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2024 IEEE International Conference on Signal Processing and Advance Research in Computing, SPARC 2024

作者： Nair, Rekha R Babu, Tina Ramasamy, Gayatri Singh, Tripty Yuan, Xiaohui Alliance University Alliance School of Advanced Computing Department of Computer Science and Engineering Bengaluru India Amrita School of Computing Bengaluru Amrita Vishwavidyapeetham Department of Computer Science and Engineering India University of North Texas Department of Computer Science and Engineering DentonTX United States

ISBN: (纸本)9798350385199

Tumor segmentation in MRI images is a crucial process in medical imaging, aimed at accurately identifying and delineating tumor regions within the brain or other tissues. Hence proposed a modified U-Net++ based segmentation with predicted mask. To further improve the performance of the proposed method, incorporated additional layers into the U Net++ architecture. U-Net++ consists of Encoder Decoder and the encoder captures image content, while the decoder delineates object boundaries. U-Net++ excels in capturing both high-level and intricate details, which significantly boosts segmentation performance. This helps in accurate tumor segmentation of complex MRI images. To measure the effectiveness of the proposed model for segmentation and detection, Accuracy, Intersection over Union(IOU), Dice Coefficient and Area under the ROC Curve(AUC) scores are used. The proposed U-Net++ brought accuracy to 99 percent, which could significantly help in the process of accurate segmentation of human tissues. Accuracy of 39 % for IOU of 80, Dice Coefficient of 89%, and 33% for the test set. This is equivalent to 8% sensitivity and accuracy and AUC of 78. The proposed method can be used as an additional diagnosis aid to help doctors determine the suitability of the treatment and the changes occurring in the body during the development of the disease © 2024 IEEE.

关键词： Magnetic resonance imaging

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A Review of Spectrum Sharing in 6G for IoT Environment 2

A Review of Spectrum Sharing in 6G for IoT Environment

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2nd International Conference on Artificial Intelligence, Blockchain, and Internet of Things, AIBThings 2024

作者： Khan, Andleeb Nand, Parma Bhushan, Bharat Alazzawi, Esraa Mohammed Jamil, Akhtar Sharda School of Engineering & Technology Department of Computer Science & Engineering UP Greater Noida India University of Baghdad Al-Jadriya Department of Computer Engineering Baghdad10071 Iraq National University of Computer and Emerging Sciences Department of Computer Science Islamabad Pakistan

ISBN: (纸本)9798331529529

With increase in demand the resources have proven to be limited. Spectrum sharing is a way to use the limited resources to their full potential. These can exceptionally improve the way IoT devices work. The ever-increasing demand in communication can be revolutionized by 6G network as it proves to be superior in terms of latency, reliability, data rates, connectivity and coverage. Hence the concept of spectrum sharing in 6G holds a great prospect for the future. This paper reviews the recent advances in the field of spectrum sharing for 6G in the IoT environment. It lays down the important techniques and the security and privacy concerns in IoT environments. It further lays down the important future prospects in terms of mm Wave, THz and optical spectrum and their most important use cases. © 2024 IEEE.

关键词： Terahertz waves

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Exploring the Usability of Adaptive Weighting Factors in Transformer Health Diagnostic Practices 14

Exploring the Usability of Adaptive Weighting Factors in Tra...

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14th International Conference on the Properties and Applications of Dielectric Materials, ICPADM 2024

作者： Prasojo, Rahman Azis Maharani, Devira Anggi Safarina, Liska Suwarno Putra, Muhammad Akmal A. Ekojono Politeknik Negeri Malang Department of Electrical Engineering Malang Indonesia School of Electrical Engineering and Informatics Institut Teknologi Bandung Bandung Indonesia

