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Classification of cervical spine disease using convolutional neural network

Multimedia Tools and Applications 2024年第41期83卷 88963-88979页

作者： Bezabh, Yohannes Agegnehu Salau, Ayodeji Olalekan Abuhayi, Biniyam Mulugeta Ayalew, Aleka Melese Department of Information Technology University of Gondar Gondar Ethiopia Department of Electrical/Electronics and Computer Engineering Afe Babalola University Ado-Ekiti Nigeria Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India

Cervical spondylosis myelopathy (CSM) is a degenerative disorder of the cervical spine’s disks and joints, leading to neurological disability. Typically, this condition causes increasing discomfort and neurologic deterioration. Cervical spine pain disease classification using deep learning can assist medical professionals in making early and more precise medical diagnosis of cervical abnormalities. The main focus of the assessment for doctors is the classification of cervical spine diseases from X-rays images, which is valuable for patients to indicate the various symptoms they are faced with. Identifying cervical spine pain injuries (CPI) is a critical aspect of the diseases segmentation and classification. Manually explaining the high-dimensional feature space makes it challenging to identify the exact category and level of severity. This paper introduces a Convolutional Neural Networks (CNN) concatenation feature extraction method to help in the diagnosis of cervical spine pain disease from X-ray images. An innovative CNN-concatenation model-based fully connected classifier and a four-class system are applied to classify X-ray image data in this research. The CNN approach is employed to automatically classify X-ray images into healthy (normal) and with cervical spine disease (abnormal). CSM is currently diagnosed mostly through clinical evaluation using imaging methods such as X-ray and computed tomography (CT). Due to its inexpensive cost and minimal radiation dose, X-ray is commonly used. The proposed approach achieved a training accuracy of 99.98%, a validation accuracy of 98.29%, and a testing accuracy of 97.82% of classification from experiments. These results indicate that the image preprocessing, data augmentation, and CNN approaches provide an efficient classification method for identifying and accurately classifying cervical spine diseases. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Feature extraction

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A Modified Hexagonal Shaped Sub-6 GHz 5G Antenna in MIMO Configuration

A Modified Hexagonal Shaped Sub-6 GHz 5G Antenna in MIMO Con...

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2024 IEEE Microwaves, Antennas, and Propagation Conference, MAPCON 2024

作者： Porwal, Rahul Garg, Rahul Kumar Raj, Nikhil Nair, M.V. Deepak Department of Electronics and Communication Engineering The LNM Institute of Information Technology Jaipur India Department of Computer Science and Engineering University of Engineering and Management Jaipur India

ISBN: (纸本)9798350379693

A line-fed Modified Hexagon shaped 2-port planar antenna is presented for 5G application. The MIMO antenna is designed through combining the techniques of a modifying the radiator and defected ground structure. In the MIMO configuration, two replicas of the MIMO element are arranged orthogonal to ensure the pattern diversity. A cross shaped structure is integrated with the ground to enhance interelement isolation. The size and operating bandwidth of the MIMO antenna are 28×56 mm2 and 2.54 GHz to 5.46 GHz, respectively. This design advantageously offers a wide -15 dB bandwidth from 3.3 GHz to 5.0 GHz with a percentage bandwidth of 40.96% and a peak realized gain of 3.1 dBi at 3.3 GHz. Maximum isolation value of 33.55 dB is achieved while it is never less than 16.85 dB for the frequency of interest. The envelope correlation coefficient (ECC) © 2024 IEEE.

关键词： 5G mobile communication systems

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An Energy Efficient IoT Based Smart Street Lighting Using Low Cost SOC 1

An Energy Efficient IoT Based Smart Street Lighting Using Lo...

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1st IEEE International Conference on electronics, Computing, Communication and Control technology, ICECCC 2024

作者： Velu, Anitha Ramamoorthy, Raghu Manasa, S.M. Navulkumar, Devakirubai Sri Sairam College of Engineering Department of Electronics and Communication Engineering Bengaluru India The Oxford College of Engineering Department of Computer Science and Engineering Bengaluru India Jayam College of Engineering and Technology Department of Computer Science and Engineering Dharmapuri India

