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International Conference on Modern Electrical and Energy systems (MEES)

作者： Nataliia Lutska Nataliia Zaiets Lidiia Vlasenko Stefan Junge Dept. of Automation and Computer Technologies of Control Systems National University of Food Technologies Kyiv Ukraine Dept. of Automation and Robotic Systems National University of Life and Environmental Sciences of Ukraine Kyiv Ukraine Dept. of Software Engineering and Cyber Security State University of Trade and Economics Kyiv Ukraine Dept. of Packaging Technology Berlin University of Applied Sciences Berlin Germany

With the economic crisis that is looming over the world due to changes in the energy sector and a reduction in the supply of certain types of products, energy and resource efficiency issues are becoming more relevant for the food industry. Also important is the preservation of the environment and the implementation of Cleaner Production trends. Most enterprises follow the concept of Industry 4.0 and increase efficiency through the implementation of intelligent methods, in particular machine learning. The paper proposes a neural network model of sugar production. The model makes it possible to predict the main estimates of resource efficiency used to correct the technological modes of production by the operator-technologist. The structure of the operational decision support system has been developed and is used by personnel to model and predict changes in resource efficiency estimates and KPIs calculated on their basis. Its feature is the integration of methods for achieving resource efficiency, improving environmental performance and ensuring KPIs. To do this, at the initial stage, an analysis of the main flows that affect resource efficiency and an assessment of their importance was carried out. Ensuring Production Performance is confirmed by the simulation and the adequacy of the created neural network accuracy of more than 95%.

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Slotted ALOHA with Code Combining for IoT networks

Slotted ALOHA with Code Combining for IoT Networks

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Communications and networking (MeditCom), IEEE International Mediterranean Conference on

作者： Dimitrios Tyrovolas Athanasios Chrysologou Georgios Chondrogiannis Sotiris Tegos Prodromos-Vasileios Mekikis Panagiotis Diamantoulakis Sotiris Ioannidis Christos Liaskos Nestor Chatzidiamantis George Karagiannidis Dept. of Electrical and Computer Engineering Aristotle University of Thessaloniki Thessaloniki Greece Dept. of Electrical and Computer Engineering Technical University of Chania Chania Greece Computer Science Engineering Department University of Ioannina Ioannina Greece Cyber Security Systems and Applied AI Research Center Lebanese American University (LAU) Lebanon

Internet of Things (IoT) networks are expected to be a key enabler technology for smart cities, due to their ability to offer real-time monitoring. In this work, we propose a novel medium access control (MAC) protocol, inspired by the synergy of slotted ALOHA and hybrid automatic repeat request with code combining (HARQ-CC), termed as code combining slotted ALOHA (CCSA), to improve the performance of IoT networks with multiple randomly deployed sensors. Specifically, we provide analytical closed-form expressions for the network’s outage probability as well as for its average throughput when CCSA is applied. Finally, we provide simulation results that reveal the authenticity of the derived expressions and reveal the capabilities of HARQ-CC to significantly improve the network’s performance when the sensors’ access is based on a random access scheme.

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Integrated Photonic Accelerator Based on Optical Spectrum Slicing for Convolutional Neural networks

arXiv

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arXiv 2023年

作者： Tsirigotis, Aris Sarantoglou, George Deligiannidis, Stavros Sozos, Kostas Bogris, Adonis Mesaritakis, Charis Dept. Information and Communication Systems Engineering University of the Aegean Palama 2 str. Karlovasi Samos83200 Greece Dept. Informatics and Computer Engineering University of West Attica Ag. Spiridonos Egaleo12243 Greece

In this work we numerically analyze a passive photonic integrated neuromorphic accelerator based on hardware-friendly optical spectrum slicing nodes. The proposed scheme can act as a fully analogue convolutional layer, preprocessing information directly in the optical domain. The proposed scheme allows the extraction of meaningful spatio-temporal features from the incoming data, thus when used prior to a simple fully connected digital single layer network it can boost performance with negligible power consumption. Numerical simulations using the MNIST dataset confirmed the acceleration properties of the proposed scheme, where 10 neuromorphic nodes can replace the convolutional layers of a sophisticated LeNet-5 network, thus reducing the number of total floating point operations per second (FLOPS) by 98% while offering a 97.2% classification accuracy. ©2022 The Author(s) Copyright © 2023, The Authors. All rights reserved.

