Mind vs. machine: Comparative analysis of metaphor-related word translation by human and AI systems by Zhengjian Li, Lang Chen

“…The present study presents a comparative analysis of the translation processes and outcomes of human translators, Neural Machine Translation (NMT) systems and Large Language Models (LLMs) focusing on the translation of Metaphor-related Words (MRW). …”
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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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Learning patterns of HIV-1 resistance to broadly neutralizing antibodies with reduced subtype bias using multi-task learning. by Aime Bienfait Igiraneza, Panagiota Zacharopoulou, Robert Hinch, Chris Wymant, Lucie Abeler-Dörner, John Frater, Christophe Fraser

“…The ability to predict HIV-1 resistance to broadly neutralizing antibodies (bnAbs) will increase bnAb therapeutic benefits. Machine learning is a powerful approach for such prediction. …”
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Applying in machine learning and deep learning in finance industry: A case study on repayment prediction by Nguyễn Phát Đạt, Hồ Mai Minh Nhật, Trương Công Vinh, Lê Quang Chấn Phong, Lê Hoành Sử

“…The present inquiry advocates for the adoption of sophisticated computational methodologies, including machine learning and deep learning, to analyze borrowers’ behavioral patterns, demographic profiles, and credit histories, thus facilitating the prognostication of loan repayment likelihood. …”
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Multivariable modelling based on statistical and machine learning techniques for monthly precipitation forecasting in the eastern Amazon by Renata Gonçalves Tedeschi, Eduardo Costa de Carvalho, Antonio Vasconcelos Nogueira Neto, Claudia Priscila Wanzeler da Costa, Julio Cezar Goncalves de Freitas, Julio Cezar Goncalves de Freitas, Rafael de Lima Rocha, Rafael de Lima Rocha, Ronnie Cley de Oliveira Alves, Ronnie Cley de Oliveira Alves, Ewerton Cristhian Lima de Oliveira

“…BackgroundAccurate precipitation forecasting is crucial for various sectors, such as agriculture, hydrology, and disaster management. In recent years, machine learning (ML) techniques have proven invaluable in improving the accuracy of rainfall prediction and identifying the complex relationships between precipitation and other meteorological variables.MethodsThis paper presents acomprehensive analysis of the use of multivariable statistical and ML models to predict monthly rainfall at 13 locations in the eastern Amazon. …”
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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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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

“…This study employs RGB imagery and machine learning techniques to detect <i>Tagosodes orizicolus</i> infestations in “Tinajones” rice crops during the flowering stage, a critical challenge for agriculture in northern Peru. …”
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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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Machine Learning Model Coupled with Graphical User Interface for Predicting Mechanical Properties of Flax Fiber by T. Nageshkumar, Prateek Shrivastava, L. Ammayapan, Manisha Jagadale, L. K. Nayak, D. B. Shakyawar, Indran Suyambulingam, P. Senthamaraikannan, R. Kumar

“…In this study, a total of 432 patterns of input and output parameters obtained from laboratory experiments were used to develop machine learning algorithms (Random forest, support vector, and XGBoost). …”
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Analyzing High-Speed Rail’s Transformative Impact on Public Transport in Thailand Using Machine Learning by Chinnakrit Banyong, Natthaporn Hantanong, Panuwat Wisutwattanasak, Thanapong Champahom, Kestsirin Theerathitichaipa, Rattanaporn Kasemsri, Manlika Seefong, Vatanavongs Ratanavaraha, Sajjakaj Jomnonkwao

“…This study investigates the impact of high-speed rail (HSR) on Thailand’s public transportation market and evaluates the effectiveness of machine learning techniques in predicting travel mode choices. …”
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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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Assessing the association of multi-environmental chemical exposures on metabolic syndrome: A machine learning approach by Yehoon Jo, Mi-Yeon Shin, Sungkyoon Kim

“…SHapley Additive exPlanations (SHAP) and partial dependence plots (PDP) revealed both linear and nonlinear exposure–response patterns, suggesting threshold effects for key chemicals. …”
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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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Spatiotemporal Analysis of Sea-Surface pH in the Pacific Ocean Based on Interpretable Machine Learning by Minlong Huang, Jin Qi, Can Zhang, Yuanyuan Wang, Yijun Chen, Jian Shao, Sensen Wu

“…Therefore, this study provides a data-driven approach to gain deeper insights into the spatiotemporal patterns of pH and its influencing factors.…”
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Student dropout prediction through machine learning optimization: insights from moodle log data by Markson Rebelo Marcolino, Thiago Reis Porto, Tiago Thompsen Primo, Rafael Targino, Vinicius Ramos, Emanuel Marques Queiroga, Roberto Munoz, Cristian Cechinel

“…Learning management systems such as Moodle generate extensive datasets reflecting student interactions and enrollment patterns, presenting opportunities for predictive analytics. …”
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Estimating shear strength of dredged soils for marine engineering: experimental investigation and machine learning modeling by Zheng Yao, Kaiwei Xu, Zejin Wang, Haodong Sun, Peng Cui, Peng Cui

“…The motivation behind this hybridization lies in the need to effectively capture nonlinear interactions and latent logical patterns among influencing factors, which are often overlooked by traditional single-algorithm models. …”
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The impact of cultural factors on digital marketing strategies with Machine learning and honey bee Algorithm (HBA) by Muhammad Khan, Masood Ahmad, Rakhmonov Dilshodjon Alidjonovich, Kalonov Mukhiddin Bakhritdinovich, Kurbanbekova Mohichehra Turobjonovna, Imomov Jamshidxon Odilovich

“…This paper analyses the impact of cultural factors on digital marketing strategies in Pakistan. Improvement of machine learning (ML) techniques combined with the Honey Bee Algorithm (HBA) has been incorporated for better solutions. …”
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