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4th International Conference on Smart Electronics and communication, ICOSEC 2023

作者： Madasamy, Sathiyakeerthi Nelson Kennedy Babu, C. Sumathi, V. Chokkalingam, Arun Pilvi Systems Inc Solution Architect LewisvilleTX75057 United States Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Tamil Nadu Chennai India Sri Sai Ram Engineering College Department of Mathematics Tamil Nadu Chennai44 India Rmk College of Engineering and Technology Department of Electronics and Communication Engineering Tamilnadu Chennai India

ISBN: (纸本)9798350300888

Image registration is a significant assignment in computer visualization and medical imaging, which involves aligning two or more descriptions of the same division or object taken from different viewpoints or at dissimilar epochs. Deep learning-based approaches have recently demonstrated promising outcomes in several image-processing applications, including image registration. Using multi-scale feature extraction methods and deep learning, the study provides a reliable image registration method in this study. Three primary steps make up the suggested method: feature extraction, feature matching, and transformation estimation. A deep convolutional neural network (CNN) is utilized in the feature extraction process to extract multi-scale information from the input images. The CNN architecture is built to manage large-scale deformations by extracting features at various scales. Following feature extraction, a strong matching technique based on the nearest neighbor distance ratio test is utilized to match the extracted features. The proposed method uses the Random Sample Consensus (RANSAC) algorithm to estimate the transformation parameters robustly, which helps to handle outliers and noise in the feature-matching process. The proposed method is assessed on several benchmark datasets, including the ETH-80 dataset and the TUM RGB-D dataset, and compared with state-of-the-art approaches. The investigational consequences demonstrate that the proposed method outstrips prevailing approaches in terms of accuracy and robustness, especially for images with large-scale deformations. The proposed technique also demonstrates good performance in the presence of noise and outliers, which further demonstrates its robustness. © 2023 IEEE.

关键词： Feature extraction

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A New Intelligent Scheduler to Improve Reactive OpenFlow communication in SDN-Based IoT Data Streams

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SSRN 2023年

作者： Batista, Ernando Alencar, Brenno M. Silva, Eliabe Canário, João Paulo Rios, Ricardo A. Dustdar, Schahram Figueiredo, Gustavo Bittencourt Prazeres, Cássio Federal Institute of Bahia Brazil Federal University of Bahia Brazil TU Wien University Distributed Systems Group Vienna Austria Stone Co. Bahia Salvador Brazil Department of Computer Science Brazil

The significant adoption of the Internet of Things (IoT) has increased the challenges in providing adequate IoT infrastructures meeting essential requirements, such as dynamicity networks and low latency. In this context, the Software-Defined Networking (SDN) paradigm and the OpenFlow protocol provide new possibilities for IoT networks. Based on the global view of network elements enabled by the Controller, SDN allows the programmability and control of the infrastructure according to the actual demands of applications. The OpenFlow protocol defines the exchange of messages between controllers and switches, enabling communication and network control. OpenFlow implements three operation modes: proactive, reactive, and hybrid. Due to the dynamic characteristic of the IoT data stream, the reactive mode is mainly used and indicated for IoT environments. As the OpenFlow controller installs rules dynamically, there is no need to know the network's sources, destinations, and paths in advance. Although reactive mode introduces dynamicity, it can generate additional delay due to switch-controller communication. This delay increases the response time of the IoT data stream. We propose an SDN-IoT scheduler based on Deep Neural Networks (DNN) to predict the time between data stream changes from IoT devices and install rules in advance, suppressing the existing delay in reactive mode. The proposal automatically uses previous data from the IoT data stream to calculate the time of the following communication from IoT devices. Our results indicate that predicting IoT data stream changes and installing OpenFlow rules in advance reduced about 51% of communications response time. © 2023, The Authors. All rights reserved.

关键词： Deep neural networks

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IoT-based Heartbeats Monitoring System

IoT-based Heartbeats Monitoring System

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International Conference on Computing, Engineering, and Design (ICCED)

作者： Mohamad Asyraf Noorza Bin Mohd Tamron Zainab S. Attarbashi Mustafa Ali Abuzaraida Nesrine Atitallah Saman Iftikhar Dini Oktarina Dwi Handayani Atikah Balqis Binti Basri Kulliyyah of Information & Communication Technology International Islamic University Malaysia Selangor Malaysia Department of Computer Science Faculty of Information & Communication Technology International Islamic University Malaysia Selangor Malaysia Computer Science Department Faculty of Information Technology Misurata University Libya Faculty of Computer Studies Arab Open University Saudi Arabia Department of Information Systems Faculty of Information and Communication Technology International Islamic University Malaysia Selangor Malaysia

