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A Deep Learning Approach for Landmines Detection Based on Airborne Magnetometry Imaging and Edge Computing

Computer Modeling in Engineering & Sciences 2024年第5期139卷 2117-2137页

作者： ahmed barnawi Krishan Kumar Neeraj Kumar Bander Alzahrani Amal Almansour Faculty of Computing and Information Technology King Abdulaziz UniversityJeddahSaudi Arabia Department of Computer Science and Engineering Thapar Institute of Engineering and TechnologyDeemed to be UniversityPatialaIndia

Landmines continue to pose an ongoing threat in various regions around the world,with countless buried landmines affecting numerous human lives.The detonation of these landmines results in thousands of casualties reported worldwide annually.Therefore,there is a pressing need to employ diverse landmine detection techniques for their removal.One effective approach for landmine detection is UAV(Unmanned Aerial Vehicle)based AirborneMagnetometry,which identifies magnetic anomalies in the local terrestrial magnetic field.It can generate a contour plot or heat map that visually represents the magnetic field strength.Despite the effectiveness of this approach,landmine removal remains a challenging and resource-intensive task,fraughtwith risks.Edge computing,on the other hand,can play a crucial role in critical drone monitoring applications like landmine detection.By processing data locally on a nearby edge server,edge computing can reduce communication latency and bandwidth requirements,allowing real-time analysis of magnetic field data.It enables faster decision-making and more efficient landmine detection,potentially saving lives and minimizing the risks involved in the process.Furthermore,edge computing can provide enhanced security and privacy by keeping sensitive data close to the source,reducing the chances of data exposure during transmission.This paper introduces the MAGnetometry Imaging based Classification System(MAGICS),a fully automated UAV-based system designed for landmine and buried object detection and localization.We have developed an efficient deep learning-based strategy for automatic image classification using magnetometry dataset traces.By simulating the proposal in various network scenarios,we have successfully detected landmine signatures present in themagnetometry images.The trained models exhibit significant performance improvements,achieving a maximum mean average precision value of 97.8%.

关键词： CNN deep learning landmine detection magnetometer mean average precision UAV

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THAPE: A Tunable Hybrid Associative Predictive Engine Approach for Enhancing Rule Interpretability in Association Rule Learning for the Retail Sector

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Computers, Materials & Continua 2024年第6期79卷 4995-5015页

作者： Monerah Alawadh ahmed barnawi Information Technology Department King Abdulaziz UniversityJeddah21589Saudi Arabia

Association rule learning(ARL)is a widely used technique for discovering relationships within datasets.However,it often generates excessive irrelevant or ambiguous rules.Therefore,post-processing is crucial not only for removing irrelevant or redundant rules but also for uncovering hidden associations that impact other factors.Recently,several post-processing methods have been proposed,each with its own strengths and weaknesses.In this paper,we propose THAPE(Tunable Hybrid Associative Predictive Engine),which combines descriptive and predictive techniques.By leveraging both techniques,our aim is to enhance the quality of analyzing generated rules.This includes removing irrelevant or redundant rules,uncovering interesting and useful rules,exploring hidden association rules that may affect other factors,and providing backtracking ability for a given product.The proposed approach offers a tailored method that suits specific goals for retailers,enabling them to gain a better understanding of customer behavior based on factual transactions in the target market.We applied THAPE to a real dataset as a case study in this paper to demonstrate its effectiveness.Through this application,we successfully mined a concise set of highly interesting and useful association rules.Out of the 11,265 rules generated,we identified 125 rules that are particularly relevant to the business context.These identified rules significantly improve the interpretability and usefulness of association rules for decision-making purposes.

