Applicability of Machine Learning and Mathematical Equations to the Prediction of Total Organic Carbon in Cambrian Shale, Sichuan Basin, China by Majia Zheng, Meng Zhao, Ya Wu, Kangjun Chen, Jiwei Zheng, Xianglu Tang, Dadong Liu

“…This study introduces three key innovations to address these challenges: (1) A Dynamic Weighting–Calibrated Random Forest Regression (DW-RFR) model integrating high-resolution Gamma-Ray-guided dynamic time warping (±0.06 m depth alignment precision derived from 237 core-log calibration points using cross-validation), Principal Component Analysis-Deyang–Anyue Rift Trough Shapley Additive Explanations (PCA-SHAP) hybrid feature engineering (89.3% cumulative variance, VIF < 4), and Bayesian-optimized ensemble learning; (2) systematic benchmarking against conventional ΔlogR (R² = 0.700, RMSE = 0.264) and multi-attribute joint inversion (R² = 0.734, RMSE = 0.213) methods, demonstrating superior accuracy (R² = 0.917, RMSE = 0.171); (3) identification of Gamma Ray (r = 0.82) and bulk density (r = −0.76) as principal TOC predictors, contrasted with resistivity’s thermal maturity-dependent signal attenuation (r = 0.32 at Ro > 3.0%). …”
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Next-Generation Text Summarization: A T5-LSTM FusionNet Hybrid Approach for Psychological Data by Bilal Khan, Muhammad Usman, Inayat Khan, Jawad Khan, Dildar Hussain, Yeong Hyeon Gu

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Research on the interaction between energy consumption pattern transformation and international trade in the digital economy era based on gig data analysis by Yiqun Wu, Shibing Zhang

“…However, problems remain, such as low energy efficiency and heavy dependence on traditional energy. By addressing data privacy compliance, enhancing model generalization, expanding to practical cases like the BRI, integrating system dynamics concepts, grounding machine learning techniques in theory, linking numerical results to theory, and highlighting research novelty through comparisons, we have provided a robust and insightful analysis. …”
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Data analysis-based framework for the design and assessment of chemical process plants: a case study in amine gas-treating systems by Rahul Gupta, Gladys Navas, Daniela Galatro

“…This work presents a process-integrity assessment framework to chemical process design that combines first principles, heuristics, vendor specifications, standards/codes, data analysis, and machine learning modelling, hypothesized as an efficient route for optimal process design. …”
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Explainable AI for Spectral Analysis of Electromagnetic Fields by Dimitris Kalatzis, Agapi Ploussi, Ellas Spyratou, Theodor Panagiotakopoulos, Efstathios P. Efstathopoulos, Yiannis Kiouvrekis

“…This study explores the application of Explainable Artificial Intelligence (XAI) techniques to the spectral analysis of electromagnetic field (EMF) measurements conducted across various frequency bands in Cyprus. …”
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DSGRec: dual-path selection graph for multimodal recommendation by Zihao Liu, Wen Qu

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SSL-SurvFormer: A Self-Supervised Learning and Continuously Monotonic Transformer Network for Missing Values in Survival Analysis by Quang-Hung Le, Brijesh Patel, Donald Adjeroh, Gianfranco Doretto, Ngan Le

“…Additionally, the presence of missing attributes within tabular data is widespread. By leveraging recent developments of Transformer and Self-Supervised Learning (SSL), we introduce <i>SSL-SurvFormer</i>. …”
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ASTER GDEM Correction Based on Stacked Ensemble Learning and ICEsat-2/ATL08: A Case Study from the Qilian Mountains by Qi Wei, Yanli Zhang, Yalong Ma, Ruirui Yang, Kairui Lei

“…Furthermore, unmanned aerial vehicle (UAV) DEM data from five sample areas were selected for profile analysis, and it was found that the corrected GDEM was closer to the real surface. …”
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Accurate total consumer price index forecasting with data augmentation, multivariate features, and sentiment analysis: A case study in Korea. by Injae Seo, Minkyoung Kim, Jong Wook Kim, Beakcheol Jang

“…Second, the monthly frequency of CPI data results in a relatively sparse time series, limiting the performance of the analysis. …”
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Exploring Data Preparation Strategies: A Comparative Analysis of Vision Transformer and ConvNext Architectures in Breast Cancer Histopathology Classification by Kaczmarek Mikołaj, Kowal Marek, Korbicz Józef