The advent of the Internet of Things (loT) has opened up a realm of possibilities in developing globally accessible systems and devices. With the current state of internet speeds, these systems offer real-time control, making them more powerful than ever. Simultaneously, the healthcare sector has been witnessing notable advancements, but it also grapples with an increasing number of health issues, with heart problems standing out as a prominent concern in many countries. These problems manifest in various symptoms, some of which can lead to more serious conditions, such as irregular heartbeats. In response to these challenges and leveraging cutting-edge technology, researchers have embarked on endeavors to enhance healthcare devices and render them more readily available to medical professionals. This paper explores the merging of loT technology with healthcare, specifically focusing on the development of a system designed to monitor and record a patient’s heartbeat. This is achieved through the deployment of a Heartbeat/SpO2 Sensor, and the data is seamlessly transmitted to a mobile application accessible remotely. The outcomes of this study demonstrate that the heartbeat data captured by Arduino is effectively transmitted to the cloud, meeting the system’s predefined objectives. This represents a significant stride in harnessing loT’s potential to enhance healthcare practices and patient monitoring.

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UAV -Based Object Tracking Using Siamese Networks and Kalman Filtering

UAV -Based Object Tracking Using Siamese Networks and Kalman...

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Multi-Agent systems for Collaborative Intelligence (ICMSCI), International Conference on

作者： S.V Ramanan M. Rajasekar G. Shanmugapriya Sujitha R Akshya. J Mani Deepak Choudhry Department of Electronics and Communication Engineering P PG Institute of Technology Coimbatore Department of Computer Science and Engineering VSB college of Engineering Technical Campus Coimbatore Department of Computer Science and Business Systems Dr.N G.P Institute of Technology Coimbatore Department of Information Technology Christ The King Engineering College Coimbatore Department of Computational Intelligence SRM Institute of Science and Technology Chennai Department of Computing Technologies SRM Institute of Science and Technology Chennai

ISBN: (数字)9798331509828

ISBN: (纸本)9798331509835

The ability to match features and keep track of objects in changing dynamic environments is still an important problem, particularly due to varying noise levels, diverse datasets, and high dimensional feature spaces. Euclidean Distance and Cosine Similarity based matching are existing methods that commonly do not generalize well, particularly in the case of cross domain scenarios, where noise and motion dynamics are powerful factors to limit the accuracy of tracking. However, Deep Metric Learning approaches can extract robust features at the cost of computational efficiency and are unsuitable for real-time applications. This research proposes a Siamese Network framework to solve these problems by using robust feature matching and multi-object tracking. Based on that, the proposed framework uses a dual-branch network architecture in which feature embeddings extracted from paired inputs are mapped through the same shared latent space. Furthermore, the adaptive learning rate leads to efficient convergence during the training. Experimental results show that the Siamese Network not only achieves better accuracy than the existing methods and the more such as Deep Metric Learning but also performs significantly more efficient computation. With an accuracy of 90.2 % at a precision of 88.5%, the framework's inference time is much faster than that of Deep Metric Learning at 2.0ms. Furthermore, the proposed method performs well on the generalization over datasets with a 15% improvement of cross-domain matching accuracy on Euclidean Distance. By striking this balance between accuracy, efficiency and adaptability, it shows its suitability for dynamic and noisy environments for real-time tracking. The proposed method fills existing gaps and achieves a new state of the art for feature matching and tracking performance.

关键词： Accuracy Dynamics Euclidean distance Feature extraction Real-time systems Robustness Object tracking Noise measurement Vehicle dynamics Noise level

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Auto Digit Selection for Most Significant Digit First Multiplication

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IEEE Embedded systems Letters 2025年

作者： Gorgin, Saeid Jaberipur, Ghassem Lee, Jeong-A Shin, Seokjoo Kim, Jungrae Computer Engineering Iranian Research Organization for Science and Technology Tehran Iran Semiconductor Systems Engineering Department College of Information and Communication Engineering SungKyunKwan University Korea Republic of Brain Pool Program for Chosun University Gwangju Korea Republic of Shahid Beheshti University Tehran Iran Chosun University Department of Computer Engineering Gwangju Korea Republic of

Performance in modern embedded systems, particularly those executing computation-intensive signal/image processing and machine learning algorithms, is critically dependent on the efficiency of multiplication operations. Serial multiplication offers key advantages in these resource-constrained environments, including low memory usage and minimal interconnect complexity. The most significant digit first (MSDF) serial multiplication scheme further enhances efficiency by enabling early termination and supporting variable precision. It is ideal for embedded applications where power and performance are critical. This paper proposes an auto-selection scheme for MSDF multiplication, where product digits are generated as a byproduct of residual maintenance, leading to speed, cost, and power efficiency improvements. We demonstrate the implementation of serial-serial and serial-parallel multipliers, where the multiplier operand in the latter is fully available throughout the computation. Simulation and synthesis results, compared to the most relevant previous works, show reductions in delay (16% and 20%), area consumption (11% and 23%), and power dissipation (7% and 20%), making our approach well-suited for embedded systems requiring efficient, low-power multiplication hardware. © 2009-2012 IEEE.

