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In the field of computer vision, it is a very challenging task to use artificial intelligence deep learning method to realize the programming and creation of NFT artwork. With the continuous development and improvement of deep learning technology, this task has become a reality. The generative adversarial network model used in deep learning can generate new images based on the extraction and analysis of image data features and has become an important tool for NFT artwork image generation. In order to better realize the NFT artwork programming, this paper analyzes the working principle of the traditional adversarial generation method and then uses the StyleGAN model to edit the higher-level attributes of the image, which can effectively control the generated style and style of the NFT artwork image. Finally, in order to improve the quality of the generated images, this paper introduces a channel attention mechanism and a spatial attention mechanism to ensure that the generated images are more reasonable and realistic. Finally, through a large number of experiments, it is proved that the NFT artwork transmission programming algorithm based on artificial intelligence deep learning proposed in this paper can control the overall style of image generation according to the needs of the transmission, and the generated image features have good details and high visual quality. [ABSTRACT FROM AUTHOR]

As the presentation form of the web, web pages contain a lot of important information in web design. As many information carriers, such as sound, graphics, and text, web pages have been integrated and have played a good role in visual transmission. It inherits many features of traditional graphic design, it can be said that it extends graphic design, and web design is richer in visual performance. At present, the research on web design in China is mainly background research. There is little research on the front-end aspects of web pages, especially in the aspect of plane vision. The lack of research in this area limits the development of web design. This paper introduces the relevant situation of web design and then analyzes the relevant elements in web design, such as visual elements, and their innovative applications. In addition, it focuses on the presentation form of visual elements in web design and summarizes the development trend of visual elements in contemporary web interface design. It proposes the strategy of innovation and application of visual elements in web design. The author hopes that, through the research of this paper, it will lay a theoretical foundation for the development of web design in the future. At the same time, this paper uses the NCMF algorithm in artificial intelligence to identify the visual elements of web pages, thereby improving the efficiency of web page design. Experiments show that the Chaos accuracy rate of the NCMF algorithm in the CETD dataset is 73.3482%. [ABSTRACT FROM AUTHOR]

With the development of artificial intelligence, machine vision technology based on deep learning is an effective way to improve production efficiency. Because of the rapid update of the automobile manufacturing industry and the large variety of products, the learning time and the number of learning samples of the deep learning model are limited, which brings great difficulties to the recognition of components. Therefore, considering the economic benefits of enterprises, this paper proposes an intelligent component recognition method appropriate for small datasets, aiming to explore an automatic system for component recognition suitable for industrial manufacturing environments. The method completes the generation of the dataset through the system architecture with the potential for automation and the image cropping method based on feature detection and then designs a deep learning network based on coarse-fine-grained feature fusion to generate an intelligent recognition model of components. Finally, the designed network achieves an accuracy of 95.11%, and compared with the traditional classical network on multiple datasets, the designed network has better performance. Thus, the proposed method can improve the production flexibility of the automobile manufacturing industry and improve equipment intelligence. [ABSTRACT FROM AUTHOR]

Machine vision is an important branch of the rapid development of modern artificial intelligence, and it is a key technology to convert the image information of monitoring targets into digital signals. However, due to the wide range of machine vision applications, this research focuses on its application in video surveillance. In the era of artificial intelligence, the detection and tracking of moving objects have always been a key issue in video surveillance. The simulation of human vision is realized by combining the relevant functions of the computer and the image acquisition device, which enables the computer to have the ability to recognize the surrounding environment through images. The intelligent video analysis technology can automatically analyze and extract the key useful information from the video source with the powerful data processing ability of the computer, so as to realize the computer's "understanding" of the video. It allows the computer to "understand" what is shown in the video or what kind of "event" happened and provides a new method and reliable basis for accident detection and accident analysis. Therefore, after a brief introduction to machine vision, moving target monitoring methods, and intelligent tracking algorithms, this paper will focus on moving target monitoring and intelligent tracking strategies for video surveillance. In addition, this paper will focus on introducing the principle of intelligent tracking algorithm through formulas and compare the accuracy and success rate of target monitoring and intelligent tracking between the machine vision-based algorithm and other algorithms during the experiment. Finally, experiments show that the monitoring and tracking effect of machine vision combined with "cloud" is the best, and the overall average can reach 85.7%. Based on this, this paper fully confirms the feasibility of the moving target monitoring and intelligent tracking algorithm based on machine vision. [ABSTRACT FROM AUTHOR]

With the rapid development of China's economy and the continuous optimization and improvement of Internet information technology, a new thing, new media, has emerged. On this basis, a kind of artistic behavior of creating, carrying, spreading, and appreciating is also called new media art. New media art has developed for a long time in foreign countries, but its development and popularization are relatively late in China, and it is still in a stage of improvement. As far as visual communication design is concerned, the emergence and development of new media have strongly promoted the development of its work and the dissemination of various works. Developing visual communication design based on new media art has become a key issue for all visual communication designers. Nowadays, deep learning is widely used in different fields in different ways, and image art representation is a hot research field in computer vision. Therefore, this paper mainly studies and analyzes the symbolism of new media art expression forms and proposes a new media art symbolism model based on artificial intelligence big data. In order to provide a method of real-time representation of art images, the deep learning method is used to model the artistic expression of images, and spatial reorganization of content features and style features is carried out. This paper designs simulation experiments to verify the effectiveness of the model, which can provide some help for the development and improvement of new media art in China. We compared the results of our model with a number of other models to test its effectiveness. We achieved a matching accuracy of 98.4% which is higher than or equal to the results of the existing models. [ABSTRACT FROM AUTHOR]

In recent years, computer vision, artificial intelligence, machine learning, and other high-tech technologies have advanced rapidly. These strategies lay a new technical foundation for online learning and intelligent education by making it easier to promote the scientific, intelligent, and data-driven growth of learners' academic emotions. However, at present, online learning can better make up for the shortcomings of traditional learning and enable people to realize distance learning. However, as an important indicator, learners' learning emotion has a direct impact on learners' learning quality and effect. Therefore, this paper analyzes distance learners' academic emotions based on online learning behavior data. It extracts online learning behavior data by using a deep learning algorithm and multimodal weighted feature fusion based on DS (Dempster-Shafer) evidence theory, establishing distance learners' academic cognition motivation model, and constructs an online learning emotion measurement framework. Finally, it is determined through a correlation study of distance learners' academic emotions and learning impacts those learners' academic emotions in class. It will have a beneficial influence on learning since learners' academic emotion is favorably connected with instructors' emotion, and learners' addition, deletion, and modification behavior is positively correlated with learners' academic emotion. [ABSTRACT FROM AUTHOR]

