Аннотация: Рассматриваются проблемы создания и применения автоматизированных систем. Особое внимание уделяется одному из важных видов автоматизированных систем — автоматизированных систем мониторинга (АСМ) состояний сложных организационно-технических комплексов (СОТК) в режиме реального времени с учетом возможной деградацией их структур, проведен обзор существующих исследований и технологических подходов к решению проблем создания и применения АСМ состояния СТО и управления в реальном масштабе времени.

Ключевые слова: интеллектуальные информационные технологии мониторинга и правления сложными объектами.

Ключевые слова по ACM классификатору: J.6 Computer-Aided? Engineering and I.2.2 Automatic Programming.

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ПЕРСПЕКТИВНЫЕ НАПРАВЛЕНИЯ РАЗВИТИЯ ИНФОРМАЦИОННЫХ ТЕХНОЛОГИЙ МОНИТОРИНГА И УПРАВЛЕНИЯ СОСТОЯНИЯМИ СЛОЖНЫХ ТЕХНИЧЕСКИХ ОБЪЕКТОВ В РЕАЛЬНОМ МАСШТАБЕ ВРЕМЕНИ

Олег Майданович, Михаил Охтилев, Борис Соколов

http://www.foibg.com/ijima/vol01/ijima01-4-p02.pdf

Abstract: The problem of prediction of British Petroleum Corp. stock prices and the Dow Jones Industrial Average stock quote is considered. For the prediction data stock quote of the largest oil companies at the stock exchange NYSE were used as input data. The obtained experimental results of prediction using FGMDH were compared with the classical GMDH and cascade neo-fuzzy neural networks. For the classical and fuzzy GMDH four classes of functions- linear, quadratic, Fourier polynomial and Chebyshev polynomial were used, and the variation in the form of membership function, the size of learning sample and freedom of choice with the developed software were performed. Experimental results of forecasting at NYSE are presented enabling to estimate efficiency of different forecasting methods and to choose the most proper method.

Keywords: fuzzy group method of data handling, stock exchange, stock prices forecasting, cascade neo-fuzzy neural networks.

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НЕЧЕТКИЙ МЕТОД ИНДУКТИВНОГО МОДЕЛИРОВАНИЯ В ЗАДАЧАХ ПРОГНОЗИРОВАНИЯ НА ФОНДОВЫХ РЫНКАХ

Юрий Зайченко

http://www.foibg.com/ijima/vol01/ijima01-4-p01.pdf

Abstract: The paper presents extensible software architecture and a prototype and an implementation of a highly configurable system for HTML validation. It is based on validation rules defined in an XML document called “extended validation schema”. It serves as an extended validation schema beside the official HTML specification, because the browsers’ and other web clients’ differences in HTML visualization makes the HTML specification insufficient and it is perfectly possible an HTML document to be syntax valid and yet not well visualized in some browser or mail-client. The extended validation schema allows definition of custom and specific validation rules in three levels - document rules, element (or tag) rules and attributes rules. The correctness of the validation schema is checked via a predefined XSD schema. The paper defines a prototype of a validation engine that consists of HTML parser, HTML validator, Storage module and Statistics module. The HTML parser parses the HTML file and breaks it into corresponding elements. The HTML validator applies the custom validations defined in the extended validation schema for every single element and attribute along with document-level validations, and also automatically corrects the errors wherever possible. The Storage module saves the validation results to a persistent storage. They can be considered for unit tests and used by the Statistics module to create additional statistics, analyses, quality assurance and bug tracking. A comparison is made with other HTML validation services and solutions. The results of an implementation of the prototype system in a software company are also presented.

Keywords: HTML validation, XML schema, quality assurance, unit tests, bugs tracking.

ACM Classification Keywords: D.4.m Software – Miscellaneous.

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HTML VALIDATION THROUGH EXTENDED VALIDATION SCHEMA

Radoslav Radev

http://www.foibg.com/ijima/vol01/ijima01-3-p09.pdf

Abstract: The executed researches belong to area of design of perspective access networks. Work is devoted to the analysis of parameters of access networks and a choice of the most significant among them. Results of researches for wire decisions of the organization of a network are given.

