Development and evaluation of statistical and artificial intelligence approaches with microbial shotgun metagenomics data as an untargeted screening tool for use in food production by Kristen L. Beck, Niina Haiminen, Akshay Agarwal, Anna Paola Carrieri, Matthew Madgwick, Jennifer Kelly, Victor Pylro, Ban Kawas, Martin Wiedmann, Erika Ganda

“…ABSTRACT The increasing knowledge of microbial ecology in food products relating to quality and safety and the established usefulness of machine learning algorithms for anomaly detection in multiple scenarios suggests that the application of microbiome data in food production systems for anomaly detection could be a valuable approach to be used in food systems. …”
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Early Warning Method of Abnormal Electricity Consumption Behavior Based on Data Driven by WAN Wei, LIU Hongqi, SUN Hong-chang, ZHANG Feng, WANG Yang, SUN Wei-qing

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Mechanism–Data Collaboration for Characterizing Sea Clutter Properties and Training Sample Selection by Wenhao Chen, Yong Zou, Zhengzhou Li, Shengrong Zhong, Haolin Gan, Aoran Li

“…These multi-features extracted from inaccurate models and imbalanced data lead to overfitting or underfitting and degrade detection performance. …”
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Detecting Galactic Rings in the DESI Legacy Imaging Surveys with Semisupervised Deep Learning by Jianzhen Chen, Zhijian Luo, Cheng Cheng, Jun Hou, Shaohua Zhang, Chenggang Shu

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Evaluation of lung cancer early detection offered by the German Social Accident Insurance for formerly asbestos-exposed employees using low-dose computed tomography – setting and s... by Felix Greiner, Jan Heidrich, Helena Keller, Dirk Taeger, Thorsten Wiethege, Volker Harth

“…To avoid selection bias, a complete set of anonymised data (approximately 22,200 individuals as of 12/2021) from the EVA-LCS will be transmitted for use in EVALUNG. …”
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Validation testing to determine the sensitivity of lateral flow testing for asymptomatic SARS-CoV-2 detection in low prevalence settings: Testing frequency and public health messag... by Jack Ferguson, Steven Dunn, Angus Best, Jeremy Mirza, Benita Percival, Megan Mayhew, Oliver Megram, Fiona Ashford, Thomas White, Emma Moles-Garcia, Liam Crawford, Tim Plant, Andrew Bosworth, Michael Kidd, Alex Richter, Jonathan Deeks, Alan McNally

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A comparative assessment of machine learning models and algorithms for osteosarcoma cancer detection and classification by Amoakoh Gyasi-Agyei

“…A publicly available raw osteosarcoma dataset was explored and then preprocessed using different combinations of data denoising techniques (including principal component analysis, mutual information gain, analysis of variance and Kendall’s rank correlation analysis) and data augmentation to derive seven different datasets. …”
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Spectroscopic Method for Detection of Soluble Solid Content in Cherry Tomato Using Deep Convolutional Generative Adversarial Network-Based Data Augmentation by WU Zhijing, LIU Fuqiang, LI Zhigang, CHEN Hui

“…Considering insufficient sample numbers in the practical detection of soluble solid content (SSC) in cherry tomato, we proposed a deep convolutional generation adversarial network (DCGAN) model to expand spectral data and SSC label data, and established a one-dimensional convolutional neural network regression (1D-CNNR) model to improve the prediction accuracy and generalization capability of the DCGAN model. …”
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Fast Islanding Detection for Distribution System including PV using Multi-Model Decision Tree Algorithm by Rasool Ebrahimi, Ghazanfar Shahgholian, Bahador Fani

“…Probable events are simulated under different system operating states to generate classification data set. The pro­po­sed method is tested on typical distribution system including the PV unit, different loads, and synchronous generator. …”
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Recipe Based Anomaly Detection with Adaptable Learning: Implications on Sustainable Smart Manufacturing by Junhee Lee, Jaeseok Jang, Qing Tang, Hail Jung

“…In Recipe-Based Learning, we first apply K-Means clustering to account for the flexible manufacturing process, which relies on diverse settings. The injection molding data are classified into setting-specific recipes to ensure data normality and uniqueness. …”
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Defect Detection and Correction in OpenMP: A Static Analysis and Machine Learning-Based Solution by Norah A. Al-Johany, Fathy E. Eassa, Sanaa A. Sharaf, Eynas H. Balkhair, Sara M. Assiri

