Design Optimization of a Composite Using Genetic Algorithms for the Manufacturing of a Single-Seater Race Car by Ioannis Tsormpatzoudis, Dimitriοs A. Dragatogiannis, Aimilios Sideridis, Efstathios E. Theotokoglou

“…While conventional chassis structures predominantly utilize metals, achieving further mass reduction and enhanced rigidity necessitates the adoption of composite sandwich materials, typically comprising carbon fiber-reinforced polymer (C.F.R.P.) laminate skins bonded to an aluminum honeycomb core. …”
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Comparative study of heuristic algorithms for coordination of industrial over current relays with nonstandard characteristics in microgrid protection by Ekta Gairola, Mahiraj Singh Rawat, Padmanabh Thakur, Sandeep Gupta, Mukesh Kumar

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Automatic Vibration Balancing System for Combine Harvester Threshing Drums Using Signal Conditioning and Optimization Algorithms by Xinyang Gu, Bangzhui Wang, Zhong Tang, Honglei Zhang, Hao Zhang

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Predicting onset of myopic refractive error in children using machine learning on routine pediatric eye examinations only by Yonina Ron, Tchelet Ron, Naomi Fridman, Anat Goldstein

“…Among them, 429 (11%) developed myopia. The models predicted myopia with up to 77% sensitivity and 92% specificity. …”
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Enhanced safety and efficiency in traction elevators: a real-time monitoring system with anomaly detection by Safa Ozdemir, Osamah N. Neamah, Raif Bayir

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Designing Predictive Analytics Frameworks for Supply Chain Quality Management: A Machine Learning Approach to Defect Rate Optimization by Zainab Nadhim Jawad, Balázs Villányi

“…The framework employs advanced ML algorithms, including extreme gradient boosting (XGBoost), support vector machines (SVMs), and random forests (RFs), to accurately predict defect rates and derive actionable insights for supply chain optimization. …”
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Machine learning unveils hypoxia-immune gene hub for clinical stratification of thyroid-associated ophthalmopathy by Lu Chen, Sha Wang, Jinwei Wang, Bei Xu, Jia Tan, Xiaobo Xia

“…Hypoxia exposure aggravated the above dysregulation and promoted proliferation and adipogenesis of orbital fibroblasts. A predictive model was developed using four machine learning algorithms and validated for its effectiveness in diagnosing TAO and assessing disease severity. …”
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Improving brain tumor classification: An approach integrating pre-trained CNN models and machine learning algorithms by Mohamed R. Shoaib, Jun Zhao, Heba M. Emara, Ahmed S. Mubarak, Osama A. Omer, Fathi E. Abd El-Samie, Hamada Esmaiel

“…This study provides valuable insights into the interplay between CNN models, feature extraction techniques, and machine learning algorithms for brain tumor classification, highlighting the efficacy of DenseNet201 combined with SVM and MLP.…”
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Associations of DNA Methylation Algorithms of Aging With Cardiovascular Disease and Mortality Risk Among US Older Adults by Xian Cui, Shiqun Sun, Hui Zhang, Yulu Gong, Darong Hao, Yaqian Xu, Chongyu Ding, Jing Wang, Tongyan An, Jinlong Liu, Jun Du, Xiangwei Li

“…However, their predictive performance for CVD varies significantly. …”
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Predicting Pathological Complete Response Following Neoadjuvant Therapy in Patients With Breast Cancer: Development of Machine Learning–Based Prediction Models in a Retrospective S... by Chun-Chi Lai, Cheng-Yu Chen, Tzu-Hao Chang

“…ObjectiveThe objective of this study was to develop robust, machine learning–based prediction models for pCR following neoadjuvant therapy, leveraging clinical, laboratory, and imaging data. …”
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medAL-suite: A software solution for creating and deploying complex clinical decision support algorithms by Ludovico Gennaro Cobuccio, Vincent Faivre, Rainer Tan, Alan Vonlanthen, Fenella Beynon, Emmanuel Barchichat, Alain Fresco, Quentin Girard, Sinan Ucak, Sylvain Schaufelberger, Ibrahim Evans Mtebene, Peter Agrea, Emmanuel Kalisa, Gillian A. Levine, Martin Norris, Sabine Renggli, Alix Miauton, Lisa Cleveley, Kristina Keitel, Julien Thabard, Valérie D’Acremont, Alexandra V. Kulinkina