Keywords: access network, parameters of access networks.

ACM Classification Keywords: С.2. Computer-communication networks, H. Information Systems - H.1 Models and Principles, K. Computing Milieux - K.6 Management of computing and information system.

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ANALYSIS AND JUSTIFICATION FOR SELECTION PARAMETERS OF WIRED ACCESS SYSTEMS

Svetlana Sakharova

http://www.foibg.com/ijima/vol01/ijima01-3-p08.pdf

Abstract: In this paper we study some problems involved in analysis of Pareto-distributed scientometric data (series of citations versus paper ranks). The problems include appropriate choices of i) the distribution type (continuous, discrete or finite-size discrete) and ii) statistical methods to obtain unbiased estimates for the powerlaw exponent (maximum likelihood procedure or least square regression.). Since relatively low magnitudes of the power exponent (less than 2), are observed massively in scientometric databases, finite-size discrete Pareto distribution (citations, distributed to finite number of paper ranks) appears to be more adequate for data analysis than the traditional ones. This conclusion is illustrated with two examples (for synthetic and actual data, respectively). We also derive empirical relationships, in particular, for the maximum and the total number of citations dependence on the Hirsch index. The latter generalize results of previous studies.

Keywords: Scientometrics, Hirsch index, Pareto distributions, data analysis, empirical relationships

ACM Classification Keywords: H. Information Systems, H.2. Database Management, H.2.8. Database applications, subject: Scientific databases; I. Computing methodologies, I.6 Simulation and Modeling, I.6.4. Model Validation and Analysis

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THEORETICAL ANALYSIS OF EMPIRICAL RELATIONSHIPS FOR PARETODISTRIBUTED SCIENTOMETRIC DATA

Vladimir Atanassov, Ekaterina Detcheva

http://www.foibg.com/ijima/vol01/ijima01-3-p07.pdf

Abstract: In this paper we propose a novel approach for flood hazard mapping by processing and analyzing a time-series of satellite data and derived flood extent maps. This approach is advantageous in cases when the use of hydrological models is complicated by the lack of data, in particular high-resolution DEM. We applied this approach to the time-series of Landsat-5/7 data acquired 2000 to 2010 for the Katima Mulilo region in Namibia. We further integrated flood hazard map with dwelling units database to derive flood risk map.

Keywords: flood hazard, flood risk assessment, Earth remote sensing, Earth observation, satellite data processing, UN-SPIDER.

ACM Classification Keywords: H.1.1 Models and Principles Systems and Information Theory; I.4.8 Image Processing and Computer Vision Scene Analysis - Sensor Fusion.

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THE USE OF TIME-SERIES OF SATELLITE DATA TO FLOOD RISK MAPPING

Sergii Skakun

http://www.foibg.com/ijima/vol01/ijima01-3-p06.pdf

Abstract: This paper highlights the current state on establishing a network of test sites in Ukraine within the Joint Experiment for Crop Assessment and Monitoring (JECAM) project of the Global Earth Observation System of Systems (GEOSS). The results achieved so far on developing methods for crop state and area estimation using satellite and in situ observations are presented. The agromonitoring portal that provides access to geospatial products is described as well.

Keywords: Earth remote sensing, GEOSS, JECAM, satellite data processing, agriculture, area estimation.

ACM Classification Keywords: H.3.4 Information Systems Systems and Software - Distributed systems; I.5.1 Computing Methodologies Models –Neural nets; I.4.8 Image Processing and Computer Vision Scene Analysis - Sensor Fusion.

