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7th International Conference on Computing Methodologies and Communication, ICCMC 2023

作者： Joyce, A. Preethi, D.M.D. Department of Computer Science and Engineering PSNA College of Engineering and Technology Dindigul India

ISBN: (纸本)9781665464086

The Social Area networking Platform (S ANP) is a rapidly developing technology that allows users to meet friends, family, and others, who share their interests and express their personal feelings. The potential privacy threats include, Paedophilia, identity theft, spoofing, data breaches, the Koob face worm, phishing, and evil twin attacks. These attacks are major threat to user privacy information. Many people are not aware of the risks of impersonation. To avoid this, it is necessary to understand social network privacy concerns. This scenario includes a study of social network privacy concerns, as well as hazards and risks from recent events. This study significantly contributes to the classification of dangers and categorization of frauds relevant to social networks. Following the completion of a comprehensive research study of privacy concerns in S ANP, optimized techniques were identified and performance for privacy threats was also evaluated. The proposed system has used Euclidean distance to evaluate the dataset based on privacy threats. It will be useful in establishing trustworthiness on social media. © 2023 IEEE.

关键词： Hazards

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A Rotation & Scale Invariant CNN Model to Detect Humans in Disaster Area from Aerial View

A Rotation & Scale Invariant CNN Model to Detect Humans in D...

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2023 International Conference on Next-Generation Computing, IoT and Machine Learning, NCIM 2023

作者： Moury, Khadiza Sarwar Gauhar, Noushin Ahsan, Sk. Md. Masudul Khulna University of Engineering & Technology Department of Computer Science and Engineering Khulna Bangladesh

ISBN: (纸本)9798350316001

Human detection in aerial images is incredibly challenging because of the characteristics aerial images possess. One of the main applications of human detection in aerial images is for search and rescue operations after a disaster. An efficient human detection approach will hugely optimize the search and rescue operation, which helps reduce the loss of lives due to disasters. A custom dataset has been made for this study using an Unmanned Aerial Vehicle (UAV) or drone. The dataset is made to simulate real-life disaster scenarios. Various types of preprocessing are done on the dataset considering the wide range of aerial images, i.e., different scales, orientations, and postures of the human body cover different areas in aerial images. A new convolutional neural network (CNN) based model has been proposed for classification in this paper. The model has been made both scaling and rotation invariant. A comparative study has been done among 12 pretrained models and compared to the proposed model. You Only Look Once (YOLOv3) has been used for transfer learning for the task of localization. The proposed classification model has shown satisfactory results with accuracy as high as 94.18%. © 2023 IEEE.

关键词： Convolutional neural networks

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LCEFL: A Lightweight Contribution Evaluation Approach for Federated Learning

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IEEE Transactions on Mobile Computing 2025年第7期24卷 6643-6657页

作者： Guo, Jingjing Li, Jiaxing Liu, Zhiquan Xiong, Yupeng Ma, Yong Vasilakos, Athanasios V. Li, Xinghua Ma, Jianfeng Xidian University School of Cyber Engineering Xi’an710071 China Jinan University College of Cyber Security Guangzhou510632 China Jiangxi Normal University Department of Computer Science and Technology Nanchang330022 China Department of Networks and Communications College of Computer Science and Information Technology Dammam31441 Saudi Arabia Grimstad4879 Norway

The prerequisite for implementing incentive mechanisms and reliable participant selection schemes in federated learning is to obtain the contribution of each participant. Available evaluation methods for participant contributions require the server to possess a test dataset, often impractical. Additionally, the excessively high complexity of these works is unacceptable when training complex models in large-scale federated learning system. To address these issues, we propose a lightweight contribution evaluation method for federated learning participants, named LCEFL, based on model projection theory, which does not require the server to provide a test dataset. In addition, a model compression method is designed to be used in LCEFL to reduce the computational complexity. Furthermore, a trusted aggregation method based on LCEFL is proposed, where the weight of each participant's local model is determined by its trust level, which can be calculated using its contribution evaluation result. Experimental results show that LCEFL can achieve nearly the same accuracy as schemes based on Shapley Value, while significantly reducing computational overhead by more than 50%. Compared to available aggregation methods, the proposed trusted aggregation scheme is able to accelerate the convergence speed of the global model and improve its accuracy by 2% to 45%. © 2002-2012 IEEE.

