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2024 International Conference on Electrical and computer engineering Researches, ICECER 2024

作者： Muhammad, Muhammad Kudu Olaniyi, Olayemi Mikail Osang, Francis Oyefolahan, Ishaq Oyebisi Adebayo, Ojeniyi Joseph Kolo, Idris Mohammed Federal University Technology Department of Computer Science Minna Nigeria National Open University of Nigeria Department of Cyber Security Abuja Nigeria National Open University of Nigeria Department of Computer Science Abuja Nigeria National Open University of Nigeria Africa Centre of Excellence on Technology Enhanced Learning Abuja Nigeria Federal University Technology Department of Cyber Security Minna Nigeria

ISBN: (纸本)9798331539733

Mobile Learning System (MLS) is facing new challenges in terms of privacy, such as data collection, storage, and sharing because of the core infrastructure and network that enables cloud computing services. Most of the data exchange in Mobile Learning System (MLS) require mandatory authorisation to allow access to the learners' information in the MLS. Therefore, this article attempts to rank learners' sensitive attributes stored in MLS. Thus, concerns about privacy breaches motivated this paper to adopt an attributes partitioning strategy into the sensitive attributes to enforce privacy during learners' profile information access. The article adopted the informed consent phenomenon to determine and formulate learners' data privacy attributes sensitivity using the Fuzzy Analytic Hierarchy Process (FAHP) Algorithm. Results from the implemented Learners' Privacy Preserving (LPP) Algorithm determined normalized weights of top-five rank-selected learners' sensitive data to include: Browsing History (1ST, Ranked), Geolocation Data (2ND, Ranked), IP Address (3RD, Ranked), web Browser (4TH, Ranked), Medical Records (5TH, Ranked) and CGPA (10TH, Ranked) respectively. This indicates that these five most sensitive features are at risk and require protection to prevent privacy breaches, thus ensuring privacy preservation that prevents unauthorised access to learners' sensitive data in the mobile learning system environment. The ranking of sensitive data in this paper could serve as inspiration for future research work on mobile learning security to improve the privacy of sensitive attributes in MLS environment © 2024 IEEE.

关键词： Contrastive Learning

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Statistical Analysis of a Deep Learning Based Trimodal Biometric System Using Paired Sampling T-Test

Statistical Analysis of a Deep Learning Based Trimodal Biome...

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2023 International Conference on science, engineering and Business for Sustainable Development Goals, SEB-SDG 2023

作者： Atanda, Oladayo Gbenga Ismaila, Wasiu Afolabi, Adeolu Olabode Awodoye, Olufemi Adeyanju Falohun, Adeleye Samuel Oguntoye, Jonathan Ponmile Landmark University Department of Computer Science Omu-Aran Nigeria Sdg 11 Sustainable Cities and Community Nigeria Lautech Department of Cyber-security Science Ogbomoso Nigeria Lautech Department of Computer Engineering Ogbomoso Nigeria Ajayi Crowther University Department of Computer Engineering Oyo Nigeria

ISBN: (纸本)9798350324785

Biometrics of individuals is distinctive to them and is constant over the course of a person's lifespan. A few trimodal biometric systems involving passive biometrics have adopted Convolutional Neural Network (CNN) technique for image recognition but the technique still suffers problems of overfitting and generalization especially with fewer datasets. Hence, the aim of this paper is to address the problems utilizing improved deep learning strategies namely;CNN-MA and CNN- MMA, and to also carry out their statistical analysis utilizing paired sampling t-test. Five hundred and seventy (570) coloured ear images, and one thousand one hundred and forty (1140) grayscale face and iris images of 190 individuals were captured through the use of a Techno F1 digital camera and CMITECH DMX-10 CCD face-iris camera, respectively. One thousand and twenty-six (1026) samples were used for training while six hundred and eighty-four (684) were used for testing. The system implementation was done on MATLAB 2016a. The statistical t- test analysis between CNN-MA and CNN-MMA showed statistical significance in terms of FPR (p = 0.014), P (p = 0.013) and RA (p = 0.005). The statistical t-test analysis between CNN- MA and CNN also showed statistical significance in terms of FPR (p = 0.000), SEN (p = 0.014), P (p = 0.000) and RA (p = 0.036). CNN-MMA and CNN-MA techniques yielded better FPR, SEN, SP, P, and RA than the CNN. © 2023 IEEE.

