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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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448 Views
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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2307 Views
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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1324 Views
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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118 Views
The dataset encompasses an extensive collection of patient information, delving into their comprehensive medical background, encompassing a myriad of features that encapsulate not only the physical but also the mental and emotional states. Furthermore, the dataset is enriched with invaluable ECG data derived from the patients. Moreover, our dataset boasts additional features meticulously extracted from the ECG records, thereby enhancing the potential for our machine learning model to undergo more effective training with our rich and diverse data.
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610 Views
This data set comes from the MetaFilter website. The question ID data of the askme section is obtained through the official dump data. After selecting a specific category, the corresponding other data is obtained using the ID, including the question title, description, questioner, tags, and all comments.
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28 Views
Sensors (VSA001) are used to capture the vibration signals on the bearing (LDK UER20), and a sampling frequency is 25.6 KHz. Samples are collected by a interval of 60 seconds, the length of each sampling is 0.1 seconds, and each sample includes 2560 signals. Multiple sets of vibration signals in normal condition are collected at various time intervals to facilitate model fine-tuning, with a representation of the practical operating conditions. For ease of use, the data file format is .csv.
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406 Views
The UCI dataset is a data repository maintained and made available by the University of California, Irvine that is widely used for machine learning and data mining research. The dataset covers a wide range of fields and topics, including but not limited to medicine, biology, social sciences, physics, engineering, and more. The uniqueness of this dataset is that it contains data from multiple different domains and sources, allowing researchers to explore and analyze the data from different perspectives and contexts.
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1923 Views
Vehicle-to-Everything (V2X) potential to support Intelligent Transportation System (ITS) is challenged by its inherent high mobility, changing topology and consequently link instability. The quest to minimize the effect of changing topology has led centroid-based clustering algorithms to exploit Cluster Head (CH) longevity approaches to improve stability while compromising on throughput performance. Most K-means based schemes particularly reselect cluster seeds at every reclustering phase.
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230 Views
This dataset is used to predict whether a patient is likely to get stroke based on the input parameters like gender, age, various diseases, and smoking status. Each row in the data provides relavant information about the patient.
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5390 Views