Robust modeling of differential gene expression data using normal/independent distributions: a Bayesian approach. by Mojtaba Ganjali, Taban Baghfalaki, Damon Berridge

“…Two publicly available gene expression data sets are analyzed using the proposed approach. …”
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Improving Brain Metabolite Detection with a Combined Low-Rank Approximation and Denoising Diffusion Probabilistic Model Approach by Yeong-Jae Jeon, Kyung Min Nam, Shin-Eui Park, Hyeon-Man Baek

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Using Machine Learning Method for Variable Star Classification Using the TESS Sectors 1–57 Data by Li-Heng Wang, Kai Li, Xiang Gao, Ya-Ni Guo, Guo-You Sun

“…By crossmatching the data with Gaia’s variable star catalogue, we obtained labeled data sets for further analysis. …”
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Future of Breast Cancer Diagnosis: A Review of DL and ML Applications and Emerging Trends for Multimodal Data by Beibit Abdikenov, Tomiris Zhaksylyk, Olzhas Shortanbaiuly, Yerzhan Orazayev, Nursultan Makhanov, Temirlan Karibekov, Victor Suvorov, Aruzhan Imasheva, Kurmash Zhumagozhayev, Aigerim Seitova

“…The findings highlight the importance of interdisciplinary collaboration, application of standardized data sets, and continuous improvement of the methodology to facilitate clinical uptake of AI-based breast cancer diagnostic systems.…”
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Very-Large-Scale Integration-Friendly Method for Vital Activity Detection with Frequency-Modulated Continuous Wave Radars by Krzysztof Ślot, Piotr Łuczak, Paweł Kapusta, Sławomir Hausman, Arto Rantala, Jacek Flak

“…A simple algorithm for respiratory activity detection in data produced by Frequency-Modulated Continuous-Wave (FMCW) radars is presented in this paper. …”
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Deep Learning Network Selection and Optimized Information Fusion for Enhanced COVID-19 Detection: A Literature Review by Olga Adriana Caliman Sturdza, Florin Filip, Monica Terteliu Baitan, Mihai Dimian

“…Information fusion techniques, which operate at the data, feature, and decision levels, enhance diagnostic performance. …”
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Application of 4PCS and KD-ICP Alignment Methods Based on ISS Feature Points for Rail Wear Detection by Jie Shan, Hao Shi, Zhi Niu

“…In order to detect the abrasion of rails, a new point cloud alignment method combining 4-points congruent sets (4PCS) coarse alignment based on internal shape signature (ISS) and K-dimensional iterative closest points (KD-ICP) fine alignment is proposed, and for the first time, the combined algorithm is applied to the detection of rail wear. …”
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Normative data of the Spanish version of the Montreal Cognitive Assessment (MoCA) in older individuals from Peru by Lucia Bulgarelli, Emilia Gyr, Jose Villanueva, Koni Mejía, Claudia Mejía, Renato Paredes, Sheyla Blumen

“…Conclusions: We provided normative data and cutoff scores for older Peruvians, supporting the clinical use of the MoCA in Peru and setting a benchmark for future test standardizations in the region.…”
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Evaluation of imaging techniques for early detection of intrathoracic cancers in symptomatic patients in primary care: a systematic review by Fiona M Walter, Jaime L Peters, Richard Neal, Natalia Calanzani, Bogdan Grigore, Wasim Hamad, Lauren Asare

“…We aimed to summarise evidence on the performance of imaging techniques that could aid the detection of intrathoracic cancers in low prevalence settings.Design Systematic review and quality appraisal using Quality Assessment of Diagnostic Accuracy Studies-2 and Grading of Recommendations Assessment, Development and Evaluation.Data sources MEDLINE, Embase and Web of Science were searched with a predesigned search strategy for articles from January 2000 to January 2024.Eligibility criteria We included studies relevant for primary care, where participants were suspected of having intrathoracic cancer and reported on at least one diagnostic performance measure. …”
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An integrated model of multiple-condition ChIP-Seq data reveals predeterminants of Cdx2 binding. by Shaun Mahony, Matthew D Edwards, Esteban O Mazzoni, Richard I Sherwood, Akshay Kakumanu, Carolyn A Morrison, Hynek Wichterle, David K Gifford

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From Tables to Computer Vision: Transforming HPDC Process Data into Images for CNN-Based Deep Learning by A. Burzyńska