“…Algorithms are subsequently automatically deployed in medAL-reader to service level clinicians in health facilities. …”
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Identifying leptospirosis hotspots in Selangor: uncovering climatic connections using remote sensing and developing a predictive model by Muhammad Akram Ab Kadir, Rosliza Abdul Manaf, Siti Aisah Mokhtar, Luthffi Idzhar Ismail

“…Conclusions This GIS-based study identified a primarily sporadic occurrence of leptospirosis in Selangor with minimal spatial clustering. The LGBM algorithm effectively predicted leptospirosis hotspots based on the analysed hydroclimatic factors. …”
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Predicting chronic pain using wearable devices: a scoping review of sensor capabilities, data security, and standards compliance by Johannes C. Ayena, Johannes C. Ayena, Amina Bouayed, Amina Bouayed, Myriam Ben Arous, Myriam Ben Arous, Youssef Ouakrim, Youssef Ouakrim, Karim Loulou, Karim Loulou, Darine Ameyed, Isabelle Savard, Leila El Kamel, Neila Mezghani, Neila Mezghani

“…Data extraction focused on device types, sensor quality, compliance with health standards, and the predictive algorithms employed.ResultsWearable devices show promise in correlating physiological markers with CP, but few studies integrate predictive models. …”
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A New Paradigm in AC Drive Control: Data-Driven Control by Learning Through the High-Efficiency Data Set—Generalizations and Applications to a PMSM Drive Control System by Madalin Costin, Ion Bivol

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The Alignment of High-resolution Solar Prominence Images Observed by the New Vacuum Solar Telescope by Yunfang Cai, Yongyuan Xiang, Kaifan Ji

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Proactive Data Placement in Heterogeneous Storage Systems via Predictive Multi-Objective Reinforcement Learning by Suchuan Xing, Yihan Wang

“…Through comprehensive evaluation using both synthetic and real-world traces from deep learning training workloads, our method demonstrates substantial improvements over state-of-the-art algorithms: achieving up to 45.1% reduction in average I/O latency, 32.5% improvement in throughput for critical applications, and 28.8% reduction in storage costs. …”
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Development of machine learning models for predicting non-remission in early RA highlights the robust predictive importance of the RAID score-evidence from the ARCTIC study by Gaoyang Li, Shrikant S. Kolan, Franco Grimolizzi, Joseph Sexton, Giulia Malachin, Guro Goll, Tore K. Kvien, Tore K. Kvien, Nina Paulshus Sundlisæter, Manuela Zucknick, Siri Lillegraven, Espen A. Haavardsholm, Espen A. Haavardsholm, Bjørn Steen Skålhegg

“…The model performance was evaluated through five independent unseen tests with nested 5-fold cross-validation. The predictive power of each feature was assessed using a composite measure derived from individual algorithm estimates.ResultsThe model demonstrated a mean AUC-ROC of 0.75-0.76, with mean sensitivity of 0.77-0.81, precision (also referred to as Positive Predictive Value) of 0.77-0.79 and specificity of 0.63-0.66 across the criteria. …”
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Predictive Analysis of Cardiovascular Disease Risk Factors in Romania using Machine Learning and Medical Statistics by Radu-Anton MOLDOVAN, Sebastian-Aurelian ŞTEFĂNIGĂ

“…Based on our findings, a series of preventive interventions and policy recommendations for cardiovascular risk reduction at the individual and population levels can be implemented. …”
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Unsupervised Machine Learning for Classifying CHIME Fast Radio Bursts and Investigating Empirical Relations by Da-Chun Qiang, Jie Zheng, Zhi-Qiang You, Sheng Yang

“…By employing Uniform Manifold Approximation and Projection for dimensionality reduction and clustering algorithms ( k -means and Hierarchical Density-Based Spatial Clustering of Applications with Noise), we successfully segregate repeaters and nonrepeaters into distinct clusters, identifying over 100 potential repeater candidates. …”
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Reconsidering the use of race, sex, and age in clinical algorithms to address bias in practice: A discussion paper by Reanna Panagides, Jessica Keim-Malpass

“…Clinical algorithms are commonly used as decision-support tools, incorporating patient-specific characteristics to predict health outcomes. …”
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