ISBN: (纸本)9798350371802

Internet of Things (IoT) has radically improved and modernized in every aspect of human existence. The term IoT is a recent trend that states the development a self-configurable network by connecting a variety of hardware objects, to internet without requiring human input to improve service delivery and automate processes by means of data transmission to the cloud. Streetlights are an essential component of any city, although they provide safer roadways, improved night vision, and increased visibility of public spaces it uses a significant amount of electricity and operates at maximum intensity from dusk till dawn. The wastage of electricity can be secured by having lights turn OFF on their own if it is not in use. This work presents an energy efficient IoT-based smart street lighting by using low-cost System On Chip (SOC) circuit. This work deploys an LDR sensor which acts as a decision device, to choose whether to switch ON or OFF the street light based on the level of ambient light. This simple switch has a relay at its output and is activated by either light or dark. This switching may be done using a cost-effective Wi-Fi module namely, ESP8266 for which the LDR sensed value is given as input. Any user can check the status of a street light whether it is ON/OFF from anywhere using internet in real time. It promises great dependability and remarkable stability for long-term. The proposed work is implemented using a NodeMCU board that is designed to supply the required light intensity at different times. © 2024 IEEE.

关键词： Internet of things

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Review On the Techniques Used for Detection of Fabric Defects Using AI 1

Review On the Techniques Used for Detection of Fabric Defect...

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1st IEEE International Conference on Advances in electronics, Communication, Computing and Intelligent Information Systems, ICAECIS 2023

作者： Batra, Isheeta Prasad, S A Hari Arvind, K.S. Faculty of Engineering & Technology Department of Computer Science and Engineering Karnataka India Faculty of Engineering & Technology Department of Electronics and Communication Engineering Karnataka India

ISBN: (纸本)9798350348057

The garment industry is the second-most polluting industry after oil. These mass-produced clothes if rejected are dumped and have an enormous impact on the environment. Therefore, to save the cost post production it is important to identify any sorts of defects pre-production when clothes are in a textile form, so that in case the fabric lacks somewhere in quality it can always be replaced, saving all the time and cost. This detection is possible using techniques like segmentation, or on the basis of texture or by using deep learning algorithms each having its own advantages and disadvantages to efficiently identify the defects in textile fabric. This paper is a comprehensive study of these three techniques and comparison made on basis of brief analysis which say that the deep learning technique is the best of the three for detection of defects keeping in contrast the type of research and developing technologies. © 2023 IEEE.

关键词： Deep learning

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Anomaly Detection and Classification in Streaming PMU Data in Smart Grids

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computer Systems Science & engineering 2023年第9期46卷 3387-3401页

作者： A.L.Amutha R.Annie Uthra J.Preetha Roselyn R.Golda Brunet Department of Computational Intelligence SRM Institute of Science and TechnologyChennai603203India Department of Electrical and Electronics Engineering SRM Institute of Science and TechnologyChennai603203India Department of Computer Science and Engineering Government College of EngineeringSalem636011India

The invention of Phasor Measurement Units(PMUs)produce synchronized phasor measurements with high resolution real time monitoring and control of power system in smart grids that make *** are used in transmitting data to Phasor Data Concentrators(PDC)placed in control centers for monitoring purpose.A primary concern of system operators in control centers is maintaining safe and efficient operation of the power *** can be achieved by continuous monitoring of the PMU data that contains both normal and abnormal *** normal data indicates the normal behavior of the grid whereas the abnormal data indicates fault or abnormal conditions in power *** a result,detecting anomalies/abnormal conditions in the fast flowing PMU data that reflects the status of the power system is critical.A novel methodology for detecting and categorizing abnormalities in streaming PMU data is presented in this *** proposed method consists of three modules namely,offline Gaussian Mixture Model(GMM),online GMM for identifying anomalies and clustering ensemble model for classifying the *** significant features of the proposed method are detecting anomalies while taking into account of multivariate nature of the PMU dataset,adapting to concept drift in the flowing PMU data without retraining the existing model unnecessarily and classifying the *** proposed model is implemented in Python and the testing results prove that the proposed model is well suited for detection and classification of anomalies on the fly.

