Environmental adaptations in metagenomes revealed by deep learning by Johanna C. Winder, Simon Poulton, Taoyang Wu, Thomas Mock, Cock van Oosterhout

“…To enhance biological interpretability of these predictions, we compared this model with a genetic algorithm (GA), which, although it had lower predictive ability, provided transparent classification rules and predictors. …”
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Revolutionizing Prenatal Care: Harnessing Machine Learning for Gestational Diabetes Anticipation by Sanmugasundaram Ravichandran, Hui-Kai Su, Wen-Kai Kuo, Manikandan Mahalingam, Kanimozhi Janarthanan, Bruhathi Sathyanarayanan, Kabilan Saravanan

“…We implemented a robust framework for diabetes prediction, leveraging a diverse array of machine learning algorithms. …”
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Validation of Self-reported Medical Condition in the Taiwan Biobank by Chi-Shin Wu, Le-Yin Hsu, Chen-Yang Shen, Wei J. Chen, Shi-Heng Wang

“…Integrating complementary databases, such as clinical diagnoses, prescription records, and medical procedures, can enhance accuracy through customized algorithms based on disease categories and participant characteristics and optimize sensitivity or positive predictive values to align with specific research objectives.…”
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Diaproteo: A supervised learning framework for early detection of diabetes mellitus based on proteomic profiles by Hamza Shahab Awan, Fahad Alturise, Tamim Alkhalifah, Yaser Daanial Khan

“…This research explores the application of supervised algorithms to predict DM using a variety of datasets such as clinical features, genetic markers, and lifestyle variables. …”
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Enhanced Light-Gradient Boosting Machine (GBM)-Based Artificial Intelligence-Blockchain-Based Telesurgery in Sixth Generation Communication Using Optimization Concept by Punitha S., Preetha K. S.

“…This proposed novel Enhanced Light GBM predicts the final output based on the criticality scores. …”
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Random Harmonic Detection and Compensation Based on Synchronous Reference Frame by Yanbo Che, Zhaojing Yin, Shuyan Yu, Qiang Sun

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A simultaneous post-LASIK sequential bilateral implantation of multifocal IOLs aimed at refraction correction. A clinical case by E. N. Eskina, A. V. Belogurova, A. I. Fisenko

“…The compliance with the modified algorithms of pre-and intraoperative behavior of the operating team, thorough preparation of the patient, careful calculation of the IOL allowed us to obtain the predictive refractive result with a high level of visual satisfaction and absence of undesirable postoperative phenomena. …”
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Neurotechnological Approaches to Cognitive Rehabilitation in Mild Cognitive Impairment: A Systematic Review of Neuromodulation, EEG, Virtual Reality, and Emerging AI Applications by Evgenia Gkintoni, Stephanos P. Vassilopoulos, Georgios Nikolaou, Apostolos Vantarakis

“…Emerging AI applications showed potential for personalized assessment and intervention through predictive modeling and adaptive algorithms. <i>Conclusions:</i> Neurotechnological approaches offer promising avenues for MCI rehabilitation, with the most substantial evidence for integrated interventions targeting multiple mechanisms. …”
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Low-Cost, LiDAR-Based, Dynamic, Flood Risk Communication Viewer by Debra F. Laefer, Evan O’Keeffe, Kshitij Chandna, Kim Hertz, Jing Zhu, Raul Lejano, Anh Vu Vo, Michela Bertolotto, Ulrich Ofterdinger

“…This hyper-local approach to visualization is enhanced by the ability to portray changes in (i) watercolor, (ii) texture, and (iii) motion (including dynamic heights) for various flood prediction scenarios. Through decoupling physics-based predictions from the visualization, a dynamic, flood risk viewer was produced with modest processing resources involving only a single, quad-core processor with a frequency around 4.30 GHz and with no graphics card. …”
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Modified Traffic Flow Model with Connected Vehicle Microscopic Data for Proactive Variable Speed Limit Control by Jie Fang, Huixuan Ye, Said M. Easa

“…The proposed VSL control algorithm adopts the Model Predictive Control (MPC) framework, which employs a modified version of the classic traffic flow model METANET to take advantage of the microscopic data in traffic flow predictions. …”
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AI-powered blockchain technology in industry 4.0, a review by Mohsen Soori, Roza Dastres, Behrooz Arezoo

