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检索条件"任意字段=2021 2nd International Conference on Machine Learning and Computer Application, ICMLCA 2021"

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Research on Malware Software Detection Technology Based on SVM on Cloud Platform for Mobile Terminal 2

Research on Malware Software Detection Technology Based on S...

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2nd international conference on Artificial Intelligence and computer Information Technology, AICIT 2023

作者： Lin, Liming Yao, Ye Li, JiaYu Yao, Jia Chen, Wending Huang, Siyang School of Software Northwestern Polytechnical University Xi'an City China School of Computer Science Northwestern Polytechnical University Xi'an City China Queen Mary University of London Engineering School Npu Northwestern Polytechnical University Xi'an City China

ISBN: (纸本)9798350302950

In recent years, the number of new malware samples has shown a trend of rapid growth. The increasing number of malware has resulted in a huge number of signature databases that the host needs to save, which is difficult to maintain. In view of the above situation, this article builds a distributed cloud platform based on Hadoop, and uses the related technology of the cloud platform to deal with the rapid growth of the number of malware. At the same time, the use of the distributed cloud platform greatly improves the detection efficiency. In order to solve the problem of low detection accuracy of unknown software, a malware detection method based on API call sequence is proposed. First use AndroGuard to extract all the function call graphs under the package of the main class in the APK file. Then, through the analysis of the function call graph, the API call sequence of all the entry nodes is obtained;further, the sequence is sorted according to the address information of the first node of each sequence. Splice the sorted sequences in order, and the resulting sequence is the time-series-based API call sequence. After dimensionality reduction through AutoEncoder, machine learning algorithm SVM is used for malware detection. Experimental results show that the accuracy of the SVM detection model based on the API call sequence is higher than that of the SVM detection model based on other features and other machine learning detection models based on the API call sequence, and can detect Android malware more effectively. © 2023 IEEE.

关键词： machine learning

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Design of Multi-sensor Vein Data Fusion Blood Sampling Robot Based on Deep learning 2

Design of Multi-sensor Vein Data Fusion Blood Sampling Robot...

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2nd international conference on computer, Control and Robotics, ICCCR 2022

作者： Sha, Ming Wang, Huaizhi Lin, Guoqin Long, Yuanjian Zeng, Yubin Guo, Sensen College of Mechatronics and Control Engineering Shenzhen University Shenzhen China

ISBN: (纸本)9781665466745

In 2020, the new crown epidemic swept the world, and the world's medical resources will be significantly challenged. Medical staff manual venipuncture has a low accuracy rate and is prone to cause cross-infection. Automatic puncture blood collection robots have the advantages of high automation and accurate puncture. However, the existing automatic puncture blood collection robots for vein recognition have low control accuracy, large volume, and modalities. Single and other issues. Based on the difficulties faced by traditional venipuncture methods, an automatic venipuncture robot based on deep learning was developed to improve the success rate of one-time venipuncture. The YOLO-v5 target detection module is used to locate the blood vessel coarsely, while the U-Net segmentation module accurately divides the blood vessel area to determine the location of the puncture point and its direction. To obtain the depth and area information of blood vessels, we use the algorithm of OpenCV. The stepper motor and servo motor in the actuator are closed-loop controlled by the bus motion controller, and the puncture training arm model is used to perform puncture tests to verify the performance of the robot. Tests show that the robot has high puncture repeatability and accuracy, which meets the application requirements. © 2022 IEEE.

关键词： Deep learning

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Research and application of heterogeneous data collection and analysis platform based on behavior simulation

Research and application of heterogeneous data collection an...

引用

icmlca 2021;2nd international conference on machine learning and computer application

作者： Zhenliang Hao ZhiJun Chang Bolin Qi Xu Li

The competition for data resources has become an important part of the current market competition. The main sources of the college entrance examination data currently studied are third-party websites, or manually input data from the official website. Both of these methods have major shortcomings. First of all, in terms of accuracy and limitation, there is no guarantee that the data is accurate. Secondly, in the process of using data, it will be clamped by a third party, so we use behavior simulation technology to collect data from the official website, and then resolve the data by solving the heterogeneity problem. Finally, the completeness and accuracy of the college entrance examination data can be guaranteed through this system platform.

