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Brain-Computer Interface (BCI) is a technology that enables direct communication between the brain and external devices, typically by interpreting neural signals. BCI-based solutions for neurodegenerative disorders need datasets with patients’ native languages. However, research in BCI lacks insufficient language-specific datasets, as seen in Odia, spoken by 35-40 million individuals in India. To address this gap, we developed an Electroencephalograph (EEG) based BCI dataset featuring EEG signal samples of commonly spoken Odia words.
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To test the effectiveness of different ambiguity models in representing real decision-making under ambiguity, we ran an incentivized experiment of choice under ambiguity. The study involved 310 participants recruited using the online international labor market, Amazon Mechanical Turk (MTurk), to participate in an experimental study implemented on the survey platform, Qualtrics. Each of the 310 subjects made 150 preference choices between two options involving variations of the four ambiguity problems with varying levels of ambiguity and risk.
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This Dataset used a non-invasive blood group prediction approach using deep learning. Rapid and meticulous prediction of blood type is a major step during medical emergency before supervising the red blood cell, platelet, and plasma transfusion. Any small mistake during transfer of blood can cause death. In conventional pathological assessment, the blood test is conducted using automated blood analyser; however, it results into time taking process.
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In practical media distribution systems, visual content usually undergoes multiple stages of quality degradation along the delivery chain, but the pristine source content is rarely available at most quality monitoring points along the chain to serve as a reference for quality assessment. As a result, full-reference (FR) and reduced-reference (RR) image quality assessment (IQA) methods are generally infeasible. Although no-reference (NR) methods are readily applicable, their performance is often not reliable.
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In practical media distribution systems, visual content usually undergoes multiple stages of quality degradation along the delivery chain, but the pristine source content is rarely available at most quality monitoring points along the chain to serve as a reference for quality assessment. As a result, full-reference (FR) and reduced-reference (RR) image quality assessment (IQA) methods are generally infeasible. Although no-reference (NR) methods are readily applicable, their performance is often not reliable.
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Guava fruit production is one of the main sources of economic growth in Asian countries, the world production of guava in 2019 was 55 million tons. Guava disease is an important factor in economic loss as well as quantity and quality of guava. The original guava fruit disease dataset consist of 38 images of phytophthora, 30 images of root and 34 images of scab guava disease with 650x650x3 pixel.
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This dataset contains solar radiation data at the high resolution of 1 second for consecutive 72 hours under dynamic weather conditions (sunny and cloudy days). This data can be useful in various fields and researchers can use it in their research for algorithms validation and performance evaluation.
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Dataset Description
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This data set is a fisheye lens video file of important intersections in Hsinchu City, provided by Hsinchu City Police Department. Original video reference:
- (This video is from the fish-eye in front of Hsinchu Railway Station on Linsen Road 2019/11/15) https://youtu.be/S-tYTAaQgIs
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Contains traffic data sets of four intersections in Hsinchu City, including about 500 images and about 5,000 point annotations
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