NCL Data2026 · dataset
Code, split files, out-of-fold predictions and latency measurements for "Coil leakage, training variance and FLOP proxies in the evaluation of real-time steel surface defect detectors"<p dir="ltr">Supplementary material for the article "Coil leakage, training variance and FLOP proxies in the evaluation of real-time steel surface defect detectors" (submitted to the Journal of Real-Time Image Processing).</p><p dir="ltr">The study re-evaluates real-time steel surface defect detectors on NEU-DET and GC10-DET with five-fold cross-validation, coil-disjoint folds for GC10-DET, repeat
NCL Data2026 · dataset
<b>An Explainable Deep Learning Framework for Heart Disease Prediction Using SMOA-Based Feature Selection and ACIO-Optimized Improved GRU</b><p dir="ltr">Heart disease is a serious disease with possibly fatal consequences. Finding high-risk factors for chronic CVD, such as smoking, cholesterol, obesity, and high blood pressure, could save several lives. Our work proposes an AI-enabled framework to help professionals and physicians identify high-risk indicators for heart disease.</p>
Teesside University Research Data Repository2026 · dataset
Brinjal Leaf Disease and Fruit Condition Image Dataset: A Field-Collected Dataset from BangladeshThis dataset is a field-collected image resource for researchers and practitioners working in agriculture, machine learning, and computer vision. It contains images of brinjal (Solanum melongena) leaves and fruits collected under field conditions in Bangladesh. The dataset covers different disease symptoms, pest infestation, nutritional deficiency, and healthy leaf and fruit conditions. The datase
figshare + Loughborough Research Repository + GRANTS Data + UP Research Data Repository2026 · Computed tomography
Supplementary file 1_Deep learning with interactive segmentation for risk stratification of cystic renal lesions on tri-phase CT: a multicenter study.pdfBackground<p>Accurate preoperative risk stratification of cystic renal lesions (CRLs) remains a clinical challenge, primarily due to morphological overlap which often leads to the overtreatment of indolent cysts. To address this, we developed and validated an integrated deep learning framework that combines interactive segmentation with tri-phase CT feature integration to refine malignancy predict
figshare + Loughborough Research Repository + GRANTS Data + UP Research Data Repository2026 · Computed tomography
Video 1_Deep learning with interactive segmentation for risk stratification of cystic renal lesions on tri-phase CT: a multicenter study.mp4Background<p>Accurate preoperative risk stratification of cystic renal lesions (CRLs) remains a clinical challenge, primarily due to morphological overlap which often leads to the overtreatment of indolent cysts. To address this, we developed and validated an integrated deep learning framework that combines interactive segmentation with tri-phase CT feature integration to refine malignancy predict
ZivaHub + Deakin Research Online + DMU Figshare2026 · dataset
A rapid-updating method for anthropogenic NO<sub>x</sub> emissions based on convolutional neural networks and TROPOMI NO₂ observations<p>Nitrogen oxides (NO<sub><i>x</i></sub> = NO + NO<sub>2</sub>) are air pollutants primarily emitted from anthropogenic sources, however, bottom-up anthropogenic NO<sub><i>x</i></sub> emission inventories often suffer from update delays. The TROPOspheric Monitoring Instrument (TROPOMI) provides near-real-time, high-resolution NO<sub>2</sub> column densities, offering new opportunities for timely
ZivaHub + Deakin Research Online + DMU Figshare + HKU DataHub + Swinburne Figshare + DaYta Ya Rona + SUNScholarData + figshare + Loughborough Research Repository + GRANTS Data + UP Research Data Repository2026 · Astronomical catalogue
DySPAM-Net dataset<p dir="ltr">This dataset contains field-collected mango (Mangifera indica L.) canopy images acquired for the development and evaluation of DySPAM-Net (Dynamic Sequential Phenology-Aware Multimodal Network), a deep-learning framework for early-stage mango yield prediction.</p><p dir="ltr">The dataset was collected from a commercial mango orchard in Gujarat, India, using smartphone-based field imag
ZivaHub + Deakin Research Online + DMU Figshare + HKU DataHub + Swinburne Figshare + DaYta Ya Rona + SUNScholarData + figshare + Loughborough Research Repository + GRANTS Data + UP Research Data Repository2026 · Astronomical catalogue
Data and code for: Evaluating Multitask Deep Learning for Multi-Crop Plant Health Recognition under Seed Variation and Crop Transfer<p dir="ltr">This item contains the data records and code supporting the study "Evaluating Multitask Deep Learning for Multi-Crop Plant Health Recognition under Seed Variation and Crop Transfer" (Cogent Food & Agriculture, under review).</p><p dir="ltr">The study compares four output formulations (single-task joint classification, independent multitask heads, multitask fusion, and fusion condition
