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Nowadays, with the development of computer science and technology, computer intelligent algorithms are more and more widely used in various industries. Every calculation formula in the computer intelligent algorithm has systematic logic and singleness, in order to expound the dynamic algorithm of football training optimized by the computer intelligent algorithm in detail. In this paper, the monitoring system using the computer intelligent algorithm can dynamically observe people or objects and systematically analyze them. This paper mainly studies the research of a football training dynamic monitoring system based on the computer intelligent algorithm and the design and optimization of the computer intelligent dynamic monitoring system in football training. Finally, the overall composition of the computer intelligent dynamic monitoring system and the application of the optimized computer intelligent dynamic monitoring system to the analysis of sample data are studied. [ABSTRACT FROM AUTHOR]

Complex systems play important roles in science and economy and have become one of the major intellectual and scientific challenges of the twenty-first century. However, the way complex system research connects to other academic fields is much less well understood, with only anecdotal evidence. In this work, we present an anatomy of complex system research by leveraging a large-scale dataset that contains more than 100 million digitalized publications. First, we find that complex system research shows a steady growth relative to the whole science in the last 60 years, with a sudden burst after 2000, which might be related to the development of computational technologies. Although early complex system study shows strong referencing behaviors to mathematics and physics, it couples significantly with computer science in the twenty-first century and affects engineering strongly. Moreover, we find empirical evidence that complex system research tends to have multidisciplinary nature, as it is often inspired by or affects a diverse set of disciplines. Finally, we find significant positive correlations between fields' reference broadness and future scientific impacts. Overall, our findings are consistent with several characteristics of complex system research, i.e., its multidisciplinary, quantitative, and computational nature, and may have broad policy implications for supporting and nurturing multidisciplinary research. [ABSTRACT FROM AUTHOR]

With the advancement of scientific collaboration in the 20th century, researchers started collaborating in many research areas. Researchers and scientists no longer remain solitary individuals; instead, they collaborate to advance fundamental understandings of research topics. Various bibliometric methods are used to quantify the scientific collaboration among researchers and scientific communities. Among these different bibliometric methods, the co-authorship method is one of the most verifiable methods to quantify or analyze scientific collaboration. In this research, the initial study has been conducted to analyze interdisciplinary research (IDR) activities in the computer science domain. The ACM has classified the computer science fields. We selected the Journal of Universal Computer Science (J.UCS) for experimentation purposes. The J.UCS is the first Journal of Computer Science that addresses a complete ACM topic. Using J.UCS data, the co-authorship network of the researcher up to the 2nd level was developed. Then the co-authorship network was analyzed to find interdisciplinary among scientific communities. Additionally, the results are also visualized to comprehend the interdisciplinary among the ACM categories. A whole working web-based system has been developed, and a forced directed graph technique has been implemented to understand IDR trends in ACM categories. Finally, the IDR values between the categories are computed to quantify the collaboration trends among the ACM categories. It was found that "Artificial Intelligence" and "Information Storage and Retrieval", "Natural Language Processing and Information Storage and Retrieval", and "Human-Computer Interface" and "Database Applications" were found the most overlapping areas by acquiring an IDR score of 0.879, 0.711, and 0.663, respectively. [ABSTRACT FROM AUTHOR]

With the development of computer science and technology and the advancement of language teaching and learning methods, computer-assisted language learning technology has made it possible to solve this problem. In the area of oral learning, some computer-assisted language learning systems at home and abroad mainly focus on the learning of words and grammar and only have one or two evaluation indicators as the basis of evaluation, which have certain functional defects and can only give an overall rating to learners' pronunciation. To address these problems, this paper takes the English speech of Chinese college students as the research object and improves the traditional computerized English pronunciation quality evaluation method by considering multiplicative evaluation indexes, such as pitch, speed, rhythm, and intonation; i.e., pitch evaluation is based on frequency central feature parameters, speech speed evaluation based on speech duration, rhythm evaluation. The method of pitch, speed, rhythm, and intonation evaluation has been experimentally verified to be reliable. Furthermore, considering the weight of the above multiplicative evaluation indexes. [ABSTRACT FROM AUTHOR]

