Reviewerly/exHarmony huggingface dataset:
Research on Medical Big Data Mining and Intelligent Analysis for Smart Healthcare

In the process of rapid socio-economic development, people’s living standards have been improved to varying degrees. At the same time, more and more people are turning their attention to physical health while satisfying their material and spiritual lives. In order to provide more effective health protection, China has established a medical system covering the whole country. While increasing medical investment, China has improved its medical level through the integration of high-tech, including the application of big data technology in smart healthcare. This article first introduces the basic framework of intelligent analysis and cloud computing, and then applies medical big data mining and formulas to the field of smart healthcare, which can adjust smart healthcare by calculating the average and standard deviation of medical care. Finally, practical analysis is conducted on big data mining in smart healthcare, and it is analyzed that it is in line with smart healthcare big data mining. The experimental results show that the proposed algorithm can effectively improve the level of medical big data preprocessing, and the data mining effect is good. Finally, this article explains the new technologies such as medical IoT, cloud computing, big data mining, and intelligent analysis in the definition of smart healthcare, as well as analyzes the basic concepts and business forms to prove the effectiveness of smart healthcare. Among them, this article obtains relevant health data from various sensors of patients, achieving 78% recognition, 87% detection, and 92% control of the tested object from Data 1 to Data 4. Therefore, combining big data mining with intelligent algorithms in smart healthcare can greatly improve assistance.
Construction of Image Segmentation and Recognition System Based on Deep Neural Network Algorithm

Neural network is a mathematical model constructed by imitating the nervous system of human brain, which is formed by complex connection of many artificial neurons. The loss back propagation algorithm promotes the self-learning of model parameters, and finally realizes the modeling of the complex relationship between pixels in the image. Similar to shallow learning algorithm, which only contains one or two layers of nonlinear transformation, deep learning has the ability to learn more advanced feature expression by simulating the principle and structure of human brain neurons and realizing multi-layer abstraction of features through multi-layer artificial neural network. In this article, a 3D image segmentation and recognition algorithm based on improved Convolutional Neural Network (CNN) is proposed, which reduces the dependence of traditional semantic segmentation model training on manually labeled data sets, improves the generalization ability of the network, and realizes the virtual reconstruction of 3D scenes by extracting the underlying features of 3D images. The comprehensive experimental results show that the image segmentation and recognition model based on the improved CNN algorithm has advantages over SVM algorithm in both accuracy and efficiency.
Research on Node classification Algorithm based on Deep Network representation learning under incomplete information

Most of the existing network representation learning models are designed under the assumption that the network is complete. In practice, large-scale networks often have incomplete/missing node content or links. Therefore, its performance will be affected by missing nodes. To solve the above problems, this paper proposes a node classification algorithm DNRL based on deep network representation learning under incomplete information. Firstly, transfer probability matrix is used to integrate structural information and attribute information dynamically, which makes up for the excessive loss caused by incomplete structural information. Then, a depth generation model (variational autoencoder) with powerful feature extraction ability is used to learn the lowdimensional representation of nodes through the joint optimization of graph attention network and autoencoder. At the same time, the potential high nonlinear features of network data are captured from three perspectives: node attributes, local structure and global structure. Finally, through evaluation and analysis, the proposed algorithm has better node classification effect.
Construction of Virtual Simulation System for Interior Design Based on Augmented Reality

As one of the most intuitive technical means of design expression, VR (Virtual Reality) technology has become a platform for designers and customers to communicate and display. Compared with traditional design expression methods, the communication of design schemes is more efficient and can better meet customer requirements. This paper attempts to study interior design based on AR (Augmented Reality) and build a virtual simulation system. The simulation experiment proves that the success rate of the system established in this paper in the test of LED lamp modeling state is high, which can reach 81.85%. The average success rate of the traditional simulation system is only 47.3%. Similarly, compared with the traditional simulation system, the return rate of the system established in this paper is lower than that of the traditional system, and the average return rate in this paper is only 17.13%. Therefore, it can be concluded that the virtual simulation system of interior design established in this paper has more advantages in interior design. It can be seen that the application of AR technology in interior design has opened up a new interior design and display mode for us.
Research on Teaching Mode of Experiential Learning Environment Based on VR Virtual Simulation Technology

