Adaptive convolutional neural network-based principal component analysis algorithm for the detection of manufacturing data by Tsun-Kuo Lin

“…Herein, an adaptive convolutional neural network (CNN)-based principal component analysis (PCA) algorithm for the detection of manufacturing data is proposed. …”
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A Data-Driven Analysis of Electric Vehicle Adoption Barriers in the Philippines: Combining SEM and ANNs by Charmine Sheena R. Saflor, Klint Allen Mariñas, Ma. Janice Gumasing, Jazmin Tangsoc

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Enhancing Latent Defect Detection in Built-In Spindle Assembly Lines Through Vibration Data Analysis by Kuo-Hao Li, Chao-Nan Wang, Yao-Chi Tang

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Condition monitoring and multi-fault classification of hydraulic systems using multivariate functional data analysis by Cevahir Yildirim, Alba M. Franco-Pereira, Rosa E. Lillo

“…To address these challenges, this study introduces a novel multivariate Functional Data Analysis (FDA) framework based on Multivariate Functional Principal Component Analysis (MFPCA) for classifying failure conditions in hydraulic systems. …”
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Research on Rehabilitation Training Based on Principal Component Analysis of Three Dimensional Motion Data of Taijiquan by Xiang LI, Jian WANG, Tianjiao LI, Xiangbin WANG

“…Objective:In order to improve the effectiveness of rehabilitation training, using the principal component analysis method to study movement characteristics of TAI CHI action.Methods:The subjects played Taijiquan "YUNSHOU" action of YANG style, meanwhile, the three dimensional motion capture system (Motion Analysis) were used for data acquisition, collected motion data of elbow, shoulder, head and body cadres, and then analyzed the data by principal component analysis method.Results:The obtained data were statistically analyzed, which showed that the selected joints motion data were correlated (<italic>P</italic>&lt;0.01), head rotation angle and torso rotation angle existed significant correlation (correlation coefficient is 0.993, <italic>P</italic>&lt;0.01), head rotation angle and shoulder flexion angle were correlated (<italic>P</italic>&lt;0.01), shoulder flexion angle and elbow flexion angle were correlated (<italic>P</italic>&lt;0.01); principal component analysis results showed that when the trunk rotation angle was taken as variables, elbow flexion angle, shoulder flexion angle, head rotation angle were considered as independent variables, all the joints were involved in completing the hand movements (<italic>P</italic>&lt;0.01).Conclusion:Taijiquan "YUNSHOU" movements can be divided into two main components, the main motion components can be decomposed in the rehabilitation training were conducted, and then whole motion integration training were conducted, it will speed up motor learning process during rehabilitation period, improve coordination of action, thus achieving the purpose of fast and efficient recovery.…”
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Enhancing Heart Attack Prediction: Feature Identification from Multiparametric Cardiac Data Using Explainable AI by Muhammad Waqar, Muhammad Bilal Shahnawaz, Sajid Saleem, Hassan Dawood, Usman Muhammad, Hussain Dawood

“…However, timely diagnosis remains a challenge due to the complex and nonlinear relationships between clinical indicators. Machine learning (ML) and deep learning (DL) models have the potential to predict cardiac conditions by identifying complex patterns within data, but their “black-box” nature restricts interpretability, making it challenging for healthcare professionals to comprehend the reasoning behind predictions. …”
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The effectiveness of curriculum standardization in data analysis and tools proficiency for undergraduate education: a case study by Lorena DelaTorre-Diaz, Héctor X. Ramírez-Pérez, David Escobar-Castillejos

“…IntroductionThe rapid evolution of technology necessitates the development of advanced computing and data analysis skills in undergraduate education. …”
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Attitudes of University Students Toward Statistics as a Pathway to Data Literacy: A Meta-Analysis by Rosaura Fernández Pascual, David Caballero Mariscal, Maria Pinto, Ana Eugenia Marín-Jiménez

“…We considered data collected through Survey of Attitudes toward Statistics (SATS) questionnaires, specifically its 36-item and 28-item versions. …”
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Fault analysis of railway track superstructure using time-frequency method from vibrational data by Shahzor Memon, Imtiaz Hussain, Lubna Moin

“…To obtain better performance, the research focuses on the time-frequency analysis of vibrational data using the Fast Fourier Transform and Short-time Fourier transform to identify the fault signatures. …”
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Time-Shifted Maps for Industrial Data Analysis: Monitoring Production Processes and Predicting Undesirable Situations by Tomasz Blachowicz, Sara Bysko, Szymon Bysko, Alina Domanowska, Jacek Wylezek, Zbigniew Sokol

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Multi-source data-driven Bayesian network for risk analysis of maritime accidents in the high sea by Xiaotong Qu, Chengbo Wang, Chengbo Wang, Ruijia Zhao, Mingxing Fang, Mingxing Fang, Mingxing Fang, Xinlian Xie

“…Incomplete data significantly hampers risk analysis for high-sea maritime accidents (HSMAs). …”
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Educational Psychology Analysis Method for Extracting Students’ Facial Information Based on Image Big Data by Maoyue Zhang