关键词： Biometrics

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Modeling of Improved Solar Energy Installation for Efficient Power Systems 14

Modeling of Improved Solar Energy Installation for Efficient...

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14th International Conference on Advanced computer Information Technologies, ACIT 2024

作者： Satsyk, Viktor Reshetylo, Oleksandr Markina, Lyudmila Khrystynets, Nataliia Bahniuk, Nataliia Melnychuk, Yuliia Lutsk National Technical University Automation and Computer-Integrated Technologies Department Lutsk Ukraine Lutsk National Technical University Department of Automation and Computer-Integrated Technologies Lutsk Ukraine Lutsk National Technical University Computer Engineering and Cyber Security Department Lutsk Ukraine Lutsk National Technical University Department of Computer Engineering and Cyber Security Lutsk Ukraine Lutsk National Technical University Department of Digital Educational Technologies Lutsk Ukraine

ISBN: (纸本)9798350350036

in the article we develop modeling and creating a prototype of improved solar power plant for economical power supply systems for consumers which require simple autonomous inexpensive practical energy solutions. On the basis of Arduino Uno microcontroller board, a software-hardware complex for controlling the angle of the solar panel has been developed, which allows to follow the position of sun and automatically turn the solar panel in its direction. With aid of developed Android application on a smartphone screen, it is possible to monitor the amount of voltage produced by the solar panel at current time moment, and to analyze the dynamics of its change over time and, accordingly, to plan effective use the generated energy. © 2024 IEEE.

关键词： Hybrid power

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Comparison of Multiple Regression Particle Swarm Optimization Algorithms and Multiple Regression Multi-Objective Seven-Spot Ladybird Optimization for Host Overload / Under-Loaded Detection in Cloud Datacenter 3

Comparison of Multiple Regression Particle Swarm Optimizatio...

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3rd International Conference on Emerging Smart Technologies and Applications, eSmarTA 2023

作者： Aqlan Alhammadi, Akram Saeed Raman, Jayakrishnan Anilakkad Mosleh, Mohammed A.S. Ali Abdullah, Hesham Mohammed It College Taiz University Aljanad University Alsaeed Faculty Engineering Engineering of Computer Networkand Cyber Security Department Taiz Yemen Cyber Forensic Consultant CyberSec Analytics Kerala India Al-Fayha College Jubail Industrial City Department of Computer Science and Engendering Saudi Arabia Jouf University Department of Computer Science and It Sakaka Saudi Arabia

ISBN: (纸本)9798350305333

Power consumption of the data centers in recent times is a big challenge presently. Virtualization plays a key role in improving efficacy. Virtual Machine consolidation (VMC) is a technique for efficient usage of a resource to reduce energy consumption by consolidating Virtual Machines (VMs) into fewer physical machines without Service level agreement violation (SLAV). In this paper, we compare two algorithms called Multiple Regression Particle Swarm Optimization (MRPSO) Algorithm and Multiple Regression Multi-Objective Seven-Spot Ladybird Optimization (MR-MOSLO) Algorithm for host overloaded and under loaded detection. The comparison result shows that the performance of MRPSO is better to MR-MOSLO when the number of VMs is few. © 2023 IEEE.