The VR computer simulation technology, computer graphics technology, multimedia technology, sensing technology, network technology and other high-tech technologies are integrated to realize a three-dimensional virtual environment similar to the real life environment, such as sight, hearing and touch. This paper attempts to build an experiential learning environment teaching mode based on VR virtual simulation technology. Edit with the help of other editing software, and import the edited material into Unity3D for presentation. By entering the virtual practice system of related disciplines, students can practice in different positions in free time and space, and understand the business contacts between different departments. The results show that virtual experimental courseware is helpful to express the teaching information of the subject, promote learners’ mastery of relevant knowledge, satisfy their desire for knowledge, and better help them to construct the meaning of what they have learned.
Research on Computer Vision Target Detection Algorithm Based on Deep Neural Network Model

Computer vision (CV) is to use computers to analyze and process the surrounding environment through images or videos, and to simulate the information processing process of human visual system. Computers often need to detect and locate foreground objects in images or videos first, so as to carry out a series of higher-level processing such as subsequent recognition, tracking and analysis. Its core mainly includes two parts: one is the detection and extraction of moving objects; The second is the tracking of moving targets. The detection and extraction of moving target is the premise of target tracking task. If the extracted target is incorrect or incomplete, it will have a great impact on the target tracking part, and even it cannot be tracked effectively. As a technical subject, CV tries to apply its theory and model to construct a CV system. Based on this, this paper takes the depth image as the research object and combines the neural network model in machine learning theory to realize the image occlusion boundary detection task. At the same time, according to the distribution of the feature vectors of pixel points, a nonlinear normalization method is designed to map the feature vectors and train and detect them according to the BP neural network model. All pixel points in the image are divided into occluded boundary points and non-occluded boundary points to achieve the purpose of detection.
Research on Cloud Virtual Machine Scheduling Strategies and Algorithm Optimization

Cloud computing is widely used in the engineering field with the advantage of low cost and high service. The data center, as the carrier of cloud applications, consumes a lot of power and wastes more and more resources in providing services to the cloud. In this paper, a new VM migration strategy is proposed to address the problem that existing research cannot effectively balance the quality of service and energy consumption. The strategy uses a combination of algorithms for load prediction, selection and placement of VMs to optimize the VM migration process at all stages simultaneously. The specific work includes the following aspects.(1)The load prediction phase uses a combination of BP neural network, differential autoregressive moving average model (ARIMA), and least squares support vector machine model (LSSVM) prediction methods to identify overloaded and underutilized hosts in the data center.(2)The virtual machine selection algorithm (HULT) based on high CPU utilization and low migration time is proposed in the virtual machine selection phase to migrate virtual machines from overloaded hosts and lightly loaded hosts.(3)The virtual machine placement phase proposes the Resource Utilization Based Best Adaptation Decreasing Virtual Machine Placement algorithm (RU-PABFD), the virtual machine placement process is based on the original Best Adaptation Decreasing algorithm, and the original algorithm is improved by adding the constraint of the remaining resource utilization of the destination host. And the combined prediction algorithm, HULT algorithm and RU-PABFD algorithm are combined to obtain the complete virtual machine scheduling policy (VMS-CP).The algorithm proposed in this paper can guarantee the quality of service and reduce the waste of resources in the data center, while maintaining the balance between energy consumption and performance.
Autonomous Role Design of Game Based on Neural Network Algorithm and Artificial Intelligence

At present, the interaction mode of role behavior in most games is deterministic interaction behavior. Such deterministic behavior often fails to reflect the autonomy of the role, and it also leads to the monotonous action of the role, which is easily predicted by the player and reduces the playability of the game. In this paper, the design of game autonomous role based on neural network algorithm and AI (artificial intelligence) is developed. Based on cognitive role modeling, this paper puts forward the design idea of game autonomous role by combining BPNN (BP neural network) and GA (genetic algorithm). Using GA to optimize BPNN, applying GA in BPNN can realize automatic optimization of learning rules, and improve the learning efficiency and the optimization speed of weight coefficient of BPNN. The research results show that using the optimized neural network algorithm to evolve the autonomous role of the game can reduce the computational complexity by about 25% compared with using the traditional evolutionary neural network algorithm. The running results of virtual characters in the system show the effectiveness of the behavior model; The effectiveness of the optimized neural network algorithm is verified by experimental analysis.
Design of Intelligent Path-finding Algorithm for Sweeping Robot Based on BP Neural Network Model

