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3rd International Conference on Advance Computing and Innovative Technologies in engineering, ICACITE 2023

作者： Shakeel, Mohammed Krishnammal, P. Muthu Vekariya, Vipul Akram, Shaik Vaseem Kumar, Parvathapuram Pavan Kapila, Dhiraj Invertis University Faculty of Computer Applications Bareilly India VIT-AP University School of Electronics Engineering Andhra Pradesh Amaravati India Parul University Department of PIET India Uttaranchal University Uttaranchal Institute of Technology India St. Martin's Engineering College Department of ECE Telangana Secunderabad India Lovely Professional University Department of Computer Science & Engineering Punjab Phagwara India

ISBN: (纸本)9798350399264

With more and more connected devices joining IoT networks, protecting their communications is becoming more difficult. The distributed ledger technology known as blockchain has the potential to solve the security issues plaguing 5G-enabled IoT networks. This article provides a layered blockchain security strategy to protect Internet of Things networks and make their deployment simpler. Using clustering, the complex multi-layer structure is simplified. The K-unknown clusters in the IoT network are generated by use of a hybrid Evolutionary Computation Methodology involving Simulated Annealing and Genetic Algorithms. The chosen cluster chiefs have authority over local authentication and permissions. By creating a private blockchain network on a local level, cluster administrators and critical nodes can streamline their interactions. This type of blockchain also facilitates authentication on a network, but it also adds an extra layer of security and integrity. The proposed framework makes use of Hyperledger Fabric, an open-source implementation of Distributed ledger technology. The encryption of data transmitted between nodes is ensured by the use of a global distributed ledger technology, which is instated in the base stations. As opposed to previously disclosed methods, simulation results show that the proposed clustering algorithm is effective. Finally, it is shown that the envisaged portable cryptocurrency architecture achieves a better latency/capacity tradeoff than a conventional global blockchain. © 2023 IEEE.

关键词： Blockchain

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UNet with Two-Fold Training for Effective Segmentation of Lung Section in Chest X-Ray 3

UNet with Two-Fold Training for Effective Segmentation of Lu...

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3rd International Conference on Intelligent Computing, Instrumentation and Control Technologies, ICICICT 2022

作者： Rajinikanth, Venkatesan Kadry, Seifedine Damasevicius, Robertas Gnanasoundharam, J. Abed Mohammed, Mazin Glan Devadhas, G. Noroff University College Faculty of Applied Computing and Technology Kristiansand94612 Norway Silesian University of Technology Faculty of Applied Mathematics Gliwice44-100 Poland St. Joseph's College of Engineering Omr Department of Electronics and Instrumentation Engineering Chennai600119 India University of Anbar College of Computer Science and Information Technology Ramadi31001 Iraq Vimal Jyothi Engineering College Department of Electronics and Instrumentation Engineering Chemperi India

ISBN: (数字)9781665410052

ISBN: (纸本)9781665410052

Segmentation and evaluation of the Region of Interest (ROI) in medical imaging is a prime task for disease screening and decision-making. Due to accuracy, Convolutional-Neural-Network (CNN) based ROI segmentation has been widely employed in recent years to evaluate a class of medical images recorded using chosen modality. The proposed work aims to demonstrate the segmentation performance of the UNet scheme with a one-fold and two-fold training process. To experimentally verify the merit of the proposed scheme, segmentation of the lung section from the chest X-ray is studied. This research includes the following parts;(i) Resizing the test image and image mask to pixels, (ii) Training the UNet with one-fold and two-fold approaches, (iii) Extracting the ROI, (iv) Comparing the ROI with the mask to compute the image metrics and (v) Validating and confirming the segmentation performance of UNet. The performance of UNet is then verified with UNet+ and UNet++. The investigational ending substantiates that the proposed approach helps to get better Jaccard (>95%), Dice ((>97%), and Accuracy (>98%) in two-fold training compared to other methods considered in this study. © 2022 IEEE.

关键词： Decision making

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A Resource-Efficient Multi-core Multi-thread RISC-V-based System-on-Chip

A Resource-Efficient Multi-core Multi-thread RISC-V-based Sy...

