Artificial Intelligence

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SYPHAX dataset was collected from Tunisia in “Sfax” city, the second largest Tunisian city after the capital. A total of 2008 images were gathered through manual collection one by one, with each image energizing text detection challenges in nature according to real existing complexity of 15 different routes (downtown, Nasryia, Sidi Mansour, Sakiet Ezziet, Sakiet Eddayer, Mahdia road, Tunis main road, Chehia, Taniour, Lafran, Elayn, Gremda, Manzel Chaker, Matar route, Gabes road) along with ring roads, intersections and roundabouts.

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The e-nose device used in this study was constructed using a gas sensor array, LCD display, micro air pumps for inhalation and exhalation, a microcontroller, and a mini-PC. Gas samples from the sample chamber were periodically drawn into the device through a hose. Each sample underwent a 30-hour sampling process at room temperature (25°C). The sampling frequency was 15 times per hour, resulting in 60 records per sample.

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SCD Dataset: This new dataset has been specifically created for the development and education of children with down syndrome. The dataset, containing a total of 13,500 Turkish question-answer pairs, has "positive" and "negative" emotion labels. In the context of human-robot interaction, accurately identifying and addressing positive and negative emotions has a significant impact on user experience and satisfaction. Neutral questions and answers provide less information in terms of sentiment analysis and are less relevant to the purpose of this study.

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The datasets are used to test an embedding imputation model. There are two different experiments: finance and mobile applications.

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A hybrid-field STAR-RIS dataset. we have provided the paired samples for hybrid-field cascaded channel estimation in STAR-RIS systems, in which the data preprocessing and normalization operations have been completed.

The simulation parameters of this dataset have been elaborated in our submitted paper. For instance, M_1 x M_2 = 4 x 8, N_1 x N_2 = 4 x 32, f_c = 28GHz, and Q=N/4.  The  description of each data file is listed as follows.

inHmix_28_32_128_K2_32pilot.mat: the training dataset and validation dataset in the ES protocol.<br/>

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This dataset was initially collected by Mrs Athira P K  with the help of  teachers and students of Rahmania school for handicapped, Kozhikode, Kerala, India. Later the dataset was extended by many other BTech and MTech students with the help of their friends.

MUDRA NITC dataset consists of videos of static and dynamic gestures of Indian sign language. In static gestures mainly static alphabets videos and  preprocessed image frames are included.

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In recent years, teaching-learning methods have emerged into a completely new dimension from what used to be a traditional approach. The in-person lectures have been converted into online virtual learning, the traditional record-keeping has been replaced by robust learning management systems which have made the teaching-learning process lot more efficient and convenient.

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These are the two lncRNA and protein interactions (LPI) datasets, which collected and built by Zhang et al. and Zheng et al., respectively.

dataset1: Wen Zhang, Qianlong Qu, Yunqiu Zhang, Wei Wang. The linear neighborhood propagation method for predicting long non-coding RNA-protein interactions. Neurocomputing 273: 526-534 (2018)

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