In medical images, image segmentation is a very important method, which can accurately locate and analyze the lesions and tissues. However, due to the complexity of medical images and noise, accurate and robust segmen...
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Recognition of text in images has become increasingly important with the rapid development of cameras, and the main technology involved is Optical Character Recognition (OCR). In order to enhance the recognition accur...
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Fog computing is an emerging paradigm that provides services near the end-user. The tremendous increase in IoT devices and big data leads to complexity in fog resource allocation. Inefficient resource allocation can l...
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The gannet optimization algorithm (GOA) is an effective group intelligence algorithm inspired by the foraging behavior of gannets. Despite its merits, considerable potential exists for enhancing its exploration and co...
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Neural network-based encoder and decoder are one of the emerging techniques for image compression. To improve the compression rate, these models use a special module called the quantizer that improves the entropy of t...
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Dealing with classification problems requires the crucial step of feature selection (FS), which helps to reduce data dimensions and shorten classification time. Feature selection and support vector machines (SVM) clas...
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In the fields of intelligent transportation and multi-task cooperation, many practical problems can be modeled by colored traveling salesman problem (CTSP). When solving large-scale CTSP with a scale of more than 1000...
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In the fields of intelligent transportation and multi-task cooperation, many practical problems can be modeled by colored traveling salesman problem (CTSP). When solving large-scale CTSP with a scale of more than 1000 dimensions, their convergence speed and the quality of their solutions are limited. This paper proposes a new hybrid ITÖ (HITÖ) algorithm, which integrates two new strategies, crossover operator and mutation strategy, into the standard ITÖ. In the iteration process of HITÖ, the feasible solution of CTSP is represented by the double chromosome coding, and the random drift and wave operators are used to explore and develop new unknown regions. In this process, the drift operator is executed by the improved crossover operator, and the wave operator is performed by the optimized mutation strategy. Experiments show that HITÖ is superior to the known comparison algorithms in terms of the quality solution.
Digital microfluidic biochip provides an alternative platform to synthesize the biochemical protocols. Droplet routing in biochemical synthesis involves moving multiple droplets across the biochip simultaneously. It i...
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A novel color image encryption scheme is developed to enhance the security of encryption without increasing the complexity. Firstly, the plain color image is decomposed into three grayscale plain images, which are con...
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A novel color image encryption scheme is developed to enhance the security of encryption without increasing the complexity. Firstly, the plain color image is decomposed into three grayscale plain images, which are converted into the frequency domain coefficient matrices(FDCM) with discrete cosine transform(DCT) operation. After that, a twodimensional(2D) coupled chaotic system is developed and used to generate one group of embedded matrices and another group of encryption matrices, respectively. The embedded matrices are integrated with the FDCM to fulfill the frequency domain encryption, and then the inverse DCT processing is implemented to recover the spatial domain signal. Eventually,under the function of the encryption matrices and the proposed diagonal scrambling algorithm, the final color ciphertext is obtained. The experimental results show that the proposed method can not only ensure efficient encryption but also satisfy various sizes of image encryption. Besides, it has better performance than other similar techniques in statistical feature analysis, such as key space, key sensitivity, anti-differential attack, information entropy, noise attack, etc.
Nowadays,the personalized recommendation has become a research hotspot for addressing information *** this,generating effective recommendations from sparse data remains a ***,auxiliary information has been widely used...
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Nowadays,the personalized recommendation has become a research hotspot for addressing information *** this,generating effective recommendations from sparse data remains a ***,auxiliary information has been widely used to address data sparsity,but most models using auxiliary information are linear and have limited *** to the advantages of feature extraction and no-label requirements,autoencoder-based methods have become quite ***,most existing autoencoder-based methods discard the reconstruction of auxiliary information,which poses huge challenges for better representation learning and model *** address these problems,we propose Serial-Autoencoder for Personalized Recommendation(SAPR),which aims to reduce the loss of critical information and enhance the learning of feature ***,we first combine the original rating matrix and item attribute features and feed them into the first autoencoder for generating a higher-level representation of the ***,we use a second autoencoder to enhance the reconstruction of the data representation of the prediciton rating *** output rating information is used for recommendation *** experiments on the MovieTweetings and MovieLens datasets have verified the effectiveness of SAPR compared to state-of-the-art models.
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