With the development of computer science, especially the application of 3D scanning technology in garment design, intelligent modeling is realized, which is impossible to achieve in traditional design methods. In this paper, we propose the 3D model construction of human garments based on the motion recovery structure method. The eigenmatrix is obtained from the camera parameters, and the transformation matrix is calculated by matching the image feature points with the help of scale-invariant feature conversion algorithm to realize the 3D reconstruction technology of human garments based on multiview image sequences. The effectiveness of this method is verified through experiments, and it has good robustness and accuracy. Through the form of style modeling, the design thinking and method can be extended to form a more reasonable garment structure and guide the innovation of garment production mode. [ABSTRACT FROM AUTHOR]

With the continuous development of economy, the economic development model is constantly changing. Especially since China's entry into WTO, the scale of economic development has reached a new height. The continuous development of economy makes the financial news module evolve towards specialization. However, with the emergence of Internet of Things technology, a large number of data appear in the network, which brings some difficulties to the classification and analysis of data economy. Emotion classification refers to the complexity and diversity of people's emotions. It can be classified from different observation angles. Because the core content of emotion is value, human emotion should be classified mainly according to the different characteristics of the movement and change of value relationship it reflects. This paper is aimed at studying the emotional classification method of financial news based on artificial intelligence and expecting to use artificial intelligence technology and classification method to classify financial news. It allows more people to know the implied information of financial information and promotes economic development. Artificial intelligence is a branch of computer science. It attempts to understand the essence of intelligence and produce a new intelligent machine that can respond in a similar way to human intelligence. This paper mainly summarizes the topic selection characteristics and subdivision topic selection characteristics of financial data news through quantitative and qualitative methods and explores the classification of financial news. In this paper, a simplified classification algorithm based on convolution function is proposed for the classification of traditional financial news networks. The experimental results show that the classification accuracy of artificial intelligence method is improved by 4% compared with the traditional emotion classification method, and the classification accuracy of positive emotion is lower than that of negative emotion by 2%. [ABSTRACT FROM AUTHOR]

Path search is a hot issue in computer science and artificial intelligence science. When the user enters the starting point and ending point to be queried in the road network path search system, the system will return the best path to the user. In this paper, the road network path search system that can run and calculate the optimal navigation path to the test data is developed by designing the software architecture through the comprehensive use of database design, programming language, shortest path algorithm, UML diagram, software development model, GIS system source data, and other methods. Based on the software design principles of scalability, flexibility, and pluggability, the design and code in this paper can be practically applied to various systems such as GIS, GPS, logistics robots, unmanned aerial vehicles, and autonomous vehicles to realize their road network path planning and navigation functions. [ABSTRACT FROM AUTHOR]

The rapid development of computer science has led to the rapid development of new media technology. Among them, VR technology is sought after by the vast market for its multidimensional experience and immersive model. VR technology plays an important role in today's art exhibition and presentation. However, in the process of transforming 2D images into 3D models, traditional VR technology has some problems, such as long loading time, low model decision-making efficiency, and chaotic operation process, which often lead to visual interference when viewers watch art. Based on this background and the interactive page algorithm based on traditional VR technology, this paper studies and improves the visual interference in immersive multimedia art design by using task technology perception matching model (TTF). The results show that the improved task technology perception matching model reduces the visual and auditory interference by 10% in the process of virtual reality art display and viewing, and the model layout is more reasonable, and the utilization rate is higher. [ABSTRACT FROM AUTHOR]

Natural language processing is an important direction in the field of computer science and artificial intelligence. It can realize various theories and methods of effective communication between humans and computers using natural language. Machine learning is a branch of natural language processing research, which is based on a large-scale English-Chinese database. Due to the relatively poor alignment corpus of English and Chinese bilingual sentences containing unknown words, machine translation is unprofessional and unbalanced, which is the problem studied in this paper. The purpose of this paper is to design and implement a length-based system for sentence alignment between English and Chinese bilingual texts. The research content of this paper is mainly divided into the following parts. First, the evaluation function of bilingual sentence alignment is designed, and on this basis, the bilingual sentence alignment algorithm based on the length and the optimal sentence pair sequence search algorithm is designed. In this paper, China National Knowledge Infrastructure (CNKI) is selected as an English-Chinese bilingual candidate website and English-Chinese bilingual web pages are downloaded. After analyzing the downloaded pages, nontext content such as page tags is removed, and bilingual text information is stored so as to establish an English-Chinese bilingual corpus based on segment alignment and retain English-Chinese bilingual keywords in the web pages. Second, extract the dictionary from the software StarDict, analyze the original dictionary format, and turn it into a custom dictionary format, which is convenient and better to use the double-sentence sentence alignment system, which is conducive to expanding the number of dictionaries and increasing the professionalism of vocabulary. Finally, we extract the stems of English words from the established corpus to simplify the complexity of English word processing, reduce the noise caused by the conversion of word parts of speech, and improve the operation efficiency. A bilingual sentence alignment system based on length is implemented. Finally, the system parameters are adjusted for comparative experiments to test the system performance. [ABSTRACT FROM AUTHOR]

