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Integration of Deep Learning and Industrial Computer Vision Library for Motorcycle and Vehicle License Plate recognition 2020

Integration of Deep Learning and Industrial Computer Vision ...

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3rd international conference on Image and Graphics Processing (ICIGP)

作者： Wu, Ting-Yu Liao, Hsien-Chou Lim, Zi-Yi Chaoyang Univ Technol 168 Jifeng E Rd Taichung Taiwan

ISBN: (纸本)9781450377201

The license plate recognition is widely used in daily life. Basically, the license plate recognition process is divided into two phases: detection and recognition. Most of the previous studies are simply use conventional pattern recognition techniques or deep learning. Therefore, the industrial computer vision library (Euresys Open eVision) and deep learning are integrated to fulfill license plate recognition and presented in this paper. The proposed approach is divided into three phases: the first phase is to apply deep learning to detect the license plate in the image with a complicated background. The second phase is the license plate correction. A perspective transformation is used to correct the angle of the license plate. Finally, optical character recognition (OCR) is used to recognize the license plate. According to the experimental results, the accuracy of the proposed approach can reach 96.7% with an average identification time of 63.4ms. It shows that the proposed approach is feasible in a practical environment.

关键词： License plate recognition deep learning pattern recognition OCR

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Improving Multi-Factor Authentication Security with Deep Learning-Based User Behaviour Analysis

Improving Multi-Factor Authentication Security with Deep Lea...

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Integrated Circuits and Communication Systems (ICICACS), IEEE international conference on

作者： Fahad Sulaiman Mohammed Alnafea Alok Singh Sengar Noor Kaylan Hamid Kuttimarks Muthu Selladurai Sharmeen Izzat Hassan R Saravanakumar Department of Accounting College of Business and Economics in Qassim University Buraida Saudi Arabia Department of Computer Science & Engineering Haridwar University Roorkee India Department of Computer Education Al Kitab University Kirkuk Iraq Shivajirao S. Jondhle College of Engineering and Technology Asangaon India Department of Computer Engineering College of Engineering Knowledge University Erbil Iraq Department of ECE Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai India

ISBN: (数字)9798331508456

ISBN: (纸本)9798331508463

With growing frequency and sophistication of cyber threats, there is an urgent need for more adaptive, secure multi-factor authentication systems. Traditional approaches often tend to make static factor-based authentication systems that just ignore the dynamic nature of user behavior and are constantly at risk from evolved attacks against authentication mechanisms. This paper presents a novel hybrid deep learning approach - that of long short-term memory networks and convolutional neural networks combined in an attempt to address these disadvantages and enhance the security of an MFA system. The approach uses LSTM to analyze sequential data on user behavior, hence recognizing long-term dependencies like login pattern and session durations. On the other hand, CNN layers will classify the attribute by learning the spatial hierarchy thus identifying small variations that can mark emerging threats to security. This method, in contrast to others, analyzes statically or all at once, providing a dynamic check into the temporal as well as spatial aspects of user interactions; thus, there is more force in its frame for anomaly detection. Experimental tests further verify the excellent performance of the system, with results indicating an accuracy of 98.2%, precision of 99.9%, recall of 99.5%, and an F1 score of 99%. The new strategy exploits MFA frameworks more effectively in minimizing their vulnerabilities against authentication attacks, while developing a dynamic and robust solution that caters to the shift in the ever-changing landscape within cybersecurity, thus advancing the possibility of practice securely.

关键词： Deep learning Hands Multi-factor authentication Accuracy Neural networks Reflection pattern recognition Convolutional neural networks Integrated circuit modeling Long short term memory

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Weakly-Supervised Gland Instance Segmentation based on Point Labeling 3

Weakly-Supervised Gland Instance Segmentation based on Point...

