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This represents a comprehensive collection of data from a Automative manufacturing unit. This unit could be involved in a range of production activities, from assembly line manufacturing to more complex, multi-stage processes. The dataset is designed to capture various operational parameters that are crucial for analyzing and optimizing manufacturing processes.
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The dataset contains data obtained by measuring hand movements while performing the letters of the Polish Sign Language alphabet. It contains data from 16 users performing all 36 letters ten times. Each single execution of a gesture is recorded in 75 samples. The experiment also included data augmentation, multiplying the number of data by 200. times.
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The CF algorithm is combined to generate personalized English text reading recommendations for various long-tail user groups. By optimizing the recommendation generation process, the recommendation accuracy of the model is enhanced, and the recommendation performance and user satisfaction of the English text reading recommendation system are improved. The Top-N algorithm model is compared with the algorithm model based on matrix decomposition in terms of recommendation accuracy and F-Measure value, and the advantages of the proposed algorithm model are proved.
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This dataset records the assessment of the effectiveness of learning objects in statistical education within nursing degree programs. It includes observations from 54 students with the following variables: - diagnostico_institucional: Assessment by the educational institution. - pre_test: Knowledge assessment prior to the educational intervention. - post_test: Knowledge assessment following the educational intervention. - edad: Age of the students. - campus: Campus of the institution where education is conducted. - sede: University site grouping several campuses together.
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To make it possible for the model to distinguish the connection between requirements and the software architecture pattern during training using GAI, the expected response for a specific requirement was labeled with a software architecture pattern with the prefix “Software architecture pattern: ” and its explanation with the prefix “Explanation: ”.
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We have obtained data from May 2022 to October 2023 for our suggested framework modelling. This set of data incorporates seasonality-related speech, which we convert into text, Facebook, and Twitter posts. On the whole, 4646 data elements have been acquired, comprising 3716 representing affected individuals and the remainder of 930 representing unaffected individuals, which generated a proportional 4:1 ratio.
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The provided dataset appears to contain weather-related information for New Delhi Safdarjung, India, spanning from January 1, 2023, to July 21, 2023. The dataset includes the following columns: Station ID, Station Name, Date, Precipitation (PRCP), Average Temperature (TAVG), Maximum Temperature (TMThe dataset includes daily observations with information on precipitation and temperature. It seems that some values are missing (NULL values), and there are variations in the units used for precipitation AX), and Minimum Temperature (TMIN).
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In the contemporary cybersecurity landscape, robust attack detection mechanisms are important for organizations. However, the current state of research in Software-Defined Networking (SDN) suffers from a notable lack of recent SDN-OpenFlow-based datasets. Here we introduce a novel dataset for intrusion detection in Software-Defined Networking named SDNFlow. The dataset, derived from OpenFlow statistics gathered from real traffic, integrates a comprehensive range of network activities.
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In medical applications, machine learning often grapples with limited training data. Classical self-supervised deep learning techniques have been helpful in this domain, but these algorithms have yet to achieve the required accuracy for medical use. Recently quantum algorithms show promise in handling complex patterns with small datasets. To address this challenge, this study presents a novel solution that combines self-supervised learning with Variational Quantum Classifiers (VQC) and utilizes Principal Component Analysis (PCA) as the dimensionality reduction technique.
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