ZivaHub + Deakin Research Online + DMU Figshare + HKU DataHub + Swinburne Figshare + DaYta Ya Rona + SUNScholarData + figshare + Loughborough Research Repository + GRANTS Data + UP Research Data Repository2026 · Astronomical catalogue
<b>Leak-Free Cross-Validation Reveals an Architecture-Independent Performance Ceiling for District-Scale Maize Yield Prediction</b><p dir="ltr">Remote sensing for crop yield prediction. The spatial data is found at the link below:</p><p dir="ltr"><a href="https://www.kaggle.com/datasets/sokazimba/zambian-districts-datasets" target="_blank" rel="noreferrer">https://www.kaggle.com/datasets/sokazimba/zambian-districts-datasets</a></p>
ZivaHub + Deakin Research Online + DMU Figshare2026 · dataset · unknown
Explainable AI for malware opcode sequence analysis and explainability-motivated saliency-map based spurious correlation robustness in imagesExplainability offers a powerful lens for understanding and improving the robustness of Machine Learning (ML) models. This work demonstrates how eXplainable AI (XAI) techniques can be used not only to interpret model behaviour, but also to develop robust training algorithms that encourage the learning of semantically meaningful features.<br><br>The first contribution is Hierarchical-LIME (H-LIME),
ZivaHub + Deakin Research Online + DMU Figshare2026 · dataset
Large Language Models - an Overview<p dir="ltr"><b>Large Language Models: Foundations and Applications in Clinical Text</b> is an educational presentation introducing the development, architecture, and clinical applications of modern language models. The presentation traces the evolution of language modeling from early neural-network concepts and rule-based systems through n-gram models, recurrent neural networks, LSTMs, GRUs, word
TU Wien Research Data2026 · dataset
Loosdorf-MSL dataset: multispectral LiDAR data for LULC classification, supporting current and prospective European NMCAs' schemes1. Overview Loosdorf-MSL presents the first 3D multispectral (MS) LiDAR dataset for land use land cover (LULC) classification based on the current and prospective LULC classification schemes of European National Mapping and Cadastral Agencies (NMCAs). By releasing Loosdorf-MSL, we aim to address the following gaps: The limited availability of publicly accessible MS LiDAR datasets for the developme
ZivaHub + Deakin Research Online + DMU Figshare2026 · dataset · unknown
Enhanced human-robot collaboration through deep learning enabled human motion predictionThrough enhanced sensing and communication, human-robot collaboration (HRC) refers to application scenarios where a robot, usually a collaborative robot (cobot), and a human occupy the same workspace and interact to accomplish collaborative tasks. Efficiency and safety are among the most important considerations in these scenarios. <br><br>Toward this target, we first consider the perception of a
ZivaHub + Deakin Research Online + DMU Figshare2026 · dataset · unknown
Intelligent methods for analyzing veracity and helpfulness of online reviewsOnline reviews are pivotal in influencing consumer purchasing decisions, making the detection of fake reviews and the prediction and ranking of review helpfulness critical areas of study. This thesis presents a comprehensive exploration of various Machine Learning solutions to address these challenges, employing an array of data representations and advanced neural network models. Initially, the in
ZivaHub + Deakin Research Online + DMU Figshare + UCL Research Data Repository2026 · dataset · unknown
Enhancing gait recognition with 3D markerless motion captureIn recent years gait has increased in popularity as a biometric and gait recognition has matured into a larger field that is is attracting international interest. Most current state-of-the-art gait recognition systems use appearance-based methods which are fast and capable of high performance on gait datasets. With the emergence of deep learning the focus of appearance-based methods has shifted, f
ZivaHub + Deakin Research Online + DMU Figshare + HKU DataHub + Swinburne Figshare + DaYta Ya Rona + SUNScholarData + figshare + Loughborough Research Repository + GRANTS Data + UP Research Data Repository2026 · Astronomical catalogue
Vision transformer-based ordinal model for semi-quantitative synovitis grading on musculoskeletal ultrasound: a retrospective study with internal and external validation<p>To develop and validate a Vision Transformer-based model for semi-quantitative grading of synovitis on musculoskeletal ultrasound and to assess its potential clinical utility. The study also examined whether transformer-based modeling offers advantages over conventional CNNs in ordinal ultrasound grading.</p> <p>This retrospective diagnostic study included 312 patients, 624 joints, and 1,248 ul
ZivaHub + Deakin Research Online + DMU Figshare2026 · dataset
Outage performance and calibration of ML-assisted resource allocation for next-generation wireless systemsNext-generation wireless networks, including 6G systems, require intelligent, adaptive strategies to mitigate link failures under dynamic channel conditions. While machine learning (ML) is a key enabler for such strategies, conventional ML approaches often yield limited performance gains and may fail to meet stringent reliability demands. This thesis develops novel ML solutions for intelligent res
