Computer-economical optimization method for solving inverse problems of determining electrophysical properties of objects in eddy current structroscopy by V. Ya. Halchenko, R. V. Trembovetska, V. V. Tychkov

“…The results confirmed the validity of a significant reduction in space without major loss of information. …”
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Radar-equivalent snowpack: reducing the number of snow layers while retaining their microwave properties and bulk snow mass by J. Meloche, N. R. Leroux, B. Montpetit, V. Vionnet, C. Derksen

“…<p>Snow water equivalent (SWE) retrieval from Ku-band radar measurements is possible with complex retrieval algorithms involving prior information on the snowpack microstructure and a microwave radiative transfer model to link backscatter measurements to snow properties. …”
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Framework for Personalizing Wearable Devices Using Real-Time Physiological Measures by Prakyath Kantharaju, Sai Siddarth Vakacherla, Michael Jacobson, Hyeongkeun Jeong, Meet Nikunj Mevada, Xingyuan Zhou, Matthew J. Major, Myunghee Kim

“…The third case study personalized gait parameters, specifically step frequency, using an electrocardiogram (ECG)-based cost function along with an optimization algorithm variant, resulting in a 43&#x0025; reduction in optimization time for one non-disabled subject. …”
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Deep learning-based technique for investigating the behavior of MEMS systems with multiwalled carbon nanotubes and electrically actuated microbeams by Muhammad Amir, Jamshaid Ul Rahman, Ali Hasan Ali, Ali Raza, Zaid Ameen Abduljabbar, Husam A. Neamah

“…Numerical simulations and graphical demonstrations are presented to verify the accuracy and efficiency of the algorithm. • The study develops a novel DNN-based model to solve non-linear systems in MEMS, particularly for oscillators with MWCNTs. • Deep learning optimizers are applied to improve the accuracy and efficiency of predicting MEMS behavior. • Numerical simulations confirm the effectiveness of the proposed methodology.…”
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Cooperative Mechanism for Energy Transportation and Storage in Internet of Energy by Weigang Hou, Guoda Tian, Lei Guo, Xiaojie Wang, Xu Zhang, Zhaolong Ning

“…For this end, we investigate cooperative energy transportation and storage for IoEs in terms of problem analysis, algorithm design, and platform development. After demonstrating the feasibility condition and proving the NP-hard of our problem, we derive the optimal solution by the reduction from a classic knapsack problem. …”
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Helium retention feature in the boron deposited layer on tungsten substrate by laser-induced breakdown spectroscopy and machine learning approach by Muhammad Amir Shabbir, Ran Hai, Zhonglin He, Zehua Liu, Fahad Rehman, Xue Bai, Jianping Mu, Ding Wu, Cong Li, Hongbin Ding

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Investigating the Use of Machine Learning Models to Understand the Drugs Permeability Across Placenta by Vaisali Chandrasekar, Mohammed Yusuf Ansari, Ajay Vikram Singh, Shahab Uddin, Kirthi S. Prabhu, Sagnika Dash, Souhaila Al Khodor, Annalisa Terranegra, Matteo Avella, Sarada Prasad Dakua

“…Owing to limited drug testing possibilities in pregnant population, the development of computational algorithms is crucial to predict the fate of drugs in the placental barrier; it could serve as an alternative to animal testing. …”
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Exploring Feature Selection with Deep Learning for Kidney Tissue Microarray Classification Using Infrared Spectral Imaging by Zachary Caterer, Jordan Langlois, Connor McKeown, Mikayla Hady, Samuel Stumo, Suman Setty, Michael Walsh, Rahul Gomes

“…Through the integration of scalable deep learning models coupled with feature selection, we have developed a classification pipeline with high predictive power, which could be integrated into a high-throughput real-time IR imaging system. …”
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Artificial intelligence as a diagnostic aid in cross-sectional radiological imaging of the abdominopelvic cavity: a protocol for a systematic review by Natalie S Blencowe, Neil J Smart, George E Fowler, Rhiannon C Macefield, Conor Hardacre, Mark P Callaway

“…Diagnostic accuracy of AI models, including reported sensitivity, specificity, predictive values, likelihood ratios and the area under the receiver operating characteristic curve will be examined and compared with standard practice. …”
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Advancing multidisciplinary management of pediatric hyperinflammatory disorders by Francesco La Torre, Giovanni Meliota, Adele Civino, Angelo Campanozzi, Valerio Cecinati, Enrico Rosati, Emanuela Sacco, Nicola Santoro, Ugo Vairo, Fabio Cardinale

“…Future research directions include the identification of predictive biomarkers, exploration of novel therapeutic targets, and development of evidence-based treatment protocols to enhance long-term outcomes in pediatric inflammatory diseases.…”
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Early breast cancer detection via infrared thermography using a CNN enhanced with particle swarm optimization by Riyadh M. Alzahrani, Mohamed Yacin Sikkandar, S. Sabarunisha Begum, Ahmed Farag Salem Babetat, Maryam Alhashim, Abdulrahman Alduraywish, N. B. Prakash, Eddie Y. K. Ng