关键词： Virtual machine

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Machine learning approaches to soil-structure interaction under seismic loading: predictive modeling and analysis

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Asian Journal of Civil engineering 2024年第8期25卷 5781-5792页

作者： Alkhdour, Ahmad shraa, Tamer Department of Civil Engineering Faculty of Engineering Al-Balqa Applied University Al-Salt Jordan Faculty of Science and Information Technology Irbid National University Cyber Security Irbid Jordan

Soil-structure interaction (SSI) under seismic loading is a rather complex phenomenon that has immense effects on the seismic performance of structures. Traditional approaches are the finite element method (FEM) and the boundary element method (BEM), which have been used rather widely in analyzing SSI. Both methods usually fail to capture the complex dynamics of the underlying process. Recent advances in machine learning offer promising alternatives for predictive modeling and analysis of SSI. This paper deals with the applicability of the XGBoost machine learning model, optimized with particle swarm optimization (PSO) in predicting Soil-Structure Interaction under Seismic Loading. The presented model shows accuracy with mean squared error (MSE): 0.04, Root Mean Squared Error (RMSE): 0.2, R-squared (R²): 0.95, and mean absolute error (MAE): 0.1. The results show the better performance of the model over traditional methods like the finite element method (FEM) and the boundary element method (BEM). Comparisons through visualization show that there were close agreements in the displacements predicted and real displacements. Stress distributions and stress–strain curves, predicted from the analysis, validate the model's accuracy. The important outcomes are that the model can deliver more accurate and reliable predictions, enhancing seismic design, and safety to a great extent. It contributes to the literature by being the first application of machine learning combined with an optimization technique;it provides a full comparison to traditional methods for the community and shows future research opportunities, for example, including real-time seismic data or exploring model transferability. Graphical Abstract: (Figure presented.). © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024.

关键词： Particle Swarm Optimization Predictive modeling Seismic loading Soil-structure interaction XGBoost

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Oral Cancer Using Deep Learning and Auto-Fluorescence Image Analysis

Oral Cancer Using Deep Learning and Auto-Fluorescence Image ...

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2024 International Conference on Advances in Modern Age Technologies for Health and engineering science, AMATHE 2024

作者： Muhammed Yaseer, P. Arul Xavier, V.M. Shyni, S.S. Karunya Institute Of Technology And Sciences Division Of Computer Science And Engineering Tamilnadu Coimbatore India Karunya Institute Of Technology And Sciences Division Of Data Science And Cyber Security Tamilnadu Coimbatore India

ISBN: (数字)9798350371567

ISBN: (纸本)9798350371567

Poor treatment outcomes result from the fact that oral cancer is frequently identified at an advanced stage, making it a serious and sometimes lethal disease. Thus, it is essential to develop quick and accurate procedures for detecting oral cancer in its early stages. In this work, we present a unique non-invasive detection technique for evaluating auto-fluorescence signals in oral tissues utilizing convolutional neural networks ResNet50 architecture. Biological tissues naturally produce auto-fluorescence signals, and variations in these signals may be a marker of the existence of malignant cells. Our approach seeks to precisely identify malignant and normal oral tissues by capturing these minute variations in auto-fluorescence patterns. Using a sizable dataset of pictures of oral tissue, we trained and evaluated our RestNet50 model, and the results showed impressive accuracy rates of 89%. The better performance of our detection method can be attributed to the effective capture of high-level features made possible by the ResNet50 architecture. Furthermore, the suggested approach demonstrated quick processing speeds, which allowed it to be used for real-time clinical applications. This approach may greatly enhance patient prognosis and treatment results by identifying oral cancer in its early stages with high accuracy of 89% when compared to VGG16 and VGG19. Although more validation research is necessary before the suggested approach can be used in clinical settings, it shows promise in facilitating early diagnosis and intervention to enhance patient outcomes. © 2024 IEEE.

