Machine Learning
A recent study [1] alerts on the limitations of evaluating anomaly detection algorithms on popular time-series datasets such as Yahoo, Numenta, or NASA, among others. In particular, these datasets are noted to suffer from known flaws suchas trivial anomalies, unrealistic anomaly density, mislabeled ground truth, and run-to-failure bias. The TELCO dataset corresponds to twelve different time-series, with a temporal granularity of five minutes per sample, collected and manually labeled for a period of seven months between January 1 and July 31, 2021.
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The dataset was generated through the execution of a Python script designed to collect a comprehensive set of data samples from six different sensors for each specific gesture. Upon launching the script, users are prompted to initiate gesture 0, Once ready, users can commence recording, with the program automatically capturing 1000 samples for that particular gesture. Subsequently, the program prompts users to perform gesture 1, and this process repeats until data for all gestures is collected.
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The Sentinel-2 L2A multispectral data cubes include two regions of interest (roi1 and roi2) each of them containing 92 scenes across Switzerland within T32TLT, between 2018 and 2022, all band at 10m resolution These areas of interest show a diverse landscape, including regions covered by forests that have undergone changes, agriculture and urban areas.
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Human biomechanics is still an active topic of research that requires more technological advancements and data collection of various human body movements. There is a need for methodologies to identify daily activities in various scenarios, such as one while carrying a school bag. Deakin university has developed an Internet of Things (IoT) enabled smart school bag consisting of motion analysis sensors that would recognize the activities performed while carrying the school bag.
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This comprehensive dataset comprises multiple files, encompassing essential information on various aspects of power systems. It includes the active and reactive power consumption data for both the 33- and 136-bus test systems, along with the resistance and reactance values of the distribution lines, and the network structure.
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Set of samples resulting from an evaluative, population-based cross-sectional study, approved by the Research Ethics Committee of Federal University of Alfenas, protocol: 415856. These samples pertain to 1,027 rural workers from the southern region of Minas Gerais, Brazil, where comprehensive data on socioeconomic factors, history of pesticide intoxication and hospitalization, and usage of personal protective equipment were collected. Blood samples were obtained to measure biomarkers of pesticide exposure as well as indicators of renal and hepatic sequelae.
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Seven years of water consumption data, along with population data, were manually collected in collaboration with the local municipality office. This data was then combined with climatic data to model the proposed machine learning algorithm. The weather data was recorded for a period of 7 years using precise meteorological instruments installed in Islamabad at coordinates 33.64° N and 72.98° E, with an elevation of 500 meters above sea level.
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We present the RQMD dataset, a comprehensive collection of diverse material samples aimed at advancing computer vision and machine learning algorithms in terrain classification tasks. This dataset contains RGB images of 5 different terrains, such as Asphalt, Brick, Grass, Gravel, and Tiles, captured using an 8-megapixel Raspberry Pi camera from a top-view perspective. Notably, the dataset encompasses images taken at different times of the day, introducing variations in lighting conditions and environmental factors.
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The significance of having sustainable water quality data cannot be overstated. It plays a crucial role in comprehending the historical variations and patterns in river conditions and also helps in understanding how industrial waste impacts the well-being of aquatic ecosystems. To achieve sustainable water management practices, it is imperative to rely on dependable and extensive data. Therefore, accurate monitoring and assessment of various water quality parameters become essential.
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