ZivaHub + Deakin Research Online + DMU Figshare + UCL Research Data Repository2026 · dataset · unknown
Deep learning for processing histopathology imagesHistopathology is the study and diagnosis of disease via tissue microscopy and it is currently the ‘gold-standard‘ in formally diagnosing many types of disease including cancers.<br>Due to increasing workloads on pathologists, there is a growing need for automated image analysis pipelines that are able to filter out obviously benign samples. However, these algorithms are sensitive to factors of va
ZivaHub + Deakin Research Online + DMU Figshare + UCL Research Data Repository2026 · dataset · unknown
Deep learning for boundary representation CAD modelsThis thesis explores utilising deep learning methodologies for tasks relating to learning from boundary representation (B-Rep) CAD models. The ambition is to use deep learning for an automatic feature recognition algorithm to identify geometric features to help automate the CAD to analysis pre-processing task of defeaturing, as it is a necessary but time-consuming operation. The three main activit
ZivaHub + Deakin Research Online + DMU Figshare + HKU DataHub + Swinburne Figshare + DaYta Ya Rona + SUNScholarData + figshare + Loughborough Research Repository + GRANTS Data + UP Research Data Repository2026 · Astronomical catalogue
Supplementary file 1_LDAR shows more consistent prognostic performance than other albumin-derived indices for 28-day ICU mortality in critically ill patients with urosepsis: evidence from a dual-cohort retrospective study.docxBackground<p>Urosepsis remains a leading cause of ICU mortality. Albumin-derived composite indices, which integrate nutritional, inflammatory, and metabolic parameters, hold prognostic promise; however, their comparative performance has not been directly assessed.</p>Methods<p>We conducted a dual-cohort retrospective study using MIMIC-IV (n = 3,060) for derivation and the Affiliated Hospital of Gu
ZivaHub + Deakin Research Online + DMU Figshare + HKU DataHub + Swinburne Figshare + DaYta Ya Rona + SUNScholarData + figshare + Loughborough Research Repository + GRANTS Data + UP Research Data Repository2026 · Astronomical catalogue
Data Sheet 1_MEMOIR-VLM—a multimodal vision-language model for Alzheimer's disease classification and question answering.pdfIntroduction<p>Alzheimer's disease (AD) affects an estimated 55 million people worldwide and is projected to nearly double by 2050, creating a need for scalable, non-invasive AI systems that can classify disease stage, support multimodal clinical reasoning, and interact in natural language. Most deep learning work on AD focuses on single-modality classification without natural-language interaction
ZivaHub + Deakin Research Online + DMU Figshare2026 · dataset
Machine learning methods for hyperspectral imaging: from reconstruction to classificationWith the growing demands for efficient and scalable food analysis, agriculture, and healthcare applications, the need for improved data acquisition and processing techniques has become increasingly significant. Near Infrared Spectroscopy has emerged as a powerful tool for non-invasive analysis in these domains, providing key insights into material composition at a spectral level. Hyperspectral ima
ZivaHub + Deakin Research Online + DMU Figshare + UCL Research Data Repository2026 · dataset · unknown
Automated hardware trojan detectionA Hardware Trojan (HT) is a malicious circuit inserted into an Integrated Circuit (IC) or a malicious modification of a circuit to change its behaviour or leak secret information. As the production of ICs is now distributed globally through the use of third parties in the outsourcing of design services, the use of off-the-shelf intellectual property (IP) and different foundry services, there are m
ZivaHub + Deakin Research Online + DMU Figshare2026 · dataset · unknown
Deep learning of dyadic interaction visual cues for human-robot collaboration in assembly tasks<p></p><p>This thesis examines the integration of multiple visual cues in dyadic interactions through deep learning to improve interactivity and intuitiveness in human-robot interactions, particularly in assembly tasks. Serving as a preliminary effort, these bodies of work seek to address the critical deficiencies in current dyadic human robot interaction methodologies, which typically depend on i
ZivaHub + Deakin Research Online + DMU Figshare2026 · dataset · unknown
Super-resolution in millimetre-wave compressive computational imagingImaging at millimetre wave (mmW) has many advantages over infrared (IR), X-ray and optical imaging. MmWs can penetrate through materials that are opaque at optical wavelengths. They do not possess any ionizing effects, and hence are harmless to human exposure. They can also be operated in all weather conditions, making them suitable for both indoor and outdoor use. Because of all these advantages,