Deep learning is a brand new field in machinelearning research which constructs structured models to extract features by simulating the cognitive aspects of the human brain. The whole training process requires only t...
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Adversarial networks are commonly used in Image reconstruction, segmentation, detection, classification and cross-modal synthesis. In our research programmer, we studied some basic adversarial networks like Generative...
Adversarial networks are commonly used in Image reconstruction, segmentation, detection, classification and cross-modal synthesis. In our research programmer, we studied some basic adversarial networks like Generative Adversarial Network (Gan), Convolutional Neural Networks (CNN) and Deep Neural Network (DNN). As a result, we saw a lot of innovations andapplications in different fields, especially in the medical field. Based on our observations, we believe there would be ceaseless new improvements in medicine. To assist the professional researchers, find and quote a variety of papers, we will conduct a review of advances in medicine within adversarial networks in the past few years.
The world is seeing a rapid growth in mobile malware applications. Traditional computer malware programmers are shifting to android malware applications. Consequently, mobile security specialists are also working very...
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In recent times, increasing amount of the data have enriched the decision making using machinelearning. Despite of the growth in the domain of machinelearning, the proximity to the physical limits of chip fabricatio...
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As an important branch of machinelearning research, reinforcement learning can obtain strategy improvement through the interaction of trial and environment and is widely applied in various fields. Research on reinfor...
As an important branch of machinelearning research, reinforcement learning can obtain strategy improvement through the interaction of trial and environment and is widely applied in various fields. Research on reinforcement learning theory is helpful for subsequent multidisciplinary projects. However, related researches are still relatively scattered. Thus, this paper introduces the theory andapplication of reinforcement learning by literature analysis and comprehensively introduces the main algorithms theory of reinforcement learning, including temporal difference learning, Q-learning and Sarsa learning, as well as their combination and effect comparison. Besides, this paper also summarizes the current applications of reinforcement learning that have received more attention, namely control systems, autonomous driving, and robots. Moreover, the current research issues and future work directions of reinforcement learning are discussed. Overall, this article concludes that the current basic algorithm construction of reinforcement learning is relatively complete, but the method of combining multiple algorithms remains to be discussed.
In this paper, in order to reduce interference between medical IoT device networks, balance network energy consumption, establish an optimal game model of joint channel distribution and power control. The model is cha...
In this paper, in order to reduce interference between medical IoT device networks, balance network energy consumption, establish an optimal game model of joint channel distribution and power control. The model is characterized by network interference and node residual energy. In addition, the model also considers the relationship between channel distribution and power control. In addition, an enhanced learning channel distribution algorithm based on non-cooperative game is designed, and it is proved that the algorithm converges on Nash equilibrium. The simulation results show that this paper has good network performance with small interference and uniform energy consumption.
In today’s world, vast amounts of information are available to us. For decades, scientists have sought to develop the most efficient methods of querying these data and extracting the information needed. In this surve...
In today’s world, vast amounts of information are available to us. For decades, scientists have sought to develop the most efficient methods of querying these data and extracting the information needed. In this survey, we describe the basics of sentiment analysis and discuss prominent work in natural language processing, focusing on the domain of movie reviews. We discuss methods that operate using lexicon-based approaches, conventional machinelearning approaches, deep learning approaches, and hybrid approaches. Compared with previous papers, this article offers more comprehensive coverage, as it includes the latest methodologies in the field, e.g., the use of BERT in sentiment analysis of movie reviews. We also highlight the limitations of research to date and point to potential directions for future work.
Internet andcomputer systems are an indispensable part of daily life. The number of web applications have increased with the development of technology and digital transformation. Web applications have high risk for s...
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ISBN:
(纸本)9781665407595
Internet andcomputer systems are an indispensable part of daily life. The number of web applications have increased with the development of technology and digital transformation. Web applications have high risk for security since the applications is not developed securely, contains vulnerabilities and easily accessible by hackers. Web application firewall is used to protect web applications from attacks. Signature-based and anomaly-based methods are used in web application firewalls. In this research, anomaly-based web application firewall model was developed using natural language processing techniques and linear support vector machinelearning algorithm. Word n-gram and character n-gram natural language processing techniques were compared by performing separate models. Proposed model achieve higher detection performance with using the character n-gram compared to other studies. According to the results of the experiment proposed model is capable of detection web attacks effectively with the overall detection accuracy rate of 99.53%.
In recent years, with the rapid development of the Internet, the Internet has become an indispensable part of society. However, with the increasing variety of malware and the application of encryption methods, the sec...
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ISBN:
(纸本)9781665438735
In recent years, with the rapid development of the Internet, the Internet has become an indispensable part of society. However, with the increasing variety of malware and the application of encryption methods, the security of Internet network is constantly threatened. How to detect and identify malicious software without affecting the normal operation of user hosts and violating user privacy by monitoring a small number of non-sensitive features while software is running dynamically, so as to protect user host information, has become an imminent issue in the field of network security. In this work, a new feature extraction method is developed and proved to be effective. This paper presents a characterization method to extract malware features from three aspects: derived features, vector space features of API and context features of API. XGBoos, LGBM, Improved TextCNN models are trained to predict test sets. Finally, these models are combined with Stacking model to output the final results.
machinelearning is applied to every aspect of people’s life. Especially in the field of games, machines gradually show their own set of mechanisms and occupy a certain position in this field. In 2016, Alpha Go and L...
machinelearning is applied to every aspect of people’s life. Especially in the field of games, machines gradually show their own set of mechanisms and occupy a certain position in this field. In 2016, Alpha Go and Lee Sedol, the world champion of Go and a professional nine-dan player, had a man-machine battle with Go and won with a total score of 4-1. However generally, games such as Go and Chess are classified as games with complete information, while other games, such as poker, are games with incomplete information. In the game of incomplete information, traditional algorithms are no longer applicable due to the lack of information and the increase in uncertainty, and an independent algorithm system needs to be studied. This paper first introduces the application of machinelearning in known-complete information games, then explains the problems of machinelearning in multi-players known incomplete information games, summarizes the development of machinelearning in the field of incomplete information games at this stage, and finally prospects the future of machinelearning in the field of games. In general, machinelearning still has problems in the game of incomplete information, and future generations need to continue to study.
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