Artificial Intelligence
Liver tumor segmentation.
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<p>There is presently no solution to the problem of an autonomous bulldozer pushing mounds of material to desired goal locations in the presence of obstacles whilst obeying the kinematic constraints of the bulldozer. Past work has solved some aspects of this problem, but not all. This research presents the first complete, practical solution to the problem. It works by creating a fixed RRT in advance, and then during operation connecting pushing poses into this RRT using Bezier curves.
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We present an Arabic Twitter dataset for online extremism detection consisting of 89K tweets with associated metadata. The dataset was manually annotated by three experts and achieved a Gwet’s AC1 score of 0.6, indicating substantial inter-annotator agreement. We performed further analysis of the tweet metadata to identify important features. For the extremism dataset, there were 89,816 tweets in total published by 52,929 unique users.
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Industrial Internet of Things (IIoTs) are high-value cyber targets due to the nature of the devices and connectivity protocols they deploy. They are easy to compromise and, as they are connected on a large scale with high-value data content, the compromise of any single device can extend to the whole system and disrupt critical functions. There are various security solutions that detect and mitigate intrusions.
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Brain-Computer Interface (BCI) has become an established technology to interconnect a human brain and an external device. One of the most popular protocols for BCI is based on the extraction of the so-called P300 wave from EEG recordings. P300 wave is an event-related potential with a latency of 300 ms after the onset of a rare stimulus. In this paper, we used deep learning architectures, namely convolutional neural networks (CNNs), to improve P300-based BCIs.
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This dataset is in support of my Research paper 'Detection of Pancreatic,Ovarian & Prostate Tumor, Cancer and Treatment by Ablation'.Due to computer crash, all work, datasets and old papers lost. Re-work may be submitted.
For Machine design, pls refer, open-access page 'Data and Designs of B-Machines'
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This document describes the details of the BON Egocentric vision dataset. BON denotes the initials of the locations where the dataset was collected; Barcelona (Spain); Oxford (UK); and Nairobi (Kenya). BON comprises first-person video, recorded when subjects were conducting common office activities. The preceding version of this dataset, FPV-O dataset has fewersubjects for only a single location (Barcelona). To develop a location agnostic framework, data from multiple locations and/or office settings is essential.
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Due to the multi-path propagation and extreme sensitivity to minor changes in the propagation medium, the coda waves open new fascinating possibilities in non-destructive evaluation and acoustic imaging. However, their noise-like structure and high spurious sensitivity for ambient conditions (temperature, humidity, and others) make it challenging to perform localized inspection in the overall coda wave evolution.
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Water consumption. Data recorded between 2017.1.1 and 2019.12.31.
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