The Artificial Intelligence Recommender System has emerged as a significant research interest. It aims at helping users find things online by offering recommendations that closely fit their interests. Recommenders for research papers have appeared over the last decade to make it easier to find publications associated with the field of researchers’ interests. However, due to several issues, such as copyright constraints, these methodologies assume that the recommended articles’ contents are entirely openly accessible, which is not necessarily the case. This work demonstrates an efficient model, known as RPRSCA: Research Paper Recommendation System Using Effective Collaborative Approach, to address these uncertain systems for the recommendation of quality research papers. We make use of contextual metadata that are publicly available to gather hidden relationships between research papers in order to personalize recommendations by exploiting the advantages of collaborative filtering. The proposed system, RPRSCA, is unique and gives personalized recommendations irrespective of the research subject. Thus, a novel collaborative approach is proposed that provides better performance. Using a publicly available dataset, we found that our proposed method outperformed previous uncertain methods in terms of overall performance and the capacity to return relevant, valuable, and quality publications at the top of the recommendation list. Furthermore, our proposed strategy includes personalized suggestions and customer expertise, in addition to addressing multi-disciplinary concerns.

A system of identification based on biometrie data is developed. The new method of Alteration by Ateb-Gabor was developed. Usage of this Alteration method gives an opportunity to utilize more control levels than usual Gabor fllter. Ateb functions are functions that have two parameters that enable more productive identification. This allows developing a new image processing method.

Development of a technology for eliminating color rendering imperfections in digital photographic images

The method of biometric identification, designed to ensure the protection of confidential information, is considered. The method of classification of biometric prints by means of machine learning is offered. One of the variants of the solution of the problem of identification of biometric images on the basis of the k-means algorithm is given. Marked data samples were created for learning and testing processes. Biometric fingerprint data were used to establish identity. A new fingerprint scan that belongs to a particular person is compared to the data stored for that person. If the measurements match, the statement that the person has been identified is true. Experimental results indicate that the k-means method is a promising approach to the classification of fingerprints. The development of biometrics leads to the creation of security systems with a better degree of recognition and with fewer errors than the security system on traditional media. Machine learning was performed using a number of samples from a known biometric database, and verification / testing was performed with samples from the same database that were not included in the training data set. Biometric fingerprint data based on the freely available NIST Special Database 302 were used to establish identity, and the learning outcomes were shown. A new fingerprint scan that belongs to a particular person is compared to the data stored for that person. If the measurements match, the statement that the person has been identified is true. The machine learning system is built on a modular basis, by forming combinations of individual modules scikit-learn library in a python environment.

The environment is full of various sounds - signals registered by human and animal hearing organs, which in terms of frequency and strength merge into a monotonous, incoherent sound. The physical carriers of signals reaching the hearing organs are acoustic (sound) waves – mechanical waves that propagate in the air and are perceived by the hearing organs. Human hearing organs can perceive sounds in a fairly wide range of frequencies and intensities. The range of sounds heard by a person is limited by two threshold curves: the lower one is the threshold of perception, the upper one is the threshold of pain sensations. The human hearing threshold is 0 dB at a frequency of 1000 Hz, and the pain threshold occurs from a sound of 140 dB [2]. As long as there have been wars, it is known that mass battle cries and drumbeats oppress the enemy. The sirens of attack planes and bombers make him feel terrified and want to run away. Now an explanation has been found for this: loud sounds excite a person, contribute to the influx of a large amount of hormones into the blood, in particular, adrenaline, as a result of which there is a feeling of danger and fear [3]. There is a description of existing approaches to assess the impact of sounds on the emotional state of man, which will be the first step towards creating a model for analyzing the impact of sounds on the emotional state of man based on the theory of discrete emotions. Generated values for emotional evaluation of sounds.