Big data thinking of top executives and corporate innovation: based on machine learning by Xuesong Tang, Qiang Liao, Wen Li, Wang Liao

“…Utilising machine learning methods to construct a big data thinking index for corporate executives, we find that such thinking significantly enhances corporate innovation levels. …”
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Predicting and analyzing ferry transit delays using open data and machine learning by Malek Sarhani, Abtin Nourmohammadzadeh, Stefan Voß, Mohammed EL Amrani

“…Given their pivotal role in the transportation systems of numerous cities, accurately predicting ferry delays is crucial for synchronizing transit services.This paper demonstrates the value of open data for improving ferry delay predictions through machine learning, focusing on two case studies. …”
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Exploring Continuous Seismic Data at an Industry Facility Using Unsupervised Machine Learning by Chengping Chai, Omar Marcillo, Monica Maceira, Junghyun Park, Stephen Arrowsmith, James O. Thomas, Joshua Cunningham

“…We applied two unsupervised machine learning algorithms to analyze continuous seismic data collected from an industrial facility in Texas, United States. …”
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Deep learning-driven approach for cataract management: towards precise identification and predictive analytics by Shuaixin Lu, Lingling Ba, Jie Wang, Min Zhou, Peiyao Huang, Xiaohua Zhang, Simo Pan, Xinmiao Zhou, Kai Wen, Jing Sun

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Using machine learning to identify Parkinson’s disease severity subtypes with multimodal data by Hwayoung Park, Changhong Youm, Sang-Myung Cheon, Bohyun Kim, Hyejin Choi, Juseon Hwang, Minsoo Kim

“…This study aims to address the clinical applicability and heterogeneity of PD using PD severity subtypes classification and digital biomarker development by combining objective multimodal data with machine learning (ML) approaches. Methods We analyzed datasets that combine clinical characteristics, physical function and lifestyle data, gait parameters in motion analysis systems, and wearable sensors collected from persons with PD (n = 102) to perform clustering for subtype classification. …”
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Data-driven network intrusion detection using optimized machine learning algorithms by Dauda Adeite Adenusi, Oladosu Oyebisi Oladimeji, Theopilus Adekunle Oyekola, Korede Solomon Olagunju

“…This study presents a comprehensive evaluation of machine learning approaches for network intrusion detection, comparing the performance of Decision Trees (DT), Random Forest (RF), K-Nearest Neighbors (K-NN), Gradient Boosting (GB), and Logistic Regression (LR) algorithms. …”
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Study on Thermal Conductivity Prediction of Granites Using Data Augmentation and Machine Learning by Yongjie Ma, Lin Tian, Fuhang Hu, Jingyong Wang, Echuan Yan, Yanjun Zhang

“…To address the insufficient generalization ability of machine learning models caused by scarce measured data on granite thermal conductivity, this study focused on granites from the Gonghe Basin and Songliao Basin in Qinghai Province. …”
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Adaptive machine learning framework: Predicting UHPC performance from data to modelling by Yinzhang He, Shaojie Gao, Yan Li, Yongsheng Guan, Jiupeng Zhang, Dongliang Hu

“…This study proposes an interpretable machine learning (ML) framework to predict the compressive strength (CS) of UHPC and analyze input variable influences. …”
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Machine learning for battery quality classification and lifetime prediction using formation data by Jiayu Zou, Yingbo Gao, Moritz H. Frieges, Martin F. Börner, Achim Kampker, Weihan Li

“…Accurate classification of battery quality and prediction of battery lifetime before leaving the factory would bring economic and safety benefits. Here, we propose a data-driven approach with machine learning to classify the battery quality and predict the battery lifetime before usage only using formation data. …”
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Integration of multimodal imaging data with machine learning for improved diagnosis and prognosis in neuroimaging by Saurabh Bhattacharya, Sashikanta Prusty, Sanjay P. Pande, Monali Gulhane, Santosh H. Lavate, Nitin Rakesh, Saravanan Veerasamy

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Performance Evaluation of Support Vector Machine and Stacked Autoencoder for Hyperspectral Image Analysis by Brahim Jabir, Bendaoud Nadif, Isabel De la Torre Diez, Helena Garay, Irene Delgado Noya

“…However, extracting insights from complex data requires sophisticated analytical approaches. …”
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Using Machine Learning to Unveil the Dynamics of Insider Trading in Financial Markets by Ilona Vladimirovna Tregub, Alexander Sebastian Wagner

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Predicting Heat Treatment Duration for Pest Control Using Machine Learning on a Large-Scale Dataset by Stavros Rossos, Paraskevi Agrafioti, Vasilis Sotiroudas, Christos G. Athanassiou, Efstathios Kaloudis

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Evolution, topics and relevant research methodologies in business intelligence and data analysis in the academic management of higher education institutions. A literature review by M. Correa-Peralta, J. Vinueza-Martínez, L. Castillo-Heredia

“…This bibliometric study analyzed 755 Scopus-indexed publications (2019–2023) using RStudio, Biblioshiny, and Microsoft Excel to elucidate key themes, influential authors, and emerging research trends. Learning analytics, educational data mining, and BI applications such as dropout prediction systems, tailored distance education strategies, and machine learning models for institutional performance predominate in the field. …”
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An integrated machine learning and hyperparameter optimization framework for noninvasive creatinine estimation using photoplethysmography signals by Parama Sridevi, Zawad Arefin, Sheikh Iqbal Ahamed

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Multimodal Machine Learning for Predicting Post-Surgery Quality of Life in Colorectal Cancer Patients by Maryem Rhanoui, Mounia Mikram, Kamelia Amazian, Abderrahim Ait-Abderrahim, Siham Yousfi, Imane Toughrai

“…In this paper, we propose a multimodal machine learning framework for the prediction of quality of life indicators in colorectal cancer patients at various temporal stages, leveraging both clinical data and computed tomography scan images. …”
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A hybrid deep learning framework for early detection of Mpox using image data by Sajal Chakroborty

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Big-Data-Assisted Urban Governance: A Machine-Learning-Based Data Record Standard Scoring Method by Zicheng Zhang, Tianshu Zhang

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Interpretable correlator Transformer for image-like quantum matter data by Abhinav Suresh, Henning Schlömer, Baran Hashemi, Annabelle Bohrdt

Subjects: “…interpretable machine learning…”
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Progress in Flood Risk Assessment Based on Data-Driven Methods by HE Xinyu, TIAN Wenchong, ZHANG Zhiyu, LIAO Zhenliang

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