Machine learning based calculation of refractive index of polyethylene glycol polymer by Walid Abdelfattah, Munthar Kadhim Abosaoda, Hardik Doshi, H.S. Shreenidhi, Manoranjan Parhi, Devendra Singh, Prabhjot Singh, Bilakshan Purohit, Kamal Kant Joshi, Ahmad Abumalek

“…This study develops advanced machine learning algorithms to accurately predict the refractive index of polyethylene glycol (PEG) polymers using temperature and molecular weight as key input variables. …”
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Bregman–Hausdorff Divergence: Strengthening the Connections Between Computational Geometry and Machine Learning by Tuyen Pham, Hana Dal Poz Kouřimská, Hubert Wagner

“…We also describe computational geometric algorithms that have been extended to this geometry, focusing on algorithms relevant for machine learning.…”
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Navigating the Digital Maze: A Review of AI Bias, Social Media, and Mental Health in Generation Z by Jane Pei-Chen Chang, Szu-Wei Cheng, Steve Ming-Jang Chang, Kuan-Pin Su

“…It also advocates for evidence-based strategies to mitigate the harms associated with algorithmic bias, urging collaboration among AI developers, mental health experts, policymakers and educators at personal, community (school), and national and international levels to cultivate a safer, more supportive digital ecosystem for future generations.…”
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Neural Network Approaches for Distributional Shifts in Environmental Sensors by Tobias Sukianto, Sebastian A. Schober, Cecilia Carbonelli, Simon Mittermaier, Robert Wille

“…Due to the distributional shift between the training and operational environment induced by sensor ageing and drift processes, the algorithms that predict air quality suffer from performance degradation during the products’ lifetime. …”
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Current status and prospects of artificial intelligence in the diagnosis and treatment of bladder tumors by GAN Xiya, WEI Wei

“…Machine learning and deep learning algorithms have played an important role in the imaging detection and diagnosis of bladder tumors, providing high accuracy in tumor detection, grading, and staging predictions. …”
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Climate change will alter Amazonian bumblebees’ distribution, but effects are species-specific by Patrícia Nunes-Silva, André Luis Acosta, Rafael Cabral Borges, Breno Magalhães Freitas, Ricardo Caliari Oliveira, Tereza Cristina Giannini, Vera Lucia Imperatriz-Fonseca

“…An ensemble modeling approach combining five different algorithms was used to predict areas of stability, habitat loss, and potential range expansion.ResultsBy 2060, B. brevivillus is projected to lose 41.6% of its current suitable habitat, with significant reductions in northern and coastal regions. …”
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Modern advancements of energy storage systems integrated with hybrid renewable energy sources for water pumping application by Marwa M. Ahmed, Haneen M. Bawayan, Mohamed A. Enany, Mahmoud M. Elymany, Ahmed A. Shaier

“…The manuscript also highlights the integration of artificial intelligence (AI) to optimize energy management, predict irrigation demands, and improve operational efficiency. …”
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Estimating Energy Consumption During Soil Cultivation Using Geophysical Scanning and Machine Learning Methods by Jasper Tembeck Mbah, Katarzyna Pentoś, Krzysztof S. Pieczarka, Tomasz Wojciechowski

“…These data, along with soil texture, served as inputs for predicting fuel consumption and field productivity. …”
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Identification of immune and major depressive disorder-related diagnostic markers for early nonalcoholic fatty liver disease by WGCNA and machine learning by Yuyun Jia, Yanping Cao, Qin Yin, Xueqian Li, Xiu Wen

“…Immune cell infiltration levels were quantified using single-sample gene set enrichment analysis (ssGSEA). A predictive model for SS/NASH was developed by evaluating nine machine-learning algorithms with 10-fold cross-validation on the datasets.ResultsFourteen genes strongly linked to both the immune system and the two conditions were identified. …”
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Carbonate Seismic Facies Analysis in Reservoir Characterization: A Machine Learning Approach with Integration of Reservoir Mineralogy and Porosity by Papa Owusu, Abdelmoneam Raef, Essam Sharaf

