Artificial Intelligence
Anxiety affects human capabilities and behavior as much as it affects productivity and quality of life. It can be considered as the main cause of depression and suicide. Anxious states are easily detectable by humans due to their acquired cognition, humans interpret the interlocutor’s tone of speech, gesture, facial expressions and recognize their mental state. There is a need for non-invasive reliable techniques that perform the complex task of anxiety detection.
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Character recognition has been widely understood as a means of mechanizing the process of understanding text in the written form to facilitate fast and efficient use of text. Indeed, text existing all around us presents information for peoples. However, tourists in foreign countries are unable to understand what indicate text on road signs, shop names, product advertisements, posters, etc. when they are unfamiliar with the native language of the visited country.
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The ADAB database (The Arabic handwriting Data Base) was developed to advance the research and development of Arabic on-line handwritten systems. This database is developed in cooperation between the Institut fuer Nachrichtentechnik (IfN) and Research Groups in Intelligent Machines, University of Sfax, Tunisia. The text written is from 937 Tunisian town/village names. A pre-label assigned to each file consists of the postcode in a sequence of Numeric Character References, which stored in the UPX file format.
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Our dataset, which is Nepali news dataset, contains 17 categories, including Art, Bank, Blog, Business, Diaspora, Entertainment, Filmy, Health, Hollywood-bollywood, Koseli, Literature, Music, National, Opinion, Society, Sports, and World.
If you use this dataset, please cite our paper.
Sitaula C, Basnet A, Aryal S. 2021. Vector representation based on a supervised codebook for Nepali documents classification. PeerJ Computer Science 7:e412 https://doi.org/10.7717/peerj-cs.412
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The dataset consists all the Telugu characters that contains Vowels, Consonants and combine characters such as Othulu (Consonant-Consonant) and Guninthamulu (Consonant-Volwels). The main objective of this dataset to recognize handwritten Telugu characters, from that convert handwritten document into editable electronic copy.
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4539 Views
Training, Test, and Validation data pertaining to the real-time packet data captured in Sonic Firewall is attached herewith.
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217 Views
The select_mmsi.xls file is the MMIS number were selected. The information include the gear type of each vessel, the startdate and enddate of random selected 5000 position points. The txt files are the raw information of each position points for all vessel.
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The data collection was carried out over several months and across several cities including but not limited to Quetta, Islamabad and Karachi, Pakistan. Ultimately, the number of images collected as part of the Pakistani dataset were, albeit in a very small quantity. The images taken were also distributed across the classes unevenly, just like the German dataset. All the 359 images were then manually cropped to filter out the unwanted image background data. All the images were sorted into folders with names corresponding to the label of the images.
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This is a supplementary data file, providing the data used to evaluate the performance of our 3D fully convolutional neural network. This network removes reverberation noise from ultrasound channel data. This dataset is simulated ultrasound channel data, simulated in Field II Pro, and has artificial reverberation and thermal noise added. This dataset will be linked to our publication, once it is accepted.
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428 Views
Holoscopic micro-gesture recognition (HoMG) database was recorded using a holoscopic 3D camera, which have 3 conventional gestures from 40 participants under different settings and conditions. The principle of holoscopic 3D (H3D) imaging mimics fly’s eye technique that captures a true 3D optical model of the scene using a microlens array. For the purpose of H3D micro-gesture recognition. HoMG database has two subsets. The video subset has 960 videos and the image subset has 30635 images, while both have three type of microgestures (classes).
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554 Views