“…With reference to image-based big data, this research integrates the application of artificial intelligence facial expression recognition technology with the research on educational theory and applies information technology to the actual teaching process, in order to promote the optimization of the teaching process and improve the learning effect. …”
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Publisher Correction: fNIRS reproducibility varies with data quality, analysis pipelines, and researcher experience by Meryem A. Yücel, Robert Luke, Rickson C. Mesquita, Alexander von Lühmann, David M. A. Mehler, Michael Lührs, Jessica Gemignani, Androu Abdalmalak, Franziska Albrecht, Iara de Almeida Ivo, Christina Artemenko, Kira Ashton, Paweł Augustynowicz, Aahana Bajracharya, Elise Bannier, Beatrix Barth, Laurie Bayet, Jacqueline Behrendt, Hadi Borj Khani, Lenaic Borot, Jordan A. Borrell, Sabrina Brigadoi, Kolby Brink, Chiara Bulgarelli, Emmanuel Caruyer, Hsin-Chin Chen, Christopher Copeland, Isabelle Corouge, Simone Cutini, Renata Di Lorenzo, Thomas Dresler, Adam T. Eggebrecht, Ann-Christine Ehlis, Sinem B. Erdoğan, Danielle Evenblij, Talukdar Raian Ferdous, Victoria Fracalossi, Erika Franzén, Anne Gallagher, Christian Gerloff, Judit Gervain, Noy Goldhamer, Louisa K. Gossé, Ségolène M. R. Guérin, Edgar Guevara, SM Hadi Hosseini, Hamish Innes-Brown, Isabell Int-Veen, Sagi Jaffe-Dax, Nolwenn Jégou, Hiroshi Kawaguchi, Caroline Kelsey, Michaela Kent, Roman Kessler, Nadeen Kherbawy, Franziska Klein, Nofar Kochavi, Matthew Kolisnyk, Yogev Koren, Agnes Kroczek, Alexander Kvist, Chen-Hao Paul Lin, Andreas Löw, Siying Luan, Darren Mao, Giovani G. Martins, Eike Middell, Samuel Montero-Hernandez, Murat Can Mutlu, Sergio L. Novi, Natacha Paquette, Ishara Paranawithana, Yisrael Parmet, Jonathan E. Peelle, Ke Peng, Tommy Peng, João Pereira, Paola Pinti, Luca Pollonini, Ali Rahimpour Jounghani, Vanessa Reindl, Wiebke Ringels, Betti Schopp, Alina Schulte, Martin Schulte-Rüther, Ari Segel, Tirdad Seifi Ala, Maureen J. Shader, Hadas Shavit, Arefeh Sherafati, Mojtaba Soltanlou, Bettina Sorger, Emma Speh, Kevin D. Stubbs, Katharina Stute, Eileen F. Sullivan, Sungho Tak, Zeus Tipado, Julie Tremblay, Homa Vahidi, Maaike Van Eeckhoutte, Phetsamone Vannasing, Gregoire Vergotte, Marion A. Vincent, Eileen Weiss, Dalin Yang, Gülnaz Yükselen, Dariusz Zapała, Vit Zemanek

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A Risk-Optimized Framework for Data-Driven IPO Underperformance Prediction in Complex Financial Systems by Mazin Alahmadi

“…The framework’s effectiveness is assessed using a hand-collected dataset that includes features from both pre-IPO prospectus and firm-specific financial data. We thoroughly evaluate the results using single-split evaluation and 10-fold cross-validation analysis. …”
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A Novel Convolutional Neural Network–Long Short-term Memory Model for Interplanetary Coronal Mass Ejection Detection by Junpei Li, Hong Chen, Jingjing Wang, Yanhong Chen, Bingxian Luo, Hao Deng

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Assessment of Machine Learning Methods for Concrete Compressive Strength Prediction by Oluwafemi Omotayo, Chinwuba Arum, Catherine Ikumapayi

“…In order to focus on the prediction of concrete without any pozzolanic content, the data points containing pozzolans were dropped, leaving 526 data points which were trained and tested on the selected ML algorithms. …”
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Research Progress of Dangerous Driving Behavior Recognition Methods Based on Deep Learning by Junjian Hou, Bingyu Zhang, Yudong Zhong, Wenbin He

“…In this paper, the research progress in the recognition methods of dangerous driving behavior based on deep learning is analyzed. Firstly, the data collection methods are categorized into four types, evaluating their respective advantages, disadvantages, and applicability. …”
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Toward Automatic Detection of Pi2 Magnetic Pulsation Using Machine Learning by Mohamed S. Abdalzaher, Essam Ghamry, Khairul Adib Yusof, Mostafa Shaaban

“…The discontinuation of the conventional index previously employed to detect Pi2 pulsations has driven this study to propose an innovative detection method leveraging machine learning (ML). This paper introduces a novel ML-based classification framework that utilizes geomagnetic field data for Pi2 pulsation detection. …”
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Metric-Based Few-Shot Transfer Learning Approach for Voice Pathology Detection by Jong-Ho Won, Deok-Hwan Kim

“…The main contributions of this study include the implementation of few-shot learning (FSL) in voice pathology detection, addressing data scarcities and class imbalances through its integration with transfer learning. …”
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Improving Access Trust in Healthcare Through Multimodal Deep Learning for Affective Computing by I. Sakthidevi, G. Fathima

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