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Computational Investigation of Multiband EMNZ Metamaterial Absorber for Terahertz Applications

computers, Materials & Continua 2022年第5期71卷 3905-3920页

作者： Ismail Hossain Md Samsuzzaman Mohd Hafiz Baharuddin Norsuzlin Binti Mohd Sahar Mandeep Singh Jit Singh Mohammad Tariqul Islam Space Science Center Universiti Kebangsaan Malaysia43600UKM BangiSelangorMalaysia Department of Computer and Communication Engineering Faculty of Computer Science and EngineeringPatuakhali Science and Technology UniversityBangladesh Department of Electrical Electronic and Systems EngineeringFaculty of Engineering and Built EnvironmentUniversiti Kebangsaan MalaysiaMalaysia

This study presents an Epsilon Mu near-zero(EMNZ)nanostructured metamaterial absorber(NMMA)for visible regime *** resonator and dielectric layers are made of tungsten(W)and quartz(fused),where the working band is expanded by changing the resonator layer’s *** to perfect impedance matching with plasmonic resonance characteristics,the proposed NMMA structure is achieved an excellent absorption of 99.99%at 571 THz,99.50%at 488.26 THz,and 99.32%at 598 THz *** absorption mechanism is demonstrated by the theory of impedance,electric field,and power loss density distributions,*** geometric parameters are explored and analyzed to show the structure’s performance,and a near-field pattern is used to explain the absorption mechanism at the resonance frequency *** numerical analysis method describes that the proposed structure exhibited more than 80%absorbability between 550 and 900 *** computer Simulation Technology(CST Microwave Studio 2019)software is used to design the proposed ***,CSTHFSS interference is validated by the simulation data with the help of the finite element method(FEM).The proposed NMMA structure is also exhibits glucose concentration sensing capability as *** the proposed broadband absorber may have a potential application in THz sensing,imaging(MRI,thermal,color),solar energy harvesting,light modulators,and optoelectronic devices.

关键词： Metamaterial absorber terahertz applications multiband EM near zero nanostructured visible regime applications

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Home Automation Using Wi-Fi: ESP32-Based System for Remote Control and Environmental Monitoring 3

Home Automation Using Wi-Fi: ESP32-Based System for Remote C...

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3rd IEEE International Conference on Distributed Computing and Electrical Circuits and electronics, ICDCECE 2024

作者： Abdul Lateef Haroon, P.S. Shafiulla, Mohammed Naveed, Shaik Mohammed Ahmed, Suhail Nawaz, Shaik Mohammed Kumar, Utkarsh Ballari Institute Of Technology & Management Department Of Electronics And Communication Engineering Ballari India Ballari Institute Of Technology And Management Department Of Computer Science And Engineering Ballari India Ballari Institute Of Technology & Management Department Of Computer Science And Engineering Ballari India Ballari Institute Of Technology & Management Department Of Artificial Intelligence And Machine Learning Ballari India

ISBN: (纸本)9798350318609

This project introduces a comprehensive home automation system leveraging Wi-Fi connectivity and ESP32 micro controllers, aimed at enhancing modern living through seamless automation and user-centric design. The background emphasizes the fusion of IoT technology with home automation, highlighting Wi-Fi's significance in enabling remote monitoring and control of household devices. Methodologically, the project integrates relay modules and DHT11 sensors, orchestrated through a user-friendly smartphone application. Results demonstrate successful implementation and testing, showcasing functionality, reliability, and user-friendliness. Real-time environmental monitoring and remote appliance control fulfill the objectives of enhancing home comfort, convenience, and energy efficiency. The conclusion underscores the project's contribution to advancing smart home technologies, offering users a scalable and customizable solution. Future research opportunities include enhancing system capabilities and addressing security and privacy concerns. © 2024 IEEE.