Cleaning is a repetitive and time-consuming job. Although there are many kinds of vacuum cleaners that can make people get rid of the traditional cleaning tools, they still can’t get rid of the cleaning work, and these vacuum cleaners still have great limitations. With the continuous development and progress of technology, the general trend of artificial intelligence (AI) is coming. As a contemporary intelligent household appliance, sweeping robot has more and more applications. In this paper, the full coverage intelligent path-finding algorithm is designed and optimized, and an intelligent path-finding algorithm of sweeping robot based on improved Back-propagation neural network (BPNN) is proposed, so as to optimize the sweeping path of sweeping robot, select the best cleaning path and eliminate the cleaning blind area of sweeping robot as much as possible. Finally, the proposed algorithm and the traditional ACO algorithm are simulated and compared. The experimental results show that the algorithm in this paper can not only be well applied in closed unknown complex environment, but also has the advantages of few local sub-regions, short and smooth path, and also has good adaptability and real-time optimization characteristics.
Research on Computer Vision Target Detection Algorithm Based on Feature Recognition

Computer vision has formed many research branches because of its different application fields and uses. Due to the influence of various environmental factors in real life, the clarity, completeness and distinctiveness of such target features are lacking to a certain extent, which brings great challenges to the existing algorithms. In this paper, a computer vision target detection algorithm based on SIFT (Scale Invariant Feature Transform) feature recognition is proposed. Firstly, the algorithm preprocesses the target image and the input image, then detects the existence of some feature points, that is, generates feature vectors, and obtains candidate matching points through the matching of feature vectors. Finally, it judges whether the target is recognized or not according to the number of matching points. The research results show that the target detection algorithm proposed in this paper can effectively combine and filter matching through multiple steps, and the matching accuracy is significantly improved compared with the traditional algorithm. The research results show that the background subtraction method is used to extract the moving target, which ensures the integrity of the detected target and improves the real-time and accuracy of the moving target detection.
Optimization Algorithm Design of 3D Animation Rendering Based on Virtual Reality

With the development of computer technology, people’s thinking mode has also changed dramatically. 3D animation and virtual reality (VR) technology can be seen everywhere around people. With the development of technology, 3D animation and VR technology are applied in more and more fields, such as education, medical treatment and commerce. Under the background of increasing international competition, the update speed of high-tech is changing with each passing day. With the wide application of VR technology by modern people, the 3D animation industry has been further developed. The effective combination of 3D animation scene graphic design and VR technology can further improve the overall perfection of 3D animation scene design. This paper improves the lighting model and shadow rendering algorithm according to the defects of the general lighting rendering algorithm in the application of 3D animation scenes. Based on this, a hybrid 3D scene lighting rendering algorithm is obtained. When optimizing the Cook Torrance lighting model, a geometric attenuation factor algorithm is used, and when improving the shadow rendering algorithm, a light shadow rendering algorithm based on variance shadow is used.
Simulation of Computer Image Processing Model Based on Random Forest Algorithm

In essence, image recognition, as a pattern classification problem, can also be processed by classical pattern classification algorithms. Under the background of network information technology, it is need to use computer vision algorithm to process the image and realize the three-dimensional reconstruction of the image for the distortion of the true three-dimensional display image of intelligent interactive system. Random Forest (RF) is a new ensemble learning algorithm developed in recent years, which has good classification accuracy. In this article, a computer image processing model based on RF algorithm is proposed to enhance the ability of depth feature learning. The experimental results show that this image processing technology is more accurate than BP neural network and can be widely used in image processing. High-level target feature representation can often improve the recognition accuracy. However, the extraction of high-level target features generally has certain computational complexity. Compared with traditional methods, the accuracy of the proposed method is greatly improved, and the generalization performance is superior, which can identify images that have been beautified and repaired.
Application of Gesture Recognition Technology Based on Deep Learning in Intelligent Laboratory

Gesture recognition technology has great value in real life applications, and it is still not mature and has many difficulties to overcome. Therefore, gesture recognition technology still has high research value. The core of this paper is to design a feasible intelligent laboratory, and improve the image segmentation and gesture tracking algorithms to improve the accuracy of gesture recognition. SSD network replaces all the connected layers of the front-end basic network with convolution layers, and extracts the result of one convolution layer for target detection. In this paper, the common convolution layer is split, and each convolution group is divided into a set of deep convolution and a set of point convolution, and each layer has its own convolution kernel by deep convolution operation. The results show that the test accuracy of the proposed algorithm is 97.406%, and it shows better classification and recognition ability when running on the self-built database. DL (Deep learning) will automatically learn the characteristics of gestures in the training set, and when the whole task is implemented end-to-end, regression model will be used to solve the detection task, and the detection result will be significantly improved. Can realize certain market application.
Research on Cloud Platform Computing Security and Integrity Protection System Based on Machine Learning

