artificial intelligence

The BirDrone dataset is compiled by aggregating images of small drones and birds sourced from various online datasets. It comprises 2970 high-resolution images (640x640 pixels), each featuring unique backdrops and lighting conditions. This dataset is designed to enhance machine learning models by simulating real-world scenarios.

 

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In deep learning, images are utilized due to their rich information content, spatial hierarchies, and translation invariance, rendering them ideal for tasks such as object recognition and classification. The classification of malware using images is an important field for deep learning, especially in cybersecurity. Within this context, the Classified Advanced Persistent Threat Dataset is a thorough collection that has been carefully selected to further this field's study and innovation.

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Nasal Cytology, or Rhinology, is the subfield of otolaryngology, focused on the microscope observation of samples of the nasal mucosa, aimed to recognize cells of different types, to spot and diagnose ongoing pathologies. Such methodology can claim good accuracy in diagnosing rhinitis and infections, being very cheap and accessible without any instrument more complex than a microscope, even optical ones.

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DIRS24.v1 presents a dataset captured in campus environment. These images are curated suitably for the utilization in developing perception modules. These modules can be very well employed in Advanced Driver Assistance Systems (ADAS). The images of dataset are annotated in diversified formats such as COCO-MMDetection, Pascal-VOC, TensorFlow, YOLOv7-PyTorch, YOLOv8-Oriented Bounding Box, and YOLOv9.

 

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This paper conducts a systematic bibliometric analysis in the Artificial Intelligence (AI) domain to explore privacy protection research as AI technologies integrate and data privacy concerns rise. Understanding evolutionary patterns and current trends in this research is crucial. Leveraging bibliometric techniques, the authors analyze 8,322 papers from the Web of Science (WoS) database, spanning 1990 to 2023.

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During our research in generating or optimizing molecules to be drug candidates by extending deep reinforcement learning and graph neural networks algorithms, we used GEOM data [1], and we had an idea to make a dataset obtained from molecules from GEOM to predit the activity towards COVID and the drug linkeness. We calculated over 200 descriptors for the molecules using RDKit [2]. We hope you enjoy using it.

 

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315 Views

This paper conducts a systematic bibliometric analysis in the Artificial Intelligence (AI) domain to explore privacy protection research as AI technologies integrate and data privacy concerns rise. Understanding evolutionary patterns and current trends in this research is crucial. Leveraging bibliometric techniques, the authors analyze 3,061 papers from the Web of Science (WoS) database, spanning 1994 to 2023.

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Internet-of-Things (IoT) technology such as Surveillance cameras are becoming a widespread feature of citizens' life. At the same time, the fear of crime in public spaces (e.g., terrorism) is ever-present and increasing but currently only a small number of studies researched automatic recognition of criminal incidents featuring artificial intelligence (AI), e.g., based on deep learning and computer vision. This is due to the fact that little to none real data is available due to legal and privacy regulations. Consequently, it is not possible to train and test deep learning models.

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This dataset comprises data created during research on AI-generated code, with a focus on software engineering use-cases. The purpose of the research was to investigate how AI should be integrated into university software engineering curricula.

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Overview

The dataset under consideration is a comprehensive compilation of code snippets, function descriptions, and their respective binary representations aimed at fostering research in software engineering. It contains a variety of code functionalities and serves as a valuable resource for understanding the behavior and characteristics of C programs. This data is sourced from the AnghaBench repository, a well-documented collection of C programs available on GitHub.

 

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