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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]

With the rapid development of computer network technology, the advantages of virtual reality technology in the field of instant messaging are becoming more and more significant. Virtual reality technology plays an important role in communication networks, including enhanced resource utilization, device redundancy, immersion, interactivity, conceptualization, and holography. In this paper, we use the basic theory of Restricted Boltzmann Machine to establish a semisupervised spatio-temporal feature model through the animation capture data style recognition problem. The bottom layer can be pretrained with a large amount of unlabeled data to enhance the model's feature perception capability of animation data, and then train the high-level supervised model with the labeled data to finally obtain the model parameters that can be used for the recognition task. The layer-by-layer training method makes the model have good parallelism, that is, when the layer-by-layer training method makes the model well parallelized, that is, when the bottom features cannot effectively represent the animation features, such as overfitting or underfitting, only the bottom model needs to be retrained, while the top model parameters can be kept unchanged. Simulation experiments show that the design assistance time of this paper's scheme for animation is reduced by 10 minutes compared to baseline. [ABSTRACT FROM AUTHOR]

The management of nursing scheduling in healthcare facilities have faced new challenges during the COVID-19 pandemic. With the rapid development of big data and artificial intelligence technology, data-driven intelligent medical services are what we need to study nowadays. This paper not only proposes reasonable solutions in areas such as refined nursing scheduling by using these scientific technologies to quickly realize the allocation of human resources in hospitals. It also accelerates the development of hospital informatization construction through computer technology, establishing a scientific and intelligent medical platform that meets the needs of users. Aiming at the problem of nursing scheduling in medical service data research, this paper proposes a complete plan by analyzing the development of the medical platform at this stage. Firstly, established an intelligent medical service platform, and studied the medical management from the perspective of data. Then, analyze the intelligent medical platform data by utilizing optimized algorithms, through reasonable analysis under various constraints, to get the basic nursing scheduling plan that meets the needs of medical institutions. Finally, considering the actual situation of emergency medical treatment, the decision classification model is introduced under the basic scheme to further screen out the optimal management scheme of modern medical treatment. [ABSTRACT FROM AUTHOR]

With the advent of the information age, computer technology is also widely used in various fields. In the field of education, physical education teaching strategy has always been the focus of many educators. In order to optimize physical education teaching strategies and improve teaching quality, this paper proposes a new intelligent computing technology. This technology has excellent innovation in the engineering design field of physical education teaching strategy reform and innovation and combines Intelligent Computing with physical education teaching strategy to explore the feasibility and effectiveness of physical education teaching strategy reform and innovation. On the basis of intelligent computing algorithm, this paper analyzes the visualization strategy scheme brought by intelligent computing classroom to physical education teaching. This paper analyzes the feasibility of students' feedback after the reform of the physical education teaching strategy. Finally, the big data discrete dynamic modeling technology is used to dynamically model and analyze the students' learning behavior and effect after the reform and innovation of physical education teaching strategy. Combined with the analysis data and students' behavior feedback, this paper analyzes the feasibility of the physical education teaching strategy after the reform and innovation. The results show that the visualization scheme of physical education teaching strategy based on intelligent computing can help students understand theoretical knowledge and realize the transformation from static classroom to dynamic classroom. It enhances students' practical activities and perceptual knowledge and is of great help to students' physical education learning effect. In the discrete dynamic modeling analysis, the feasibility of physical education teaching strategy reform is very important. [ABSTRACT FROM AUTHOR]

Based on the development background of the interweaving and integration of computer technology and Internet technology, China's artificial intelligence industry is quietly rising. In the social life of the information age, the artificial intelligence industry represented by machine deep learning is playing a very important role. This study in combination with the background of the new health reform, in view of the reform of the medical industry, analyzes the connotation of financial wisdom based on the important role of the hospital financial development problems, puts forward the development direction of artificial intelligence hospital financial wisdom development measures, designed to meet the changing external environment demand, reduces human costs, and improves the overall efficiency of hospital financial fund management. Based on the evaluation results, this paper proposes the correct direction for the development of hospital intelligence finance by using the BP neural network model. After the analysis, it is found that the development of artificial intelligence is an important measure to promote the development of hospital intelligent finance. In other words, hospital intelligent financial management is the product of the continuous progress of artificial intelligence technology. At the present stage, the intelligent financial management problems of hospitals are mainly as follows: (1) lack of financial informatization, (2) lack of perfect financial risk early warning system, and (3) the phenomenon of "information island" in the financial system. After analyzing the above problems, the research believes that the development of hospital intelligent finance based on artificial intelligence needs to solve the above thorny problems, so as to improve the outcome of hospital intelligent finance development. The following work should be done: (1) strengthen the design of information sharing module, (2) intelligent control of the cost of hospitals, and (3) intelligent treatment of hospital accounting. Combining the development of artificial intelligence and hospital intelligent finance theory, combined with the actual trend of financial intelligence development under the background of artificial intelligence development in the new era, it provides scientific basis for the development of hospital intelligent finance. [ABSTRACT FROM AUTHOR]