关键词： Federated Learning Computational Modeling Accuracy Servers Training Data Models Machine Learning Mobile Computing Particle Measurements Convergence Federated Learning Contribution Evaluation Trust Evaluation Model Projection Theory Secure Aggregation Federated Learning Computational Complexity Test Dataset Global Model Selection Of Participants Model Projections Aggregation Method Shapley Value Speed Of The Model Contributions Of Participants Number Of Participants Accuracy Of Model Computational Cost Local Data Learning Task Layer Model Participation Outcomes Updated Model Value Of Participation Model Aggregation Server Aggregates Global Convergence Iterative Rounds Trust Value Local Updates MNIST Dataset Total Amount Of Data Local Training Aggregation Algorithm Complex Model Structure

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A secured deep learning based smart home automation system

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International Journal of Information technology (Singapore) 2024年第8期16卷 5239-5245页

作者： Sanjay, Chitukula Jahnavi, Konda Karanth, Shyam Department of Computer Science and Engineering Manipal Institute of Technology Manipal Academy of Higher Education Karnataka Manipal India

With the expansion of modern technologies and the Internet of Things (IoT), the concept of smart homes has gained tremendous popularity with a view to making people’s lives easier by ensuring a secured environment. Several home automation systems have been developed to report suspicious activities by capturing the movements of residents. However, these systems are associated with challenges such as weak security, lack of interoperability and integration with IoT devices, timely reporting of suspicious movements, etc. Therefore, the given paper proposes a novel smart home automation framework for controlling home appliances by integrating with sensors, IoT devices, and microcontrollers, which would in turn monitor the movements and send notifications about suspicious movements on the resident’s smartphone. The proposed framework makes use of convolutional neural networks (CNNs) for motion detection and classification based on pre-processing of images. The images related to the movements of residents are captured by a spy camera installed in the system. It helps in identification of outsiders based on differentiation of motion patterns. The performance of the framework is compared with existing deep learning models used in recent studies based on evaluation metrics such as accuracy (%), precision (%), recall (%), and f-1 measure (%). The results show that the proposed framework attains the highest accuracy (98.67%), thereby surpassing the existing deep learning models used in smart home automation systems. © The Author(s) 2024.

关键词： CNN IoT Motion recognition and security Sensors Smart homes

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Mobile Application on Drowsiness Detection When Driving Car

Mobile Application on Drowsiness Detection When Driving Car

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International Conference on VLSI and Microwave and Wireless Technologies, ICVMWT 2021

作者： Singhal, Akshat Kumar, Sunil Department of Computer Science and Engineering Meerut Institute of Engineering and Technology Meerut India

ISBN: (纸本)9789811903113

With the increase in the number of vehicles on our roads, the number of road accidents is also increased drastically. One of the major reasons found for this mishappening is driver fatigue. By this proposed system, the author tries to measure a driver’s fatigue by detecting drowsiness as this is the only way to calculate fatigue. This paper presents the module for advanced driver assistance system (ADAS) which monitors and calculates driver’s drowsiness and warned driver through an alert system which will subsequently lead to decrease in the no. of road accidents. The designing of this system is done in such a way that one need not install any external hardware device. The rear camera of the mobile is used to monitor the eyes and mouth region of the driver. The system will alert you through an alarm if it recognizes the blinking of eyes or yawning or both. If the driver’s eye remains closed or mouth remains open for a certain period which can be done through the algorithm discussed below, then it is considered that the driver is drowsy. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Advanced driver assistance systems

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End-To-End Encryption Enabled Lightweight Mutual Authentication Scheme for Resource Constrained IoT Network

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computers, Materials & Continua 2025年第2期82卷 3223-3249页