关键词： Association rule learning post-processing predictive machine learning rule interpretability

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A Secure Key Agreement Scheme for Unmanned Aerial Vehicles-Based Crowd Monitoring System

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Computers, Materials & Continua 2022年第3期70卷 6141-6158页

作者： Bander Alzahrani ahmed barnawi Azeem Irshad Areej Alhothali Reem Alotaibi Muhammad Shafiq Faculty of Computing and Information Technology King Abdulaziz UniversityJeddahSaudi Arabia Department of Computer Science and Software Engineering International Islamic University IslamabadPakistan Department of Information and Communication Engineering Yeungnam UniversityGyeongsan38541Korea

Unmanned aerial vehicles(UAVs)have recently attractedwidespread attention in civil and commercial applications.For example,UAVs(or drone)technology is increasingly used in crowd monitoring solutions due to its wider air footprint and the ability to capture data in real time.However,due to the open atmosphere,drones can easily be lost or captured by attackers when reporting information to the crowd management center.In addition,the attackers may initiate malicious detection to disrupt the crowd-sensing communication network.Therefore,security and privacy are one of the most significant challenges faced by drones or the Internet of Drones(IoD)that supports the Internet of Things(IoT).In the literature,we can find some authenticated key agreement(AKA)schemes to protect access control between entities involved in the IoD environment.However,the AKA scheme involves many vulnerabilities in terms of security and privacy.In this paper,we propose an enhancedAKAsolution for crowdmonitoring applications that require secure communication between drones and controlling entities.Our scheme supports key security features,including anti-forgery attacks,and confirms user privacy.The security characteristics of our scheme are analyzed byNS2 simulation and verified by a random oracle model.Our simulation results and proofs show that the proposed scheme sufficiently guarantees the security of crowd-aware communication.

关键词： IoT unmanned aerial vehicles authentication crowd monitoring

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Attribute-Based Secure Data Sharing with Efficient Revocation in Fog Computing

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Journal of Information Security 2017年第3期8卷 203-222页

作者： Asma Alotaibi ahmed barnawi Mohammed Buhari Department of Information Technology King Abdulaziz University Jeddah Saudi Arabia

Fog computing is a concept that extends the paradigm of cloud computing to the network edge. The goal of fog computing is to situate resources in the vicinity of end users. As with cloud computing, fog computing provides storage services. The data owners can store their confidential data in many fog nodes, which could cause more challenges for data sharing security. In this paper, we present a novel architecture for data sharing in a fog environment. We explore the benefits of fog computing in addressing one-to-many data sharing applications. This architecture sought to outperform the cloud-based architecture and to ensure further enhancements to system performance, especially from the perspective of security. We will address the security challenges of data sharing, such as fine-grained access control, data confidentiality, collusion resistance, scalability, and the issue of user revocation. Keeping these issues in mind, we will secure data sharing in fog computing by combining attributebased encryption and proxy re-encryption techniques. Findings of this study indicate that our system has the response and processing time faster than classical cloud systems. Further, experimental results show that our system has an efficient user revocation mechanism, and that it provides high scalability and sharing of data in real time with low latency.

关键词： Attribute-Based Encryption Fine-Grained Access Control Fog Computing Proxy Re-Encryption User Revocation

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A Survey on Methods and Applications of Intelligent Market Basket Analysis Based on Association Rule

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Journal on Big Data 2022年第1期4卷 1-25页

作者： Monerah M.Alawadh ahmed M.barnawi King Abdulaziz University Jeddah21589The Kingdome of Saudi Arabia

The market trends rapidly changed over the last two decades.The primary reason is the newly created opportunities and the increased number of competitors competing to grasp market share using business analysis techniques.Market Basket Analysis has a tangible effect in facilitating current change in the market.Market Basket Analysis is one of the famous fields that deal with Big Data and Data Mining applications.MBA initially uses Association Rule Learning(ARL)as a mean for realization.ARL has a beneficial effect in providing a plenty benefit in analyzing the market data and understanding customers’behavior.An important motive of using such techniques is maximizing the business profit as well as matching the exact customer needs as closely as possible.In this survey paper,we discussed several applications and methods of MBA based on ARL.Also,we reviewed some association rule learning measurements including trust,lift,leverage,and others.Furthermore,we discuss some open issues and future topics in the area of market basket analysis and association rule learning.

关键词： Intelligent market basket analysis association rule learning market basket analysis apriori algorithm association rule measurements

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