关键词： Microcontrollers

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Blockchain-Based Online E-voting System

Blockchain-Based Online E-voting System

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2023 International Conference on Smart Computing and Application, ICSCA 2023

作者： Ali, Youssef Abdelrahman Fekry Ahmed, Omar Tarek Mohamed Diab, Mohamad Ahmad Mohamad Sayed, Mohamed Abd Elhalim Abd Elaziz, Mohamed Khaled Aboshosha, Bassam.W. Higher Institute of Engineering El-Shorouk Academy Department of Communication and Computer Engineering El-Shorouk City11937 Egypt

ISBN: (纸本)9798350347050

Recently, blockchain technology has become popular. Through a highly secure, decentralised system enabled by this technology, anyone can interact securely without the need for a middleman. Machine learning can help with many of the constraints that blockchain-based systems have in addition to its own strengths. Blockchain technology and machine learning together have the potential to produce very effective and beneficial outcomes. Hence, when blockchain and ML converge, they surely would benefit from each other. Blockchain can enhance the security of ML platforms, and ML can provide automation and optimization to blockchain solutions. In this work, we advocate the importance of enhancing blockchain with ML algorithms, as a proof of principle we address the issue of secure and intelligent e-voting. The use of blockchain technology has brought tremendous different application domains, e-voting is one of them. Most existing e-Voting systems require central authority during the process of authentication and verification of the voter. In this paper, we propose a safe online voting approach based on blockchain and ML to provide a solution to this issue. We use blockchain to ensure integrity and transparency of the votes, and ML for automating the verification process of eligible voters based on AI-for face authentication. The proposed solution offers automation, security, and mobility to the voting system. © 2023 IEEE.

关键词： E-learning

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Predictive Modeling of Compressive Strength in Coconut Shell-Embedded Concrete using Ensemble Regression Models 2

Predictive Modeling of Compressive Strength in Coconut Shell...

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2nd IEEE International Conference on Integrated Intelligence and communication Systems, ICIICS 2024

作者： Tipu, Rupesh Kumar Shah, Owais Ahmad Vats, Satvik Tomar, Arjit Khan, Imran Ahmed Whig, Pawan Ali, Kunwar Babar Verma, Aditya School of Engineering & Technology K. R. Mangalam University Gurugram India Department of Electronics & Communication Engineering Dayananda Sagar University Bengaluru India Department of Computer Science & Engineering Graphic Era Hill University Dehradun India Department of Computer Science & Engineering Noida International University Gautam Budh Nagar India Department of Electronics & Communication Engineering Jamia Millia Islamia University New Delhi India Department of Electronics & Communication Engineering Vivekananda Institute of Professional Studies New Delhi India Meerut Institute of Engineering & Technology Meerut India Department of Computer Science & Engineering Graphic Era Deemed to be University Dehradun India

ISBN: (纸本)9798331504960

This study is based on the exploration of ensemble regression models using Adaboost Regressor, Random Forest Regressor, Gradient Boosting Regressor, and XGBoost Regressor for predicting the compressive strength of coconut shell concrete. In the study normalization technique, and 5-fold cross-validation for the training and testing of the models over a dataset of 483 samples are employed, constituting various concrete mix features. It produced the result showing competitive performance with the top performance being Adaboost Regressor that resulted in a score of 0.81 R2 and a minimal MSE of 1.16. With the help of SHAP, it has been analyzed and observed that influential pivotal effects have been outlined by influential features including Age, Coarse Aggregate, and Water content in the predictions. Age of concrete has emerged highest important factor that puts it in a leading role for determining compressive strength. The research itself gives insight into the optimal formulation of concrete, harmonizing between structural integrity and sustainability. Ensemble models, coupled with SHAP, give inspiring directions for advancing concrete technology in eco-friendly construction practices. © 2024 IEEE.

关键词： Sustainable development

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Hybrid Optimization for Enhanced Retinal Image Classification Using Neural Networks

Hybrid Optimization for Enhanced Retinal Image Classificatio...