关键词： Fluorescence

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Toward Correlated Sequential Rules

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第10期5卷 5340-5351页

作者： Chen, Lili Gan, Wensheng Chen, Chien-Ming Tongji University Department of Computer Science and Technology Shanghai200082 China Jinan University College of Cyber Security Guangzhou510632 China Pazhou Laboratory Guangzhou510330 China Nanjing University of Information Science and Technology School of Artificial Intelligence Nanjing210044 China

The goal of high-utility sequential pattern mining (HUSPM) is to efficiently discover profitable or useful sequential patterns in a large number of sequences. However, simply being aware of utility-eligible patterns is insufficient for making predictions. To compensate for this deficiency, high-utility sequential rule mining (HUSRM) is designed to explore the confidence or probability of predicting the occurrence of consequence sequential patterns based on the appearance of premise sequential patterns. It has numerous applications, such as product recommendation and weather prediction. However, the existing algorithm, known as HUSRM, is limited to extracting all eligible rules while neglecting the correlation between the generated sequential rules. To address this issue, we propose a novel algorithm called correlated high-utility sequential rule miner (CoUSR) to integrate the concept of correlation into HUSRM. The proposed algorithm requires not only that each rule be correlated but also that the patterns in the antecedent and consequent of the high-utility sequential rule be correlated. The algorithm adopts a utility-list structure to avoid multiple database scans. Additionally, several pruning strategies are used to improve the algorithm's efficiency and performance. Based on several real-world datasets, subsequent experiments demonstrated that CoUSR is effective and efficient in terms of operation time and memory consumption. All codes are accessible on GitHub: https://***/DSI-Lab1/CoUSR. © 2020 IEEE.

关键词： Forecasting

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Provably Secure Efficient Key-Exchange Protocol for Intelligent Supply Line Surveillance in Smart Grids

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IEEE Internet of Things Journal 2025年第12期12卷 21784-21792页

作者： Ayub, Muhammad Faizan Li, Xiong Mahmood, Khalid Alenazi, Mohammed J.F. Das, Ashok Kumar Wang, Guijuan University of Electronic Science and Technology of China School of Computer Science and Engineering Sichuan Chengdu12599 China National Yunlin University of Science and Technology Graduate School of Intelligent Data Science Yunlin Douliu64002 Taiwan Department of Computer Engineering Riyadh11451 Saudi Arabia International Institute of Information Technology Center for Security Theory and Algorithmic Research Hyderabad500 032 India Korea University College of Informatics Department of Computer Science and Engineering Anam-ro Seoul145 Korea Republic of Ministry of Education Shandong Computer Science Center Qilu University of Technology Shandong Academy of Sciences Key Laboratory of Computing Power Network and Information Security Jinan250353 China Shandong Provincial Key Laboratory of Industrial Network and Information System Security China Shandong Fundamental Research Center for Computer Science Jinan 250353 China

Intelligent supply line surveillance is critical for modern smart grids. Smart sensors and gateway nodes are strategically deployed along supply lines to achieve intelligent surveillance. They collect data continuously and transmit it to the control centre in real-time. It enables real-time monitoring, fault detection, and efficient energy management across distribution networks. This advanced surveillance system ensures continuous monitoring of supply lines, detecting anomalies, and optimizing operations to maintain the stability and reliability of the smart grid. However, the reliance of all participating nodes on public communication channels to transmit supply line surveillance data exposes these systems to critical cyber attacks. These cyber attacks include impersonation, physical tampering, ephemeral secret leakage, and desynchronization attacks. To address these issues, existing key-exchange protocols often fail to ensure robust security while imposing high computation and communication overheads, limiting their practicality for resource-constrained environments. Therefore, we propose a secure and efficient key exchange and tamper-resistant authentication protocol using Elliptic Curve Cryptography (ECC) and Physical Unclonable Functions (PUFs). The PUF mechanism provides robust resistance against physical tampering and cloning attacks, ensuring enhanced physical security for supply line devices. We validate the security robustness of the devised protocol using the Random or Real (ROR) model. Furthermore, informal security analysis demonstrates the protocol's robustness in rigorously resisting various attacks, including physical tampering, impersonation, ephemeral secret leakage and desynchronization. Moreover, we determine the performance evaluation that reveals the protocol's superior efficiency compared to competing protocols, achieving significant reductions of 28.64% in computation overhead and 9.96% in communication overhead. © 2014 IEEE.

