An improved neurogenetic model for recognition of 3D kinetic data of human extracted from the Vicon Robot system

في هذه الأيام ، انه من الضروري التمييز بين نوع السلوك البشري ، تلعب تقنيات الذكاء الاصطناعي دورًا كبيرًا في ذلك المجال. تم دمج خصائص خوارزمية الشبكة العصبية الاصطناعية (FANN) والخوارزمية الجينية لإنشاء آلية عمل مهمة تساعد في هذا المجال. حيث يمكن استخدام النظام المقترح للمهام الأساسية في الحياة ، مثل التحليل والأتمتة والتحكم والتعرف والمهام الأخرى. التقاطع والطفرة هما الآليتان الأساسيتان اللتان تستخدمهما الخوارزمية الجينية في النظام المقترح لاستبدال عملية الانتشار العكسي في ANN. بينما تركز تقنية الشبكة العصبية الاصطناعية المغذية على معالجة المدخلات ، يعتمد هذا العمل على عملية كسر خوارزمية الشبكة العصبية الاصطناعية المغذية. بالإضافة إلى ذلك ، يتم حساب النتيجة من كل ANN أثناء عملية التفكك ، والتي تعتمد على تقسيم خوارزمية الشبكة العصبية الاصطناعية إلى عدة شبكات ANN بناءً على عدد طبقات ANN ، وبالتالي ، كل طبقة في الشبكة العصبية الاصطناعية الأصلية يتم تقييمها. يتم اختيار أفضل الطبقات لمرحلة التقاطع بعد عملية الكسر ، بينما تمر الطبقات الأخرى بعملية الطفرة. ثم يتم تحديد مخرجات هذا الجيل من خلال دمج الشبكات العصبية الاصطناعية في شبكة ANN واحدة ؛ ثم يتم فحص النتيجة لمعرفة ما إذا كانت العملية تحتاج إلى إنشاء جيل جديد. ان أداء النظام جيدًا وأنتج نتائج دقيقة عند استخدامه مع البيانات المأخوذة من نظام Vicon Robot ، والذي تم تصميمه بشكل أساسي لتسجيل السلوكيات البشرية بناءً على بيانات ثلاثية الابعاد وتصنيفها على أنها طبيعية أو عدوانية.These days, it is crucial to discern between different types of human behavior, and artificial intelligence techniques play a big part in that.  The characteristics of the feedforward artificial neural network (FANN) algorithm and the genetic algorithm have been combined to create an important working mechanism that aids in this field. The proposed system can be used for essential tasks in life, such as analysis, automation, control, recognition, and other tasks. Crossover and mutation are the two primary mechanisms used by the genetic algorithm in the proposed system to replace the back propagation process in ANN. While the feedforward artificial neural network technique is focused on input processing, this should be based on the process of breaking the feedforward artificial neural network algorithm. Additionally, the result is computed from each ANN during the breaking up process, which is based on the breaking up of the artificial neural network algorithm into multiple ANNs based on the number of ANN layers, and therefore, each layer in the original artificial neural network algorithm is assessed. The best layers are chosen for the crossover phase after the breakage process, while the other layers go through the mutation process. The output of this generation is then determined by combining the artificial neural networks into a single ANN; the outcome is then checked to see if the process needs to create a new generation. The system performed well and produced accurate findings when it was used with data taken from the Vicon Robot system, which was primarily designed to record human behaviors based on three coordinates and classify them as either normal or aggressive

Структура и функции репликативного нейроподобного модуля

Описывается технология построения нейросетевой системы искусственного интеллекта на стыке декларативного программирования и машинного обучения на основе моделирования кортикальных колонок. Используя доступный материал и сравнительно простые явления, эволюционные механизмы создали сложноорганизованные разумные системы. Из этого следует вывод, что искусственный интеллект также должен основываться на простых, но масштабируемых и биоправдоподобных алгоритмах, в которых стохастическая динамика корковых нейронных модулей позволяет быстро и эффективно находить решения сложных проблем. Цель исследования – алгоритмическая формализация на уровне репликативных нейросетевых комплексов. Базовый модуль искусственного интеллекта представлен как специализация и формализация понятия «китайская комната», введенного Джоном Сёрлом. Приведены результаты экспериментов по прогнозированию бинарных последовательностей. Компьютерная симуляця показала высокую эффективность реализации предложенных алгоритмов, при этом вместо использования для каждой задачи тщательно подобранного и адаптированного отдельного метода с частично эквивалентной переформулировкой задач были применены стандартный единый подход и единые параметры алгоритма. Делается вывод, что результаты экспериментов показывают возможность эффективных прикладных решений на базе предложенной технологии. Представленная технология позволяет создать самообучающиеся и планирующие деятельность системы

Calculation of the cyclic characteristics of the electroencephalogram for investigation of the electrical activity of the brain

