Use of machine learning in predicting continuity of HIV treatment in selected Nigerian States. by Mukhtar Ijaiya, Erica Troncoso, Marang Mutloatse, Duruanyanwu Ifeanyi, Benjamin Obasa, Franklin Emerenini, Lucien De Voux, Thobeka Mnguni, Shantelle Parrott, Ejike Okwor, Babafemi Dare, Oluwayemisi Ogundare, Emmanuel Atuma, Molly Strachan, Ruby Fayorsey, Kelly Curran

“…We trained multiple supervised ML algorithms on de-identified client-level electronic medical records data from a cohort of PLHIV across four Nigerian states. …”
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Impact of occupancy behavior on building energy efficiency: What’s next in detection and monitoring technologies? by Wenjie Song, John Calautit

“…Personalization and adaptability emerge as key themes, particularly in multi-occupant contexts, while multi-sensor data fusion promises to enhance detection stability and reduce false positives. …”
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Real-Time Cloth Simulation in Extended Reality: Comparative Study Between Unity Cloth Model and Position-Based Dynamics Model with GPU by Taeheon Kim, Jun Ma, Min Hong

“…To overcome the limitations of traditional CPU-based physics simulations, we designed and optimized highly parallelized algorithms utilizing Unity’s Compute Shader framework. …”
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K-LM: Knowledge Augmenting in Language Models Within the Scholarly Domain by Vivek Kumar, Diego Reforgiato Recupero, Rim Helaoui, Daniele Riboni

“…The use of superior algorithms and complex architectures in language models have successfully imparted human-like abilities to machines for specific tasks. …”
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Machine learning for defect condition rating of wall wooden columns in ancient buildings by Yufeng Li, Wu Ouyang, Zhenbo Xin, Houjiang Zhang, Shuqi Sun, Dian Zhang, Wenbo Zhang

“…The RBF neural network model achieved the highest accuracy (94.57 %) on the feature fusion dataset, while Grey Wolf Optimizer (GWO) optimization further improved accuracy to 96.74 %. …”
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Clinical applications of photon-counting CT in neuroimaging by LÜ Haiying, LU Yong, HE Naying

“…In the future, it is necessary to gradually overcome these limitations through continued hardware upgrades, algorithmic optimization, and the advancement of multicenter prospective studies to fully unleash the clinical potential of PCCT.…”
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Evaluating Network Performance in a Web-Based Augmented Reality System for Lipmatte Color Recommendation by Zulfikri Paidi, Nurhanna Md Abd Wahid, Nurzaid Muhd Zain, Mahfudzah Othman

“…This research highlights the significant potential of AR technology in modernizing the beauty shopping experience while illustrating the critical role of network performance in achieving optimal user satisfaction. …”
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Trading off Iodine and Radiation Dose in Coronary Computed Tomography by Guillaume Fahrni, Thomas Saliba, Damien Racine, Marianna Gulizia, Georgios Tzimas, Chiara Pozzessere, David C. Rotzinger

“…This review explores the balance between radiation and CM volumes, emphasizing patient-specific protocol optimization to improve diagnostic accuracy while minimizing risks. …”
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Predicting Oncological and Functional Outcomes by Nephrectomy Type for T1 Renal Tumors Using Machine Learning Models by Dongrul Shin, Maisy Song, Jungyo Suh, Cheryn Song

“…Purpose Determining the optimal surgical approach for patients with T1 renal tumors requires balancing long-term oncological and renal functional outcomes. …”
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Research Progress on Atmospheric Turbulence Perception and Correction Based on Adaptive Optics and Deep Learning by Qinghui Liu, Yihang Di, Mengmeng Zhang, Zhenbo Ren, Jianglei Di, Jianlin Zhao

“…Particular emphasis is placed on deep learning‐enabled intelligent correction paradigms, while critical analysis is provided regarding prospective research trajectories and implementation challenges.…”
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Development of Adaptive Testing Method Based on Neurotechnologies by E. V. Chumakova, D. G. Korneev, M. S. Gasparian

“…SGD, Adam, NAdam and RMSprop implemented in Keras were compared as optimizers to achieve faster convergence. Adam showed the best results in terms of accuracy, while the MSE loss function (mean square error) was used together with the optimizer. …”
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Machine learning for predicting strength properties of waste iron slag concrete by Matiur Rahman Raju, Syed Ishtiaq Ahmad, Md Mehedi Hasan, Noor Md. Sadiqul Hasan, Md Monirul Islam, Md. Abdul Basit, Ishraq Tasnim Hossain, Saif Ahmed Santo, Md Shahrior Alam, Mahfuzur Rahman