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International SoC Design Conference, ISOCC

作者： Binh Kieu-Do-Nguyen Khai-Duy Nguyen Nguyen The Binh Tuan-Kiet Dang Duc-Hung Le Cuong Pham-Quoc Ngoc-Thinh Tran Cong-Kha Pham Trong-Thuc Hoang Department of Computer and Network Engineering The University of Electro-Communications (UEC) Tokyo Japan Faculty of Computer Science and Engineering Ho Chi Minh City University of Technology (HCMUT) Hochiminh Vietnam Faculty of Electronics and Telecommunications University of Science (HCMUS) Hochiminh Vietnam

ISBN: (数字)9798350377088

ISBN: (纸本)9798350377095

In the past, a small size microcontroller was needed to control and gather data from resource-constrained electronic devices aimed at Internet of Things (IoT) applications. While the server nodes receive the requests with a high demand for calculation. However, to minimize network traffic transactions and the overall workload that the server node must handle, a portion of these operations must now be completed locally. Additionally, multiple tasks must be processed simultaneously by IoT devices. In this study, we suggested a multi-core, multi-thread enable System-on-Chip (SoC) that consume small resources. The suggested architecture achieves a notable speed increase over single-core design by utilizing the advantages of RISC-V Instruction Set Architecture (ISA).

关键词： Multicore processing Microcontrollers Instruction sets Memory management Telecommunication traffic System-on-chip Internet of Things Servers

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Automatic Design of a Cascoded-Inverter-Based Charge-Sensitive Amplifier Using gm/ID Technique and Particle Swarm Optimization in 28 nm CMOS

Automatic Design of a Cascoded-Inverter-Based Charge-Sensiti...

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International Conference on Mixed Design of Integrated Circuits and Systems (MIXDES)

作者： Piotr Kaczmarczyk Department of Measurement and Electronics Faculty of Electrical Engineering Automatics AGH University of Science and Technology Computer Science and Biomedical Engineering Krakow Poland

In this paper, an automated design procedure of a cascoded-inverter-based charge-sensitive amplifier (CSA) using the g m /I D method and particle swarm optimization (PSO) is presented. Assuming certain fixed values, such as transistors' length, supply voltage, feedback resistance, and input, output and feedback capacitances, the algorithm is able to find the proper transistors' width and cascode biasing voltages, to meet specified requirements, such as power consumption, gain, bandwidth, input/output DC level and input-referred noise. A CSA designed in 28 nm CMOS technology using the presented method achieves 500 V/V gain, 3.7 MHz bandwidth and 71e − ENC, consuming 2.2 μW of power from a 1.1 V supply.

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Pneumonia Detection in Chest X-ray using InceptionV3 and Multi-Class Classification 3

Pneumonia Detection in Chest X-ray using InceptionV3 and Mul...

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3rd International Conference on Intelligent Computing, Instrumentation and Control Technologies, ICICICT 2022

作者： Rajinikanth, Venkatesan Kadry, Seifedine Damasevicius, Robertas Pandeeswaran, C. Abed Mohammed, Mazin Glan Devadhas, G. Noroff University College Faculty of Applied Computing and Technology Kristiansand94612 Norway Silesian University of Technology Faculty of Applied Mathematics Gliwice44-100 Poland St. Joseph's College of Engineering Omr Department of Electronics and Instrumentation Engineering Chennai600119 India University of Anbar College of Computer Science and Information Technology Ramadi31001 Iraq Vimal Jyothi Engineering College Department of Electronics and Instrumentation Engineering Chemperi670632 India

ISBN: (数字)9781665410052

ISBN: (纸本)9781665410052

The lung is a imperative internal organs in human physiology. The abnormality in the lung will cause severe respiratory problems. Pneumonia is a severe lung infection, and early screening and treatment are essential to heal the illness. This research aims to implement a pre-trained InceptionV3 scheme to detect pneumonia in chest X-ray pictures. This scheme consists of the following phases;(i) Image collection and resizing, (ii) Deep-features extraction using InceptionV3, (iii) Feature reduction with firefly algorithm, (iv) Multi-class classification, and (v) Validation. A four-class classifier is employed in the proposed scheme to classify the X-ray into normal, mild, moderate, and severe classes using 5-fold cross-validation. The experimental outcome of the K-Nearest Neighbor (KNN) classifier confirms that this scheme offered a classification accuracy of 85.18% on the considered image database. © 2022 IEEE.