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Research On Detection Of Malicious Software 2

Research On Detection Of Malicious Software

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2nd international conference on E-Commerce and Internet Technology (ECIT)

作者： Peng, Boyuan Univ Sci & Technol China Hefei Peoples R China

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.

关键词： Deep learning machine learning network security API Malicious software

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machine learning accelerates the technological development of brain science 2

Machine learning accelerates the technological development o...

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2021 2nd international conference on Internet of Things, Artificial Intelligence and Mechanical Automation, IoTAIMA 2021

作者： Chen, Mengya Li, Longfei School of Computer Engineering and Science Shanghai University Shanghai China

The development of machine learning provides a strong research foundation for the field of brain science. Compared with traditional research methods, the strategy based on machine learning is more suitable for the big data background of the current development of the field, and has a broad application prospect. This paper introduces the application status of machine learning in brain science research from three aspects: neuron reconstruction, neuron classification, neuron synthesis, and summarizes the development direction of machine learning technology in the field. © Published under licence by IOP Publishing Ltd.

关键词： Neurons

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Simulation of Formation Control for AUVs with Reinforcement learning Toolbox

Simulation of Formation Control for AUVs with Reinforcement ...

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icmlca 2021;2nd international conference on machine learning and computer application

作者： Xiaomin Guo Xiaowen Li Bing Wang Hongwei Gao Yan Li

Autonomous Underwater Vehicle (AUV) plays an important role in plenty of fields. It is a challenging task to design a high-performance formation controller for AUVs. In this paper, a formation control policy of dual underwater vehicles based on the deep reinforcement learning method Deep Deterministic Policy Gradient method (DDPG) is proposed. The goal of our scenario is that the back AUV follows the front AUV at a certain safe distance. The policy trains the speed and direction of the back AUV, meanwhile, the customized reward function is used to ensure that the distance between the back AUV and the front AUV is within the safe distance range, and minimize the lateral deviation. The simulation results show the effectiveness of the deep reinforcement learning method in AUV formation control.

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application of machine learning in Games with incomplete information

Application of Machine Learning in Games with incomplete inf...

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icmlca 2021;2nd international conference on machine learning and computer application

作者： Xinyi Zhou

machine learning 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 machine learning in known-complete information games, then explains the problems of machine learning in multi-players known incomplete information games, summarizes the development of machine learning in the field of incomplete information games at this stage, and finally prospects the future of machine learning in the field of games. In general, machine learning still has problems in the game of incomplete information, and future generations need to continue to study.

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Brain tumor detection: An application based on machine learning 2

Brain tumor detection: An application based on machine learn...

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2nd international conference for Emerging Technology, INCET 2021

作者： Gaikwad, Sharmila Patel, Saarah Shetty, Ajinkya MCT's Rajiv Gandhi Institute of Technology Dept. of Computer Engineering Mumbai India

ISBN: (纸本)9781728170299

Since years machine learning have played a significant part in the healthcare sector. Out of the various deep learning approaches, Convolutional Neural Networks has shown the most promising results in the classification task. The objective of this paper was to build an application of CNN by proposing a medical expert-based approach to detect Brain Tumor. Annually thousands of people are being diagnosed with Brain Tumor and various treatments such as surgery and radiotherapy are being used currently and to detect the tumor MRI is the imaging technique widely used which is very time-consuming as tumors are highly variable in their size, shape, and structure. Also, there is a risk of human error as there may occur variations by different expert physicians leading to inaccurate results which call for the need for an automatic detection system. The proposed approach helped to classify the MRI images as tumorous and non-tumorous, restraining the visual exploration by doctors to make it less time-consuming since the methods used in the system can detect tumors with the use of MRI images. The architecture was based on CNN along with Transfer learning model VGG-16. The proposed system contained a dataset from Kaggle which was then preprocessed followed by augmentation and passed to the architecture where it was capable of obtaining accurate results rapidly. © 2021 IEEE.