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CROP STATE AND AREA ESTIMATION IN UKRAINE BASED ON REMOTE AND INSITU OBSERVATIONS

Nataliia Kussul, Andrii Shelestov, Sergii Skakun, Oleksii Kravchenko, Bohdan Moloshnii

http://www.foibg.com/ijima/vol01/ijima01-3-p05.pdf

Abstract: In order to view wide contrast details in an image scene, a wide dynamic range (WDR) image sensor is required. However, these wide dynamic range images cannot be accurately viewed on a regular display device due to its limited dynamic range. Without the proper use of a WDR image compression algorithm, the details of images will be lost. Tone-mapping algorithms are used to adapt the captured wide dynamic range scenes to the low dynamic range displays available. This paper explores the utilization of an exponent-tone mapping algorithm for colored and monochrome WDR images in lure of a regular display. The exponent-based tone mapping algorithm utilizes only the Bayer (CFA) of the WDR image to produce tone mapped image results. High quality results are achieved without the use of additional image processing techniques such as histogram clipping. The image results are then compared with other conventional tone mapping operators available.

Keywords: Tone mapping, Wide dynamic range, High Dynamic Range Image, Image enhancement.

ACM Classification Keywords: A.0 General Literature - Conference proceedings; I.4.0 Image processing and Computer Vision- General (or .3 enhancement)

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AN IN-DEPTH ANALYSIS AND IMAGE QUALITY ASSESSMENT OF AN EXPONENTBASED TONE MAPPING ALGORITHM

Chika Ofili, Stanislav Glozman, Orly Yadid-Pecht?

http://www.foibg.com/ijima/vol01/ijima01-3-p04.pdf

Abstract: Colonoscopy is the gold standard method for the diagnosis of colorectal cancer (CRC). It detects the first clinical manifestation of CRC, known as polyps. One night prior to a colonoscopy procedure, patients are instructed to take laxative agents in order to completely cleanse the colon. This process is called bowel preparation. Contemporary sensitivity of colonoscopy for detecting polyps of a size larger than 10 mm is 98% with the limitation in detection mainly due to poor visualization related to inadequate bowel preparation. Unfortunately, there is not yet a metric (formally recommended by means of guidelines) for the quantification of bowel preparation. Scales used nowadays are not objective, because generally colonoscopists estimate the level of cleanliness after the conclusion of the colonoscopic test. This limitation leads to the formalization of the present study, which focuses on the development of a novel cleansing evaluation system for bowel preparation and the assessment of its clinical efficacy. The proposed system consists of a computer-based tool that can automatically measure the quantity of stool and waste matter existing within the patient during a colonoscopy procedure. As these metrics can be obtained automatically, the proposed method can lead to future quality control in daily medical practice. Furthermore, it can be used to create best practice standards for colonoscopy training or as part of medical skill evaluation.

Keywords: Colonoscopy; Colon preparation; Efficacy; Quality measurement metrics; Video segmentation

ACM Classification Keywords: A.0 General Literature - Conference proceedings; J.3. Life and Medical Sciences

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AUTOMATED SYSTEM FOR QUANTIFYING THE LEVEL OF PREPARATION IN COLONOSCOPY

Leticia Angulo-Rodríguez?, Xuexin Gao, Dobromir Filip, Christopher N. Andrews and Martin P. Mintchev

http://www.foibg.com/ijima/vol01/ijima01-3-p03.pdf

Abstract: Membrane computing is a recent area that belongs to natural computing.. P-systems are the structures which have been defined, developed and implemented to simulate the behavior and the evolution of membrane systems which we find in nature. Diophantine equations are those equations that have integer solutions. Currently, the extended Euclidean algorithm works to find integer solutions. .This paper shows a step by step procedure that solves a Diophantine equation by processing the extended Euclidean Algorithm

Keywords: Extended Euclidean Algorithm, Membrane systems .

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SOLVING DIOPHANTINE EQUATIONS WITH A PARALLEL MEMBRANE COMPUTING MODEL

Alberto Arteta, Nuria Gomez, Rafael Gonzalo

http://www.foibg.com/ijima/vol01/ijima01-3-p02.pdf

Abstract: This paper presents some ideas about a new neural network architecture that can be compared to a Taylor analysis when dealing with patterns. Such architecture is based on lineal activation functions with an axo-axonic architecture. A biological axo-axonic connection between two neurons is defined as the weight in a connection in given by the output of another third neuron. This idea can be implemented in the so called Enhanced Neural Networks in which two Multilayer Perceptrons are used; the first one will output the weights that the second MLP uses to computed the desired output. This kind of neural network has universal approximation properties even with lineal activation functions. There exists a clear difference between cooperative and competitive strategies. The former ones are based on the swarm colonies, in which all individuals share its knowledge about the goal in order to pass such information to other individuals to get optimum solution. The latter ones are based on genetic models, that is, individuals can die and new individuals are created combining information of alive one; or are based on molecular/celular behaviour passing information from one structure to another. A swarm-based model is applied to obtain the Neural Network, training the net with a Particle Swarm algorithm.

