Machine Learning-Based Prediction Performance Comparison of Marshall Stability and Flow in Asphalt Mixtures by Muhammad Farhan Zahoor, Arshad Hussain, Afaq Khattak

“…Eight key input parameters were considered for modeling. We used three feature importance analysis techniques (Random Forest, Permutation Importance, and Lasso Regression) to determine which parameters were the most significant. …”
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Realistic synthetic dataset generation for cyber-physical systems: a performance evaluation by Yaa Acquaah, Kaushik Roy

“…However, the correlation analysis revealed differences in feature relationships between real and synthetic datasets. …”
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Machine learning methods in the differential diagnosis of difficult-to-classify types of diabetes mellitus by N. V. Rusyaeva, I. I. Golodnikov, I. V. Kononenko, T. V. Nikonova, M. V. Shestakova

“…Errors in determining the type of diabetes lead to incorrect treatment tactics, which leads to poor glycemic control, the development of complications, a decrease in the patient's quality of life, and increased mortality.The key method for diagnosing MODY is sequencing of genes associated with this disease, and LADA is an immunological blood test in combination with the features of the clinical picture. …”
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Identifying Big Five Personality Traits through Controller Area Network Bus Data by Yameng Wang, Nan Zhao, Xiaoqian Liu, Sinan Karaburun, Mario Chen, Tingshao Zhu

“…Data were collected from 92 participants who were asked to drive a car along a pre-defined 15 km route. Using statistical methods and the discrete Fourier transform, some time-frequency features related to driving were extracted to establish models for identifying participants’ Big Five personality traits. …”
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A Gamified Assessment Tool for Antisocial Personality Traits (Antisocial Personality Traits Evidence-Centered Design Gamified): Randomized Controlled Trial by Yaobin Tang, Yongze Xu, Qunli Zhou, Ran Bian

“…MethodsThis research featured a 3-phase evidence-centered design framework. …”
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A hybrid variational mode decomposition framework for enhanced cardiac output estimation using impedance cardiography by Priya Darshini Kumari, Ksh Milan Singh, Zefree Lazarus Mayaluri, Prabodh Kumar Sahoo, Satyabrata Lenka, Ganapati Panda, Sujeevan Kumar Agir

“…Experimental results demonstrate that the proposed VMD-NLM-DWT approach achieves a maximum of 1.2 dB improvement in signal-to-noise ratio (SNR), an average 13% reduction in mean squared error (MSE), and 9% lower percent root mean square difference (PRD) compared to leading two-stage denoising methods. …”
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Enhancing Atmospheric Turbulence Phase Screen Generation with an Improved Diffusion Model and U-Net Noise Generation Network by Hangning Kou, Min Wan, Jingliang Gu

“…The model effectively captures the statistical distribution of turbulence phase screens using small training datasets. …”
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MODELING HOUSE SELLING PRICES IN JAKARTA AND SOUTH TANGERANG USING MACHINE LEARNING PREDICTION ANALYSIS by Sugha Faiz Al Maula, Nicoletta Almira Dyah Setiawan, Elly Pusporani, Sa'idah Zahrotul Jannah

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Improving the Incident Management Process Based on a Use Case Approach by A. A. Mikryukov, A. V. Kuular

“…The analysis of statistical data is carried out; the efficiency is estimated as a result of the application of the algorithm based on the use case analysis. …”
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Data-driven framework for prediction of mechanical properties of waste glass aggregates concrete by Kennedy C. Onyelowe, Shadi Hanandeh, Viroon Kamchoom, Ahmed M. Ebid, Hamza Imran, Miguel Angel Duque Vaca, Greys Carolina Herrera Morales, Nestor Ulloa, Krishna Prakash Arunachalam

“…Results indicate that the Firefly and Wolf algorithms exhibited the highest prediction accuracy across all four properties, with Wolf emerging as the overall best-performing model due to its superior generalization ability, lower error rates, and high correlation with experimental results. …”
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Enhancing Stock Price Forecasting with CNN-BiGRU-Attention: A Case Study on INDY by Madilyn Louisa, Gumgum Darmawan, Bertho Tantular