关键词： Magnetic resonance imaging

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8th Workshop on Software Architecture Erosion and Architectural Consistency, SAEroCon 2021, 1st international Workshop on Software Architecture and machine learning, SAML 2021, 4th international Workshop on Context-aware, Autonomous and Smart Architecture, CASA 2021, 5th Workshop on Formal Approaches for Advanced Computing Systems, FAACS 2021, 2nd international Workshop on Model-Driven Engineering for Software Architecture, MDE4SA 2021 and 1st international Workshop on Mining Software Repositories for Software Architecture, MSR4SA 2021 which complemented 15th European conference on Software Architecture, ECSA 2021

8th Workshop on Software Architecture Erosion and Architectu...

引用

8th Workshop on Software Architecture Erosion and Architectural Consistency, SAEroCon 2021, 1st international Workshop on Software Architecture and machine learning, SAML 2021, 4th international Workshop on Context-aware, Autonomous and Smart Architecture, CASA 2021, 5th Workshop on Formal Approaches for Advanced Computing Systems, FAACS 2021, 2nd international Workshop on Model-Driven Engineering for Software Architecture, MDE4SA 2021 and 1st international Workshop on Mining Software Repositories for Software Architecture, MSR4SA 2021 which complemented 15th European conference on Software Architecture, ECSA 2021

ISBN: (纸本)9783031151156

The proceedings contain 15 papers presendted at a virtual meeting. The special focus in this conference is on Software Architecture Erosion and Architectural Consistency. The topics include: Interactive Elicitation of Resilience Scenarios Based on Hazard Analysis Techniques;Towards an Extensible Approach for Generative Microservice Development and Deployment Using LEMMA;applying Knowledge-Driven Architecture Composition with Gabble;architectural Optimization for Confidentiality Under Structural Uncertainty;foundations and Research Agenda for Simulation of Smart Ecosystems Architectures;Blended Graphical and Textual Modelling of UML-RT State-machines: An Industrial Experience;toward Awareness Creation of Common Challenges Women are Facing in Academia: A Study from a German Perspective;mapping Source Code to Modular Architectures Using Keywords;hierarchical Code-to-Architecture Mapping;Building the MSR Tool Kaiaulu: Design Principles and Experiences;self-adaptive machine learning Systems: Research Challenges and Opportunities;behavioral Maps: Identifying Architectural Smells in Self-adaptive Systems at Runtime;an Architectural Approach for Enabling and Developing Cooperative Behaviour in Diverse Autonomous Robots.

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Predictions of diabetes and diet recommendation system for diabetic patients using machine learning techniques 2

Predictions of diabetes and diet recommendation system for d...

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2nd international conference for Emerging Technology, INCET 2021

作者： Bhat, Salliah Shafi Ansari, Gufran Ahmad B.S. Abdur Rahman Crescent Institute of Science and Technology School of Computer Information and Mathematical Sciences Department of Computer Applications Chennai600048 India

ISBN: (纸本)9781728170299

Diabetes is a major metabolic disease that can seriously affect the whole human body. Nowadays, diabetes has become a common disease to mankind from young to old. The number of reported diabetic patients is escalating day by day, due to innumerable reasons toxic or chemical contents mixed with the food, obesity, working culture and bad diet plan, unusual life style, eating food habits and environmental factors. Hence, diagnosing of diabetes is essential to save the human lives. machine learning Techniques can be used to develop an efficient healthcare system to predict different type of diabetic diseases in advance. In this paper a machine learning Techniques is used for diagnosis of diabetes and recommended proper diet for diabetic Patient through Diet Recommendation System (DRS). The proper data analysis is used for the selection of proper diet for diabetic Patients. © 2021 IEEE.

关键词： Recommender systems

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