Combining computer vision technology with process design, a new design and production method is obtained, which breaks through the limitations of traditional jewelry creation and provides new possibilities for the realization of complex jewelry structures. When technology no longer becomes the bottleneck of artistic expression, the space of art will be greatly expanded. Science and technology leading design method has become a new way to assist jewelry artists in subjective creation. According to various thoughts and ideas in design, establishing the corresponding algorithm rules and parameters can generate the scheme through calculation. The design result obtained in this way not only has a scientifically logical basis but also obtains the result beyond the normal imagination space due to the intelligent design process. This paper tries to apply computer vision technology to modern jewelry design, analyzes several aspects of computer vision application in process design, and combines the latest technical means to put forward algorithms for verification. The results prove that computer vision can improve the efficiency of crafts design significantly. [ABSTRACT FROM AUTHOR]

In web design, we need to make full use of computer multimedia technology, which can effectively improve the quality of web design and provide greater convenience. The neural network needs to calculate a large amount of training data in image optimization, and the calculation speed cannot keep up with real-time data processing, resulting in the problem of poor quality of optimized images. This paper analyzes the problems existing in the traditional optimized BP neural network algorithm and puts forward an optimized BP neural network image optimization method which combines the increase of momentum term with the adaptive adjustment of learning rate. This method can speed up the iteration speed and jump out of the situation of premature local minimum. The test results show that the user satisfaction of the text visual effect of the web interface optimized by this method is more than 98% and the user satisfaction of other methods is only about 90%. The visual satisfaction of the web interface optimized by this method is significantly higher than that of the comparative method. The web interface visual optimization effect of the method in this article is better, and it can meet the satisfaction requirements of most users. [ABSTRACT FROM AUTHOR]

As a convenient device for observing neural activity in the natural environment, portable EEG technology (PEEGT) has an extensive prospect in expanding neuroscience research into natural applications. However, unlike in the laboratory environment, PEEGT is usually applied in a semiconstrained environment, including management and engineering, generating much more artifacts caused by the subjects' activities. Due to the limitations of existing artifacts annotation, the problem limits PEEGT to take advantage of portability and low-test cost, which is a crucial obstacle for the potential application of PEEGT in the natural environment. This paper proposes an intelligent method to identify two leading antecedent causes of EEG artifacts, participant's blinks and head movements, and annotate the time segments of artifacts in real time based on computer vision (CV). Furthermore, it changes the original postprocessing mode based on artifact signal recognition to the preprocessing mode based on artifact behavior recognition by the CV method. Through a comparative experiment with three artifacts mark operators and the CV method, we verify the effectiveness of the method, which lays a foundation for accurate artifact removal in real time in the next step. It enlightens us on how to adopt computer technology to conduct large-scale neurotesting in a natural semiconstrained environment outside the laboratory without expensive laboratory equipment or high manual costs. [ABSTRACT FROM AUTHOR]

There are some problems in the process of video intelligent description and analysis of volleyball, such as poor effective information extraction rate and poor dynamic tracking effect. Based on this, combined with long-term and short-term memory network and attention mechanism, this paper designs an intelligent description model of volleyball video based on deep learning algorithm and studies how to improve the extraction rate of volleyball video information through intelligent detection hardware and image recognition technology. This paper first introduces the application of image recognition technology and deep learning algorithm in the intelligent description of volleyball video, then designs the volleyball video and image recognition model based on deep learning algorithm according to the requirements of volleyball video intelligent description, and selects three correlation factors related to the impact indicators of volleyball skills. This study selects three characteristic parameters associated with volleyball video analysis indexes, namely, take-off, bounce, and hand movement, combined with image sensing hardware assisted sensor network to realize real-time monitoring of action state in volleyball video analysis system. The experimental results show that, compared with the current mainstream sports video intelligent analysis and image recognition methods with data analysis as the core, the intelligent volleyball sports video intelligent description and image recognition system based on the integration of deep learning algorithm and sensor hardware assistance has the advantages of good detection effect, high data effectiveness, low cost, and high efficiency of volleyball sports video analysis. It can effectively improve the efficiency of volleyball video intelligent description. [ABSTRACT FROM AUTHOR]

