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The potato plant disease detection dataset comprises 5,748 images of potato leaves categorized into six classes: Early Blight (1,000), Fungi (748), Healthy (1,000), Late Blight (1,000), Pest (1,000), and Virus (1,000). The dataset was collected from various open-access sources and integrated class-wise for comprehensive analysis. This dataset provides a robust foundation for training and evaluating convolutional neural networks in plant disease detection.
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The Asian Subcontinent Dataset (ASCD) is a multisensor dataset that includes geospatial data collected from different countries within the Asian subcontinent, specifically India, Bangladesh, and Sri Lanka. The dataset consists of imagery captured through two main sources: Google Earth Pro software and IKONOS-2 satellite images. ASCD includes 14 distinct land cover classes, ranging from urban areas to natural environments, with 1,674 images. The number of images in each land cover class varies between 100 and 204.
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This dataset presents the recognition of handwritten hieroglyphic alphabets. In this dataset we use consisting of 18 distinct classes of hieroglyphs alphabets. The dataset is designed to facilitate research in the field of ancient script recognition, particularly focusing on handwriting variability and pattern recognition. Each class represents a unique hieroglyph, with samples collected to ensure a diverse range of writing styles. To create this dataset, 25 students each handwrote samples for all 18 classes of hieroglyphs. Afterward, we carefully photographed each image.
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As the world increasingly becomes more interconnected, the demand for safety and security is ever-increasing, particularly for industrial networks. This has prompted numerous researchers to investigate different methodologies and techniques suitable for intrusion detection systems (IDS) requirements. Over the years, many studies have proposed various solutions in this regard, including signature-based and machine learning (ML)-based systems. More recently, researchers are considering deep learning (DL)-based anomaly detection approaches.
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The Sketchy images refer to hand-drawn drawings, while SCIST are those with unclear or weak semantic information, represent a distinctive cases from natural scenes.The primary objective of this dataset is to facilitate the style transfer, whether originating from manual sketches or digital renderings, into enriched and artistically embellished counterparts through the utilization of software.
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This abstract presents a dataset focused on circular objects within a production line environment, with diameters ranging from 30 to 70 cm. The dataset is designed to facilitate the development and evaluation of computer vision algorithms tailored for detecting and analyzing circular objects in industrial settings. It comprises a diverse collection of images captured under varying lighting conditions, backgrounds, orientations, and scales, mimicking real-world production line scenarios.
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Image representation of Malware-benign dataset. The Dataset were compiled from various sources malware repositories: The Malware-Repo, TheZoo,Malware Bazar, Malware Database, TekDefense. Meanwhile benign samples were sourced from system application of Microsoft 10 and 11, as well as open source software repository such as Sourceforge, PortableFreeware, CNET, FileForum. The samples were validated by scanning them using Virustotal Malware scanning services. The Samples were pre-processed by transforming the malware binary into grayscale images following rules from Nataraj (2011).
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