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

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

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

Parametric Multispectral Mappings and Comparative Genomics

This article describes new algorithms that allow for viewing genetic sequences in the form of their multispectral images. We presented examples of the construction of such mappings with a demonstration of the practical problems of comparative genomics. New DNA visualization tools seem promising, thanks to their informativeness and representativeness. The research illustrates how a novel sort of multispectral mapping, based on decomposition in several parametric spaces, can be created for comparative genetics. This appears to be a crucial step in the investigation of the genetic coding phenomenon and in practical activities, such as forensics, genetic testing, genealogical analysis, etc. The article gives examples of multispectral parametric sets for various types of coordinate systems. We build mappings using binary sub-alphabets of purine/pyrimidine and keto/amino. We presented 2D and 3D renderings in different characteristic spaces: structural, integral, cyclic, spherical, and third-order spherical. This research is based on the method previously developed by the author for visualizing genetic information based on new molecular genetic algorithms. One of the types of mappings, namely two-dimensional, is an object of discrete geometry, a symmetrical square matrix of high dimension. The fundamental properties of symmetry, which are traced on these mappings, allow us to speak about the close connection between the phenomenon of genetic coding and symmetry when using the developed mathematical apparatus for representing large volumes of complexly organized molecular genetic information

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

Parametric Multispectral Mappings and Comparative Genomics

This article describes new algorithms that allow for viewing genetic sequences in the form of their multispectral images. We presented examples of the construction of such mappings with a demonstration of the practical problems of comparative genomics. New DNA visualization tools seem promising, thanks to their informativeness and representativeness. The research illustrates how a novel sort of multispectral mapping, based on decomposition in several parametric spaces, can be created for comparative genetics. This appears to be a crucial step in the investigation of the genetic coding phenomenon and in practical activities, such as forensics, genetic testing, genealogical analysis, etc. The article gives examples of multispectral parametric sets for various types of coordinate systems. We build mappings using binary sub-alphabets of purine/pyrimidine and keto/amino. We presented 2D and 3D renderings in different characteristic spaces: structural, integral, cyclic, spherical, and third-order spherical. This research is based on the method previously developed by the author for visualizing genetic information based on new molecular genetic algorithms. One of the types of mappings, namely two-dimensional, is an object of discrete geometry, a symmetrical square matrix of high dimension. The fundamental properties of symmetry, which are traced on these mappings, allow us to speak about the close connection between the phenomenon of genetic coding and symmetry when using the developed mathematical apparatus for representing large volumes of complexly organized molecular genetic information

Scaling and Visualization of Nucleotide Sequences

Algorithms for scaling and visualization of nucleotide sequences developed in this study allow identifying relationships between the biochemical parameters of DNA and RNA molecules with scale invariance, fractal clusters, nonlinear ordering and symmetry and noise immunity of visual representations in orthogonal coordinate systems. The algorithms are capable of displaying structures of the nucleotide sequences of living organisms by visualizing them in spaces of various dimensions and scales. Approximately one hundred genes (protozoa, plants, fungi, animals, viruses) were analysed and examples of visualization of the nucleotide composition of genomes of various species have been presented. The developed method contributes to an in-depth understanding of the principles of genetic coding and simplifying the perception of genetic information due to the algorithmic interpretation of the basic properties of polynucleotide fragments with visualization of the final geometric structure of the genetic code