“…A rule-based correction engine is employed to repair detected defects through minimally invasive code transformations, such as inserting critical sections, correcting directive placement, and adjusting data-sharing clauses. …”
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MT-Former: Multi-Task Hybrid Transformer and Deep Support Vector Data Description to Detect Novel anomalies during Semiconductor Manufacturing by Hyunsu Jeong, Chiho Yoon, Hyunseok Lim, Jaesuk Chang, Sampa Misra, Chulhong Kim

“…For SK Hynix data, MT-former achieved higher AUC as compared with a Deep-SVDD model, by 8.19% for anomaly detection and by 9.59% for classifying the existing classes. …”
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The Capabilities of Optical and C-Band Radar Satellite Data to Detect and Understand Faba Bean Phenology over a 6-Year Period by Frédéric Baup, Rémy Fieuzal, Clément Battista, Herivanona Ramiakatrarivony, Louis Tournier, Serigne-Fallou Diarra, Serge Riazanoff, Frédéric Frappart

“…Using Sentinel-1, Sentinel-2, and Landsat-8 data, temporal variations in NDVI and radar backscatter coefficients (γ⁰_VV, γ⁰_VH, and γ⁰_VH/VV) are examined to assess crop growth, detect anomalies, and evaluate the impact of climatic conditions and sowing strategies. …”
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Toward an Operational System for Automatically Detecting <i>Xylella fastidiosa</i> in Olive Groves Based on Hyperspectral and Thermal Remote Sensing Data by Annarita D’Addabbo, Raffaella Matarrese, Francesco Lovergine, Alberto Refice, Antonella Belmonte, Fabio Bovenga, Antonia Gallo, Serafina Serena Amoia, Raied Abou Kubaa, Giovanni Mita, Donato Boscia

“…First, we assess whether the size of a data set, composed of a limited number of labelled examples, is sufficient to train accurate classifiers. …”
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Enhancing precision in multiple sclerosis lesion segmentation: A U-net based machine learning approach with data augmentation by Oezdemir Cetin, Berkay Canel, Gamze Dogali, Unal Sakoglu

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Enhanced detection of Mpox using federated learning with hybrid ResNet-ViT and adaptive attention mechanisms by V. Maheskumar, R. Vijayarajeswari

“…This framework provides a scalable, privacy-preserving solution for Mpox detection, particularly suitable for deployment in resource-constrained settings.…”
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PyReX: A Recursion Based Crossover Detection Algorithm in Python for Along‐Track Geophysical Measurements by K. V. N. G. Vikram, D. V. P. Krishna, K. M. Sreejith

“…Usual approach of crossover detection considers consecutive data points in tracks as segments and checks for intersections between all combinations of these segments. …”
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Challenges in detecting and predicting adverse drug events via distributed analysis of electronic health record data from German university hospitals. by Anna Maria Wermund, Torsten Thalheim, André Medek, Florian Schmidt, Thomas Peschel, Alexander Strübing, Daniel Neumann, André Scherag, Markus Loeffler, Miriam Kesselmeier, Ulrich Jaehde, POLAR_MI Consortium

“…The Medical Informatics Initiative Germany (MII) aims to facilitate the interoperability and exchange of electronic health record data from all German university hospitals. The MII use case "POLyphamacy, drug interActions and Risks" (POLAR_MI) was designed to retrospectively detect medication-related risks in adult inpatients. …”
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Machine-learning detection of stress severity expressed on a continuous scale using acoustic, verbal, visual, and physiological data: lessons learned by Marketa Ciharova, Khadicha Amarti, Ward van Breda, Ward van Breda, Martin J. Gevonden, Sina Ghassemi, Annet Kleiboer, Christiaan H. Vinkers, Christiaan H. Vinkers, Christiaan H. Vinkers, Christiaan H. Vinkers, Milou S. C. Sep, Milou S. C. Sep, Milou S. C. Sep, Milou S. C. Sep, Sophia Trofimova, Alexander C. Cooper, Xianhua Peng, Xianhua Peng, Mieke Schulte, Mieke Schulte, Eirini Karyotaki, Eirini Karyotaki, Eirini Karyotaki, Pim Cuijpers, Pim Cuijpers, Pim Cuijpers, Heleen Riper, Heleen Riper

“…Use of multimodal data, meaning data coming from multiple sources, might contribute to machine-learning stress severity detection. …”
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Enhancing epidemic preparedness: a data-driven system for managing respiratory infections by Moslem Sarani, Katayoun Jahangiri, Manoochehr Karami, Mohammadreza Honarvar

“…This study details the development of a data-driven early warning system (EWS) designed to improve outbreak detection and response utilizing the data integration and visualization capabilities of Microsoft Power BI. …”
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