Communication in society had developed within cultural and geographical boundaries prior to the invention of digital technology. The latest advancements in communication technology have significantly surpassed the conventional constraints for communication with regards to time and location. These new platforms have ushered in a new age of user-generated content, online chats, social network and comprehensive data on individual behavior. However, the abuse of communication software such as social media websites, online communities, and chats has resulted in a new kind of online hostility and aggressive actions. Due to widespread use of the social networking platforms and technological gadgets, conventional bullying has migrated from physical form to online, where it is termed as Cyberbullying. However, recently the digital technologies as machine learning and deep learning have been showing their efficiency in identifying linguistic patterns used by cyberbullies and cyberbullying detection problem. In this research paper, we aimed to evaluate shallow machine learning and deep learning methods in cyberbullying detection problem. We deployed three deep and six shallow learning algorithms for cyberbullying detection problems. The results show that bidirectional long-short-term memory is the most efficient method for cyberbullying detection, in terms of accuracy and recall.

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The composition of legumes and sugar beet contains a large number of useful mineral and vitamin substances. The use of composite flour from leguminous crops for the preparation of bakery products helps increase food and biochemical properties. The main objects of this research are chickpea flour, bean flour, dry sugar beet powder, and wheat flour of the first grade. The main problem is an insufficient amount of minerals and vitamins, so the purpose of this work is to enrich bakery products and replace sugar in the recipe with sugar beet powder. The results showed that composite flour and sugar beet increased calcium content by 13.54 mg/100 g, iron ‒ by 0.57 mg/100 g, potassium ‒ by 141.03 mg/100 g, phosphorus ‒ by 38.89 mg/100 g, vitamin A ‒ by 0.002 mg/100 g, vitamin B2 ‒ by 0.016 mg/100 g, vitamin E ‒ by 0.32 mg/100 g, and vitamin PP ‒ by 0.405 mg/100 g. Microbiological indicators meet the established norms and requirements; the amount of mesophilic aerobic and facultative-anaerobic microorganisms, yeast, and mold in the test bun was the least compared to the control sample. As a result, it was proved that the use of composite flour of leguminous crops contributes to an increase in the nutritional and biological values of bakery products, and the application of dried sugar beet powder makes it possible to completely exclude sugar from the formulation of the resulting product. Employing this technology and formulations for obtaining bakery products makes it possible to expand the range of bakery products, reduce the duration of the manufacturing process, improve the quality of finished products, increase labor productivity. That also contributes to the improvement of the socio-economic indicators of bakery and confectionery enterprises

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