The Effect of Extended Smartphone Screen Time on Continuous Partial Attention by M. Fırat

“…Students attributed this finding to hypnotic algorithms, distracting redundancy, marketing and advertising, passive receiver mode, short video flow, and surprising content. …”
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Assessing the Impact of Mixed Pixel Proportion Training Data on SVM-Based Remote Sensing Classification: A Simulated Study by Jianyu Gu, Russell G. Congalton

“…Support vector machine (SVM) algorithms have been widely utilized in the remote sensing community due to their high performance with small training datasets. …”
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Performance Analysis of Eye Movement Event Detection Neural Network Models with Different Feature Combinations by Birtukan Adamu Birawo, Pawel Kasprowski

“…Deep learning approaches have recently demonstrated superior performance across various fields, so researchers have also used them to identify eye movement events. …”
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The Scope and Direction of Changes in the Revenues of Local Government Units in Poland in the New Act of October 1, 2024 by Dagmara Hajdys

“…These include the "determinants" adopted for the various algorithms used to determine the expenditure needs of each LGU. …”
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Utilizing machine learning and digital twin technology for rock parameter estimation from drilling data by Abdullah Khan, Yiming Li, Muhammad Shoaib, Umair Sajjad, Fuxin Rui

“…This review provides an in-depth analysis of recent advancements and research, highlighting the transformative impact of these innovations on the field. …”
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The Necessity of CT Hip Scans in the Investigation of Occult Hip Fractures and Their Effect on Patient Management by Thomas Gatt, Daniel Cutajar, Lara Borg, Ryan Giordmaina

“…The focus of further research should be towards the development of investigation algorithms which decrease the reliance on CT and defining the optimal surgical criteria for occult hip fractures.…”
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Detecting Unbalanced Network Traffic Intrusions With Deep Learning by S. Pavithra, K. Venkata Vikas

“…To overcome these challenges, this project proposes a novel hybrid Intrusion Detection System using machine learning algorithms, which includes XGBoost, Long Short-Term Memory (LSTM), Mini-VGGNet, and AlexNet, which is used to handle the unbalanced network traffic data. …”
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Assessment and simulation of eco-environmental quality changes in rapid rural urbanization: Xiong’an New Area, China by Zhongli Lin, Hanqiu Xu, Xiong Yao, Zhipeng Zhu

“…Many studies have employed RSEI to achieve rapid, objective, and effective quantitative assessment of the spatio-temporal changes of regional EEQ. However, research that combines RSEI with machine learning algorithms to conduct multi-scenario simulation of EEQ is still relatively scarce. …”
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Multi-Fingerprints Indoor Localization for Variable Spatial Environments: A Naive Bayesian Approach by Chengjie Hou, Zhizhong Zhang

“…Fingerprint-based indoor localization has been a hot research topic. However, the current fingerprint-based indoor localization approaches still rely on a single fingerprint database, where the average level of data at reference points is used as the fingerprint representation. …”
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Posttraumatic epilepsy: clinical, diagnostic and therapeutic features by N. E. Maslov, A. A. Litvinova, P. S. Kovalev, N. N. Maslova, N. V. Yuryeva, E. I. Khamtsova

“…It has been proved that no clear algorithms for PTE diagnostics and treatment are currently available, which often leads to insufficient patient care.…”
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On Predictive Modeling for the Al2O3 Data Using a New Statistical Model and Machine Learning Approach by Mahmoud El-Morshedy, Zahra Almaspoor, Gadde Srinivasa Rao, Muhammad Ilyas, Afrah Al-Bossly

“…In this article, we focused on predictive modeling for real data by means of a new statistical model and applying different machine learning algorithms. The importance of statistical methods in various research fields is modeling the real data and predicting the future behavior of data. …”
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Interactive Mitigation of Biases in Machine Learning Models for Undergraduate Student Admissions by Kelly Van Busum, Shiaofen Fang

“…Bias and fairness issues in artificial intelligence (AI) algorithms are major concerns, as people do not want to use software they cannot trust. …”
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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

“…  This research paper evaluates the transformative impact of artificial intelligence (AI) in combating financial crimes and money laundering within international trade from 2010 to 2024. …”
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Twitter Sentiment Classification towards Telecommunication Provider Users in Indonesia by Fernanda Mulya Syah Putra, Sindhu Rakasiwi, Noval Ariyanto

“…By applying machine learning algorithms such as Naïve Bayes and Support Vector Machine (SVM), this research analyzes customer perceptions of telecommunication service providers in Indonesia. …”
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An eye for an ‘I:’ a critical assessment of artificial intelligence tools in migration and asylum management by Lucia Nalbandian

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From Misinformation to Insight: Machine Learning Strategies for Fake News Detection by Despoina Mouratidis, Andreas Kanavos, Katia Kermanidis

“…Beyond technical advancements, we explore the ethical implications of automated misinformation detection, addressing concerns related to censorship, algorithmic bias, and the trade-off between content moderation and freedom of expression. …”
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Advances in sensor technologies for breast cancer detection: a comprehensive review of imaging and non-imaging approaches by Emmy Bhatti, Prabhpreet Kaur

“…However, challenges persist, including accessibility, monetary barriers, and standardization in clinical applications. This research highlights the potential of a multi-sensor system, driven by real-time monitoring and powered by machine learning algorithms, to address the gaps in breast cancer diagnosis. …”
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Scalable earthquake magnitude prediction using spatio-temporal data and model versioning by Rahul Singh, Bholanath Roy

“…Multiple machine learning algorithms, including Gradient Boosting, Light Gradient Boosting Machine (LightGBM), XGBoost, and Random Forest, are evaluated on dataset sizes of 20%, 35%, 65%, and 100%, with performance metrics such as Mean Absolute Error, Mean Squared Error, Root Mean Squared Error, and R 2. …”
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Recent Advances in In Situ 3D Surface Topographical Monitoring for Additive Manufacturing Processes by Vignesh Suresh, Badrinath Balasubramaniam, Li-Hsin Yeh, Beiwen Li

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An exploration of machine learning approaches for early Autism Spectrum Disorder detection by Nawshin Haque, Tania Islam, Md Erfan

“…These results underscore the potential of machine learning in aiding the early detection of ASD in children and toddlers, offering promising avenues for future research and clinical applications.…”
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