关键词： Authentication Protocol

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Artificial Intelligent based Cost Optimized Smart Energy Distribution and Management 2

Artificial Intelligent based Cost Optimized Smart Energy Dis...

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2nd IEEE International Conference on Emerging Research in Computational science, ICERCS 2024

作者： Anitha, R. Siva Prasad, G. Senthil, M. Sarfaraz Ahmed, A. Raja, Laxmi Vijayakumar, S.D. Government Engineering College Department of Electronics and Communication Engineering Kerala India QIS College of Engineering and Technology Department of Computer Science and Engineering Andhra Pradesh India Sri Eshwar College of Engineering Department of Computer Science and Engineering Tamil Nadu Coimbatore India Karpagam Academy of Higher Education Department of Cyber Security Coimbatore India Builders Engineering College Department of Electronics and Communication Engineering Tamil Nadu India

ISBN: (纸本)9798331534950

The ever-increasing need for energy, along with the requirement to cut emissions of greenhouse gases and maximize efficiency in energy use, has been a driving force behind the development of cutting-edge technology in the field of intelligent energy distribution and management. This research article proposes an Artificial Intelligence (AI)-based cost-optimized smart energy distribution and management system. The purpose of this research is to present a new solution to addressing these difficulties. In order to intelligently manage the distribution and consumption of energy resources in a manner that is both cost-effective and efficient, the system that has been developed makes use of several artificial intelligence approaches, such as machine learning, optimization algorithms, and data analytics. The system's goal is to maximize energy utilization while keeping costs to a minimum and achieve the best possible optimization of energy distribution. In order to forecast energy demand and supply, the AI algorithms analyses data from the past as well as data that is being collected in real time. They also take into account aspects such as the current weather, the current time of day, and the preferences of individual users. The system makes real-time adjustments to the distribution of energy based on these projections, taking into account issues such as load balancing, the integration of renewable energy sources, and grid stability. Additionally, the system supports demand response capabilities, which enables users to actively participate in the management of energy by modifying their patterns of consumption based on real-time pricing signals. This allows users to actively participate in the management of energy. This demand-side management function not only promotes energy efficiency but also gives customers more agency in terms of the decisions they make regarding their energy consumption. © 2024 IEEE.

关键词： Artificial Intelligence Cost Optimization Demand Response Energy Distribution Energy Management Machine Learning Optimization Renewable Energy Smart Energy

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Navigating intent-based networking: from user descriptions to deployable configurations

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Neural Computing and Applications 2025年 1-36页

作者： AlSamarneh, Ala’ A. Al-Hammouri, Ahmad T. Al-Jarrah, Omar Y. Department of Network Engineering and Security Jordan University of Science and Technology P.O. Box 3030 Irbid22110 Jordan The Beacom College of Computer and Cyber Sciences Dakota State University 820 N Washington Ave. MadisonSD57042 United States

Network automation development has accompanied network evolution due to its significant role in speeding up and simplifying network operations. Emerging networking and computing paradigms such as information-centric networks, next-generation networks, cloud, and edge computing and recent innovative technologies, such as the Internet of things (IoT), enabled novel network services (such as the Internet of Vehicles (IoV), context-aware applications, virtual reality, and augmented reality) that demand complex configurations and management. Intent-based networking (IBN) is a promising networking paradigm that provides abstract and autonomous network management. IBN promises to simplify configuring networking devices, allowing network engineers and service providers to focus on providing the expected services and continuously verifying that the network operates within the desired status. An IBN process starts by expressing the user requirement in a high-level descriptive format. Then, the IBN system translates these requirements to a low-level deployable format in a process called intent translation. In this work, we formally define the intent translation process and propose a generic intent translation system. Furthermore, we review the research on intent translation published between 2018 and 2022. We analyze and classify the proposed intent translation schemes and discuss the challenges and recent trends in intent translation. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

关键词： Internet of things

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