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检索条件"任意字段=7th International Conference on Computing in Engineering and Technology, ICCET 2022"

共 11084 条记录，以下是541-550 订阅

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Deep Learning Transforming the Manufacturing Industry: A Case Study 23

Deep Learning Transforming the Manufacturing Industry: A Cas...

引用

23rd IEEE international conference on High Performance computing and Communications, 7th IEEE international conference on Data Science and Systems, 19th IEEE international conference on Smart City and 7th IEEE international conference on Dependability in Sensor, Cloud and Big Data Systems and Applications, HPCC-DSS-SmartCity-DependSys 2021

作者： Ahmad, Hafiz Mughees Rahimi, Afshin Hayat, Khizer University of Windsor Mechanical Automotive and Materials Engineering WindsorON Canada Ifiveo Canada INC. WindsorON Canada

ISBN: (纸本)9781665494571

the traditional manufacturing industry uses decades-old technology and manual processes that are expensive, laborious, redundant, and a slight human error can cost millions of dollars. With the industrial revolution 4.0, industries are adopting intelligent additions and modifications into their systems involving robotics and internet of things (IoT) based technology, which can help in their operations. Artificial intelligence and machine learning have revolutionized every field, and the manufacturing industry has also started to reap its benefits. In this case study, we have explained the applications of deep learning-based approaches contributing to the automation of surveillance of production lines. Several cameras are installed to collect live feeds on the floor lines where the manually operated cranes and different tasks it performs are detected and tracked by state-of-the-art Convolutional Neural Network-based YOLOv3, an object detection model, and DeepSORT tracking algorithm. the post-processing is done on the matrices generated. It varies based on the use case and helps the manufacturers with automated inspection, quality control, and labor productivity. this process will save millions of dollars per year just in the crane maintenance operation for our industry partner's client. the developed technology can be replicated in almost every manufacturing industry using traditional methods and provide them better key insights into their production lines. © 2021 IEEE.

关键词： Manufacturing industries Deep learning Cranes Tracking Surveillance Object detection Maintenance engineering

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FWIB: Fuzzy Weighted Iterative Bayes Algorithm 7

FWIB: Fuzzy Weighted Iterative Bayes Algorithm

引用

7th international conference on Intelligent computing and Signal Processing, ICSP 2022

作者： Wang, Tianhan Zhang, Weidong Lin, Dayong Beijing Institute of Technology School of Information and Electronics Beijing China Beijing Institute of Control Engineering Space Optoelectronic Measurement and Perception Lab Beijing China

ISBN: (纸本)9781665478571

Within the theoretical framework of Naive Bayes(NB), Iterative Bayes(IB), as an important variant, was proposed to alleviate the attribute interdependence problem based on output adjustment technique and shows certain performance enhancement. However, like NB, IB also suffers from biased classification and poor robustness against noise on class imbalanced data. To address these problems, cost-sensitive learning is introduced, and by assigning fuzzy-membership values to training data, we propose a class of Fuzzy Weighted Iterative Bayes algorithms (FWIB-CE, FWIB-HE, FWIB-CL-HL) to integrate more prior information from the sample distribution. the experimental results on ten typical imbalanced datasets show our methods have significant outperformance over NB and IB under G-mean evaluation in classification and have higher robustness against noise interference. © 2022 IEEE.

关键词： Classification (of information)

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Fake Account Detection on Social Media using Random Forest Classifier 7

Fake Account Detection on Social Media using Random Forest C...

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7th international conference on Intelligent computing and Control Systems, ICICCS 2023

作者： Nikhitha, Kancharla Venkata Bhavya, Karnati Nandini, D. Usha Sathyabama Institute of Science and Technology Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350397253

As the growth of online social networks increases, everyone is associated and linked with social media. A massive amount of personal data is being attacked and stolen by cyber attackers. these fraudulent profiles also spread negative publicity, false news, and various malicious programs. One of the best ways is to detect these fake accounts, not only the best way but also it is the first step to stopping the negative news and false rumours. there are many efficient ways and techniques to detect these fake accounts. However, these techniques are based on or depend on the account features. the main aim of this research is to examine three machine learning algorithms i.e., random forest, logistic regression, and decision tree. the level of efficiency in the three mentioned methodologies are compared and the highest accuracy has been achieved for random forest classifier. © 2023 IEEE.

