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21st IEEE International Colloquium on Signal Processing and Its Applications, CSPA 2025

作者： Ahmed Abdalmaged, Islam Mohamedalfatih Saud Al-Humairi, Safaa Najah Hezam Al-Hakimi, Asmaa Mahfoudh Kumari, Aparna Management and Science University School of Graduate Studies Selangor Shah Alam 40100 Malaysia Management and Science University Faculty of Information Sciences and Engineering Selangor Shah Alam 40100 Malaysia Institute of Technology Nirma University Department of Computer Science and Engineering Ahmedabad India

ISBN: (纸本)9798331522193

The fast growth of the 'Internet of Things' (IoT) has impacted on several sectors, including fuel management. This paper describes the design, fabrication, and implementation of an IoT-based smart fuel monitoring system to improve the effectiveness of using fuel resources while minimizing losses and optimizing processes. A Rasp berry Pi controller with various sensors tracks essential parameters such as fuel level, temperature, pressure, pH, and gas levels. The system also provides for processing and presenting the collected data through a mobile application and a website, making it possible to manage the system further from the users via remote control. The system was tested using two fuel types, Benzene and Diesel, and it was effective in fuel parameters management. Results established that the pressure of Benzene was reasonably constant, and it was about 14.8 psi after reaching equilibrium. In contrast, the pressure returned to 14.7 psi value for Diesel, which was its initial value. The temperature readings were constant throughout the experiment, with the benzene temperature being higher than Diesel. The system also differentiated between pH levels, keeping Benzene at 6.9 while Diesel at around 7. These results prove the capabilities and efficiency of this system in anomaly detection, fuel adulteration, and fuel theft prevention, thus solving modern solutions in fuel management. © 2025 IEEE.

关键词： Fuel management system IoT-based fuel monitoring Operational efficiency Real-time data acquisition Sensor integration

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Automated Recognition and Analysis of Escape Response in Caenorhabditis Elegans

IEEE Transactions on Computational Biology and Bioinformatic...

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IEEE Transactions on Computational Biology and Bioinformatics 2025年第3期22卷 1201-1210页

作者： Wenyue Zhan Weiyang Chen Yi Pan School of Cyber Science and Engineering Qufu Normal University Qufu China Faculty of Computer Science and Control Engineering Shenzhen University of Advanced Technology Shenzhen China Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China

Significant progress in medical research in recent years has led to the successful treatment of numerous illnesses. Unfortunately, effective treatments for several neurological disorders—particularly those affecting motor neurons—remain elusive, not only significantly impairing patients' quality of life but also placing a substantial financial burden on both individuals and society. To address this gap, scientists are focusing on model organisms to unravel pathogenic mechanisms and develop treatment strategies. Among various model organisms, Caenorhabditis elegans ( C. elegans) has shown special relevance in neurological disorder research. Escape responses, including reversals and omega turns, are controlled by motor neurons. Thus, the escape response in C. elegans provides researchers with a reliable observational metric to evaluate the functional state of motor neurons. We developed a head and tail position localization model to determine the positions of the nematode's head and tail, enabling automated counting of escape responses. We further clarified the association between escape response and motor neuron function by comparing the escape response of normal nematodes to those with motor neuron deficiencies. Consequently, the automated counting of C. elegans escape responses is a useful tool for assessing the health of motor neurons. It may offer strong backing for the exploration of neurodegenerative disease therapeutics.

关键词： Videos Motors Tail Neurons Head Gray-scale Trajectory Phenotypes Computational modeling Turning

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Blockchain-based cross-domain authorization system for user-centric resource sharing

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Blockchain(Research and Applications) 2023年第2期4卷 67-81页

作者： Yuki Ezawa Shohei Kakei Yoshiaki Shiraishi Masami Mohri Masakatu Morii Department of Electrical and Electronic Engineering Kobe UniversityKobeHyogo657-8501Japan Department of Computer Science Nagoya Institute of TechnologyNagoyaAichi466-8555Japan Faculty of Informatics Kindai UniversityHigashi-OsakaOsaka577-8502Japan

User-centric data sharing is essential to encourage citizens'active participation in the digital *** key to smart cities,a form of the digital economy,is the promotion of public use of citizen ***,it is not easy to utilize data without citizens’*** this study,we took a technological approach to these *** access(UMA)is a well-known framework for delegating resource access rights to others on the *** UMA,authorization mechanisms are designed to be centralized so that resource owners can centrally manage access rights for various resources stored in different ***,the lack of transparency in the authorization mechanism is a barrier to its implementation in large-scale systems such as smart *** this study,we developed a blockchain-based cross-domain authorization architecture that enables a resource-sharing ecosystem in which organizations that wish to utilize data can freely trade with each *** proposed architecture solves the transparency problem that conventional authorization systems have had by designing the authorization mechanism on blockchain *** implemented the proposed architecture as smart contracts and evaluated its processing *** resultant time required for delegating access rights and accessing resources was less than 500 ***,we found that the fluctuation in the processing time overhead was *** on these results,we concluded that performance degradation with the proposed architecture is minor.

