A Prediction-Based Anomaly Detection Method for Traffic Flow Data with Multi-Domain Feature Extraction by Xianguang Jia, Jie Qu, Yingying Lyu, Mengyi Guo, Jinke Zhang, Fengxiang Guo

“…In the anomaly detection experiment, it was verified that constructing a high-accuracy prediction model and conducting reasonable error analysis can effectively enable anomaly detection in the data.…”
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Analyzing the Accuracy of Satellite-Derived DEMs Using High-Resolution Terrestrial LiDAR by Aya Hamed Mohamed, Mohamed Islam Keskes, Mihai Daniel Nita

“…The accurate estimation of Digital Elevation Models (DEMs) derived from satellite data is critical for numerous environmental applications. …”
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Modeling the relationship among the stock market, gold price, oil price and exchange rate: A VECM and VDA approach by Agrawal Pravin Kumar, Kumar Mohit

“…This study investigates the dynamic interrelationships among the Indian stock market benchmark index (Nifty 50), gold prices, oil prices (Brent and WTI), and the USD/INR exchange rate, using high-frequency daily data from January 2009 to March 2023. By employing a Vector Error Correction Model (VECM) and Variance Decomposition Analysis (VDA), the study explores both the short-term and long-term dynamics between these asset classes. …”
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A big data dynamic approach for adaptive music instruction with deep neural fuzzy logic control by Dong Li, Zhenfang Liu

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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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Depth and Lineament Maps Derived from North Cameroon Gravity Data Computed by Artificial Neural Network by Marcelin Mouzong Pemi, Joseph Kamguia, Severin Nguiya, Eliezer Manguelle-Dicoum

“…The results achieved in this study establish the possibility of enhancing the quality of the analysis, interpretation, and modeling of gravity data collected on sparse grid of recording stations.…”
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Construction Of Almost Unbiased Estimator For Population Mean Using Neutrosophic Information by Florentin Smarandache, Rajesh Singh, Anamika Kumari, Shobh Nath Tiwari

“…The theoretical conclusions are validated by the empirical analysis, which made use of the real data sets.…”
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Iraqi Stock Market Prediction Using Artificial Neural Network and Long Short-Term Memory by Sama Hayder Abdulhussein AlHakeem, Nashaat Jasim Al-Anber, Hayfaa Abdulzahra Atee, Mahmod Muhamad Amrir

“…To obtain forecast models of stock market data that can accurately portray reality and obtain future forecasts, these models must take all data considerations from linear and none linear trends, different influences, and other data factors, hence the research problem of obtaining a method that gives predictions of Iraq's stock market indicators that are accurate and reliable in stock analysis. …”
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A Method of Inverting Rock Grain Size Based on Nuclear Magnetic Resonance Logging Data and Application by Jinbo Wu, Yitao Hu, Hairong Zhang

“…Rock grain size parameter is the key parameter of reservoir rock physics analysis. The study found that the relationship between the NMRT2spectrum and rock grain size distribution curve is directly related to NMRT2distribution and grain size distribution of rock, so you can use T2 to retrieve the size distribution of rock NMR spectral data. …”
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Estimating Nitrogen Dioxide Levels Using Open Data and Machine Learning: A Comparative Modeling Study by D. Varam, R. Mitra, F. Kamran, D. A. Abuhani, H. Sulieman, I. Zualkernan

“…This study investigates NO₂ levels in Italy, analyzing spatial and seasonal variations to better understand pollutant distribution. Using open-source data, we employed machine learning models to estimate NO₂ concentrations, achieving strong predictive accuracy based on the mean absolute percentage error and the root mean-squared error. …”
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Advancing state of health estimation for electric vehicles: Transformer-based approach leveraging real-world data by Kosaku Nakano, Sophia Vögler, Kenji Tanaka

“…Despite the challenges posed by noisy EV real-world data, the model shows high accuracy, with a mean absolute error of 0.72% and a root mean square error of 1.17%. …”
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A parameterized model for tower crane energy consumption was developed based on theoretical formulation and field data by Fan Zhang, Chunli Zhang, Yan Fu, Jun Liu, Jiarui Bu, Peng Duan, Si Chen

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Combined data-driven and knowledge-driven methodology for system inertia estimation based on the successional difference method by Linjun Shi, Yijie Zhao, Feng Wu, Keman Lin

“…Firstly, the post-fault system data are collected and the frequency rate of change caused by disturbance is calculated by the successional difference method, which effectively reduces the accidental error and improves the interpretability of the model at the same time. …”
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Machine Learning-Based Harvest Date Detection and Prediction Using SAR Data for the Vojvodina Region (Serbia) by Gordan Mimić, Amit Kumar Mishra, Miljana Marković, Branislav Živaljević, Dejan Pavlović, Oskar Marko

“…Data from the Sentinel-1 satellite were used in the study. …”
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Enhanced trajectory reconstruction from sparse and noisy GPS data: A progressive chunked transformer approach by Yonghui Liu, Qian Li, Inhi Kim

“…Trajectory reconstruction from sparse and noisy GPS data is critical for applications such as urban mobility analysis, transportation planning, and navigation systems. …”
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A Cloud Vertical Structure Optimization Algorithm Combining FY-4A and DSCOVR Satellite Data by Zhuowen Zheng, Jie Yang, Taotao Lv, Yulu Yi, Zhiyong Lin, Jiaxin Dong, Siwei Li

“…Some bias remains in complex cases, e.g., multi-layer thin clouds at low altitudes, and error tracing analysis suggests this may be related to cloud layer number misclassification. …”
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Machine learning-based feature selection for ultra-high-dimensional survival data: a computational approach by Nahid Salma, Majid Khan Majahar Ali, Raja Aqib Shamim

“…Gene interaction network analysis confirmed their role in RCC progression. Despite SCAD’s strong performance, it left 31% of data variability unexplained, suggesting hybrid ML models that integrate ensemble learning, two-component regression structures, and deep learning-based feature selection could further enhance gene selection and predictive accuracy. …”
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The Role of Forcing and Parameterization in Improving Snow Simulation in the Upper Colorado River Basin Using the National Water Model by Yanjun Gan, Yu Zhang, Cezar Kongoli, Ming Pan

“…We evaluated the newly developed Analysis of Record for Calibration (AORC) forcing data for SWE simulation and examined the impact of bias correction applied to AORC precipitation and temperature. …”
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Survey of Deep Learning and Machine Learning Approaches for Major Depressive Disorder Detection Using EEG Data by Sumathi Balakrishnan, Raja Kumar Murugesan, Eng Lye Lim, Amna Faisal, Humaira Ashraf

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A Novel DNA Techniques to Strengthen Cryptographic Permutation Tables in Encryption Algorithm by Nik Azura Nik Abdullah, Nur Hafiza Zakaria, Azni Haslizan Ab Halim, Abdul Alif Zakaria, Samsul Ariffin Abdul Karim

“…Subsequently, the paper proposes a new cryptographic DNA-based Permutation Table called the DNA P-Box as a secure cryptographic primitive. Security analysis of the DNA P-Box is then performed, focusing on three key aspects: Correlation Coefficient, Bit Error Rate (BER), and Key Sensitivity. …”
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