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Classification of suspected objects and severity assessment of COVID-19 from chest X-ray images using deep transfer learning

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Research on Biomedical engineering 2023年第3期39卷 705-718页

作者： Verma, Karan Kumar, Ashok Swaraj, Aman Sagar, Aditi Department of Computer Science and Engineering National Institute of Technology Delhi Delhi India Department of Electronics and Communication Engineering Government Mahila Engineering College Rajasthan Ajmer India Department of Computer Science and Engineering Indian Institute of Technology Roorkee India

Purpose: Coronavirus disease 2019 (COVID-19) has infected about 418 million people across the globe. So, the analysis of biomedical imaging accompanied with artificial intelligence (AI) approaches has transpired a vital role in diagnosing COVID-19. Until now, numerous classification approaches have been demonstrated for the detection of COVID-19. The assessment of COVID-19 patients according to severity level is not so far investigated. For this motivation, the classification of COVID-19 chest X-ray (CXR) images according to severity of the infection is presented in this work. Methods: Primarily, the 1527 CXR images are pre-processed to reshape images into unique size, denoised, and enhanced images through median filter and histogram equalization (HE) techniques, respectively. Afterward, reshaped, denoised, and enhanced CXR images are augmented using synthetic minority oversampling technique (SMOTE) to achieve the balanced dataset of 1752 CXR images. After augmentation, a pre-trained VGG16 and residual network 50 (Resnet50) deep transfer learning models with random forest (RF) and support vector machine (SVM) classifiers are utilized for feature extraction and classification of 1752 CXR images into diverse class labels such as normal, severe COVID-19, and non-severe COVID-19. Results: Our proposed ResNet50 model with SVM classifier provides the highest accuracy of about 95% for severity assessment and classification of COVID-19 CXR images as compared to other permutations. For the ResNet50 model with SVM classifier model, the average value of precision, recall, and F1-score are 91%, 94%, and 92%, respectively. Conclusion: The multi-class classification deep transfer learning models are presented to determine the severity assessment and classification of COVID-19 by using CXR images. Out of these proposed models, the ResNet50 model with SVM classifier will be highly favorable for doctors to classify patients according to their severity assessment and detection of COV

关键词： COVID-19

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AN 8-WAY RF FEED NETWORK FOR 5.3 GHZ C-BAND SAR: DESIGN, SIMULATION, AND ANALYSIS USING STANDARD METHODS

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Telecommunications and Radio engineering (English translation of Elektrosvyaz and Radiotekhnika) 2025年第5期84卷 21-43页

作者： Kothapudi, Venkata Kishore Thokala, Niharika Vallabhaneni, Ramyasri Sanaka, Poojitha Kumar, Vijay Pappula, Lakshman Gottapu, Srinivasa Kiran Bhattacharyya, Arka Guntur District Andhra Pradesh Vadlamudi522213 India Department of Communication Engineering School of Electronics Engineering Vellore Institute of Technology Tamil Nadu Vellore632014 India Department of Electrical Electronics and Communication Engineering GITAM School of Technology GITAM University Andhra Pradesh Visakhapatnam530045 India School of Science and Engineering Engineering and Computer Science Department Benedict College 1600 Harden St ColumbiaSC29204 United States

This study proposes the design and analysis of an eight-way power divider for unequal division at 5.3 GHz for C-band frequency. Many transmission line pieces make up the current power divider. These transmission lines were printed on a substrate material (RT/duroid 5880, 1.6 mm thick, permittivity 2.2, and loss tangent 0.0002). The eight-way power divider’s unequal amplitudes are constructed using Taylor’s 1-parameter method to achieve the side projection levels –17 dB down as from the main beam. The proposed power divider consists of using two transmission lines with a 3/4 frequency connected to branched microstriplines, providing excellent isolation and matching between the power divider’s two output ports. This power divider is composed of three stages. A 1:2 power divider is used in the first stage, a 1:4 power divider is used in the second stage, and a 1:8 power divider is used in the third stage. According to the simulation results, 1:2 delivers half of the input power, 1:4 produces one-fourth of the power, and 1:8 delivers one-eighth of the input power. All of these assessments were done with the advanced design system (ADS) simulation program. According to both simulated and measured data, the return loss exceeds –35 dB, and the isolation across the output terminals exceeds –40 dB. The proposed circuit operates effectively at C-band frequencies in terms of return loss and isolation (4 GHz and 8 GHz). © 2025 by Begell House, Inc.

