Multi-Objective Optimization Information Fusion and Its Applications for Logistics Centers Maximum Coverage

From past the development direction of logistics centers covering problem, the main solution is almost always relying on modern computer and gradually developed intelligent algorithm, at the same time, the previous understanding of dynamic covering location model is not "dynamic", in order to improve the unreasonable distribution of logistics centers deployment time, improve the service coverage, coverage as the optimization goal to logistics centers, logistics centers as well as each one can be free to move according to certain rules of "dot", according to the conditions set by the site moved to a more reasonable. The innovation of all algorithms in this paper lies in that the logistics centers themselves are regarded as the subject of free "activities", and they are allowed to move freely according to these rules by setting certain moving rules. Simulation results show that the algorithm has good coverage effect and can meet the requirements of logistics centers for coverage effect.

Application of Decision Tree Intelligent Algorithm in Data Analysis of Physical Health Test

With the continuous development of China’s cultural industry, people’s health has become one of the topics of the highest concern. Therefore, all the application models of physical health test data in the actual analysis have become the current research focus and trend direction of healthy constitution. This paper summarizes the significant problems in the analysis of physical health test data, through the comprehensive analysis and investigation of physical health test data, combined with the measurement of the test indicators, through the analysis and processing system of youth physical health data, the use process of national youth group physical health standard data management software, and decision tree intelligent algorithm in physical health. The research steps of test data analysis and application model summarize the application characteristics of physical health test data in the application process. Based on this, a decision tree intelligent algorithm is proposed, and the corresponding functions and optimization formulas of the algorithm are substituted. In the process of actual sample checking calculation, each weight range and corresponding errors are inferred and analyzed by combining examples. This paper summarizes the application model and optimization model of health test data analysis based on decision tree intelligent algorithm. Through the repeated test of the research data, the feasible area and application scope of the algorithm are obtained, and the practical optimization scheme and application ideas under the algorithm are obtained.

Computer Intelligent Algorithm in the Recovery of the Elbow Joint Sports Injury Model

In this study, the inverse kinematics mathematics computer intelligent algorithm model is used to study the sports injuries of the elbow joint of adolescents. At the same time, we simulated the movement parameter changes during the rehabilitation training of the patient’s wrist and proposed a joint angular velocity function based on cubic fitting. Research has found that when the training scene changes greatly or the target task is changed, the smoothness of the elbow joint movement will change. The research conclusions of this article provide a theoretical basis for the selection of man-machine action points and the formulation of rehabilitation training methods. This article establishes the degree-of-freedom simulation model of the operating arm, which is the number of independent position variables in the operating arm, and these position variables determine the positions of all parts in the mechanism.

Accurate Digital Marketing Communication Based on Intelligent Data Analysis

In digital marketing, the core advantages of scientific and technological means such as artificial intelligence and big data analysis gradually appear and pay attention to them. This paper studies the accuracy of digital marketing and proposes an intelligent algorithm based on data analysis, which improves the effect of marketing communication. Through the combination of intelligent algorithms and big data analysis, the data are convincing. Through the comparison and improvement of intelligent algorithm logistic regression and XGBoost, this paper puts forward an improved algorithm of XGBoost based on Bayesian optimization parameters, which can improve the efficiency of digital marketing communication and enhance the social influence of digital marketing.

Exploration of Artificial Intelligence Security in Multi Unmanned System Cooperation

In modern science and technology, artificial intelligence has become one of the most important and promising technologies in today’s society and plays a very important role in people’s life. In artificial intelligence, cooperation is a very important research direction, which includes the cooperation between sensors, coordinated man-machine interface and actuators on multiple UAVs. Therefore, based on the exploration of artificial intelligence security, this paper studies artificial intelligence in multi unmanned system cooperation. Firstly, this paper expounds the development of cooperative system, and then describes the purpose of multi unmanned system cooperation. Then, this paper studies the intelligent algorithm applied to the cooperation of multiple unmanned systems in the field of artificial intelligence. Finally, aiming at the existing security problems of artificial intelligence, this paper tests the functions of multiple unmanned systems. The test results show that when multiple unmanned systems work together, the accuracy of artificial intelligence in dealing with things is basically more than 90%. At the same time, it can be nearly 100% scientific, and can budget a variety of treatment schemes. This shows that in the multi unmanned system cooperation, artificial intelligence can almost meet its needs, but it still needs to be further improved.

Synthesis and Comparison of Improved Edge Detection Technique Based on Metaheuristic and Intelligent Algorithm Optimization

The edge is the most significant information in image processing applications. Moreover, and the accurate and continue edge commonly leads to accurate related steps like object tracking and region clustering. In fact, it is the first step of image analysis and understanding. The accuracy edge detection results have an impact on the comprehension machine system. In this paper we present various improved edge detection techniques by our research team, of similar color and grey level images, using the information theory approach based on other energy information inspired from Shannon entropy and utilizing as well the metaheuristic and intelligent method combined with multilevel thresholding approach in various color spaces, and like the ant colony optimization with the graph cut approach for indexing images before the segmentation step. In addition, particular swarm optimization is done, and finally the fuzzy technique is used. The effectiveness and accuracy of these approaches are evaluated by many metric measurements and compared with the common operators. The PR metric, has a significant mean value (about 20) than PR of Canny operator (about 9). And also, we can denote that all improved techniques achieve significant results with ameliorated running time.

Quasi-Mapping and Satisfying IoT Availability with a Penalty-Based Algorithm

The Internet of things and medical things (IoT) and (IoMT) technologies have been deployed to simplify humanity’s life, which the complexity of communications between their layers was increased by rising joining the applications to IoT and IoMT-based infrastructures. The issue is challenging for decision-making and the quality of service where some researchers addressed the reward-based methods to tackle the problems by employing reinforcement learning (RL) algorithms and deep neural networks (DNNs). Nevertheless, satisfying its availability remains a challenge for the quality of service due to the lack of imposing a penalty to the defective devices after detecting faults. This paper proposes a quasi-mapping method to transfer the roles of sensors and services onto a neural network’s nodes to satisfy IoT-based applications’ availability using a penalty-backwarding approach into the NN’s weights and prunes weak neurons and synaptic weights (SWs). We reward the sensors and fog services, and the connection weights between them when are covered the defective nodes’ output. Additionally, this work provides a decision-making approach to dedicate the suitable service to the requester using employing a threshold value in the NN’s output layer according to the application. By providing an intelligent algorithm, the study decides to provide a service based on its availability and updating initial information, including faulty devices and new joined components. The observations and results prove decision-making accuracy for different IoT-based applications by approximately 95.8–97% without imposing the cost. The study reduces energy consumption and delay by approximately 64.71% and 47.4% compared without using neural networks besides creating service availability. This idea affects deploying IoT infrastructures to decision-making about providing appropriate services in critical situations because of removing defective devices and joining new components by imposing penalties and rewards by the designer, respectively.