Machine Learning
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We elaborate on the dataset collected from our testbed developed at Washington University in St. Louis, to perform real-world IIoT operations, carrying out attacks that are more prelevant against IIoT systems. This dataset is to be utilized in the research of AI/ML based security solutions to tackle the intrusion problem.
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The credit risk evaluation data generated by a commercial bank’s personal consumption loans.
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This dataset contains actual field/experimental data for the following environmental engineering applications, namely:
- Concentration data generated from filtration systems which treat influents, having contaminant materials, via adsorption process.
- Streamflow height data collated for 50 states/cities in America for the historical period between 1900-2018.
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Computer vision and image processing have made significant progress in many real-world applications, including environmental monitoring and protection. Recent studies have shown that computer vision and image processing can be used to quantify water turbidity, a crucial physical parameter in water quality assessment. This paper presents a procedure to determine water turbidity using deep learning methods, specifically, convolutional neural network (CNN). At first, water samples were located inside a dark cabin before digital images of the samples were captured with a smartphone camera.
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This dataset is extracted from GitHub and contains 172,919 java source codes written by 3,128 authors. It can be used for authorship attribution.
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a novel two-electrode, frequency-scan electrical impedance tomography (EIT) system for gesture recognition
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This dataset was produced as a part of my PhD research on Android malware detection using Multimodal Deep Learning. It contains raw data (DEX grayscale images), static analysis data (Android Intents & Permissions), and dynamic analysis data (system call sequences). For the conference research paper, please refer to https://sbic.org.br/eventos/cbic_2021/cbic2021-32/
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