“…The study utilized a substantial dataset with a total of 61,584 images, and the most effective model attained an impressive Root Mean Square Error (RMSE) of 0.81, underscoring the model's remarkable capacity to accurately detect and predict casting quality issues. The model's efficacy was evaluated through its application to both large and small, differently distributed data sets. …”
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Genomics and integrative clinical data machine learning scoring model to ascertain likely Lynch syndrome patients by Ramadhani Chambuso, Takudzwa Nyasha Musarurwa, Alessandro Pietro Aldera, Armin Deffur, Hayli Geffen, Douglas Perkins, Raj Ramesar

“…Group regularisation methods in combination with 10-fold cross-validation were performed for feature selection on the training data. Results Out of 4800 CRC patients frorm the TCGA datasets with clinicopathological and somatic genomics data, we ascertained 524 patients with complete data. …”
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A Multi-Machine and Multi-Modal Drift Detection (M2D2) Framework for Semiconductor Manufacturing by Chin-Yi Lin, Tzu-Liang (Bill) Tseng, Tsung-Han Tsai

“…The semiconductor industry currently lacks a robust, holistic method for detecting parameter drifts in wide-bandgap (WBG) manufacturing, where conventional fault detection and classification (FDC) practices often rely on static thresholds or isolated data modalities. …”
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Apple Detection via Near-Field MIMO-SAR Imaging: A Multi-Scale and Context-Aware Approach by Yuanping Shi, Yanheng Ma, Liang Geng

“…This research provides a beneficial reference and technical basis for using SAR data in fruit detection and yield estimation in precision agriculture.…”
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Learning to Train and to Explain a Deep Survival Model with Large-Scale Ovarian Cancer Transcriptomic Data by Elena Spirina Menand, Manon De Vries-Brilland, Leslie Tessier, Jonathan Dauvé, Mario Campone, Véronique Verrièle, Nisrine Jrad, Jean-Marie Marion, Pierre Chauvet, Christophe Passot, Alain Morel

“…<b>Conclusions:</b> The deep learning-based models for survival prediction with RNA-seq data could be used to detect and interpret the gene-sets associated with survival in ovarian cancer patients and open a new avenue for future research.…”
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metaGEENOME: an integrated framework for differential abundance analysis of microbiome data in cross-sectional and longitudinal studies by Ahmed Abdelkader, Nur A. Ferdous, Mohamed El-Hadidi, Tomasz Burzykowski, Mohamed Mysara

“…Abstract Background Detecting biomarkers is a key objective in microbiome research, often done through 16S rRNA amplicon sequencing or shotgun metagenomic analysis. …”
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Detection method on pantograph-catenary arcing of electric locomotives based on multi-frequency-band characteristics of current signals by LUO Yinbei, GE Ting, SUN Zeyong

“…These characteristic components were then utilized to devise a measurement scheme and data preprocessing algorithm, and historical data were incorporated, leading to the establishment of feature sets. …”
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Classifying active and inactive states of growing rabbits from accelerometer data using machine learning algorithms by Mónica Mora, Lucile Riaboff, Ingrid David, Juan Pablo Sánchez, Miriam Piles

“…In this study, the objective was to automatically detect rabbit activity/inactivity states from accelerometer data in growing rabbits. …”
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Kernel Two-Dimensional Nonnegative Matrix Factorization: A New Method to Target Detection for UUV Vision System by Jian Xu, Pengfei Bi, Xue Du, Juan Li, Tianhao Jiang

“…Our contributions can be summarized as follows: (1) K2DNMF intends to use the kernel method for the matrix factorization both on the column and row directions of the two-dimensional image data in order to transform the original low-dimensional space with nonlinearity into a higher dimensional space with linearity; (2) In the K2DNMF method, a good subspace approximation to the original data can be obtained by the orthogonal constraint on column basis matrix and row basis matrix; (3) The column basis matrix and row basis matrix can extract the feature information of underwater target images, and an effective classifier is designed to perform underwater target recognition; (4) A series of related experiments were performed on three sets of test samples collected by the UUV vision system, the experimental results demonstrate that K2DNMF has higher overall target detection accuracy than the traditional underwater target recognition methods.…”
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Application of a modified set of GoogLeNet and ResNet-18 convolutional neural networks towards the identification of environmentally derived-MPLs in the Yadkin-pee dee river basin by Wesley Allen Williams, Anirudh Arunprasad, Shyam Aravamudhan

“…Due to their heterogenous nature and difficulty in characterization, a diverse set of pictorial training data from µ-Raman was used to perform transfer learning on 2 CNNs of interest: GoogLeNet (GN) and ResNet-18 (RN). …”
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