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3rd IEEE international conference on Communications, Information System and Computer Engineering, CISCE 2021

作者： Wang, Da-Han Ye, Haili Li, Jianmin Zhu, Shunzhi Zhu, Chenyan Fujian Key Labarotory of Pattern Recognition and Image Understanding School of Computer and Information Engineering Xiamen University of Technology Fujian Xiamen China Medical Diagnostic Systems Co. Ltd. Fujian Xiamen China

ISBN: (纸本)9780738112152

Colon cancer is a common digestive tract cancer occurring in the colon. How to effectively segment the glands in the pathological image of the colon is a challenging task. As well as distinguishing the glands from the background, it is necessary to distinguish the boundaries between the different instances of the glands. Common instance segmentation methods require pixel-level instance annotation, which takes a lot of time and resources. The pixel-level weakly supervised instance segmentation method cannot provide sufficient supervision, which makes the quality of gland segmentation poor. In this paper, we propose a weakly supervised gland segmentation method based on point labeling. We trained a glandular point detection model to predict high confidence points in gland images using the supervision information of point labeling. Then, the high-confidence point-assisted training instance segmentation model is used to implement the instance segmentation of the glands. We tested our method on the 2015 MICCAI Gland Challenge dataset, and the experimental results show that our method can effectively segment the instance of the Gland, and its performance is even better than that part of the method by training using the fully supervised. © 2021 IEEE.

关键词： Image analysis

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Exploration of pattern recognition Methods for Motor Imagery EEG Signal with Convolutional Neural Network Approach 3

Exploration of Pattern Recognition Methods for Motor Imagery...

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3rd international conference on Emerging Electrical Energy, Electronics and Computing Technologies 2021, ICE4CT 2021

作者： Zahra, Hanina N Zakaria, Hasballah Hermanto, Beni R School of Electrical Engineering and Informatics Bandung Institute of Technology West Java Bandung40132 Indonesia

As an application of EEG, Motor Imagery based Brain-Computer Interface (MI BCI) plays a significant role in assisting patients with disability to communicate with their environment. MI BCI could now be realized through various methods such as machine learning. Many attempts using different machine learning approaches as MI BCI applications have been done with every one of them yielding various results. While some attempts managed to achieve agreeable results, some still failed. This failure may be caused by the separation of the feature extraction and classification steps, as this may lead to the loss of information which in turn causes lower classification accuracy. This problem can be solved by integrating feature extraction and classification by harnessing a classification algorithm that processed the input data as a whole until it produces the prediction, hence the use of convolutional neural network (CNN) approach which is known for its versatility in processing and classifying data all in one go. In this study, the CNN exploration involved a task to classify 5 different classes of fingers' imaginary movement (thumb, index, middle, ring, and pinky) based on the processed raw signal provided. The CNN performance was observed for both non-augmented and augmented data with the data augmentation techniques used include sliding window, noise addition, and the combination of those two methods. From these experiments, the results show that the CNN model managed to achieve an averaged accuracy of 47%, meanwhile with the help of augmentation techniques of sliding window, noise addition, and the combined methods, the model achieved even higher averaged accuracy of 57,1%, 47,2%, and 57,5% respectively. © Published under licence by IOP Publishing Ltd.

关键词： Brain computer interface

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AI Based Adaptive QoS Optimization for IoT Devices in Healthcare Networks

AI Based Adaptive QoS Optimization for IoT Devices in Health...

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Advancements in Electrical, Electronics, Communication, Computing and Automation (ICAECA), international conference on

作者： S. Karthika K. Juliana Gnanaselvi K. Palanisamy S. Umamaheswari Department of Computer Science& Business Systems Dr. N. G. P Institute of Technology Coimbatore India Department of Computer Science Rathinam College of Arts and Science Coimbatore India Department of CSE Karpagam Academy of Higher Education Coimbatore India Department of ECE Kumaraguru College of Technology Coimbatore India

