Smart Homes by Navodit Bhardwaj, Pallavi Joshi

“…This paper proposes a unified machine learning (ML) framework for dynamic energy optimization, integrating advanced prediction, anomaly detection, and reinforcement learning (RL) techniques. …”
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Adjoint‐Based Online Learning of Two‐Layer Quasi‐Geostrophic Baroclinic Turbulence by F. E. Yan, H. Frezat, J. Le Sommer, J. Mak, K. Otness

“…Two online approaches are considered: A full adjoint‐based online approach, related to traditional adjoint optimization approaches that require a “differentiable” dynamical model, and an approximately online approach that approximates the adjoint calculation and does not require a differentiable dynamical model. …”
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Machine learning-based process quality control of screen-printed titanium dioxide electrodes by Anesu Nyabadza, Lola Azoulay-Younes, Mercedes Vazquez, Dermot Brabazon

“…Models were optimized and accelerated through feature engineering and extraction techniques, allowing them to be trained in under 1 min. …”
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Toward generalizable prediction of antibody thermostability using machine learning on sequence and structure features by Ameya Harmalkar, Roshan Rao, Yuxuan Richard Xie, Jonas Honer, Wibke Deisting, Jonas Anlahr, Anja Hoenig, Julia Czwikla, Eva Sienz-Widmann, Doris Rau, Austin J. Rice, Timothy P. Riley, Danqing Li, Hannah B. Catterall, Christine E. Tinberg, Jeffrey J. Gray, Kathy Y. Wei

“…Further, transferring such models for alternative physicochemical properties of scFvs can have potential applications in optimizing large-scale production and delivery of mAbs or bsAbs.…”
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Immune Microenvironment Characterization and Machine Learning-Guided Identification of Diagnostic Biomarkers for Ulcerative Colitis by Zheng Q, Wang L, Zhang Y, Peng J, Hou J, Wang H, Ma Y, Tang P, Li Y, Li H, Chen Y, Li J, Chen Y

“…Immune cell infiltration was assessed via the ImmuCellAI algorithm, while differential expression analysis and WGCNA were performed to identify key immune-related genes. Moreover, machine learning models, including Random Forest and Best Subset Selection, were used to construct and validate an optimal diagnostic framework. …”
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Reliable Autism Spectrum Disorder Diagnosis for Pediatrics Using Machine Learning and Explainable AI by Insu Jeon, Minjoong Kim, Dayeong So, Eun Young Kim, Yunyoung Nam, Seungsoo Kim, Sehoon Shim, Joungmin Kim, Jihoon Moon

“…Using <i>R</i> and the <i>caret</i> package (version 6.0.94), we developed and compared several ML algorithms, validated using 10-fold cross-validation and optimized by grid search hyperparameter tuning. XAI techniques were employed to improve model transparency, offering insights into how features contribute to predictions, thereby enhancing clinician trust. …”
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