关键词： Smart Grid PMU data incremental learning classifying anomalies artificial intelligence

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Optimized Deep Learning Classification Model for Intelligent Edge devices

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Journal of engineering Science and technology Review 2024年第3期17卷 88-94页

作者： Naveen, Soumyalatha Kounte, Manjunath R Department of Computer Science and Engineering Manipal Institute of Technology Bengaluru Manipal Academy of Higher Education Karnataka Manipal India School of Electronics and Communication Engineering REVA University Karnataka Bangalore560064 India

Deep learning models enable state-of-the-art accuracy in computer vision applications. However, the deeper, computationally expensive, and densely connected architecture of deep neural networks (DNN) have limitations for deploying the model on resource-constraint embedded IoT devices. We propose an efficient neural network compression framework that performs filter pruning, fine-tuning and 8-bit quantization to reduce computational complexity, inference time, and memory footprint. Furthermore, reducing the bit widths of activation and weights helps design a compact deployment model on resource-limited IoT devices such as smartphones. The proposed system is evaluated extensively on the CIFAR-10 dataset for Resnet34 and VGG16 models. In addition, we examine the efficacy of a larger model. The result shows that pruning followed by quantization compresses the neural network and compared to the baseline model, achieved an accuracy of 78.01% for Resnet34 and 82.34% for Vgg16 after pruning and quantization which is © 2024 School of Science, DUTH. All rights reserved.

关键词： Object recognition

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Virtual Trial Room for Online Shopping 10

Virtual Trial Room for Online Shopping

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10th International Conference on Communication and Signal Processing, ICCSP 2024

作者： Kavin, A.M. Jithesh, T. Kalaiarasi, G. Selvi, M. Yogitha, R. Suresh Babu, T.N. Sathyabama Institute of Science and Technology Department of Computer Science and Engineering Chennai India Adhiparasakthi Engineering College Department of Electronics and Communication Engineering Melmaruvathur India

ISBN: (纸本)9798350353068

Virtual Trial Room - An Innovative Solution It provides a customized and immersive shopping experience those who save online have to attempt. With improvement Popularity of e-commerce, consumer base the problem is that you cannot get dressed in advance it is smooth to shop for Allows builders to test with this problem simply Clothing within the virtual *** normally takes a lot of time to try on things in a store. However, in situations like Online Shopping, it might not even be able to try on clothing. The popularity of online purchasing has greatly increased nowadays especially after pandemic breakdowns. When buying things like clothing, it's important to understand how the clothing will look on the buyer. This is the main cause of the decline in online clothing ***, a virtual trial room for online shopping would be a tremendous luxury for online retailers that could offer a large selection to users. This would allow people to see how clothes fit them individually. This would be a fantastic tool for Online shopping marketers to expand their market. By establishing a virtual trial room setting, we hope to improve accessibility and time efficiency for trying on clothing. In this project, we provide an Open-CV-based virtual trial room application for online shopping. The Virtual trial Room is an innovative solution that offers a personalized and immersive shopping experience to online shoppers. With the increasing popularity of e-commerce, customers face the challenge of not being able to try on clothes before purchasing them. The Virtual trial Room addresses this issue by allowing customers to virtually try on clothes in a digital environment © 2024 IEEE.

关键词： Sales

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Ruppert's Delaunay Triangulation Refinement Scheme for Optimal RSUs Placement in Vehicle to Infrastructure Communication Network

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China Communications 2023年第7期20卷 303-320页

作者： Selvakumari P Chinnasamy A Sree Rathna Lakshmi NVS Sheela D Department of Computer Science and Engineering Tagore Engineering CollegeChennai 600048India Department of Data Science and Business Systems School of ComputingSRMIST kattankulathur CampusChennai 603203India Department of Electronics and Communication Engineering Department of ECE at Agni College of TechnologyChennai 603103India Department of Electronics and Communication Engineering Saveetha School of Engineering SIMATSChennai 600124India

Road Side Units(RSUs)are the essential component of vehicular communication for the objective of improving safety and mobility in the road *** are generally deployed at the roadside and more specifically at the intersections in order to collect traffic information from the vehicles and disseminate alarms and messages in emergency situations to the neighborhood vehicles cooperating with the ***,the development of a predominant RSUs placement algorithm for ensuring competent communication in VANETs is a challenging issue due to the hindrance of obstacles like water bodies,trees and *** this paper,Ruppert’s Delaunay Triangulation Refinement Scheme(RDTRS)for optimal RSUs placement is proposed for accurately estimating the optimal number of RSUs that has the possibility of enhancing the area of coverage during data *** RDTRS is proposed by considering the maximum number of factors such as global coverage,intersection popularity,vehicle density and obstacles present in the map for optimal RSUs placement,which is considered as the core improvement over the existing RSUs optimal placement *** is contributed for deploying requisite RSUs with essential transmission range for maximal coverage in the convex map such that each position of the map could be effectively covered by at least one RSU in the presence of *** simulation experiments of the proposed RDTRS are conducted with complex road traffic *** results of this proposed RDTRS confirmed its predominance in reducing the end-to-end delay by 21.32%,packet loss by 9.38%with improved packet delivery rate of 10.68%,compared to the benchmarked schemes.

