A Synthetic Data Generation Approach With Dynamic Camera Poses for Long-Range Object Detection in AI Applications by Misbah Bibi, Anam Nawaz Khan, Muhammad Faseeh, Qazi Waqas Khan, Rashid Ahmad, do-Hyeun Kim

“…Accurate long-range object detection is essential for applications such as security and surveillance. However, existing datasets often lack the complexity needed to represent real-world outdoor environments, resulting in limited performance of object detection algorithms at extended distances.Synthetic data generation offers a way to address these limitations by creating varied and realistic training scenarios. …”
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Exploration of the Application Rules and Clinical Significance of Acupoints in Acupuncture Treatment of Migraine Based on Data Mining [Response to Letter] by He Y, Wu Y, Li X

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Application of CIBERSORTx and BayesPrism to deconvolution of bulk RNA-seq data from human myocardium and skeletal muscle by Marcella Conning-Rowland, Chew W. Cheng, Oliver Brown, Marilena Giannoudi, Eylem Levelt, Lee D. Roberts, Kathryn J. Griffin, Richard M. Cubbon

“…Here, we describe the application and in silico validation of two pipelines to deconvolute human right atrium, left ventricle and skeletal muscle bulk RNA-seq data. …”
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Transformers deep learning models for missing data imputation: an application of the ReMasker model on a psychometric scale by Monica Casella, Nicola Milano, Pasquale Dolce, Davide Marocco

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Cluster Analysis of Comparative Genomic Hybridization (CGH) Data Using Self-Organizing Maps: Application to Prostate Carcinomas by Torsten Mattfeldt, Hubertus Wolter, Ralf Kemmerling, Hans‐Werner Gottfried, Hans A. Kestler

“…In this paper we present the application of a self‐organizing map (Genecluster) as a tool for cluster analysis of data from pT2N0 prostate cancer cases studied by CGH. …”
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A novel family of beta mixture models for the differential analysis of DNA methylation data: An application to prostate cancer. by Koyel Majumdar, Romina Silva, Antoinette Sabrina Perry, Ronald William Watson, Andrea Rau, Florence Jaffrezic, Thomas Brendan Murphy, Isobel Claire Gormley

“…To address this, a family of beta mixture models (BMMs) is proposed that (i) objectively infers methylation state thresholds and (ii) identifies differentially methylated CpG sites (DMCs) given untransformed, beta-valued methylation data. The BMMs achieve this through model-based clustering of CpG sites and by employing parameter constraints, facilitating application to different study settings. …”
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Application of Empirical Orthogonal Function Interpolation to Reconstruct Hourly Fine Particulate Matter Concentration Data in Tianjin, China by Hongwu Zhou, Haidong Pan, Shuang Li, Xianqing Lv

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Correction to: A robust implicit Jungck iterative scheme: stability, data dependency, and applications to nonlinear integral equations by Kapil Kumar, Hind Alamri, Vivek Kumar, Faik Gürsoy, Ioannis K. Argyros, Nawab Hussain

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Statistical Inferences to the Parameter and Reliability Characteristics of Gamma-mixed Rayleigh Distribution under Progressively Censored Data with Application by Kousik Maiti, Suchandan Kayal

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Assessing the Preprocessing Benefits of Data-Driven Decomposition Methods for Phase Permutation Entropy—Application to Econometric Time Series by Erwan Pierron, Meryem Jabloun

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A Rule Based Feature Selection Approach for Target Classification in Wireless Sensor Networks with Sensitive Data Applications by Zhiyong Hao, Bin Liu

“…Hence, minimizing energy consumption of sensors while maintaining a given classification accuracy is a key problem in this research area, especially for sensitive data applications. This paper proposes a rule based feature selection approach rather than all-features approach that aims at increasing the energy efficiency of the system without losing much classification accuracy. …”
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Modified Two-Parameter Ridge Estimators for Enhanced Regression Performance in the Presence of Multicollinearity: Simulations and Medical Data Applications by Muteb Faraj Alharthi, Nadeem Akhtar

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Analysing the Transformation of Sports Practices in Less-Developed Mountains Based on Huts Attendance Data. Application to the Valais Alps by Jacques Mourey, Christophe Clivaz, Philippe Bourdeau

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Chitosan Biomedical Applications for the Treatment of Viral Disease: A Data Mining Model Using Bibliometric Predictive Intelligence by Worasak Klongthong, Veera Muangsin, Chupun Gowanit, Nongnuj Muangsin

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Research on Application of Big Data in Internet Financial Credit Investigation Based on Improved GA-BP Neural Network by Fei-Peng Wang

“…The accuracy rate of each sample method is over 90%, and some accuracy rate is even more than 90%, which indicates that the model is applicable to the credit data of big data in internet finance.…”
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Application of machine learning and temporal response function modeling of EEG data for differential diagnosis in primary progressive aphasia by Heather Dial, Lokesha S. Pugalenthi, G. Nike Gnanateja, Junyi Jessy Li, Maya L. Henry

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An Unsupervised Machine Learning Approach to Identify Spectral Energy Distribution Outliers: Application to the S-PLUS DR4 Data by F. Quispe-Huaynasi, F. Roig, N. Holanda, V. Loaiza-Tacuri, Romualdo Eleutério, C. B. Pereira, S. Daflon, V. M. Placco, R. Lopes de Oliveira, F. Sestito, P. K. Humire, M. Borges Fernandes, A. Kanaan, C. Mendes de Oliveira, T. Ribeiro, W. Schoenell

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Application of big data technology in visitor behavioral characteristics identification and energy efficiency assessment of intelligent scenic spots by Wei Zhou, Jin Cui

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Teachers' perspectives on the application of technology in mathematics education in primary schools: A dataset from VietnamMendeley Data by Quoc Anh Vuong, Dien Thi Bui, Hue Thi Thu Dang, Anh Vinh Le, Duc Lan Do

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Online monitoring data processing methods for railway slopes and its application: A case study of the Shuohuang Railway by Mu GU

“…The effectiveness of system monitoring closely correlates with the data processing model. Taking the online monitoring system for slope deformation on the Shuohuang Railway as an example, this study focuses on three crucial aspects of data processing: data preprocessing, noise suppression, and deformation trend prediction. …”
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