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automatic Number Plate Recognition System using Machine Learning Techniques

Automatic Number Plate Recognition System using Machine Lear...

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electrical Electronics and computing Technologies (ICEECT), international conference on

作者： Avnish Dayal Aryan Porwal Avinash Gupta Aditya Kumar Srivastava Mansi Mahendru Department of Computer Science ABES Engineering College Ghaziabad UP India

ISBN: (数字)9798350378092

ISBN: (纸本)9798350378108

The system employs video processing technology to detect the image of a vehicle's number plate from a video stream. Subsequently, various algorithms are applied to extract the number from the detected plate. This system can be used at college entrances, cantonment area and other places with strict access restrictions. The system detects the number plate whenever a vehicle passes by, converting it into both images and text. Subsequently, this information is stored in the database for future reference or analysis. This research paper introduces ANPR system which is designed to optimize accuracy, efficiency, and ease of implementation which includes combination of Python programming language, YOLOv8 (You Only Look One-level), and EasyOCR, the proposed system addresses the complexities associated with detection of license plate and recognition in diverse real-world situations. Core of the system lies in the adoption of YOLOv8, an object detection model renowned for its real-time capabilities and high accuracy. EasyOCR, a user-friendly Optical Character Recognition (OCR) library, is seamlessly integrated into the ANPR system. This addition enhances the system's ability to accurately extract alphanumeric information from detected license plates, bridging the gap between detection and recognition phases. Due to this the system ensures efficient noise reduction, image segmentation, and overall improvement in the quality of input images, contributing to the system's reliability. The paper presents an in-depth evaluation of the ANPR system's performance through datasets. Additionally, the research explores potential applications of the ANPR system in real-world scenarios, emphasizing its applicability in traffic management, law enforcement, and security systems.

关键词： Technological innovation Accuracy Databases System performance Streaming media Character recognition Security Reliability License plate recognition Python

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Brunnstrom Stage Evaluation Using a Single Sensor for Hemiplegic Upper-Extremities Based on Deep Features 6th

Brunnstrom Stage Evaluation Using a Single Sensor for Hemipl...

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6th international conference on computing, control, and Industrial engineering, CCIE 2021

作者： Wang, Xingwei Meng, Long Wu, Xuejiao Zhang, Anjing Chen, Wei Center of Biomedical Engineering School of Information Science and Technology Fudan University Shanghai200433 China Center of Rehabilitation Therapy The First Rehabilitation Hospital of Shanghai Shanghai200090 China Department of Neurological Rehabilitation Medicine The First Rehabilitation Hospital of Shanghai Kongjiang Branch Shanghai200093 China

ISBN: (纸本)9789811939266

Motor function Evaluation is an indispensable part of rehabilitation treatment for stroke patients. As a rule, this process is manually performed by the experience of therapists which is subjective. Recently, research on rehabilitation assessment based on data provided by wearable sensors has provided convenient and practical solutions. In order to objectively evaluate the function of hemiplegic upper-extremities (HUEs) patients, we propose 2-dimension convolution neural network (2DCNN) for the Brunnstrom Stage (BS) classification. As a contrast, an attention Bidirectional Long Short-term Memory model (Attention-BiLSTM) and traditional machine learning approaches Support Vector Machine (SVM), Random Forest (RF) and multi-layer perceptron (MLP) were also adopted, which uses the surface electromyography (sEMG) and inertial measurement unit (IMU) data provided by a single wearable sensor on the upper extremity of stroke patients. The 2DCNN can achieve classification accuracy of 93.46% in segments for 3 levels (BS IV, BS V and healthy status), which is better than Attention-BiLSTM and traditional machine learning approaches (p © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Wearable sensors

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Accuracy and Interpretability: Developing a Computer-Aided Diagnosis System for Pneumonia Detection in Chest X-Ray Images

Accuracy and Interpretability: Developing a Computer-Aided D...

