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检索条件"任意字段=2023 3rd International Conference on Electrical Engineering and Computer Technology, ICEECT 2023"

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Brain Tumor Segmentation and Classification in MRI Images Using Deep Learning Techniques 3

Brain Tumor Segmentation and Classification in MRI Images Us...

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3rd IEEE international conference on Mobile Networks and Wireless Communications, ICMNWC 2023

作者： Archana Reddy, R. Al-Jawahry, Hassan M. Ramadan, Ghazi Mohamad Alkhafaji, Mohammed Ayad Buvaneswari, B. School of Computer Science and Artificial Intelligence SR University Telangana Warangal India The Islamic University Najaf Iraq College of MLT Ahl Al Bayt University Karbala Iraq National University of Science and Technology Dhi Qar Iraq Panimalar Engineering College Department of Information Technology Chennai India

ISBN: (纸本)9798350317022

The brain tumors are formed when groups of abnormal cells develop in the brain and possess the capacity to infiltrate nearby tissues. The early detection is necessary to aid doctors in treating cancer patients to increase their survival rate. The BraTS-2019 and BraTS-2020 dataset is utilized in this research for the segmentation and detection of brain tumor. Min-max normalization is used in this experiment for data preprocessing and fed to Kernel based Fuzzy C-means clustering (KFCM) which minimizes the computational time during segmentation process. Then, the Gray Level Cooccurrence Matrix (GLCM) is utilized for effective feature extraction and Convolutional Neural Network (CNN) is utilized for classification of tumors. The proposed KFCM-CNN performance is estimated by utilizing accuracy, precision, recall, specificity and f1-score. The obtained result demonstrations that the KFCM-CNN model achieves better accuracy of 99.62% on BraTS-2019 and 99.58% on BraTS-2020 dataset which ensures accurate classification compared to other existing techniques like ensemble and U-net model. © 2023 IEEE.

关键词： Brain Tumor Convolutional Neural Network Gray Level Cooccurrence Matrix Kernel based Fuzzy C-means clustering Min-Max Normalization

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Comparison and Analysis of the Accuracy of Multiple Machine Learning Algorithms in the Field of Spam Classification 3

Comparison and Analysis of the Accuracy of Multiple Machine ...

引用

3rd international conference on Machine Learning and computer Application, ICMLCA 2022

作者： Liu, Junchen School of Science Qingdao University of Technology Shandong Qingdao266525 China

ISBN: (纸本)9781510664814

With the continuous advancement of science and technology, network data dissemination technology has been rapidly developed, but at the same time, the problem of spam is becoming more and more serious. This paper aims to analyse the classification principle of each machine learning classifier and compare the differences in the classification effect of each classifier. For the classification of spam, this paper mainly does the following aspects of the work: first, the selection of mail datasets and the pre-processing of email text information, the second is the extraction of the text characteristics of the email, The data is then divided into two parts, one for training and one for testing, using each machine to train the model, using the test set to test the model. Finally, parameters such as accuracy, precision, recall, and F1 score are calculated according to the mail classification results, comparing each model. The results show that the best classification effect is the neural network model and the naïve Bayes model, which can obtain better generalization ability in the test data and should be applied more in practice. © 2023 SPIE.

关键词： Classification (of information)

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STTSRL: Design of Triple-Node Upset Self-Recovery Latch Based on Schmidt Trigger 3

STTSRL: Design of Triple-Node Upset Self-Recovery Latch Base...

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2023 3rd international conference on Mechatronics technology and Aerospace engineering, ICMTAE 2023

作者： Zhou, Jing Zhang, Jishan Zheng, Chun School of Computer Engineering Anhui Sanlian University Anhui Hefei230601 China

With the continuous progress of semiconductor technology, the size of transistors has reduced, and the sensitivity of logic circuits to soft errors caused by radiation has enhanced. The multiple-node upset caused by radiation has affected the reliability of the circuit. This paper proposes a triple-node upset (TNU) self-recovery latch based on the Schmidt trigger (ST) for radiation hardness. The feedback loop is formed by connecting C-elements (CEs) and STs. Using the error filtering mechanism of CE and the hysteresis effect of ST is to realize the reliability of STTSRL. Under the 22 nm PTM process in HSPICE simulation, the STTSRL latch has TNU self-recoverability. Compared with the average value of the same recoverability latch proposed previously, the power consumption, delay, area, number of transistors, and PDAP are reduced by 44.85%, 53.32%, 40.34%, 19.24%, and 83.54%, respectively. © Published under licence by IOP Publishing Ltd.