关键词： Convolution

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UCI and EICN Integrated Model for Copyright Distribution Management

UCI and EICN Integrated Model for Copyright Distribution Man...

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Metaverse Computing, networking and Applications (MetaCom), IEEE International Conference on

作者： Soonhong Kwon Wooyoung Son Jong-Hyouk Lee Dept. of Computer and Information Security & Convergence Engineering for Intelligent Drone Sejong University Seoul Republic of Korea Protocol Engineering Lab. Sejong University Seoul Republic of Korea

With the recent surge in interest surrounding the metaverse environment, the significance of effective copyright license management has grown considerably. While systematic copyright management is crucial, the limitations of existing copyright identification systems pose challenges to maintaining proper control. To address these issues, this paper proposes an integrated model combining Unique Content Identifier (UCI) and Extended Integrated Copyright Number (EICN) to develop a robust copyright identification system that safeguards the rights of copyright holders.

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A Neural network-based System Identification Model to Predict Output Current and Voltage of Solar Photovoltaic Panels

A Neural Network-based System Identification Model to Predic...

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Science, engineering and Business for Sustainable Development Goals (SEB-SDG), International Conference on

作者： Emmanuel Adetiba Olumide O. Oluleye Ayodele H. Ifijeh Victoria Oguntosin Olatayo M. Olaniyan Olubunmi Adewale Akinola Gbenga Afolabi Jacob O. Odetola. Abdultaofeek Abayomi Dept. of Electrical & Information Engineering Covenant University Ota Ogun State Nigeria HRA Institute for -Systems Science Durban University of Technology Durban South Africa Dept. of Computer Engineering Federal University Oye-Ekiti Ekiti State Nigereia Dept. of Information and Communication Technology Mangosuthu University of Technology Durban South Africa

Solar irradiance is the energy per unit area received by the Sun as electromagnetic radiation. It is one of the most important renewable energy sources. Photovoltaic or other solar technologies are used to generate power more accurately than direct sun irradiation. Solar irradiance research and measurement have a variety of critical applications, including forecasting power generation from solar power plants, climate modeling, and weather forecasting. This paper presents a neural network-based system identification model developed using measured parameters from solar panels with various wattage specifications, namely, 10W, 20W, 40W, and 100W. The parameters that were measured to train the ANN model for the prediction of the output current and voltage include the angle of panel orientation, panel temperature, ambient temperature, irradiance, and wattage. Several training experiments were conducted and the best ANN model produced at 500 epochs gave an accuracy of 99.81% and a loss of 0.1940. The model was deployed into an intelligent Web App that was also developed in this study. This app could be a potential tool for renewable energy engineers and researchers.

关键词： Temperature measurement Renewable energy sources Voltage measurement Current measurement Weather forecasting Solar energy Predictive models

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Implementing a Novel Blockchain Algorithm for Enhanced Data Integrity in Cloud systems

Implementing a Novel Blockchain Algorithm for Enhanced Data ...

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Trends in Quantum Computing and Emerging Business Technologies (TQCEBT), International Conference on

作者： V. Balaji S. Ambika S Varshadevi R. Thalapathi Rajasekaran J. Sangeetha Department of CSE (Cyber Security) Easwari Engineering College Chennai Department of Information Technology E.G.S.Pillay Engineering College Nagapattinam Tamil Nadu India Dept. of Computer Science and Engineering GRT Institute of Engineering and Technology Tiruttani Tamil Nadu India Dept. of Computer Science and Engineering Saveetha School of EngineeringSIMATS Chennai Sri Venkateswara College of Engineering and Technology Tirupachur Thiruvalluvar India