Cloud computing has gained widespread popularity in various fields due to its benefits, including on-demand resource provisioning, scalability, and cost-effectiveness. However, the rapid expansion of cloud computing services has raised significant concerns about the security and integrity of cloud data. This paper proposes a security and integrity protection system for cloud computing based on machine learning algorithms. The proposed system employs various machine learning algorithms, including Random Forest, Support Vector Machine, and Naive Bayes, to detect and prevent security threats in cloud computing. Additionally, the proposed system employs encryption techniques to ensure data confidentiality in the cloud. Experimental results demonstrate that the proposed system can effectively detect and prevent security threats, thus preserving the integrity of cloud data.
Study on Limit Equilibrium Stability Analysis Method of Rock and Soil Slope Based on Improved Genetic Algorithm

The equilibrium stability of rock-soil slope has always been the focus of civil industry and specialty, and the establishment of its stability is one of the difficult and hot issues faced by civil engineering. The traditional slope limit analysis methods are mostly based on the basic theory and operation principle of LimiState software to simulate the inclined layered soil slope of common slope projects. Although the results obtained by limit analysis are accurate, they still lack certain applicability. In this paper, the limit equilibrium stability of rock and soil slopes will be analyzed by improving genetic algorithm, and Powell algorithm will be added as a local process on the basis of genetic algorithm, which can ensure the global search ability of genetic algorithm and increase its search speed. The improved genetic algorithm will search the minimum safety factor of rock and soil slopes, which will bring new method analysis for the limit equilibrium stability of rock and soil slopes and provide technical support and theoretical construction for the development of civil engineering.
Research on Elderly Fall Prevention Recognition System for Intelligent Elderly Care and Machine Vision

In order to improve the sensitivity and specificity of elderly fall recognition, a set of elderly fall prevention recognition system based on intelligent elderly care and machine vision is designed. The system was jointly developed based on the EAIDK-310 embedded development board and OpenCV library. The moving object detection algorithm based on the hybrid Gaussian model was adopted to detect the moving objects, and the multi-parameter comprehensive evaluation algorithm based on the three contour features of human aspect ratio, inclination angle and centroid height was adopted to recognize the fall behavior, thereby realizing the elderly fall recognition. The test results show that the average sensitivity and specificity of this system for the elderly fall behavior recognition is 94.23% and 95.86%, and the system can process about 10 frames of images per second, which has a certain realtime performance and can realize realtime recognition of the elderly fall behavior.
BP-based Evaluation Paradigm of Automobile Marketing Professional Information Teaching Practise

This topic builds a BP based practise teaching evaluation index system of automobile marketing major based on the characteristics of automobile marketing major in light of the lack of comprehensive and efficient information-based evaluation methods in the practise teaching of automobile marketing major. In this research, a novel adaptive variation genetic algorithm (AGA) is used to address the issue that it is challenging to identify the best solution during the optimisation phase of traditional BP. Additionally, this research uses the entropy weight approach rather than BP assessment since it is too subjective. The construction of an intelligent assessment model for teaching automotive marketing practise is based on the EM-AGA-BP. The method's excellent accuracy-up to 96.42%-has been shown. The EM-AGA-BP model's average accuracy, Precision, F1, and AUC are 0.952, 0.965, 0.927, and 0.974, respectively. The assessment model developed in this study may significantly raise the accuracy of internship evaluations in automobile marketing. More excellent marketing skills may be offered for the automotive business via the study of this topic.
A New Method for Patch

ViT introduced the structure Transformer from NLP to computer vison. To mimic the relationship between sentence and words, ViT cut picture to patches. Then MLP-Mixer learned and continued the method of using segmented pictures to become patches, and designed a network built purely by MLP. These networks verify the effectiveness of slicing pictures. However, from previous work, both ViT and MLP-Mixer need to be pretrained on very large data sets to achieve better results. Analyzing the operation process of the two, we find that the way of slice feeding into the network well reflects the relationship between the parts of the picture, but lacks the grasp of the overall information of the picture. To solve this problem, we designed some new patches to assist the network to analyze the global, strip and single channel information of the picture. In the object recognition task, our network has achieved the effect comparable to the original network structure without large-scale pretraining.