Artificial Intelligence
PAPILA dataset contains fundus images and clinicaldata from 244 patients, with images from both eyes of each patient. This dataset is specifically designed to support research on early glaucoma diagnosis by leveraging comprehensive data from both eyes. Additionally, it includes segmentation information for each patient’s optic disc and cup, alongside diagnostic outcomes based on clinical data. For our analysis, we focused on images labeled as normal (0) and glaucoma (1),selecting data from 210 patients.
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The operator controls the vehicle to drive in an environment with dense distribution of obstacles. During the driving process, the spatial environment data is collected by liDAR and camera, and the map of the operable area is processed according to the change of longitudinal slope, which is used to show the distribution of the operable area in the current driving space of the vehicle. Then, according to the distribution of operable areas and driving behavior data, the study of humanoid driving is carried out.
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To download the dataset without purchasing an IEEE Dataport subscription, please visit: https://zenodo.org/records/13738598
Please cite the following paper when using this dataset:
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This dataset offers a comprehensive collection of financial data associated with the development of medical software, providing insights into the various cost components involved in creating and maintaining such systems. It encompasses expenses from the initial concept and design phase through to development, testing, deployment, and ongoing maintenance. The data has been meticulously gathered from a variety of completed and ongoing medical software projects, highlighting both typical and outlier cost scenarios.
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This dataset contains information about code smell, which is a very important issue in software engineering.
It is built by collecting the method having code smell from GitHub using the SonarCloud tool.
There are 5 code smells and 1 normal class with 500 examples each.
the metadata: method (function),smellkey, smellid
Smell Type
ID
Description
Reference
java:S100
0
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466 Views
5G cellular networks are particularly vulnerable against narrowband jammers that target specific control subchannels in the radio signal. One mitigation approach is to detect such jamming attacks with an online observation system, based on machine learning. We propose to detect jamming at the physical layer with a pre-trained machine-learning model that performs binary classification. Based on data from an experimental 5G network, we study the performance of different classification models.
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258 Views
This dataset contains audio recordings and transcriptions of toxic speech derived from Indonesian conversations during YouTube videos where scammers are confronted. The dataset captures two separate interactions that escalate into toxic exchanges. Each interaction has been verified by native Indonesian speakers and labeled into two classes: toxic and non-toxic. The dataset includes both the original and preprocessed versions of the speech and text data. The original speech files total 136MB, while the preprocessed speech files are 111,7MB.
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The dataset is derived from Monte Carlo simulations, generating EV charging power curves. For training the Physics-Informed Neural Networks (PINNs), we have statistically organized the data with the x-axis representing the State of Charge (SoC) state space, the y-axis representing time, and the z-axis representing the corresponding number of electric vehicles. The z-axis data has been normalized. The uploaded data is intended for training within the PINN framework to obtain the EV aggregation model and its parameters.
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Cora, Citeseer, and Pubmed are commonly used citation network datasets. Among these, Citeseer has the most dense features, while Pubmed has more nodes and edges.
ACM is a network of papers where each node represents a paper. In contrast to citation networks, edges connect papers that share the same authors.
Flickr serves as a social network that captures the connections between users originating from image and video hosting websites. These users are categorized into nine groups according to their personal interests.
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The limited availability of Guitar notes datasets hinders the training of any artificial intelligence model in this field. TaptoTab dataset aims to fill this gap by providing a collection of notes recordings. This dataset is collected as part of an honours project at the Faculty of Computer and Information Sciences, Ain Shams University. The dataset is composed of audio data that has been self-collected, focusing on capturing a comprehensive range of guitar notes. The dataset consists of recordings of guitar notes played on each of the six strings, covering up to the 12th fret.
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527 Views