In the field of intelligent transportation systems (ITS), video surveillance is a hot research topic; this surveillance is used in a variety of applications, such as detecting the cause of an accident, tracking down a specific vehicle, and discovering routes between major locations. Object detection and shadow elimination are the main tasks in this area. Object detection in computer vision is a critical and vital part of object and scene recognition, and its applications are vast in the fields of surveillance and artificial intelligence. Additionally, other challenges arise in regard to video surveillance, including the recognition of text. Based on shadow elevation, we present in this work an inner-outer outline profile (IOOPL) algorithm for detecting the three levels of object boundaries. A system of video surveillance monitoring of traffic can be incorporated into this method. It is essential to identify the type of detected objects in intelligent transportation systems (ITS) to track safely and estimate traffic parameters correctly. This work addresses the problem of not recognizing object shadows as part of the object itself in-vehicle image segmentation. This paper proposes an approach for detecting and segmenting vehicles by eliminating their shadow counterparts using the delta learning algorithm (Widrow-Hoff learning rule), where the system is trained with various types of vehicles according to their appearance, colors, and build types. An essential aspect of the intelligent transportation system is recognizing the type of the detected object so that it can be tracked reliably and the traffic parameters can be estimated correctly. Furthermore, we propose to classify vehicles using a machine learning algorithm consisting of artificial neural networks trained using the delta learning algorithm, a high-performance machine learning algorithm, to obtain information regarding their travels. The paper also presents a method for recognizing the number plate using text correlation and edge dilation techniques. In regard to video text recognition, number plate recognition is a challenging task. [ABSTRACT FROM AUTHOR]

In order to improve the effect of UAV intelligent control, this paper will improve machine vision technology. Moreover, this paper adds scale information on the basis of the LSD algorithm, uses the multiline segment standard to merge these candidate line segments for intelligent recognition, and uses the LSD detection algorithm to improve the operating efficiency of the UAV control system and reduce the computational complexity. In addition, this paper combines machine vision technology and multiagent decision-making technology for UAV intelligent control and builds an intelligent control system, which uses intelligent machine vision technology for recognition and multiagent decision-making technology for motion control. The research results show that the UAV intelligent control system based on machine vision and multiagent decision-making proposed in this paper can achieve reliable control of UAVs and improve the work efficiency of UAVs. [ABSTRACT FROM AUTHOR]

Artificial intelligence is a very broad science, which consists of different fields, such as machine learning, and computer vision. In recent years, the world nuclear industry has developed vigorously. At the same time, incidents of loss of radioactive sources also occur from time to time. At present, most of the search for radioactive sources adopt manual search, which is inefficient, and the searchers are vulnerable to radiation damage. Sending a robot to the search an area where there may be an uncontrolled radioactive source is different. Not only does it improve efficiency, it also protects people from radiation. Therefore, it is of great practical significance to design a radioactive source search robot. This paper mainly introduces the design and implementation of a radioactive source intelligent search robot based on artificial intelligence edge computing, aiming to provide some ideas and directions for the research of radioactive source intelligent search robot. In this paper, a research method for the design and implementation of a radioactive source intelligent search robot based on artificial intelligence edge computing is proposed, including intelligent edge computing and gamma-ray imaging algorithms, which are used to carry out related experiments on the design and implementation of radioactive sources, an intelligent search robot based on edge computing. The experimental results of this paper show that the average resolution of the radioactive source search robot is 90.55%, and the resolution results are more prominent. [ABSTRACT FROM AUTHOR]

Target tracking is an important subject in computer vision technology, which has developed rapidly in recent ten years, and its application have become wider and wider. In this process, it has transferred from a simple experimental tracking environment to a complex real scene where more challenges need to be solved. The rapid development of deep learning has promoted the research progress of digital vision. Target tracking technology is an important foundation of digital vision research, which makes it develop from academia to industry. In this paper, a method of target tracking using MDNet is introduced. Starting with the attention mechanism, two attention mechanisms are added to extract and integrate the better features. Case partitioning is used to reduce the investment of tracking module and minimize the network size during tracking, and its result can be prevented from getting worse. Finally, the experiment is analyzed in detail. [ABSTRACT FROM AUTHOR]

Artificial intelligence is a very challenging science, and people who are engaged in this work must understand computer knowledge, psychology, and philosophy. Artificial intelligence includes a wide range of sciences; it is composed of different fields, such as machine learning and computer vision. In recent years, with the rise and joint drive of technologies such as the Internet, big data, the Internet of Things, and voice recognition, the rapid development of artificial intelligence technology has presented new features such as deep learning, cross-border integration, and human-machine collaboration. Intelligent English translation is an innovation and experiment of the English language industry in the field of science and technology. Machine translation is the use of computer programmes to translate natural language into a specific natural language. This interdisciplinary subject includes three subjects: artificial intelligence, computational linguistics, and mathematical logic. Machine translation is becoming more and more popular with the public, and its advantages are mainly that machine translation is much cheaper than manual translation. From this study, it can be seen that the accuracy rate of the traditional ICTCLAS method is 76.40%, while the accuracy rate of the research method in this article is 94.58%, indicating that the research method used in this article is better. Machine translation greatly reduces the cost of translation because there are very few processes that require human participation, and the translation is basically done by computers. It is also an important research content in the field of English major research. Due to the wide range of language cultures and the influence of local culture, thought, and semantic environment, traditional human translation methods have many shortcomings. Nowadays, the demand for translation is unsatisfactory. Based on this, this paper analyzes the application status of intelligent translation, the prospect of intelligent translation, and realizes the optimization design of the human interface system for the intelligent translation of English. Through experiments, it is found that the multimedia intelligent English translation system based on deep learning not only improves the accuracy of English translation but also greatly improves the efficiency of people learning English. [ABSTRACT FROM AUTHOR]