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2024 International Conference on Innovative Computing, Intelligent communication and Smart Electrical Systems, ICSES 2024

作者： Anwar Basha, H. Deepak, R. Babu, M. Bhanu, Shaik Thasleem Amutha, B. Madhavan, R. Rajalakshmi Institute of Technology Department of Computer Science and Engineering Tamilnadu Chennai India Nitte Meenakshi Institute of Technology Department of Computer Science and Engineering Karnataka Bengaluru India Rajalakshmi Engineering College Department of Electronics and Communication Engineering Tamilnadu Chennai India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Mathematics Tamilnadu Chennai India Jeppiaar Institute of Technology Department of Computer Science and Business Systems Tamilnadu Chennai India

ISBN: (纸本)9798331543617

The present study proposes a hybrid optimization algorithm that involves the integration of Neural Networks (NN), Genetic Algorithms(GA), and Particle Swarm Optimization(PSO) to improve the accuracy and efficiency of retinal image analysis. First, a wide range of features are extracted from retinal images and normalized. Then a NN is constructed with its weights and biases initialized to process these features. The fitness of each feature subset is measured with the help of a network's output, quantified by a Mean Squared Error(MSE)-based fitness function. Receiving fitness values, the GA component picks out the most important subsets of features, which are refined with crossover and mutation operations to retain diversity and improve the search process. In a parallel process, PSO dynamically adjusts the position of particles representing subsets of features through modification of velocity and position vectors under the influences of cognition and society. With the proposed integrated approach, iteratively, an optimal feature subset is generated by minimizing error and maximizing classification performance. Finally, after convergence, this last optimized subset of features is used in the final tuning of the NN to achieve a far-reaching improvement in the accuracy of classification. This method takes advantage of the complementary strengths of NN, GA, and PSO;therefore, it is able to provide an effective solution for the optimization of features within medical image analysis. © 2024 IEEE.

关键词： Ophthalmology

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Robust Deep Transfer Learning Based Object Detection and Tracking Approach

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Intelligent Automation & Soft Computing 2023年第3期35卷 3613-3626页

作者： C.Narmadha T.Kavitha R.Poonguzhali V.Hamsadhwani Ranjan walia Monia B.Jegajothi Electronics and Communication Engineering Periyar Maniammai Institute of Science&Technology(PMIST)Thanjavur613403India Department of Computer Science and Engineering Periyar Maniammai Institute of Science&TechnologyThanjavur613403India Department of Electrical and Electronics Engineering Periyar Maniammai Institute of Science and TechnologyThanjavur613403India Electrical Engineering Department Model Institute of Engineering&Technology(Autonomous)Jammu181122India Department of Computer Science and Engineering Shri Mata Vaishno Devi UniversityKatra182320India Center for Energy Research Chennai Institute of TechnologyChennai600069India

At present days,object detection and tracking concepts have gained more importance among researchers and business ***,deep learning(DL)approaches have been used for object tracking as it increases the perfor-mance and speed of the tracking *** paper presents a novel robust DL based object detection and tracking algorithm using Automated Image Anno-tation with ResNet based Faster regional convolutional neural network(R-CNN)named(AIA-FRCNN)*** AIA-RFRCNN method performs image anno-tation using a Discriminative Correlation Filter(DCF)with Channel and Spatial Reliability tracker(CSR)called DCF-CSRT *** AIA-RFRCNN model makes use of Faster RCNN as an object detector and tracker,which involves region proposal network(RPN)and Fast *** RPN is a full convolution network that concurrently predicts the bounding box and score of different *** RPN is a trained model used for the generation of the high-quality region proposals,which are utilized by Fast R-CNN for detection ***,Residual Network(ResNet 101)model is used as a shared convolutional neural network(CNN)for the generation of feature *** performance of the ResNet 101 model is further improved by the use of Adam optimizer,which tunes the hyperparameters namely learning rate,batch size,momentum,and weight ***,softmax layer is applied to classify the *** performance of the AIA-RFRCNN method has been assessed using a benchmark dataset and a detailed comparative analysis of the results takes *** outcome of the experiments indicated the superior characteristics of the AIA-RFRCNN model under diverse aspects.

