artificial intelligence
This is a dataset that contains the testing results presented in the manuscript "Exploring the Potential of Offline LLMs in Data Science: A Study on Code Generation for Data Analysis", and it aims to assess offline LLMs' capabilities in code generation for data analytics tasks. Best utilization of the dataset would occur after thorough understanding of the manuscript. A total of 250 testing results were generated. They were merged, leading to the creation of this current dataset.
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This dataset supports a systematic review on the integration of Digital Twins (DT), Extended Reality (XR), and Artificial Intelligence (AI) in Reconfigurable Manufacturing Systems (RMS). The data was collected during a search performed on March 3, 2024, using the Scopus database. Articles published since 2018 were screened based on predefined inclusion and exclusion criteria, resulting in 37 articles selected for qualitative analysis.
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C-SRR Radiation Patterns. The pixels from the radiation pattern generated by positioning the C-SRR over the phantom model with and without cancerous tissue were extracted using various window shapes and sizes to form the dataset. For this, pixel sampling operations such as average, minimum, maximum, and median are performed. Pixel reconfiguration to triangle, square, symmetric and asymmetric is also performed. In average pixel sampling, the average value of the pixel color is divided by the total number of pixels.
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The increasing prevalence of encrypted traffic in
modern networks poses significant challenges for network security,
particularly in detecting and classifying malicious activities
and application signatures. To overcome this issue, deep learning
has turned out to be a promising candidate owing to its ability
to learn complex data patterns. In this work, we present a
deep learning-based novel and robust framework for encrypted
traffic analysis (ETA) which leverages the power of Bidirectional
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173 Views
To provide a standardized approach for testing and benchmarking secure evaluation of transformer-based models, we developed the iDASH24 Homomorphic Encryption track dataset. This dataset is centered on protein sequence classification as the benchmark task. It includes a neural network model with a transformer architecture and a sample dataset, both used to build and evaluate secure evaluation strategies.
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161 Views
Endemic fish species are key components in seafood culinary excursions. Despite the increasing interest in leveraging technology to enhance various seafood culinary activities, there is a shortage of comprehensive datasets containing images of seafood used in artificial intelligence research, mainly those showcasing endemic fish. This research endeavors to bridge this gap by increasing the accuracy of fish recognition and introducing a new dataset comprising images of native fish for application in various machine-learning investigations.
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172 Views
To download this dataset without purchasing an IEEE Dataport subscription, please visit: https://zenodo.org/records/13896353
Please cite the following paper when using this dataset:
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725 Views
ABSTRACT: Gender inequality continues to hinder the full social and economic potential of women, particularly in developing countries. This study examines the role of artificial intelligence in addressing gender disparities within micro, small, and medium-sized enterprises in India. Utilizing a cross-sectional quantitative methodology, data was collected from 300 MSMEs through stratified random sampling to evaluate AI's influence on gender diversity and the reduction of gender-based discrimination.
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This dataset was developed in the context of the NANCY project and it is the output of the experiments involving cyberattacks against services that are running in a 5G coverage expansion scenario. The coverage expansion scenario involves a main operator and a micro-operator which extends the main operator’s coverage and can also provide additional services, such as Artificial Intelligence-based cyberattack detection.
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251 Views
The advancement and ubiquity of digital networks have fundamentally transformed numerous spheres of human activity. At the heart of this phenomenon lies the Transmission Control Protocol (TCP) model, whose influence is particularly notable in the exponential growth of the Internet due to its potential ability to transmit flexibly through an advanced Congestion Control (CC). Seeking an even more efficient CC mechanism, this work proposes the construction of Deep Learning Neural Networks (MLP, LSTM, and CNN) for classifying network congestion levels.
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156 Views