Comparative study of imputation strategies to improve the sarcopenia prediction task by Shakhzod Karimov, Dilmurod Turimov, Wooseong Kim, Jiyoun Kim

“…Methods Following imputation, we utilized machine learning models, including logistic regression, gradient boosting, support vector machine, and random forest, to classify sarcopenia. …”
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Tuning into urban birdsong: enhancing nature connectedness with an AI-powered wearable by Zhuying Li, Si Cheng, Xiaoqing Sun, Xipei Ren, Yan Wang, Min-Ling Zhang

“…This article introduces GoChirp, an AI-powered wearable device for enhancing nature relatedness within urban landscapes. By utilizing machine learning algorithms, GoChirp recognizes bird sounds in the environment and provides haptic feedback that mimics the bird flapping, directing users’ attention to avian life and fostering a heightened awareness and connection to nature. …”
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Learning Analytics for Bridging the Skills Gap: A Data-Driven Study of Undergraduate Aspirations and Skills Awareness for Career Preparedness by Joel Weijia Lai, Lei Zhang, Chun Chau Sze, Fun Siong Lim

“…To address this issue, a comprehensive survey was conducted among undergraduate students to gather data on their career aspirations, their awareness of the skills required for their chosen careers, and their perceptions of how well their university supports their skill development. Using machine learning methods such as hierarchical clustering and <i>k</i>-nearest neighbors for classification, coupled with non-parametric statistical analysis such as the Mann–Whitney <i>U</i> and Chi-squared (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msup><mi>χ</mi><mn>2</mn></msup></semantics></math></inline-formula>) tests to understand students’ perceptions of their career preparedness, the findings from this study provide valuable insights into how higher education institutions can prepare students for the workforce and highlight areas where improvements are needed to better support students in achieving their career goals.…”
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Early Detection of Pancreatic Cancer: Opportunities Provided by Cancer-induced Paraneoplastic Phenomena and Artificial Intelligence by Wei-Chih Liao

“…Novel computer-aided detection tools based on AI technologies, including deep learning and radiomic analysis with machine learning, have achieved accurate detection and might supplement human interpretation to improve the sensitivity for early PDAC on CT images. …”
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Improved feature reduction framework for sign language recognition using autoencoders and adaptive Grey Wolf Optimization by Rajeev Goel, Sandhya Bansal, Kavita Gupta

“…This paper proposes technique that integrates machine learning and swarm intelligence to effectively address this issue. …”
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Generating context-specific sports training plans by combining generative adversarial networks. by Juquan Tan, Jingwen Chen

“…Personalized sports training plans are essential for addressing individual athlete needs, but traditional methods often need to integrate diverse data types, limiting adaptability and effectiveness. Existing machine learning (ML) and rule-based approaches cannot dynamically generate context-specific training programs, reducing their applicability in real-world scenarios. …”
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Impact of Rutting on Traffic Safety: A Synthesis of Research Findings by Ali Fares, Man-Nok Wong, Tarek Zayed, Nour Faris

“…It also established rutting limits and developed machine learning-based prediction models for accident rates caused by rutting under varying conditions. …”
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Phishing detection algorithm based on attention and feature fusion by ZHANG Sirui, YAN Zhiwei, DONG Kejun, YUCHI Xuebiao

“…In the first stage, the algorithm leveraged the lightweight characteristics of the machine learning model to pre-recall a subset of suspicious domain names from a multitude of domain names. …”
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Exploring ethical considerations in medical research: Harnessing pre-generated transformers for AI-powered ethics discussions. by Takuya Mori, Takuya Watanabe, Shinji Kosugi

“…This limitation may be attributed to biased opinions resulting from machine learning optimization, preventing comprehensive identification of certain ethical issues.…”
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Time‐Lagged Effects of Ionospheric Response to Severe Geomagnetic Storms on GNSS Kinematic Precise Point Positioning by Zhe Yang, Y. T. Jade Morton