With the development of computer science and information technology, human society is gradually stepping into the Internet and big data. The medical and health industry can realize the integration and readjustment of existing resources, improve the operation efficiency of the industry, and tap the huge potential of the industry with the support of big data technology. However, the medical data in the new era has the characteristics of massive, high latitude, complex structure, and complex information, which is not conducive to the direct classification of health data. The preprocessing of health data can improve the quality of dataset, reduce the size of data, and improve the efficiency and accuracy of data classification. Based on this and according to the characteristics of health dataset and the existing pretreatment technology, this paper analyzes and improves the algorithm of abnormal data detection and data protocol in the process of reprocessing data cleaning. This paper analyzes and studies feature selection algorithms based on Bayesian inference algorithm and focuses on feature selection algorithms based on random forest. In order to solve the problem that the original algorithm ignored the relationship between the importance degrees of each feature in a single tree, a feature importance degree calculation method based on local importance degree was proposed. Through experimental analysis and comparison, the improved algorithm can select better feature subset and improve the performance of the classification model. Then, TAN classifier, BAN classifier, and MBN classifier were constructed based on preprocessed hypothyroidism data, and the performances of these four classifiers were compared through experiments. The final results show that BAN classifier has the best average classification effect. [ABSTRACT FROM AUTHOR]

The data are ever increasing with the increase in population, communication of different devices in networks, Internet of Things, sensors, actuators, and so on. This increase goes into different shapes such as volume, velocity, variety, veracity, and value extracting meaningful information and insights, all are challenging tasks and burning issues. Decision-making based on multicriteria is one of the most critical issues solving ways to select the most suitable decision among a number of alternatives. Deep learning algorithms and multicriteria-based decision-making have effective applications in big data. Derivations are made based on the use of deep algorithms and multicriteria. Due to its effectiveness and potentiality, it is exploited in several domains such as computer science and information technology, agriculture, and business sector. The aim of the proposed study is to present a systematic literature study in order to show the applications of deep learning algorithms and multicriteria decision approaches for the problems of big data. The research finds novel means to make the decision support system for the problems of big data using multiple criteria in integration with machine learning and artificial intelligence approaches. [ABSTRACT FROM AUTHOR]

Recent advancements in computer science include some optimization models that have been developed and used in real applications. Some metaheuristic search/optimization algorithms have been tested to obtain optimal solutions to speed controller applications in self-driving cars. Some metaheuristic algorithms are based on social behaviour, resulting in several search models, functions, and parameters, and thus algorithm-specific strengths and weaknesses. The present paper proposes a fitness function on the basis of the mathematical description of proportional integrative derivate controllers showing that mean square error is not always the best measure when looking for a solution to the problem. The fitness developed in this paper contains features and equations from the mathematical background of proportional integrative derivative controllers to calculate the best performance of the system. Such results are applied to quantitatively evaluate the performance of twenty-one optimization algorithms. Furthermore, improved versions of the fitness function are considered, in order to investigate which aspects are enhanced by applying the optimization algorithms. Results show that the right fitness function is a key point to get a good performance, regardless of the chosen algorithm. The aim of this paper is to present a novel objective function to carry out optimizations of the gains of a PID controller, using several computational intelligence techniques to perform the optimizations. The result of these optimizations will demonstrate the improved efficiency of the selected control schema. [ABSTRACT FROM AUTHOR]