“…The proposed model achieves a superior classification accuracy of 98.8%, significantly outperforming conventional CNN implementations in terms of both computational speed and predictive accuracy. These findings suggest that the developed system holds substantial potential for early, reliable, and cost-effective breast cancer screening in real-world clinical environments.…”
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Development of a robust FT-IR typing system for Salmonella enterica, enhancing performance through hierarchical classification by Diego Fredes-García, Javiera Jiménez-Rodríguez, Alejandro Piña-Iturbe, Pablo Caballero-Díaz, Tamara González-Villarroel, Fernando Dueñas, Aniela Wozniak, Aiko D. Adell, Andrea I. Moreno-Switt, Patricia García

“…The accuracy of classifiers was validated using a validation set to determine sensitivity, specificity, positive predictive value, and negative predictive value. Initial classifiers showed high accuracy for Abony, Agona, Enteritidis, and Infantis serovars, with sensitivities close to 100%. …”
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A New Support Vector Regression Model for Equipment Health Diagnosis with Small Sample Data Missing and Its Application by Qinming Liu, Wenyi Liu, Jiajian Mei, Guojin Si, Tangbin Xia, Jiarui Quan

“…Then, the dynamic weight is presented to combine the single-variable prediction method with the multiple-variable prediction method based on certain principles, and the missing data are filled with the combined prediction methods. …”
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Human adaptation to adaptive machines converges to game-theoretic equilibria by Benjamin J. Chasnov, Lillian J. Ratliff, Samuel A. Burden

“…Abstract Here we test three learning algorithms for machines playing general-sum games with human subjects. …”
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Current Signature-Based Bearing Fault Severity Classification Using a Robust Multilevel Cascaded Framework by Korawege N. C. Jayasena, Battur Batkhishig, Babak Nahid-Mobarakeh, Ali Emadi

“…Early and accurate classification of bearing fault severity is essential for predictive maintenance, as it enhances cost-effectiveness, ensures safety, and extends product life. …”
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REVOLUTIONIZING LUXURY: THE ROLE OF AI AND MACHINE LEARNING IN ENHANCING MARKETING STRATEGIES WITHIN THE TOURISM AND HOSPITALITY LUXURY SECTORS by Maria Nascimento CUNHA, Manuel PEREIRA, António CARDOSO, Jorge FIGUEIREDO, Isabel OLIVEIRA

“…AI and ML applications, such as chatbots for 24/7 customer service and predictive analytics for tailoring travel recommendations, have greatly improved customer interaction and operational efficiencies. …”
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Iron Ore Information Extraction Based on CNN-LSTM Composite Deep Learning Model by Haili Chen, Mengxiang Xia, Yaping Zhang, Ruonan Zhao, Bingran Song, Yang Bai

“…The composite model performs best with superior predictive performance compared to CNN, LSTM, decision tree (DT), random forest (RF), and extreme gradient boosting (XGBoost) models. …”
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Towards a digital twin: Digitization and model-based optimization of the innovative high-gradient magnetic separatorMendeley Data by Marko Tesanovic, Torben Bardel, Robin Karl, Sonja Berensmeier

“…Furthermore, process efficiency is often not fully realized due to the reliance on fixed operational recipes.This study presents a digital twin framework for a pilot-scale HGMS system, integrating real-time monitoring, automated control, advanced mechanistic models, and multi-objective optimization using Bayesian algorithms. The framework was validated for robustness, scalable data handling, and predictive control. …”
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Sustainable Energy and Exergy Analysis in Offshore Wind Farms Using Machine Learning: A Systematic Review by Hamid Reza Soltani Motlagh, Seyed Behbood Issa-Zadeh, Abdul Hameed Kalifullah, Arife Tugsan Isiacik Colak, Md Redzuan Zoolfakar

“…This PRISMA-ScR review synthesizes recent advancements in ML techniques, including Random Forest, Long Short-Term Memory networks, and hybrid models, demonstrating significant improvements in predictive accuracy and operational efficiency. In addition, it critically identifies current gaps in existing software tools, such as inadequate real-time data processing and limited user interface design, which hinder the practical implementation of ML solutions. …”
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Early detection of Cercospora beticola and powdery mildew diseases in sugar beet using uncrewed aerial vehicle-based remote sensing and machine learning by Koç Mehmet Tuğrul, Rıza Kaya, Kemal Özkan, Merve Ceyhan, Uğur Gürel, Fatih Yavuz Fidantemiz

“…Additionally, the study demonstrated that early detection of diseases is possible using K-nearest neighbors and logistic regression algorithms, exhibiting high discrimination and predictive accuracy.…”
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