With the continuous development of computer technology, many institutions in society have higher requirements for the efficiency and reliability of identification systems. In sectors with a high-security level, the use of traditional key and smart card system has been replaced by the identification system of biometric technology. The use of fingerprint and face recognition in biometric technology is a biometric technology that does not constitute an infringement on the human body and is convenient and reliable. The biometric technology has been continuously improved, and the existing biometric technologies are based on unimodal biometric features. The unimodal biometric technology has its own limitations such as proposing single information and checking data affected by the environment, which makes it difficult for the technology to play its advantages in practical applications. In this paper, we use CNN-SRU deep learning to preprocess a large amount of complex data in the perceptual layer. The data collected in the perceptual layer are first transmitted to CNN convolutional neural network for simple classification and analysis and then arrives at the LSTM session to update again and optimize the screening to improve the biometric performance. The results show that the CNN-LSTM, CNN-GRU, and CNN algorithms show a decreasing trend in accuracy under the three error evaluation criteria of RMSE, MAE, and ME, from 0.35 to 0.07, 0.58 to 0.19, and 0.38 to 0.15, respectively. The recognition rate of multifeature fusion can reach 95.2%; the recognition efficiency of the multibiometric authentication system and accuracy rate has been significantly improved. It provides a strong guarantee for the regional standardization, high integration, generalization, and modularization of multibiometric identification system application products. [ABSTRACT FROM AUTHOR]

With the development of science and technology, the rapid development of social economy, the motor as a new type of transmission equipment, in the production and life of people occupies a pivotal position. Under the rapid development of computer and electronic technology, manufacturing equipment is becoming larger, faster, more continuous, and more automated. This has resulted in complex, expensive, accident-damaging, and high-impact equipment for electric motors; even routine maintenance requires significant equipment maintenance and maintenance costs. If a fault occurs, it will cause serious damage to the entire equipment and can even have a major impact on the entire production process, leading to a serious economic and social life. In this paper, a CNN-based machine learning fault diagnosis method is proposed to address the problem of high incidence of motor faults and difficulty in identifying fault types. A fault reproduction test is constructed by machine learning techniques to extract vibration time domain data for normal operating conditions, rotor eccentricity, stator short circuit, and bearing inner ring fault; divide the data segment into 15 speed segments, extract 13 typical time domain features for each speed segment; and perform mathematical statistics for fault diagnosis. Compared with the traditional algorithm, the method has more comprehensive feature information extraction, higher diagnostic accuracy, and faster diagnostic speed, with a fault diagnosis accuracy of 98.7%. [ABSTRACT FROM AUTHOR]

Every country, including China, is deeply concerned and interested in the topic of agricultural machinery automation. The world's population is growing at an astronomical rate, and as a result, the need of food is also growing at an astronomical rate. Farmers are forced to apply more toxic pesticides since traditional methods are not up to the task of meeting the rising demand. This has a major impact on agricultural practices, and in the long run, the land becomes barren and unproductive. Intelligent technologies such as Internet of Things, wireless communication, and machine learning can help with crop disease and pesticide storage management, as well as water management and irrigation. In this paper, we design and analyze an intelligent system that automatically predicts the agricultural land features for irrigation purpose. Initially, the dataset is collected and preprocessed using normalization. The features are extracted using principal component analysis (PCA). For automatic prediction by the equipment, we propose heterogeneous fuzzy-based artificial neural network (HF-ANN) with genetic quantum spider monkey optimization (GQ-SMO) algorithm. Analyses and comparisons are made between the proposed approach and current methodologies. The findings indicate the effectiveness of the proposed system. [ABSTRACT FROM AUTHOR]

Managers have a deeper understanding of computer technology, network technology, and communication technology, which is also a necessary condition for the implementation of project management information. It plays an important role in improving the performance of regional resource-based industry and high-tech service industry. By analyzing the current situation of the company's project management, the project management and management processes are standardized and the management of project quality, progress, contract, and cost through the design system is completed, so that the various resources of the project can be effectively and rationally allocated and utilized. Information communication inside and outside the project is more convenient, smooth, and fast, and more standardized operating procedures can be carried out. This paper also improves the level of local government governance, shapes the cultural connotation of innovation, openness, and cooperation, and strengthens the cultivation and flow of social elements. A series of policy suggestions are put forward in building the capacity system of both sides of industrial integration, thus greatly improving the overall project management level and the company's decision-making and analysis ability, so as to achieve fine and precise management objectives. [ABSTRACT FROM AUTHOR]