Keywords: Neural Networks, Swarm Computing, Particle Swarm Optimization.

ACM Classification Keywords: F.1.1 Theory of Computation - Models of Computation, I.2.6 Artificial Intelligence - Learning, G.1.2 Numerical Analysis - Approximation.

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POLYNOMIAL APPROXIMATION USING PARTICLE SWARM OPTIMIZATION OF LINEAR ENHANCED NEURAL NETWORKS WITH NO HIDDEN LAYERS

Luis F. de Mingo, Miguel A. Muriel, Nuria Gómez Blas, Daniel Triviño G.

http://www.foibg.com/ijima/vol01/ijima01-3-p01.pdf

Abstract. The paper is devoted to the implementation of logic-objective approach to the solving of a polyhedron contour images (in particular partially covered images) recognition problem in a complex scene represented on the display screen. A way of predicate value calculation for representation the display screen is described in the paper. Examples of a program run constructing descriptions of both separate pictures and classes of objects are presented. For recognition of partially covered objects on the complex scene the concept of partial deducibility is used. Additionally the certainty level of the correct recognition is calculated.

Keywords: artificial intelligence, pattern recognition, predicate calculus.

ACM Classification Keywords: I.2.4 ARTIFICIAL INTELLIGENCE Knowledge Representation Formalisms and Methods – Predicate logic.

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SOFTWARE FOR THE RECOGNITION OF POLYHEDRON CONTOUR IMAGES IN THE FRAMEWORK OF LOGIC-OBJECTIVE RECOGNITION SYSTEM

Natalya Bondar, Tatiana Kosovskaya

http://www.foibg.com/ijima/vol01/ijima01-2-p09.pdf

Abstract: This paper considers the problem of recognizing and classifying the odorants to preset classes of volatile matters. It is assumed that the data registered by sensory elements and been liable to processing has been distorted by errors – fuzzy values. The possibility-theoretical method of piecewise-linear approximation of functional dependencies is proposed to solve the problem.

Keywords: possibility-theoretical method, odorants, fuzzy errors.

ACM Classification Keywords: I.6 Simulation and Modeling.

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ABOUT POSSIBILITY-THEORETICAL METHOD OF PIECEWISE-LINEAR APPROXIMATION OF FUNCTIONAL DEPENDENCIES IN PROBLEM OF ODOURS’ RECOGNITION

Veda Kasyanyuk, Iryna Volchyna

http://www.foibg.com/ijima/vol01/ijima01-2-p08.pdf

Abstract: Mathematical model of vapor identification system is developed. Calibrating signals from vapor sensors are used to identify unknown input to vapor sensors and approximate output from eventual sensor system. Approximation formulas are resulted from pareto-optimum solution of multi-criterion problem. The developed method can be used to create new measuring-calculating systems within "device + PC = device with added benefits" framework.

Keywords: identification, an odorant, impacted data, measuring system, pareto-optimization

ACM Classification Keywords: I.6 Simulation and Modeling

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PARETO-OPTIMUM APPROACH TO MATHEMATICAL MODELING OF ODOURS IDENTIFICATION SYSTEM