关键词： Object detection tracking deep learning deep transfer learning image annotation

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Retinal Blood Vessel Tracking and Diameter Estimation via Gaussian Process With Rider Optimization Algorithm

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IEEE Journal of Biomedical and Health Informatics 2024年第3期28卷 1173-1184页

作者： Ahmad, Nehal Lai, Kuan-Ting Tanveer, M. Indian Institute of Technology Indore Simrol Department of Mathematics Indore453552 India National Taipei University of Technology Department of International Program of Electrical Engineering and Computer Science Taipei106 Taiwan National Taipei University of Technology Department of Electronic Engineering Taipei106 Taiwan

Retinal blood vessels structure analysis is an important step in the detection of ocular diseases such as diabetic retinopathy and retinopathy of prematurity. Accurate tracking and estimation of retinal blood vessels in terms of their diameter remains a major challenge in retinal structure analysis. In this research, we develop a rider-based Gaussian approach for accurate tracking and diameter estimation of retinal blood vessels. The diameter and curvature of the blood vessel are assumed as the Gaussian processes. The features are determined for training the Gaussian process using Radon transform. The kernel hyperparameter of Gaussian processes is optimized using Rider Optimization Algorithm for evaluating the direction of the vessel. Multiple Gaussian processes are used for detecting the bifurcations and the difference in the prediction direction is quantified. The performance of the proposed Rider-based Gaussian process is evaluated with mean and standard deviation. Our method achieved high performance with the standard deviation of 0.2499 and mean average of 0.0147, which outperformed the state-of-the-art method by 6.32%. Although the proposed model outperformed the state-of-the-art method in normal blood vessels, in future research, one can include tortuous blood vessels of different retinopathy patients, which would be more challenging due to large angle variations. We used Rider-based Gaussian process for tracking blood vessels to obtain the diameter of retinal blood vessels, and the method performed well on the 'STrutred Analysis of the REtina (STARE) Database' accessed on Oct. 2020 (https://***/~ahoover/stare/). To the best of our knowledge, this experiment is one of the most recent analysis using this type of algorithm. © 2013 IEEE.

关键词： Blood vessels

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Signal Processing Techniques for Classifying Heartbeat Patterns in ECG Signals for Cardiac Disease Detection Using Graph Convolutional Networks 4

Signal Processing Techniques for Classifying Heartbeat Patte...

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4th IEEE International Conference on Mobile Networks and Wireless communications, ICMNWC 2024

作者： Alzubaidi, Laith H. Priyadharshini, C. Vasanthakumar, G.U. Samala, Srinivas Ananthi, S. College of Technical Engineering The Islamic University of Al Diwaniyah Department of Computers Techniques Engineering Al Diwaniyah Iraq Bharathiyar Institute of Engineering for Women Department of Electronics and Communication Engineering Salem India Nitte Meenakshi Institute of Technology Department of Computer Science and Engineering Bengaluru India Sr University Department of Electronics and Communication Engineering Warangal India New Prince Shri Bhavani College of Engineering and Technology Department of Electrical and Electronics Engineering Chennai India

ISBN: (纸本)9798350352931

In recent days, the information about electrical activity is obtained in the form of Electro-Cardo-Diograms (ECG) which is become a typical way of monitoring heart rhythm and function of patients. In existing, the Convolutional Neural Networks (CNN) is utilized for the effective detection of Cardiac Disease (CD). However, the CNN model may not extract the spatial features from the signals effectively. To overcome this issue, Graph Convolutional Network (GCN) is proposed to extract the spatial features from signal effectively. Initially, the data is taken from Massachusetts institute of Technology and Beth Israel Hospital (MIT-BIH) dataset. Then, min-max normalization is utilized to preprocess the data and the high frequency noise is removed with Butterworth Frequency Filter (BFF). After that, the preprocessed data is further processed into classification with proposed GCN model. In the classification, the convolutional layers are utilized to extract the spatial features by using graph-based structure. Finally, the fully connected layer with softmax activation function is utilized to classify the CD into normal or abnormal labels. From the results, the proposed GCN model established better results than existing CNN including precision (99.75%), recall (98.60%), F1-score (98.20%) and Area Under Curve (99.70%) respectively. © 2024 IEEE.