As globalization picks up speed, product innovation—the driving force behind business growth and market competition—becomes increasingly important for advancing product design technology. Computer-aided technology is one of the key components of product design, including issues with scientific analysis, creative thinking, problem-solving, and handling conflicts. Product design technology is gaining more and more attention. Computer-aided technology is now a trendy topic that is being studied by many academics due to the quick growth of computer technology. However, most of them focus on the product information model and solution and do not start from the technical methods and innovation of product design, resulting in many problems in the later solution and information description. Therefore, this paper studies product technology problems based on computer-aided technology and briefly explains the CAI technology-driven technology and the establishment of integrated process model. In this model, the assembly information model diagram is constructed to analyze the conflicts and problems in the assembly model, and then, the CAI-driven integrated innovation technology is called to complete the assembly model design. Conflicts in the assembly design can be minimized by using this paradigm. Finally, the qualitative examination of product design reveals that brainstorming is the process that uses the least amount of energy. The shafting assembly system is reviewed and examined at the same time, and the assembly accuracy accounts for the highest 35 percent of the system, completely indicating the extraordinary impact of computer-aided product design. [ABSTRACT FROM AUTHOR]

With the development of artificial intelligence and computer technology, the deep neural network algorithm is applied to the intelligentization of various fields of production and life. However, from the current application status, the application of artificial intelligence technology has many shortcomings. Based on this, this paper starts with the deep neural network algorithm, takes face recognition as the research tool, and deeply studies how to use the deep neural network algorithm to demonstrate the application of intelligent face recognition in complex environments. A face recognition neural network algorithm is proposed, and the accuracy of the algorithm is checked by testing. The results show that the average accuracy of a single sample in the LFW dataset is 99.17%, and the efficiency of using a single sample is close to that of many smelting models, which can be applied to various intelligent recognition scenarios. [ABSTRACT FROM AUTHOR]

In order to accurately locate the fabricated building components during assembly, the fabricated building components are monitored by laser scanning. At the same time, BIM Technology is proposed to carry out construction management in the construction process, realize safe site management and construction, reduce unnecessary mistakes of human operation, and improve construction quality. With the progress of computer technology, the emergence of the Internet of things has brought convenience to many fields. This research uses the application of phase ranging to carry out omnidirectional laser scanning on the fabricated building components, obtain the point cloud data, and use BIM Technology to simulate and analyze the building, establish the accurate positioning of building components, and prevent the offset phenomenon in the assembly process, so as to improve the construction quality and efficiency. In recent years, the integration of information technology into prefabricated buildings plays a vital role in the transformation and upgrading of China's construction industry. At the same time, it will also raise the information management level of prefabricated buildings to a new level. In order to improve the quality of building construction, this paper proposes a positioning method of prefabricated building components based on BIM Technology and laser scanning. On this basis, a complete architecture and detailed process application are established, and the promotion policies conducive to the development of intelligent construction of prefabricated buildings in BIM are put forward. [ABSTRACT FROM AUTHOR]

In this paper, we propose an assisted driving system implemented with a Jetson nano-high-performance embedded platform by using machine vision and deep learning technologies. The vehicle dynamics model is established under multiconditional assumptions, the path planner and path tracking controller are designed based on the model predictive control algorithm, and the local desired path is reasonably planned in combination with the behavioral decision system. The behavioral decision algorithm based on finite state machine reasonably transforms the driving state according to the environmental changes, realizes the following of the target vehicle speed, and can take effective emergency braking in time when there is a collision danger. The system can complete the motion planning by the model predictive control algorithm and control the autonomous vehicle to smoothly track the replanned local desired path to complete the lane change overtaking action, which can meet the demand of ADAS. The path planner is designed based on the MPC algorithm, solving the objective function with obstacle avoidance function, planning the optimal path that can avoid a collision, and using 5th order polynomial to fit the output local desired path points. In 5∼8 s time, the target vehicle decelerates to 48 km/h; the autonomous vehicle immediately makes a deceleration action and gradually reduces the speed difference between the two vehicles until it reaches the target speed, at which time the distance between the two vehicles is close to the safe distance, obtained by the simulation test results. The system can still accurately track the target when the vehicle is driving on a curve and timely control the desired speed change of the vehicle, and the target vehicle always maintains a safe distance. The system can be used within 50 meters. [ABSTRACT FROM AUTHOR]