ISBN: (纸本)9798350374605

This paper examines the potential of using adaptive weighting factors in the Health Index (HI) method for transformer health diagnostics. Typically, HI methods rely on weighting factors that are set by experts. While effective, this approach often static and may not always incorporate the latest insights or data. This study aims to enhance the HI method's accuracy and reliability by integrating adaptive, data-driven weighting factors. To achieve this, the usability of deep neural network algorithms to derive these weighting factors from actual transformer oil data is investigated. The dataset includes 500 high-voltage transformer oil samples, each with five oil testing features and known oil quality conditions. This data is used to develop and evaluate the approach. The dataset is analyzed at different scales (5, 25, 50, 100, 250, and 500 randomized samples) to ensure a comprehensive and repeatable evaluation. The main procedure involves feeding the dataset into a neural network, which then extracts the optimal weighting factors. These factors are subsequently applied in the conventional HI formula to assess their accuracy in classifying transformer oil quality. The study focuses on three key areas: firstly, the influence of the dataset size on the precision of the adaptive weighting factors, secondly, the effect of varying the neural network's structure, and third, the choice of optimizers. The aim of this study is to shift from a predominantly expertbased approach to a more data-driven methodology for reliable transformer health diagnostics framework. © 2024 IEEE.

关键词： Adaptive Weighting Factors Health Index Methodology Neural Network Algorithms Transformer Health Diagnostics Transformer Oil Data Analysis

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Performance Enhancement of High-gain STDA Antennas with Reflector for 4G LTE and Sub-6 GHz 5G Applications: Design, Measurement, and Analysis

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Progress In Electromagnetics Research C 2024年 139卷 87-94页

作者： Wasim, Mohd Khera, Shelej Islam, Tanvir Malik, Praveen Kumar Asha, Sivaji Das, Sudipta School of Electronics and Electrical Engineering Lovely Professional University Phagwara144411 India Department of Electrical and Computer Engineering University of Houston HoustonTX77204 United States Saveetha Engineering College Saveetha Nagar Thandalam Chennai602105 India Department of Electronics and Communication Engineering IMPS College of Engineering and Technology West Bengal Malda732103 India

The paper focuses on the design, measurement, and performance analysis of a high-gain cross-orthogonal series fed two dipole antenna (STDA) arrays with side-wall reflectors. The antenna is specifically designed for 4G Long Term Evolution (LTE) and sub-6 GHz 5G band applications. The designed antenna is capable of operating at multiple frequencies aiming to support 4G LTE and the sub-6 GHz 5G application bands. To improve the radiation characteristics and prevent coupling effects in the presence of side-wall reflectors, parasitic strip pair directors are included in the antenna design. Furthermore, the performance of the designed STDA is evaluated by forming different array configurations, such as 2 × 1, 2 × 2, and 2 × 3 arrays. The various array configurations are proposed to investigate the effect of the projected array arrangements on the radiation pattern, impedance bandwidth, and gain characteristics. The results of the measurements show that the radiation characteristics of the antenna have been improved significantly. The proposed antenna operates at six distinct frequencies for S11 ≤ 10 dB. The operating frequencies at 1.8, 2.35, and 2.6 GHz can be utilized for LTE and 3.2, 4.2, and 5.2 GHz which can support sub-6 GHz 5G bands. The antenna is characterized by its compact size, measuring around 89 mm × 71 mm, while still achieving high gain of 12.3 dB for single STDA element with parasites and with reflector. These results emphasize the importance of the proposed design, which incorporates parasitic strip pair directors and side-wall reflectors. This design methodology plays a crucial role in enhancing the performance of the prescribed STDA array for both 4G LTE and sub-6 GHz 5G applications. © 2024, Electromagnetics Academy. All rights reserved.

关键词： Dipole antennas

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An AutoML Approach Integrated with Live Weather Data in Rain Forecasting System (RFS) 1

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8th International Conference on Inventive Communication and Computational Technologies, ICICCT 2024

作者： Mazahir Mehdi Zaidi, Syed Rizvi, Sheenu Arora, Deepak Tiwari, Shivam Department of Computer Science and Engineering Amity School of Engineering & Technology Lucknow Amity University Uttar Pradesh Lucknow India Department of Computer Science and Engineering Sharda University Greater Noida India

