Digital Twins: Strategic Guide to Utilize Digital Twins to Improve Operational Efficiency in Industry 4.0 by Italo Cesidio Fantozzi, Annalisa Santolamazza, Giancarlo Loy, Massimiliano Maria Schiraldi

“…The proposed methodology integrates key enabling technologies such as the Internet of Things (IoT), Artificial Intelligence (AI), and Machine Learning to create accurate digital replicas of manufacturing systems. …”
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Quantum-inspired K-nearest neighbors classifier for enhanced printer source identification in forensic document analysis by Saad M. Darwish, Raad A. Ali, Adel A. Elzoghabi

“…However, identifying consistent patterns across different printers remains challenging, especially when perpetrators attempt to obscure these artifacts. Machine learning models in this field must identify discriminative features that differentiate printers while minimizing noise. …”
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Fractal-Based Robotic Trading Strategies Using Detrended Fluctuation Analysis and Fractional Derivatives: A Case Study in the Energy Market by Ekaterina Popovska, Galya Georgieva-Tsaneva

“…Findings from this research show how the combination of fractals with statistical and machine learning models can improve complex trading strategies implementation for the energy markets.…”
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Minimizing Delay and Power Consumption at the Edge by Erol Gelenbe

“…Thus, substantial research has been conducted to identify optimum task allocation schemes in this context using non-linear optimization, machine learning, and market-based algorithms. Prior work has mainly focused on two methodologies: (i) formulating non-linear optimizations that lead to NP-hard problems, which are processed via heuristics, and (ii) using AI-based formulations, such as reinforcement learning, that are then tested with simulations. …”
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Covariate-adjusted construction of gene regulatory networks using a combination of generalized linear model and penalized maximum likelihood. by Omid Chatrabgoun, Alireza Daneshkhah, Parisa Torkaman, Mark Johnston, Nader Sohrabi Safa, Ali Kashif Bashir

“…Many machine learning techniques have been used to construct gene regulatory networks (GRNs) through precision matrix that considers conditional independence among genes, and finally produces sparse version of GRNs. …”
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Optimal Robust Time-Domain Feature-Based Bearing Fault and Stator Fault Diagnosis by G. Geetha, P. Geethanjali

“…In machine learning, the extraction of features is necessary for intelligent motor fault diagnosis. …”
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Zernike Moments Description of Solar and Astronomical Features: Python Code by Hossein Safari, Nasibe Alipour, Hamed Ghaderi, Pardis Garavand

“…Hence, ZMs are helpful in machine learning to identify and track the features of several.…”
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Many Local Pattern Texture Features: Which Is Better for Image-Based Multilabel Human Protein Subcellular Localization Classification? by Fan Yang, Ying-Ying Xu, Hong-Bin Shen

“…According to our experimental results from binary relevance multilabel machine learning models, the completed local binary pattern, and local tetra pattern are more discriminative for describing IHC images when compared to the traditional local binary pattern descriptor. …”
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A novel deep learning-based 1D-CNN-optimized GRU approach for heart disease prediction by Jini Mol G., Ajith Bosco Raj T.

“…This research proposes an ensemble classification method leveraging machine learning (ML) to predict cardiac problems, providing physicians with actionable insights for personalized diagnoses and treatments. …”
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Computational linguistics and natural language processing techniques for semantic field extraction in Arabic online news by Maulana Ihsan Ahmad, Moh. Kanif Anwari

“…Using quantitative methods, Arabic news articles were collected and processed with Python, a popular programming language in data analysis, and applied various NLP techniques and machine learning models to accurately extract semantic fields. …”
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Beyond the ROC Curve: Activity Monitoring to Evaluate Deep Learning Models in Clinical Settings by Hyunwoo CHOO, Kyung Hyun LEE, Sungsoo HONG, Sungjun HONG, Ki-Byung LEE, Chang Youl LEE

