Bayesian optimization with Gaussian-process-based active machine learning for improvement of geometric accuracy in projection multi-photon 3D printing by Jason E. Johnson, Ishat Raihan Jamil, Liang Pan, Guang Lin, Xianfan Xu

“…The proposed active learning framework uses Bayesian optimization to inform optimal experimentation in order to adaptively collect the most informative data for effective training of a Gaussian-process-regression-based machine learning model. …”
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Integrating Learning-Driven Model Behavior and Data Representation for Enhanced Remaining Useful Life Prediction in Rotating Machinery by Tarek Berghout, Eric Bechhoefer, Faycal Djeffal, Wei Hong Lim

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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

“…Traditional tool wear identification methods are usually based on the framework of “feature extraction + machine learning”, but these methods often have problems of low efficiency and low recognition accuracy. …”
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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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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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Comparison of simulating visibility using XGBoost and IMPROVE method: a case study in East China by Xin Zhang, Yue Wang, Zibo Zhuang, Yuxi Liu, Chengduo Yuan, Lei Su, Jingyuan Shao, Pak-Wai Chan

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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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Comprehensive multi-omics integration uncovers mitochondrial gene signatures for prognosis and personalized therapy in lung adenocarcinoma by Wenjia Zhang, Lei Zhao, Tiansheng Zheng, Lihong Fan, Kai Wang, Guoshu Li

“…By leveraging an ensemble of machine learning algorithms, we developed an Artificial Intelligence-Derived Prognostic Signature (AIDPS) model based on mitochondrial-related genes and validated its prognostic accuracy across multiple independent datasets. …”
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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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Simulation and explanatory analysis of dissolved oxygen dynamics in Lake Ulansuhai, China by Fan Zhang, Xiaohong Shi, Shengnan Zhao, Ruonan Hao, Biao Sun, Guohua Li, Shihuan Wang, Hao Zhang

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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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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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Machine learning based multi-stage intrusion detection system and feature selection ensemble security in cloud assisted vehicular ad hoc networks by C. Christy, A. Nirmala, A. Mary Odilya Teena, A. Isabella Amali

“…A new method for improving VANET security, a multi-stage Lightweight IntrusionDetection System Using Random Forest Algorithms (MLIDS-RFA), focuses on feature selection and ensemble models based on machine learning (ML). A multi-step approach is employed by the proposed system, with each stage dedicated to accurately detecting specific types of attacks. …”
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Three-Dimensional In Situ Stress Distribution in a Fault Fracture Reservoir, Linnan Sag, Bohai Bay Basin by Jiageng Liu, Yanzhong Wang, Jing Li, Xiaoyu Meng, Jiayi Teng, Zhicheng Wang, Mingzhi Li, Rui Zhu

“…Machine learning techniques, specifically a back propagation (BP) neural network, are utilized to invert the boundary conditions of the study area. …”
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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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Federated learning with tensor networks: a quantum AI framework for healthcare by Amandeep Singh Bhatia, David E Bernal Neira

“…In today’s context, Federated Learning (FL) stands out as a crucial remedy, facilitating the rapid advancement of distributed machine learning while effectively managing critical concerns regarding data privacy and governance. …”
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A Global Irradiance Prediction Model Using Convolutional Neural Networks, Wavelet Neural Networks, and Masked Multi-Head Attention Mechanism by Walid Mchara, Lazhar Manai, Mohamed Abdellatif Khalfa, Monia Raissi, Salah Hannechi

“…Existing approaches, including statistical methods, conventional machine learning models, and standalone deep learning techniques like LSTM, fail to integrate local features and long-term dependencies simultaneously, creating a need for more robust solutions. …”
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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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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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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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