Conformal Segmentation in Industrial Surface Defect Detection with Statistical Guarantees by Cheng Shen, Yuewei Liu

“…The latter constructs a prediction set on which a given guarantee for detection will be obtained. …”
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Combining K-Means Clustering and Random Forest to Evaluate the Gas Content of Coalbed Bed Methane Reservoirs by Jie Yu, Linqi Zhu, Ruibao Qin, Zhansong Zhang, Li Li, Tao Huang

“…Judging from the evaluation effect of the research block, the prediction accuracy of the new method is significantly higher than that of the original method, and more accurate gas content prediction values can be obtained for different types of reservoirs. …”
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Real-time monitoring of soil moisture in cotton fields using electromagnetic induction technology by Yang Gao, Quanze Hu, Jiaojiao Hui, Lin Chang, Mei Zeng, Qingsong Jiang

“…Currently, electromagnetic induction technology provides a non-destructive alternative, using the inversion simulation of soil apparent electrical conductivity (ECa) to construct an empirical θg model, but the prediction accuracy of the constructed model is questioned when dealing with dynamic or highly uncertain data, resulting in large errors in the prediction results and cannot meet the needs of practical applications. …”
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TCN–Transformer Spatio-Temporal Feature Decoupling and Dynamic Kernel Density Estimation for Gas Concentration Fluctuation Warning by Yanping Wang, Longcheng Zhang, Zhenguo Yan, Jun Deng, Yuxin Huang, Zhixin Qin, Yuqi Cao, Yiyang Wang

“…This study addresses the problems of multi-source data redundancy, insufficient feature capture timing, and delayed risk warning in the prediction of gas concentration in fully mechanized coal-mining operations by constructing a three-pronged technical approach that integrates feature dimensionality reduction, hybrid modeling, and intelligent early warning. …”
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Making JavaScript Render Decisions to Optimize Security-Oriented Crawler Process by Onur Aktas, Ahmet Burak Can

“…This approach seeks to reduce computational requirements and quicken the security evaluation process through the use of machine learning algorithms. By utilizing a dataset containing the source code from the main pages of 17,160 websites, our experimental results demonstrate a 20% reduction in execution time compared to full JavaScript rendering, indicating an improvement in resource usage without any significant reduction in coverage. …”
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Travel Patterns Analysis Using Tensor-Based Model from Large-Scale License Plate Recognition Data by Zhaoxin Liu, Xiaolu Wang, Yufeng Bi, Jun Kong, Run Xu, Yuanpei Chen, Jinjun Tang

“…Firstly, we apply clustering algorithms and the principal component analysis method to extract main feature variables, which can achieve the purpose of dimensionality reduction and eliminate the complex collinearity among variables. …”
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An Application of Site Selection for Solid Waste Management System Using Neutrosophic Set by A.Savitha Mary, D. Sarukasan, C. Kayelvizhi, L. Jethruth Emelda Mary, F. Josephine Daisy, K. Pitchaimani

“…To improve the efficiency and accuracy of waste management site selection, novel computational algorithms have been developed using a proposed distance formula. …”
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CoMSeC: A Comparative Analysis of Various Service Classification Techniques by Malabika Das, Ansh Sarkar, Sujata Swain

“…These embeddings are further processed using Dense Layers for dimensionality reduction and then used as inputs to different clustering algorithms. …”
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Dynamic Multi-Behaviour, Orientation-Invariant Re-Identification of Holstein-Friesian Cattle by Maarten Perneel, Ines Adriaens, Jan Verwaeren, Ben Aernouts

“…To perform reliable animal re-identification, most available algorithms require standardised animal poses. However, this lack of versatility prevents widespread application of these algorithms in behavioural research and commercial environments. …”
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Stable matching-enhanced MOEA/D for solving multi-objective optimal power flow problems by Ebrahim Akbari, Amin Khodabakhshian, Abolfazl Rahimnejad, Stephen Andrew Gadsden