ISBN: (数字)9798350384277

ISBN: (纸本)9798350384284

Keeping data safe and sound in cloud systems is important. So, new ways need to be found so private information can't get changed or seen without permission by wrong people. This research gives a new blockchain method to make data reliability better in cloud systems. We show how our suggested method is better and more useful than others in keeping data safe. We do this by doing a deep study and comparing with other ways that are currently used. When we look at how many transactions a system can handle, the time it takes for things to happen and its ability to grow bigger without problems, our suggested method does better than other solutions in Comparing Performance. It handles 1000 transactions each second, with a time delay of only 10 milliseconds. It can work well for up to one hundred nodes on the network while still showing it's efficient and able to keep growing bigger over time. The Data Comparison shows that our suggested way is very good at finding out false information, and it can find 98% of this. Besides, it quickly recovers data with a downtime of only 2 hours. This makes it different from old ways by making sure data can't be changed and there are strong checks on who did what. security Check shows our new plan works well. It stops people spending money twice, stands up against cheating tactics and keeps a strong Byzantine Fault Tolerance (BFT) at 99.9%. The Proof of Stake agreement is both easy on energy and safe. security of smart contracts is also talked about, to make them safer.

关键词： Fault tolerance Quantum computing Fault tolerant systems Smart contracts Market research Blockchains Safety

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Explore the Impact of Quantum Computing to Enhance Cryptographic Protocols and network security Measures

Explore the Impact of Quantum Computing to Enhance Cryptogra...

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Computing, Power and Communication Technologies (IC2PCT), IEEE International Conference on

作者： S. Ambika V. Balaji R.Thalapathi Rajasekaran P.N. Periyasamy N. Kamal Department of Information Technology E. G. S. Pillay Engineering College Nagapattinam Tamil Nadu India Department of CSE (Cyber Security) Easwari Engineering College Chennai India Dept. of Computer Science and Engineering Saveetha School of Engineering SIMATS Chennai India Department of Computer Science and Engineering Hindusthan Institute of Technology India Dept. of Computer Science and Engineering GRT Institute of Engineering and Technology Tiruttani Tamil Nadu India

The revolutionary effects of quantum computing on security procedures for networks and cryptographic protocols are the focus of this study. Our research shows that conventional cryptographic algorithms are susceptible to quantum assaults; specifically, RSA and ECC encryption are very vulnerable. On the other hand, cryptographic algorithms that are immune to quantum attacks, such as multivariate polynomial cryptography and lattice-based algorithms, show promise in meeting these challenges. Key exchange rates between 200 kbps and 450 kbps and error rates as low as 0.5% are some of the significant achievements shown by Quantum Key Distribution (QKD) implementations. These applications show that QKD is a viable solution for protecting communication channels up to 200 km away. On top of that, there are remarkable outcomes when network security systems incorporate quantum advancements. The detection accuracy of Quantum-Enhanced Intrusion Detection systems (QIDS) is 98.5% and the false positive rate is minimally 0.2%. The detection accuracy of quantum-secured communication systems is 99.2% and the false positive rate is an extremely low 0.1%. While quantum firewalls keep a detection accuracy of 97.8% and a false positive rate of 0.3%, quantum-safe cryptographic modules show a low latency of 3 ms.

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Construction Site Modeling Objects Using Artificial Intelligence and BIM Technology: A Multi-Stage Approach

Construction Site Modeling Objects Using Artificial Intellig...

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Smart information systems and Technologies (SIST), IEEE International Conference on

作者： Serhii Dolhopolov Tetyana Honcharenko Volodymyr Savenko Olena Balina Iryna Bezklubenko Tamara Liashchenko Dept. of Cyber Security and Computer Engineering Kyiv National University of Construction and Architecture Kyiv Ukraine Dept. of Information Technologies of Design and Applied Mathematics Kyiv National University of Construction and Architecture Kyiv Ukraine Dept. of Organization and Management of Construction Kyiv National University of Construction and Architecture Kyiv Ukraine Dept. of Information Technologies Kyiv National University of Construction and Architecture Kyiv Ukraine

This study presents a multi-stage approach for building object models (BOMs) on a construction site, aimed at creating an “evolutionary” digital twin. The integration of building information modeling (BIM) and artificial intelligence is used to achieve this goal, with the use of photo modeling using moving cameras and the potential integration of IoT technologies also discussed. A comprehensive artificial intelligence system, combining Convolutional Neural network (CNN) and Feed Forward Neural network (FFNN) architectures, has been developed to detect, categorize, and evaluate BIM projects throughout their life cycle. The scalability prospects for point cloud and mesh models, as well as the use of big data technology to optimize the representation of the digital twin, are also addressed. The study determines the effectiveness of construction site conformance detection during the construction of a BIM model, providing consistency and a quantitative evaluation of the processes taking place on the construction site. The findings of this research can be used to enhance BIM modeling methods and concepts toward a multi-stage representation of the digital twin of the construction site.

