Machine Learning
Well logs are interpreted/processed to estimate the in-situ reservoir properties (petrophysical, geomechanical, and geochemical), which is essential for reservoir modeling, reserve estimation, and production forecasting. The modeling is often based on multi-mineral physics or empirical formulae. When sufficient amount of training data is available, machine learning solution provides an alternative approach to estimate those reservoir properties based on well log data and is usually with less turn-around time and human involvements.
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This repository contains the datasets produced using different data generation strategies to train data driven models (e.g., decision trees, gradient tree boosting, and deep neural networks), and to evaluate their performances. The data generation strategies are described, and the results are presented in the conference paper: "Training Data Generation Strategies for Data-driven Security Assessment of Low Voltage Smart Grids" J. Cuenca, E. Aldea, E. Le Guern-Dall'o, R. Féraud, G. Camilleri, and A. Blavette. IEEE ISGT EU 2024, Dubrovnik, Croatia, Oct 2024.
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Human pose estimation has applications in numerous fields, including action recognition, human-robot interaction, motion capture, augmented reality, sports analytics, and healthcare. Many datasets and deep learning models are available for human pose estimation within the visible domain. However, challenges such as poor lighting and privacy issues persist. These challenges can be addressed using thermal cameras; nonetheless, only a few annotated thermal human pose datasets are available for training deep learning-based human pose estimation models.
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229 Views
The dataset contains the ground-based observations of crop growth stages for Canada's prairie provinces (Manitoba, Saskatchewan and Alberta) from 2019 to 2020. Crop growth stages were visually observed from the side of the fields on a weekly cycle until the fields were harvested. The BBCH (Biologische Bundesanstalt, Bundessortenamt und CHemische Industrie) scale was used to stage growth.
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282 Views
Concept-1K is a novel dataset designed to facilitate research on incremental learning in large language models. It comprises 1,023 concepts represented as knowledge triplets, focusing on recently emerged topics to minimize data leakage. By providing a fine-grained approach to evaluating model performance, Concept-1K enhances the understanding of how these models learn and retain new information.
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This paper presents an innovative Internet of Things (IoT) system that integrates gas sensors and a custom Convolutional Neural Network (CNN) to classify the freshness and species of beef and mutton in real time. The CNN, trained on 9,928 images, achieved 99% accuracy, outperforming models like ResNet-50, SVM, and KNN. The system uses three gas sensors (MQ135, MQ4, MQ136) to detect gases such as ammonia, methane, and hydrogen sulfide, which indicate meat spoilage.
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192 Views
The TiHAN-V2X Dataset was collected in Hyderabad, India, across various Vehicle-to-Everything (V2X) communication types, including Vehicle-to-Vehicle (V2V), Vehicle-to-Infrastructure (V2I), Infrastructure-to-Vehicle (I2V), and Vehicle-to-Cloud (V2C). The dataset offers comprehensive data for evaluating communication performance under different environmental and road conditions, including urban, rural, and highway scenarios.
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246 Views
The dataset provides crop-type surveys for Canada's prairie provinces (Manitoba, Saskatchewan and Alberta) in 2020 and 2021. The data were collected via windshield survey(driving through the countryside with GPS-enabled data collection software and satellite imagery). Crop-type points and their geographic coordinates on the ground were gathered using data collection software. Field boundaries were identified on satellite imagery. A single observation point is dropped in a homogeneous area within the field.
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183 Views
Endemic fish species are key components in seafood culinary excursions. Despite the increasing interest in leveraging technology to enhance various seafood culinary activities, there is a shortage of comprehensive datasets containing images of seafood used in artificial intelligence research, mainly those showcasing endemic fish. This research endeavors to bridge this gap by increasing the accuracy of fish recognition and introducing a new dataset comprising images of native fish for application in various machine-learning investigations.
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157 Views
This dataset consists of MRI images of brain tumors, specifically curated for tasks such as brain tumor classification and detection. The dataset includes a variety of tumor types, including gliomas, meningiomas, and glioblastomas, enabling multi-class classification. Each MRI scan is labeled with the corresponding tumor type, providing a comprehensive resource for developing and evaluating machine learning models for medical image analysis. The data can be used to train deep learning algorithms for brain tumor detection, aiding in early diagnosis and treatment planning.
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447 Views