Through the information exchange of the Internet of Things, resources in multiple fields can be integrated to provide users with more comprehensive, diverse, and high-quality services. The computer monitoring system can effectively improve the intelligence and networking of the office and is widely used in many industries. The application of Internet of Things technology can effectively improve the intelligence and networking of computer monitoring systems. The Internet of Things technology can realize the interaction between things and people, help the computer monitoring system to obtain the required information, and truly realize comprehensive monitoring. Based on this, this paper first introduces the basic technology of the Internet of Things technology and analyzes the main points of the computer monitoring technology based on the Internet of Things at this stage and then takes a computer monitoring system as the research object, and further analyzes the computer monitoring system based on the Internet of Things technology to help relevant personnel better apply IoT technology to computer monitoring systems. [ABSTRACT FROM AUTHOR]

With the rapid economic development and rising consumption levels in recent years, people are becoming more and more demanding in terms of style and fashion of clothes. As a result, customer demand for personalised clothing is increasing and the need to respond quickly to consumer demands is also becoming a competitive issue for clothing companies. The automation and intelligence of the garment design and production process is an important part of the implementation of intelligent manufacturing in the garment industry and a necessary way to transform and upgrade the garment industry. Successful clothing styles always have a distinct style identity. The style of the garment can not only conveys the designer's vision but also express the emotional needs of the consumer. In contrast, traditional garment design involves only designers and a single style. With so many styles available, the user has only been able to combine them repeatedly and has not been able to create an innovative design. In addition, apparel design and product development is still a highly empirical task. To be specific, most apparel companies can only respond to a rapidly changing market by increasing the number of designers. However, this blind expansion of staff inevitably leads to increased production costs. As a result, how to effectively develop garment products without relying on the empirical knowledge of garment designers is one of the important issues in achieving intelligent manufacturing in garment enterprises. With the rapid development of computer and network technologies, artificial intelligence, machine learning, and expert systems are widely used in various industries. Nevertheless, the application of these advanced technologies in the field of garment design is still not deep enough. This is mainly due to the uncertainty and imprecision of garment design knowledge. Also, with the rapid development of the fashion industry and the arrival of the trend of personalisation, people's demand for clothing has gradually shifted from mass appeal in terms of comfort and aesthetics to personalisation in terms of self-polishing and temperament. The personalisation of clothing encompasses a wide range of preferences in terms of style and fit. The bottom-up design process and the relatively independent setup of functional modules in traditional clothing technology have prevented the different design levels from being interlinked. This does not reflect the composition of the garment elements in the process of forming features and makes it difficult to grasp the overall design state of the garment. Therefore, in order to address these above issues, this paper proposes a garment design model based on the Bayesian classifier and decision tree algorithm to investigate how computer technologies can be applied to model garment design knowledge. This model can enable inexperienced designers to develop garment products quickly and efficiently to meet the customisation needs of customers, thus enhancing the market competitiveness of garment enterprises. [ABSTRACT FROM AUTHOR]

The paper proposes an A-ResNet model to improve ResNet. The residual attention module with shortcut connection is introduced to enhance the focus on the target object; the dropout layer is introduced to prevent the overfitting phenomenon and improve the recognition accuracy; the network architecture is adjusted to accelerate the training convergence speed and improve the recognition accuracy. The experimental results show that the A-ResNet model achieves a top-1 accuracy improvement of about 2% compared with the traditional ResNet network. Image recognition is one of the core technologies of computer vision, but its application in the field of tea is relatively small, and tea recognition still relies on sensory review methods. A total of 1,713 images of eight common green teas were collected, and the modeling effects of different network depths and different optimization algorithms were explored from the perspectives of predictive ability, convergence speed, model size, and recognition equilibrium of recognition models. [ABSTRACT FROM AUTHOR]

Machine translation is an ongoing field of research from the last decades. The main aim of machine translation is to remove the language barrier. Earlier research in this field started with the direct word-to-word replacement of source language by the target language. Later on, with the advancement in computer and communication technology, there was a paradigm shift to data-driven models like statistical and neural machine translation approaches. In this paper, we have used a neural network-based deep learning technique for English to Urdu languages. Parallel corpus sizes of around 30923 sentences are used. The corpus contains sentences from English-Urdu parallel corpus, news, and sentences which are frequently used in day-to-day life. The corpus contains 542810 English tokens and 540924 Urdu tokens, and the proposed system is trained and tested using 70 : 30 criteria. In order to evaluate the efficiency of the proposed system, several automatic evaluation metrics are used, and the model output is also compared with the output from Google Translator. The proposed model has an average BLEU score of 45.83. [ABSTRACT FROM AUTHOR]