Human face plays an indispensable role in emotional expression and information exchange, which attracts large number of researchers to study face recognition. Nowadays, with the rapid development of computer graphics, artificial intelligence, and other technologies, the ability of the human vision system to recognize facial expressions and facial organs is enhanced. More and more experts think about how to make the computer vision system have this capability. By combining artificial intelligence and computer graphics, this paper studies how to optimize the 3D face network model and extract geometric features for 3D face recognition. We propose a 3D face network model based on Poisson equation to realize face hole recognition and boundary preprocessing. Besides, we also establish the 3D face surface equation and equal face value extraction and enhance the face feature based on facial semantic information. We study the hole repair of 3D face model based on Poisson equation integrating semantic information and achieve the purpose of optimizing the 3D face model. After the optimization, the method is compared with mesh repair and Poisson repair, which demonstrates that our Poisson-based 3D face hole repairing model obtains the best results among compared methods. [ABSTRACT FROM AUTHOR]

Moving target detection and tracking technology is the core technology in the field of computer vision. It integrates image processing, pattern recognition and intelligence, and artificial intelligence and automatic control are the keys to an intelligent video surveillance system. The method acquires video image signals through visible light or infrared sensors, performs digital image processing on the video images, detects moving targets, and then extracts moving targets for target recognition. Then, the moving target is predicted and tracked according to the image features and spatiotemporal features, and the contour shape, position, and motion trajectory of the target are obtained, which provides data support for subsequent tasks. This paper uses the convolutional neural network model HyperNet as the technical support to study the deep learning (DL) tennis video target extraction. The final experimental results show that the loss value of the loss function in the training process of this method is stably maintained between 1.5% and 2.5%, and the speed performance is also greatly improved. The number of boxes for extracting candidate regions is significantly reduced, the calculation time for each frame will not exceed 1.8 s, the orientation accuracy of target extraction is 96.32%, and the size accuracy is 91.05%. [ABSTRACT FROM AUTHOR]

With the development of artificial intelligence and the rapid development of the computer industry, the practicability of computer vision programs is gradually improved. In this paper, the badminton path tracking algorithm based on computer vision analyzes the badminton trajectory and speed. This paper is aimed at analyzing the image processing technology and path tracking algorithm by using computer vision to obtain relevant data and then exploring the factors of badminton path and ball speed transformation, which provides reference significance for badminton players in future training. The path tracking algorithm is used to predict the rotation angle, the ball speed, and the athlete's body information during the badminton movement through sensors, and the position information of the moving target is captured based on the visual field tracking and target dynamic tracking. Combined with specific badminton players, we first analyze the angle of each limb and the speed of the racket in the process of movement and record the data. Determine different positioning points for different actions, such as pushing the ball, picking the ball, hooking the ball, and rubbing the hair. In this process, we aim at the connection between the highest point and the lowest point of the badminton trajectory and the ball speed. This process fully combines the theoretical knowledge of the path tracking algorithm. The experimental results show that different service skills have different effects on the trajectory and speed of badminton. In the test of relevant data by using the push and receive skills, the lowest point of the ball served by player A in the first three times is higher than that by player B. The most significant difference between the lowest points of the five times is the second time, with a difference of 0.2 m, and the third time, with a minimum difference of 0.03 m. [ABSTRACT FROM AUTHOR]

Deep learning is a subfield of artificial intelligence that allows the computer to adopt and learn some new rules. Deep learning algorithms can identify images, objects, observations, texts, and other structures. In recent years, scene text recognition has inspired many researchers from the computer vision community, and still, it needs improvement because of the poor performance of existing scene recognition algorithms. This research paper proposed a novel approach for scene text recognition that integrates bidirectional LSTM and deep convolution neural networks. In the proposed method, first, the contour of the image is identified and then it is fed into the CNN. CNN is used to generate the ordered sequence of the features from the contoured image. The sequence of features is now coded using the Bi-LSTM. Bi-LSTM is a handy tool for extracting the features from the sequence of words. Hence, this paper combines the two powerful mechanisms for extracting the features from the image, and contour-based input image makes the recognition process faster, which makes this technique better compared to existing methods. The results of the proposed methodology are evaluated on MSRATD 50 dataset, SVHN dataset, vehicle number plate dataset, SVT dataset, and random datasets, and the accuracy is 95.22%, 92.25%, 96.69%, 94.58%, and 98.12%, respectively. According to quantitative and qualitative analysis, this approach is more promising in terms of accuracy and precision rate. [ABSTRACT FROM AUTHOR]

Pedestrian detection and tracking based on computer vision has gradually become an international pattern recognition, which is one of the most active research topics in the field of computer vision and artificial intelligence. Using the theoretical results in the field of pattern recognition and computer vision technology, we are committed to detect and track pedestrians from video sequences. In addition to computer vision-based passer-by detection and tracking technology as the key, in the advanced computer vision action and analysis, it has a direct impact on the accuracy and robustness of its understanding. We analyzed various targets, such as subsequent recognition motion and pedestrian motion, and described them as high-level application processing, such as action understanding. In addition, because of the unique texture of human clothes compared with the surrounding natural landscape, they are highly "prominent" from the perspective of human visual system, and they are particularly prominent in the peripheral part of human contact with the background. In this paper, a binary function based on importance is proposed. As the space representation of image itself is not sensitive to noise and local signal, space representation is used. In addition, as an observation model, it can reduce the adverse effects of background noise and local noise on the tracking algorithm. Through the function block tracking, the pedestrian's body can be tracked in detail. At the same time, the color band learning method is used to update the target template online to deal with the changes of target appearance caused by sunshine, pedestrian posture, and other factors. According to the experimental results, even if the appearance and posture of pedestrians change greatly, it has a stable tracking effect. [ABSTRACT FROM AUTHOR]

With the progress of science and technology and the arrival of the big data era, people increasingly rely on computers to deal with daily life and related affairs. In recent years, machine learning has become more and more popular and has achieved good results in some fields, which also makes machine learning widely used. Among them, visual neural network technology can more intelligently analyze the emotional expression of oil painting, which is one of the current research hotspots, involving machine vision, pattern recognition, image processing, artificial intelligence, and other fields. However, in the art field, oil painting is still very different from other images. At present, there is no deep learning algorithm to identify the application of emotional expression analysis in oil painting theme creation. This paper will start with the neural network algorithm and combine the big data recognition technology to analyze the emotional expression of the oil painting subject in the public environment and establish the emotional expression analysis model of oil painting creation based on big data and neural network. The experiment shows that the graphics synthesized by this model have high resolution and good definition, but the speed is slow in the process of experimental operation. It takes about one hour to complete a round of image optimization. [ABSTRACT FROM AUTHOR]