关键词： road side units(RSUs) ruppert’s delau-nay triangulation refinement area of global coverage optimal RSUs placement

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Control Technique of MPPT Utilizing for Boost Converter Applied to Small Renewable Energy System and a DC Microgrid for Remote Area 4

Control Technique of MPPT Utilizing for Boost Converter Appl...

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4th IEEE International Maghreb Meeting of the Conference on Sciences and Techniques of Automatic Control and computer engineering, MI-STA 2024

作者： Zentani, Ahmed Almaktoof, Ali Kahn, M.T.E. Cape Peninsula University of Technology Department of Electrical Electronics and Computer Engineering Cape Town South Africa

ISBN: (纸本)9798350372632

The growing concerns about the environment, depletion of fossil fuels, and increasing energy consumption have led to a rise in the adoption of renewable energy sources. However, integrating these green energy sources into the existing power system requires infrastructure modifications. One popular approach is the establishment of off-grid operations through the use of microgrids. Microgrids are small-scale power systems that can operate both connected to and disconnected from the main power grid. This study focuses on a DC microgrid that utilizes a renewable energy system to power DC loads in case of a conventional grid failure. The system incorporates various components such as a universal diode rectifier, a boost converter, a small wind turbine (SWT) connected to a permanent magnet synchronous generator, and the Incremental Conductance (IC) algorithm. These components are employed to maximize the output power of the boost converter and maintain precise control over the output voltage, ensuring reliability. To efficiently operate the switching element and achieve a fast Maximum Power Point Tracking (MPPT) technique and voltage regulation despite varying wind speeds, a modified IC MPPT control method is utilized. A MATLAB/Simulink model representing an SWT system for a DC microgrid is developed to assess the dependability of the proposed system under changing wind speeds. © 2024 IEEE.

关键词： Renewable energy

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Improved channel estimation for underwater wireless optical communication OFDM systems by combining deep learning and machine learning models

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Optical and Quantum electronics 2025年第3期57卷 1-21页

作者： Salama, Wessam M. Aly, Moustafa H. Department of Computer Engineering Faculty of Engineering Pharos University Canal El Mahmoudia Street Beside Green Plaza Complex 21648 Alexandria Egypt OSA Member Department of Electronics and Communications Engineering College of Engineering and Technology Arab Academy for Science Technology and Marine Transport Alexandria1029 Egypt

Recent studies on channel estimation in wireless communication systems have focused on deep learning methods. Our primary contribution is based on the use of DenseNet121 hybrid with Random Forest (RF), Gated Recurrent Units (GRU), Long Short-Term Memory Networks (LSTM), and Recurrent Neural Networks (RNN) to improve the channel estimation and lower the error rate. In order to mitigate inter-symbol interference and map the datasets, this paper introduces M-quadrature amplitude modulation (16-QAM) and orthogonal frequency division multiplexing (OFDM), which is based on quadrature phase shift keying (QPSK). Additionally, the existence or lack of cyclic prefixes forms the basis of our simulation. Additionally, the suggested models are investigated using pilot samples 2, 4, 8, and 64. Labeled OFDM signal samples, where the labels match the signal received after applying OFDM and passing through the medium, are used to train the proposed models. The DenseNet121 functions as a powerful feature extractor to extract intricate spatial information from received signal data. Sequential models like as RNN, LSTM, and GRU are used to model temporal dependencies in the retrieved features. RF is also utilized to exploit non-linear relationships and interactions between features to further increase prediction accuracy and reduce bit error rate (BER). By comparing the models using key metrics like accuracy, bit error rate (BER), and mean squared error (MSE), superior performance is attained based on the DenseNet121_RNN_GRU_RF model. Additionally, the DLMs are assessed against traditional methods like minimal mean square error (MMSE) and least squares (LS). Using the DenseNet121_RNN_GRU_RF model indicates a considerable gain over alternative architectures, with an improvement of 36.3% over DensNet121-RNN-LSTM-RF, according to a comparison of the suggested models without cyclic prefix for OFDM_QPSK. The improvement in percentages of roughly 63.3% over DensNet121-RNN-LSTM, 68.18% over De

关键词： Orthogonal frequency division multiplexing

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