关键词： Transistors

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Intelligent Safe Driving Assistance System Based On Improved YOLOv5 3

Intelligent Safe Driving Assistance System Based On Improved...

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3rd international conference on Neural Networks, Information and Communication engineering, NNICE 2023

作者： Ni, Peiyang Qiu, Chaoyun Li, Xiaojie Liu, Ruixuan Wu, Jingwen Guan, Junlin School of Computer Science and Information Security Guilin University of Electronic Technology Guilin China

ISBN: (纸本)9798350335972

The car accident caused by driver's violation behaviors has always been one of the biggest dangers that threaten the safety of human life and property. In order to reduce the occurrence of car accidents, vehicle-mounted assistance driving systems have emerged as the times require. However, the cost of assistance driving systems in the current market is relatively high, and they only appear in high-end car brands, leaving gaps in the mid-to-low-end market. Starting from the low cost and reliability, this paper designs a set of assistance driving system to intelligently detect and warn the driver's violation behavior. The vision algorithm of the system is YOLOv5 optimized through attention mechanism and edge detection, and it's deployed on Jetson Xavier NX to ensure data security and model robustness. The algorithm combines hardware equipment, client, back-end server and Hadoop big data technology to realize the functions of drunk driving detection, intelligent monitoring of driver behavior and early warning. The solution proposed in this paper uses self-optimized open-source vision algorithms, and the main expenses come from the procurement, development of hardware equipment and cloud service. The cost is low, and it provides the possibility for the introduction of assisted driving systems in the low-end automobile market. After actual testing, the system is low in cost, easy to maintain, and at the same time meets the requirements of accuracy and real-time performance, ensuring the safety of drivers and passengers during driving. © 2023 IEEE.

关键词： Costs

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Design of a LDO with High PSRR 3

Design of a LDO with High PSRR

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2023 3rd international conference on electrical, Electronics, and Computing technology, EECT 2023

作者： Sun, Yichun Li, Jinghu Chen, Deyan Luo, Zhicong Chen, Riqing School of Computer and Information Sciences Fujian Agriculture and Forestry University Fuzhou China School of Mechanical and Electrical Engineering Fujian Agriculture and Forestry University Fuzhou China

One of the common ways to increase the PSRR(power supply rejection ratio) of LDO (low dropout linear regulator) is increasing the gain of error amplifier, but the stability of LDO system will also be reduced. A new high gain error amplifier is designed to push the poles inside the amplifier away from the origin, which ensures the stability of LDO system and improves the PSRR of low frequency band. At the TT process corner, the range of adjustable output voltage of LDO is 1.361∼1.860 V under the simulation condition of 27 °C. When power supply ranges from 2.3 V to 3.3 V, the linear regulation rate is 238.3 μV/V;When the load changes from 0 to 20 mA, the load regulation rate is 9.6 μV/mA;At load current of 20 μA, the power supply rejection ratio of LDO is -97 dB@dc, and the phase margin of the whole circuit is greater than 66 degrees. The chip is verified in a 0.18 μm CMOS process. Setting the power supply voltage in the range of 2.3 to 3.3 V, the output voltage is stable at 1.8 V, and the error is only 1.9 mV, which is less than 0.2%. © Published under licence by IOP Publishing Ltd.

关键词： System stability

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Protecting Online Transactions: A Cybersecurity Solution Model 3

Protecting Online Transactions: A Cybersecurity Solution Mod...

