Evolutionary tree-based quasi identifier and federated gradient privacy preservations over big healthcare data

<span>Big data has remodeled the way organizations supervise, examine and leverage data in any industry. To safeguard sensitive data from public contraventions, several countries investigated this issue and carried out privacy protection mechanism. With the aid of quasi-identifiers privacy is not said to be preserved to a greater extent. This paper proposes a method called evolutionary tree-based quasi-identifier and federated gradient (ETQI-FD) for privacy preservations over big healthcare data. The first step involved in the ETQI-FD is learning quasi-identifiers. Learning quasi-identifiers by employing information loss function separately for categorical and numerical attributes accomplishes both the largest dissimilarities and partition without a comprehensive exploration between tuples of features or attributes. Next with the learnt quasi-identifiers, privacy preservation of data item is made by applying federated gradient arbitrary privacy preservation learning model. This model attains optimal balance between privacy and accuracy. In the federated gradient privacy preservation learning model, we evaluate the determinant of each attribute to the outputs. Then injecting Adaptive Lorentz noise to data attributes our ETQI-FD significantly minimizes the influence of noise on the final results and therefore contributing to privacy and accuracy. An experimental evaluation of ETQI-FD method achieves better accuracy and privacy than the existing methods.</span>

Application of the Correlation Measurement Method for Reconstructing of the Velocity Profile with Spatial and Temporal Discretization in Studies of the Hydrodynamics of Turbulent Flows Based on the Matrix Conductometry Method

The correlation method for measuring of the coolant fl rate is used in the operation of nuclear power plants and is widespread in research practice including study of turbulent fl hydrodynamics. However the question of its applicability and possibilities in studies using the matrix conductometry method remains open. Earlier the algorithm for determining of the correlation fl rate using a conductometric measuring system was highlighted and the error of the results obtained was estimated and the dependence of the influence of noise and the time of data collection on the reliability of results was investigated. These works were carried out using two independent mesh sensors and the issue of the resolution of local velocity components was not covered. The purpose of this work was to test the correlation method for measuring velocity with temporal and spatial sampling using two-layer mesh conductometric sensors.As the result velocity cartograms were obtained over the cross-section of the experimental model with quasi-stationary mixing and the value of the average flow rate is in good agreement with the values obtained from the standard flow meters of the stand. Also measurements were carried out at a non-stationary setting of the experiment and realizations of the flow rate and velocity components of the flow at the measuring points were obtained.Analysis of the obtained values allows to conclude about the optimal data collection time for correlation measurements, as well as the reliability of results.

The Influence of Noise in the Neurofeedback Training Sessions in Student Athletes

Considering that athletes constantly practice and compete in noisy environments, the aim was to investigate if performing neurofeedback training in these conditions would yield better results in performance than in silent ones. A total of forty-five student athletes aged from 18 to 35 years old and divided equally into three groups participated in the experiment (mean ± SD for age: 22.02 ± 3.05 years). The total neurofeedback session time for each subject was 300 min and were performed twice a week. The environment in which the neurofeedback sessions were conducted did not seem to have a significant impact on the training’s success in terms of alpha relative amplitude changes (0.04 ± 0.08 for silent room versus 0.07 ± 0.28 for noisy room, p = 0.740). However, the group exposed to intermittent noise appears to have favourable results in all performance assessments (p = 0.005 for working memory and p = 0.003 for reaction time). The results of the study suggested that performing neurofeedback training in an environment with intermittent noise can be interesting to athletes. Nevertheless, it is imperative to perform a replicated crossover design.

Research on High Robust Multimedia Image Encryption Based on Gyrator Transform Domain Model

The encryption and privacy protection of multimedia image resources are of great value in the information age. The utilization of the gyrator transform domain model in multimedia image encryption can select parameters more accurately, so it has a wider scope of utilization and further ameliorates the stability of the whole system. On account of this, this paper first analyzes the concept and connotation of gyrator transform, then studies the image encryption algorithm on account of gyrator transform, and verifies the robustness of the gyrator transform algorithm under the influence of noise interference, shear attack, and other factors through the high robust multimedia image encryption and result analysis of gyrator transform.

The Influence of Noise on the Exact Solutions of the Stochastic Fractional-Space Chiral Nonlinear Schrödinger Equation

In this paper, we consider the stochastic fractional-space Chiral nonlinear Schrödinger equation (S-FS-CNSE) derived via multiplicative noise. We obtain the exact solutions of the S-FS-CNSE by using the Riccati equation method. The obtained solutions are extremely important in the development of nuclear medicine, the entire computer industry and quantum mechanics, especially in the quantum hall effect. Moreover, we discuss how the multiplicative noise affects the exact solutions of the S-FS-CNSE. This equation has never previously been studied using a combination of multiplicative noise and fractional space.

Recent Trends of Controlling Chaotic Resonance and Future Perspectives

Stochastic resonance is a phenomenon in which the effects of additive noise strengthen the signal response against weak input signals in non-linear systems with a specific barrier or threshold. Recently, several studies on stochastic resonance have been conducted considering various engineering applications. In addition to additive stochastic noise, deterministic chaos causes a phenomenon similar to the stochastic resonance, which is known as chaotic resonance. The signal response of the chaotic resonance is maximized around the attractor-merging bifurcation for the emergence of chaos-chaos intermittency. Previous studies have shown that the sensitivity of chaotic resonance is higher than that of stochastic resonance. However, the engineering applications of chaotic resonance are limited. There are two possible reasons for this. First, the stochastic noise required to induce stochastic resonance can be easily controlled from outside of the stochastic resonance system. Conversely, in chaotic resonance, the attractor-merging bifurcation must be induced via the adjustment of internal system parameters. In many cases, achieving this adjustment from outside the system is difficult, particularly in biological systems. Second, chaotic resonance degrades owing to the influence of noise, which is generally inevitable in real-world systems. Herein, we introduce the findings of previous studies concerning chaotic resonance over the past decade and summarize the recent findings and conceivable approaches for the reduced region of orbit feedback method to address the aforementioned difficulties.