“…Our findings demonstrate that the AMOC curve significantly enhances the evaluation of time-series predictions, enabling more accurate and continuous performance monitoring of machine learning models in clinical settings. This approach can improve model deployment and ultimately lead to better patient outcomes in healthcare. …”
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Image-Based Underwater Inspection System for Abrasion of Stilling Basin Slabs of Dam by Yonglong Li, Hua Zhang, Shuang Wang, Haoran Wang, Jialong Li

“…This image algorithm used SLIC superpixel and the SVM machine learning method to detect the coarse aggregate exposure automatically. …”
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A quantum entanglement-based algorithm for discriminating non-orthogonal qubits by Mohammed Zidan, Mohamed N. El-Qersh, Mahmoud Abdel-Aty, Montasir Qasymeh, Hichem Eleuch

“…Distinguishing unknown non-orthogonal qubits is an essential requirement for addressing various challenges in quantum computation, including quantum machine learning, quantum communications, and quantum technologies. …”
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A flexible multi-scale approach for downscaling GRACE-derived groundwater storage anomaly using LightGBM and random forest in the Tashk-Bakhtegan Basin, Iran by Arezo Mohtaram, Hossein Shafizadeh-Moghadam, Hamed Ketabchi

“…This was accomplished using a robust and flexible multi-scale approach, leveraging machine learning algorithms, specifically random forest and LightGBM. …”
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Identification of Working Trucks and Critical Path Nodes for Construction Waste Transportation Based on Electric Waybills: A Case Study of Shenzhen, China by Jun Bi, Qiuyue Sai, Fujun Wang, Yakun Chen

“…Management should strengthen the hot nodes to reduce traffic accidents. First, several machine learning methods are used to improve the prediction accuracy of electric waybill generation, where the garbage truck recorded on the electric waybill is regarded as a working truck. …”
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Gaussian Process-Based Response Surface Method for Slope Reliability Analysis by Bin Hu, Guo-shao Su, Jianqing Jiang, Yilong Xiao

“…A new response surface method (RSM) for slope reliability analysis was proposed based on Gaussian process (GP) machine learning technology. The method involves the approximation of limit state function by the trained GP model and estimation of failure probability using the first-order reliability method (FORM). …”
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Multivariable Time Series Prediction for the Icing Process on Overhead Power Transmission Line by Peng Li, Na Zhao, Donghua Zhou, Min Cao, Jingjie Li, Xinling Shi

“…The phase-space reconstruction theory and machine learning method were then applied to establish the prediction model, which fully utilized the history of multivariable time series data in local monitoring systems to represent the mapping relationship between icing load and micrometeorology factors. …”
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Analysis of Key Commuting Routes Based on Spatiotemporal Trip Chain by Wenbin Yao, Caijun Chen, Hongyang Su, Nuo Chen, Sheng Jin, Congcong Bai

“…Different from other researches that analyse the commuting pattern using machine learning algorithms based on all data, this study first extracts commuting pattern vehicles and then designs a key commuting route mining algorithm framework for commuting pattern vehicles. …”
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High-Bandwidth Chiplet Interconnects for Advanced Packaging Technologies in AI/ML Applications: Challenges and Solutions by Shenggao Li, Mu-Shan Lin, Wei-Chih Chen, Chien-Chun Tsai

“…The demand for chiplet integration using 2.5D and 3D advanced packaging technologies has surged, driven by the exponential growth in computing performance required by artificial intelligence and machine learning (AI/ML). This article reviews these advanced packaging technologies and emphasizes critical design considerations for high-bandwidth chiplet interconnects, which are vital for efficient integration. …”
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Interpretable and integrative deep learning for discovering brain-behaviour associations by Corentin Ambroise, Antoine Grigis, Josselin Houenou, Vincent Frouin

“…Abstract Recent advances highlight the limitations of classification strategies in machine learning that rely on a single data source for understanding, diagnosing and predicting psychiatric syndromes. …”
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