关键词： Integrated circuit design

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Enhanced Security with Improved Defensive Routing Mechanism in Wireless Sensor Networks

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computer Systems science & engineering 2023年第6期45卷 2795-2810页

作者： R.Sabitha C.Gokul Prasad S.Karthik Department of Computer Science&Engineering SNS College of TechnologyCoimbatoreTamilnaduIndia Department of Electronics and Communication Engineering SNS College of EngineeringCoimbatoreTamilnaduIndia

In recent scenario of Wireless Sensor Networks(WSNs),there are many application developed for handling sensitive and private data such as military information,surveillance data,tracking,***,the sensor nodes of WSNs are distributed in an intimidating region,which is non-rigid to *** recent research domains of WSN deal with models to handle the WSN communications against malicious attacks and *** traditional models,the solution has been made for defending the networks,only to specific ***,in real-time applications,the kind of attack that is launched by the adversary is not ***,on developing a security mechanism for WSN,the resource constraints of sensor nodes are also to be *** that note,this paper presents an Enhanced Security Model with Improved Defensive Routing Mechanism(IDRM)for defending the sensor network from various ***,for efficient model design,the work includes the part of feature evaluation of some general attacks of *** IDRM also includes determination of optimal secure paths and Node security for secure routing *** performance of the proposed model is evaluated with respect to several factors;it is found that the model has achieved better security levels and is efficient than other existing models in WSN *** is proven that the proposed IDRM produces 74%of PDR in average and a minimized packet drop of 38%when comparing with the existing works.

关键词： Enhanced security model wireless sensor networks improved defensive routing mechanism secure paths node security

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Sarcasm Detection in X Data Using Node Embedding and Graph Convolutional Networks 4

Sarcasm Detection in X Data Using Node Embedding and Graph C...

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4th International Conference on Advancement in electronics and communication engineering, AECE 2024

作者： Tyagi, Aditya Dayal Garg, Setu Sharma, Swati Tomar, Vivek Verma, Kimmi Computer Science & Application Noida India Its Engineering College Electronics & Communication Engineering Noida India Kiet Group of Institutions Computer Science & Engineering Ghaziabad India Computer Science & Engineering Dehradun India Abes Engineering College Computer Science & Engineering Ghaziabad India

ISBN: (纸本)9798350364729

An extensive amount of attention has been paid to the implications that the detection of sarcasm on online social networks such as Facebook, X etc. could have for the reason of sarcasm detection, sentiment analysis, content regulation, and public opinion. In light of the fact that traditional methods frequently struggle to comprehend the nuances of sarcasm, there is a growing interest in more advanced methodology that make uses of network architectures and deep learning. For the purpose of identifying instances of sarcasm in X data, this study presents a novel method that combines Graph Convolutional Networks (GCNs) and node embeddings. Our solution makes use of a generalized convolutional neural network (GCN), constructs a user-word interaction network, and incorporates word2vec for initial text embedding in order to capture both textual and contextual characteristics. Using our methodology, we demonstrate that it is superior to traditional methods in terms of both accuracy and robustness. © 2024 IEEE.

关键词： Graph embeddings

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Block chain and Deep Learning Based Secure communication using SAE-LSTM &salp Swarm Optimizer for Multivariate Industrial IoT-oriented Infrastructure 15

Block chain and Deep Learning Based Secure Communication usi...

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15th International Conference on Computing communication and Networking Technologies, ICCCNT 2024

作者： MathalaiRaj, J. Sivaranjani, S. Rajalakshmi, J. Jayachandran, T. Maheswari, M. Kavin Kumar, K. Chandrasekaran, Gokul Nadar Saraswathi College of Engineering and Technology Department of Computer Science and Engineering Theni India Sethu Institute of Technology Department of Computer Science and Engineering Virudhnagar India Sethu Institute of Technology Department of Electronics and Communication Engineering Virudhunagar India Nandha Engineering College Department of Electronics and Communication Engineering Erode India Kongu Engineering College Department of Electronics and Communication Engineering Erode Perundurai638060 India Karpagam Institute of Technology Department of Electronics and Communication Engineering Coimbatore India

