Non-invasive monitoring of blood glucose by means of wearable tracking technology
The secular outcome of our investigation is development of new monitoring service for glucose control related to diabetes. It is based on the main results of research: 1) New innovative wearable sensor that carry non-invasive measurement of glucose level. Sensor uses several independent technologies, simultaneously: radio-frequency with different levels of signal, ultrasonic, electromagnetic and thermal; 2) Special mobile application as the principal interface monitor for personal usage; 3) The unique proprietary algorithm, based on neural net, which calculates the weighted average and returns the user's glucose level. The algorithm précises the results of measurements, based on genetic neural net ideas; 4) Special designed Data Base Storage in our cloud software specialized for gathering information and giving the predictions for patient. All results together makes the essence of the research.
In work the developed model of adaptive management by the vertically integrated companies based on the system approach supporting the mechanism of an operational management in a uniform cycle of strategic planning, within the limits of faster time is presented. Thus for a finding of optimum values of operating parameters special algorithms of a class of genetic algorithms are used, neural networks the example of the developed system of adaptive management for the vertically-integrated oil company is etc. presented.
Abstract— In this paper a new multi-agent genetic algorithm for multi-objective optimization (MAGAMO) is presented. The algorithm based on the dynamical interaction of synchronized agents which are interdepended genetic algorithms (GAs) having own separate evolutions of their populations. This approach has some similarities with well known “island model” of GA. In both methods is used a migration of individuals from agents (“islands”) to the main process (“continent”). In contrast, the intelligent agents in MAGAMO are able to decompose the dimensions space to form evolutions of subpopulations (instead of distribution of initial population as in the standard “island model”). In the same time, the main (central) process is responsible for the coordination of agents only and their selection according Pareto rules (without evolution). Intelligent agents seek local suboptimal solutions for a global optimization, which will be completed in the result of the interaction of all agents. In the result of this, the amount of needed recalculating the fitness-functions can be significantly reduced. It is especially important for the multi-objective optimization related to a large-scale problem. Besides, the proposed approximating approach allows solving complex optimization problems for real big systems (like an oil company, plants, corporations, etc.).
Development of linguistic technologies and penetration of social media provide powerful possibilities to investigate users’ moods and psychological states of people. In this paper we discussed possibility to improve accuracy of stock market indicators predictions by using data about psychological states of Twitter users. For analysis of psychological states we used lexicon-based approach, which allow us to evaluate presence of eight basic emotions in more than 755 million tweets. The application of Support Vectors Machine and Neural Networks algorithms to predict DJIA and S&P500 indicators are discussed.
In this research we analyzed the problem of solving transcendental equations' systems. More detailed is analyzed the approach of solution based on genetic algorithms because it is less examined than the one based on numerical methods. The research will be useful for different kinds of physical and mathematical calculations containing transcendental equations of high complexity. The research is available for students and graduates who are familiar with the basics of numerical methods, mathematical analysis, discrete mathematics and combinatorial algorithms.
The following topics were dealt with: human/computer interfaces; texture, depth and motor perception; neural nets; fuzzy systems; learning; product/process design; simulation; robotics; visual system cybernetics; batch processes; image compression and interpretation; AI applications; fuzzy adaptive control; decision modelling; agile manufacturing; service sector; inductive algorithms; complex systems; Petri nets; real time imaging; KBS; machine recognition; requirements engineering; inspection and shop floor control; environmental decision making; medicine; supervisory control; discrete event systems; power systems; software methods; heuristic search; vision systems; database systems; information modelling; facility design and material handling; conflict resolution; emergency management; genetic algorithms; decision making and path planning; IVHS; senses approximation; intelligent user interface; robust controllers for mechanical systems; cognitive and learning systems; command and control systems; pilot associate systems; neural net applications; real time systems; mobile robot visual processes; medical applications; utility energy systems; machine recognition; computing systems design; software engineering; military applications; data analysis; stochastic processes; guided vehicles; and stability and compensation.
This book constitutes the refereed proceedings of the 6th IAPR TC3 International Workshop on Artificial Neural Networks in Pattern Recognition, ANNPR 2014, held in Montreal, QC, Canada, in October 2014. The 24 revised full papers presented were carefully reviewed and selected from 37 submissions for inclusion in this volume. They cover a large range of topics in the field of learning algorithms and architectures and discussing the latest research, results, and ideas in these areas.
The paper theorizes on the general architectonics of idealized cognitive models (ICMs) and their involvement in metonymy and metaphor. The article posits that an ICM's structure should reflect the architecture of the neural network/s engaged in processing of a given concept. The ICM nodes, or cogs, construct a complex, hierarchically organized neural connections, with the superior nodes being highly selective, invariant and prototypical. Signals travelling from one cog to another within one ICM are essentially metonymical, while a cog shared by two or more ICMs marks a metaphoric shift.
The authors proposed and mathematically described model of a new type of the Fermi-Pasta-Ulam recurrence (the FPU auto recurrence) and hypothesized an adequate description of the heart's electrical dynamics within the observed phenomenon. The dynamics of the FPU auto recurrence making appropriate electrical dynamics of the normal functioning of the heart in the form of an electrocardiogram (ECG) was obtained by a computer model study. The model solutions in the form of the FPU auto recurrence – ECG Fourier spectrum were evaluated for resistance to external disturbances in the form of random effects, as well as periodic perturbation at a frequency close to the heart beating rate of about 1 Hz. In addition, in order to simulate the dynamics of myocardial infarction model, studied the effect of the surface area of the myocardium on the stability and shape of the auto recurrence – ECG spectrum. It has been found that the intense external disturbing periodic impacts at a frequency of about 1 Hz lead to a sharp disturbance spectrum shape FPU auto recurrence – ECG structure. In addition, the decrease in the surface of the myocardium by 50% in the model led to the destruction of structures of the auto recurrence – ECG, which corresponds to the state of atrial myocardium. Research models have revealed a hypothetical basis of coronary heart disease in the form of increasing the energy of high-frequency harmonics spectrum of the auto recurrence by reducing the energy of low-frequency harmonic spectrum of the auto recurrence, which ultimately leads to a sharp decrease in myocardial contractility. In order to test the hypothesis has been studied more than 20,000 ECGs both healthy people and patients with cardiovascular disease. As a result of these studies, it was found that the dynamics of the electrical activity of normal functioning of the heart can be interpreted by the display of the detected by authors the FPU auto recurrence, and coronary heart disease is a violation of the energy ratio between the low and high frequency harmonics of the FPU auto recurrence Fourier spectrum equal to the ECG spectrum. Thus, the hypothesis has been confirmed.