Current Issue : October-December Volume : 2022 Issue Number : 4 Articles : 5 Articles
In terms of image processing, encryption plays the main role in the field of image transmission. Using one algorithm of deep learning (DL), such as neural network backpropagation, increases the performance of encryption by learning the parameters and weights derived from the image itself. The use of more than one layer in the neural network improves the performance of the algorithm. Also, in the process of image encryption, randomness is an important component, especially when used by smart learning methods. Deep neural networks are related to pixels used to manipulate position and value according to the predicted new value given from a variable neural system. It also includes messy encrypted images used via applying randomness and increasing the key space in addition to using the logistic and Henon map for complexity. The main goal of any encryption method is to increase the complexity of the encrypted image to be difficult or impossible to decrypt the image without the proposed key. One of the important measurements for image encryption is the histogram and how it can be uniformed by the proposed method. Variables of randomness are used as features for the deep learning system, with feedback during iteration. An ideal image processing encryption yields high messy images by keeping the quality. Experimental results showed the backpropagation algorithm achieved better results than other algorithms....
People may quickly employ imagig devices to acquire and use image data thanks to the rapid development of computer networks and communication technologies. However, imaging devices obtain massive data through real-time acquisition, and a large number of invalid images affect the imaging device system’s endurance on the one hand while also requiring a significant amount of time for analysis on the other hand, so there is a critical need to find a way to automate the mining of valuable information in the data. In this paper, we propose an intelligent imaging device system, which embeds a target intelligent recognition algorithm, improves the YOLOv3 model by using a method based on depth-separable convolutional blocks and inverse feature fusion structure, and finally achieves fast target detection while improving detection accuracy through the design of distance-based nonextreme suppression and loss function. By preprocessing the images and automatically identifying and saving images containing target animals, the range of the imaging device system equipment can be improved and the workload of researchers searching for target animals in images can be reduced. In this paper, we propose a method for intelligent preservation of contained target images by deploying lightweight target recognition algorithms on edge computing hardware with the intelligence of the intelligent imaging device system as the research goal. After simulation experiments, the use of this method can improve the endurance of the imaging device system and reduce the time of manual processing at a later stage....
Because the current methods used in mining engineering image feature recognition have some problems, such as poor classification accuracy, operation efficiency, and inability to recognize rotation features, in order to promote the development of mineral processing in China and improve resource recovery, simulated annealing algorithm is applied to the process of mining engineering image feature extraction in this paper. Based on the simulated annealing algorithm, this paper introduces the image recognition technology based on the simulated annealing algorithm and uses this image recognition technology to study the separation of ore and rock according to the differences between ore and waste rock in morphology and R, G, and B primary color components. At the same time, based on the local binary mode theory, the local variance of pixels is calculated successively to obtain the variance diagram of mining engineering image. At the same time, the simulated annealing algorithm is used to calculate the vector in each direction in the variance diagram of mining engineering image, and then, the vector is combined as the image eigenvalue. The obtained eigenvalue is combined with the binary pattern feature to realize mining recognition method and feature recognition. Finally, the experimental research shows that the algorithm proposed in this paper can quickly extract the spatial data information of mining engineering image variance and reuse the information of image local binary pattern. Compared with the traditional mining engineering image feature extraction algorithm, the recognition accuracy of this algorithm can reach 85%....
Early diagnosis of breast cancer is an important component of breast cancer therapy. A variety of diagnostic platforms can provide valuable information regarding breast cancer patients, including image-based diagnostic techniques. However, breast abnormalities are not always easy to identify. Mammography, ultrasound, and thermography are some of the technologies developed to detect breast cancer. Using image processing and artificial intelligence techniques, the computer enables radiologists to identify chest problems more accurately. The purpose of this article was to review various approaches to detecting breast cancer using artificial intelligence and image processing. The authors present an innovative approach for identifying breast cancer using machine learning methods. Compared to current approaches, such as CNN, our particle swarm optimized wavelet neural network (PSOWNN) method appears to be relatively superior. The use of machine learning methods is clearly beneficial in terms of improved performance, efficiency, and quality of images, which are crucial to the most innovative medical applications. According to a comparison of the process’s 905 images to those of other illnesses, 98.6% of the disorders are correctly identified. In summary, PSOWNNs, therefore, have a specificity of 98.8%. Furthermore, PSOWNNs have a precision of 98.6%, which means that, despite the high number of women diagnosed with breast cancer, only 830 (95.2%) are diagnosed. In other words, 95.2% of images are correctly classified. PSOWNNs are more accurate than other machine learning algorithms, SVM, KNN, and CNN....
At present, sports dance teaching still tends to “demonstration” training. Students have limited time and space for autonomous learning, and their enthusiasm for participation is not high, which leads to a decline in classroom learning efficiency. In view of this, video teaching has become popular in sports dance classrooms, providing a new model for sports dance teaching. Video recommendation is particularly important for the improvement of teaching quality. A sports dance video recommendation method based on style is proposed. The factorization machine model is used to combine features and process high-dimensional sparse features, the deep neural network model is adopted as the value function network of the deep Q-learning algorithm, and the deep Q-learning algorithm is used as the decision function to solve the recommendation accuracy and diversity question. Through the application experiment of sports dance video recommendation, it is resulted that the recommendation accuracy of the proposed model is slightly higher than that of traditional recommendation algorithm and the recommendation diversity is obviously better than that of traditional recommendation algorithm. The advantages and feasibility of the proposed model are verified....
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