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Interpretable machine learning approach to predict Hepatitis C virus NS5B inhibitor activity using voting-based LightGBM and SHAP by Teuku Rizky Noviandy, Aga Maulana, Irvanizam Irvanizam, Ghazi Mauer Idroes, Nur Balqis Maulydia, Trina Ekawati Tallei, Muhammad Subianto, Rinaldi Idroes

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Modelos matemáticos para predição da chuva de projeto para regiões do Estado de Minas Gerais Mathematical models for the estimation of rainfall in selected regions of Minas Gerais... by Carlos R. de Mello, Antônio M. da Silva, José M. de Lima, Daniel F. Ferreira, Marcelo S. de Oliveira

“…The most frequent precipitation was tested by the arithmetic mean, the weighted mean by the inverse-square-distance and the geo-statistical prediction (kriging). The models produced good statistical parameters, with low mean errors, showing their accuracy, specially when the kriging method for estimating the most frequent precipitation was used.…”
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Multimodal malware classification using proposed ensemble deep neural network framework by Sadia Nazim, Muhammad Mansoor Alam, Safdar Rizvi, Jawahir Che Mustapha, Syed Shujaa Hussain, Mazliham Mohd Su’ud

“…Deep learning (DL) aids in accurately classifying complex malware features. The cross-domain research in data fusion strives to integrate information from multiple sources to augment reliability and minimize errors in detecting sophisticated cyber threats. …”
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Prediction of Spatial Winter Wheat Yield by Combining Multiscale Time Series of Vegetation and Meteorological Indices by Hao Xu, Hongfei Yin, Jia Liu, Lei Wang, Wenjie Feng, Hualu Song, Yangyang Fan, Kangkang Qi, Zhichao Liang, WenJie Li, Xiaohu Zhang, Rongjuan Zhang, Shuai Wang

“…When the time length of the feature variables was shortened to MP or GP, the growing degree days (GDD), average minimum temperature (AveTmin), and effective precipitation (EP) showed stronger nonlinear relationships with the statistical yields.…”
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Accurate, Fast, and Non-Destructive Net Charge Measurement of Levitated Nanoresonators Based on Maxwell Speed Distribution Law by Peng Chen, Nan Li, Tao Liang, Peitong He, Xingfan Chen, Dawei Wang, Huizhu Hu

“…The error of net charge measurement is less than 7.3% when the pressure is above 0.1 mbar, while it can be less than 0.76% at 10 mbar. …”
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Generative adversarial network–enabled microstructural mapping from surface profiles for laser powder bed fusion by Jingwen Gao, Chenyang Zhu, Shubo Gao, Weiming Ji, Ming Xue, Kun Zhou

“…However, conventional quality control of LPBF-fabricated parts, including microstructure characterisation, often relies on trial-and-error experiments. These methods can be time-consuming, resource-intensive, and potentially destructive to specimens. …”
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Multi-objective optimization of wear parameters of hybrid composites (LM6/B4C/fly ash) using grey relational analysis. by Charles Sarala Rubi, Jayavelu Udaya Prakash, Sunder Jebarose Juliyana, Sachin Salunkhe, Robert Cep, Emad Abouel Nasr

“…The responses have a narrow margin of error, according to confirmation studies. There exists a good agreement between them.…”
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Geographically Aware Air Quality Prediction Through CNN-LSTM-KAN Hybrid Modeling with Climatic and Topographic Differentiation by Yue Hu, Yitong Ding, Wenjing Jiang

“…Third, we innovatively integrated Kolmogorov–Arnold Networks (KANs) with attention mechanisms to replace traditional fully connected layers, achieving enhanced feature weighting capacity. Comparative experiments demonstrated superior performance with a 23.6–59.6% reduction in Root-Mean-Square Error (RMSE) relative to baseline LSTM models, along with consistent outperformance over CNN-LSTM hybrids. …”
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Road Pothole Recognition and Size Measurement Based on the Fusion of Camera and LiDAR by Yongxiang Cai, Mingxing Deng, Xin Xu, Wei Wang, Xiaowei Xu

“…In practical scenarios, the relative error in size measurement is generally within 15%, with an average data processing time of 45.6 ms per frame, thereby satisfying the system’s real-time requirement of 100 ms per frame.…”
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