关键词： Classification (of information)

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Title-Based Fake News Detection Using LSTM

Title-Based Fake News Detection Using LSTM

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International Conference on computer and Information technology (ICCIT)

作者： Ratul Bhattacharjee Md Abu Sufian Md Abu Talha Md Shaikat Ahmed Faiza Tasnim Sadia Afrin Sara K.M. Nazmul Islam Department of Computer Science and Engineering (CSE) Faculty of Science & Technology (FST) American International University–Bangladesh (AIUB) Dhaka Bangladesh Department of Electrical and Electronics Engineering (EEE) Faculty of Engineering (FE) American International University–Bangladesh (AIUB) Dhaka Bangladesh Department of Computer Science and Engineering (CSE) Faculty of Engineering (FE) Ahsanullah University of Science and Technology (AUST) Dhaka Bangladesh Department of Computer Science and Engineering (CSE) Faculty of Science & Engineering (FSE) Northern University Bangladesh (NUB) Dhaka Bangladesh

ISBN: (数字)9798331519094

ISBN: (纸本)9798331519100

In today’s information-driven society, the rapid dissemination of fake news through digital platforms has emerged as a critical issue, undermining public trust and distorting societal discourse. As online misinformation continues to spread, there is a growing demand for advanced automated techniques to effectively detect and combat false information. This study introduces a Long Short-Term Memory (LSTM) based model tailored for fake news detection, concentrating on the analysis of news titles. The model exploits the inherent temporal dependencies and contextual clues within headlines to accurately differentiate between authentic and fabricated news. Tested on a comprehensive dataset, the model achieved an impressive accuracy of 93.5%, with precision and recall rates reaching 92.8% and 94.2%, respectively. These findings demonstrate the efficacy of leveraging news titles in the detection of fake news and underscore the potential of LSTM networks as a robust tool in the ongoing battle against misinformation.

关键词： Support vector machines Logistic regression Accuracy Text categorization Refining Focusing Natural language processing Fake news Long short term memory Context modeling

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Automated Door to Prevent COVID-19 using Fuzzy Logic 4

Automated Door to Prevent COVID-19 using Fuzzy Logic

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4th International Conference on Electrical, Communication and computer engineering, ICECCE 2023

作者： Khokhar, Salah-Ud-Din Sohaib, Rana Muneeb, Abdul Bibi, Irfana Noor, Muhammad Yasir Imran, Muhammad School of Intelligent Manufacturing and Control Engineering Qilu Institute of Technology Shandong Jinan China Department of Computer Science G.C. University Lahore Punjab Lahore Pakistan Department of Computer Science Faculty of Computing and Information Technology University of Punjab Punjab Lahore Pakistan Department of Electronics G.C. University Lahore Punjab Lahore Pakistan

ISBN: (纸本)9798350369694

Due to the vagueness and uncertainties due to the coronavirus, it is crucial to implement the standard operating procedures (SOPs) issued by the World Health Organization and the authorities. Therefore an effective control system is required that minimizes the vagueness and uncertainties of people who are on the front line during the period of this pandemic checking the entering persons in an indoor area where it is necessary to maintain a distance of six feet from one another to avoid the spread of coronavirus. In this research work, a robust and efficient system has been proposed that takes protective mask and feverishness values as input and applies them using the fuzzy logic algorithm. If these input values are according to the rules defined in the system, then the door will open as an output. Otherwise, the door will not open. It is, finally, analyzing the output values. This fuzzy model had been implemented using a microcontroller. The genuine difference in the duty cycle of the microcontroller’s output pulse was measured using a 100 MHz digital oscilloscope. Our proposed system was compared with different systems reported in the literature. This work reduces the human effort as well as the negligence of people and builds up the management system without the labor in a mint. © 2023 IEEE.

关键词： Fuzzy logic

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A Squeeze-Excitation ResNet Approach for Effective Classification of Parasitic Eggs

A Squeeze-Excitation ResNet Approach for Effective Classific...

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2024 International Visualization, Informatics and technology Conference, IVIT 2024

作者： Muthulakshm, M. Venkatesan, K. Rahayu, Syarifah Bahiyah Elangovan, Karthickeien Amrita School of Engineering Amrita Vishwa Vidyapeetham Dept. of Electronics and Communication Engineering Chennai India Amrita School of Computing Amrita Vishwa Vidyapeetham Department of Computer Science and Engineering Chennai India National Defence University Malaysia Faculty of Defense Science and Technology Kuala Lumpur Malaysia National Defence University Malaysia Cyber Security and Digital Industrial Revolution Centre Kuala Lumpur Malaysia

