Machine Learning
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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The dataset consists of samples of DDoS attacks. The samples were generated either by dedicated tools such as Loic, Hulk, Thorshammer, or combined from publicly available source such as from DDoS Evaluation Dataset (CIC-DDoS2019).
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To train the machine learning model, a dataset was generated containing data for «Budennovskoye» field, part of which is shown in title figure. (AR and SP are given for 90 centimeter intervals, for which, in turn, the actual values K_fpo. obtained by pumping out (pump out) was determined. As a result, the input variable set consisted of 19 values, including the rock code (AR, SP). The target column isK_f_pump_out .
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This data set contains tweets related to haptics from 2008 to 2021.
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