ISBN: (数字)9789819777105

ISBN: (纸本)9789819777099

The increasing climate unpredictability highlights the critical need for accurate rain prediction methods. Traditional approaches often struggle to adapt and accurately forecast rain across varied climatic conditions. This research work investigates the fusion of automated machine learning (AutoML) with real-time weather data to tackle these challenges. Previous methodologies have relied on manual algorithm selection and optimization, leading to inefficiencies and suboptimal outcomes. In contrast, the proposed AutoML-based approach automates algorithm selection and optimization, resulting in heightened accuracy and efficiency. Integration of diverse meteorological data streams and advanced preprocessing techniques enhances data quality for model training. Evaluation against traditional methods showcases significant enhancements in rain prediction accuracy, with scores ranging from 80.60 to 86.09% across distinct algorithms. Specifically, the Random Forest Classifier achieved an accuracy score of 85.43% and an AUC score of 0.88, Gaussian NB achieved 80.60% accuracy and 0.82 AUC, K Neighbors Classifier achieved 82.89% accuracy and 0.82 AUC, and XGBoost achieved 86.09% accuracy and 0.89 AUC. The developed approach not only advances rain forecasting but also offers potential applications in agricultural planning, urban water management, and disaster preparedness. Future research avenues may explore further enhancements to the AutoML framework and its extension to other environmental prediction domains. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Rain

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Secure mmWave-NOMA Multi-BS Vehicular Communications Using Cooperative Jamming

Secure mmWave-NOMA Multi-BS Vehicular Communications Using C...

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2024 IEEE Global Communications Conference, GLOBECOM 2024

作者： Yan, Yiting Ju, Ying Hu, Jiawei Liu, Lei Pei, Qingqi Yau, Kok-Lim Alvin Wu, Celimuge Zhang, Ning Xidian University School of Telecommunications Engineering Xi'an710071 China Universiti Tunku Abdul Rahman School of Internet Engineering and Computer Science Selangor 43200 Malaysia The University of Electro-Communications Graduate School of Informatics and Engineering Tokyo182-8585 Japan University of Windsor Department of Electrical and Computer Engineering WindsorN9B-3P4 Canada

ISBN: (纸本)9798350351255

The fronthaul network architecture is the key to dealing with the massive traffic effectively and providing high-quality service, and the multiple base stations (BSs) deployed by it face the gigantic data transmission, which has given the demand for high-capacity communication and information security in the vehicular network. In this paper, we combine the millimeter wave (mmWave) communication and non-orthogonal multiple access (NOMA) technologies to escalate the communication capacity of multiple vehicle users (VUs), and propose a blockage-based cooperative jamming strategy to solve potential security risks in the vehicular network. In particular, with the help of jam-mers selected by this strategy, transmission security is enhanced simultaneously without escalating the instability of connections caused by the time-varying nature of vehicular networks under the NOMA transmission mechanism when the base station (BS) does not fully understand the channel state information (CSI) of VUs. Then we comprehensively analyze the specific distribution of roadways and the distance distribution of VUs under the NOMA strategy, and derive the performance metrics of the network based on the stochastic geometry method. Numerical results show that the proposed cooperative jamming scheme can effectively improve the secrecy performance of the vehicular network. © 2024 IEEE.

关键词： Jamming

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A Generalized Framework for Brain Tumor and Pneumonia Detection Using Streamlite Application 4

A Generalized Framework for Brain Tumor and Pneumonia Detect...

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4th International Conference for Emerging Technology, INCET 2023

作者： Kishore, S. Nair, Rekha R Mehra, Vishal Babu, Tina Dayananda Sagar University Department of Computer Applications School of Engineering Bengaluru India Dayananda Sagar University Department of Computer Science and Engineering School of Engineering Bengaluru India

ISBN: (纸本)9798350335750

One of the dangerous disease pneumonia, which can affect one or both lungs, is frequently brought on by viruses, fungi or bacteria. Radiotherapists with advanced training are required to evaluate chest X-rays used to diagnose pneumonia. Brain tumours are another fatal disease that can have a profound impact on a patient's quality of life and alter everything for them and their loved ones. Therefore, creating an automatic system for diagnosing pneumonia would help treat the disease quickly, especially in remote areas. The work proposes a general framework for MRI and CT images that can automatically detect diseases such as brain tumours and pneumonia with single framework using VGG16. The proposed work focusing on developing streamlit web application for deployment. The proposed work gained an accuracy of 97.87% compared to Inception, Xception and ResNet for brain tumor detection and 94.3% for Pnemonia detection model. The training and testing data are compared using a CNN-based classifier to determine the optimal outcome. © 2023 IEEE.