“…The insights obtained from this research have the potential to be integrated into physics‐based and machine‐learning models to enhance forecasting capabilities of space weather impacts.…”
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Prediction of High-ozone Events Using GAM, SMOTE, and Tail Dependence Approaches in Texas (2005–2019) by Benjamin Brown-Steiner, Xiong Zhou, Matthew J. Alvarado, Brook T. Russell

“…Finally, the feature selection via the tail dependence method performs comparably to other forms of machine learning-based feature selection and we find that there are multiple parameter sets that can predict MDA8 O3 with equal success.…”
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Rolling Bearing Fault Diagnosis Based on Sensitive Feature Transfer Learning and Local Maximum Margin Criterion under Variable Working Condition by Shiyuan Liu, Xiao Yu, Xu Qian, Fei Dong

“…In real industrial scenarios, the working conditions of bearings are variable, and it is therefore difficult for data-driven diagnosis methods based on conventional machine-learning techniques to guarantee the desirable performance of diagnosis models, as the models assume that the distributions of both the training and testing data are the same. …”
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An Intelligent Deep Learning Model for CO2 Adsorption Prediction by Hanan Ahmed Hosni Mahmoud, Nada Ali Hakami, Alaaeldin M. Hafez

“…The adsorbed amount will be classified and predicted by the D-CNN. Three tree machine learning models, namely, gradient decision model (GDM), scalable boosting tree model (SBT), and gradient variant decision tree model (GVD), were fused. …”
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Connecting electronic health records to a biomedical knowledge graph to link clinical phenotypes and molecular endotypes in atopic dermatitis by Francesca Frau, Paul Loustalot, Margaux Törnqvist, Nina Temam, Jean Cupe, Martin Montmerle, Franck Augé

“…Since biomedical knowledge graphs (BKGs) are limited to the integration of prior knowledge data and do not integrate real-world data (RWD) that would allow for the incorporation of patient level information, we propose a first step towards using RWD, BKGs and graph machine learning (ML) to enable a fully integrated precision medicine strategy. …”
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Improving Electrical Fault Detection Using Multiple Classifier Systems by José Oliveira, Dioeliton Passos, Davi Carvalho, José F. V. Melo, Eraylson G. Silva, Paulo S. G. de Mattos Neto

“…Machine Learning-based fault detection approaches in energy systems have gained prominence for their superior performance. …”
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Artificial intelligence in dentistry: Assessing the informational quality of YouTube videos. by Sachin Naik, Abdulaziz Abdullah Al-Kheraif, Sajith Vellappally

“…The terms used for the search were "artificial intelligence in dentistry," "machine learning in dental care," and "deep learning in dentistry." …”
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Remote sensing forest health assessment – a comprehensive literature review on a European level by Drechsel Johannes, Forkel Matthias

“…We conclude that the availability of new satellite systems and advances in artificial intelligence and machine learning should be translated into FHA practice according to ICP standards.…”
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Linking Animal Feed Formulation to Milk Quantity, Quality, and Animal Health Through Data-Driven Decision-Making by Oreofeoluwa A. Akintan, Kifle G. Gebremedhin, Daniel Dooyum Uyeh

“…Leveraging advanced analytical techniques, such as machine learning and optimization algorithms, have created highly accurate feed formulations tailored to specific livestock needs. …”
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A novel classification method for balance differences in elite versus expert athletes based on composite multiscale complexity index and ranking forests. by Yuqi Cheng, Dawei Wu, Ying Wu, Youcai Guo, Xinze Cui, Pengquan Zhang, Jie Gao, Yanming Fu, Xin Wang

“…We calculated the CMCI and used four machine learning algorithms-Logistic Regression, Support Vector Machine(SVM), Naive Bayes, and Ranking Forest-to combine these features and assess each participant's balance ability. …”
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An explainable sentiment prediction model based on the portraits of users sharing representative opinions in social sensors by Bin Wang, Enhui Wang, Zikun Zhu, Yangyang Sun, Yaodong Tao, Wei Wang

“…We build users’ portraits from three aspects: attribute features, interest features, and emotional features. Six machine learning algorithms are used to predict emotional tendency based on users’ portraits. …”
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