作者： Shafi Ullah Haidawati Muhammad Nasir Kushsairy Kadir Akbar Khan Ahsanullah Memon Shanila Azhar Ilyas Khan Muhammad Ashraf Department of Computer Engineering Balochistan University of Information TechnologyEngineering and Management SciencesQuetta87300Pakistan Computer Engineering Section Malaysian Institute of Technology Universiti Kuala LumpurKuala Lumpur50250Malaysia Electrical Engineering Section Universiti Kuala LumpurBritish Malaysian InstituteSalengor53100Malaysia Electrical Engineering Department Mehran UniversityKhairpur-Mirs76062Pakistan Department of Mathematics College of Science Al-ZulfiMajmaah UniversityAl-Majmaah11952Saudi Arabia

Machine-to-machine (M2M) communication networks consist of resource-constrained autonomous devices, also known as autonomous Internet of things (IoTs) or machine-type communication devices (MTCDs) which act as a backbone for Industrial IoT, smart cities, and other autonomous systems. Due to the limited computing and memory capacity, these devices cannot maintain strong security if conventional security methods are applied such as heavy encryption. This article proposed a novel lightweight mutual authentication scheme including elliptic curve cryptography (ECC) driven end-to-end encryption through curve25519 such as (i): efficient end-to-end encrypted communication with pre-calculation strategy using curve25519;and (ii): elliptic curve Diffie-Hellman (ECDH) based mutual authentication technique through a novel lightweight hash function. The proposed scheme attempts to efficiently counter all known perception layer security threats. Moreover, the pre-calculated key generation strategy resulted in cost-effective encryption with 192-bit curve security. It showed comparative efficiency in key strength, and curve strength compared with similar authentication schemes in terms of computational and memory cost, communication performance and encryption robustness.

关键词： Mutual authentication lightweight end-to-end encryption elliptic curve cryptography industrial internet of things curve25519 machine-to-machine communication

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Comparative Study between ResNet and EfficientNet Family for Classification of Leukemia 26

Comparative Study between ResNet and EfficientNet Family for...

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26th International Conference on computer and Information technology, ICCIT 2023

作者： Ibtekar, Tushar Haque, Md. Rakibul Srizon, Azmain Yakin Rajshahi University of Engineering & Technology Department of Computer Science & Engineering Rajshahi Bangladesh

ISBN: (纸本)9798350359015

Leukemia, a prevalent hematological malignancy, presents significant challenges in diagnosis and classification. Traditional diagnostic methods, often reliant on microscopic examination, suffer from subjectivity and inconsistency. In an era where timely and accurate diagnosis is critical, identifying the most effective deep learning architecture for leukemia classification becomes imperative. This paper addresses this urgent need by conducting a systematic comparative study between two leading deep learning architectures, ResNet and EfficientNet. Utilizing a generalized method architecture for both models, the study aims to discern which architecture offers a superior balance of accuracy, computational efficiency, and clinical adaptability. EfficientNetB5, the top-performing model in our study, achieved an impressive accuracy of 95.87% and an ROC area of 0.94, underscoring its robustness and reliability in leukemia classification. Performance evaluation metrics, including F1-Score, Recall, Precision, Accuracy, are utilized to conduct a thorough assessment of the effectiveness of these architectural models. By providing a transparent and replicable research process, this study not only contributes to the broader discourse on methodological rigor in deep learning research but also offers a standardized approach for leukemia classification. The findings are intended to guide future research and clinical applications, thereby addressing a critical challenge in healthcare. © 2023 IEEE.

关键词： Comparative study Deep learning EfficientNet Leukemia ResNet

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Disease Detection Based on Iris Recognition

Disease Detection Based on Iris Recognition

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2023 International Conference on Energy, Materials and Communication engineering, ICEMCE 2023

作者： Shanthalaksmi Revathy, J.R. Yogeshwaran, R. Vishal, S.K. Velammal College of Engineering and Technology Department of Computer Science and Engineering Madurai India

