One of the biggest expense in software development is the maintenance. Therefore, it's critical to comprehend what triggers maintenance and if it may be predicted. Numerous research outputs have demonstrated that specific methods of assessing the complexity of created programs may produce useful prediction models to as-certain the possibility of maintenance due to software failures. As a routine it is performed prior to the release, and setting up the models frequently calls for certain, object-oriented software measurements. It's not always the case that software developers have access to these measurements. In this paper, machine learning is applied on the available data to calculate the cumulative software failure levels. A technique to forecast a software's residual defectiveness using machine learning can be looked into as a solution to the challenge of predicting residual flaws. Software metrics and defect data were separated out of the static source code repository. Static code is used to create software metrics, and reported bugs in the repository are used to gather defect information. By using a correlation method, metrics that had no connection to the defect data were removed. This makes it possible to analyze all the data without pausing the programming process. Large, sophisticated software's primary issue is that it is impossible to control everything manually, and the cost of an error can be quite expensive. Developers may miss errors during testing as a consequence, which will raise maintenance costs. Finding a method to accurately forecast software defects is the overall objective.

Holographic Intellectual Voice Assistant (HIVA) aims to facilitate human computer interaction using audiovisual effects and 3D avatar. HIVA provides complete information about the university, including requests of various nature: admission, study issues, fees, departments, university structure and history, canteen, human resources, library, student life and events, information about the country and the city, etc. There are other ways for receiving the data listed above: the university's official website and other supporting apps, HEI (Higher Education Institution) official social media, directly asking the HEI staff, and other channels. However, HIVA provides the unique experience of “face-to-face” interaction with an animated 3D mascot, helping to get a sense of ‘real-life’ communication. The system includes many sub-modules and connects a family of applications such as mobile applications, Telegram chatbot, suggestion categorization, and entertainment services. The Voice assistant uses Russian language NLP models and tools, which are pipelined for the best user experience.

Deep neural networks (DNNs) with a step-by-step introduction of inputs, which is constructed by imitating the somatosensory system in human body, known as SpinalNet have been implemented in this work on a Galaxy Zoo dataset. The input segmentation in SpinalNet has enabled the intermediate layers to take some of the inputs as well as output of preceding layers thereby reducing the amount of the collected weights in the intermediate layers. As a result of these, the authors of SpinalNet reported to have achieved in most of the DNNs they tested, not only a remarkable cut in the error but also in the large reduction of the computational costs. Having applied it to the Galaxy Zoo dataset, we are able to classify the different classes and/or sub-classes of the galaxies. Thus, we have obtained higher classification accuracies of 98.2, 95 and 82 percents between elliptical and spirals, between these two and irregulars, and between 10 sub-classes of galaxies, respectively.

Limited ability for artists and designers to create outline images on vertical surfaces such as walls. Methods, such as hand painting, are time-consuming, labor-intensive, and limit the creativity of professionals and art enthusiasts. Picasso Robotics System aims to revolutionize this process by combining advanced printing technology with artistic creativity, reducing the time required to create each painting while maintaining precision. The main motivation of the project is to bridge the gap in the efficiency and accuracy of traditional methods by integrating design selection and remote control through a dedicated mobile app with artificial intelligence capabilities expanding creative control and simplifying workflow. The mobile application is based on an innovative image processing system based on contour vector elements. This technology provides high accuracy and efficiency in creating designs and also allows you to easily scale and adapt images to different formats.

The concept of stop words introduced by H. P. Lun in the mid-20th century plays a huge role in today’s NLP practice. Stop words are used to reduce noisy text data, remove uninformative words, speed up text processing, and minimize the amount of memory required to store data.The Kyrgyz language is an agglutinative Turkic language for which no scientific study of stop words has been previously published in English. In our study, we combined frequency analysis with rule-based linguistic analysis. First, we found the most frequently used words, set a threshold, and removed words below the threshold. This way we got a list of the most frequently used words. Then we reduced the list by excluding from the list all words that do not belong to the category of function words of the Kyrgyz language. Finally, we got a list of 50 words that can be considered stop words in the Kyrgyz language. In our analysis, we used a single corpus of sentences collected and posted as an open source project by one of the local broadcasters.