Research on intelligent classification of coastal land cover by integrating remote sensing images and deep learning by Xinhao Lin, Junmiao Hei, Yixiao Wang, Ang Zhang

“…Traditional methods, like pixel-based and object-oriented classification, often struggle with high complexity and inaccurate results due to limitations in handling spatial relationships and spectral data.MethodsThis research addresses these shortcomings by integrating deep learning models, particularly convolutional neural networks (CNNs) and spatially dependent learning techniques, to develop a robust classification model for coastal land cover using remote sensing data. …”
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A data science approach to mitigating data challenges in serious gaming by Germain Abdul-Rahman, Noman Haleem, Andrej Zwitter

“…Abstract Citizen science initiatives offer an unprecedented scale of volunteer-driven data collection but often face scrutiny regarding their methodology, research design, data collection, and analysis. …”
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Discovering User Sentiment Patterns in Libraries with a Hybrid Machine Learning and Lexicon-Based Approach by Dini Nurmalasari, Dini Hidayatul Qudsi, Nessa Chairani, Heri R Yuliantoro

“…The need to enhance library services is the focus of this study, which relies on user feedback for data-driven decision-making. Text data from library user surveys conducted at Politeknik Caltex Riau (PCR) is analyzed to categorize sentiment and identify areas for improvement. …”
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A Cross-sectional Study on Stature Estimation from Arm Lengths among North Indian Population using Machine Learning by Arunima Dutta, Gyamar Anya

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A statistical framework for analysis of trial-level temporal dynamics in fiber photometry experiments by Gabriel Loewinger, Erjia Cui, David Lovinger, Francisco Pereira

“…By exploiting signal autocorrelation, our methodology yields joint 95% confidence intervals that account for inspecting effects across the entire trial and improve the detection of event-related signal changes over common multiple comparisons correction strategies. We reanalyze data from a recent study proposing a theory for the role of mesolimbic dopamine in reward learning, and show the capability of our framework to reveal significant effects obscured by standard analysis approaches. …”
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The Use of Artificial Intelligence in Combating Financial Crimes and Money Laundering in International Trade A Data-Driven Analysis (2010–2024) by Balouz Mohamed

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Multi-Task Faces (MTF) Data Set: A Legally and Ethically Compliant Collection of Face Images for Various Classification Tasks by Rami Haffar, David Sanchez, Josep Domingo-Ferrer

“…Human facial data offers valuable potential for tackling classification problems, including face recognition, age estimation, gender identification, emotion analysis, and race classification. …”
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Predicting high anger intensity using ecological momentary assessment and wearable-derived physiological data in a trauma-affected sample by Olivia Metcalf, Karen E. Lamb, David Forbes, Meaghan L. O’Donnell, Tianchen Qian, Tracey Varker, Sean Cowlishaw, Sophie Zaloumis

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The data mining and high-performance network model of tourism electronic word of mouth for analysis of factors influencing tourists’ purchasing behavior by Wei Chen

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Enhanced Consumer Healthcare Data Protection Through AI-Driven TinyML and Privacy-Preserving Techniques by S. Aanjankumar, Monoj Kumar Muchahari, Shabana Urooj, Ishmeet Kaur, Rajesh Kumar Dhanaraj, Hanan Abdullah Mengash, S. Poonkuntran, Parag Ravikant Kaveri

“…The analysis of experimental results validates the model’s efficiency in securing healthcare data, with implications for broader applications in secure, privacy-preserving medical data security analysis.…”
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Assessment of Hull and Propeller Degradation Due to Biofouling Using Tree-Based Models by Nikos Themelis, George Nikolaidis, Vasilios Zagkas

“…The power prediction models are data-driven based on machine learning algorithms. …”
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‘Machine Learning’ multiclassification for stage diagnosis of Alzheimer’s disease utilizing augmented blood gene expression and feature fusion by Manash Sarma, Subarna Chatterjee

“…Conclusion Using machine learning multiclassification techniques on blood gene expression profile data from ADNI and NCBI, we achieved the most promising results to date for diagnosing multiple stages of Alzheimer’s disease. …”
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Multi-Modal Comparative Analysis on Audio Dub Detection Using Artificial Intelligence by Divya Jennifer Dsouza, Anisha P. Rodrigues, Roshan Fernandes

“…This research aims at discovering dubs in audio files using machine learning and deep learning techniques in various modes of execution, such as offline or static, batch, and online or streaming means. …”
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Analysis of the most influential factors affecting outcomes of lung transplant recipients: a multivariate prediction model based on UNOS Data by Reza Safdari, Marsa Gholamzadeh, Hamidreza Abtahi, Mehrnaz Asadi Gharabaghi

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Artificial Intelligence in the Analysis of Upper Gastrointestinal Disorders by Chang Seok Bang

“…While early studies prioritized the automatic diagnosis of cancer or pre-cancerous lesions, the current expanded scope of AI includes benign lesions, quality control, and machine learning analysis of big data. However, the limited commercialization of medical AI and the need to justify its application in each field of research are restricting factors. …”
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Anomaly Detection of Marine Diesel Engines: A Novel Approach using Transformer Neural Networks for Reconstruction and Residual Analysis by Qin Liang, Erik Vanem, Knut Erik Knutsen, Vilmar Æsøy, Houxiang Zhang

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An interpretable machine learning study for developing a binary classifier for predicting rehospitalization from skilled nursing facilities by Zhouyang Lou, Zachary Hass, Nan Kong

“…However, analysis pipelines are underdeveloped in data balancing, feature selection, and model interpretability. …”
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The spatial-temporal probability assessment for slope instability based on uncertainty of machine learning-based prediction by Tai-feng Li, Xian-pu Xiao, Rong-hui Yan, Kang Xie, You-ming Zhao, Qian-li Zhang, Mao-qin Che

“…This paper aims to address the issue of uncertainty and limited analysis depth in machine learning (ML)-based slope cumulative displacement (SCD) prediction, leading to inaccuracies in slope safety assessment. …”
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Empowering data-driven load forecasting by leveraging long short-term memory recurrent neural networks by Waqar Waheed, Qingshan Xu, Muhammad Aurangzeb, Sheeraz Iqbal, Saadat Hanif Dar, Z.M.S. Elbarbary

“…Conventional statistical analysis and machine learning methods struggle with accurately capturing the intricate temporal relationships present in load data. …”
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Enhancing Privacy in IoT Networks: A Comparative Analysis of Classification and Defense Methods by Ahmet Emre Ergun, Ozgu Can, Murat Kantarcioglu

“…Although traffic encryption is employed to protect the privacy of IoT devices, attackers can still leverage Machine Learning (ML) and Deep Learning (DL) techniques to classify device types by analyzing packet characteristics, such as size and timing. …”
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