“…This study offers a comparative analysis of two state-of-the-art deep learning architectures, Vision Transformer (ViT) and ConvNeXT for breast cancer histopathology image classification, focusing on the impact of data preparation strategies. …”
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An evaluation of calibrated and uncalibrated high-resolution RGB data in time series analysis for coal spoil characterisation: A comparative study by Sureka Thiruchittampalam, Bikram Pratap Banerjee, Nancy F. Glenn, Simit Raval

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Predictions of Equatorial Vertical Plasma Drift Using TEC Data and a Neural Network Model by S. A. Reddy, X. Pi, C. Forsyth, A. Aruliah, A. Smith

“…To address daily prediction, the Vertical drIfts: Predicting Equatorial ionospheRic dynamics (VIPER) model has been developed. VIPER is a machine learning model that is trained on total electron content (TEC) data to predict low‐latitude vertical plasma drift observed by the C/NOFS mission across the period 2009–2015. …”
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Big Data Analytics in E-commerce Driving Business Decisions Through Customer Behavior Insights by Pande Pooja, Kulkarni Adokshaja Krishnarao, P Balamurugan, S Balakrishnan, Ramalingam Vanaja, R Revathi

“…This proposition seeks to find out the significant opportunities BIG Data Analysis can offer to e-commerce and generate analytic decisions about customer behavior. …”
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Multimodal Explainability Using Class Activation Maps and Canonical Correlation for MI-EEG Deep Learning Classification by Marcos Loaiza-Arias, Andrés Marino Álvarez-Meza, David Cárdenas-Peña, Álvaro Ángel Orozco-Gutierrez, German Castellanos-Dominguez

“…Our approach involves the following: (i) evaluating different deep learning (DL) models for subject-dependent MI-EEG discrimination; (ii) employing class activation mapping (CAM) to visualize relevant MI-EEG features; and (iii) utilizing a questionnaire–MI performance canonical correlation analysis (QMIP-CCA) to provide multidomain interpretability. …”
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Towards Automatic Bias Analysis in Multimedia Journalism by Reemt Hinrichs, Hauke Steffen, Hayastan Avetisyan, David Broneske, Jörn Ostermann

“…Abstract This work investigates the application of machine learning for the analysis of video journalism to get insights into media bias in the German video journalism landscape. …”
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Enhancing County-Level GDP Estimation Accuracy With Downscaled NPP-VIIRS Nighttime Light Data by Weihua Lin, Weixing Xu, Zhaocong Wu, Jiaheng Cao

“…This article demonstrates that the downscaled NTL data generated by NTL-CMDM offer a promising data source for more accurate and fine-scale socioeconomic analysis.…”
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Integrative analysis of bulk and single-cell transcriptomic data reveals novel insights into lipid metabolism and prognostic factors in hepatocellular carcinoma by Feiyu Qi, Guiming Zha, Yanfang Zhang, Sihua Liu, Yuhang Yang, Wanliang Sun, Dongdong Wang, Zhong Liu, Zheng Lu, Dengyong Zhang

“…The single-cell sequencing data was subjected to dimensionality reduction, which facilitated the annotation of distinct cell subpopulations and marker gene expression analysis within each subpopulation. …”
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Neutrosophic OWA-TOPSIS Model for Decision-Making in AI Systems with Large Volumes of Data by Juan Roberto Pereira Salcedo, Karla Melissa Ruiz Quezada, Edison Luis Cruz Navarrete, Pedro Manuel García Arias

“…The theoretical contribution to the academic literature expands notions of AI multi-criteria decision making process; the practical application lends itself to scalable possibilities within big data reliant cases, especially predictive sentiment analysis or resource allocation/optimization. …”
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Investigation of cold formed steel angle compression through high throughput design FEA and machine learning by Pradeep Thangavel, Manikandan Palanisamy, Divesh Ranjan Kumar, Warit Wipulanusat, Jirapon Sunkpho, Krishanu Roy

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An analysis of spatial changes in the manufacturing industry in china’s three major urban clusters from 2015 to 2019 using POI data by Chenxi Jin, Chenjing Fan, Yiwen Gong, Xinran Huang, Shiqi Li, Runhan Liu, Chunwei Guo, Yuxin Liu

“…Here, we analyzed spatial patterns of the manufacturing industry using point-of-interest (POI) data and a machine learning classification algorithm based on the Naive Bayes classifier. …”
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