With the development of today's society, medical technology is becoming more and more important in people's daily diagnosis and treatment and the number of computed tomography (CT) images and MRI images is also increasing. It is difficult to meet today's needs for segmentation and recognition of medical images by manpower alone. Therefore, the use of computer technology for automatic segmentation has received extensive attention from researchers. We design a tooth CT image segmentation method combining attention mechanism and ENet. First, dilated convolution is used with the spatial information path, with a small downsampling factor to preserve the resolution of the image. Second, an attention mechanism is added to the segmentation network based on CT image features to improve the accuracy of segmentation. Then, the designed feature fusion module obtains the segmentation result of the tooth CT image. It was verified on tooth CT image dataset published by West China Hospital, and the average intersection ratio and accuracy were used as the metric. The results show that, on the dataset of West China Hospital, Mean Intersection over Union (MIOU) and accuracy are 83.47% and 95.28%, respectively, which are 3.3% and 8.09% higher than the traditional model. Compared with the multiple watershed algorithm, the Chan–Vese segmentation algorithm, and the graph cut segmentation algorithm, our algorithm increases the calculation time by 56.52%, 91.52%, and 62.96%, respectively. It can be seen that our algorithm has obvious advantages in MIOU, accuracy, and calculation time. [ABSTRACT FROM AUTHOR]

With the rapid development of the economy and society, the sustainable development of resources and environment has been paid more and more attention. As an important part of the national environmental supervision system, resources and environmental audits have attracted great attention from the society. In order to explore the application effect of intelligent networking technology in resource and environment audit, this study carries out monitoring sampling, information collection, and later data processing optimization in resource and environment audit projects through the reference of artificial intelligence technology and method and combined with Internet of things big data analysis, which brings new Turks to the development of natural resource and environment audit and helps to improve the efficiency of audit work. Through the application of new technology in the sampling process of environmental information, the staff use new technology to collect, analyze, and mine data and explore how the new technology can promote the efficiency of resource and environmental audit and better promote the construction of ecological civilization. [ABSTRACT FROM AUTHOR]

Several primary school students in Fujian Province have perceived studying mathematics as challenging. To deal with this issue, computer technology advancements, specifically artificial intelligence (AI), present an opportunity to evaluate individual students' learning challenges and give individualized support to optimize their success in mathematics classes. It is also possible to use virtual reality (VR) to assist learners in acquiring complex mathematical and logical ideas and to lessen learners' mistakes. As a result, researchers, particularly beginners, are missing out on a complete perspective of the study of AI in teaching mathematics. That is why we are exploring the role of AI in math education by developing a "fuzzy-based tweakable convolution neural network with a long short-term memory (FT-CNN-LSTM-AM)" method. For this investigation, the students' datasets are taken and educated by mathematical teaching via the application of AI. The proposed method is utilized to predict the students' performance in mathematical education. A grey wolf optimizer is employed to boost the effectiveness of the proposed method. Furthermore, the performance of the proposed method is analyzed and compared with existing approaches to gain the highest reliability. [ABSTRACT FROM AUTHOR]

In the process of global economic integration, the rapid development of various industries not only brings various opportunities but also presents new challenges to enterprises. With the continuous expansion of enterprise scale, more and more enterprises join the trend of international competition, and the internal system of enterprise group organization is complex, resulting in the weakening of the management and control ability of the enterprise itself and the decline of operating efficiency. In order to improve this situation, the fusion and sharing of financial information has become a necessary development strategy to optimize management, strengthen internal control systems, and improve business performance in the era of big data. Technology promotes the development of productivity, and with the continuous maturity of computer technology, blockchain technology comes into being. Blockchain technology is of great significance in promoting industrial transformation and technological innovation. The development of the new era should strengthen the basic research of blockchain and regard blockchain as an important breakthrough for independent innovation of core technologies. This work first analyzes the necessity and feasibility of applying blockchain technology to the enterprise financial information fusion and sharing system. Then, it specifically describes how the blockchain technology is applied to the enterprise financial information fusion and sharing system, and improves the enterprise financial information fusion and sharing system through the blockchain technology. This forms an enterprise financial information fusion and sharing system between companies, which improves the limitations and disadvantages of the current financial sharing platform. Finally, using the AHP to analyze and compare the indicators of the traditional financial sharing model and the financial information fusion sharing model under the blockchain technology, it is concluded that the blockchain technology brings positive changes to the enterprise financial information fusion sharing model. [ABSTRACT FROM AUTHOR]

