Implementation of the healthy heart tool- an algorithm with potential cardiometabolic health benefits in persons with severe mental illness by Elisabeth Haug Lund-Stenvold, Petter A. Ringen, Ole A. Andreassen, Torfinn L. Gaarden, Cecilie B. Hartberg, Erik Johnsen, Silje Myklatun, Kåre Osnes, Kjetil Sørensen, Arne Vaaler, Serena Tonstad, John A. Engh, Anne Høye

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Comprehensive review of dimensionality reduction algorithms: challenges, limitations, and innovative solutions by Aasim Ayaz Wani

“…This review introduces a unified classification—linear, nonlinear, hybrid, and ensemble approaches—and assesses them against eight core challenges: dimensionality selection, overfitting, instability, noise sensitivity, bias, scalability, privacy risks, and ethical compliance. We outline solutions such as intrinsic dimensionality estimation, robust neighborhood graphs, fairness-aware embeddings, scalable algorithms, and automated tuning. …”
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Development of a justification process for selecting alternative risk reduction measures by Pavlo Saik, Vitalii Tsopa, Olena Yavorska, Serhii Cheberiachko, Mariia Brezitska, Andrii Yavorskyi, Vasyl Lozynskyi, Vasyl Lozynskyi

“…An eleven-step risk management process was designed to determine alternative preventive measures, characterized by feedback loops that enable the selection of optimal risk reduction strategies.ResultsThis study presents algorithms for solving three types of decision-making problems regarding the selection of combinations of preventive measures from a defined set of alternatives. …”
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Scalable Nonparametric Supervised Learning for Streaming and Massive Data: Applications in Healthcare Monitoring and Credit Risk by Mohamed Chaouch, Omama M. Al-Hamed

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Focus on Disaster Risk Reduction by ResNet-CDMV Model After Natural Disasters by Zhaojia Tang, Yu Han

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Auditing Flood Vulnerability Geo-Intelligence Workflow for Biases by Brian K. Masinde, Caroline M. Gevaert, Michael H. Nagenborg, Marc J. C. van den Homberg, Jacopo Margutti, Inez Gortzak, Jaap A. Zevenbergen

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Predicting the Likelihood of Operational Risk Occurrence in the Banking Industry Using Machine Learning Algorithms by Hamed Naderi, Mohammad Ali Rastegar Sorkhe, Bakhtiar Ostadi, Mehrdad Kargari

“…This study investigates and predicts the likelihood of operational risk occurrence in the banking industry using machine learning algorithms. …”
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A dimension reduction assisted credit scoring method for big data with categorical features by Tatjana Miljkovic, Pei Wang

“…We argue that current studies related to the development of credit scoring models have largely ignored recent developments in statistical methods for sufficient dimension reduction. To contribute to the field of financial innovation, this study proposes a Dimension Reduction Assisted Credit Scoring (DRA-CS) method via distance covariance-based sufficient dimension reduction (DCOV-SDR) in Majorization-Minimization (MM) algorithm. …”
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Development of a Quantitative Assessment Algorithm for Operational Risks in Mining Engineering by Marina Nevskaya, Anna Shabalova, Liubov Nikolaichuk, Natalya Kirsanova

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Nonlinear Volatility Risk Prediction Algorithm of Financial Data Based on Improved Deep Learning by Wangsong Xie

“…To increase the prediction accuracy of financial data, a new nonlinear volatility risk prediction algorithm is proposed based on the improved deep learning algorithm. …”
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Enhancing cybersecurity via attribute reduction with deep learning model for false data injection attack recognition by Faheed A.F. Alrslani, Manal Abdullah Alohali, Mohammed Aljebreen, Hamed Alqahtani, Asma Alshuhail, Menwa Alshammeri, Wafa Sulaiman Almukadi

“…The ARDL-FDIAR technique uses Z-score normalization to scale the input data. The attribute reduction process gets invoked using the modified Lemrus optimization algorithm (MLOA) to choose optimal feature sets. …”
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Multi-objective Optimal Scheduling of Water-Carbon in Cascade Reservoirs during Impoundment for Carbon Emission Reduction by ZHOU Yan-lai, NING Zhi-hao, HE Jun-tao

