Recursive Time Series Prediction Modeling of Long-Term Trends in Surface Settlement During Railway Tunnel Construction by Feilian Zhang, Qicheng Wei, Zhe Wu, Jiawei Cao, Danlin Jian, Lantian Xiang

“…To prevent surface settlement and surrounding rock collapse during railroad tunnel construction, while also ensuring the safety of the tunnel and existing structures, we propose a recursive prediction model for the long-term trend of surface settlement utilizing a singular spectrum analysis (SSA), improved sand cat swarm optimization (ISCSO), and a kernel extreme learning machine (KELM). …”
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Enhancing Order Fulfillment Through Production Process Reengineering Using Manufacturing Execution System as a Reference Model by Pin-Yu Liao, Dah-Chuan Gong, Zhen Zeng

“…Through the reengineering of processes at this machining factory, this case company’s order fulfillment will be improved. …”
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Multi-Agent Meta Reinforcement Learning for Reliable and Low-Latency Distributed Inference in Resource-Constrained UAV Swarms by Marwan Dhuheir, Aiman Erbad, Bechir Hamdaoui, Samir Brahim Belhaouari, Mohsen Guizani, Thang X. Vu

“…Extensive simulations show that our model outperforms competitive methods by around 29% in terms of latency and around 23% in terms of transmission power improvements while delivering results comparable to the traditional LDTP optimization solution by around 9% in terms of latency.…”
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From Reality to Virtuality: Revolutionizing Livestock Farming Through Digital Twins by Elanchezhian Arulmozhi, Nibas Chandra Deb, Niraj Tamrakar, Dae Yeong Kang, Myeong Yong Kang, Junghoo Kook, Jayanta Kumar Basak, Hyeon Tae Kim

“…A Digital Twin (DT), an aspect of cutting-edge digital agriculture technology, represents a virtual replica or model of any physical entity (physical twin) linked through real-time data exchange. …”
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A New Breast Cancer Discovery Strategy: A Combined Outlier Rejection Technique and an Ensemble Classification Method by Shereen H. Ali, Mohamed Shehata

“…Only then does the diagnostic model in the BCDS for precise diagnosis begin to be trained. …”
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A Novel High Performance Object Identification Approach in Care Homes Using Gaussian Preprocessing by Sara Auweiler, Melina Mueller, Diana Puhla, Pascal Penava, Ricardo Buettner

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Distributed energy sharing algorithm for Micro Grid energy system based on cloud computing by Wenwei Su, Yan Shi

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A novel method for power transformer fault diagnosis considering imbalanced data samples by Jun Chen, Yong Wang, Lingming Kong, Yilong Chen, Mianzhi Chen, Qian Cai, Gehao Sheng

“…Hyperparameter tuning is achieved through the Bayesian optimization algorithm to identify the model parameter set that maximizes test set accuracy.ResultsAnalysis of the transformer fault case library reveals that the model proposed in this paper reduces diagnostic time by nearly half compared to traditional machine learning diagnosis models. …”
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Enhanced SVM-based model for predicting cyberspace vulnerabilities: Analyzing the role of user group dynamics and capital influx. by Yicheng Long

“…To address the limited adaptability of traditional support vector machine (SVM) models in identifying nonlinear attacks, this study introduces a distribution-driven, dynamically adaptive kernel optimization approach. …”
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Hybrid CNN-LSTM Model with Custom Activation and Loss Functions for Predicting Fan Actuator States in Smart Greenhouses by Gregorius Airlangga, Julius Bata, Oskar Ika Adi Nugroho, Boby Hartanto Pramudita Lim

“…Experimental results demonstrate the superior performance of the hybrid CNN-LSTM model, achieving an accuracy of 0.9992, precision of 0.9989, recall of 0.9996, and an F1 score of 0.9992, significantly outperforming traditional machine learning methods such as Random Forest and Gradient Boosting, as well as standalone CNN and LSTM architectures. …”
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A Hybrid AI Framework for Enhanced Stock Movement Prediction: Integrating ARIMA, RNN, and LightGBM Models by Adel Alarbi, Wagdi Khalifa, Ahmad Alzubi