How to effectively identify errors in athletes' movements, and then achieve the goal of correcting or eliminating them, is a problem that the sports world is currently trying to solve. Sports field is a field where sensor-based human movement recognition can play a role. Obtaining human body data through sensors can provide data basis for research on the characteristics of human movement, improvement of sports level, and technical movement analysis of sports. The main research direction of this paper is the AI+IoT joint recognition of human movements. The target human movements involved include standing, walking, running, shooting, jump shot, jumping, dribbling, walking dribbling, and running dribbling. Computer vision, as a branch of the artificial intelligence research field, uses computers to simulate human visual cognitive ability, excavate useful information from digitized images or videos without manual intervention, and strive to achieve human understanding of visual signals, and realize the ability to understand visual signals from a low level. Conversion of data input to high-level knowledge output. The system scoring module is the core functional module of the system. It extracts the angle feature of the trainer's skeleton data, finds the corresponding frame to correspond, and prompts the trainer's current standard degree of movement in the form of score. In terms of judgment accuracy, the accuracy of all subjects after watching the complete video is higher than the accuracy of the time blocking point, with extremely significant differences, r = -10.80, df =52, p < 0.001. This research improves the universality of target detection and extraction in the real natural environment of the machine vision system and improves the complexity and robustness of human action recognition. [ABSTRACT FROM AUTHOR]

Machine vision is a noncontact measurement method. In some jobs that are not suitable for artificial work environment or artificial vision, machine vision is usually used to replace artificial vision to meet the traditional requirements. Therefore, this paper quotes machine vision into the intelligent scoring and style research of calligraphy post. Firstly, it briefly introduces the related concepts and steps of calligraphy post and briefly explains the style of calligraphy post. Then for the third part of the machine vision localization research algorithm, the correlation function algorithm was proposed in the research field to explain and analyze. Finally, by comparing other research methods of machine vision, it shows that machine vision is more conducive to the study of calligraphy style intelligent scoring, through the experimental study of people on the calligraphy style and calligraphy style of people on the intelligent score. At the end, it is proposed that machine vision can greatly promote the study of intelligent scoring and style of calligraphy and also reflect the accuracy and usability of intelligent scoring for calligraphy style. On-contact test of machine vision refers to not touching the tested object, so as to obtain the test result. It is a three-dimensional testing technology. Among them, the test system of machine vision is relatively simple. It is easy to move and easy to collect data, and the cost of this noncontact test is low. It has more accurate testing technology. A plurality of test quantities can be simultaneously tested by image laser detection of the test quantity without touching the object. [ABSTRACT FROM AUTHOR]

With the application of deep learning method in the field of image processing, the image-related intelligent interaction technology has also been rapidly developed. Visual question answering (VQA) collects the image information by asking questions related to the image and ultimately achieves the purpose for enriching the image understanding. Vision and language are the two core parts of human intelligence to understand the real world, and also the basic components to realize artificial intelligence, and a lot of research has been carried out in their respective fields. With the continuous promotion and application of deep learning in the fields of computer vision and natural language processing, visual question answering technology across the visual field and natural language disciplines has become a research hotspot in recent years. Visual question answering (VQA) for intelligent interaction collects image information by asking relevant questions to the content of the image and finally achieves the purpose of enriching image understanding. At the same time, as an emerging research direction, the challenges faced by the visual question answering system are huge, and we need to learn and excavate. Through the comprehensive comparison and analysis of the existing models and methods of visual question answering, this paper summarizes the shortcomings and development directions of the current research work and analyzes several models of visual question answering technology for the processing of image input and question input of the visual question answering model. The working principle of the model and the common public data set of the model: it is concluded that extending the structured knowledge base and applying mature technologies such as text question answering and natural language processing to deal with VQA problems are the future development directions of the VQA model. [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]

In recent years, with the rapid development of science and technology, people's demand for the quality of daily production and life has gradually increased, and all localities are gradually urbanized. The convenient use of water conservancy, transportation, environment, medical treatment, power grid and other aspects of cities in any country affects people's healthy life. Therefore, it is urgent to build a smart city. For smart city, intelligent identification and management is a very large and complex problem. As one of the high and new technologies, the intelligent recognition of machine vision derived from artificial intelligence has always been a hot spot of great concern. It just provides convenience for the development of smart city and becomes the development direction of smart city construction in the future. Based on this, the role of machine vision is to connect with the computer through wireless sensors such as cameras to simulate an eye that can represent human visual function. This simulated eye can be recognized intelligently in real time. It transmits the recognized information to the computer, and the computer will analyze and process the obtained information for judgment and recognition. This paper will mainly use the optimal threshold segmentation algorithm based on Machine Vision video processing to solve the problem of urban intelligent recognition, and use a specific algorithm to solve some difficulties and obstacles in intelligent recognition. The traditional optimal threshold segmentation algorithm, the optimal threshold segmentation algorithm and the improved optimal threshold segmentation algorithm are experimentally compared. After experimental comparison, it is found that the optimal threshold segmentation algorithm, the improved optimal threshold segmentation algorithm and the traditional optimal threshold segmentation algorithm can intelligently identify the types of buildings and urban traffic signs in the city, Compared with the improved optimal threshold segmentation algorithm, the improved optimal threshold segmentation algorithm improves the response speed, real-time performance, stability and accuracy of the algorithm. Therefore, the optimal threshold segmentation algorithm after the well meets the needs of building the system, and can be useful in the process of intelligent recognition. This improvement is also necessary, which is conducive to the subsequent system construction. [ABSTRACT FROM AUTHOR]

The lungs are COVID-19's most important focus, as it induces inflammatory changes in the lungs that can lead to respiratory insufficiency. Reducing the supply of oxygen to human cells negatively impacts humans, and multiorgan failure with a high mortality rate may, in certain circumstances, occur. Radiological pulmonary evaluation is a vital part of patient therapy for the critically ill patient with COVID-19. The evaluation of radiological imagery is a specialized activity that requires a radiologist. Artificial intelligence to display radiological images is one of the essential topics. Using a deep machine learning technique to identify morphological differences in the lungs of COVID-19-infected patients could yield promising results on digital images of chest X-rays. Minor differences in digital images that are not detectable or apparent to the human eye may be detected using computer vision algorithms. This paper uses machine learning methods to diagnose COVID-19 on chest X-rays, and the findings have been very promising. The dataset includes COVID-19-enhanced X-ray images for disease detection using chest X-ray images. The data were gathered from two publicly accessible datasets. The feature extractions are done using the gray level co-occurrence matrix methods. K-nearest neighbor, support vector machine, linear discrimination analysis, naïve Bayes, and convolutional neural network methods are used for the classification of patients. According to the findings, convolutional neural networks' efficiency linked to imaging modalities with fewer human involvements outperforms other traditional machine learning approaches. [ABSTRACT FROM AUTHOR]