关键词： Left-to-Right Arithmetic Machine Learning Algorithms MSDF Multiplication Online Arithmetic

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Designing a Secure and Scalable Data Sharing Mechanism Using Decentralized Identifiers(DID)

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computer Modeling in Engineering & sciences 2024年第10期141卷 809-822页

作者： Iuon-Chang Lin I-Ling Yeh Ching-Chun Chang Jui-Chuan Liu Chin-Chen Chang Department of Management Information Systems National Chung Hsing UniversityTaichung402TaiwanChina Information and Communication Security Research Center Feng Chia UniversityTaichung407TaiwanChina Department of Information Engineering and Computer Science Feng Chia UniversityTaichung407TaiwanChina

Centralized storage and identity identification methods pose many risks,including hacker attacks,data misuse,and single points of ***,existing centralized identity management methods face interoperability issues and rely on a single identity provider,leaving users without control over their ***,this paper proposes a mechanism for identity identification and data sharing based on decentralized *** scheme utilizes blockchain technology to store the identifiers and data hashed on the chain to ensure permanent identity recognition and data *** is stored on InterPlanetary File System(IPFS)to avoid the risk of single points of failure and to enhance data persistence and *** the same time,compliance with World Wide Web Consortium(W3C)standards for decentralized identifiers and verifiable credentials increases the mechanism’s scalability and interoperability.

关键词： Self-sovereign identity decentralized identifier blockchain

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Descriptive Exam Evaluation through Semantic Mining and Analysis

Descriptive Exam Evaluation through Semantic Mining and Anal...

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Human-Centered Cognitive systems (HCCS)

作者： Raybal Akhtar Muhammad Safyan Muddesar Iqbal Sohail Sarwar Department of Informatics and Systems University of Management and Technology Lahore Pakistan Department of Computer Science Government College University Lahore Lahore Pakistan Department of Communication & Network Engineering Prince Sultan University Riyadh Saudi Arabia Department of Computer Science Bahria University Islamabad Islamabad Pakistan

Online examination evaluation methods are used as an alternative to the traditional examination’s way of evaluation methods. They offer a number of advantages while addressing issues associated with outdated conventional approaches. Online evaluation methods are mainly used for objective and short answers based exams but may not suitable for assessing the depth knowledge in candidates. So, candidate’s descriptive examinations are conducted to assess their depth of knowledge. Descriptive evaluation methods have been put into practice for quite some time but they greatly rely on keyword-matching techniques between the questions and answers. Due to this limitation, the semantic meanings of these answers are ignored. In current research, the deficiencies in prevalent systems have been addressed by calculating the semantic and syntactic similarity between answers. Moreover, the repetitive words and similarity between counteractive answers has also been considered. We have used SQuAD 1 dataset in current research. Our model has achieved a value of 0.88 in terms of accuracy for while computing similarity among listed items.

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Highly Accurate Crow Search Optimized CNN Classifier for Cyclone Prediction

Highly Accurate Crow Search Optimized CNN Classifier for Cyc...

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Data science, Agents & Artificial Intelligence (ICDSAAI), International Conference on

作者： R. Ramya Kavin K. S R. Anuja A. T. R. Krishna Priya D. Karthikeyan Kiruba Thangam Raja Department of Electronics and Communication Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences (SIMATS) Tamilnadu India Department of Electrical and Electronics Engineering Government College of Engineering Tirunelveli Tamilnadu India Department of Computer Science and Engineering Rohini College of Engineering and Technology Kanyakumari Tamilnadu India Department of Electrical and Electronics Engineering SRM Institute of Science and Technology Chennai India Department of Software and Systems Engineering School of Computer Science Engineering and Information Systems (SCORE) VIT University Vellore

ISBN: (数字)9798331537555

ISBN: (纸本)9798331537562

Accurate and timely prediction of cyclone events is essential for effective disaster management and risk mitigation. This study introduces a highly precise cyclone prediction model that combines Convolutional Neural Networks (CNNs) with the optimization capabilities of the Crow Search Algorithm (CSA). The resulting model, called Crow Search Optimized CNN (CSO-CNN), harnesses the feature extraction and classification strengths of CNNs, while the CSA is used to optimize the hyperparameters of the CNN architecture for optimal performance. The model is trained and validated on a comprehensive dataset that includes satellite imagery and meteorological data from a wide range of cyclone events. The CSO-CNN achieves remarkable classification accuracy, surpassing traditional CNN models and other leading techniques. Its ability to quickly and accurately assess the likelihood of cyclone formation provides crucial decision support for disaster management authorities, enabling timely and effective response strategies. By integrating the CSA optimization technique with the CNN framework, this study presents a novel approach to cyclone prediction. It highlights the potential of combining advanced Machine Learning (ML) algorithms to tackle complex environmental challenges and deepen our understanding of intricate atmospheric phenomena.