ISBN: (数字)9798331525439

ISBN: (纸本)9798331525446

This paper proposes an artificial intelligence-based adaptive Quality of Service (QoS) optimization framework for Internet of Things (IoT) devices in healthcare networks. IoT devices, such wearable monitoring and remote diagnostic tools, struggle to maintain optimal performance due to fluctuating network circumstances and hardware limitations. The suggested system uses machine learning and deep learning techniques to dynamically update QoS settings in order to achieve consistent, low-latency, and energy-efficient data transport. The AI model forecasts congestion, distributes resources optimally, and enhances network performance by continuously learning from real-time data and network conditions. This customisable solution improves healthcare delivery by providing timely, continuous services, resulting in better patient outcomes and ensuring the long-term viability of IoT healthcare networks. The framework's adaptive nature addresses scalability and flexibility challenges in healthcare IoT systems by constantly learning and optimising performance as network conditions change. The simulation results demonstrate how well the proposed solution works to improve QoS, ensure seamless healthcare delivery, and improve patient outcomes in dynamic network situations. This study paves the way for AI-powered healthcare IoT networks that are more reliable, responsive, and intelligent.

关键词： Performance evaluation Adaptive systems Medical services Quality of service Throughput Real-time systems pattern recognition Internet of Things Convolutional neural networks Optimization

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Multimodal Corpus Analysis of Autoblog 2020: Lecture Videos in Machine Learning 23rd

Multimodal Corpus Analysis of Autoblog 2020: Lecture Videos ...

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23rd international conference on Speech and Computer, SPECOM 2021

作者： Hernandez, Abner Yang, Seung Hee Pattern Recognition Lab Friedrich Alexander University Erlangen-Nürnberg Erlangen Germany Department of Artificial Intelligence in Biomedical Engineering Friedrich Alexander University Erlangen-Nürnberg Erlangen Germany

ISBN: (纸本)9783030878016

This paper introduces a lecture video corpus, Autoblog 2020. With the increase of online learning in universities, there is a demand for a systematic toolchain development for lecture video processing. However, the existing lecture video corpus does not satisfy the requirement for such tasks, and lecture transcription and analyses are relatively unexplored areas in speech and natural language research. Autoblog 2020 Corpus is developed towards the end goal of free video-to-blog post conversion software that supports making video presentations more accessible. It will include automatic editing of disfluencies, automatic speech recognition (ASR), and spoken term extraction so that researchers can process and share their contents more efficiently. In this paper, we present a description of the corpus, linguistic analyses and preliminary experiment results regarding ASR, keyword extraction, and segmentation. The results will be used in future work to develop a video-to-blog post conversion. © 2021, Springer Nature Switzerland AG.

关键词： Speech recognition

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Research on the Fusion pattern recognition System Based on the Concept of Production Education Integration and Application of Generative Countermeasure Network 1

引用

3rd EAI international conference on Multimedia Technology and Enhanced Learning, ICMTEL 2021

作者： Lv, Conggang Jiangxi Tourism and Commerce Vocational College Nancang330039 China

ISBN: (数字)9783030825652

ISBN: (纸本)9783030825645

In order to highlight the practical application value of network data and information fusion behavior under the background of industry-education integration, a fusion pattern recognition system that applies generative confrontation network under the concept of industry-education integration is designed. First, the cyclic neural network is used to generate independent text information packets. While establishing the generation of the confrontation network framework, various reinforcement learning parameters are adjusted to realize the construction of the hardware execution environment of the recognition system. On this basis, build an embedded network framework, with the help of EEPROM chip and LD3320 chip circuit, to supervise the fusion process of network data information identification and implementation behavior, and realize the construction of the system’s software execution environment. Combined with the related hardware equipment structure, complete the research on the fusion pattern recognition system of the application generation confrontation network under the concept of integration of production and education. Comparative experiment results show that with the application of the above system, the mean value of network data information fusion time is reduced from 17.9 s to 11.2 s, while the maximum amount of information processed by a single fusion process reaches 9.3 × 1012T which can be used in the context of the integration of production and education Effectively highlight the practical application capabilities of network data information fusion behavior. © 2021, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.

关键词： Integration

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Research on Electricity Information Acquisition System Based on Sample Data Mining Model 3

Research on Electricity Information Acquisition System Based...