In the past, the traditional small-scale 3D reconstruction technology developed maturely and mainly relied on manual handheld devices for data acquisition. Due to multiple factors, inaccuracy, and other problems, data will be omitted, and sometimes staff will face risks directly. Nowadays, with the development of computer science and technology, three-dimensional reconstruction technology has been innovated continuously, and it has also made a breakthrough in the application of large-scale reconstruction of dangerous areas. In this paper, the expensive sensors in the past are abandoned, and the cheaper and more efficient lidar is used instead. The laser point cloud and image are combined to describe the mining heritage scene in depth, and the academic achievements are transformed into productivity. The results show that (1) comparing the performance of the algorithm, the overall error of the proposed method is 5.86, and the average time consumption is about 10.23 ms. This filtering algorithm can restore the landscape of mining heritage to a great extent and optimize the problems of noise and outliers. (2) Our designed model has a very low loss value, and the point cloud accuracy is as high as 92.9%. Compared with other model methods, the model has excellent performance. (3) After the completion of the model, the overall satisfaction effect is above 70%, which can well restore the mining heritage style. Finally, the experimental effect of 3D reconstruction is good, which is more conducive to the research of reconstruction protection. There are still some work details and performance problems that need to be optimized and solved. [ABSTRACT FROM AUTHOR]

The longest common subsequence (LCS) problem is a classic computer science problem. For the essential problem of computing LCS between two arbitrary sequences s1 and s2, this paper proposes an algorithm taking O(n+r) space and O(r+n2) time, where r is the total number of elements in the set (i,j)|s1[i]=s2[j]. The algorithm can be more efficient than relevant classical algorithms in specific ranges of r. [ABSTRACT FROM AUTHOR]

The fast advancement of computer science and information technology has resulted in a significant revolution in the commercial painting sector. The professional painting industry has experienced a dramatic breakthrough with the rapid expansion of computer science and technology. With the rapid development and comprehensive popularization of computer technology, more and more people use computer image processing technology in oil painting created as a new way of creation. Some artists use a variety of ways to study the way of oil painting creation and use image processing technology to stimulate the creator's imagination and improve the efficiency of drawing sketches in optimizing creative ideas, drawing, and processing original works. However, the value of art comes from creation. Oil painting creation based on computer image processing is a new type of artistic production that still requires the integration of different artists' ideas and creative concepts. The image processing technology is a tool and a way for artists to make art while also realizing the convergence of science, technology, and art. This paper uses computer image processing technology to study oil painting creation. First, it briefly discusses computer image processing technology, focusing on picture preprocessing and image graying, and then uses this technique in formation of oil painting. Second, it establishes the oil painting mathematical, direction field, and color conversion models completing the oil painting image creation by using this model. Finally, the advantages of this technology are analyzed from the two aspects of oil painting creation, painting technique, and brush direction. The results show that 75% of artists are very satisfied with the digital painting technique, and 65% of artists are very satisfied with the painting brush direction, which shows that the application effect of computer image processing technology in oil painting creation is remarkable. [ABSTRACT FROM AUTHOR]

Architecture is closely related to human development. Different architectural shapes and texture structures reflect the changes of society and the level of social development. They are not only important materials for studying history but also carry the profound significance of human history. However, due to chemical effects, the acid, alkali, salt, and corrosive solvents are applied and produced in the process of industrial production, as well as the corrosive media contained in the atmosphere, groundwater, surface water, and soil will corrode the ancient buildings. The whole picture of many ancient buildings cannot be completely preserved, let alone used as research materials to deeply explore history and humanities. With the development of computer science and technology, we hope to protect and repair traditional ancient buildings through modern technology, which not only respects the long history of the Chinese nation but also helps us better study history through ancient buildings. This paper takes the ancient buildings of traditional villages in Guanzhong, Shanxi Province as an example, optimizes them on the basis of predecessors, and explores the protection and restoration of ancient buildings based on the improved priority algorithm. The results show that the proposed algorithm performs better in the protection and restoration model of ancient buildings than the traditional algorithm. [ABSTRACT FROM AUTHOR]