Andriy Zavorotnyy, Veda Kasyanyuk

http://www.foibg.com/ijima/vol01/ijima01-2-p07.pdf

Abstract: Mobile devices are becoming increasingly sophisticated. These devices are inherently sensors for collection and communication of textual and voice signals. In a broader sense, the latest generation of smart cell phones incorporates many diverse and powerful sensors such as GPS (Global Positioning Systems) sensors, vision sensors (i.e., cameras), audio sensors (i.e., microphones), light sensors, temperature sensors, direction sensors (i.e., magnetic compasses), and acceleration sensors (i.e., accelerometers). The availability of these sensors in mass-marketed communication devices creates exciting new opportunities for data mining and data mining applications. So, it is not surprising that modern mobile devices, particularly cell phones of last generations that work on different mobile operating systems, got equipped with quite sensitive sensors. This paper is devoted to one approach that solves human activity classification problem with help of a mobile device carried by user. Current method is based on K-Nearest? Neighbor algorithm (K-NN). Using the magnitude of the accelerometer data and K-NN algorithm we could identify general activities performed by user.

Keywords: human activity classification; K-NN algorithm; mobile devices; accelerometer; Android platform

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ACTIVITY RECOGNITION USING K-NEAREST NEIGHBOR ALGORITHM ON SMARTPHONE WITH TRI-AXIAL ACCELEROMETER

Sahak Kaghyan, Hakob Sarukhanyan

http://www.foibg.com/ijima/vol01/ijima01-2-p06.pdf

Abstract: In this paper is described a method for automatic analysis of missing block neighbor area. The analysis, which is based on Canny edge detection and calculation of homogeneity coefficient in missing block neighbor area, provides suboptimal rectangular support area for each block. The suboptimal support area for each block is used in Selective Extrapolation algorithm. Paper includes experiment results of proposed method which are compared with results of selective extrapolation where the support area size is fixed for all blocks.

Keywords: Image processing, selective extrapolation, Canny edge detection, missing blocks concealment.

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ON A MODIFICATION OF THE FREQUENCY SELECTIVE EXTRAPOLATION METHOD

Gevorg Karapetyan and Hakob Sarukhanyan

http://www.foibg.com/ijima/vol01/ijima01-2-p05.pdf

Abstract: In this paper, an algorithm for a fingerprint closed pore extraction is proposed. A closed pore is considered as a segment of binarized fingerprint image. Segment contains maximal information about a pore shape, orientation or other significant features. The proposed algorithm is based on the consecutive performance of some simple and well known image processing procedures, namely image binarization, segmentation, inversion, whitening etc. Segmentation is a process of splitting an image into non-overlapping partitions with connected pixels of the same intensity interval. After segmentation a pore is presented as a white segment in a black background. Inversion transforms the white pore segment into a black segment. Whitening is an operation to change pixels of the segment of certain size to pixels of intensity 255. This operation deletes black pores from the inverted image. Thus we can extract all the pores by comparing the intermediate images. The proposed algorithm consists of mentioned operations applied by appropriate choosing of thresholds. An example of application of described algorithm to show the effectiveness of our approach to the pore extraction problem is given.

Keywords: fingerprint, closed pores, segmentation, binarization, inversion.

ACM Classification Keywords: Image Processing and Computer Vision

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SEGMENTATION BASED FINGERPRINT PORE EXTRACTION METHOD

David Asatryan, Grigor Sazhumyan

http://www.foibg.com/ijima/vol01/ijima01-2-p04.pdf

Abstract: In this paper, a methodology for reliable password generation from fingerprints is developed. In contrast to traditional biometric systems, proposed algorithm does not authenticate user by matching his or her biometrics. Reference data gives no information about the password and fingerprint. In hand with cryptography, this method can provide highly secure protection for cryptographic keys used in Digital Signatures and Digital Rights Management systems.

Keywords: Password Generation, Confidentiality, Authentication, Privacy, Security, Fingerprints, Image Processing, Pattern Recognition, Template Matching.

ACM Classification Keywords: D.4.6 Security and Protection (K.6.5)

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CORRELATION-BASED PASSWORD GENERATION FROM FINGERPRINTS

Gurgen Khachatrian, Hovik Khasikyan

http://www.foibg.com/ijima/vol01/ijima01-2-p03.pdf

Abstract: The article is devoted to thorough study of a new regression method performance. The proposed method based on convex correcting procedures over sets of predictors is subject to modifications and tested in comparison with the acknowledged regression utility. The modifications touch both resource consumption and quality aspects of the method and tests are performed with sets of generated samples.