关键词： Tumors

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Human Activity Recognition Using Deep Learning Techniques for Healthcare Applications 15

Human Activity Recognition Using Deep Learning Techniques fo...

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

作者： Shekhar, Raj Tomar, Deepak Singh Sharan, Bhagwati Pateriya, R.K. Maulana Azad National Institute of Technology Department of Computer Science and Engineering Bhopal India Srm University-AP School of Engineering and Sciences Department of Computer Science and Engineering Andhra Pradesh India

ISBN: (纸本)9798350370249

The idea of smart healthcare is gradually gaining traction with the rapid advancement in information technologies. Smart healthcare is the intelligent transformation of the current medical system to make it more reliable, efficient, and individualized through the intelligent use of next-generation technologies like artificial intelligence, and the Internet of Things (IoT). This study offered the recent advancement of Deep learning (DL) techniques for healthcare systems, and the use of DL techniques to identify human physical activity with wearable sensors. In this paper, convolutional neural networks (CNN), CNN-LSTM, Multi Headed CNN-LSTM, and Multi Headed CNNBiLSTM are used. The obtained outcomes of these Deep Learning models are compared in terms of accuracy, precision, recall and F1 score. The Multi-Headed CNN-BiLSTM model exhibits better performance than the rest of the models when applied to the UCI HAR dataset. © 2024 IEEE.

关键词： Convolutional neural networks

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Enhanced Fraud Detection in Ethereum Transactions: Fusion of Modified Genetic Algorithms and Deep Learning with Limited Attributes

Enhanced Fraud Detection in Ethereum Transactions: Fusion of...

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2024 IEEE International Conference on Signal Processing and Advance Research in Computing, SPARC 2024

作者： Srivastava, Atul Kumar, Alok Pillai, Anuradha Sharma, Vijay Shankar AUUP Amity School of Engineering and Technology Lucknow India Symbiosis Institute of Technology Department of Computer Science and Engineering Pune India Manipal University Department of Computer and Communication Engineering Jaipur India

ISBN: (纸本)9798350385199

Ethereum smart contracts, the new way of transactions and a popular name in the world of cryptocurrencies, have gathered a huge base of research and scientific attention. They are so helpful that they allow us to eliminate the need for a separate third-party library to allow unknown parties to see contract details on a computer. But since we can all see that online commerce is growing day by day and will continue to grow, it can never be fully free from scams and unethical operations. So, to correctly detect all such unethical and malicious transactions, this paper used a deep learning model. And to further enhance the model, this study used metaheuristic optimization as well. It employs an algorithm called Genetic Algorithm and to provide better optimization in the explorations phase of Cuckoo Search (CS) to achieve its goal of detecting fraudulent transactions. The algorithm covers the loopholes in the CS strategy. Furthermore, to provide strong grounds for research in our paper, this proposed model was compared with various types of approaches such as Light Gradient Boosting Machine, Support Vector Classification, Multi-Layer Perceptron, XGBoost, Logistic Regression, and Random Forest. The proposed model outperforms other significant models like SVC, KNN, LGBM Classifier, RF etc. with the accuracy of 98.6%. © 2024 IEEE.

关键词： Genetic algorithms

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Tailoring Fano resonance characteristics for GSS4T1-based reconfigurable photonics

Tailoring Fano resonance characteristics for GSS4T1-based re...

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2024 Frontiers in Optics, FiO 2024

作者： Foteinopoulou, Stavroula Tsitsas, Nikolaos L. Department of Electrical and Computer Engineering University of New Mexico NM87131-0001 United States School of Informatics Aristotle University of Thessaloniki Thessaloniki54124 Greece

We present a GSS4T1-based metagrating designed to exhibit a homogeneous-optical-medium response, when GSS4T1 amorphous, or a judicious Fano response, when GSS4T1 crystalline, which enables a negative beam steering tha... 详细信息

关键词： Photonics

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