The performance of image classification technology based on deep network has been greatly improved, making computer vision enter the stage of industrialization and be gradually applied to many aspects of human work and life. As a typical classification task in computer vision, human behavior recognition has immeasurable potential value in medical, family, transportation, and other scenarios. At the same time, in the field of competitive sports, the integration of artificial intelligence technology and sports technical and tactical analysis is undoubtedly an important way to innovate and improve the technical and tactical level. Taking karate as an example, the study of athletes' training and competition videos is an important means and method for technical and tactical analysis in competitive sports. Traditional tactical intelligence analysis methods have many shortcomings, such as high labor cost, serious data loss, long delay, and low accuracy. Therefore, based on the convolutional neural network, this paper establishes a new graph convolution model for automatic intelligent analysis of karate athletes' technical action recognition, action frequency statistics, and trajectory tracking. The technology effectively makes up for the disadvantages of traditional tactical intelligence analysis methods. The research results show that the new topology map construction method has a significant effect on improving the accuracy of behavior recognition and also lays a foundation for technical and tactical analysis. [ABSTRACT FROM AUTHOR]

Information is obtained from human eyes for thinking divergence, and further associated with computer equipment, so human beings endow computers with the ability of "vision" to convey and feel information. This field has developed for many years, and many aspects can be in line with other research directions, such as artificial intelligence, which has become popular in recent years, and pattern recognition, which has been applied a lot. In order to sort out the structure and content of multitarget recognition smoothly, this paper starts from the perspective of shallow vision, uses theory and practical experiments, and chooses the core technology with the largest weight from massive computer technologies, so that the recognition algorithm can compare with the recognition algorithm. The research shows that (1) CNN shows its unique feature ability and incomparable detection accuracy from many models, and the error rate can be reduced from 28.07% to 18.40%. (2) The method of candidate region is complex, and the larger the region, the more difficult it is to calculate. The method based on regression is far beyond it in both precision and speed and is more suitable for the research of this subject. (3) When the mAP increases, the speed is forced to slow down. If the image resolution is high with the same model, the mAP will be high (SSD and YOLO models are often used). Experiments show that the recognition effect is obvious. At the end of the article, the advantages and disadvantages of this study are summarized. In the field of computer vision, people need to do more in-depth research. Follow-up can optimize multitarget recognition and detection and strive to improve the accuracy. [ABSTRACT FROM AUTHOR]

Neural network algorithms and intelligent algorithms are hot topics in the field of deep learning. In this study, the neural network algorithm and intelligence are optimized, and it is used in simulation experiments to improve the target image recognition ability of the algorithm in the machine vision environment. First, this paper introduces the application of neural networks in the field of machine vision. Second, in the experiment, the improved VGG-16 convolutional neural network (CNN) model is applied to metal block defect detection. Experimental results show that the optimized network can classify metal block defects with the maximum accuracy of 99.28%. Then, the intelligent algorithm based on neural network is studied, and the CIFAR-10 data set is taken as the experimental target for training test and verification test. Using BP algorithm, particle swarm optimization algorithm (PSO-BP), and improved neural network algorithm, respectively, the convergence speed of ICS algorithm based on BP neural network is compared. In contrast, ICS-BP algorithm has the fastest convergence speed and converges when the number of iterations is 32, followed by PSO-BP algorithm. [ABSTRACT FROM AUTHOR]

With the development of social economy and the progress of science and technology, the process of urbanization continues to deepen and the progress of the construction industry changes with each passing day. People are paying more and more attention to the safety of civil engineering. Accidents frequently occurred during the construction process, which exposed the lack of attention to safety issues during the construction process, the lack of effective supervision of safe construction, and the lack of safety awareness of construction personnel, which had an impact on the construction of civil engineering. In this article, a solution to the safety management system for construction personnel based on artificial intelligence machine vision technology is proposed for the study of the safety management of civil engineering structures. First of all, through the research and analysis of the problems in the safety management of construction workers, the types of safety accidents of construction workers are summarized and the effect of machine understanding of the construction scene is realized through target detection and description of the spatial interaction between the two. A real-time detection and early warning platform is built, and early warnings are automatically sent out when a predefined dangerous scene occurs, so as to achieve the purpose of preventing construction accidents. The experimental results in this paper show that, in the use of artificial intelligence machine vision technology to manage the safety of civil engineering construction, the safety management of workers in the construction is realized, and the results show that the level of construction safety management has increased to 97.4%, ensuring the quality of civil engineering construction and safety. [ABSTRACT FROM AUTHOR]

Intelligent farming machines are becoming a new trend in modern agriculture. The intelligence and automation allow planting to become data-driven, leading to more timely and cost-effective production and management of farms and improving the quality and output of farm products. This paper presents a proposal for developing a type of intelligent tea picking machine based on active computer vision and Internet of Things (IoT) techniques. The intelligent tea picking machine possesses an active vision system for new tip positioning and can automatically implement tea picking operation in the natural environment. The active vision system provided with a cross-light path of projection and camera is designed according to the actual characteristics of picking surface, where new tips can be recognized by referring to the color factor and their height information is easily acquired by fringe projection profilometry. Furthermore, the machine attaches wireless communication equipment to transmit the real-time status of the tea picking process to an intermediary platform and eventually to the Internet for extensive data analysis. The data such as color factor and quantity of new tips collected through IoT can be used for different quality and production evaluations. The focus of this paper can promote the automation and intelligence of tea pickers and agricultural machinery. [ABSTRACT FROM AUTHOR]