“…Artificial Intelligence (AI) algorithms can be employed to enhance the security of the blockchain networks in the era of industry 4.0. …”
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Advancements in AI-Powered Electric Vehicle Routing: Multi-Constraint Optimization and Infrastructure Integration Approaches for Evolving EVs&#x2014;A Survey by P. Anandha Prakash, R. Radha

“…Electric vehicles (EVs) have become essential tools in the worldwide shift to sustainable transportation, driven by advances in battery technology, charging infrastructure, and routing algorithms. This paper presents a comprehensive investigation of the emerging EV ecosystem through three key domains: electric automobiles, routing algorithms, and charging infrastructure. …”
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Investigating amplitude amplification in optimization-based control for a continuous stirred tank reactor by Kip Nieman, Helen Durand, Saahil Patel, Daniel Koch, Paul M. Alsing

“…Our previous works have studied the use of quantum computers and quantum algorithms for process control applications. Our prior work evaluated the use of a gate-based quantum amplitude amplification algorithm when applied to a model predictive control optimization problem, specifically evaluating a linear systems example. …”
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Multimodal dataset for sensor fusion in fall detection by Carla Taramasco, Miguel Pineiro, Pablo Ormeño-Arriagada, Diego Robles, David Araya

“…Despite the existence of numerous fall detection systems (FDSs) that utilize machine learning and predictive modeling, accurately distinguishing between everyday activities and genuine falls continues to pose significant challenges, exacerbated by the varied nature of residential settings. …”
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Overcoming the Barriers That Obscure the Interlinking and Analysis of Clinical Data Through Harmonization and Incremental Learning by Vasileios C. Pezoulas, Konstantina D. Kourou, Fanis Kalatzis, Themis P. Exarchos, Evi Zampeli, Saviana Gandolfo, Andreas Goules, Chiara Baldini, Fotini Skopouli, Salvatore De Vita, Athanasios G. Tzioufas, Dimitrios I. Fotiadis

“…Lexical and semantic matching methods are used to align the structure of the heterogeneous, curated cohort data along with incremental learning algorithms including class imbalance handling and hyperparameter optimization to enable the development of disease prediction models. …”
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GeoFactory: an LLM performance enhancement framework for geoscience factual and inferential tasks by Zhou Chen, Xiao Wang, Xinan Zhang, Ming Lin, Yuanhong Liao, Juanzi Li, Yuqi Bai

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Advanced time-series InSAR analysis to estimate surface deformation and utilization of hybrid deep learning for susceptibility mapping in the Jakarta metropolitan region by Wahyu Luqmanul Hakim, Muhammad Fulki Fadhillah, Joong-Sun Won, Yu-Chul Park, Chang-Wook Lee

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Fundus camera-based precision monitoring of blood vitamin A level for Wagyu cattle using deep learning by Nanding Li, Naoshi Kondo, Yuichi Ogawa, Keiichiro Shiraga, Mizuki Shibasaki, Daniele Pinna, Moriyuki Fukushima, Shinichi Nagaoka, Tateshi Fujiura, Xuehong De, Tetsuhito Suzuki

“…This study developed a handheld camera system capable of capturing cattle fundus images and predicting vitamin A levels in real time using deep learning. 4000 fundus images from 50 Japanese Black cattle were used to train and test the prediction algorithms, and the model achieved an average 87%, 83%, and 80% accuracy for three levels of vitamin A deficiency classification (particularly 87% for severe level), demonstrating the effectiveness of camera system in vitamin A deficiency prediction, especially for screening and early warning. …”
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An Adaptive Weight Physics-Informed Neural Network for Vortex-Induced Vibration Problems by Ping Zhu, Zhonglin Liu, Ziqing Xu, Junxue Lv

“…In this study, a VIV dataset of a cylindrical body with different degrees of freedom is used to compare the performance of the PINN and three PINN optimization algorithms. The findings suggest that, compared to a standard PINN, the AW-PINN lowers the mean squared error (MSE) on the test set by 50%, significantly improving the prediction accuracy. …”
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Improving Mental Health Diagnosis with Hybrid Ensemble Models: A Data-Driven Approach by Malave Sachin, Khemani Bharti, Kelkar Rucha, Balekundri Urvi, Bogar Shravani, Kolekar Areen

“…This study examines how emotional and behavioural indicators might be used to predict mental health issues using machine learning (ML) algorithms. …”
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