ISBN: (纸本)9798350393378

Disease detection using iris recognition technology has emerged as a transformative paradigm in the realm of healthcare diagnostics. Leveraging the intricate patterns within the iris, this study explores the development and evaluation of a sophisticated machine learning model for disease identification. Through meticulous pre-processing and feature extraction, the iris patterns are translated into a comprehensive set of data points. Utilizing state-of-the-art machine learning algorithms, the model achieves a remarkable accuracy rate, revolutionizing the precision of disease diagnostics. Ethical considerations play a pivotal role in this research, with a strong emphasis on patient privacy and algorithmic fairness. Rigorous anonymization protocols and bias-mitigating strategies are integrated, ensuring that patient data is handled responsibly and diagnostic outcomes are equitable across diverse demographic groups. Looking forward, the potential applications of iris recognition in healthcare are vast. From real-time disease detection to secure access control within medical facilities, the technology's versatility promises transformative shifts in healthcare delivery. Moreover, the integration of iris recognition with telemedicine platforms opens doors for remote diagnostics, bridging healthcare disparities and ensuring accessibility to even the most remote populations. © 2023 IEEE.

关键词： Access control

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Covid Analysis Using Machine Learning Technique 4th

Covid Analysis Using Machine Learning Technique

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4th International Conference on Inventive Computation and Information Technologies, ICICIT 2022

作者： Gussain, Rohit Kumar, Nitin Sharma, Minshul Dehraj, Pooja Department of Computer Science and Engineering Noida Institute of Engineering and Technology Noida India

ISBN: (纸本)9789811974014

The recent pandemic, covid-19 has largely affected people’s lives, health, and productivity. The first case of Covid-19 was recorded on December 31, 2019, in Wuhan, China. Since then, the number of cases has increased exponentially, and subsequently, numerous precautions have been taken to prevent and cure the virus. By May 26, 2021, totally, 168 million cases were reported worldwide, with 3.49 million deaths, and the pandemic is currently underway, with people continuing to get affected and fighting for their lives from this deadly virus. The World Health Organization (WHO) has also released various precautions and vaccines to combat the pandemic, but these are insufficient to reduce the number of infected cases or save people's lives. The proposed research study discusses about the utilization of artificial intelligence (AI), machine learning (ML), and data science techniques for gaining a better understanding of covid-19 virus. This technological advancement can easily make proper judgments about covid-19, as well as the predictions on confirmed & recovered cases and deaths were made by using this technology. The datasets also include previous and current information about covid-19. The proposed research study also discusses about a tool called "Prophet." Prophet is a Facebook open-source tool, which uses the Sklearn model API. The proposed study initially creates a prophet instance and then use its fit and predict methods. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Viruses

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Long-Short Term Memory decision model for device argumentation in ambient assisted living

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Journal of Ambient Intelligence and Humanized Computing 2024年第12期15卷 4073-4083页

作者： Kumar, G. S. Madhan ShivaPrakash, S.P. Krinkin, Kirill Shichkina, Yulia A. Department of Information Science and Engineering JSS Science and Technology University Karnataka Mysore570006 India School of Computer Science and Engineering Constructor University Bremen Germany Department of Technologies of Artificial Intelligence in Physiology and Medicine Saint Petersburg Electrotechnical University "LETI" Saint Petersburg Russia

The global elderly population is on the rise, posing significant challenges in both developed and developing nations. These challenges include issues related to mobility, falls, and the performance of daily tasks. However, the demands of today’s fast-paced lifestyles make it difficult to provide constant care for the elderly, driving them towards a preference for independent living. To address this need for independent living, the concept of Ambient assisted living (AAL) has been developed. AAL is an intelligent system that employs sensors and devices to monitor and assist elderly individuals in their daily activities. Nevertheless, conflicts can arise when multiple sensors report conflicting activities concurrently, leading to device argumentation. To tackle this issue, this paper introduces a novel Long Short-Term Memory (LSTM)-based learning technique for aiding decision-making during device argumentation concerning activity occurrences. The LSTM model classifies user activities and sensor status values, employing an innovative approach to identify device argumentation within these classified activities. This LSTM decision-making (LDM) model effectively resolves device argumentation and accurately identifies the target activity. An experiment using a combined dataset from the ARAS and CASE datasets demonstrates the superiority of the proposed model, achieving an accuracy of 90.30%, precision of 85.66%, recall of 95.23%, and an F1-Score of 90.22%. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

关键词： Intelligent systems

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