ISBN: (纸本)9798350354447

The identification and classification of different kinds of parasite eggs in microscopic samples represent a critical challenge in the field of Soil-transmitted helminth infection diagnosis. Traditional methods are often labor-intensive and time-consuming. The emergence of deep learning models has shown promising results in automating this process by extracting intricate features from complex images. This study aims to develop an automated system for accurately classifying parasite egg types in microscopic images by leveraging the ability of squeeze excitation layers to learn the global information from the input. The proposed system employs features extracted by ResNet50 and ResNet101 with Squeeze Excitation (SE) layers for analysis. The extracted features are then input into a Support Vector Classifier. The study systematically evaluates the features extracted from ResNet50+SE and ResNet101+SE. Results from the evaluation demonstrate the efficacy of the ResNet50+SE in accurately classifying parasite egg types in microscopic images with an accuracy of 0.94. The study provides valuable insights into the choice of squeeze-excitation block added Resnet in the context of parasitology, contributing to the advancement of automated medical image analysis. The findings hold great potential for improving diagnostic processes and supporting epidemiological studies through efficient and accurate parasite detection. © 2024 IEEE.

关键词： Deep learning

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Leveraging Artificial Intelligence in Online Educational Platforms to Enhance Continuous Professional Development in the Teaching Profession 2

Leveraging Artificial Intelligence in Online Educational Pla...

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2nd International Conference Computational and Characterization Techniques in engineering and Sciences, IC3TES 2024

作者： Amankulovich, Khujakulov Sherzod Tokhirovich, Mannabov Javlon Bakhridinovich, Kadirov Tulkin Kuvandikovich, Taylakov Umid Ananth, Christo Kumar, T. Ananth Samarkand Regional National Center for Training Teachers in New Methods Uzbekistan Uzbekistan Samarkand Agroinnovations and Research University Uzbekistan Samarkand State University Uzbekistan&Adjunct Faculty Department of Electronics and Communication Engineering Vel Tech Rangarajan Dr.Sagunthala R&d Institute of Science and Technology Avadi Chennai India Ifet College of Engineering Computer Science and Engineering Tamilnadu India

ISBN: (纸本)9798350364699

To keep up with the dynamic nature of the modern classroom, teachers must have access to online learning platforms for professional development purposes. This investigation looks at how these platforms can be used to improve teachers' skills, make information sharing easier, and open doors to CPD opportunities. This study examines the relationship between online resources and methods to demonstrate how digital tools have an impact on teacher training and professional development by leveraging AI. It also draws attention to the fact that these platforms are helping with information-methodological support for teachers as well as the ongoing reforms in our nation. Incorporating empirical observations and case studies, the analysis demonstrates how effective online platforms are in fostering a collaborative and innovative environment for professional learning. Digital transformation has the potential to radically alter how teachers obtain professional development, and this study emphasizes that fact. This will enable teachers to effectively address the demands of modern classrooms. © 2024 IEEE.

关键词： Contrastive Learning

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Improved Power Transfer Efficiency Using a Dual Transmitter Receiver Design With Integrated Decoupling Coils for IPT EV Charging

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IEEE Access 2025年 13卷 91362-91373页

作者： Bilal Alam Saad Mekhilef Marizan Mubin Kok Soon Tey Addy Wahyudie Department of Electrical Engineering Power Electronics and Renewable Energy Research Laboratory University of Malaya Kuala Lumpur Malaysia School of Engineering Swinburne University of Technology Melbourne VIC Australia Department of Computer System and Technology Faculty of Computer Science and Information Technology Universiti Malaya Kuala Lumpur Malaysia Department of Electrical and Communication Engineering United Arab Emirates University (UAE-U) Al Ain United Arab Emirates

In high-power inductive power transfer (IPT) systems, multiple coils are used to enhance efficiency, but interactions between coils on the same side can reduce overall performance. Traditionally, decoupling coils are added to minimize these interactions, however they don’t contribute to power transfer and may even hinder system efficiency. This article introduces a new IPT system and coil structure that integrates decoupling coils, which not only improve mutual interaction between same-side coils but also actively contribute to power transfer. The coils are optimized for high mutual inductance, strong coupling coefficient, reduced self-inductance, and minimized side-by-side mutual inductance, performing well even with X-axis, Y-axis, and angular misalignments. Using 3-D Finite Element Analysis (ANSYS-Maxwell), the magnetic coupler’s performance is thoroughly evaluated, and a downsized experimental setup demonstrates the system’s feasibility. The proposed system achieves a maximum dc-dc efficiency of 94.27%, successfully delivering 1 kW across a 120-mm air gap with zero-voltage switching, presenting a significant advancement for multi-transmitter and multi-receiver systems.

关键词： Coils Receivers Transmitters Mathematical models Couplings Inductance Costs Voltage Rectifiers Integrated circuit modeling

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