Exploring the Role of Artificial Intelligence in Dental Implantology: A Scholarly Review by Abdulrahman Ahmed Aseri

“…AI-based technologies, such as machine learning algorithms, neural networks, and computer vision, are being increasingly utilized to interpret diagnostic imaging, predict implant outcomes, and customize treatment protocols tailored to individual patient needs. …”
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Employee loyalty evaluation using machine learning in technology-based small and medium-sized enterprises by Yong Shi, Yuan Wang, Hongkun zuo

“…Through several machine learning models and algorithms to predict employee loyalty, the feasibility of machine learning to predict employee loyalty is proved, and the evaluation of talent in TSMEs is supported by decision analysis. …”
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Gauge-fixing quantum density operators at scale by Amit Jamadagni, Eugene Dumitrescu

“…We provide a theory, algorithms, and simulations of nonequilibrium quantum systems using a one-dimensional (1D) completely positive (CP), matrix-product (MP) density-operator (ρ) representation. …”
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AI-enhanced digital therapeutics for cognitive impairment: integrating mobile applications, virtual reality, and wearable devices by Dian Jiao

“…The integration of artificial intelligence enables dynamic personalization, predictive analytics, and multi-modal data fusion, creating unified therapeutic ecosystems that enhance outcomes despite persistent challenges related to data security, ethics, and algorithmic transparency. …”
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Facial expression recognition based on adaptation of the classifier to videos of the user by E.N. Churaev, A.V. Savchenko

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Evaluation of Machine Learning Applications for the Complex Near-Critical Phase Behavior Modelling of CO₂–Hydrocarbon Systems by Daulet Magzymov, Meruyert Makhatova, Zhasulan Dairov, Murat Syzdykov

“…We compared the physical Peng–Robinson equation of state model to machine learning algorithms under varying temperatures, pressures, and composition, including challenging near-critical scenarios. …”
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AI-driven model for optimized pulse programming of memristive devices by Benjamin Spetzler, Markus Fritscher, Seongae Park, Nayoun Kim, Christian Wenger, Martin Ziegler

“…However, programming memristive AI hardware to achieve a desired synaptic weight configuration remains challenging because it requires accurate and energy-efficient algorithms for selecting the optimal weight-update pulses. …”
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Gas permeability, diffusivity, and solubility in polymers: Simulation-experiment data fusion and multi-task machine learning by Brandon K. Phan, Kuan-Hsuan Shen, Rishi Gurnani, Huan Tran, Ryan Lively, Rampi Ramprasad

“…To address this challenge, we present a multi-tiered multi-task learning framework empowered with advanced machine-crafted polymer fingerprinting algorithms and data fusion techniques. This framework combines scarce “high-fidelity” experimental data with abundant diverse “low-fidelity” simulation or synthetic data, resulting in predictive models that display a high level of generalizability across novel chemical spaces. …”
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Insights into Nitrogen-Associated Protein 50 (NAP50) as a Tyrosyl–DNA Phosphodiesterase in Dinoflagellates by Lu Tang, Nora Fung-Yee Tam, Winnie Lam, Thomas Chun-Hung Lee, Steven Jing-Liang Xu, Fred Wang-Fat Lee

“…The three-dimensional structure and biological functions of NAP50 are predicted using deep learning algorithms. Based on evolutionary relationships and functional predictions, NAP50 may play a role in repairing plastid DNA damage and potentially contribute to the transcription of plastid genes in dinoflagellates.…”
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A Comparative Analysis of Automated Machine Learning Libraries for Electricity Price Forecasting by O’Leary Christian, Lynch Conor, Toosi Farshad Ghassemi

“…Reliable and accurate electricity price forecasting algorithms can be used to inform efficient energy consumption schedules and maximise profits for electricity traders. …”
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Guidelines for releasing a variant effect predictor by Benjamin J. Livesey, Mihaly Badonyi, Mafalda Dias, Jonathan Frazer, Sushant Kumar, Kresten Lindorff-Larsen, David M. McCandlish, Rose Orenbuch, Courtney A. Shearer, Lara Muffley, Julia Foreman, Andrew M. Glazer, Ben Lehner, Debora S. Marks, Frederick P. Roth, Alan F. Rubin, Lea M. Starita, Joseph A. Marsh