“…The results highlight the effectiveness of the method in addressing OPF problems with conflicting objectives such as cost minimization, loss reduction, and voltage stability enhancement. This research provides a new perspective on applying stable matching mechanisms into evolutionary algorithms for power system optimization.…”
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Partitioning variability in animal behavioral videos using semi-supervised variational autoencoders. by Matthew R Whiteway, Dan Biderman, Yoni Friedman, Mario Dipoppa, E Kelly Buchanan, Anqi Wu, John Zhou, Niccolò Bonacchi, Nathaniel J Miska, Jean-Paul Noel, Erica Rodriguez, Michael Schartner, Karolina Socha, Anne E Urai, C Daniel Salzman, International Brain Laboratory, John P Cunningham, Liam Paninski

“…Here we introduce a new video analysis tool that combines the output of supervised pose estimation algorithms (e.g. DeepLabCut) with unsupervised dimensionality reduction methods to produce interpretable, low-dimensional representations of behavioral videos that extract more information than pose estimates alone. …”
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Accuracy measurement of automatic remeshing tools by Mattia Sullini

“…While in the applications for which remeshing algorithms have historically been developed, the problem can be addressed without affecting the mesh polycount, for the authoring of 3D assets intended for Real-Time Rendering applications, maintaining a low polycount is mandatory. …”
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Improved estimation of two-phase capillary pressure with nuclear magnetic resonance measurements via machine learning by Oriyomi Raheem, Misael M. Morales, Wen Pan, Carlos Torres-Verdín

“…In contrast, nuclear magnetic resonance (NMR) measurements, which provide information on pore body size distribution, are faster and can be leveraged to estimate capillary pressure using machine learning algorithms. Recently, artificial intelligence methods have also been applied to capillary pressure prediction (Qi et al., 2024).Currently, no readily applicable predictive model exists for estimating an entire capillary pressure curve directly from standard petrophysical logs and core data. …”
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The Persistent Threat of Chronic Inflammation on the Mortality Among Cervical Cancer Survivors: A Mendelian Randomization and Machine Learning Analysis Using UK Biobank and Chinese... by Wang J, Chen Z, Guan M, Ma Z, Peng L, Chen J, Fiori PL, Carru C, Capobianco G, Coradduzza D, Zhou L

“…After feature selection with 3 algorithms (LASSO regression, Boruta and Support vector machines), the gradient boosting machine (GBM) model outperformed other models by achieving an area under the curve (AUC) of 0.930 and a Brier score of 0.027 in 1-year overall survival (OS) prediction. …”
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Performance Evaluation of Uplink Cell-Free Massive MIMO Network Under Weichselberger Rician Fading Channel by Birhanu Dessie, Javed Shaikh, Georgi Iliev, Maria Nenova, Umar Syed, K. Kiran Kumar

“…However, estimating the channel with high-performance, low-cost computational methods is still a problem. Different algorithms have been developed to address these challenges in channel estimation. …”
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Artificial neural networks-based multi-objective optimization of immersion cooling battery thermal management system using Hammersley sampling method by Muhammed Donmez, Mehmet Ihsan Karamangil

“…Multi-objective optimization, using ANN-based multi objective genetic algorithms, is conducted on a 16S1P configuration at 4C discharge and 0.008 kg/s. …”
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Low-complexity detection for MIMO visible light communication system using generalized spatial modulation by Wei-Chiang Wu, Gan-Lin Chen

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An Approach Based on the Use of Commercial Codes and Engineering Judgement for the Battle of Water Demand Forecasting by Alfredo Iglesias-Rey, Carlos Alfonso López Hojas, F. Javier Martínez-Solano, Pedro L. Iglesias-Rey

“…Initial data filtering, input variable determination, and simultaneous application of RNN, LSTM, and GRU forecasting algorithms are conducted. Results are evaluated based on Battle of Water Demand Forecasting criteria, refining parameters iteratively for enhanced prediction accuracy. …”
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Novel meta-ensemble modelling approach and comparison of machine-learning models for rebar price estimation by Sahin Tolga Guvel, Abdulkadir Budak, Ibrahim Karataş

“…The voting meta-ensemble machine-learning algorithm exhibited the best performance for all lag periods investigated. …”
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Leveraging AI for Enhanced Operational Risk Management in Sports Events by Jibraili Malak, Rharib Abderrahim, Jibraili Zineb

“…The results reveal that, although AI offers many opportunities to minimise operational failures, concerns remain about the complexity of algorithms and the management of real-time data. Despite these obstacles, the paper concludes that future prospects are promising, with potential innovations such as the automation of critical processes and the prediction of operational incidents.…”
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