“…To this end, this study utilizes an unsupervised comparative hierarchical and K-means ML classification of the whole 3D seismic data spectrum and a suite of spectral bands to overcome the cluster “facies” number uncertainty in ML data partition algorithms. This comparative ML, which was leveraged with seismic resolution data preconditioning, predicted geologically plausible seismic facies, i.e., seismic facies with spatial continuity, consistent morphology across seismic bands, and two ML algorithms. …”
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FEATURES OF THERAPEUTIC SUPPORT FOR PATIENTS WITH POLYTRAUMA by I.A. Mormol, O.A. Borzykh, I.P. Kaidashev

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A Critical Review of the Prospect of Integrating Artificial Intelligence in Infectious Disease Diagnosis and Prognosis by Shuaibu Abdullahi Hudu, Ahmed Subeh Alshrari, Esra’a Jebreel Ibrahim Abu-Shoura, Amira Osman, Abdulgafar Olayiwola Jimoh

“…By analyzing diverse datasets, including clinical symptoms, laboratory results, and imaging data, AI algorithms can significantly enhance early detection and personalized treatment strategies. …”
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A Cautionary Perspective on Artificial Intelligence and Novel Imaging Technologies in Patient Selection for Retrograde Intrarenal Surgery by Samir Muter, Noorulhuda Al-Ani

“…This commentary critically examines these clinical algorithms, highlighting the absence of prospective validation, the potential for overreliance, and the possible limitations of their clinical applicability. …”
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Knowledge and Perception of Practicing Anesthetists on Current Techniques, Clinical Applications, and Limitations of Artificial Intelligence in Anesthesiology: An Indian Study by Manasij Mitra, Maitraye Basu, Amrita Ghosh, Ranabir Pal

“…Their concepts on techniques, applications, and safety of AI including levels and potentials of use were significant so far as predictive algorithms, assessing vital parameters and perioperative care were concerned. …”
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Postsurgery Classification of Best-Corrected Visual Acuity Changes Based on Pterygium Characteristics Using the Machine Learning Technique by Fatin Nabihah Jais, Mohd Zulfaezal Che Azemin, Mohd Radzi Hilmi, Mohd Izzuddin Mohd Tamrin, Khairidzan Mohd Kamal

“…A retrospective of the secondary dataset of 93 samples of pterygium patients with different pterygium attributes was used and imported into four different machine learning algorithms in RapidMiner software to predict the improvement of BCVA after pterygium surgery. …”
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Explainable Feature Engineering for Multi-class Money Laundering Classification by Petre-Cornel GRIGORESCU, Antoaneta AMZA

“…This paper provides insight into typical money laundering typologies used in the financial crime domain and provides a concrete set of methods through the use of which fraudulent transactions may be classified using traditional machine learning algorithms and proving the efficacy of tree-based models in not only predictive power, but also explainability and ease of interpretation of results.…”
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A Modular, Model, Library Framework (DebrisLib) for Non-Newtonian Geophysical Flows by Ian E. Floyd, Alejandro Sánchez, Stanford Gibson, Gaurav Savant

“…Numerical modelers have developed a variety of non-Newtonian algorithms to simulate this range of physical processes. …”
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Edges are all you need: Potential of medical time series analysis on complete blood count data with graph neural networks. by Daniel Walke, Daniel Steinbach, Sebastian Gibb, Thorsten Kaiser, Gunter Saake, Paul C Ahrens, David Broneske, Robert Heyer

“…<h4>Purpose</h4>Machine learning is a powerful tool to develop algorithms for clinical diagnosis. However, standard machine learning algorithms are not perfectly suited for clinical data since the data are interconnected and may contain time series. …”
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ROBUST NUMERICAL METHODS FOR SOLVING THE poorly determined taskS OF ELECTRODYNAMICS AND NONLINEAR DYNAMICS by A. A. Kuraev, T. L. Popkova, V. V. Matveenko

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Traumatic Brain Injury and Artificial Intelligence: Shaping the Future of Neurorehabilitation—A Review by Seun Orenuga, Philip Jordache, Daniel Mirzai, Tyler Monteros, Ernesto Gonzalez, Ahmed Madkoor, Rahim Hirani, Raj K. Tiwari, Mill Etienne

“…This literature review explores the current and potential applications of AI in TBI management, focusing on AI’s role in diagnostic tools, neuroimaging, prognostic modeling, and rehabilitation programs. AI-driven algorithms have demonstrated high accuracy in predicting mortality, functional outcomes, and personalized rehabilitation strategies based on patient data. …”
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