Agro-business is highly dependent on rice quality and its protection from diseases. There are several prerequisites for the procedures and the strategies that are productive and efficient for expanding the harvest yield. The advancement in computer science has supported various domains; agricultural innovation is one of them. The apparatuses which utilize the strategies of advanced artificial intelligence and machine learning have been featured in this paper. These techniques attain abnormally productive outcomes for the recognition of infections engrossing the images of leaves, fields of harvest, or seeds. In this context, this work presents a survey that focuses on accuracy agribusiness for expanding the conception of rice, which is one of the main harvests on the planet. In this paper, the overview and examination of various papers distributed in the most recent eight years with various methodologies identified with crop diseases identification, the health of seedlings, and quality of grain have been introduced. Experiments are performed for knowledge extraction using Web of Science and Scopus databases to analyze research trends in the domain of rice disease identification using artificial intelligence using global analysis, year-wise and country-wise citations, and so on to support various researchers working in this domain. [ABSTRACT FROM AUTHOR]

With the continuous in-depth development of science and technology in my country, the field of artificial intelligence has also made considerable progress. For example, the comprehensiveness and operability of technology have been enhanced. Artificial intelligence, as a comprehensive and intersecting research field in the fields of computer science, information science, and mathematics, has also made a certain contribution to the progress of international students in colleges and universities. This article is aimed at studying the problems based on basic theories and time teaching methods, mainly through a comprehensive data analysis of artificial intelligence technology and fusion differential evolution algorithm, and then improving the learning ability of students and comprehensive quality. This paper proposes a swarm intelligence algorithm, K -means clustering algorithm, and vector machine algorithm and summarizes the process and application fields of the swarm intelligence algorithm. In addition, the CMAC learning algorithm is proposed, which shows that robots under the background of industrial intelligence and machine learning can organize themselves to form groups to perform complex tasks. The experimental results of this paper show that the communication range is between 0 and 1, which can enable more robots to communicate in order to improve the individual's social learning opportunities, thereby enhancing the individual's learning ability to improve the performance of learning behavior and improving the efficiency of practical teaching and research, making the efficiency reach 0.9. [ABSTRACT FROM AUTHOR]

With the rapid rise in the number of people buying houses, the demand for interior space design has also increased accordingly. The diversification of existing room types and the diversity of the public's perception of fashion make interior designers in short supply. The future of computer science and technology in the field of automatic design of indoor areas will be immeasurable. This paper proposes an automatic layout method for spatial area design based on convolutional neural networks (CNN). CNN methods are a fast and efficient method. By mimicking the designer's design process, it proposes a two-stage algorithm that defines the room first and the wall later, and the algorithm also provides a large-scale dataset called RPLAN that contains more than 80,000 interior layout plans from real residential buildings. Starting from the prediction living room, the automatic layout of the indoor areas is completed by iteration. A large number of empirical results show that the interior area design effect of this method is comparable to the interior design floor plan of professional designers. [ABSTRACT FROM AUTHOR]

Under the normal conditions of the new economy, people's living standards have improved; the amount of urban domestic waste has increased suddenly; and the differentiation of waste has become more and more complicated. However, the garbage transportation method at this stage has been difficult to meet the needs of reality. Therefore, it is particularly urgent to reform the original and simple treatment methods and adopt the latest and more reasonable garbage transportation. The urban waste transportation route optimization described in this article is quite complicated in the specific problems faced in the optimization of the urban domestic waste transportation route. This paper proposes to combine computer science to study the system optimization model and combine information management and other disciplines to develop and apply research on the dynamic management software of urban waste logistics systems. In the modern service industry, logistics occupies an important position. The optimization of urban waste transportation routes based on genetic algorithms also promotes the development of high-end modern service industries and is an important task to promote urban development and maintained hygiene and improve the healthcare of the citizens. The optimization of urban waste transportation routes is conducive to creating a clean and tidy urban environment. [ABSTRACT FROM AUTHOR]

Artificial intelligence (AI) is a subfield of computer science concerned with developing intelligent machines capable of performing tasks similar to those performed by humans. This human-created intelligence began more than 60 years ago. The goal of previous generations of applications was to demonstrate generic human-like behaviour. The goal has expanded with the advancement and increased compliance of this technology. It includes areas such as healthcare, gaming, and smart devices. The COVID-19 epidemic has posed a significant barrier to maintaining a sustainable strategy for mental health support clients with major mental illnesses and clinicians who have had to shift delivery modes quickly. In this study, we have conducted a systematic literature review (SLR) to provide an overview of the current state of the literature related to software measurement of healthcare using artificial intelligence. The study followed a secondary research strategy. The systematic literature review aim was to analyze software measurement of mental health illness in terms of previous literature. This study screened out of 28 research papers out of 1076 initial searches. We used Science Direct, IEEE Xplore, Springer Link, ACM, and Hindawi as database search engines. The research objective was to explore the needs of software applications and automation in the healthcare sector to bring efficiency to the systems. The research concluded that the healthcare setting crucially requires the implementation of software automation. [ABSTRACT FROM AUTHOR]

