Machine Learning

We use a total of 16 datasets, detailed descriptions of which are provided in Table II. Among them, 11 datasets are from the UCI database, the DLBCL-Harvard dataset is from the ELVIRA biomedical database, Yale and ORL

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The training trajectory datasets are collected from real users when exploring the volume dataset on our interactive 3D visualization framework. The format of the training dataset collected is trajectories of POVs in the Cartesian space. Multiple volume datasets with distinct spatial features and transfer functions are used to collect comprehensive training datasets of trajectories. The initial point is randomly selected for each user. Collected training trajectories are cleaned by removing POV outliers due to users' misoperations to improve uniformity.

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<p>This dataset contains symptoms and disease information. It contains total of 1325 symptoms covered with 391 disease.This dataset is refernced from website MedLinePlus. This dataset have training and testing dataset and can be used to train disease prediction algorithm . It is created on own for project disease prediction and do not involves any funding or promotional terms.</p>

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Subjects are categorized into three groups based on office blood pressure threshold: Normal (N), Prehypertension (P), and Stage 1 Hypertension (S). Each group contains 100 subjects, and all records have duration of at least 8 minutes. This study uses sliding window with length of 1 second and step size of 1 second to segment records. PPG, ECG and BP yield 167432 segments, respectively. MAP, DBP, and SBP are defined as average, minimum, and maximum of each BP segment, respectively. Max-Min normalization is applied to PPG and ECG segments. 

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This dataset comprises three benchmarks: Digits-5, PACS, anf office_caltech_10. Digits-5 is a set of handwritten digit images sampled from five domains: MNIST, MNIST-M, USPS, SynthDigits, and SVHN.  All sample are images of numbers ranging from 0 to 9.  PACS is composed of four different datasets, each representing a different visual domain: Photo, Art Painting, Cartoon, and Sketch. It contains 9,944 images, including 1,792 real photos, 2,048 art paintings, 2,344 cartoon images, and 2,760 sketches.

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Bengaluru has been ranked the most congested city in India in terms of traffic for several years now. This hackathon is aimed at creating innovative solutions to the traffic management problem in Bengaluru, and is being co-organised by the Bengaluru Traffic Police, the Centre for Data for Public Good, and the Indian Institute of Science (IISc). The prizes are being sponsored by the IEEE Foundation.

Last Updated On: 
Thu, 10/17/2024 - 05:18
Citation Author(s): 
Raghu Krishnapuram, Rakshit Ramesh, and Arun Josephraj

Numerous studies have focused on exploring Android malware in recent years, covering areas such as malware detection and application analysis. As a result, there is a pressing need for a reliable and scalable malware dataset to support the development and evaluation of effective malware studies. Although several benchmarks for Android malware datasets are widely used in research, they have significant limitations. Firstly, many of these datasets are outdated and do not capture current malware trends. Additionally, some have become obsolete or inaccessible, limiting their usefulness.

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Health degradation issues in automotive power electronics converter systems (PECs) arise due to repetitive thermomechanical stress experienced during real-world vehicle operation. This stress, caused by heat generated during semiconductor operation within PECs, leads to the degradation of semiconductor's operating life. Estimating the power semiconductor junction temperature (Tj) is crucial for assessing semiconductor degradation in operation. Although physics-of-failure-based models can estimate Tj, they require substantial computational power.

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Wild-SHARD presents a novel Human Activity Recognition (HAR) dataset collected in an uncontrolled, real-world (wild) environment to address the limitations of existing datasets, which often need more non-simulated data. Our dataset comprises a time series of Activities of Daily Living (ADLs) captured using multiple smartphone models such as Samsung Galaxy F62, Samsung Galaxy A30s, Poco X2, One Plus 9 Pro and many more. These devices enhance data variability and robustness with their varied sensor manufacturers.

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This dataset consists of near-infrared spectral images of eight different varieties of corn seeds, classified as FH759, JL59,JY54,JY205, LH205,XX5, ZY2207, SY81. Each variety contains images of embryonic and endosperm surfaces, with 50 samples per image. The wavelength range is 881-1715 nm.

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