Improving Discharge Predictions in Ungauged Basins: Harnessing the Power of Disaggregated Data Modeling and Machine Learning by Aggrey Muhebwa, Colin J. Gleason, Dongmei Feng, Jay Taneja

“…This approach may overlook the spatial heterogeneity of river systems and their impact on discharge patterns. We hypothesize that integrating spatiotemporal hydrologic knowledge into the data modeling process (distributed/disaggregated modeling) can improve the performance of discharge prediction models. …”
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Short-Term Electric Load Forecasting for an Industrial Plant Using Machine Learning-Based Algorithms by Oğuzhan Timur, Halil Yaşar Üstünel

“…Their capacity to analyze intricate patterns and enhance prediction accuracy renders them a favored option for enhancing energy management and operational efficiency. …”
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Enhancing DDoS Attack Classification through SDN and Machine Learning: A Feature Ranking Analysis by Aymen AlAwadi, Kawthar Rasoul ALesawi

“…Due to the growing dependence of digital services on the Internet, Distributed Denial of Service (DDoS) attacks are a common threat that can cause significant disruptions to online operations and financial losses. Machine learning (ML) offers a promising way for early DDoS attack detection due to its ability to analyze large datasets and identify patterns. …”
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Mind vs. machine: Comparative analysis of metaphor-related word translation by human and AI systems by Zhengjian Li, Lang Chen

“…LLMs demonstrate translation patterns which are more aligned with human translators, helping them achieve higher evaluation scores, though their performance remains inconsistent, particularly with novel metaphors. …”
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Machine Learning Approaches for Fault Detection in Internal Combustion Engines: A Review and Experimental Investigation by A. Srinivaas, N. R. Sakthivel, Binoy B. Nair

“…The former uses physical models of engine components to diagnose defects, while the latter employs statistical analysis of sensor data to identify patterns indicating faults. Various methods for ICE fault identification, such as vibration analysis, thermography, acoustic analysis, and optical approaches, are reviewed. …”
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Machine learning and multi-omics analysis reveal key regulators of proneural–mesenchymal transition in glioblastoma by Can Xu, Jin Yang, Huan Xiong, Xiaoteng Cui, Yuhao Zhang, Mingjun Gao, Lei He, Qiuyue Fang, Changxi Han, Wei Liu, Yangyang Wang, Jin Zhang, Ying Yuan, Zhaomu Zeng, Ruxiang Xu

“…PMTRG expression patterns in patient tissues and different cell subsets were examined by proteomics and single-cell transcriptome data analysis. …”
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Novel application of unsupervised machine learning for characterization of subsurface seismicity, tectonic dynamics and stress distribution by Mohammad Salam, Muhammad Tahir Iqbal, Raja Adnan Habib, Amna Tahir, Aamir Sultan, Talat Iqbal

“…Our study pioneers an innovative use of unsupervised machine learning, a powerful tool for navigating unclassified data, to unravel the complexities of subsurface seismic activities and extract meaningful patterns. …”
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Shoreline dynamics prediction using machine learning models: from process learning to probabilistic forecasting by Afshar Adeli, Afshar Adeli, Ali Dastgheib, Ali Dastgheib, Dano Roelvink, Dano Roelvink, Dano Roelvink

“…Through comprehensive testing across one complex shoreline evolution scenario, this research identifies the ConvLSTM model—trained on 2D gridded data— as the optimal machine learning approach suited for addressing specific shoreline complexities and evolution patterns. …”
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Robust fault detection and classification in power transmission lines via ensemble machine learning models by Tahir Anwar, Chaoxu Mu, Muhammad Zain Yousaf, Wajid Khan, Saqib Khalid, Ahmad O. Hourani, Ievgen Zaitsev

“…This research introduces a novel approach for fault detection and classification by analyzing voltage and current patterns across transmission line phases. Leveraging a comprehensive dataset of diverse fault scenarios, various machine learning algorithms—including Random Forest (RF), K-Nearest Neighbors (KNN), and Long Short-Term Memory (LSTM) networks—are evaluated. …”
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Predictive Modeling of Energy Consumption for Cooling Ventilation in Livestock Buildings: A Machine Learning Approach by Carlos Alejandro Perez Garcia, Patrizia Tassinari, Daniele Torreggiani, Marco Bovo

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Detection of <i>Tagosodes orizicolus</i> in Aerial Images of Rice Crops Using Machine Learning by Angig Rivera-Cartagena, Heber I. Mejia-Cabrera, Juan Arcila-Diaz

