Power and Area Efficient Cascaded Effectless GDI Approximate Adder for Accelerating Multimedia Applications Using Deep Learning ModelRead the full article
Computational Intelligence and Neuroscience is a forum for the interdisciplinary field of neural computing, neural engineering and artificial intelligence. The journal’s focus is on intelligent systems for computational neuroscience.
Chief Editor, Professor Cichocki, engages in world-leading research in the field of artificial intelligence and biomedical applications of advanced data analytics technologies.
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Characterization of Group Behavior of Corruption in Construction Projects Based on Contagion Mechanism
With the rapid development of construction projects, more and more engineering corruption problems have emerged. Therefore, this paper proposes a SEIR (susceptible-exposed-infected-recovered) based corruption model to better understand the propagation process of corruption cases in construction projects. In this model, the data samples are collected from the 2018 Engineering Corruption Case Judgment Document, the propagation parameters are obtained through actual case analysis with the help of complex networks, the change process and key influencing factors of actual nodes in engineering corruption cases are simulated by Python. The study results indicate that the personnel conforms to the “4–9 transmission law,” in which the early stage is a period of high incidence of corruption cases. The network of corruption cases is somewhat vulnerable, and its spread is about minus 8 times the change in crackdown rate and 10 times the change in infection rate. The variation range of the susceptible population S and the removed person R in the propagation simulation curve can predict the relationship between corruption infection rate and crackdown rate, which can provide theoretical guidance for preventing the occurrence of corruption.
Decision Scheduling for Cloud Computing Tasks Relying on Solving Large Linear Systems of Equations
With the continuous reform and innovation of Internet technology and the continuous development and progress of social economy, Big Data cloud computing technology is more and more widely used in people’s work and life. Many parallel algorithms play a very important role in solving large linear equations in various applications. To this end, this article aims to propose and summarize a cloud computing task scheduling model that relies on the solution of large linear equations. The method of this paper is to study the technology of solving large-scale linear equations and propose an M-QoS-OCCSM scheduling model. The function of the experimental method is to solve the problem of efficiently executing N mutually dependent parallel tasks within limited resources, while fully satisfying users’ expectations of task completion time, bandwidth rate, reliability, and cost. In this paper, the application experiment of large-scale linear equations in task scheduling is used to study task scheduling algorithms. The results show that when the task load is 10 and 20, the convergence speed of the MPQGA algorithm is 32 seconds and 95 seconds faster than that of the BGA algorithm, respectively.
Research on Effect of Load Stimulation Change on Heart Rate Variability of Women Volleyball Athletes
Objective. To explore the effect of different training load stimulation on heart rate variability level of Chinese elite female volleyball players. Through two-year follow-up experiment, this paper uses OmegaWave Sport Technology system to track and test the heart rate variability level and central nervous system parameters of 25 elite Chinese women volleyball players who participated in the national adult volleyball training in 2019 and 2020. It is found that the HRV time-domain index of the players under the stimulation of three stages of training load during the winter training in 2020 is determined. Frequency-domain index has significant influence on response stability of central nervous system. In order to further explore the influence of HRV on response stability of central nervous system, a feature classification method based on distance evaluation is proposed for experimental data processing. Through the multimodal human-machine interaction (M-HMI), advanced machine learning is used to promote the cooperative interaction between human and intelligent body. After analysis, SDNN and LF n.u. have a significant impact on the average reaction time. It shows that some indexes tested by the OmegaWave system can reflect the real-time physical function state of athletes sensitively and play an active role in diagnosis of fatigue of athletes’ central nervous system. HRV time-domain and frequency-domain indexes, as parameters to evaluate the body functional state of excellent female volleyball players in the preparation process of competition, can sensitively reflect the level of autonomic nerve regulation of athletes in three different load stages.
A Social-aware and Mobile Computing-based E-Commerce Product Recommendation System
E-commerce product recommendation system can help users to find their own products quickly from a large number of products. To address the shortcomings of the current e-commerce product recommendation system, such as low efficiency and large recommendation errors, we designed an intelligent recommendation system based on social awareness and mobile computing. The behavioral characteristics of the current e-commerce product recommendation system are analyzed; the e-commerce product recommendation system is built according to the data processing technology of mobile computing, and the key technologies of the e-commerce product recommendation system are designed. The test results show that the proposed system overcomes the shortcomings of the traditional e-commerce product recommendation system, speeds up the speed of users to find the products they really need from a large number of products, improves the accuracy of e-commerce product recommendations, and the error of e-commerce product recommendations is much lower than that of the traditional, which has higher practical application value.
Earnings Management Behavior of Enterprise Managers Based on Evolutionary Game Theory
Today, earnings mismanagement in China’s enterprises has become a serious problem as managers conduct financial fraud by means of earnings management, hindering China’s overall economic development. Upon shareholders’ requirements and investors’ concerns, managers should disclose real financial information. The essay analyzes the revenue function generated by the manager and the shareholder through an evolutionary theory model where the managers team of the enterprise and shareholders are both game parties. After building the model, the essay utilizes Python to stimulate the theoretical model to analyze both parties’ behavior to explain the process of evolutionary game theory.
Novel Method for Safeguarding Personal Health Record in Cloud Connection Using Deep Learning Models
It is a new online service paradigm that allows consumers to exchange their health data. Health information management software allows individuals to control and share their health data with other users and healthcare experts. Patient health records (PHR) may be intelligently examined to predict patient criticality in healthcare systems. Unauthorized access, privacy, security, key management, and increased keyword query search time all occur when personal health records (PHR) are moved to a third-party semitrusted server. This paper presents security measures for cloud-based personal health records (PHR). The cost of keeping health records on a hospital server grows. This is particularly true in healthcare. As a consequence, keeping PHRs in the cloud helps healthcare institutions save money on infrastructure. The proposed security solutions include an optimized rule-based fuzzy inference system (ORFIS) to determine the patient’s criticality. Patients are classified into three groups (sometimes known as protective rings) based on their severity: very critical, less critical, and normal. In trials using the UCI machine learning archive, the new ORFIS outperformed existing fuzzy inference approaches in detecting the criticality of PHR. Using a graph-based access policy and anonymous authentication with a NoSQL database in a private cloud environment improves data storage and retrieval efficiency, granularity of data access, and response time.