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3rd international conference on Advance Computing and Innovative Technologies in engineering, ICACITE 2023

作者： Surya, S. Jagtap, Suvarna Ranjeet Ramnarayan, Ramnarayan Priyadarshini, Mankali Ibrahim, Read Khalid Alazzam, Malik Bader Porur Chennai600116 India Yashwantrao Mohite College Maharashtra Pune411038 India Uttaranchal University Uttaranchal Institute of Technology Uttarakhand Dehradun24800 India Sr University School of Computer Science and Artificial Intelligence Telangana Warangal506371 India Al-Farahidi University Medical Technical College Baghdad10021 Iraq Ajloun National University Information Technology College Ajloun Jordan

ISBN: (纸本)9798350399264

The increasing reliance on online transactions and e-commerce platforms has increased cybersecurity threats, putting businesses and individuals at risk of data breaches, theft, and other malicious attacks. This study proposes a cybersecurity solution model to protect online transactions and mitigate the risks of cyberattacks. The model emphasizes the importance of OTP verification and password protection as protective measures to secure transactions. The study highlights the different types of cybersecurity threats faced by e-commerce platforms, including debit or credit card fraud, data misuse, and vulnerabilities in electronic payment systems. Human errors can also increase the risk of cyberattacks, making it crucial for businesses to implement security measures and strategies to prevent and mitigate cyber threats. The proposed cybersecurity solution model emphasizes the need for a multi-layered security approach, incorporating technologies such as firewalls, antivirus software, and encryption. Additionally, the model recommends regular security audits and employee training to raise awareness of cybersecurity risks and promote best practices. The study concludes that businesses and individuals must take proactive steps to protect their online transactions and safeguard against cybersecurity threats. By implementing the proposed cybersecurity solution model, businesses can mitigate the risks of cyberattacks and ensure secure online transactions, promoting trust and confidence in e-commerce platforms. © 2023 IEEE.

关键词： Cybersecurity

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Vibration Control of a Smart Composite Engine Fan Blade by Neural Network Controller 3

Vibration Control of a Smart Composite Engine Fan Blade by N...

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3rd international conference on computer, Control and Robotics, ICCCR 2023

作者： Mamela, Tebogo Lucky Li, Baoming He, Weiguo Zhang, Shunqi School of Mechatronic Shanghai University Engineering and Automation Shanghai China Zhejiang CHR Intelligent Equipment Co Ltd Jingun Zhejiang32144 China

ISBN: (纸本)9781665492126

This research analyzes the adoption of an artificial neural network (ANN) method for a smart engine fan blade bonded with a piezoelectric (PZT) patch made of macro fiber composite (MFC) for vibration control and suppression. The MFC material that functions as a sensor and an actuator is embedded on the surface of the fan blade to provide structural active damping on the structure. The finite element model (FEM) also the system state-space model of the engine fan blade is formulated to establish the structural numerical model as a basis to initialize the impulse input disturbance to measure the performance of the controllers. To predict the future reaction of the smart fan blade, the recommended controller is trained using the nonlinear autoregressive moving average with an exogenous input model of order 2 (NARMA-L2). The PID-supplemented compensation method improves the controller's performance, and the NARMA-L2 and fuzzy generate the necessary control signal. The performance will then be assessed by contrasting the reactions of the NARMA-L2-PID, fuzzy-PID, and classical PID controllers under a specified dynamic excitation. The outcomes show that the proposed controller model is effective and reliable regardless of the input disturbance exerted on the structure's vibration response. © 2023 IEEE.

关键词： Vibration control

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Investigation into the Categorization of Crohn's Segments from Capsule Endoscopy Videos:: Introducing A Thick Data Categorization Framework 3

Investigation into the Categorization of Crohn's Segments fr...

引用

3rd international conference on Innovative Research in Applied Science, engineering and technology, IRASET 2023

作者： Fiaidhi, Jinan Mohammed, Sabah Zezos, Petros Lakehead University Department of Computer Science Thunder Bay Canada Northern Ontario School of Medicine Nosm University Thunder Bay Canada