With the development of Internet technology and computer technology, the network has provided convenience to enterprises while putting forward new requirements for the development of the accounting industry. The combination of traditional financial accounting methods and computerized information technology enables accurate and rapid transmission of financial data. At the same time, the application of network technology optimizes the financial accounting process of enterprises and greatly improves the efficiency of accounting work and accountants can devote more time and energy to the analysis of enterprise financial information. However, with the application of Internet technology, the change in financial accounting has also generated new problems. The article focuses on the topic of financial accounting; first, it briefly introduces the development history of financial accounting and the Internet; second, it discusses the changes in accounting work mode and its characteristics under the network environment and analyzes the advantages and problems of combining network technology and financial accounting; and finally, it puts forward the countermeasures to solve the "Internet+" era financial accounting work for the current situation. Finally, the countermeasures to solve the financial accounting work in the era of "Internet+" are proposed to improve the professional ability of financial personnel. [ABSTRACT FROM AUTHOR]

The electrical activity produced during the heartbeat is measured and recorded by an ECG. Cardiologists can interpret the ECG machine's signals and determine the heart's health condition and related causes of ECG signal abnormalities. However, cardiologist shortage is a challenge in both developing and developed countries. Moreover, the experience of a cardiologist matters in the accurate interpretation of the ECG signal, as the interpretation of ECG is quite tricky even for experienced doctors. Therefore, developing computer-aided ECG interpretation is required for its wide-reaching effect. 12-lead ECG generates a 1D signal with 12 channels among the well-known time-series data. Classical machine learning can develop automatic detection, but deep learning is more effective in the classification task. 1D-CNN is being widely used for CVDS detection from ECG datasets. However, adopting a deep learning model designed for computer vision can be problematic because of its massive parameters and the need for many samples to train. In many detection tasks ranging from semantic segmentation of medical images to time-series data classification, multireceptive field CNN has improved performance. Notably, the nature of the ECG dataset made performance improvement possible by using a multireceptive field CNN (MRF-CNN). Using MRF-CNN, it is possible to design a model that considers semantic context information within ECG signals with different sizes. As a result, this study has designed a multireceptive field CNN architecture for ECG classification. The proposed multireceptive field CNN architecture can improve the performance of ECG signal classification. We have achieved a 0.72 F 1 score and 0.93 AUC for 5 superclasses, a 0.46 F 1 score and 0.92 AUC for 20 subclasses, and a 0.31 F 1 score and 0.92 AUC for all the diagnostic classes of the PTB-XL dataset. [ABSTRACT FROM AUTHOR]

With the development of computer technology, animation is more and more used because of its simple, effective, and higher performance. Machine learning has become the core of artificial intelligence at present. Intelligent learning algorithms are widely used in practical problems such as evaluation. Knowledge-based automatic animation production system faces two challenges: (1) lack of learning ability and waste of data on the website; (2) the quality of animation produced that depends on the level of system designer and the inability of system users to participate in animation production.In order to solve these two problems, an active animation learning system enables the animation system to constantly learn experience and produce the most popular animation, for the first time, for animation production system design and implementation of applied research. Image retrieval technology is a research center in the field of image application. It is widely used in many fields, such as electronic commerce. Animation design will use dynamic image and machine learning to innovate. [ABSTRACT FROM AUTHOR]

With the improvement of the ability to acquire natural information, wireless sensor networks also need to transmit corresponding information in terms of collecting information. Wireless sensor nodes have great application prospects as a key component of wireless sensors. Therefore, different wireless sensors play an important decisive role in the operation of wireless network applications. With the continuous development of wireless sensor networks, existing wireless sensor network nodes exhibit limitations and shortcomings such as inflexible structure, low variability, and low versatility. Specifically, the learning and neural networks obtained by different artificial intelligence expert systems in computing technology are different. On the one hand, it can meet the needs of users for information systems to a certain extent, and on the other hand, it can also help accelerate the development of computer science. At present, the new generation of information technology industry is listed in the seven emerging strategic industries of the country. The new cloud computing technology has gradually expanded to important corporate governance capabilities in terms of information technology. The intelligent application of cloud computing technology replaces traditional enterprise management technology. Efficiency management and risk management can improve the quality and business capabilities of the entire enterprise, improve system applications according to the actual situation of the enterprise, improve system applications, and implement health and the sustainable development of the enterprise, thereby promoting the sustainable development of the computer technology industry. [ABSTRACT FROM AUTHOR]

Virtual reality is a computer technology that produces a simulated environment. It is completely immersive and gives users the viewpoint that they are somewhere else. In recent times, it has become a highly interactive and visualization tool that has gained interest among educators and scholars. Art learning is a teaching-learning approach that is dependent on learning "through the arts" and "with the arts;" it can be a procedure in which art develops the medium of teaching-learning and an important model in some subjects of the curriculum. In this work, we develop a grey wolf optimization with the residual network form of virtual reality application for environmental art learning (GWORN-EAL) technique. It aims to provide metacognitive actions to improve environmental art learning for young children or adults. The GWORN-EAL technique is mainly based on the stimulation of particular features of the target painting over a default image. The color palette of the recognized image of the Fauve painter was mapped to the target image using the Fauve vision of the painter and represented by vivid colors. For optimal hyperparameter tuning of the ResNet model, the GWO algorithm is employed. The experimental results indicated that the GWORN-EAL technique has accomplished effectual outcomes in several aspects. A brief experimental study highlighted the improvement of the GWORN-EAL technique compared to existing models. [ABSTRACT FROM AUTHOR]

