Computational Intelligence

The dataset contains Software Development Effort Estimation (SDEE) metrics values extracted from around 1800 Open Source Software (OSS) repositories of GitHub.
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The present dataset is based on implementing of 3 approaches with respect to the acquisition of driver data. The same one that we propose to use a sensor of concentration of alcohol in the environment (physiological), a sensor that measure the temperature of the defined points on driver’s face (biological) and another one that allows to identify and recognize the thickness of the pupil (visual characteristics).
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Copyright (C) 2016-2019 Shandong University
Dataset for "An artificial fusion intelligence-driven approach to energy-aware scheduling of stochastic nonlinear heterogeneous super-systems”
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Design of novel RF front-end hardware architectures and their associated measurement algorithms.
Research objectives, includes:
RO1: Novel architecture based upon Adaptive Wavelet Band-pass Sampling (AWBS) of RF Analog-to-Information Conversion (AIC).
RO2: Integration of AWBS for increasing the wideband sensing capabilities of real-time spectrum analyzers by using AICs.
RO3: Propose online calibration methods and algorithms for front-end hardware non-idealities compensation.
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This database has five different linter quality classes (short cotton fibers), linter has wide applicability in the production of surgical tissue, paper money among other applications. The images available were used to classify the product in an industrial process, through the use of computer vision techniques.
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We photographed Giemsa-stained thick blood smear slides from 150 P. falciparum infected patients at Chittagong Medical College Hospital, Bangladesh, using a smartphone camera for the different microscopic field of views. Images are captured with 100x magnification in RGB color space with a 3024×4032 pixel resolution. An expert slide reader manually annotated each image at the Mahidol-Oxford Tropical Medicine Research Unit (MORU), Bangkok, Thailand. We de-identified all images andtheir annotations, and archived them at the National Library of Medicine (IRB#12972).
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Abstract
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Emergency managers of today grapple with post-hurricane damage assessment that is often labor-intensive, slow,costly, and error-prone. As an important first step towards addressing the challenge, this paper presents the development of benchmark datasets to enable the automatic detection ofdamaged buildings from post-hurricane remote sensing imagerytaken from both airborne and satellite sensors. Our work has two major contributions: (1) we propose a scalable framework to create benchmark datasets of hurricane-damaged buildings
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