“…Among the tested ML algorithms, Decision Tree (DT) and XGBoost showed the highest accuracy (R2 = 0.95135) in predicting concrete strength properties, while models like SVM and Symbolic Regression underperformed. …”
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Identification of anaerobic bacterial strains by pyrolysis-gas chromatography-ion mobility spectrometry by Tim Kobelt, Jonas Klose, Rumjhum Mukherjee, Rumjhum Mukherjee, Martin Lippmann, Szymon P. Szafranski, Szymon P. Szafranski, Meike Stiesch, Meike Stiesch, Stefan Zimmermann

“…Preliminary experiments have demonstrated that pattern recognition algorithms can predict the genus of isolated bacteria with a precision of up to 97%.…”
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Battery Energy Storage Systems in Microgrids: A Review of SoC Balancing and Perspectives by Thales Augusto Fagundes, Guilherme Henrique Favaro Fuzato, Lucas Jonys Ribeiro Silva, Augusto Matheus dos Santos Alonso, Juan C. Vasquez, Josep M. Guerrero, Ricardo Quadros Machado

“…In addition, this article explores optimization processes aimed at reducing operational costs while considering SoC equalization. …”
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Implications of machine learning techniques for prediction of motor health disorders in Saudi Arabia by Ehab M. Almetwally, I. Elbatal, Mohammed Elgarhy, Amr R. Kamel

“…The dataset was partitioned into a training set comprising 70 % of the data and a testing set containing 30 % of the data using the ML algorithms. The results of the implemented algorithms demonstrated that the RF technique attained an accuracy of 100 %, while the DT technique scored an accuracy of 63 %, the NB approach scored an accuracy of 69 %, the K-NN technique scored an accuracy of 76 %, and the SVM technique scored an accuracy of 90 %. …”
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Large Language Models in Transportation: A Comprehensive Bibliometric Analysis of Emerging Trends, Challenges, and Future Research by Mahbub Hassan, Md. Emtiaz Kabir, Muzammil Jusoh, Hong Ki An, Michael Negnevitsky, Chengjiang Li

“…Key research themes focus on autonomous mobility, traffic optimization, and sustainable transportation networks. …”
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Artificial intelligence as a transforming factor in motility disorders–automatic detection of motility patterns in high-resolution anorectal manometry by Miguel Mascarenhas, Francisco Mendes, Joana Mota, Tiago Ribeiro, Pedro Cardoso, Miguel Martins, Maria João Almeida, João Rala Cordeiro, João Ferreira, Guilherme Macedo, Cecilio Santander

“…This is the first worldwide study proving the accuracy of a ML model for differentiation of motility patterns in HR-ARM, demonstrating the value of artificial intelligence models in optimizing HR-ARM availability while reducing interobserver variability and increasing accuracy.…”
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Dataset of apples for grading by sweetness, ripeness and varietyMendeley Data by Shilpa Gaikwad, Sonali Kothari, Ignisha Rajathi G

“…Ripeness evaluation includes 29,160 images documenting the complete maturation cycle over 18 days, while variety classification contains 1683 images from three distinct cultivars. …”
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Projecting Forest Fire Probability in South Korea Under Climate Change, Population, and Forest Management Scenarios Using AI & Process-Based Hybrid Model (FLAM-Net) by Hyun-Woo Jo, Myoungsoo Won, Florian Kraxner, Seong Woo Jeon, Yowhan Son, Andrey Krasovskiy, Woo-Kyun Lee

“…Enhancements included improving backpropagation for optimization and introducing algorithms for national-specific fire ignition dynamics. …”
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Artificial Intelligence in Primary Malignant Bone Tumor Imaging: A Narrative Review by Platon S. Papageorgiou, Rafail Christodoulou, Panagiotis Korfiatis, Dimitra P. Papagelopoulos, Olympia Papakonstantinou, Nancy Pham, Amanda Woodward, Panayiotis J. Papagelopoulos

“…Through machine learning and deep learning techniques, AI leverages computational algorithms and large datasets to enhance medical imaging interpretation and support clinical decision-making. …”
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