Keywords: forecasting, bias-variance decomposition, convex combinations, variables selection.

ACM Classification Keywords: G.3 Probability and Statistics - Correlation and regression analysis, Statistical computing.

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ON SOME PROPERTIES OF REGRESSION MODELS BASED ON CORRELATION MAXIMIZATION OF CONVEX COMBINATIONS

Oleg Senko, Alexander Dokukin

http://www.foibg.com/ijima/vol01/ijima01-2-p02.pdf

Abstract: We consider the technique using nonsmooth optimization methods for pattern recognition problems. The results of numerical experiments of comparison of the proposed approach with support vector machines are presented.

Keywords: cluster, decision rule, discriminant function, linear and nonlinear programming, nonsmooth optimization

ACM Classification Keywords: G.1.6 Optimization - Gradient methods, I.5 Pattern Recognition; I.5.2 Design Methodology - Classifier design and evaluation

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NON SMOOTH OPTIMIZATION METHODS IN THE PROBLEMS OF CONSTRUCTING A LINEAR CLASSIFIER

Yurii I. Zhuravlev, Yuryi Laptin, Alexander Vinogradov, Nikolay Zhurbenko, Aleksey Likhovid

http://www.foibg.com/ijima/vol01/ijima01-2-p01.pdf

Abstract: An analysis of distinctions between a mathematical notion of an algorithm and a program is presented in the paper. The notions of the number of steps and the run used memory size for a Pascal, Turbo Prolog, Visual Prolog or Refal-5 program run are introduced. For every of these programming languages a theorem setting conditions upon a function implementation for polynomial time effectiveness is presented. For a Turbo or Visual Prolog program It is proved that a polynomial number of steps is sufficient for its belonging to the class FP. But for a Pascal or Refal-5 program it is necessary that it additionally has a polynomially bounded run memory size.

Keywords: complexity theory, class FP, programming languages Pascal, Turbo Prolog, Visual Prolog and Refal- 5.

ACM Classification Keywords: F.2.m ANALYSIS OF ALGORITHMS AND PROBLEM COMPLEXITY Miscellaneous.

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POLYNOMIAL-TIME EFFECTIVENESS OF PASCAL, TURBO PROLOG, VISUAL PROLOG AND REFAL-5 PROGRAMS

Nikolay Kosovskiy, Tatiana Kosovskaya

http://www.foibg.com/ijima/vol01/ijima01-1-p09.pdf

Abstract: The paper is devoted to the modifying of Maslov inverse method for a special form of predicate formulas used in the solution of artificial intelligence problems. An algorithm of the inverse method application for such type formulas is justified. Upper and lower bounds of the number of steps in such an application are obtain. Upper bounds coincide with those of other deduction algorithms, but the exhaustion is greatly reduced while construction particular derivation.

Keywords: artificial intelligence, pattern recognition, predicate calculus, inverse method of S.Yu.Maslov, complexity theory.

ACM Classification Keywords: I.2.4 ARTIFICIAL INTELLIGENCE Knowledge Representation Formalisms and Methods – Predicate logic, I.5.1 PATTERN RECOGNITION Models – Deterministic, F.2.2 Nonnumerical Algorithms and Problems – Complexity of proof procedures.

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THE INVERSE METHOD FOR SOLVING ARTIFICIAL INTELLIGENCE PROBLEMS IN THE FRAMEWORKS OF LOGIC-OBJECTIVE APPROACH AND BOUNDS OF ITS NUMBER OF STEPS