The purpose of the study is experimental verification of the proposed EEG analysis method based on the construction of a connectivity graph of the analyzed signal, in which the amplitudes are displayed by vertices, and their relative position relative to each other by arcs. The display of the EEG signal in the graph structure causes the appearance of cyclic structures with the possibility of calculating their numerical characteristics. As a result of the study, criteria for initialization of the initial conditions of the counting algorithm have been developed. The following parameters were calculated: the number of cycles and the Euler number in the EEG recording. Coil representations of graphs are given. The proposed algorithm has a scaling parameter, the choice of which affects the final results. The second free parameter of the proposed algorithm is the degree of artificial signal coarsening. Variants of the algorithm application for multichannel EEG signals with multichannel signal processing by channel-by-channel detection of semantic units and construction of a generalized semantic connectivity graph are considered. An example of an analyzed multichannel EEG signal, which was pre-processed with reduction of all amplitudes to natural numbers in accordance with the calculated characteristics, is given. An example of an EEG of a subject with closed eyes during quiet wakefulness and an EEG of a subject with open eyes is given. In Conclusion, it is shown that the final indicators can vary significantly (from zero to tens of thousands or more) depending on the particular derivation of the EEG channel. Analysis of the cyclic structures of the electroencephalogram seems to be a potential way to assess various human states due to the possibility of distinguishing them using the proposed method. The study has a limited, pilot characte

The Matrix Method of Representation, Analysis and Classification of Long Genetic Sequences

The article is devoted to a matrix method of comparative analysis of long nucleotide sequences by means of presenting each sequence in the form of three digital binary sequences. This method uses a set of symmetries of biochemical attributes of nucleotides. It also uses the possibility of presentation of every whole set of N-mers as one of the members of a Kronecker family of genetic matrices. With this method, a long nucleotide sequence can be visually represented as an individual fractal-like mosaic or another regular mosaic of binary type. In contrast to natural nucleotide sequences, artificial random sequences give non-regular patterns. Examples of binary mosaics of long nucleotide sequences are shown, including cases of human chromosomes and penicillins. The obtained results are then discussed

Computer Visualization of Julia Sets for Maps beyond Complex Analyticity

Using the computer program creating Julia sets for two-dimensional maps we have made computer animation showing how Julia sets for the Peckham map alters when the parameter of the map is changing. The Peckham map is a one-parameter map which includes the complex map z=z^2+c, and is nonanalytical for other values of the parameter. Computer animation of Julia fractal sets allows seeing how patterns typical for complex maps gradually destroy

Spectral Decomposition of Mappings of Molecular Genetic Information in the System Basis of Single Nucleotide Functions

This paper presents and visualizes examples of large amounts of genetic information using a new class of cognitive computer graphics algorithms. These algorithms are related to the semiotics of perception and allow the interpretation of those properties of nucleotide sequences that are difficult to perceive by simple reading or by standard means of statistical analysis. This article summarizes previously presented algorithms for visualizing long nucleic acids based on the primary Hadamard–Walsh function system. The described methods allow us to produce one-dimensional mappings of nucleic acids by levels corresponding to their scale-integral physicochemical parameters and construct a spectral decomposition of the nucleotide composition. An example of the spectral decomposition of parametric representations of molecular genetic structures is given. In addition, a multiscale composition of genetic functional mappings visualizing the structural features of nucleic acids is discussed

Computer Visualization of Julia Sets for Maps beyond Complex Analyticity

Using the computer program creating Julia sets for two-dimensional maps we have made computer animation showing how Julia sets for the Peckham map alters when the parameter of the map is changing. The Peckham map is a one-parameter map which includes the complex map z=z^2+c, and is nonanalytical for other values of the parameter. Computer animation of Julia fractal sets allows seeing how patterns typical for complex maps gradually destroy

Spectral Decomposition of Mappings of Molecular Genetic Information in the System Basis of Single Nucleotide Functions

This paper presents and visualizes examples of large amounts of genetic information using a new class of cognitive computer graphics algorithms. These algorithms are related to the semiotics of perception and allow the interpretation of those properties of nucleotide sequences that are difficult to perceive by simple reading or by standard means of statistical analysis. This article summarizes previously presented algorithms for visualizing long nucleic acids based on the primary Hadamard–Walsh function system. The described methods allow us to produce one-dimensional mappings of nucleic acids by levels corresponding to their scale-integral physicochemical parameters and construct a spectral decomposition of the nucleotide composition. An example of the spectral decomposition of parametric representations of molecular genetic structures is given. In addition, a multiscale composition of genetic functional mappings visualizing the structural features of nucleic acids is discussed

A Comparative Analysis of Different Strains of Coronavirus Based on Genometric Mappings

Coronaviruses are viruses that infect the respiratory system of humans. Besides high mortality rates among the population, they brought about several economic crises on a global scale. Methods. To study and identify features in the genetic composition of the nucleotide sequences of various coronaviruses, we applied copyright algorithms and visualization, which allowed us to compare the biochemical parameters of diverse RNA coronaviruses in a visual form. Results. The article provides examples of different approaches to imaging coronaviruses. We have provided examples of coronavirus RNA structure visualization in various parametric spaces (1-D and 2-D). We employed various visualization types, including structural, integral, and frequency. The research discussed methods of visualization. Our team developed visualization and comparative analysis of coronavirus serotypes and visualization of SARS-CoV-2 coronavirus datasets. Discussion followed on the visualization results. The presented techniques and the results allowed for displaying the structure of RNA sequences of coronaviruses in spaces of various dimensions. Conclusions. According to our findings, the proposed method contributes to the visualization of the genetic coding of coronaviruses. We discussed the issues of machine learning and neural network technology concerning the analysis of coronaviruses based on the presented approach. The described line of research is essential for the study and control of complex quantum mechanical systems, such as RNA or DNA