*.csv ; *.xlsx
The Dravidian Spam SMS dataset has Spam and Ham messages in English, Tamil, Telugu, Kannada, and Malayalam languages. Nearly 7700 messages were collected by sending friends and other contacts a Google form. Language experts (reading and writing skills) were used to label the messages of corresponding languages carefully. The dataset also includes the Tamil verbatim messages written in English. For example, “Nee Nalama”. The Ham messages are mostly normal. Spam messages include business, annoying, and unnecessary messages an anonymous user sends.
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The following dataset contains social network (Vkontakte) profile characteristics of 1358 Russian-speaking subjects and measured psychological traits, verbal and fluid intelligence. The user’s profiles, posts and reposts were processed, emotional coloring, sentiment, intent characteristics were extracted from them. Each participant answered BigFive inventory, Raven’s advanced progressive matrices, Verbal intelligence test.
The Big Five Inventory in Russian adaptation7 was used for measuring five main domains.
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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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dataset for An open-loop solution for a stochastic problem with imperfect state information and chance-constraints adjusted by an optimal gain.
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Contains the SUS survey and application dataset and the UEQ application dataset.
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We propose a planning method for offshore wind farm electrical collector system (OWF-ECS) with double-sided ring topology meeting the “N-1” criterion on cable faults in the paper. This data set includes two OWF with 30/62 wind turbines, with candidate lines options to be chosen from/optimized.
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Gestational diabetes is a type of high blood sugar that develops during pregnancy. It can occur at any stage of pregnancy and cause problems for both the mother and the baby, during and after birth. The risks can be reduced if they are early detected and managed, especially in areas where only periodic tests of pregnant women are available. Intelligent systems designed by machine learning algorithms are remodelling all fields of our lives, including the healthcare system. This study proposes a combined prediction model to diagnose gestational diabetes.
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This data is the Federal Communication Commission (FCC) F(50,50) signal strength variation curves for the Very High Frequency (VHF) Channel 7-13 and the Ultra High Frequency (UHF) Channel 14-69. The signal strength for both curves is in dBuV/m for an Effective Radiated Power (ERP) per dipole of 1 kW. All data are based on a 9 m mobile antenna height measurement for 30 m to 600 m antenna heights within a transmitter-receiver separation ranging from 1.5 km to 100 km.
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