ISBN: (纸本)9798350398366

Crohn's disease (CD) is a complex disorder that causes patchy inflammation and ulcerative effects in the gut from mouth to anus, which requires diagnosis for proper treatment. Capsule endoscopy (CE) procedure emerged as important investigation for screening the small bowel and the entire colon. However, examining the CE videos by gastroenterologists is a tedious and time consuming task as the capsule camera takes more than eighty thousand frames, with a frequency rate of 2 frames per second, for one segment of the gastrointestinal tract. However, having an automatic way to categorize the patchy frames that hold the markers of Crohn's disease will help the examiners to provide more reliable diagnosis and treatment. In this article, we are describing a thick data analysis framework that can help categorizing Crohn's disease sequences through the use of triplet-loss Siamese neural network that can learn from few shots provided by the expert to detect frames with markers of the Crohn's disease as well as to eliminate those frames having reduced mucosal view. Additionally, the framework uses a fuzzy filler to produce intermediate sets of frames having similar markers. These sets can be annotated and returned back to their position at the original CE video. The use of our framework showed promising results in re-orienting the original CE video into marked sequences of Crohn's disease frames. We have trained and tested our framework using two notable CE datasets (KVASIR Capsule and CrohnIPI) that contain the expert annotation on related markers of Crohn's disease. © 2023 IEEE.

关键词： Data Analytics

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Detection of rotating small targets in remote sensing images based on improved yolov5s 3

Detection of rotating small targets in remote sensing images...

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3rd international conference on Optics and Communication technology, ICOCT 2023

作者： Zhang, JiaLin Lin, KaiFan School of Computer and Information Engineering Harbin University of Commerce Harbin150028 China

ISBN: (纸本)9781510672529

Aiming at the characteristics of clustering, multi-scale changes, low contrast, and complex background in remote sensing images, this paper proposes an improved rotation frame algorithm based on yolov5s network, which reduces the traditional horizontal detection frame in remote sensing images by rotating frames. Missing detection of small targets due to aggregation and rotation problems;by using the attention mechanism module CBAMC3 that combines space and channels in the Backbone part to replace the original C3 module, the feature representation ability of targets of different scales in remote sensing images is improved;In the loss function, the Focal Loss function is used to reduce the complex background of the remote sensing image and the certain influence of the imbalance of positive and negative samples, and strengthen the training weight of the positive samples;at the same time, the Copy Paste data enhancement is used to enrich the number of samples on the data set. Enhance the generalization ability of the *** result on the DOTA dataset reaches map86.4%, which is 3.1% higher than the 83.3% of the original yolov5s, which proves that the model has high detection accuracy in remote sensing detection and has certain reference value for detecting images under remote sensing. © 2023 SPIE.

关键词： Remote sensing

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Detection of Pneumonia Based on ResNet Improved by Attention Mechanism 3

Detection of Pneumonia Based on ResNet Improved by Attention...

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3rd IEEE international conference on Power, Electronics and computer Applications, ICPECA 2023

作者： Zhou, Zishun Liu, Yuxiang Wang, Qingzheng Toe, Teoh Teik University of Electronic Science and Technology of China School of Computer Science and Engineering Chengdu China Nanyang Technological University NTU Business AI Lab Singapore

ISBN: (纸本)9781665472784

In recent years, Covid-19 is one of the major health challenges facing the human population. Due to the highly infectious nature of Covid-19 and the difficulty of detecting symptoms in the early stages, it is definitely necessary to combine X-ray for the diagnosis of pneumonia. Using traditional neural networks such as VGG, ResNet, and DenseNet to diagnose pneumonia based on X-ray images faces a number of difficulties. These models have insufficient spatial information extraction capability and are prone to overfitting on the training set. The attention mechanism is a means to improve model performance by helping the model better extract channel and spatial features from the feature maps. To identify pneumonia more accurately, we combined the ResNet network and CBAM attention mechanism to design the ResNet101-cbam model with a series of data augmentation methods as well as training strategies. We used the same approach to add attention mechanisms to ResNet50, ResNet101 and ResNet152 and tested their performance. The results show that ResNet101-cbam is the best performing model overall. It achieved a recall of 0.8205, a precision of 0.822, and an accuracy of 0.8285 on the test set, while the original pretrained ResNet101 had a precision of 0.7280 and an accuracy of 0.7644. Its performance were better than the more complex model: ResNet152-cbam, a little bit, but the training speed is improved by more than 25%. More importantly, the model with the added attention mechanism effectively overcomes the effects of positive and negative sample imbalance. The ResNet101-cbam model can be used as a medical aid, which can improve diagnostic efficiency and help us better deal with large-scale pneumonia epidemics. © 2023 IEEE.

关键词： Diagnosis

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