Decoding the chicken gastrointestinal microbiome by PB. Burrows, F. Godoy-Santos, K. Lawther, A. Richmond, N. Corcionivoschi, SA. Huws

“…Metataxonomic profiles produced across the 602 datasets illustrated the presence of 3 phyla, 25 families and 30 genera, of which core genera (defined by presence in over 90% of datasets) belonged to Lactobacillus, Faecalibacterium, Butyricicoccus, Eisenbergiella, Subdoligranulum, Oscillibacter, Clostridium & Blautia. …”
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An Improved Pearson’s Correlation Proximity-Based Hierarchical Clustering for Mining Biological Association between Genes by P. M. Booma, S. Prabhakaran, R. Dhanalakshmi

“…Microarray gene expression datasets has concerned great awareness among molecular biologist, statisticians, and computer scientists. …”
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Multifractal Applications in Hydro-Climatology: A Comprehensive Review of Modern Methods by Shamseena Vahab, Adarsh Sankaran

“…Complexity evaluation of hydro-climatic datasets is a challenging but essential pre-requisite for accurate modeling and subsequent planning. …”
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Identification of Biomarkers for Esophageal Squamous Cell Carcinoma Using Feature Selection and Decision Tree Methods by Chun-Wei Tung, Ming-Tsang Wu, Yu-Kuei Chen, Chun-Chieh Wu, Wei-Chung Chen, Hsien-Pin Li, Shah-Hwa Chou, Deng-Chyang Wu, I-Chen Wu

“…RUVBL1 was consistently overexpressed in all three datasets, although we found inconsistent CNIH expression possibly affected by the diverse major risk factors for ESCC across different areas.…”
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Improvement of the Nonparametric Estimation of Functional Stationary Time Series Using Yeo-Johnson Transformation with Application to Temperature Curves by Sameera Abdulsalam Othman, Haithem Taha Mohammed Ali

“…In this article, Box-Cox and Yeo-Johnson transformation models are applied to two time series datasets of monthly temperature averages to improve the forecast ability. …”
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Detection of Fuzzy Association Rules by Fuzzy Transforms by Ferdinando Di Martino, Salvatore Sessa

“…We present a new method based on the use of fuzzy transforms for detecting coarse-grained association rules in the datasets. The fuzzy association rules are represented in the form of linguistic expressions and we introduce a pre-processing phase to determine the optimal fuzzy partition of the domains of the quantitative attributes. …”
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Cerebral Edema Associated with Ventricular Reservoirs in Two Patients: A Case Report by Marshall C. Cress, Angela N. Spurgeon, Douglas C. Miller, N. Scott Litofsky

“…We report two cases of ventricular catheter-reservoir-associated edema in patients receiving treatment for CNS metastasis.…”
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BucketAugment: Reinforced Domain Generalisation in Abdominal CT Segmentation by David Jozef Hresko, Peter Drotar

“…Our results indicate that BucketAugment significantly enhances domain generalization across diverse medical datasets, requiring only minimal modifications to existing network architectures. …”
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Diagnosis and prognosis of melanoma from dermoscopy images using machine learning and deep learning: a systematic literature review by Hoda Naseri, Ali A. Safaei

“…Future research should focus on creating accessible, large datasets and enhancing model interpretability to increase clinical applicability. …”
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PMWFCM: A Possibility based MultiKernel Weighted Fuzzy Clustering Algorithm for classification of driving behaviors by Ning Ma, Kaijun Wu, Yubin Yuan, Jiawei Li, Xiaoqiang Wu

“…Validation on three different types of datasets demonstrates that the PMWFCM algorithm performs exceptionally well in terms of average accuracy, normalized information, average time, robustness, and convergence. …”
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Application of Google Earth Engine to Monitor Greenhouse Gases: A Review by Damar David Wilson, Gebrekidan Worku Tefera, Ram L. Ray

“…Google Earth Engine (GEE) is a cloud-based platform revolutionizing geospatial analysis by providing access to vast satellite datasets and computational capabilities for monitoring environmental and societal issues. …”
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SaliencyMix+: Noise-Minimized Image Mixing Method With Saliency Map in Data Augmentation by Hajeong Lee, Zhixiong Jin, Jiyoung Woo, Byeongjoon Noh

