Hyperspectral image classification has become an important issue in remote sensing due to the significant amount of spectral information in HSI. The costly and time-consuming annotation task of HSIs makes the number of labeled samples is limited. To address the above problem, we propose an enhanced semi-supervised support vector machine algorithm for spectral-spatial HSI classification. To fully capture the spectral and spatial information of HSI, we use local binary pattern to obtain spatial feature. The captured spatial features are concatenated with the spectral features to yield the hybrid spectral-spatial features. Self-training mechanism is then adopted to gradually select confident unlabeled samples with their pseudo-labels and add them to the labeled set. To further improve the classification performance of the semi-supervised support vector machine, we choose a cuckoo search algorithm based on the chaotic catfish effect to find its optimal combination of parameters. The experimental results on two publicly available HSI datasets show that the proposed model achieves excellent classification accuracy for each category in hyperspectral images, and also has superior overall accuracy compared with other comparative algorithms. Adequate experiments and analysis illustrate the promising potential and prospect of our proposed model for HSI classification.

The biennial "Computer Sciences and Information Technologies" conference in Yerevan in September of 2023 was the 14th in this series. This event is of regional interest, provide a networking ecosystem to scientists in areas such as the Mathematical Logic and Logical Reasoning, Discrete Mathematics, Pattern Recognition and Cognitive Sciences, and Applications. 17 selected papers of this conference, included in Special Issue of PRIA journal provide up to date research results and research topics that make a sensitive input to the field known as "Logic Combinatorial Pattern Recognition". Results from Algebra and Mathematical Logic help in structural and semantic areas of pattern recognition, graph theoretical investigations help with clustering and image analysis, other selected papers and results are devoted directly to semantic pattern recognition, to cognitive sciences in general, or they provide application platforms where different artificial intelligence technologies are converging.

When solving artificial intelligence problems related to the study of complex structured objects, a convenient tool for describing such objects is the language of predicate calculus. The paper presents two algorithms for checking the isomorphism of pairs of elementary conjunctions of predicate formulas (they coincide up to variable names and the order of conjunctive terms). The first of the algorithms checks elementary conjunctions containing a single predicate symbol for isomorphism. Furthermore, if the formulas are isomorphic, it finds a one-to-one correspondence between the arguments of these formulas. If all predicates are binary, the proposed algorithm is an algorithm for checking two directed graphs for isomorphism. The second algorithm checks elementary conjunctions containing multiple predicate symbols for isomorphism. Estimates of their time complexity are given for both algorithms.

Based on an analysis of statistical data on railway and road traffic, as well as laboratory studies, mathematical models are developed that describe the “human–monitoring system–vehicle–traffic system” system. The issues of classifying operators according to their tendency to fall asleep and create emergency situations are explored. A statistical analysis of the accident rate of vehicle drivers was carried out based on their susceptibility to accidents. The degree of effectiveness and safety of monitoring systems is taken into account, as well as the influence of psychological factors caused by drivers excessive trust in the monitoring system. The risks associated with system malfunctions and insufficient efficiency of its operation are calculated. The use of an ineffective driver monitoring system does not reduce, but increases the likelihood of an accident. The design and principles of operation of a driver vigilance telemechanical control system (DVTCS) are described. The device is designed for continuous monitoring of the drivers vigilance and attentiveness while driving rolling stock. The work of DVTCS is based on scientific results according to which episodic changes in skin resistance reflect the level of alertness and wakefulness. It has been shown that due to more reliable, continuous, and nondistracting monitoring of the drivers physiological state the DVTCS provides a higher level of traffic safety than its “Safety Handle” counterpart. Statistical data from operational and laboratory data have been analyzed, indicating a high level of operational safety of the DVTCS. A comparison of Russian and international requirements for the safety level of DVTCS has been carried out. Methods for further improvement of the device are noted.

The article is devoted to the achievements of the leading scientific school of Academician V.A. Soifer in the field of biomedical image processing. The main stages of development of research in the field of analysis of medical data are given. Various tasks in processing, analysis, and recognition of medical images, as well as their specifics, are considered. Methods, algorithms, and systems obtained during joint research with major medical institutions in the Russian Federation are described.