关键词： Butterworth filters

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A Review on DeepLungNet: CNN-Based Lung Cancer Detection Techniques Using CT Images

A Review on DeepLungNet: CNN-Based Lung Cancer Detection Tec...

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2024 International Conference on Advances in Data engineering and Intelligent Computing Systems, ADICS 2024

作者： Nair, Surabhi S. John, Asha Susan Raju, Juby Musaliar College Of Engineering And Technology Department Of Electronics And Communication Engineering Pathanamthitta India Kalasalingam Academy Of Research And Education Department Of Electronics And Communication Engineering Virudhanagar Tamil Nadu Krishnan kovil626 126 India Hindustan Institute Of Technology And Science Department Of Computer Science Engineering TamilNadu India

ISBN: (纸本)9798350364828

The disease known as lung cancer, which is common and frequently deadly, starts in the cells of the lungs and causes symptoms like exhaustion, chest pain, and persistent coughing. Since small cell lung cancer (SCLC) is less frequent but more dangerous than non-small cell lung cancer (NSCLC), knowledge of its causes, including smoking, pollution, and genetic factors, is essential. Survival rates are greatly increased by early identification by about 20%. This study comprehensively analyzes deep learning and machine learning models-based lung cancer prediction methods from 2016 to 2023. The review emphasizes how well these models work to achieve greater accuracy. Expanding on this, a new method is suggested that combines data augmentation and denoising preprocessing methods with a CNN in a ResNet architecture for classification. The goal of this hybrid model is to maintain and improve accuracy levels in the identification of benign and malignant lung pictures. This methodology offers promising promises for more accurate and reliable lung cancer prediction by utilizing advances in both preprocessing and classification techniques. © 2024 IEEE.

关键词： Diseases

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Comment Analyzer by Sentimental Analysis through Natural Language Processing 10

Comment Analyzer by Sentimental Analysis through Natural Lan...

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

作者： Chinnasamy, P. Ayyasamy, Ramesh Kumar Madhukar, Gadde Karthik, Gaddamidi Bhargav, Tanneru Nayak, Dungavath Yuva Kiran Mlr Institute of Technology Department of Computer Science and Engineering Hyderabad India Faculty of Information and Communication Technology Kampar Malaysia

ISBN: (纸本)9798350353068

Comment analyzers were widely employed across industries for sentiment analysis, social media monitoring, and customer feedback evaluation. These tools facilitated insight into public opinions and sentiments expressed in comments on various platforms. Sentiment analysis is a natural language processing technique that determines the emotional tone of the text, helping to categorize it as positive, negative, or neutral, making it valuable for understanding public opinion and customer feedback. With the explosive increase of comments, it is difficult to analyze them manually. The issue arises because online content is huge and complicated, making it tough to grasp the subtle emotions and attitudes people have in their comments. This mystery surrounding digital conversations needs a solution: a Comment Analyzer with Sentiment Analysis. Text categorization in the form of sentiment analysis incorporates NLP, machine learning, data mining, information retrieval, Power BI for data analysis, and other study areas. These limitations highlight the need for more sophisticated sentiment analysis techniques, such as the Vader sentiment analysis provided in this study, to overcome existing system shortcomings and increase sentiment classification accuracy. The Comment Analyzer using Sentiment Analysis represents a pioneering approach to unraveling the cryptic sentiments embedded within the myriad comments and discussions that saturate the digital sphere. We are using data sets such as YouTube video comments and Ecommerce Product comments. © 2024 IEEE.

关键词： Sentiment analysis

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