In recent years, fueled by the breakthroughs in the technology in quantum computation, there has been a rising interest in the noisy intermediate-scale quantum (NISQ) era. In addition to a large number of qubits, the study of error correction and quantum algorithms has made great progress. However, as a primary goal of quantum computation, making practicable quantum computers in the NISQ era still needs further study, mainly focusing on quantum computer organization, architecture, and circuit synthesis. This paper studies the quantum circuit synthesis in a small-scale universal quantum computing device called a "quantum system-on-chip" (QSoC). We analyze the quantum compilation of the hybrid architecture for a small-scaled universal quantum computational device with a specific size (quantum chip). Two kinds of on-chip circuit synthesis algorithms are proposed and discussed. [ABSTRACT FROM AUTHOR]

Vague graphs (VGs), which are a family of fuzzy graphs (FGs), are a well-organized and useful tool for capturing and resolving a range of real-world scenarios involving ambiguous data. In graph theory, a dominating set (DS) for a graph G ∗ = X , E is a subset S of the vertices X such that every vertex not in S is adjacent to at least one member of S. The concept of DS in FGs has received the attention of many researchers due to its many applications in various fields such as computer science and electronic networks. In this paper, we introduce the notion of ϵ 1 , ϵ 2 , 2 -Regular vague dominating set and provide some examples to explain various concepts introduced. Also, some results were discussed. Additionally, the ϵ 1 , ϵ 2 , 2 -Regular strong (weak) and independent strong (weak) domination sets for vague domination set (VDS) were presented with some theorems to support the context. [ABSTRACT FROM AUTHOR]

Fractional variants of distance-based parameters have application in the fields of sensor networking, robot navigation, and integer programming problems. Complex networks are exceptional networks which exhibit significant topological features and have become quintessential research area in the field of computer science, biology, and mathematics. Owing to the possibility that many real-world systems can be intelligently modeled and represented as complex networks to examine, administer and comprehend the useful information from these real-world networks. In this paper, local fractional strong metric dimension of certain complex networks is computed. Building blocks of complex networks are considered as the symmetric networks such as cyclic networks C n , circulant networks C n 1,2 , mobious ladder networks M 2 n , and generalized prism networks G m n . In this regard, it is shown that LSFMD of C n n ≥ 3 and G m n n ≥ 6 is 1 when n is even and n / n − 1 when n is odd, whereas LSFMD of M 2 n is 1 when n is odd and n / n − 1 when n is even. Also, LSFMD of C n 1,2 is n / 2 ⌈ m + 1 / 2 ⌉ where n ≥ 6 and m = ⌈ n − 5 / 4 ⌉. [ABSTRACT FROM AUTHOR]

This umbrella review is motivated to understand the shift in research themes on brain-computer interfacing (BCI) and it determined that a shift away from themes that focus on medical advancement and system development to applications that included education, marketing, gaming, safety, and security has occurred. The background of this review examined aspects of BCI categorisation, neuroimaging methods, brain control signal classification, applications, and ethics. The specific area of BCI software and hardware development was not examined. A search using One Search was undertaken and 92 BCI reviews were selected for inclusion. Publication demographics indicate the average number of authors on review papers considered was 4.2 ± 1.8. The results also indicate a rapid increase in the number of BCI reviews from 2003, with only three reviews before that period, two in 1972, and one in 1996. While BCI authors were predominantly Euro-American in early reviews, this shifted to a more global authorship, which China dominated by 2020–2022. The review revealed six disciplines associated with BCI systems: life sciences and biomedicine (n = 42), neurosciences and neurology (n = 35), and rehabilitation (n = 20); (2) the second domain centred on the theme of functionality: computer science (n = 20), engineering (n = 28) and technology (n = 38). There was a thematic shift from understanding brain function and modes of interfacing BCI systems to more applied research novel areas of research-identified surround artificial intelligence, including machine learning, pre-processing, and deep learning. As BCI systems become more invasive in the lives of "normal" individuals, it is expected that there will be a refocus and thematic shift towards increased research into ethical issues and the need for legal oversight in BCI application. [ABSTRACT FROM AUTHOR]