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Distributed computing and electrical Circuits and Electronics (ICDCECE), IEEE international conference on

作者： Krishna Mridha Apu Chandra Barman Shekhar Biswas Shakil Sarkar Sunanda Biswas Masrur Ahsan Priyok Computer Engineering Marwadi University Rajkot India Department of Pharmacy Marwadi University Rajkot India Computer Science and Engineering BRAC University Software Engineering Tianjin University China

People of all ages can contract pneumonia, a deadly respiratory illness that is more common in underdeveloped countries. For successful treatment and higher survival rates, pneumonia must be accurately and quickly diagnosed. While chest X-ray imaging is a frequently used diagnostic technique for detecting pneumonia, its interpretation can be arbitrary and error-prone. In this study, we used a convolutional neural network (CNN) architecture to construct a computer-aided diagnostic system for pneumonia identification in chest X-ray images. Our dataset comprises 5,863 anterior-posterior view X-ray pictures that have been divided into two categories: normal and pneumonia. The photos were chosen from the Guangzhou Women and Children's Medical Center's pediatric patients between the ages of one and five, and they were first checked for quality control before being judged by two highly qualified doctors. A third expert also reviewed the assessment set to verify correctness. For the test set, our CNN model performed well, with 99% accuracy, 99% precision, 99.25% f1-score, and 98% specificity. To improve the interpretability of our model, we also included explainable AI techniques like Grad-CAM and Grad-CAM++. These approaches offer insights into CNN's decision-making process. Our computer-aided diagnosis system offers a trustworthy and unbiased method for detecting pneumonia in chest X-ray pictures, which can help with the quick identification and treatment of this potentially fatal condition.

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Closed-form Solution for the Finite-horizon Linear-quadratic control Problem of Linear Fractional-order Systems

Closed-form Solution for the Finite-horizon Linear-quadratic...

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2021 American control conference, ACC 2021

作者： Dabiri, Arman Chahrogh, Laya Karimi MacHado, J. A. Tenreiro Southern Illinois University Edwardsville Department of Mechanical and Mechatronics Engineering EdwardsvilleIL62026 United States Southern Illinois University Edwardsville Department of Computer Science EdwardsvilleIL62026 United States Institute of Engineering Polytechnic Institute of Porto Department of Electrical Engineering Porto4249-015 Portugal

ISBN: (纸本)9781665441971

This paper is devoted to finding an explicit solution for the finite-horizon linear-quadratic regulator optimal control problem of linear time-invariant fractional-order systems based on a spectral method. The proposed technique does not pose the burden of regular Riccati fractional differential equations that demand integrating left- and right-sided fractional differential operators. The obtained solution exhibits spectral convergence with an efficient computation time. For this purpose, the closed-form of the optimal solution and the finite horizon state-feedback control are both obtained at Chebyshev-Gauss-Lobatto points by adopting operational matrices of left- and right-sided fractional-order differentiation. The validity and effectiveness of the proposed method are examined through an illustrative example. © 2021 American automatic control Council.

关键词： Linear control systems

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A Study on LiFi and Visible Light Data Connectivity Exploring High-Speed, Secure Data Transmission in Wireless Communication

A Study on LiFi and Visible Light Data Connectivity Explorin...

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2025 international conference on Intelligent control, computing and Communications, IC3 2025

作者： Sharma, Swati Chandrashekar, Rakesh Begum, Rowsonara Singh, Navdeep Inti, Lakshmi Bindhu Shakir, Sarah Abbas Galgotias university Department of Electrical Electronics & Communication Engineering Uttar Pradesh Gerater Noida203201 India New Horizon College of Engineering Bangalore India Mlr Institute of Technology Department of Computer Science and Engineering Telangana Hyderabad India Lovely Professional University Phagwara India Griet Department of Information Technology Bachupally Telangana Hyderabad India College of Medical Technology The Islamic University Radiology Techniques Department Najaf Iraq