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3rd international conference on Computer Information Science and Application Technology (CISAT)

作者： Dang Zhongkui Fu Lei State Grid NingXia Informat & Telecommun Co Guyuan 756000 Peoples R China

In power user information collection and detection, power companies generally have a variety of different detection needs, or need to solve the problem while having additional requirements for certain aspects. Therefore, the SVM classification technology is used in the paper to carry out more detailed pattern recognition of power consumption characteristics for small-scale users or users with major suspicions. Moreover, given the imbalance of the abnormal electricity detection data set, a comprehensive processing model of unbalanced samples is constructed. Meanwhile, the differential evolution algorithm is applied to complete the SVM parameter optimization, which not only solves the problem that the SVM classification performance is significantly affected by the parameters, but also ensures the operating efficiency of the integrated classification model.

关键词： pattern recognition

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A Brute-Force Attack Detection Method Based on the Combination of Artificial Neural Network and Auto Encoder

A Brute-Force Attack Detection Method Based on the Combinati...

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Cloud Computing, Big Data Applications and Software Engineering (CBASE), international conference on

作者： Xiaojun Ma Qingyuan Li Xiaofei Wang Quan Wen Yingming Zeng Beijing Institute of Computer Technology and Application Beijing China

ISBN: (数字)9798331506582

ISBN: (纸本)9798331506599

The ongoing advancement of information technology has led to the emergence of a multitude of network attack methodologies and techniques. As a prevalent form of network attack, it is imperative to undertake targeted research on the deliberate disruption of network traffic. Two issues currently impede the efficacy of machine learning and deep learning models in detecting brute force attacks. First, the presence of noise in the traffic data reduces the accuracy and robustness of detection. Second, when an attacker modifies the frequency, intensity, time, and other parameters of their attack to evade detection, the characteristics of the attack traffic become more complex and diverse. This, in turn, results in a notable decline in detection accuracy. In order to address the aforementioned issues, this paper presents a novel detection model that integrates the capabilities of an artificial neural network (ANN) and an auto-encoder (AE). The proposed model leverages the ANN's proficiency in recognizing intricate data patterns and the AE's effectiveness in removing noise, thereby facilitating precise detection of brute-force attacks. The experimental results demonstrate that the proposed detection model exhibits an enhanced accuracy rate of 99.93% in CICIDS-2017, exhibiting superior overall detection performance compared to existing algorithmic models such as AE and DAE. Additionally, it demonstrates a reduced false alarm rate and effective mitigation of the impact of noise and interference on attack data.

关键词： Time-frequency analysis Accuracy Prevention and mitigation Noise Telecommunication traffic Feature extraction Brute force attacks Robustness pattern recognition Software engineering

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Query Processing And Optimization Method of Ethnic Music Data Based on Deep Belief Network

Query Processing And Optimization Method of Ethnic Music Dat...

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Data Analytics, Computing and Artificial Intelligence (ICDACAI), international conference on

作者： Sixian He Siyuan He Xin Gao College of Music and Dance North Minzu University School Yinchuan China College of Music Ningxia University Yinchuan China Faculty of Music and Performing Arts Universiti Pendidikan Sultan Idris Tanjung Malim Perak Malaysia School of Materials Science & Engineering North Minzu University Yinchuan China

ISBN: (数字)9798331519032

ISBN: (纸本)9798331519049

With the development of the Internet and the large-scale digital music industry, the acquisition and listening of music are presented to users in a more convenient way. How to find the music that users like to hear from the massive digital music data on the Internet has become a key issue in the field of ethnic music information retrieval. Key issues and main goals addressed. There is a complex relationship between ethnic music data, which is suitable for modeling and query processing with data structure. However, the existing query processing algorithms only focus on structural features, and are not optimized for folk music metadata and music content data, and the query efficiency is not high. Currently, the amount of data in various media is exploding, and digital music is everywhere and increasing day by day. At the same time, various music applications have also greatly contributed to the advancement of information technology, such as music sharing, which made the ancient peer-to-peer technology mainstream at the end of the 20th century. The method used in this paper is a deep belief network. Compared with other classification algorithms, its error convergence speed is faster, and the final recognition error for the data set is relatively smaller.

关键词： Query processing Refining Signal processing algorithms Optimization methods Music Prototypes Signal processing Internet Peer-to-peer computing pattern matching

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