Wearable technology is one of the greatest applications of the Internet of Things. The popularity of wearable devices has led to a massive scale of personal (user-specific) data. Generally, data holders (manufacturers) of wearable devices are willing to share these data with others to get benefits. However, significant privacy concerns would arise when sharing the data with the third party in an improper manner. In this paper, we first propose a specific threat model about the data sharing process of wearable devices’ data. Then we propose a K-anonymity method based on clustering to preserve privacy of wearable IoT devices’ data and guarantee the usability of the collected data. Experiment results demonstrate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

With the rapid development of computer science and Internet technology, face recognition technology is widely used in such as public security, judicial and criminal investigation, public security, information security and access control system, such as public security system need to find out the criminals in the system library, or from the entrance control system quickly identify and match the identity of relevant personnel information. As a stable, intuitive and highly recognizable biometric feature, human face is being paid more and more attention by researchers. Compared with other bioinformation recognition methods, face recognition is characterized by direct, friendliness and convenience. Users are not easy to resist, and compared with other recognition technologies, they are easy to be accepted by users, so it has received attention and research. This paper designs a face recognition system based on a convolutional neural network. Compared with the traditional face recognition method, the convolutional neural network model does not need manual complex and time-consuming feature extraction algorithm design, only need to design an effective neural network model, and then end-to-end training on a large number of training samples for simple and efficient training, can get a good identification effect. The design uses the target detection algorithm to conduct accurate, real-time, efficient face recognition, and can accurately identify the people in the camera. [ABSTRACT FROM AUTHOR]

Recently, wide applications of fuzzy set theory have attracted the attention of both researchers and practitioners. Fuzzy portfolio develops as a new area in the research field of investment portfolio. This paper investigates the major research hotspots, development trend, and evolution of fuzzy portfolio, which provides a systematic review of the current fuzzy portfolio literature. CiteSpace, the most commonly used bibliometrics software, is used in this article. According to the 602 articles with 15132 references, several conclusions can be summarized as follows: (1) Fuzzy portfolio becomes increasingly interdisciplinary with the connections among "Computer Science" and so on. (2) Most contributive authors are Markowitz and Zadeh. (3) South China University of Technology makes excellent performance in this research area and China is the most influential country. (4) European Journal of Operations Research is the cradle of plenty of crucial fuzzy portfolio investigations. (5) We find some research hotspots helpful to make scientific predictions of future trends by analyzing the keywords. By utilizing the effective bibliometric methods, we provide a comprehensive analysis and deep insights into the fuzzy portfolio research, enabling the individuals, especially the new beginners who are interested in this area to learn fuzzy portfolio, which will be of great help for their future explorations. [ABSTRACT FROM AUTHOR]

This paper is to study the minimal realization theory for a fuzzy multiset language in the framework of category theory, which has already provided the tools and techniques for the advancement of several features of theoretical computer science. Specifically, by using the well-known categorical concepts, it is shown herein that there is a minimal realization (called the Nerode realization) for each fuzzy multiset language, and all minimal realizations for a given fuzzy multiset language are isomorphic to it. [ABSTRACT FROM AUTHOR]

For applied scientists and engineers, graph theory is a strong and vital tool for evaluating and inventing solutions for a variety of issues. Graph theory is extremely important in complex systems, particularly in computer science. Many scientific areas use graph theory, including biological sciences, engineering, coding, and operational research. A strategy for the orthogonal labelling of a bipartite graph G with n edges has been proposed in the literature, yielding cyclic decompositions of balanced complete bipartite graphs K n , n by the graph G. A generalization to circulant-balanced complete multipartite graphs K n , n , ⋯ , n ⏟ m ; m , n ≥ 2 , is our objective here. In this paper, we expand the orthogonal labelling approach used to generate cyclic decompositions for K n , n to a generalized orthogonal labelling approach that may be used for decomposing K n , n , ⋯ , n ⏟ m . We can decompose K n , n , ⋯ , n ⏟ m into distinct graph classes based on the proposed generalized orthogonal labelling approach. [ABSTRACT FROM AUTHOR]

