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检索条件"主题词=detection algorithms"

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Blackleg detection in Potato Plants using Convolutional Neural Networks 6th

Blackleg Detection in Potato Plants using Convolutional Neur...

引用

6th International-Federation-of-Automatic-Control (IFAC) Conference on Sensing, Control and Automation Technologies for Agriculture (AGRICONTROL)

作者： Afonso, Manya Blok, Pieter M. Polder, Gerrit van der Wolf, Jan M. Kamp, Jan Wageningen Univ & Res Wageningen Netherlands

Potato blackleg is a tuber-borne bacterial disease caused by species within the genera Dickeya and Pectobacterium that can cause decay of plant tissue and wilting through the action of cell wall degrading enzymes released by the pathogen. In case of serious infections, tubers may rot before emergence. Management is largely based on the use of pathogen-free seed potato tubers. For this, fields are visually monitored both for certification and also to take out diseased plants to avoid spread to neighboring plants. Imaging potentially offers a quick and non-destructive way to inspect the health of potato plants in a field. Early detection of blackleg diseased plants with modern vision techniques can significantly reduce costs. In this paper, we studied the use of deep learning for detecting blackleg diseased potato plants. Two deep convolutional neural networks were trained on RGB images with healthy and diseased plants. One of these networks (ResNetl8) was experimentally found to produce a precision of 95 % and recall of 91 % for the disease class. These results show that convolutional neural networks can be used to detect blackleg diseased potato plants. (C) 2019, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.

关键词： Neural networks Machine learning Image processing detection algorithms Agriculture

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Survey on Deadlocks in Distributed Database Systems

Survey on Deadlocks in Distributed Database Systems

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Intelligent Communication Technologies and Virtual Mobile Networks (ICICV), International Conference on

作者： Y. Bhavani K. Praveen Kumar K. Dharmateja P. Pranathi R. Sowmya Kakatiya Institute of Technology & Science Warangal India

ISBN: (数字)9781665419604

ISBN: (纸本)9781665429986

In Distributed Database System (DBS) and multitasking system, the occurrence of deadlocks is one of the most serious problems. If a site request for a resource that is already in the another site which is waiting for another resource then the scenario is called as distributed deadlock. Different distributed environments require a suitable deadlock detection algorithm to detect deadlocks. Different distributed environments needs to maintain their platforms by avoiding deadlocks. To achieve this environment, it is required to fed with optimized deadlock detection and avoidance algorithms. In this article, different deadlock detection algorithms that uses Wait For Graph and resolution algorithms to trace out deadlocks were discussed. An optimization technique is used for resolving deadlock in an efficient manner. A comparison between different deadlock detection algorithms based on different parameters like, delay time, message size, number of messages and whether the algorithm detects false deadlocks or not were performed. Based on the comparisons, a few deadlock detection algorithms were suggested for the distributed environment.

关键词： Heuristic algorithms System recovery Database systems Delays Resource management detection algorithms Optimization

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Design of Radar Target Detector based on CFAR Feature Plane

Design of Radar Target Detector based on CFAR Feature Plane

引用

IEEE International Conference on Radar

作者： Wenjing Zhao Guolong Cui Xianxiang Yu Xue Yao School of Information and Communications Engineering University of Electronic Science and Technology of China Chengdu China School of Electronic and Information Engineering Chongqing Three Gorges University Chongqing China

ISBN: (纸本)9781665468893

The processing framework resorting to the constant false alarm rate (CFAR) feature plane interprets CFAR detector design as a learning machine composed of input layer, hidden layer (invariant feature extraction), design layer and output layer, which provides a novel way to the design of CFAR detector. In this paper, we investigate the design criterion to the design layer under this framework. On the CFAR feature plane composed of two maximum invariant statistics, two new detectors with simple controllable parameters are designed through feature fusion by weighted arithmetic mean and weighted geometric mean. Finally, simulation experiments verify the effectiveness of the two methods.

关键词： Simulation Design methodology Radar detection Detectors Radar Feature extraction detection algorithms

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Evading Signature-Based Antivirus Software Using Custom Reverse Shell Exploit

Evading Signature-Based Antivirus Software Using Custom Reve...

