Artificial Intelligence

The steel tube dataset comprises comprehensive information on various attributes related to steel tubes, encompassing dimensions, material composition, manufacturing processes, and performance characteristics. This dataset facilitates in-depth analysis of steel tube properties, aiding researchers, engineers, and industry professionals in optimizing designs, ensuring structural integrity, and advancing materials science in the context of steel tube applications.

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The Web has long become a major platform for online criminal activities. URLs are used as the main vehicle in this domain. To counter this issues security community focused its efforts on developing techniques for mostly blacklisting of malicious URLs.

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To make it possible for the model to distinguish the connection between requirements and the software architecture pattern during training using GAI, the expected response for a specific requirement was labeled with a software architecture pattern with the prefix “Software architecture pattern: ” and its explanation with the prefix “Explanation: ”.

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Zip includes commented Jupyter notebook with associated data files. Abstract for paper is:

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Zip includes commented Jupyter notebook with associated data files. Abstract for paper is:

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The dataset Provides S-parameter measurements of an AI enhanced wireless power transfer system using two ISO/ICE 14443-1 Coils with series capacitance compensation at 13.56 MHz under three configurations of vertical and horizontal misalignment, inter-coil distance, and azimuthal tilt. The structure and components of the system is shown in the Image attached to the Dataset This measured data validates the implementation of the system at the three coil configurations discussed in the publication.

 

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Wearable devices, such as data gloves and electronic skins, can perceive human hand's actions, behaviors and even emotions with the help of knowledge learning and inference. Curvature or magnetism sensing in such devices often lacks comprehensive gesture interactive information, meanwhile, the limited computing power of wearable applications restricts the multi-mode fusion of different sensing data and the deployment of deep learning networks.

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We have obtained data from May 2022 to October 2023 for our suggested framework modelling. This set of data incorporates seasonality-related speech, which we convert into text, Facebook, and Twitter posts. On the whole, 4646 data elements have been acquired, comprising 3716 representing affected individuals and the remainder of 930 representing unaffected individuals, which generated a proportional 4:1 ratio.

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The existing datasets lack the diversity required to train the model so that it performs equally well in real fields  under varying environmental conditions. To address this limitation, we propose to collect a small number of in-field data and use the GAN to generate synthetic data for training the deep learning network. To demonstrate the proposed method, a maize dataset 'IIITDMJ_Maize'  was collected using a drone camera under different weather conditions, including both sunny and cloudy days. The recorded video was processed to sample image frames that were later resized to 224 x 224.

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