Understanding human activity and behavior, particularly real-time understanding in video feeds, is one of the most active areas of research in Computer Vision (CV) and Artificial Intelligence (AI) nowadays. To advance the topic of integrating learning engagement research with university teaching practice, accurate and efficient assessment, and analysis of students' classroom learning behavior engagement is very important. The recently proposed classroom behavior recognition algorithms have some limitations, such as the inability to quickly and accurately identify students' classroom behaviors because they do not consider the motion information of students between consecutive frames. In recent years, action recognition algorithms based on Convolutional Neural Networks (CNN) have improved significantly. To address the limitations of existing algorithms, in this study, a 3D-CNN is selected as a network model for classroom student behavior recognition, which increases information multisourcing and classroom student localization with high accuracy and robustness. For better analysis of human behavior in videos, the 3D convolution extends the 2D convolution to the spatial–temporal domain. In the proposed system, first of all, a real-time picture stream of each student is obtained by combining real-time target detection and tracking. Then, a deep spatiotemporal residual CNN is used to learn the spatiotemporal features of each student's behavior, so, as to achieve real-time recognition of classroom behaviors for multistudent targets in classroom teaching scenarios. To verify the effectiveness of the proposed model, different experiments are conducted using the labeled classroom behavior dataset. The experimental results demonstrate that the proposed model exhibits better performance in classroom behavior recognition. The accurate recognition of classroom behaviors can assist the teachers and students to understand the classroom learning situation and help to promote the development of smart classroom. [ABSTRACT FROM AUTHOR]

The use of computer vision for target detection and recognition has been an interesting and challenging area of research for the past three decades. Professional athletes and sports enthusiasts in general can be trained with appropriate systems for corrective training and assistive training. Such a need has motivated researchers to combine artificial intelligence with the field of sports to conduct research. In this paper, we propose a Mask Region-Convolutional Neural Network (MR-CNN)- based method for yoga movement recognition based on the image task of yoga movement recognition. The improved MR-CNN model is based on the framework and structure of the region-convolutional network, which proposes a certain number of candidate regions for the image by feature extraction and classifies them, then outputs these regions as detected bounding boxes, and does mask prediction for the candidate regions using segmentation branches. The improved MR-CNN model uses an improved deep residual network as the backbone network for feature extraction, bilinear interpolation of the extracted candidate regions using Region of Interest (RoI) Align, followed by target classification and detection, and segmentation of the image using the segmentation branch. The model improves the convolution part in the segmentation branch by replacing the original standard convolution with a depth-separable convolution to improve the network efficiency. Experimentally constructed polygon-labeled datasets are simulated using the algorithm. The deepening of the network and the use of depth-separable network improve the accuracy of detection while maintaining the reliability of the network and validate the effectiveness of the improved MR-CNN. [ABSTRACT FROM AUTHOR]

Artificial intelligence (AI) has evolved into a powerful tool that has wide-spread application in computer vision such as computer-aided inspection, industrial control systems, and navigation of robots. Monitoring the condition of machineries and mechanical components for the presence of faults with the aid of image-based automated analysis is one major application of computer vision. Diagnosing machinery faults from images can be made feasible with the adoption of deep learning and machine learning techniques. The primary objective of this study is to detect malfunctions in photovoltaic (PV) modules by utilizing a combination of deep learning and machine learning methodologies, with the assistance of RGB images captured via unmanned aerial vehicles. Six test conditions of PV modules such as good panel, snail trail, delamination, glass breakage, discoloration, and burn marks were considered in the study. The overall experimentation was carried out in two phases: (i) deep learning phase and (ii) machine learning phase. In the initial deep learning phase, the final fully connected layer of six pretrained networks, namely, DenseNet-201, VGG19, ResNet-50, GoogLeNet, VGG16, and AlexNet, was utilized to extract PVM image features. During the machine learning phase, feature selection from the extracted features was carried out using the J48 decision tree algorithm. Post selection of features, three families of classifiers such as tree, Bayes, and lazy were applied to determine the best feature extractor-classifier pair. The combination of DenseNet-201 features with k-nearest neighbour (IBK) classifier produced the overall classification accuracy of 100.00% among all other pretrained network features and classifiers considered. [ABSTRACT FROM AUTHOR]

Considering the problem that teachers' command gesture tracking error is due to their poor detection ability in the intelligent education of big data or technology, the application method of interactive visualization and computer vision in the intelligent education of big data or technology is designed. Three-dimensional coordinate transformations of teaching content information and visual processing of teaching information. Use computer vision to detect teachers' actions and complete computer vision detection of interactive teaching. Using the rendering algorithm, the AI education environment is optimized without affecting computer vision detection. So far, the interactive visualization and computer vision application method design in intelligent big data technology education has been completed. The experimental results show that combining these two technologies can significantly improve the tracking effect of teachers' instruction gestures, optimize the design effect of teaching content, and raise teaching levels. [ABSTRACT FROM AUTHOR]

This study addresses the underlying challenges of computer vision adoption in the Kenyan agricultural sector and how to solve these hurdles to commercialize this technology. Technological advancements have revolutionized the agriculture sector, where artificial intelligence enhances yields, mitigates losses, and manages natural resources, leading to increased productivity. Kenya is still lagging in the commercialization of computer vision to improve its agricultural sector, which is the largest source of GDP. Kenya has remarkable skills and expertise in artificial intelligence that can support artificial intelligence implementation; the government policies, data availability, and high cost incurred in starting a computer vision company are problematic. Through better government policies on subsidies and data, research and development investments, and AI forums, Kenya will solve the challenges of adopting computer vision. While computer vision has the potential to revolutionize the agricultural industry by improving crop yield, detecting diseases, and increasing efficiency, there are several barriers to its adoption, including inadequate infrastructure, lack of technical expertise, and limited funding. This study aims to identify the challenges hindering the implementation of computer vision technology in the Kenyan agricultural sector and propose potential solutions to address these challenges. [ABSTRACT FROM AUTHOR]