In today's society, people's lives are increasingly inseparable from computer information. Due to the continuous improvement of technology and the rapid development of internet technology, the network environment is becoming more and more complex, which makes it easy to cause loopholes in the information retrieval system when people use the network. Therefore, it is especially important to search for legal knowledge by computer. In order to adapt to this change and demand, we need a retrieval system to provide the corresponding search function, legal information content, and management and other services, so as to achieve the purpose of computer legal information retrieval. The legal information retrieval system is computer based, draws conclusions from the analysis of relevant data, and then applies them to judicial trial cases, criminal investigations, and other fields to provide a reference for relevant legal issues. The system is designed to combine computer technology with a criminal investigation and other fields, and then analyze the data to draw the corresponding conclusions. The retrieval algorithms used are mainly image and content retrieval algorithms, and image retrieval algorithms mainly use image segmentation technology, while content retrieval algorithms mainly use the cuckoo algorithm. At present, the information construction and economic and social development in China have become one of the issues of common concern and need to be solved by all countries in the world. The study of the legal information retrieval system is of great importance in the construction of information technology and the development of economic society. [ABSTRACT FROM AUTHOR]

With the continuous development of computer technology, analysis techniques based on various types of sports data sets are also evolving. One typical representative is image-based motion recognition technology, which enables video action recognition with a certain degree of feasibility. In basketball technical action videos, technical action has obvious characteristics. The athletes in the footage in sports videos are relatively fixed, and the scenes are relatively homogeneous, so technical action analysis of basketball technical action videos has certain advantages. However, there are many challenges in basketball technical action recognition, mainly including the fact that basketball techniques are numerous and complex. To address the above issues, this research proposes a 3D convolutional neural network framework that two different resolution image inputs are performed on the basketball technical action dataset. The experimental results show that the algorithmic process designed in this study is effective for action recognition on the basketball technical action dataset. [ABSTRACT FROM AUTHOR]

With the rapid development of computer network technology, the concept of "Internet +" has become more and more popular in recent years. The combination of the Internet and finance has particularly attracted people's attention, and the operating modes of many industries have also changed. Since the use of Internet technology can achieve data sharing and information exchange, the "Internet + Finance" model has broken the barriers of information asymmetry in the financial sector in the past and has made great contributions to China's multiple improvements. The financial market is very important to China's economic development. The identification of the ID function of the wireless sensor network is susceptible to interference and the identification accuracy is reduced. We propose an adaptive identification feature recognition algorithm based on an improved minimum gray tree. After calculating the similarity, the nearest neighbor matching algorithm is directly used to obtain the minimum matching cost corresponding to the wireless sensor network registration that is regarded as the recognized identity so as to realize the identity function adaptive recognition. In this regard, the simulation results show that the proposed algorithm has high recognition accuracy. With the pace of financial innovation, financial institutions have achieved rapid development on the basis of Internet service platforms. At the same time, as the core of preventing money laundering activities, financial institutions are also facing many issues in identifying "customers" in their work. This article analyzes the main content, implementation effects, and difficulty of customer identification in financial institutions and proposes relevant improvement plans. [ABSTRACT FROM AUTHOR]

With the rapid development of computer technology and network technology and the widespread popularity of electronic equipment, communication among people is more dependent on the Internet. The Internet has brought great convenience to people's lives and work, and the Internet data is constantly being recorded. People's data information and behavior information, which provides the basis for data mining and recommendation systems, mining users' information and behaviors, and providing "user portraits" for each user, can provide better services to users and it is also an important part of the recommendation system. In one step, this article takes MOOC education resources as the research goal. In order to improve the effective management of MOOC platform resources based on traditional methods, this article combines relevant data sets and recommendation techniques to initially build a learning platform, implements a deep neural network algorithm, and recommends related services. The request and response data were explained, and through the online learning data set, based on the learner's historical learning records, the learning resources were simulated and recommended to the learners. The resource customization module was elaborated. Through the results of resource recommendation, a personalized learning resource recommendation platform was initially realized, which more intuitively demonstrated the recommendation effect and better realized the teaching management of the MOOC platform. [ABSTRACT FROM AUTHOR]