“…Many different VEPs have been released, and there is tremendous variability in their underlying algorithms, outputs, and the ways in which the methodologies and predictions are shared. …”
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AI and Custom Marketing for Cultural Initiatives in Regional Projects: Perspectives on the Territorial Integration of Communities with Tourism by Mariflora Caruso, Laura Leopardi, Saverio Giulio Malatesta

“…This approach aims to predict and meet individual preferences, redefining engagement and outreach strategies in cultural settings.…”
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Early Warning of Nerve Agent Release in Large Indoor Environments Based on Encoder-Decoder Coupling Physics-Informed Neural Network by Shuobei Sun, Yang Peng, An Wang, Yiwen Xie, Yang Hu, Zhongyu Hou

“…Extensive experiments are conducted, and the results indicate that our model attains the highest performance among all the algorithms proposed in this paper. The difference between the actual and predicted results is small, and the scatter points are distributed around the fitted curves. …”
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Unveiling the effect of urinary xenoestrogens on chronic kidney disease in adults: A machine learning model by Bowen Zhang, Liang Chen, Tao Li

“…Four ML algorithms—random forest classifier (RF), XGBoost (XGB), k-nearest neighbors (KNN), and support vector machine (SVM)—were used alongside traditional logistic regression to predict CKD. …”
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Examining wildfire dynamics using ECOSTRESS data with machine learning approaches: the case of South‐Eastern Australia's black summer by Yuanhui Zhu, Shakthi B. Murugesan, Ivone K. Masara, Soe W. Myint, Joshua B. Fisher

“…With these data, we predicted over 90% of all wildfire occurrences 1 week ahead of these wildfire events. …”
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Estimating Trends in Cardiovascular Disease Risk for the EXPOSE (Explaining Population Trends in Cardiovascular Risk: A Comparative Analysis of Health Transitions in South Africa a... by Shaun Scholes, Jennifer S Mindell, Mari Toomse-Smith, Annibale Cois, Kafui Adjaye-Gbewonyo

“…Laboratory- and non–laboratory-based World Health Organization (WHO) and Globorisk algorithms were used to calculate the predicted 10-year total (fatal and nonfatal) CVD risk. …”
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AI Innovations in Liver Transplantation: From Big Data to Better Outcomes by Eleni Avramidou, Dominik Todorov, Georgios Katsanos, Nikolaos Antoniadis, Athanasios Kofinas, Stella Vasileiadou, Konstantina-Eleni Karakasi, Georgios Tsoulfas

“…As a result, algorithms are being developed to assess steatosis in pre-implantation biopsies and predict liver graft function, with AI applications displaying great accuracy across various studies included in this review. …”
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Opportunities and limitations of introducing artificial intelligence technologies into reproductive medicine by V. A. Lebina, O. Kh. Shikhalakhova, A. A. Kokhan, I. Yu. Rashidov, K. A. Tazhev, A. V. Filippova, E. P. Myshinskaya, Yu. V. Symolkina, Yu. I. Ibuev, A. A. Mataeva, A. N. Sirotenko, T. T. Gabaraeva, A. I. Askerova

“…Studies show that machine learning algorithms can process images of embryos to assess their quality, thus facilitating the selection of the most viable among them for transfer. …”
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AIoT Monitoring for Early Identification of Diseases in Grapevines: Complete Study by Mihaela Hnatiuc, Domnica Alpetri, Sorin-Robertino Sintea, Bogdan Hnatiuc, Gabriel Margarit Raicu, Mirel Paun, Ionica Dina

“…Machine learning (ML) algorithms, running on a server with an NVIDIA R3900 card, process this data to predict potential infections caused by pathogens such as Plasmopara viticola, Uncinula necator, and Botrytis. …”
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Control over biopower in cognitive and surveillance capitalism by Stanković-Pejnović Vesna

“…Digital networks do not only collect data on users, but they "cluster" these users with the help of algorithms and encourage specific desired behaviors. …”
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