“…This study proposes the Autoregressive Integrated Moving Average Ensemble Recurrent Light Gradient Boosting Machine (AR-ERLM), an innovative model designed to enhance the precision and reliability of stock movement predictions. …”
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Effect of Substrate Temperature on Bead Track Geometry of 316L in Directed Energy Deposition: Investigation and Regression Modeling by Deviprasad Chalicheemalapalli Jayasankar, Stefan Gnaase, Dennis Lehnert, Artur Walter, Robin Rohling, Thomas Tröster

“…Using Design of Experiments (DoE) methods, individual bead marks are generated and analyzed to assess geometric characteristics. Regression models, including both linear and quadratic approaches, are constructed to predict machine parameters for achieving the desired bead geometry at different substrate temperatures. …”
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Fault diagnosis of inter‐turn short circuits in PMSM based on deep regulated neural network by Ahmed Mesai Belgacem, Mounir Hadef, Enas Ali, Salah K. Elsayed, Prabhu Paramasivam, Sherif S. M. Ghoneim

“…Abstract Permanent Magnet Synchronous Machine (PMSM) is widely utilised in numerous industrial applications due to its precise control capabilities. …”
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A Fault Diagnosis Model for Rotating Machinery Using VWC and MSFLA-SVM Based on Vibration Signal Analysis by Lei You, Wenjie Fan, Zongwen Li, Ying Liang, Miao Fang, Jin Wang

“…This paper proposes a novel fault diagnosis model, which extracts features by combining vibration severity, dyadic wavelet energy time-spectrum, and coefficient power spectrum of the maximum wavelet energy level (VWC) at the feature extraction stage. …”
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Neural Network Models for Ionospheric Electron Density Prediction at a Fixed Altitude Using Neural Architecture Search by Yang Pan, Mingwu Jin, Shun‐Rong Zhang, Simon Wing, Yue Deng

“…Neural networks (NNs) emerge as a powerful modeling tool for Ne prediction. However, heavy manual adjustments are time consuming to determine the optimal NN structures. …”
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Study on the Method of Vineyard Information Extraction Based on Spectral and Texture Features of GF-6 Satellite Imagery by Xuemei Han, Huichun Ye, Yue Zhang, Chaojia Nie, Fu Wen

“…Then, this study constructed vineyard information extraction models by integrating spectral and texture features, using machine learning algorithms including Naive Bayes (NB), Support Vector Machines (SVMs), and Random Forests (RFs). …”
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A Comprehensive Survey on Split-Fed Learning: Methods, Innovations, and Future Directions by Geetabai S. Hukkeri, R. H. Goudar, G. M. Dhananjaya, Vijayalaxmi N. Rathod, Shilpa Ankalaki

“…In this work we presented Split-Fed Learning (SFL), a new framework that combines the concepts of Federated Learning (FL) and Split Learning (SL), to provide privacy-aware and scalable training of machine learning models in settings with distributed data. …”
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Predicting PbS Colloidal Quantum Dot Solar Cell Parameters Using Neural Networks Trained on Experimental Data by Hoon Jeong Lee, Arlene Chiu, Yida Lin, Sreyas Chintapalli, Serene Kamal, Eric Ji, Susanna M. Thon

“…Recent advances in machine learning (ML) have enabled predictive programs for photovoltaic characterization, optimization, and materials discovery. …”
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Bio-inspired MXene membranes for enhanced separation and anti-fouling in oil-in-water emulsions: SHAP explainability ML by Nadeem Baig, Sani I. Abba, Jamil Usman, Ibrahim Muhammad, Ismail Abdulazeez, A.G. Usman, Isam H. Aljundi

“…Accurate preprocessing and model interpretation enhance decision-making and optimization in membrane fouling and separation efficiency studies, ensuring robust and reliable ML models.…”
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CFD modelling and simulation of anaerobic digestion reactors for energy generation from organic wastes: A comprehensive review by Muhammad Usman Farid, Indiana A. Olbert, Andreas Bück, Abdul Ghafoor, Guangxue Wu

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