ISBN: (纸本)9798350370249

The rapid development of the Industrial Internet of Things (IIoT) imposes the needs for digitization of industrial procedures so as to enhance the efficiency of the network. Cyber-Physical System (CPS) has important role in applications of industries and infrastructure towards IoT. There are several challenges in IIoT environment such as centralization, scalability, privacy, security and communication latency. To overcome these issues, Deep learning based Sparse Autoencoder-Long Short-Term Memory (SAE-LSTM) with Salp swarm optimizer SSA based IoT-oriented infrastructure is used to detect the attacks. The major objective of this work is to propose a deep learning and blockchain based smart contract technique for a secure communication in smart city using Salp Swarm Optimizer algorithm for determining data privacy and security for the IIoT's application service. Blockchain & data aggregation with Proof-of-Work (PoW) generates a distributed network for communication phase of CPS. The protocols to forward the data in the network is established by Software-Defined Networking (SDN). Smart contract is employed to execute the transaction. The suggested technique is implemented and the performance is carried out by estimating Accuracy, Precision, Recall and F1 Score. © 2024 IEEE.

关键词： Blockchain and data aggregation with Proof-of-Work (PoW) Decision Sparse Autoencoder-Long Short-Term Memory (DSAE-LSTM) Industrial IoT Salp swarm optimizer SSA Smart contract

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Enhancing Rumour Detection: A Hybrid Deep Learning Approach with ELMo Embeddings & CNN 2

Enhancing Rumour Detection: A Hybrid Deep Learning Approach ...

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2nd IEEE International Conference on Interdisciplinary Approaches in Technology and Management for Social Innovation, IATMSI 2024

作者： Patel, Amitesh Kapoor, Aman Mahato, Mrinmoy Raut, Subhradeep Bahadur Sinha, Bam Iiit Ranchi Computer Science and Engineering Jharkhand India Iiit Ranchi Electronics & Communication Engineering Jharkhand India Nit Sikkim Computer Science and Engineering Ravangla India

ISBN: (纸本)9798350360523

Microblogs have gained significant traction as a news media source in recent times. On the contrary, unsupported information that is widely shared on social media platforms has the potential to drive significant disruption by misguiding individuals. This is exemplified by synthetic text, commonly referred to as 'readfakes,' which further spreads online disinformation. It is crucial to develop models that possess the ability to detect and prevent the spread of rumours. Linguistic characteristics are easily obtainable and serve as crucial attributes in the initial phases of dissemination. Concurrently, the choice of features is substantial with respect to the interpretability and performance of the classifier. This research presents a hybrid model that addresses the urgent requirement for an automated rumour detection system. The model incorporates deep learning, more specifically a Convolutional Neural Network (CNN), and ELMo embeddings that have undergone refinement using a Multinomial Naive Bayes classifier. Training and evaluating this model with the PHEME rumour dataset. The text is transformed into ELMo embeddings at the outset and then proceeds to traverse the CNN architecture in order to extract features. The optimized vector is then used to train the Multinomial Naive Bayes classifier, which is positioned in the CNN's output layer, to categorize rumors. © 2024 IEEE.

关键词： Classification (of information)

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Leveraging Machine Learning Models for Proactive Disaster Forecasting 1

Leveraging Machine Learning Models for Proactive Disaster Fo...

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1st International Conference on AIML-Applications for engineering and Technology, ICAET 2025

作者： Affan, Daimi Syed Ahmed, Askari Arslaan Maaz, Ahmed Syed Asma, Iqbal Computer Science and Engineering Muffakham Jah College of Engineering and Technology Hyderabad India Electronics and Communication Engineering Deccan College of Engineering and Technology Hyderabad India

ISBN: (纸本)9798350355611

The increasing frequency and intensity of natural disasters, such as earthquakes, tsunamis, floods, and forest fires, necessitate the development of advanced early warning systems. Current disaster prediction systems are hampered by limitations in sensor technology, which is often expensive and primarily effective for large-scale disasters. This project aims to enhance disaster prediction accuracy and timeliness by utilizing real-time data and advanced machine learning algorithms, including XGBoost, Voting classifier, and Random Forest classifier. Additionally, recognizing the long-term health impacts of poor air quality, the project extends its scope to predict the Air Quality Index (AQI). The project seeks to enable proactive risk management and effective disaster response by integrating real-time environmental data and machine learning models. We aim to improve proactive risk management and disaster response by employing real-time data. The outcomes of this project promise significant advancements in disaster preparedness, ultimately safeguarding human lives and infrastructure. © 2025 IEEE.