关键词： Access control Authorization Blockchain Hyperledger fabric User-managed access

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A secure IoT limited Devices by applying Multi-Round Lightweight Cryptography 25

A secure IoT limited Devices by applying Multi-Round Lightwe...

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25th International Arab Conference on Information technology, ACIT 2024

作者： Al-Mousa, Mohammad Rasmi Abdulqawi, Mohammad Badarneh, Hazem Jihad Al-Shaikh, Ala'a Asassfeh, Mahmoud Al-Sherideh, Ala'a Saeb Samara, Ghassan Elrashidi, Ali Alazaidah, Raed Zarqa University Department of Cybersecurity Zarqa Jordan Zarqa University Department of Computer Science Zarqa Jordan University of Business and Technology Department of Electrical Engineering Jeddah21432 Saudi Arabia Zarqa University Faculty of Information Technology Zarqa Jordan

ISBN: (纸本)9798331540012

The Internet of Things (IoT) is considered the base factor, that helps in developing many sectors. Additionally, IoT provides novel applications and services, i.e. smart homes, connected cars, and industrial automation. However, this development aggravates the security and privacy issues. This is because the connected devices may be vulnerable to cyber attacks in addition to generated data. Accordingly, it is of utmost importance to deploy appropriate security measures to alleviate these attacks. To overcome the mentioned problem, this work employs lightweight cryptography on limited processing capabilities of IoT devices i.e. limited memory, computing power, and battery life. These limited capabilities obstruct employing cryptography methods that require nontrivial computational resources. This work presents a multiround lightweight cryptography approach for IoT devices, which defines the security and protection of generated and transferred data. Multi-round cryptography mitigates the required device resources while ensuring robust protection and maintaining the stability of IoT devices. © 2024 IEEE.

关键词： Cyber attacks

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Assessing Property Damage from Natural Disasters: Self-Supervised and Supervised Learning Models for Remote Sensing Imagery Analysis

SSRN

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SSRN 2024年

作者： Zi, Xing Zhu, Taoyuan Shi, Yunxiao Tao, Xian Li, Jun Prasad, Mukesh School of Computer Science Faculty of Engineering and Information Technology University of Technology Sydney Sydney2007 Australia

The trend of conducting property safety inspections is increasingly being recognized and adopted by governments and the public. In this research, a comparison of various supervised learning models based on Convolutional Neural Networks (CNN) and self-supervised learning models, specifically MoCoV2 and DinoV2, was performed. The findings illustrated that self-supervised models, notably DinoV2, exhibited exceptional prowess in the task of property damage classification, demonstrating robust performance metrics. In the image pairs dataset depicting the state of properties before and after disaster events, the predictive precision of positive samples achieved a remarkable 99.9%. The impact of the model's feature attention on its performance was stressed in this study, and a comprehensive analysis of the model's behavior when handling different classes or features was undertaken through visualization of the model's feature attention. It was observed that there were instances where the model excessively focused on the edges of the images or irrelevant areas, thereby neglecting crucial features. These findings suggest potential avenues for further research and development of feature attention mechanisms to enhance model performance and robustness across diverse visual tasks. © 2024, The Authors. All rights reserved.

关键词： Remote sensing

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Analysis of energy consumption for SPI and I2C communications in ultra-low power embedded systems

Analysis of energy consumption for SPI and I2C communication...

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Proceedings of the International Convention MIPRO

作者： Josip Zidar Ivan Aleksi Tomislav Matić Faculty of Electrical Engineering Computer Science and Information Technology Osijek

Developing ultra-low-power devices requires low-power components, algorithms, and communication protocols. For environmental monitoring along the supply chain, products may travel a long way from the distributor to the customer. In addition, extreme climatic circumstances, such as temperatures below 0°C, affect battery capacity. In order to monitor package’s environment, it is crucial to ensure that all embedded system parts use as little energy as possible. This paper addresses how much power the SPI and I2C communication protocols use. These protocols are widely used in low-power embedded devices for sensor communication. Based on its results, this research article presented recommendations for developing ultra-low power embedded devices that use the SPI/I2C protocol.

