Target detection is an important task in the field of computer vision and its application has been extended to the medical field. In the medical field, target detection techniques can be used for diagnosis, treatment ...
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ISBN:
(数字)9798350389487
ISBN:
(纸本)9798350389494
Target detection is an important task in the field of computer vision and its application has been extended to the medical field. In the medical field, target detection techniques can be used for diagnosis, treatment and research. For example, by performing target detection on medical images, lesion regions or foci can be automatically identified and localized to help doctors perform accurate diagnosis and treatment. In traditional machine learning methods, commonly used features include morphological features, texture features, and histogram features. And in deep learning methods, the commonly used structures include Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN), etc. In addition, there are many special problems and challenges for medical images, such as image quality, variation between modalities, and lack of data. Therefore, target detection in the medical field requires special techniques and algorithms to address these issues. By integrating expertise in the medical field, combining traditional and deep learning techniques, and developing improved and optimized algorithms for the special problems of medical images, the accuracy and efficiency of medical target detection can be improved, thus contributing to the advancement of medical research and clinical practice.
With the growing popularity of the Internet, Web applications have become increasingly essential in our daily lives. Web application programming interfaces (Web APIs) play a crucial role in facilitating interaction be...
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Short-term traffic forecasting is an important means to prevent urban congestion. In this paper, the urban traffic operation time series data sets are used for modeling, combining with various characteristic variables...
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AI is black-box and non-explainable, in other words, due to the complexity of the decision-making process of AI, people are unable to know why and how AI makes the decision. For these reasons, people will question and...
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ISBN:
(数字)9798350389524
ISBN:
(纸本)9798350389531
AI is black-box and non-explainable, in other words, due to the complexity of the decision-making process of AI, people are unable to know why and how AI makes the decision. For these reasons, people will question and worry about AI’s decision-making. Against this background, researchers have proposed the concept of Explainable Artificial Intelligence (XAI). The aim of this paper is to help readers be able to better understand XAI through extensive surveys. In this paper, we discuss the concept of XAI, the motivation of the research, the current status of the research, and the difficulties in the research process. At the same time, we also discuss the specific methods to realize AI explainability by dividing AI explainability methods into two ways: transparent design and ex post facto interpretation. In addition, we select the three fields of healthcare, finance, and autonomous driving, discuss the application of XAI in these three fields, and select some representative results as examples through surveys. Finally, we summarize this article and present our outlook on the future development of XAI.
Various communication methods allow modern people to communicate with each other more frequently, resulting in a more diverse network structure in today's social network due to its own dynamics. Compared with the ...
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A game measurement model considering the attacker’s knowledge background is proposed based on the Bayesian game theory aiming at striking a balance between the protection of sensitive information and the quality of *...
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A game measurement model considering the attacker’s knowledge background is proposed based on the Bayesian game theory aiming at striking a balance between the protection of sensitive information and the quality of *** quantified the sensitive level of information according to the user’s personalized sensitive information protection *** on the probability distribution of sensitive level and attacker’s knowledge background type,the strategy combination of service provider and attacker was analyzed,and a game-based sensitive information protection model was *** the combination of strategies under Bayesian equilibrium,the information entropy was used to measure the leakage of sensitive ***,in the paper the influence of the sensitive level of information and the attacker’s knowledge background on the strategy of both sides of the game was considered *** on,the leakage of the user’s sensitive information was ***,the feasibility of the model was described by experiments.
Code completion, which can accelerate the software development process and improve the quality of software products, is an essential part of today's integrated development environments. It has become an important ...
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Mobile Edge computing (MEC) pushes computing resources from the network center to the network edge to provide efficient and reliable computingservices. However, due to the mobility and diversity of mobile users (MUs)...
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In recent years, blockchain research has set off an upsurge in academia, and it is called the next generation of value Internet. Because of its decentralization, anonymity, security, immutability, traceability and oth...
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With the increasing growth of electronic medical data, the difficulties of data sharing among medical institutions and the leakage of data privacy have become the focus of the public and medical workers. The blockchai...
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