In the context of the development of artificial intelligence (AI) technology, the planning and dissemination of radio and television must not only learn to quickly and deeply process a large amount of content and extract structured information such as classification, themes, tags, and styles from information sources, but also learn to use audience behaviors to explore and discover potential content, jump out of the editor's subjective vision, and realize personalized content presentation. On the basis of summarizing and analyzing previous research results, this study expounded the research status and significance of the planning and dissemination mode of radio and television, elaborated on the development background, current status, and future challenges of AI technology, introduced the methods and principles of computer vision, natural language processing, semantic analysis, and video generation, discussed the content production, interview, and editing of radio and television, explored the review, archiving, and system solutions of radio and television, proposed the planning strategies for radio and television assisted by AI technology, analyzed the smart distribution, reception, and feedback of radio and television dissemination, discussed the interactive application and virtual reality of radio and television dissemination, analyzed the dissemination mode of radio and television assisted by AI technology, and finally carried out a case application and its analysis. The study results show that AI technology can be used for smart lyrics, smart writing, smart broadcasting, smart translation, smart recommendation, smart stripping, and smart retrieval, and so on. The AI-assisted radio and television planning gives audiences the right to choose news, provides them with more targeted news content by means of data capture and resource integration, and caters to audiences' all-round and in-depth information needs through a question-and-answer method, innovating dissemination logic and expression approaches, and enhances the audience's sense of experience and leadership in obtaining information. The AI-assisted radio and television dissemination mode has the advantages of improving the effectiveness of information communication, promoting the optimization and integration of news resources, and enhancing audience stickiness. [ABSTRACT FROM AUTHOR]

Computer vision is the science that enables computers and machines to see and perceive image content on a semantic level. It combines concepts, techniques, and ideas from various fields such as digital image processing, pattern matching, artificial intelligence, and computer graphics. A computer vision system is designed to model the human visual system on a functional basis as closely as possible. Deep learning and Convolutional Neural Networks (CNNs) in particular which are biologically inspired have significantly contributed to computer vision studies. This research develops a computer vision system that uses CNNs and handcrafted filters from Log-Gabor filters to identify medicinal plants based on their leaf textural features in an ensemble manner. The system was tested on a dataset developed from the Centre of Plant Medicine Research, Ghana (MyDataset) consisting of forty-nine (49) plant species. Using the concept of transfer learning, ten pretrained networks including Alexnet, GoogLeNet, DenseNet201, Inceptionv3, Mobilenetv2, Restnet18, Resnet50, Resnet101, vgg16, and vgg19 were used as feature extractors. The DenseNet201 architecture resulted with the best outcome of 87% accuracy and GoogLeNet with 79% preforming the worse averaged across six supervised learning algorithms. The proposed model (OTAMNet), created by fusing a Log-Gabor layer into the transition layers of the DenseNet201 architecture achieved 98% accuracy when tested on MyDataset. OTAMNet was tested on other benchmark datasets; Flavia, Swedish Leaf, MD2020, and the Folio dataset. The Flavia dataset achieved 99%, Swedish Leaf 100%, MD2020 99%, and the Folio dataset 97%. A false-positive rate of less than 0.1% was achieved in all cases. [ABSTRACT FROM AUTHOR]

The second cause of disability in Mexico is visual impairment; 26% population is blind. Even though blind people maintain a lower quality's life than sighted people, there is not enough social interest to develop comprehensive solutions that improve it. Although many voice-activated emerging technologies use artificial intelligence to get human-machine communication through intelligent virtual assistants, such as Alexa, Siri, Google Home, and Cortana, among others, in reality, there have not been developed specific tools just for blind people that help them to improve their independent behavior because they always depend on other people in doing their daily tasks, more even when they need to move around unknown environments for them. This document reports the development of an autonomous mobility system to be operated by blind people. By audio reconstruction, it is possible to dictate, to the blind pedestrian, in real-time, the presence of traffic lights, crosswalks, and information about their current location. This system employs real-time computer vision tools, artificial intelligence, audio playback systems, and location systems, and it improves the independent behavior of blind people because they could move through unknown environments without the assistance of any sighted person, giving them greater independence and consequently increasing their life's quality. [ABSTRACT FROM AUTHOR]

Movement object detection method is the basis and key of the modem intelligent video surveillance system. It combines advanced technologies in many fields, such as artificial intelligence, image processing, and pattern recognition, and is the research area of computer vision technology. Therefore, it is important to explore the motion detection and target recognition algorithms. The data collection method has concluded that T > 60 is absent from four different videos. The T value is traversed from 0–255 to select the largest T value as the split threshold. Through the analysis, the range of the threshold selection can be reduced to 0-60, thus improving the operational efficiency. [ABSTRACT FROM AUTHOR]

With advances in computer vision and artificial intelligence technology, facial expression recognition research has become a prominent topic. Current research is grappling with how to enable computers to fully understand expression features and improve recognition rates. Most single face image datasets are based on the psychological classification of the six basic human expressions used for network training. By outlining the problem of facial recognition by comparing traditional methods, deep learning, and broad learning techniques, this review highlights the remaining challenges and future directions of deep learning and broad learning research. The deep learning method has made it easier and more effective to extract expression features and improve facial expression recognition accuracy by end-to-end feature learning, but there are still many difficulties in robustness and real-time performance. The broad learning system (BLS) is a broad network structure that is expanded by increasing the number of feature nodes and enhancement nodes appropriately to reinforce the structure and is also effective in facial expression recognition. However, outliers and noises in unbalanced datasets need BLS to solve in the future. Finally, we present several problems that still need to be addressed in facial expression recognition. [ABSTRACT FROM AUTHOR]

People are realizing that linguistic competence is a vital aspect of communicative competence as language study and instruction have progressed. Grammar instruction is not only important but also the primary means of achieving communication skills in foreign language classes. This article focuses on the computer vision and machine learning-based virtual reality technology college English immersive context teaching approach. The goal is to increase pupils' capacity to learn English. The experimental groups used VR innovation immersive environment education from the standpoint of constructivist theory in a compared teaching experiment with 2 classifications of freshman at a university, while the control class used standard multimedia equipment and conventional teaching techniques. Students only passively acquire a lot of material from instructors in the class, they have limited opportunity to engage in the exchange of information and express thoughts in the chosen language, and they are "immersed" in the Chinese environment most of the time. With an average score of 2.8 points higher than the control group, the overall English level was likewise higher. English teaching system based on computer big data method, optimization of oral English teaching system based on computer-aided technology, and artificial intelligence are the existing methods used for comparison. This demonstrates that using constructivism theory and virtual reality technology in college English immersing contextual education may genuinely increase students' English levels. [ABSTRACT FROM AUTHOR]

