Analysis on the Impact of Data Augmentation on Target Recognition for UAV-Based Transmission Line Inspection by Chunhe Song, Wenxiang Xu, Zhongfeng Wang, Shimao Yu, Peng Zeng, Zhaojie Ju

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Using Machine Learning to Propose a Qualitative Classification of Risk of Soil Erosion by Dione Pereira Cardoso, Paulo Cesar Ossani, Marcelo Angelo Cirillo, Marx Leandro Naves Silva, Junior Cesar Avanzi

“…Following this, machine learning techniques were applied to analyze the data. …”
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Experimental investigation of shaft misalignment effects on bearing reliability through vibration signal analysis using machine learning and deep learning by Fransiskus Tatas Dwi Atmaji, Jamasri, Hari Agung Yuniarto, I Made Miasa

“…In contrast, the LSTM model, trained directly on raw time-series data, outperformed all other models, achieving a classification accuracy of 99.7%.This study contributes a novel dataset, an original misalignment simulation platform, and a comprehensive comparative analysis of modelling approaches. …”
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A novel data representation framework based on nonnegative manifold regularisation by Yan Jiang, Wei Liang, Jintian Tang, Hongbo Zhou, Kuan-Ching Li, Jean-Luc Gaudiot

“…Representation learning techniques have been frequently applied in multimedia content analysis and retrieval. …”
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Integrating petrophysical data into efficient iterative cluster analysis for electrofacies identification in clastic reservoirs by Mohammed A. Abbas, Watheq J. Al-Mudhafar, Aqsa Anees, David A. Wood

“…Efficient iterative unsupervised machine learning involving probabilistic clustering analysis with the expectation-maximization (EM) clustering algorithm is applied to categorize reservoir facies by exploiting latent and observable well-log variables from a clastic reservoir in the Majnoon oilfield, southern Iraq. …”
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Enhancing Genomic Data Representation Through BERT-LSTM Hybrid Architecture by Kyeong Ho Kim, Minji Kim, Sohui Kim, Jong-Min Lee

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Advanced Phishing Detection: Leveraging t-SNE Feature Extraction and Machine Learning on a Comprehensive URL Dataset by Taha Etem, Mustafa Teke

“…We evaluated several machine learning algorithms on both full and reduced datasets, including Logistic Regression, Naive Bayes, k-Nearest Neighbors (kNN), Decision Trees, and Random Forest. …”
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Empirical Analysis of Data Sampling-Based Decision Forest Classifiers for Software Defect Prediction by Fatima Enehezei Usman-Hamza, Abdullateef Oluwagbemiga Balogun, Hussaini Mamman, Luiz Fernando Capretz, Shuib Basri, Rafiat Ajibade Oyekunle, Hammed Adeleye Mojeed, Abimbola Ganiyat Akintola

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DLProv: a suite of provenance services for deep learning workflow analyses by Débora Pina, Liliane Kunstmann, Adriane Chapman, Daniel de Oliveira, Marta Mattoso

“…Deep learning (DL) workflows consist of multiple interdependent and repetitive steps, including data preparation, model training, evaluation, and deployment. …”
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Building and explaining data-driven energy demand models for Indian states by Kieran M R Hunt, Hannah C Bloomfield

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scFocus: Detecting branching probabilities in single-cell data with SAC by Chunlin Chen, Zeyu Fu, Jiajia Yang, Huaqing Chen, Jiabao Huang, Shitian Qin, Chuhuai Wang, Xiaoqian Hu

“…This demonstrates its applicability on different types of datasets and showcases its potential in discovering biological changes due to experimental treatments through multi-batch dataset processing. Finally, an online analysis tool based on scFocus was developed, helping researchers and clinicians in the process and visualization of single-cell RNA sequencing data as well as the interpretation of these data through branch probabilities in a streamlined and intuitive way.…”
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Deep learning-based strategies for evaluating and enhancing university teaching quality by Ying Gao

“…The research process involves multiple complex stages, including data collection, preprocessing, feature extraction, model construction, training, validation, and results analysis. …”
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IDMPF: intelligent diabetes mellitus prediction framework using machine learning by Leila Ismail, Huned Materwala

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A data driven approach to classify descriptors based on their efficiency in translating noisy trajectories into physically-relevant information by Simone Martino, Domiziano Doria, Chiara Lionello, Matteo Becchi, Giovanni M Pavan

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Bridging the gap between R and Python in bulk transcriptomic data analysis with InMoose by Maximilien Colange, Guillaume Appé, Léa Meunier, Solène Weill, W. Evan Johnson, Akpéli Nordor, Abdelkader Behdenna

“…Abstract We introduce InMoose, an open-source Python environment aimed at omic data analysis. We illustrate its capabilities for bulk transcriptomic data analysis. …”
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The story of a knowledge-based and learning organization by Jan Hjelte, Kristina Westerberg

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Comparative Analysis of Transformers to Support Fine-Grained Emotion Detection in Short-Text Data by Robert H. Frye, David C. Wilson

“…Understanding a person’s mood and circumstances by way of sentiment or finer-grained emotion detection can play a significant role in AI systems and applications, such as in chat dialogue or reviews. Analysis of emotion from text typically requires specialized text or document understanding, and recent work has focused on transformer learning approaches. …”
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Numerical Analysis of Damage in Composites: From Intra-Layer to Delamination and Data-Assisted Methods by Alireza Taherzadeh-Fard, Alejandro Cornejo, Sergio Jiménez, Lucia G. Barbu

“…Additionally, the role of data-assisted (driven) techniques, such as machine learning, in enhancing predictive capabilities is explored. …”
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Topological data analysis of pattern formation of human induced pluripotent stem cell colonies by Iryna Hartsock, Eunbi Park, Jack Toppen, Peter Bubenik, Elena S. Dimitrova, Melissa L. Kemp, Daniel A. Cruz

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Lavender hydrosol analysis using UV spectroscopy data and partial least squares regression by Sára Preiner, Bálint Levente Tarcsay, Dóra Pethő, Norbert Miskolczi

“…The UV–Vis absorbance spectra of the extracts were recorded and the composition analyzed using GC–MS. The composition data obtained allowed for the calculation of changes within the quantities of different EO components in the samples.The partial least squares regression technique (PLS) was utilized to establish a connection between changes in the composition of the hydrosol and the changes in the UV–Vis spectra. …”
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