关键词： Risk management

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Optimizing Sheep Breed Classification with Bat Algorithm-Tuned CNN Hyperparameters

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SN computer science 2024年第2期5卷 219页

作者： Ravikiran, H.K. Jayanth, J. Sathisha, M.S. Bindu, K. Department of Electronics and Communication Engineering Navkis College of Engineering Karnataka Hassan 573217 India Department of Electronics and Communication Engineering GSSSIETW Karnataka Mysuru 570016 India Department of Computer Science and Engineering Navkis College of Engineering Karnataka Hassan 573217 India Department of Electronics and Communication Engineering Global Academy of Technology Karnataka Bengaluru 560098 India

Efficiently classifying sheep breeds through image analysis is pivotal in modern animal husbandry, influencing critical management and breeding decisions. This study delves into automating this process by harnessing Convolutional Neural Networks (CNNs), with a particular focus on optimizing key hyperparameters—the learning rate and dropout rate—essential for refining model performance. Manual hyperparameter tuning is often time-consuming and demands expertise. To overcome this challenge, we introduce an innovative approach that utilises the Bat algorithm, a bio-inspired optimization technique. This algorithm mimics bat echolocation behaviors, skillfully navigating the complex hyperparameter search space to determine optimal values. By dynamically adjusting CNN hyperparameters, our research aims to boost classification accuracy while simplifying the tuning process. Empirical results highlight significant gains in classification accuracy and emphasizes the Bat algorithm's efficacy. The optimized CNN model, empowered by fine-tuned hyperparameters, demonstrates superior performance, promising practical applicability in real-world sheep breed classification scenarios. This study meticulously adjusts pulse rate and loudness, revealing an optimal combination of [0.001, 0.24404868], which substantially improves model performance. The findings emphasize the Bat algorithm's role in streamlining hyperparameter tuning and its potential impact on automated sheep breed classification. © 2024, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： BAT algorithm Classification CNN Dropout Hyperparameter tuning Learning rate

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Development of Human Activity Prediction Systems in Smart Homes 1

Development of Human Activity Prediction Systems in Smart Ho...

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1st IEEE International Conference on Smart Power Control and Renewable Energy, ICSPCRE 2024

作者： Sukanya, K. Prashanth, Addagatla Addagatla, Ugendhar TKR College of Engineering and Technology Electronics and Communication Engineering Hyderabad Meerpet India Institute of Aeronautical Engineering Electronics and Communication Engineering Hyderabad Dundigal India Mvsr Engineering College Computer Science and Engineering Hyderabad Nadergul India

ISBN: (纸本)9798350377002

Human activity prediction using machine learning in smart homes is an emerging research area with significant potential for enhancing the functionality and efficiency of residential environments. This study explores the application of machine learning techniques to predict human activities within smart homes and highlights the benefits it brings to residents. By analyzing data collected from various sensors and devices, such as motion sensors, smart appliances, and environmental sensors, machine learning models can accurately forecast activities like occupancy patterns, usage of appliances, and daily routines. Predicting user activity aids in energy efficiency by optimizing consumption patterns, enhances recommender systems for personalized suggestions, and boosts anomaly detection for identifying irregularities in various domains. Leveraging predictive analytics, organizations can streamline operations, improve user experience, and strengthen security measures effectively. The findings of this study demonstrate the potential of human activity prediction using machine learning in smart homes to create intelligent, efficient, and personalized living spaces. © 2024 IEEE.

关键词： Smart homes

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Enhanced Health Monitoring System for Internet of Medical Things using Machine Learning 15

Enhanced Health Monitoring System for Internet of Medical Th...

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15th International Conference on Computing communication and Networking Technologies, ICCCNT 2024

作者： Swedheetha, C. Manikandan, P. Rajalakshmi, J. Muthuraja, T. Kavin Kumar, K. Chandrasekaran, Gokul Sriram Srivathsan, N. Velammal College of Engineering and Technology Department of Computer Science and Engineering India Kalasalingam Academy of Research and Education Krishnankoil Department of Electronics and Communication Engineering Virudhunagar India Sethu Institute of Technology Department of Electronics and Communication Engineering Virudhnagar India Kongu Engineering College Perundurai Department of Electronics and Communication Engineering Erode638060 India Karpagam Institute of Technology Department of Electronics and Communication Engineering Coimbatore India Velammal College of Engineering and Technology Department of Computer Science and Engineering Madurai India

ISBN: (纸本)9798350370249

This brief looks at mobile operation, speech recognition, and voice robotization. Patients with physical limitations, such as Parkinson's disease and cerebral palsy, as well as those with speech impairments, can express their requirements with this application. The initiative enhances the general well-being and independence of people with impairments. Increasing the automation of current technologies is the main objective of this design. © 2024 IEEE.

关键词： Neurodegenerative diseases

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