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Blockchain-Enhanced Framework for Secure Third-Party Vendor Risk Management and Vigilant security Controls

Blockchain-Enhanced Framework for Secure Third-Party Vendor ...

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IEEE International Conference on Big Data

作者： Deepti Gupta Lavanya Elluri Avi Jain Shafika Showkat Moni Omer Aslan Dept. of Computer Information Systems Texas A&M University - Central Texas Texas USA College of Business University of Tampa Florida USA Dept. of Electrical Engineering and Computer Science Embry-Riddle Aeronautical University Florida USA Department of Software Engineering Bandırma Onyedi Eylül University Bandırma Turkey

ISBN: (数字)9798350362480

ISBN: (纸本)9798350362497

In an era of heightened digital interconnectedness, businesses increasingly rely on third-party vendors to enhance their operational capabilities. However, this growing dependency introduces significant security risks, making it crucial to develop a robust framework to mitigate potential vulnerabilities. This paper proposes a comprehensive secure framework for managing third-party vendor risk, integrating blockchain technology to ensure transparency, traceability, and immutability in vendor assessments and interactions. By leveraging blockchain, the framework enhances the integrity of vendor security audits, ensuring that vendor assessments remain up-to-date and tamperproof. This proposed framework leverages smart contracts to reduce human error while ensuring real-time monitoring of compliance and security controls. By evaluating critical security controls—such as data encryption, access control mechanisms, multi-factor authentication, and zero-trust architecture—this approach strengthens an organization’s defense against emerging cyber threats. Additionally, continuous monitoring enabled by blockchain ensures the immutability and transparency of vendor compliance processes. In this paper, a case study on iHealth’s transition to AWS Cloud demonstrates the practical implementation of the framework, showing a significant reduction in vulnerabilities and marked improvement in incident response times. Through the adoption of this blockchain-enabled approach, organizations can mitigate vendor risks, streamline compliance, and enhance their overall security posture. Our findings highlight the importance of employing blockchain to enforce security controls and maintain compliance with healthcare regulations such as HIPAA. In this paper, we present a comprehensive set of security controls and demonstrate how blockchain technology enhances their effectiveness, ensuring greater transparency, accountability, and automation in vendor assessments. By reducing human error, enabl

关键词： Multi-factor authentication Smart contracts Organizations Real-time systems Regulation Blockchains security Time factors Risk management Resilience

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A Novel Transfer Learning Framework for Diagnosing Covid-19 from Chest X-Ray Images with Minimal Computational Resources

SSRN

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SSRN 2023年

作者： Abdelwahed, Ahmed Helmi, Ahmed M. Issa, Mohamed Computer and Systems Department Faculty of Engineering Zagazig University Egypt Computer Engineering Dept. Engineering and Information Technology College Buraydah Private Colleges Saudi Arabia Faculty of Computer Science and Information Technology Egypt−Japan University for Science and Technology Egypt

Deep learning proved effective in detecting COVID-19 from chest X-ray images. Although computing is the current bottleneck in training and deploying models, few studies focus on developing efficient frameworks while many aim only at performance. We propose a novel framework for detecting COVID-19 cases from chest X-ray images efficiently. Our study utilizes four high-performing architectures, including MobileNetV3, RegNetX, NasNetMobile, and EfficientNetV2, which use minimal resources, to extract features from X-ray images and classify COVID-19 cases using various statistical models and machine learning algorithms. We evaluated our proposed framework using metrics such as accuracy, precision, recall, specificity, f1-score, and AUC scores. The false negative rate for each feature extractor and classifier pair is reported, and the average inference time, to measure resource efficiency, is calculated. The results show that EfficientNetV2 was the best-performing feature extractor, while MobileNetV3 had the best average inference time and the lowest false negative rate. The Neural network classifier performed the best, with state-of-the-art accuracy reaching 99.28%, followed by both RBF-SVM and XGBoost. The proposed framework is more robust, reliable, and resource-efficient for COVID-19 detection compared to existing literature. Our findings indicate the prospects of using the proposed framework in efficient clinical diagnosis. © 2023, The Authors. All rights reserved.

关键词： COVID-19

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