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Hyper Elliptic Curve Based Certificateless Signcryption Scheme for Secure IIoT Communications

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computers, Materials & Continua 2022年第5期71卷 2515-2532页

作者： Usman Ali Mohd Yamani Idna Idris Jaroslav Frnda Mohamad Nizam Bin Ayub Roobaea Alroobaea Fahad Almansour Nura Modi Shagari Insaf Ullah Ihsan Ali Department of Computer System and Technology Faculty of Computer Science and Information TechnologyUniversity of MalayaKuala Lumpur50603Malaysia Department of Computer Science University of SwatSaidu Sharif19130Pakistan Center for Research in Mobile Cloud Computing University of MalayaKuala Lumpur50603Malaysia Department of Quantitative Methods and Economic Informatics Faculty of Operation and Economics of Transport and CommunicationsUniversity of Zilina01026 ZilinaSlovakia Department of Computer Science College of Computers and Information TechnologyTaif UniversityTaif21944Saudi Arabia Department of Computer Science College of Sciences and Arts in RassQassim UniversityBuraydah51452Saudi Arabia Department of Computer Science Hamdard Institute of Engineering and TechnologyIslamabad44000Pakistan

Industrial internet of things (IIoT) is the usage of internet of things(IoT) devices and applications for the purpose of sensing, processing andcommunicating real-time events in the industrial system to reduce the unnecessary operational cost and enhance manufacturing and other industrial-relatedprocesses to attain more profits. However, such IoT based smart industriesneed internet connectivity and interoperability which makes them susceptibleto numerous cyber-attacks due to the scarcity of computational resourcesof IoT devices and communication over insecure wireless channels. Therefore, this necessitates the design of an efficient security mechanism for IIoTenvironment. In this paper, we propose a hyperelliptic curve cryptography(HECC) based IIoT Certificateless Signcryption (IIoT-CS) scheme, with theaim of improving security while lowering computational and communicationoverhead in IIoT environment. HECC with 80-bit smaller key and parameterssizes offers similar security as elliptic curve cryptography (ECC) with 160-bitlong key and parameters sizes. We assessed the IIoT-CS scheme security byapplying formal and informal security evaluation techniques. We used Realor Random (RoR) model and the widely used automated validation of internet security protocols and applications (AVISPA) simulation tool for formalsecurity analysis and proved that the IIoT-CS scheme provides resistance tovarious attacks. Our proposed IIoT-CS scheme is relatively less expensivecompared to the current state-of-the-art in terms of computational cost andcommunication overhead. Furthermore, the IIoT-CS scheme is 31.25% and 51.31% more efficient in computational cost and communication overhead,respectively, compared to the most recent protocol.

关键词： IoT security authentication protocols hyperelliptic curve cryptography certificateless public key cryptography

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SpamVis: A Visual Interactive System for Spam Review Detection 28th

SpamVis: A Visual Interactive System for Spam Review Detecti...

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28th International Conference on Knowledge Based and Intelligent information and engineering Systems, KES 2024

作者： Lam, Nguyen Thanh Thao Le, Nu Uyen Phuong Islam, Md Rafiqul Sakib, Md KowsarHossain Prome, Shanjita Akter Sanin, Cesar Szczerbicki, Edward Zhou, Jianlong College of Business & Management VinUniversity Hanoi12426 Viet Nam Faculty of Engineering Architecture and Information Technology University of Queensland BrisbaneQLD4072 Australia Business Information Systems Australian Institute of Higher Education SydneyNSW Australia School of Computer Science Taylor's University Malaysia Faculty of Management and Economics Gdansk University of Technology Gdansk Poland School of Computer Science University of Technology Sydney Australia

