This paper presents a web-based environment, designed to provide researchers of Old Church Slavonic manuscripts with an intuitive, semantically sound framework and the respective tools for collection, description, categorization, analysis, and presentation of their respective findings. And while the digital repertorium described here concentrates mostly on the paleographic features of the South Slavic manuscripts from the fourteenth century, a period of considerable literary achievements in the Balkans, we feel that the incorporation of the state-of-the-art methods form the field of digital presentation and preservation of cultural heritage will contribute to the better understanding of a still under-researched topic.
This article deals with a new handwritten dataset, Kyrgyz-MNIST, and recognition of handwritten Kyrgyz letters through the Artificial Neural Network. There were collected 89 023 manuscript samples of 36 Kyrgyz letters. Since 33 of the 36 letters in the Kyrgyz language are similar to the Russian letters, the special letters “ң”, “ү”, and “ө” were studied with special emphasis. The system of handwriting recognition works with Image processing methods and Machine Learning algorithms helped to recognize Kyrgyz manuscripts and convert them into computer letters. As a result, the CNN method recognized the handwriting letters more accurately. In total 36 letters were recognized with an average accuracy of 99.1%. Newly studied, the special letters in Kyrgyz Language are also recognized with more than 99% correct.
ABSTRACT: The paper delves into the fundamentals of information systems security, providing an analysis of the security levels of both information and network resources. It describes various types of attacks on information systems along with their detection methods. Additionally, it characterizes systems based on anomaly detection methods in information system behaviors and outlines key aspects of constructing attack detection systems. The study further investigates network anomalies and provides detailed characterizations of attack types. Novel methods for detecting attacks on information systems have been developed, including a new approach involving the utilization of threat expectation reports for attack detection purposes. Moreover, a method for detecting anomalies based on behavior in information systems has been devised. Specifically, the presented algorithm focuses on establishing a model of regular or typical system/user functionality. Through this approach, the system compares current activity rates with the normal activity profile, identifying significant deviations as potential sources of attack or network anomalies.
Analysing dataset representing ten years (1999-2008) of clinical care at 130 US hospitals and integrated delivery networks which includes over 50 features representing patient and hospital outcomes, we have implemented various machine learning and deep learning models to achieve higher accuracy in classifying diabetic patients in order to predict the early re-admission cases. To overcome the sever challenge we have faced with class imbalance in our datasets, we have introduced SMOTE sampling technique. Having resampled our data and implemented our deep learning architecture, we have obtained an accuracy of 98%. The relatively higher performances which were obtained upon using both support vector machine and deep learning models are suggestive of implementing these algorithms in order to overcome challenges related to hospital diabetic re-admission cases in both managing the patents and preventing this devastating disease.
Abstract. In the conditions of rapid development of technologies for evaluation and growth of scientific research, the issue of effective organizational process of scientific competitions becomes especially important. This article is devoted to the analysis of prospects and innovations in the development of automated expert systems used in the organization of scientific competitions, with the main focus on the formation of topics of research works.