关键词： Social networking (online)

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Secure Data Transmission Design Using 32-Bit Memory Block 7

Secure Data Transmission Design Using 32-Bit Memory Block

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2023 7th international conference On computing, Communication, Control And Automation, ICCUBEA 2023

作者： Dhadwad, Dnyaneshwari Vaidya, Yogita Coep Technological University Department of Electronics Engineering Maharashtra Pune India

ISBN: (纸本)9798350304268

As technology advances at an ever-increasing rate, the difficulty of protecting data from hackers rises in the advanced world. By using cutting-edge methods, hackers are attempting to get past security measures. Citizens' worries about data privacy are rising as a result. Data breaches can be prevented by using hardware or software. the FPGA board is adaptable and simple to operate. FPGA also offers effectiveness. the study uses software to implement the Advanced Encryption Standard encryption technique. It uses the AES cryptographic security method. AES is implemented by using the 2014.7 ISE Design Suite from Xilinx. the Spartan-6 FPGA displays the results. On the Spartan-6, we addressed the Advanced Encryption Standard Algorithm's implementation and resource requirements. © 2023 IEEE.

关键词： Advanced Encryption Standard Cryptography Data Security Encryption Key Generation

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Efficient Fetal Health Monitoring and Classification with Machine Learning 7

Efficient Fetal Health Monitoring and Classification with Ma...

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7th international conference on Intelligent computing and Control Systems, ICICCS 2023

作者： Singh, Kabir Shyry, Prayla Franklin, Ramya G Sathyabama Institute of Science and Technology Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350397253

the goal of the research on fetal health categorization using machine learning is to create a model that can precisely predict the condition of a fetus during pregnancy. this is crucial because prompt action following early identification of fetal health issues might enhance the pregnancy's outcome. this research study suggests classifying fetal health status using machine learning approaches based on ultrasound images and other clinical parameters. To train and test our model, we will gather a sizable dataset of ultrasound images and clinical characteristics from pregnant women. To categorize the state of fetal health, the proposed model will employ machine learning algorithms and image processing techniques. Our approach should categorize fetal health status with a high degree of accuracy, and it will help obstetricians and gynecologists provide better treatment for pregnant patients. © 2023 IEEE.

关键词： Genetic programming

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Single-Line-to-Ground Fault Line Selection Method in Distribution Network Based on Multi-scale Wavelet Decomposition of Transient Signals 7

Single-Line-to-Ground Fault Line Selection Method in Distrib...

引用

7th international conference on Intelligent computing and Signal Processing, ICSP 2022

作者： Gao, Lixue Wu, Shouyuan Wang, Mingxu Wang, Zunxian Yang, Yongxin Shandong University School of Electrical Engineering China

ISBN: (纸本)9781665478571

In the traditional method of using the steady-state quantity for fault line selection takes a long time, while the method using the transient quantity is easy to be disturbed or misjudged although the judgment is quick. For these problems, a method for judging the faulty line by using the transient characteristic quantities of the phase voltage is proposed. Firstly, the phase voltage characteristics after a single phase-to-ground fault on the line are analyzed. Secondly, the discrete-wavelet-transform (DWT) is used to extract the amplitude coefficients of the high-frequency components of the three-phase voltage of the line and locate the frequency band, by constructed an eigenvalue that characterizes the magnitude of the phase voltage high-frequency signal, the fault line selection criterion is established. Finally, simulation data of a single-phase-to-ground fault model for the distribution network verifies the effectiveness of the proposed fault line selection method. © 2022 IEEE.

关键词： Discrete wavelet transforms

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Service Placement in Edge computing with AI Based Techniques 7th

Service Placement in Edge Computing with AI Based Techniques

引用

7th international conference on computing in engineering and technology, iccet 2022

作者： Ali, Shadan Kumar, Mohit Department of Computer Science and Engineering Dr. B.R. Ambedkar NIT Jalandhar India Department of IT Dr. B. R. Ambedkar NIT Jalandhar India

ISBN: (纸本)9789811927188

the Internet of things (IoT) is a rising technology where real-world physical objects are connected to the network and trying to make the smart environment solve some real-life problems automatically in an efficient way. these IoT devices generate a large amount of data at a constant rate and demand high storage and computational resources. the cloud paradigm comes into the picture to acknowledge this problem and allows IoT devices to use the resources as per the requirement, but it has several limitations, mainly security challenges, bandwidth, and unable to handle time-sensitive data. Hence, an emerging computing paradigm named edge computing addressed these issues by gracefully offering the services near the data sources to time-sensitive data. However, these services can have different computational requirements, and the capacities of edge servers also change dynamically. therefore, many artificial intelligence (AI) based service placement algorithms have been proposed to counter these issues and improve QoS metrics. In this article, we rigorously reviewed several AI-based states-of-art approaches for service placement and observed some research gaps. Further, we have proposed an efficient algorithm for an integrated edge-cloud architecture to place the service in a well-organized way. © 2022, the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Edge computing

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A Binocular Vision-Guided Puncture Needle Automatic Positioning Method 7

A Binocular Vision-Guided Puncture Needle Automatic Position...