With the continuous advancement of medicine and computer science, medical image processing technology is also constantly advancing, and at the same time, it puts forward the needs of its own development. The purpose of this article is to combine the three-dimensional reconstruction of medical images and virtual reality (VR) technology in nursing experiment teaching to help students understand more easily and to simplify the teachers' teaching process and make the VR application technology. It is the most popular and effective in medical teaching. This article proposes the C-V model and the geometric active contour model to help us more clearly understand the pathology in this environment, where the specific symptoms appear, and bring a more easy-to-understand model for teaching and improving teaching quality. This article also designs nursing experiment teaching. The experimental results of this paper show that, after using VR courseware for teaching, the optimal test rate of the experimental class is 15% higher than that of the control class, and the transition rate is 8%. The actual test excellent rate and success rate of the experimental class are much higher than those of the control class. Therefore, it can be concluded that the application of VR technology in nursing teaching helps teachers improve their practical ability. The excellent teaching feedback rate is 95%, which is higher than 80.5% in the control group, indicating that the patient teaching simulation is approved by the observation group. The program can effectively improve the feedback rate of excellent teaching and provide students with better teaching services. [ABSTRACT FROM AUTHOR]

As computer science and information technology develop rapidly, virtual reality (VR) technology has evolved from theory to application. As a key technology in modern society, VR technology is increasingly influencing more and more aspects of people's daily lives, including sports training. VR technology can be seen as an assistive technology that provides specific support for athletes' sports training through various means such as simulating training scenarios and conducting data analysis. This paper focuses on exploring the application of VR technology in football training and the combination of sports training and VR technology. In addition, a feasibility analysis of the application of VR technology in football is carried out, and software such as Poser 8.0, 3ds MAX, and EON Studio in the virtual football training system are introduced. The aim is to further provide theoretical support for the development and research of virtual football sports system software and to study virtual systems that are not disturbed by external natural conditions. It will break through the limitations of sports training due to factors such as weather, player injuries, lack of training space, and funding. This is conducive to improving teaching and training methods, promoting the mastery of technical movement essentials and the improvement of football skills. [ABSTRACT FROM AUTHOR]

Machine learning and data analytics are two of the most popular subdisciplines of modern computer science which have a variety of scopes in most of the industries ranging from hospitals to hotels, manufacturing to pharmaceuticals, mining to banking, etc. Additionally, mining and hospitals are two of the most critical industries where applications when deployed security, accuracy, and cost effectiveness are the major concerns, due to the huge involvement of man and machines. In this paper, the problem of finding out the location of man and machines has been focused on in case of an accident during the mining process. The primary scope of the research is to guarantee that the projected position is near to the real place so that the trained model's performance can be tested. The solution has been implemented by first proposing the MLAELD (Machine Learning Architecture for Excavators' Location Detection), in which Bluetooth Low Energy (BLE) beacons have been used for tracking the live locations of excavators preceded by collecting the data of the signal strength mapping from multiple beacons at each specific point in a closed area. Second, machine learning techniques are proposed to develop and train multioutput regression models using linear regression, K-nearest neighbor regression, decision tree regression, and random forest regression. These techniques can predict the live locations of the required persons and machines with a high level of precision from the last beacon strengths received. [ABSTRACT FROM AUTHOR]

Fuzzy graph (FG) models take on the presence being ubiquitous in environmental and fabricated structures by humans, specifically the vibrant processes in physical, biological, and social systems. Owing to the unpredictable and indiscriminate data which are intrinsic in real life and problems being often ambiguous, it is very challenging for an expert to exemplify these problems through applying an FG. Vague graph structure (VGS), belonging to the FG family, has good capabilities when facing with problems that cannot be expressed by FGs. VGS can handle the vagueness connected with the incompatible and determinate information of any real-world problem, where FGs may not succeed to bear satisfactory results. The previous definitions' restrictions in FGs have led us to propose new definitions in the VGS. Domination is one of the key issues that has many applications in computer science and social networks. Today, many researchers are trying to prove its application in medical sciences and psychology. Therefore, in this paper, different concepts related to domination in VGSs such as Ni-dominating set, vague full dominating set, minimal N i -DS, and strong capacity Ni-dominating set are defined using some examples. Finally, an application of domination in medical sciences has been presented. [ABSTRACT FROM AUTHOR]