*.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.

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. 

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.

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.

the focus of this dataset is to provid an open-loop solution for a stochastic problem with imperfect state information and
chance-constraints adjusted by an optimal gain.

dataset for An open-loop solution for a stochastic problem with imperfect state information and chance-constraints adjusted by an optimal gain.

Contains the SUS survey and application dataset and the UEQ application dataset.

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.

The study describes the use of nano aluminum nitride in developing thermally conductive but electrically insulative flexible materials for use in high-voltage insulation. The study begins with the detailed protocol for hydrophobic surface treatment of nanofillers for enhanced interaction with base silicone gum. TEM and XRD analysis are used to understand the shape, dimensions, and phase of the filler. XRD analysis revealed the coexistence of aluminum oxide in traces along with aluminum nitride. Successful surface functionalization was confirmed through FTIR analysis.