This article elucidates the technocratic utopia envisioned by Academician Axel Berg, detailing his perspective on the future and the strategies he believed would bring it to fruition. Specific initiatives undertaken by Berg are highlighted, notably the establishment of the Scientific Council “Cybernetics”—a nexus for the advancement of cybernetics in the former Soviet Union and the Russian Federation—and his endeavors in digital education. Two distinct articles within this compilation further consider these domains of Berg’s work and of those who continued his legacy.

The article describes the results of Belarusian scientists in the field of image recognition and analysis obtained over the last 30 years. The main groups of scientists working at the National Academy of Sciences of Belarus and universities are shown. Conferences held by Belarusian scientists are shown, with special attention paid to the most popular conference Pattern Recognition and Information Processing. Information is provided on the development of the knowledge-intensive IT sector and companies working in the field of computer vision. Data on the training of scientific personnel in this field are provided.

This article tells the history of a scientific school, the center of which is currently located at the Federal Research Center “Computer Science and Control” of the Russian Academy of Sciences, and its main interests are focused around the problems of image recognition and processing. Several of the following articles in this issue are devoted to the present state of this school.

This article presents the results of research into methods and algorithms for processing image sequences aimed at application in autonomous navigation systems. The methods are grouped in the following areas: pre-processing, generation of 2D and 3D scene models, object recognition. and determination of motion parameters from a sequence of images. The article presents methods and algorithms proposed and researched by the authors of the article over the past decade. Description of the methods and algorithms is accompanied by examples and results of experimental studies.

Herein, a historical analytical survey of work of “Discrete Modelling of Pattern Recognition” (DM-Lab) research group in Armenia is presented. The group is since 1973, supervised by worldwide recognized scientist Yurii Ivanovich Zhuravlev and lead by his former student Levon Aslanyan. The general start of attention to computational mathematics and computational systems in Armenia is concerned with the times of cybernetics as a research direction, and the names of great scientists and policy makers, such as Andranik Iosifyan and Sergei Mergelyan. In early 1950’s a large number of HighTech and defense related organizations were established in area, and their theoretical, scientific cluster was formed around the Yerevan Research Institute of Mathematical Machines, and Computer Center of Academy of Sciences and Yerevan State University. This was the time for intensive stuff and student exchanges inside the larger country USSR. Rimma Podlovchenko, Rafik Tonoyan, Igor’ Zaslavski, Yuri Shoukourian started teaching at Yerevan State University in 70’s, a number of students were delegated to the recognized cybernetical centers, in Moscow, Kiev, Novosibirsk. And one of the results of these developments was appearance of DM-Lab in Armenia, composed by alumnus of Novosibirsk and Moscow State Universities, led by Levon Aslanyan, and supervised globally by RF Academician Yuri Ivanovich Zhuravlev. Further research and education activities lead to defenses of candidate and doctoral dissertations, in Armenia, and at the council of Computer Center of Academy of Sciences of Russian Federation. The initial stuff of DM-Lab group included Gevorg Tonoyan, Levon Asatryan, Vilik Karakhanyan. Local members of the group were Hasmik Sahakyan, Vladimir Sahakyan, Irina Arsenyan, Levon Kazaryan and large number of young PhD students. Research directions at the DM-Lab were and are related to the pattern recognition theory – to mathematical models of forming and analyzing learning sets, studying their properties such as the class compactness hypothesis, in terms of isoperimetry; to forming the logic of interrelations of classes, in terms of logic separation; setting up new approaches in data mining area, etc. All these studies involve intensive research over the years, addressing topics related to the geometry of n-dimensional unite cube and lattices in general, Boolean function minimization, discrete optimization problems, and algorithmic studies coming from data science and artificial intelligence. International relations and activities of the group includes: long term representation of Armenia in the ISO technical groups, representation of Armenia in ICT research programmes of European Council, membership at the ITHEA virtual research institute with its conferences and publishing house. 10’s of research projects were implemented during these years. Projects were funded by UNDP, NATO Research, INTAS, EC Esprit, IST and Horizone, RFBR, and other international and local sources. 16 candidate and 2 doctoral theses were defended by the group members.