ISBN: (纸本)9798331527495

As an innovative wireless communication technology, LiFi, derived from visible light to transmit data, will be a new choice of alternative to classical radio frequency (RF) communications. Data can be transferred backward and forth between the user and a computer with high speed using rapid modulation of LED light bulbs, a technique known as LiFi. An Internet of Things (IoT) based visible light data connectivity system for efficient data transmission over LiFi technology is introduced in this paper in the form of IoTVLDC. Visible light communication (VLC) smart systems are called LiFi., they make use of enhanced bandwidth and resistance to electromagnetic interference. LiFi can operate within the 60 GHz spectrum and achieves data rates in excess of 10 Mbps, compared to the several Mbps reacheable through conventional WiFi systems. Several gaps exist: in particular, how LiFi performs in dynamic environments and energy efficiency metrics, as well as how it can coexist with RF based systems. This study will then evaluate these aspects and propose a hybrid system that combines traditional technologies and LiFi to provide seamless connectivity between lit and non lit. The study will also examine the possibility of its use in real world IoT applications, for example smart homes, industrial automation and secure communications in networks. This paper will demonstrate through extensive experimentation that LiFi technology can provide sustainable, secure, and more efficient communication solutions for the future of wireless networking. © 2025 IEEE.

关键词： Bandwidth

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A Study on LiFi and Visible Light Data Connectivity Exploring High-Speed, Secure Data Transmission in Wireless Communication

A Study on LiFi and Visible Light Data Connectivity Explorin...

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Intelligent control, computing and Communications (IC3), international conference on

作者： Swati Sharma Rakesh Chandrashekar Rowsonara Begum Navdeep Singh Lakshmi Bindhu Inti Sarah Abbas Shakir Department of Electrical Electronics & Communication Engineering Galgotias university Gerater Noida Uttar Pradesh New Horizon College of Engineering Bangalore India Department of Computer Science and Engineering MLR Institute of Technology Hyderabad Telangana India Lovely Professional University Phagwara India Department of Information Technology GRIET Bachupally Hyderabad Telangana India Radiology Techniques Department College of Medical Technology The Islamic University Najaf Iraq

ISBN: (数字)9798331527495

ISBN: (纸本)9798331527501

关键词： Wireless communication Radio frequency Lighting Switches Light fidelity Energy efficiency Data communication Internet of Things Wireless fidelity Visible light communication

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Big Data Analytics in E-Healthcare Using Hadoop and Hive 4th

Big Data Analytics in E-Healthcare Using Hadoop and Hive

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4th international conference on Futuristic Trends in Networks and computing Technologies, FTNCT 2021

作者： Choudhary, Richa School of Computer Science University of Petroleum and Energy Studies Dehradun India

ISBN: (纸本)9789811950360

New technologies like big data, cloud computing, machine learning could play a vital role in providing healthcare services to patients. The healthcare industry is producing a large volume of data which is increasing exponentially. Healthcare industry data is unstructured and is collected in different varieties. There is a dire need of processing these huge volumes of healthcare datasets to provide personalized treatment to the patients, to provide predictive analytics so the pre-diagnosis can take place. To predict the insights from existing patient data requires new tools and techniques as healthcare data is complex. Big data technologies such as Hadoop, MapReduce, Pig, Hive, and others provide the platform for healthcare data processing. Increasing rates of severe health diseases are impacting human life. One such kind of disease is cancer, this work presents the association between smoking and lung cancer. This paper presents the implementation of hive architecture for analyzing the lung cancer rate among active smokers dataset from centers of disease control and prevention government agency. The results obtained show that active smokers have a higher rate of lung cancer. It also addresses various challenges of implementing big data techniques over healthcare data. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Big data

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Research on the Influence of Inventory Optimization Level on System Reliability

Research on the Influence of Inventory Optimization Level on...