引用

IEEE Southeastcon

作者： Andrew Johnson Rami J. Haddad Georgia Southern University Statesboro United States

Antivirus software is considered to be the primary line of defense against malicious software in modern computing systems. The purpose of this paper is to expose exploitation that can evade Antivirus software that uses signature-based detection algorithms. In this paper, a novel approach was proposed to change the source code of a common Metasploit-Framework used to compile the reverse shell payload without altering its functionality but changing its signature. The proposed method introduced an additional stage to the shellcode program. Instead of the shellcode being generated and stored within the program, it was generated separately and stored on a remote server and then only accessed when the program is executed. This approach was able to reduce its detectability by the Antivirus software by 97% compared to a typical reverse shell program.

关键词： Malware Servers detection algorithms Payloads

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Design of MSPRN Target Recognition Algorithm for Vehicle Automatic Driving

Design of MSPRN Target Recognition Algorithm for Vehicle Aut...

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Artificial Intelligence Technology (ACAIT), Asian Conference on

作者： Min Yang College of Mechanical Engineering Nanjing University of Science and Technology ZiJin College Nanjing Jiangsu Province China

ISBN: (纸本)9781665426312

Vehicle automatic driving technology can effectively improve the safety performance of vehicle driving. This research is to meet the needs of vehicle automatic driving, and propose a target recognition algorithm with better performance. According to the existing research, the algorithm is optimized and improved based on the traditional Faster R-CNN algorithm, and a network target recognition algorithm based on Multi Strategy Regional candidate box is proposed to optimize the anchor box. Through the comparative analysis of recognition and recall rate between traditional R-CNN algorithm and MSPRN algorithm, it can be seen that MSPRN algorithm has better algorithm performance and is suitable for target detection and recognition in vehicle automatic driving.

关键词： Analytical models Target recognition Vehicle driving Object detection Safety detection algorithms Artificial intelligence

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Zombie Hosts Identification Based on DNS Log

Zombie Hosts Identification Based on DNS Log

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IEEE International Conference on Network Infrastructure and Digital Content (IC-NIDC)

作者： Renjie Wang Yangsen Zhang Ruixue Duan Zhuofan Huang Institute of Intelligent Information Processing Beijing Information Science and Technology University Beijing China Beijing Laboratory of National Economic Security Early-warning Engineering Beijing China

ISBN: (纸本)9781665405836

Although the academia has done a lot of research on DNS abnormal behavior, whether from the perspective of traffic or irregular domain name recognition, the mechanism behind DNS is ignored in the pre-processing of DNS logs and other data. In addition, most studies focus on traffic anomaly detection and unconventional domain name recognition, and lack of systematic research on the combination of the two, so the proposed algorithm has no practical application. This paper proposes a clustering method based on DNS client IP address traffic characteristics, which divides DNS logs into five access modes. Then, a DNS log preprocessing algorithm is designed to preprocess the logs that may exist in zombie hosts. Finally, a two-layer GRU network detection algorithm based on domain name text features is proposed. Experimental results show that this method can effectively identify zombie hosts in DNS logs.

关键词： Systematics Conferences Clustering methods Clustering algorithms Feature extraction IP networks detection algorithms

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Old Sinhala Newspaper Article Segmentation for Content Recognition Using Image Processing

Old Sinhala Newspaper Article Segmentation for Content Recog...

引用

From Innovation to Impact (FITI)

作者： P. P. A. Gayashan K. A. V. G. Perera G. D. Shashiwadana Nirmani L. Ranathunga Department of Information Technology Faculty of Information Technology University of Moratuwa Katubedda Srilanka

ISBN: (数字)9781665483254

ISBN: (纸本)9781665483261

As an automation approach of the Old Newspaper digitization, the content segmentation plays a major role. This study segments the degraded and mediocre quality old Sinhala newspapers into separate articles together with main elements classification, character segmentation, feature extraction, and character recognition. As a remedial measure for the misspelled word generation, a word correction technique was introduced at the end of the process to improve the accuracy of the article digitization. This paper highlights the first step of this study, where newspaper page segmentation into separate articles through heuristic knowledge embedded approach is carried out. This approach includes image detection, line detection, text area identification, margin detection, and column separation of newspaper pages. The results of this research are intriguingly comparable to other existing literature.