引用

7th international conference on Cloud computing and Big Data Analytics, ICCCBDA 2022

作者： Gu, Haoran Fu, Zhuang Fu, Zeyu Lu, Chenzhuo Fei, Jian Guan, Yisheng School of Mechanical Engineering Shanghai Jiao Tong University Shanghai China School of Medicine Shanghai Jiao Tong University Shanghai China School of Electromechanical Engineering Guangdong University of Technology Guangzhou China

ISBN: (纸本)9781665487115

In the traditional Magnetic Resonance Imaging(MRI)-guided breast puncture surgery, doctors perform puncture surgery manually based on the MRI images and experience. the puncture needle may not be able to reach the location of the lesion accurately due to the inexperience or fatigue of the doctor. In this paper, a binocular vision-guided automatic positioning method for breast puncture needles is proposed. First, the combined image processing algorithm and the three-frame difference method are used to detect the needle tip;then, the puncture needle is guided to the target position by position-based visual servoing. the experimental results show that the average error of feature point detection is within 3 pixels, and the final positioning error is within 2.5mm. Compared with the traditional manual positioning method, the positioning accuracy of this method is higher, and the puncture process is also more stable, which improves the efficiency of surgery. © 2022 IEEE.

关键词： Visualization Magnetic resonance imaging Surgery Magnetic resonance Medical services Manuals Breast

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Cloud-Based Storage Solutions for Facial Recognition Attendance Systems 7

Cloud-Based Storage Solutions for Facial Recognition Attenda...

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7th international conference on Circuit Power and computing Technologies, ICCPCT 2024

作者： Dani, B. Alex, T. Ayisha Naisam, M.P.V. Melbin, G. Shihabudeen, H. College of Engineering Aranmula Kerala India University Institute of Technology Kerala India College of Engineering Kidangoor Kerala India

ISBN: (数字)9798350372816

ISBN: (纸本)9798350372816

the Automated Attendance System Using Face Recognition presents a resolution for attendance supervision, by utilizing the power of facial recognition technology to automate the tracking process. this innovative system is proposed to improve attendance recording in diverse sectors such as education, corporate, and beyond, by automating the identification process with the best precision and efficiency. through the integration of compact yet sophisticated algorithms, the system not only delivers seamless attendance tracking but also prioritizes the security and confidentiality of sensitive data, ensuring compliance with privacy regulations with the help of cloud storage. this comprehensive report delves into the development, functionality, implementation strategies, and prospects of the Face Attendance System, highlighting its potential to rewrite the traditional attendance management paradigms and improve organizational efficiency. Using machine learning and many other technologies it is made possible to take attendance using computer vision and related tools. the system provides a complete solution for proxy attendance. © 2024 IEEE.

关键词： Efficiency

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Hybrid Deep Learning Model for Cyber-Attack Detection 7

Hybrid Deep Learning Model for Cyber-Attack Detection

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7th international conference on Intelligent computing and Control Systems, ICICCS 2023

作者： Premkumar, M. Lakshmi, R. Velrajkumar, P. Priya, S.Gayathri Tanguturi, Rama Chaithanya Murali, S. Sivaramkrishnan, M. Department of Information Technology Namakkal Mallasamudharam India RNS Institute of Technology Bengaluru Affiliated to VTU Department of Computer Science and Engineering Belgaum India CMR Institute of Technology Bengaluru India R. M. D. Engineering College Kavaraipettai Department of Electronics and Communication Engineering Chennai India PACE Institute of Technology and Sciences Department of Computer Science and Engineering Andhra Pradesh Ongole India Velammal College of Engineering and Technology Department of Computer Science and Engineering Madurai India Karpagam College of Engineering Department of EEE Coimbatore India

ISBN: (纸本)9798350397253

Better detection and avoidance of intrusions solutions are in high demand because of the worldwide upsurge in hacking on computer networks. New technologies like fog computing, cloud computing, and the Internet of things have dramatically increased the potential for cyberattacks and other forms of cyber risk. these attacks can compromise computer network infrastructures, web services, and social media platforms, resulting in economic and reputation loss. Because of its usefulness in detecting and halting malicious actions, intrusion detection systems (IDS) play a crucial part in network defence mechanisms. In this study, a hybrid Deep Learning (DL) network was used to identify the cyberattack. this effort began with the collection and processing of cyber-attack data from the NSL-KDD. Convolutional Neural Networks (CNNs), Bidirectional Long Short-Term Memory (Bi-LS TMs), and hybrid CNN+Bi-LSTMs are the DL models trained with 80% of processed data. the remaining 20% of the data is used for testing the models after they have been trained. Both positive and negative metrics are used to assess the results of the testing phase. When compared to other networks, the recommended CNN+Bi-LSTM model achieves the highest score for positive metrics and the lowest score for negative metrics. © 2023 IEEE.

关键词： Feature extraction

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