“…[Methods] Given that current studies on cascade reservoir impoundment scheduling have not yet incorporated carbon reduction objectives, this study proposed a multi-objective water-carbon scheduling model for cascade reservoirs during impoundment period based on the carbon emission factor method.An early storage strategy for cascade reservoirs was developed,and three objectives—minimizing flood control risk,maximizing power generation,and minimizing greenhouse gas emissions—were established.The Non-dominated Sorting Genetic Algorithm Ⅱ (NSGA-Ⅱ) was employed to derive optimal scheduling schemes for the impoundment period.…”
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ТHЕ ALGORITHM OF ANALYSIS OF AGRICULTURAL RISKS UNDER INFLUENCE OF INCOMPLETE INFORMATION ABOUT THEIR PARAMETERS by L. O. Lomovskykh, O. V. Mandych, O. O. Kovalenko, N. A. Karasova, A. Orzeł

“… The risk analysis algorithms, which are offered by domestic scientists for Ukrainian farmers, are limited in use and are often obsolete. …”
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MOUD 2.0: a clinical algorithm and implementation evaluation protocol for sublingual and injectable buprenorphine treatment of opioid use disorder by Brandon L. Joa, Eric N. Fung, Michael S. Weinstein, Lara C. Weinstein, Lara C. Weinstein

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Enhancing Long-Term Flood Forecasting with SageFormer: A Cascaded Dimensionality Reduction Approach Based on Satellite-Derived Data by Fatemeh Ghobadi, Amir Saman Tayerani Charmchi, Doosun Kang

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Mitigating Algorithmic Bias in AI-Driven Cardiovascular Imaging for Fairer Diagnostics by Md Abu Sufian, Lujain Alsadder, Wahiba Hamzi, Sadia Zaman, A. S. M. Sharifuzzaman Sagar, Boumediene Hamzi

“…<b>Background/Objectives</b>: The research addresses algorithmic bias in deep learning models for cardiovascular risk prediction, focusing on fairness across demographic and socioeconomic groups to mitigate health disparities. …”
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Revolutionizing Cardiac Risk Assessment: AI-Powered Patient Segmentation Using Advanced Machine Learning Techniques by Joan D. Gonzalez-Franco, Alejandro Galaviz-Mosqueda, Salvador Villarreal-Reyes, Jose E. Lozano-Rizk, Raul Rivera-Rodriguez, Jose E. Gonzalez-Trejo, Alexei-Fedorovish Licea-Navarro, Jorge Lozoya-Arandia, Edgar A. Ibarra-Flores

“…Cardiovascular diseases stand as the leading cause of mortality worldwide, underscoring the urgent need for effective tools that enable early detection and monitoring of at-risk patients. This study combines Artificial Intelligence (AI) techniques—specifically the k-means clustering algorithm—alongside dimensionality reduction methods like Principal Component Analysis (PCA) and Uniform Manifold Approximation and Projection (UMAP) to identify patient groups with varying levels of heart attack risk. …”
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Development and validation of an algorithm to estimate the risk of severe complications of COVID-19: a retrospective cohort study in primary care in the Netherlands by Martijn W Heymans, Amber A van der Heijden, Petra J M Elders, Giel Nijpels, Karin M A Swart, Koos van der Velden, Joline W J Beulens, Ron M C Herings, Bernard A M van der Zeijst, Eric G Hiddink, Reinier A R Herings, Hein P J van Hout

“…Applied to different vaccination scenarios, the proportion of people needed to be vaccinated to reach a 50% reduction of severe complications was 67.5%, 50.0%, 26.1%, 16.0%, 10.0% and 8.4% for the worker, naive, influenza, 60plus, oldest first and sCOVID scenarios, respectively.Conclusion The sCOVID algorithm performed well to predict the risk of severe complications of COVID-19 in the first and second waves of COVID-19 infections in this Dutch population. …”
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Evacuation Route Determination in Indoor Architectural Environments Based on Dynamic Fire Risk Assessment by Jiaojiao Bai, Xikui Lv, Liangtao Nie, Mingjing Fang

“…To address this issue, this study proposes an enhanced A* algorithm to determine evacuation paths based on dynamic fire risk assessment. …”
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Predicting cardiovascular outcomes in Chinese patients with type 2 diabetes by combining risk factor trajectories and machine learning algorithm: a cohort study by Qi Huang, Xiantong Zou, Zhouhui Lian, Xianghai Zhou, Xueyao Han, Yingying Luo, Shuohua Chen, Yanxiu Wang, Shouling Wu, Linong Ji

“…Conclusions The ML-CVD-C model, incorporating dynamic cardiovascular risk trajectories and a machine learning algorithm, significantly improves risk prediction accuracy for Chinese patients with diabetes. …”
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