Tatiana Kosovskaya, Nina Petukhova

http://www.foibg.com/ijima/vol01/ijima01-1-p08.pdf

Abstract: Most processes, found in medicine, are nonlinear, chaotic, have a high level of complexity. The decisions in health care are often stereotyped, managed by habits preferences, previous experience and official directives. These decisions might be not completely conscious. There are a lot of papers, devoted to modeling diagnostics or treatment conduction, but still behavior responses of medical practitioners were not studied, no universal comprehensive and effective model was created. Besides nonlinear nature of biomedical phenomena, pathologies, its chaotic expression, all the information process in medicine at each of its stages, including information perception by available diagnostic tools, analysis, decision making and implementation of therapeutic interventions, are complex, chaotic. We made attempts to integrate this process, bringing scheme into harmony. Each stage requires creation some mathematical model, that might be described by generalized equation. These equations can be substituted into one, that could be solved in closed system. We do not aim to find some absolute kind of decision, its statistically calculated optimal way of solution, but accent on a special mood, the state of expert, which could give a possibility to make only one correct decision with failure in input parameters. In such cases the lack of prior data is compensated by doctor’s experience.

Keywords: imaging, mathematical modeling, intervention, choise, error analysis, Monty Hall paradox, method of branches and boundaries.

ACM Classification Keywords: H. Information Systems: H.1 MODELS AND PRINCIPLES: H.1.0 General; G.1.0 Mathematics of Computing General Error analysis; G.2 DISCRETE MATHEMATICS: G.2.1 Combinatorics: Combinatorial algorithms; G.2.2 Graph Theory.

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CHOICE OF DIAGNOSTIC DECISION MAKING IN MEDICINE AND INTERVENTION MISTAKE PREDICTION USING MATHEMATICAL MODELS

Ivan Melnyk, Rostyslav Bubnov

http://www.foibg.com/ijima/vol01/ijima01-1-p07.pdf

Abstract: Number of The grouping information problem in its two basic manifestations recovering function, represented by empirical data (observations) and problem of classification (clusterization) and conception of its solving by the standard recurrent procedures are proposed and discussed. It is turn out that in both case correspond procedures can be designed on the base of so called neurofunctional transformations (NfT— transformations). Conception of such transformations implements the idea of superposition of standard functions by certain sequence of recurrent applications of the superposition. Least Square Method is used for designing the elementary functional transformations and implemented by necessary developed of M-P-inverse? technique. It is turn out that the same approach may be designed and implemented for solving the classification problem. Besides, the special classes of beam dynamics with delay were introduced and investigated to get classical results regarding gradients. These results were applied to optimize the NfT—transformations?.

Keywords: Grouping information problem, generalized artificial neuronets, learning samples, beam dynamics, Fuzzy likelihood equation, Multiset theory.

ACM Classification Keywords: G.2.m. Discrete mathematics: miscellaneous,G.2.1 Combinatorics. G.3 Probability and statistics, G.1.6. Numerical analysis I.5.1.Pattern Recognition H.1.m. Models and Principles: miscellaneous:

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RECURRENT PROCEDURE IN SOLVING THE GROUPING INFORMATION PROBLEM IN APPLIED MATHEMATICS

V. Donchenko, Yu. Krivonos, Yu. Krak

http://www.foibg.com/ijima/vol01/ijima01-1-p06.pdf

Abstract: ESI (European Software Institute), Center Eastern Europe and BASSCOM, in collaboration with Association Autism developed a project of a model for employment provision to people with ASD. The model includes trainings and workshops for IT companies for work with people with ASD, as well as theoretical/ practical IT training for the job candidates. The job positions for the employees with autism spectrum disorders are software products testing for bugs, data processing in IT systems, administration and office functions and other suitable activities using IT. This paper describes the course of the project and the sustainable results of the pilot model. The training program and the methodology for adapted and real employment, developed within the framework of the project are also described.

Keywords: IT training and employment, autism, social iInclusion.

ACM Classification Keywords: J. Computer Applications - J.4 Social and Behavioral Sciences, K.3 Computers and Education - Assistive technologies for persons with disabilities, K.3 Computers and Education - Employment, K.3 Computers and Education - Handicapped persons/special needs,

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MODEL FOR IT TRAINING AND EMPLOYMENT OF PEOPLE WITH AUTISM SPECTRUM DISORDERS

Ekaterina Detcheva, Mirena Velkova, Ani Andonova

http://www.foibg.com/ijima/vol01/ijima01-1-p05.pdf