关键词： Accuracy Atmospheric modeling Disaster management Predictive models Prediction algorithms Feature extraction Cyclones Satellite images Convolutional neural networks Optimization

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Revolutionizing Connectivity and Data Processing in IoT Ecosystems through Edge Computing

Revolutionizing Connectivity and Data Processing in IoT Ecos...

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2024 IEEE International Conference on Recent Advances in science and Engineering Technology, ICRASET 2024

作者： Kumari, V. Sheeja Pillai, N. Muthuvairavan Jayarin, P.Jesu David, D. Beulah Jenin, J.S. Saveetha Institute of Medical and Technical Sciences Saveetha University Saveetha School of Engineering Department of Computational Intelligence Thandalam Tamil Nadu Chennai India R.M.D Engineering College Department of Computer Science and Business Systems Tamil Nadu Thiruvallur India Saveetha Institute of Medical and Technical Sciences Saveetha University Saveetha School of Engineering Department of Information Technology Thandalam Tamil Nadu Chennai India Care College of Engineering Department of Electronics and Communication Engineering Tamil Nadu Trichy India

ISBN: (纸本)9798350388602

This work, which explores the performance of the existing five IoT sensor classification algorithms and develops five more algorithms, intends to later compare the obtained classification accuracies. In this evaluation, the algorithms included are Decision Tree, Random Forest, K-Nearest Neighbors, Support Vector Machine, and Naive Bayes, while the proposed ones are called EdgeNet, IoTBoost, EdgeSVM, DeepEdge, and EdgeFusionNet. The performances are evaluated using the key measures of precision and processing time. Results show that the algorithms under study are usually more accurate than well-known ones, with EdgeFusionNet reaching 94% accuracy which is the highest accuracy. In the selection of algorithms, existing ones show different performance but the Decision Tree and Naive Bayes demonstrate relatively faster processing times compared to the Random Forest and the Support Vector Machine. Yet, the proposed EdgeSVM algorithm shows almost the fastest processing time which exceeds Zokfl2 and other algorithms onference. On top of that, extra measures comprise memory usage, energy efficiency, scalability, model size, and robustness are evaluated for the suggested algorithms. Thus, the results may be inferred that the suggested algorithms provide the advanced solutions for the IoT sensor data classification, that have higher accuracy and comparable or better time complexity in comparison with the existing ones. These outcomes give useful information for researchers as well as technicians that using IoT techniques for the sake of various applications purposes. © 2024 IEEE.

关键词： Support vector machines

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Lung Cancer Diagnosis and Classification Using Hybrid Neural Network Techniques

Lung Cancer Diagnosis and Classification Using Hybrid Neural...

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Sustainable communication Networks and Application (ICSCNA), International Conference on

作者： M. Yasmin A. Andrew Roobert Department of Computer Science and Business Systems Francis Xavier Engineering College Tirunelveli Tamil Nadu Department of Electronics and Communication Engineering Francis Xavier Engineering College Tirunelveli Tamil Nadu

Lung cancer remains a formidable global health challenge, with early and accurate diagnosis being a critical determinant of patient outcomes. In response to this pressing need, this research presents a novel approach for lung cancer diagnosis and classification utilizing hybrid neural network techniques. By harnessing the synergistic power of convolutional neural networks (CNNs) and recurrent neural networks (RNNs), we have developed an integrated model capable of effectively processing diverse data sources to enhance diagnostic precision. The first component, CNNs, is employed for feature extraction from medical imaging data, such as X-ray images and computed tomography (CT) scans. These networks excel in capturing intricate patterns and subtle abnormalities in medical images, contributing to the early detection of lung cancer. The second component, RNNs, leverages sequential patient data, including clinical histories, genetic information, and longitudinal records, providing valuable contextual information for comprehensive analysis. The hybrid neural network model facilitates a holistic approach to lung cancer diagnosis and classification, as it unifies the strengths of both data-driven imaging analysis and sequential data interpretation. Experimental evaluations of this approach reveal its potential to achieve superior accuracy, outperforming traditional methods, and thus allowing for timely and precise cancer diagnosis. This advancement holds significant promise in improving the prognosis and management of lung cancer patients, potentially leading to more personalized treatment strategies and ultimately reducing the burden of this devastating disease. As such, this research contributes to the ongoing efforts to enhance lung cancer diagnosis, making strides towards improved patient care and survival rates.

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