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Electronic Information engineering and Computer science (EIECS), 2021 international conference on

作者： Hongyan Wang Gang Xiang Ruishi Lin Dongpeng Li Yu Peng Gongcheng Zhou Langfu Cui Qingzhen Zhang Liang Li School of Automation Science and Electrical Engineering Beihang University Beijing China Beijing Aerospace Automatic Control Institute Beijing China High Tech Research Institute Beijing China

This paper aims to investigate the impact of spare parts management on system reliability in complex equipment systems, primarily focusing on two factors: the system's continuous operational duration and the total cost associated with spare parts storage. By establishing a multi-objective optimization model, we have identified an optimal spare parts storage strategy that strikes a balance between the total cost and the system's continuous operational duration. The experimental results indicate that, within the assumed 3-year effective operational cycle of the complex equipment system, when equipping it with 2, 2, and 3 spare parts for the gyroscope, accelerometer, and servo motor respectively, the system's longest continuous operating duration is 37,300 hours, and the minimum cost is 84,000 RMB. Compared to the single-objective optimization model, this model not only reduces the total cost but also ensures that the system will not be interrupted due to spare parts shortages within its normal lifecycle. This study provides valuable insights and optimization methods for improving the reliability of complex systems.

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SU-Net: A retinal segmentation model based on improved U-Net network 4

SU-Net: A retinal segmentation model based on improved U-Net...

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4th international conference on Robotics, Intelligent control and Artificial Intelligence, RICAI 2022

作者： Yang, Mengzhu Wang, Yongfang Li, Guoqiang Zhang, Lintao Zhu, Dong Wang, Chengchao School of Information Science and Engineering Linyi University Linyi276005 China

ISBN: (纸本)9781450398343

Image segmentation plays a very important role in medical diagnosis. It can extract information such as the area of interest, human tissue, and lesion size. Diseases of the nervous system, leukemia, and diabetes can cause eye problems. To observe the changes in the distribution, structure, and morphological characteristics of blood vessels in retinal images by image segmentation, and it also can help diagnose the degree of lesions of the above diseases to a certain extent. Although the commonly used artificial segmentation is the gold standard, it has the disadvantages of being time-consuming, power-consuming, and unable to reproduce, so the research on accurate and efficient automatic image segmentation method is the focus of image segmentation research. Because of the problems, such as partial feature data loss, low segmentation accuracy, and pathological information segmentation errors that may occur in the traditional U-Net model during retinal image segmentation, we proposed an improved U-Net based retinal image segmentation model-SU-Net. In this method, an attention module is added to the U-Net coding process, which can fully capture the context information to improve the accuracy of image feature extraction. The effectiveness of the proposed method was verified by testing on the publicly available retina data set. The average IoU, Dice coefficient, and global segmentation accuracy were taken as evaluation indexes. Compared with the U-Net model, experiments show that the accuracy of IoU, Dice, and global segmentation has increased by 0.7, 0.9, and 0.2, and reached 82.4%, 82.2%, and 95.5% respectively. © 2022 ACM.

关键词： Image segmentation

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Slotted ALOHA with Code Combining for IoT Networks

Slotted ALOHA with Code Combining for IoT Networks

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Communications and Networking (MeditCom), IEEE international Mediterranean conference on

作者： Dimitrios Tyrovolas Athanasios Chrysologou Georgios Chondrogiannis Sotiris Tegos Prodromos-Vasileios Mekikis Panagiotis Diamantoulakis Sotiris Ioannidis Christos Liaskos Nestor Chatzidiamantis George Karagiannidis Dept. of Electrical and Computer Engineering Aristotle University of Thessaloniki Thessaloniki Greece Dept. of Electrical and Computer Engineering Technical University of Chania Chania Greece Computer Science Engineering Department University of Ioannina Ioannina Greece Cyber Security Systems and Applied AI Research Center Lebanese American University (LAU) Lebanon

Internet of Things (IoT) networks are expected to be a key enabler technology for smart cities, due to their ability to offer real-time monitoring. In this work, we propose a novel medium access control (MAC) protocol, inspired by the synergy of slotted ALOHA and hybrid automatic repeat request with code combining (HARQ-CC), termed as code combining slotted ALOHA (CCSA), to improve the performance of IoT networks with multiple randomly deployed sensors. Specifically, we provide analytical closed-form expressions for the network’s outage probability as well as for its average throughput when CCSA is applied. Finally, we provide simulation results that reveal the authenticity of the derived expressions and reveal the capabilities of HARQ-CC to significantly improve the network’s performance when the sensors’ access is based on a random access scheme.

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