With the continuous development and improvement of artificial intelligence technology, machine learning technology has also been extensively developed, which has promoted the development of computer vision, image processing, natural language processing, and other fields. Purpose. This article aims to apply the image processing technology based on machine learning in the detection of childhood diseases and propose the application of image processing technology to the detection of childhood diseases. This article introduces machine learning, image recognition technology, and related algorithms in detail and experiments on image recognition technology based on machine learning. The experimental results show that image recognition technology based on machine learning can well identify white blood cells that are difficult to distinguish with the naked eye, with a recognition rate of up to 90%. Applying image recognition technology based on machine learning in disease diagnosis has greatly improved the level of medical diagnosis. [ABSTRACT FROM AUTHOR]

Accurate detection and recognition of various kinds of fruits and vegetables by using the artificial intelligence (AI) approach always remain a challenging task due to similarity between various types of fruits and challenging environments such as lighting and background variations. Therefore, developing and exploring an expert system for automatic fruits' recognition is getting more and more important after many successful approaches; however, this technology is still far from being mature. The deep learning-based models have emerged as state-of-the-art techniques for image segmentation and classification and have a lot of promise in challenging domains such as agriculture, where they can deal with the large variability in data better than classical computer vision methods. In this study, we proposed a deep learning-based framework to detect and recognize fruits and vegetables automatically with difficult real-world scenarios. The proposed method might be helpful for the fruit sellers to identify and differentiate various kinds of fruits and vegetables that have similarities. The proposed method has applied deep convolutional neural network (DCNN) to the undertakings of distinguishing natural fruit images of the Gilgit-Baltistan (GB) region as this area is famous for fruits' production in Pakistan as well as in the world. The experimental outcomes demonstrate that the suggested deep learning algorithm has the effective capability of automatically recognizing the fruit with high accuracy of 96%. This high accuracy exhibits that the proposed approach can meet world application requirements. [ABSTRACT FROM AUTHOR]

With the rapid development of computer vision and artificial intelligence, people are increasingly demanding image decomposition. Many of the current methods do not decompose images well. In order to find the decomposition method with high accuracy and accurate recognition rate, this study combines convolutional neural network and probability map model, and proposes a single-image intrinsic image decomposition method that is on both standard dataset images and natural images. Compared with the existing single-image automatic decomposition algorithm, the visual effect comparable to the user interaction decomposition algorithm is obtained, and the method of this study also obtains the lowest error rate in the quantitative comparison on the standard dataset image. The multi-image collaborative intrinsic image decomposition method proposed in this study obtains the decomposition result of consistent foreground reflectivity on multiple sets of image pairs. In this study, the eigenimage decomposition is applied to the illumination uniformity in the small change detection, and the promising reflectivity layer image obtained by the decomposition helps to improve the accuracy of the cooperative saliency detection. This study proposes an algorithm for the cooperation between CNN and probability graph model, and introduces how to combine the probability graph model with the traditional CNN to accomplish the pixel-level eigendecomposition task. This study also designs a single-image and multi-image intrinsic image decomposition results analysis experiments, then analyzes the probabilistic graphical model coordination intrinsic image decomposition results, and finally analyzes the convolutional neural network coordination intrinsic decomposition performance to draw the conclusion of this study. The effect on the Msrc-v2 dataset was increased by 0.8% over the probability plot model. [ABSTRACT FROM AUTHOR]

Mobile application (app) use is increasingly becoming an essential part of our daily lives. Due to their significant usefulness, people rely on them to perform multiple tasks seamlessly in almost all aspects of everyday life. Similarly, there has been immense progress in artificial intelligence (AI) technology, especially deep learning, computer vision, natural language processing, and robotics. These technologies are now actively being implemented in smartphone apps and healthcare, providing multiple healthcare services. However, several factors affect the usefulness of mobile healthcare apps, and usability is an important one. There are various healthcare apps developed for each specific task, and the success of these apps depends on their performance. This study presents a systematic review of the existing apps and discusses their usability attributes. It highlights the usability models, outlines, and guidelines proposed in previous research for designing apps with improved usability characteristics. Thirty-nine research articles were reviewed and examined to identify the usability attributes, framework, and app design conducted. The results showed that satisfaction, efficiency, and learnability are the most important usability attributes to consider when designing eHealth mobile apps. Surprisingly, other significant attributes for healthcare apps, such as privacy and security, were not among the most indicated attributes in the studies. [ABSTRACT FROM AUTHOR]

Large-scale structural health monitoring and damage detection of concealed underwater structures are always the urgent and state-of-art problems to be solved in the field of civil engineering. With the development of artificial intelligence especially the combination of deep learning and computer vision, greater advantages have been brought to the concrete crack detection based on convolutional neural network (CNN) over the traditional methods. However, these machine learning (ML) methods still have some defects, such as it being inaccurate or not strong, having poor generalization ability, or the accuracy still needs to be improved, and the running speed is slow. In this article, a modified fully convolutional network (FCN) with more robustness and more effectiveness is proposed, which makes it convenient and low cost for long-term structural monitoring and inspection compared with other methods. Meanwhile, to improve the accuracy of recognition and prediction, innovations were conducted in this study as follows. Moreover, differed from the common simple deconvolution, it also includes a subpixel convolution layer, which can greatly reduce the sampling time. Then, the proposed method was verified its practicability with the overall recognition accuracy reaching up to 97.92% and 12% efficiency improvement. [ABSTRACT FROM AUTHOR]

The mouse is one of the wonderful inventions of Human-Computer Interaction (HCI) technology. Currently, wireless mouse or a Bluetooth mouse still uses devices and is not free of devices completely since it uses a battery for power and a dongle to connect it to the PC. In the proposed AI virtual mouse system, this limitation can be overcome by employing webcam or a built-in camera for capturing of hand gestures and hand tip detection using computer vision. The algorithm used in the system makes use of the machine learning algorithm. Based on the hand gestures, the computer can be controlled virtually and can perform left click, right click, scrolling functions, and computer cursor function without the use of the physical mouse. The algorithm is based on deep learning for detecting the hands. Hence, the proposed system will avoid COVID-19 spread by eliminating the human intervention and dependency of devices to control the computer. [ABSTRACT FROM AUTHOR]

Mimicking the central nervous system and by extension and creating various additional methods of computation such as artificial neural networks, machine learning, deep learning, or genetic algorithms have led to the artificial intelligence field which aims to solve given problems in a flexible, intelligent, and learnable way. Computational neuroscience is concerned with simulating real neural systems to predict brain workings and disorders from subneuronal systems to network plasticity as hypotheses to be tested later in real neural tissues and hence to understand the principles governing them. [Extracted from the article]