关键词： Image segmentation Technological innovation Image recognition Automation Feature extraction Character recognition detection algorithms

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Video steganography network based on 3DCNN

Video steganography network based on 3DCNN

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Digital Society and Intelligent Systems (DSInS), International Conference on

作者： Yangping Lin Zhiqiang Ning Jia Liu Mingshu Zhang Pei Chen Xiaoyuan Yang College of Cryptography Engineering Engineering University of PAP Xi’an China

ISBN: (纸本)9781665406314

In recent years, the steganography scheme based on neural network has made many significant progress on images, but it is still in the exploratory stage in the field of video steganography. By using long skip connections to extract the spatio-temporal information in the video, this paper proposes a 3DCNN full-video steganography network. The network takes a pair of cover and secret video sequences as input, and uses a stego network to output a spatio-temporal residual sequence, which is added to the cover video as a small disturbance. A video classification network is proposed, which can be used to identify the cover video frame and the stego video frame to assist the message receiver to extract the secret message correctly. We chose UCF101 video data set as the training and testing set of the network model. We used various video quality evaluation indicators (PSNR, SSIM, Pixel distribution) to measure the performance evaluation of the stego video network, and proved the anti-detection of the stego video by using some stego detection algorithms. Under the training and testing of the data set of stego videos generated by the stego network, the classification accuracy of the proposed video classification network reaches about 93%.

关键词： Training Steganography Video sequences Receivers Feature extraction Quality assessment detection algorithms

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Evaluation and analysis of college students' physical education relying on the algorithm of emergent words detection

Evaluation and analysis of college students' physical educat...

引用

International Conference on Mechanical, Control and Computer Engineering (ICMCCE)

作者： Shengxiang Zhe Student Center Yunnan University of Business Management Kunming Yunnan China

With the continuous improvement of physical education reform, attention should be paid to the evaluation of students' satisfaction with the quality of physical education and the establishment of a physical education quality evaluation system. This paper relies on the detection algorithm of emergent words, adopts the method of investigation and analysis, obtains the four main factors that affect the evaluation results of the classroom teaching quality of college physical education teachers, and conducts impact analysis, aiming to provide a reference for the evaluation of college physical education quality.

关键词： Training Education Object recognition detection algorithms

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VPD-Map: Visual Pointcloud Descriptor with Top-view Feature Map for Viewpoint-Free Loop Closure in Direct Visual Odometry

VPD-Map: Visual Pointcloud Descriptor with Top-view Feature ...

引用

Conference on Vehicle Control and Intelligence (CVCI)

作者： Ruitao Zhang Yafei Wang Shaoteng Wu School of Mechanical Engineering Shanghai Jiao Tong University Shanghai China

ISBN: (纸本)9781665408486

Loop closing is a crucial step to improve the accuracy of a SLAM system, which can reduce drift caused by frontend odometry and ensure global consistency of mapping process. In a traditional visual SLAM system, this is usually done by extracting and matching image features. The accuracy of the loop closure detection and the precision of the pose-constraint provided by loop closing is the bottleneck restricting the loop closing accuracy. Since image features is sensitive to large viewpoint changes, in this paper, we propose a novel neural network architecture VPD-Map, which takes visual pointcloud to provide a global 3D visual descriptor for fast loop closure detection and a top-view feature map for pose-constraint prior. Since the descriptor and feature map are based solely on visual pointcloud information, it is robust to viewpoint changes. VPD-Map can also serve as the backend of a visualSLAM system. Experiment on loop closure detection shows that this descriptor can perform viewpoint-free loop closure detection and outperforms traditional loop detection algorithms like bag-of-word model on the KITTI Odometry Dataset.

关键词： Visualization Simultaneous localization and mapping Three-dimensional displays Neural networks Feature extraction Task analysis detection algorithms

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