“…Data augmentation is vital in deep learning for enhancing model robustness by artificially expanding training datasets. However, advanced methods like CutMix blend images and assign labels based on pixel ratios, often introducing label noise by neglecting the significance of blended regions, and SaliencyMix applies uniform patch generation across a batch, resulting in suboptimal augmentation. …”
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Exploring Unique Extracellular Vesicles Associated Signatures: Prognostic Insights, Immune Microenvironment Dynamics, and Therapeutic Responses in Pancreatic Adenocarcinoma by Kai Nan, Ming Zhang, Zilong Geng, Yuankai Zhang, Lin Liu, Zhi Yang, Peng Xu

“…Initially, we identified differentially expressed lncRNAs (DELs) from The Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression (GTEx) project datasets. Subsequently, we validated the expression of these DELs within extracellular vesicles and assessed their prognostic implications in PAAD using the GSE133684 and TCGA datasets. …”
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Novel Dynamic Partial Reconfiguration Implementation of K-Means Clustering on FPGAs: Comparative Results with GPPs and GPUs by Hanaa M. Hussain, Khaled Benkrid, Ali Ebrahim, Ahmet T. Erdogan, Huseyin Seker

“…Using General Purpose Processors (GPPs) to process large datasets may take a long time; therefore many acceleration methods have been proposed in the literature to speed up the processing of such large datasets. …”
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Towards unbiased skin cancer classification using deep feature fusion by Ali Atshan Abdulredah, Mohammed A. Fadhel, Laith Alzubaidi, Ye Duan, Monji Kherallah, Faiza Charfi

“…The proposed model addresses potential biases associated with skin conditions, particularly in individuals with darker skin tones or excessive hair, by incorporating feature fusion to assimilate insights from diverse datasets. Extensive experiments were conducted using publicly accessible datasets to evaluate SWNet’s effectiveness.This study utilized four datasets-Mnist-HAM10000, ISIC2019, ISIC2020, and Melanoma Skin Cancer-comprising skin cancer images categorized into benign and malignant classes. …”
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An Effective Approach for Modular Community Detection in Bipartite Network Based on Integrating Rider with Harris Hawks Optimization Algorithms by Bader Fahad Alkhamees, Mogeeb A. A. Mosleh, Hussain AlSalman, Muhammad Azeem Akbar

“…The quantitative assessment of the proposed approach, as well as thorough comparative evaluation, was meticulously conducted in terms of fitness and modularity over the citation networks datasets (cit-HepPh and cit-HepTh) and bipartite network datasets (Movie Lens 100 K and American Revolution datasets). …”
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TerraDactyl: A tool for connecting environmental data to when and where by Ariel Levi Simons, Hector Baez, Neha Acharya-Patel, Caren C. Helbing, Jim Jeffers, Julie Stanford, Rachel S. Meyer

“…Analyses on multiple environmental datasets are enhanced when there is a common set of variables, improving the ability of researchers to collaborate across a wide variety of projects. …”
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DeepExtremeCubes: Earth system spatio-temporal data for assessing compound heatwave and drought impacts by Chaonan Ji, Tonio Fincke, Vitus Benson, Gustau Camps-Valls, Miguel-Ángel Fernández-Torres, Fabian Gans, Guido Kraemer, Francesco Martinuzzi, David Montero, Karin Mora, Oscar J. Pellicer-Valero, Claire Robin, Maximilian Söchting, Mélanie Weynants, Miguel D. Mahecha

“…Machine learning techniques show promise but require well-structured, high-quality, and curated analysis-ready datasets. Earth observation datasets comprehensively monitor ecosystem dynamics and responses to climatic extremes, yet the data complexity can challenge the effectiveness of machine learning models. …”
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Self-Organized Criticality of Rainfall in Central China by Zhiliang Wang, Chunyan Huang

“…In this paper, our objective is to find the evidence of self-organized criticality (SOC) for rain datasets in China by employing the theory and method of SOC. …”
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Data driven prediction of fragment velocity distribution under explosive loading conditions by Donghwan Noh, Piemaan Fazily, Songwon Seo, Jaekun Lee, Seungjae Seo, Hoon Huh, Jeong Whan Yoon

“…The model's predictive capability is demonstrated through the accurate predictions for both training and testing datasets, showing its potential for the real-time prediction of fragmentation performance.…”
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