Machine Learning

The detection of anomalous structures in natural image data is of utmost importance for numerous tasks in the field of computer vision. The development of methods for unsupervised anomaly detection requires data on which to train and evaluate new approaches and ideas. We introduce the MVTec Anomaly Detection (MVTec AD) dataset containing 5354 high-resolution color images of different object and texture categories. It contains normal, i.e., defect-free, images intended for training and images with anomalies intended for testing.

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188 Views

According to US NOAA, unexploded ordnances (UXO) are ”explosive weapons such as bombs, bullets, shells, grenades, mines, etc. that did not explode when they were employed and still pose a risk of detonation”. UXOs are among the most dangerous, threats to human life, environment and wildlife protection as well as economic development. The risks associated with UXOs do not discriminate based on age, gender, or occupation, posing a danger to anyone unfortunate enough to encounter them.

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1314 Views

Maternal, sexual and reproductive healthcare (MSRH) are sensitive urgent public health issues that require timely trustworthy authentic medical responses. Unfortunately, curative healthcare systems of Low Middle-Income Countries (LMICs) are insufficiently responsive to such healthcare needs. Such needs vary among social groups often founded on social inequalities like income, gender and education.

Last Updated On: 
Sun, 06/30/2024 - 14:01

In the era of advanced artificial intelligence, the integration of emotional intelligence into AI systems has become crucial for developing Responsible Software Systems that are not only functional but also emotionally perceptive. The Microe dataset, a pioneering compilation focusing on micro-expressions, aims to revolutionize AI systems by enhancing their capability to recognize and interpret subtle emotional cues. This dataset encompasses over eight classes of common emotions, meticulously captured and categorized to aid in the synthesis and recognition of micro-expressions.

Last Updated On: 
Tue, 07/16/2024 - 11:30

In the realm of global agriculture, the imperative of sustaining an ever-expanding population is met with challenges in optimizing crop production and judicious resource management. SmartzAgri heralds a groundbreaking approach to modern agriculture. This innovative system represents a convergence of machine learning algorithms and Internet of Things (IoT) technology, aimed at reshaping traditional paradigms of crop recommendation.

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3417 Views

While deep learning has catalyzed breakthroughs across numerous domains, its broader adoption in clinical settings is inhibited by the costly and time-intensive nature of data acquisition and annotation. To further facilitate medical machine learning, we present an ultrasound dataset of 10,223 Brightness-mode (B-mode) images consisting of sagittal slices of porcine spinal cords (N=25) before and after a contusion injury.

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20 Views

The removal of surgical tools from the brain is a critical aspect of post-operative care. Surgical sponges such as cotton balls are one of the most commonly retained tools, as they become visually indistinguishable from the surrounding brain tissue when soaked with blood and can fragment into smaller pieces. This can lead to life-threatening immunological responses and invasive reoperation, demonstrating the need for new foreign body object detection methods.

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45 Views

Modern automotive embedded systems include a large number of electronic control units (ECU) responsible for managing sophisticated systems such as engine control, ABS brake systems, traction control, and power steering systems. To ensure the reliability and effectiveness of these functions, it is essential to apply rigorous test approaches and standards. The integration of diagnostic functions in automotive embedded systems demands consistent tests and a detailed analysis of data.

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165 Views

The major language used on social media platforms is primarily dialectal, posing unique challenges for Natural Language Processing. To address this, a large, manually annotated corpus of approximately 30,500 Saudi dialect tweets in the food delivery app domain was introduced. The corpus was annotated with positive, negative, and neutral sentiment categories. Additionally, the existing SauDiSenti lexicon was expanded by 30%, providing an improved resource for sentiment analysis in the Saudi dialect. the corpus and expanded lexicon have been evaluated using machine learning classifiers.

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82 Views

In order to train the joint contrastive representation learning module, we constructe a large Text Annotated Distortion, Appearance and Content (TADAC) image database.

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380 Views

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