In recent times, the number of spam reviews through various online platforms has emerged as a prime challenge, profoundly impacting businesses and consumers. These fake reviews not only distort clients' perceptions of products and services but also erode trust within the digital ecosystem. Despite the advent of machine learning (ML) techniques for identifying spam reviews, comparing text, and pinpointing groups of spammers, there remain notable gaps in both accuracy and the combination of interactive visualization for real-time decision-making. This paper presents SpamVis, a visual interactive system that leverages deep learning (DL) and ML blended with advanced visualization techniques for spam review detection, enabling analysts to conduct complicated analytical queries. The system allows users to input via click-on or touch to generate interactive charts and plots tailored for spam review analysis. The findings of the baseline test carried out on 67,395 review texts demonstrate that Bidirectional Encoder Representations from Transformers (BERT) carried out the best accuracy (86%) compared to other models. Our outcomes suggest that SpamVis can alleviate the gaps concerning accuracy and visualization needs in contemporary techniques, guiding analysts to make informed decisions for mitigating spam reviews and enhancing consumer trust. Furthermore, SpamVis empowers users to seamlessly discover the online reviews of various social media platforms in real-time, such as Facebook, Youtube, etc., giving them practical insights to navigate the online marketplace effectively. © 2024 The Authors.

关键词： Adversarial machine learning

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Cloud-Powered Smart Rehabilitation and Assistive Robotics for Stroke Recovery with Recurrent Neural Networks 5

Cloud-Powered Smart Rehabilitation and Assistive Robotics fo...

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5th International Conference on Data Intelligence and Cognitive Informatics, ICDICI 2024

作者： Sampoornam, M. Maria Rajasekaran, Mothiram Visumathi, J. Pattanaik, Balachandra Kalaichelvi, S. Sujatha, S. J.J College of Engineering and Technology Department of Information Technology Tamil Nadu Tiruchirappalli India Pine Candle Way Saint AugustineFL United States Veltech Rangarajan Dr.Sagunthala R&d Institute of Science and Technology Department of Information Technology Tamil Nadu Chennai India Wollega University Department of Electrical and Computer Engineering Nekemte Ethiopia Pg and Research Department of Computer Science and Applications Elayampalayam Tamil Nadu Tiruchengode India Saveetha University Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Biomedical Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350389609

Innovative rehabilitation treatments are needed since stroke is a major cause of long-term impairment globally. In this paper, it presents a smart robotics-assisted rehabilitation system for stroke patients that uses cloud computing and is enhanced using Recurrent Neural Networks (RNNs). The system utilizes a combination of wearable sensors, robotic equipment, and cloud computing to create individualized rehabilitation plans for each patient. To quickly evaluate patients' progress and adjust rehabilitation programs, RNNs are used for real-time processing of continuous sensor data streams. Cloud computing allows for easy scaling, universal accessibility, and compatibility with preexisting healthcare systems. Initial results show promise in enhancing stroke recovery outcomes, highlighting the potential of the proposed method to transform rehabilitation techniques in the smart healthcare realm. The urgent need for effective and tailored stroke rehabilitation programs is something this framework intends to solve, which should lead to better results and a higher quality of life for patients. © 2024 IEEE.

关键词： Recurrent neural networks

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Expanding Horizon: The Role of Reconfigurable Intelligent Surfaces in Enabling Massive Connectivity

Expanding Horizon: The Role of Reconfigurable Intelligent Su...

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2024 International Conference on Horizons of Information technology and engineering, HITE 2024

作者： Ayub, Muhammad Shoaib Adasme, Pablo Rodriguez, Demostenes Zegarra Saadi, Muhammad Shongwe, Thokozani Kanadilovna, Ainur Zhumadillayeva Universidad de Santiago de Chile Department of Electrical Engineering Santiago Chile Federal University of Lavras Brazil Department of Computer Science Lavras Brazil Nottingham Trent University Department of Computer Science United Kingdom University of Johannesburg Department of Electrical & Electronic Engineering Technology Johannesburg South Africa L. N. Gumilev Eurasian National University Faculty of Information Technology Astana Kazakhstan

ISBN: (纸本)9798331516055

This comprehensive article explores the integration of reconfigurable intelligent surfaces (RIS) with massive multiple input and multiple output (mMIMO) systems for future wireless networks, focusing on millimeter-wave (mmWave), beyond 5G (B5G), and sixth-generation (6G) technologies. It provides an in-depth understanding of the theoretical foundations of RIS and mMIMO, discussing their advantages, limitations, and unique capabilities in achieving massive connectivity. This article also highlights role of RIS in enhancing the performance of various wireless systems, including IoT, 5G, B5G, and 6G communications. The paper concludes by identifying open challenges and opportunities in RIS for massive connectivity, offering a roadmap for future research in this rapidly evolving field. © 2024 IEEE.

关键词： 5G mobile communication systems

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