Establishment and Solution Test of Wear Prediction Model Based on Particle Swarm Optimization Least Squares Support Vector Machine by Xiao Huang, Yongguo Wang, Yuhui Mao

“…To address these problems, this paper proposes a tool wear state identification model based on particle swarm optimization (PSO) and least squares support vector machine (LS-SVM), namely the PSO-LS-SVM model. …”
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Mobile Traffic Prediction at the Edge Through Distributed and Deep Transfer Learning by Alfredo Petrella, Marco Miozzo, Paolo Dini

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What are the future directions for microplastics characterization? A regex-llama data mining approach for identifying emerging trends by FERNANDO GOMES, SHEKHAR BHANSALI, FABÍOLA MARANHÃO, VIVIANE VALLADÃO, KARINE VELASCO

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Multiphase Transport Network Optimization: Mathematical Framework Integrating Resilience Quantification and Dynamic Algorithm Coupling by Linghao Ren, Xinyue Li, Renjie Song, Yuning Wang, Meiyun Gui, Bo Tang

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Applications of Artificial Intelligence in Drug Repurposing by Zhaoman Wan, Xinran Sun, Yi Li, Tianyao Chu, Xueyu Hao, Yang Cao, Peng Zhang

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AI4EF: Artificial Intelligence for Energy Efficiency in the building sector by Alexandros Menelaos Tzortzis, Georgios Kormpakis, Sotiris Pelekis, Ariadni Michalitsi-Psarrou, Evangelos Karakolis, Christos Ntanos, Dimitris Askounis

“…AI4EF (Artificial Intelligence for Energy Efficiency) is an advanced, user-centric tool designed to support decision-making in building energy retrofitting and efficiency optimization. Leveraging machine learning (ML) and data-driven insights, AI4EF (Artificial Intelligence for Energy Efficiency) enables stakeholders such as public sector representatives, energy consultants, and building owners—to model, analyze, and predict energy consumption, retrofit costs, and environmental impacts of building upgrades. …”
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Hyperparameter optimisation in deep learning from ensemble methods: applications to proton structure by Juan Cruz-Martinez, Aron Jansen, Gijs van Oord, Tanjona R Rabemananjara, Carlos M R Rocha, Juan Rojo, Roy Stegeman

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SLC3A2 as a key anoikis−related gene for prognosis and tumor microenvironment remodeling in melanoma by Xiaojin Liu, Jiaheng Xie, Yingying Xiao

“…A total of 150 anoikis-related genes were identified, and 101 machine learning algorithms and their combinations (including Cox regression, random survival forest, and gradient boosting machine) were systematically evaluated to identify the optimal prognostic model. …”
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Variance Reduction Optimization Algorithm Based on Random Sampling by GUO Zhenhua, YAN Ruidong, QIU Zhiyong, ZHAO Yaqian, LI Rengang

“…The stochastic gradient descent (SGD) algorithms have been applied to machine learning and deep learning due to their superior performance. …”
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Review of machine learning-assisted multi-property design of high-entropy alloys: phase structure, mechanical, tribological, corrosion, and hydrogen storage properties by Yunlong Li, Jialiang Tan, Cheng Qian, Xiaochao Liu, Rui Nie

“…In recent years, the rapid development of artificial intelligence has led to the widespread adoption of machine learning (ML) as a powerful tool in HEAs research. …”
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Machine learning-based brain magnetic resonance imaging radiomics for identifying rapid eye movement sleep behavior disorder in Parkinson’s disease patients by Yandong lian, Yibin Xu, Linlin Hu, Yuguo Wei, Zhaoge Wang

“…Additionally, multi-factor logistic regression analysis identified clinical predictors associated with PD-RBD, and these clinical features were integrated with the radiomics signatures to develop predictive models using various machine learning algorithms. …”
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Strategies for Automated Identification of Food Waste in University Cafeterias: A Machine Vision Recognition Approach by Yongxin Li, Chaolong Zhang, Hui Xu, Yuantong Yang, Han Lu, Lei Deng

“…To ensure the effective implementation of food waste reduction in college cafeterias, Capital Normal University developed an automatic plate recognition system based on machine vision technology. …”
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Defining Disease Phenotypes in Primary Care Electronic Health Records by a Machine Learning Approach: A Case Study in Identifying Rheumatoid Arthritis. by Shang-Ming Zhou, Fabiola Fernandez-Gutierrez, Jonathan Kennedy, Roxanne Cooksey, Mark Atkinson, Spiros Denaxas, Stefan Siebert, William G Dixon, Terence W O'Neill, Ernest Choy, Cathie Sudlow, UK Biobank Follow-up and Outcomes Group, Sinead Brophy

“…<h4>Methods</h4>This study linked routine primary and secondary care EHRs in Wales, UK. A machine learning based scheme was used to identify patients with rheumatoid arthritis from primary care EHRs via the following steps: i) selection of variables by comparing relative frequencies of Read codes in the primary care dataset associated with disease case compared to non-disease control (disease/non-disease based on the secondary care diagnosis); ii) reduction of predictors/associated variables using a Random Forest method, iii) induction of decision rules from decision tree model. …”
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Mapping Spatiotemporal Dynamic Changes in Urban CO₂ Emissions in China by Using the Machine Learning Method and Geospatial Big Data by Wei Guo, Yongxing Li, Ximin Cui, Xuesheng Zhao, Yongjia Teng, Andreas Rienow

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Predicting Ozone Concentrations in Ecologically Sensitive Coastal Zones Through Structure Mining and Machine Learning: A Case Study of Chongming Island, China by Yan Liu, Tingting Hu, Yusen Duan, Jingqi Deng

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Statistical and machine learning analysis of diesel engines fueled with Moringa oleifera biodiesel doped with 1-hexanol and Zr2O3 nanoparticles by K. Sunil Kumar, Abdul Razak, M. K. Ramis, Shaik Mohammad Irshad, Saiful Islam, Anteneh Wogasso Wodajo

“…The novelty lies in the synergistic use of these additives for improving fuel efficiency and reducing emissions, combined with advanced statistical and machine learning models for optimization and prediction. …”
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Is there a competitive advantage to using multivariate statistical or machine learning methods over the Bross formula in the hdPS framework for bias and variance estimation? by Mohammad Ehsanul Karim, Yang Lei

“…This study aimed to systematically evaluate and compare the performance of traditional statistical methods and machine learning approaches within the hdPS framework, focusing on key metrics such as bias, standard error (SE), and coverage, under various exposure and outcome prevalence scenarios.…”
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Ozone Pollution in China Affected by Climate Change in a Carbon Neutral Future as Predicted by a Process‐Based Interpretable Machine Learning Method by Huimin Li, Yang Yang, Hang Su, Hailong Wang, Pinya Wang, Hong Liao

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Design of an integrated model using deep reinforcement learning and Variational Autoencoders for enhanced quantum security by Harshala Shingne, Diptee Chikmurge, Priya Parkhi, Poorva Agrawal

“…This work addresses these challenges by proposing the integration of AI and machine learning optimization techniques into quantum communication protocols to enhance both security and efficiency. …”
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Towards a Sustainable Disruptive Growth Model: Integrating Foresight, Wild Cards and Weak Signals Analysis by Rafael Popper, Yuli Villarroel, Raimund Popper

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