The study of large datasets to uncover hidden patterns and trends has become increasingly important and valuable in recent years. These large datasets are characterized by wide availability, structural complexity, and significant volume of information.
The problem of building a face recognition pipeline faces numerous challenges such as changes in lighting, pose, and facial expressions. The main stages of the pipeline include detection, alignment, feature extraction, and face representation. Each of these stages is critically important for achieving accurate recognition.
In the context of rapidly increasing volumes of video data, the problem of their efficient search and analysis becomes more acute. This research aims to develop and test an innovative system to improve the speed and accuracy of video search, utilizing the capabilities of Deep Convolutional Neural Networks (DCNN) and Feedforward Neural Networks (FFNN).
This paper proposes an approach to the detection and extraction of specific features in an ideogram image. Ideogram is a depiction of a healthy chromosome used in a karyotyping process - a procedure designed to diagnose chromosomal abnormalities.
Extraction of ideogram features is a part of a general algorithm for the detection of chromosomal abnormlities. According to the general algorithm, both chromosomes and ideograms have to be parsed and converted into a single data format for further comparison.
It is acknowledged that each person's life, group of people and nation is formed depending on geographical, economic, political, cultural and religious conditions. Lifestyle is formed as a result of daily repetition and consists of the following factors: nutrition, exercise, the presence of bad habits, moral and spiritual development, and so on. In recent decades, lifestyle has been considered an integral part of well-being, leading to increased research. According to the scientist's study, more than half of health problems are related to diet.