“…Both models exhibited highly comparable performance, with VGG16 attaining a precision of 98.274% and ResNet50 achieving a precision of 98.245%, demonstrating their effectiveness in identifying infestation patterns with high reliability. To automate the analysis of complete UAV-acquired images, a web-based application was developed. …”
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Enhancing hand-drawn diagram recognition through the integration of machine learning and deep learning techniques by Vanita Agrawal, MVV Prasad Kantipudi, Jayant Jagtap

“…Additionally, deep learning techniques, which are well known for their ability to find intricate patterns and features in data, are incorporated into the proposed system. …”
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An EWS-LSTM-Based Deep Learning Early Warning System for Industrial Machine Fault Prediction by Fabio Cassano, Anna Maria Crespino, Mariangela Lazoi, Giorgia Specchia, Alessandra Spennato

“…This research details the creation and evaluation of an EWS that incorporates deep learning methods, particularly using Long Short-Term Memory (LSTM) networks enhanced with attention layers to predict critical machine faults. The proposed system is designed to process time-series data collected from an industrial printing machine’s embosser component, identifying error patterns that could lead to operational disruptions. …”
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A Survey on Machine Learning Enhanced Integrated Sensing and Communication Systems: Architectures, Algorithms, and Applications by Mikael Ade Krisna Respati, Byung Moo Lee

“…This technology utilizes the same communication resources for communicating and sensing within the same framework, enabling more efficient use of resources. Currently, machine learning (ML) has been developed in the field of communications, including sensing and wireless communications, due to its ability to tackle complex optimization problems, estimate complex issues, and extract and exploit spatial/temporal patterns that can improve ISAC performance. …”
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Model test on the effects of shield machine cutterhead vibration on tunnel face stability in sandy ground by Meng-Bo Liu, Jun-Hua Xiao, Shao-Ming Liao, Zhi-Yong Liu, Jun-Zuo He, Yan-Qing Men, Jia-Cheng Sun

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The influence of digital transformation on the total factor productivity of enterprises: the intermediate role of human-machine cooperation by Qi Xiong, Jingyi Yang, Xiuwu Zhang, Yarui Deng, Yao Gui, Xiaoyang Guo

“…The findings reveal that (1) digital transformation significantly enhances TFP, a conclusion that remains valid after considering endogeneity issues and conducting a series of robustness checks, thereby refuting the productivity paradox associated with digital transformation; (2) furthermore, the enabling effect of digital transformation on TFP varies significantly across enterprises due to differences in ownership, factor intensity, asset size, degree of marketization, tax preference, and geographical location; (3) in terms of the impact mechanism, digital transformation promotes TFP by enabling efficient human–machine collaboration patterns. This study not only complements research on the influencing factors of microenterprise TFP, providing empirical evidence for improving enterprise production efficiency, but also offers insights for local governments to formulate differentiated digital policies.…”
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Segmentation of Endothelial Cell Boundaries of Rabbit Aortic Images Using a Machine Learning Approach by Saadia Iftikhar, Andrew R. Bond, Asim I. Wagan, Peter D. Weinberg, Anil A. Bharath

“…The results obtained from the image segmentation technique developed here may be used for the study of shape and alignment of ECs, and hence patterns of blood flow, around arterial branches.…”
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Automatic detection of cognitive events using machine learning and understanding models’ interpretations of human cognition by Quang Dang, Murat Kucukosmanoglu, Michael Anoruo, Golshan Kargosha, Sarah Conklin, Justin Brooks

“…The findings highlight the potential of machine learning applied to pupillary signals for rapid, individualized detection of cognitive events.…”
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Predicting intensive care need in women with preeclampsia using machine learning – a pilot study by Camilla Edvinsson, Ola Björnsson, Lena Erlandsson, Stefan R. Hansson

“…In this study we aimed to develop a prediction model for severe outcomes using routine biomarkers and clinical characteristics.Methods We used machine learning models based on data from an intensive care cohort with severe preeclampsia (n=41) and a cohort of preeclampsia controls (n=40) with the objective to find patterns for severe disease not detectable with traditional logistic regression models.Results The best model was generated by including the laboratory parameters aspartate aminotransferase (ASAT), uric acid and body mass index (BMI) with a cross-validation accuracy of 0.88 and an area under the curve (AUC) of 0.91. …”
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Predicting female football outcomes by machine learning: behavioural analysis of goals as high stress events by Aratz Olaizola, Ibai Errekagorri, Elsa Fernández, Julen Castellano, John Suckling, Karmele Lopez-de-Ipina

“…This study aims to enhance the performance and in-game success in women’s football by developing machine learning (ML) models that predict match outcomes based on player and team behaviour following goals. …”
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