Mobile devices within Fifth Generation(5G)networks,typically equipped with Android systems,serve as a bridge to connect digital gadgets such as global positioning system,mobile devices,and wireless routers,which are v...
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Mobile devices within Fifth Generation(5G)networks,typically equipped with Android systems,serve as a bridge to connect digital gadgets such as global positioning system,mobile devices,and wireless routers,which are vital in facilitating end-user communication ***,the security of Android systems has been challenged by the sensitive data involved,leading to vulnerabilities in mobile devices used in 5G *** vulnerabilities expose mobile devices to cyber-attacks,primarily resulting from security ***-permission apps in Android can exploit these channels to access sensitive information,including user identities,login credentials,and geolocation *** such attack leverages"zero-permission"sensors like accelerometers and gyroscopes,enabling attackers to gather information about the smartphone's *** underscores the importance of fortifying mobile devices against potential future *** research focuses on a new recurrent neural network prediction model,which has proved highly effective for detecting side-channel attacks in mobile devices in 5G *** conducted state-of-the-art comparative studies to validate our experimental *** results demonstrate that even a small amount of training data can accurately recognize 37.5%of previously unseen user-typed ***,our tap detection mechanism achieves a 92%accuracy rate,a crucial factor for text *** findings have significant practical implications,as they reinforce mobile device security in 5G networks,enhancing user privacy,and data protection.
In recent years, deep learning-based Synthetic Aperture Radar (SAR) image detection, recognition, and segmentation models achieve remarkable accuracy when trained on large amounts of SAR image samples. However, the ac...
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A capsule neural network faces significant challenges in achieving high accuracy on complex datasets due to its high computational complexity and limited ability to represent features. To overcome these limitations, t...
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Effective recommender systems play a crucial role in accurately capturing user and item attributes that mirror individual preferences. Some existing recommendation techniques have started to shift their focus towards ...
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Smartphones contain a vast amount of information about their users, which can be used as evidence in criminal cases. However, the sheer volume of data can make it challenging for forensic investigators to identify and...
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Permanent magnet synchronous motors (PMSMs) are commonly used in various electrical drive applications due to their high efficiency and performance. However, these systems are susceptible to several types of faults. T...
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The convergence of blockchain technology and artificial intelligence (AI) presents a promising solution for enhancing safety within the Internet of Vehicles (IoV) ecosystem. This paper introduces the "Blockchain-...
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The convergence of blockchain technology and artificial intelligence (AI) presents a promising solution for enhancing safety within the Internet of Vehicles (IoV) ecosystem. This paper introduces the "Blockchain-Based Collision Avoidance with AI for Vehicles" (BCA-CAR) algorithm, which aims to provide advanced and intelligent collision avoidance capabilities in IoV. BCA-CAR combines the security and data integrity features of blockchain with the real-time decision-making capabilities of AI to prevent collisions and improve road safety. The algorithm consists of five key phases: Data Collection and Processing, AI Collision Risk Assessment, Decision and Smart Contract Execution, Data Validation and Trust (Blockchain Integration), and Learning and Improvement. In the Data Collection and Processing phase, data from vehicle sensors, cameras, V2V and V2I communication, and external infrastructure is collected and preprocessed. The AI Collision Risk Assessment phase utilizes machine learning models to analyze real-time data and predict collision risks. In the Decision and Smart Contract Execution phase, smart contracts on the blockchain automate collision avoidance actions. The Data Validation and Trust phase ensures the authenticity and integrity of data through blockchain technology. Finally, the Learning and Improvement phase leverages historical collision data to enhance predictive models and overall system performance. BCA-CAR's primary objective is to enhance safety by preventing collisions, ensuring data trustworthiness, and providing intelligent collision avoidance capabilities. This innovative algorithm has the potential to revolutionize road safety in the era of IoV by reducing accidents, improving traffic management, and enhancing the security and privacy of vehicular communication. The findings highlight that Support Vector Regression (SVR) demonstrates strong predictive accuracy and adaptability within the Internet of Vehicles (IoV), offering a reliable modeli
In the context of Intelligent Transportation Systems (ITS), the role of vehicle detection and classification is indispensable for streamlining transportation management, refining traffic control, and conducting in-dep...
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The learning and teaching power of the students in different courses can be different according to their intelligence and talent. One student can be smart in a single course while he/she is lazy in other courses. Afte...
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This study introduces the System for Calculating Open Data Re-identification Risk (SCORR), a framework for quantifying privacy risks in tabular datasets. SCORR extends conventional metrics such as k-anonymity, l-diver...
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This study introduces the System for Calculating Open Data Re-identification Risk (SCORR), a framework for quantifying privacy risks in tabular datasets. SCORR extends conventional metrics such as k-anonymity, l-diversity, and t-closeness with novel extended metrics, including uniqueness-only risk, uniformity-only risk, correlation-only risk, and Markov Model risk, to identify a broader range of re-identification threats. It efficiently analyses event-level and person-level datasets with categorical and numerical attributes. Experimental evaluations were conducted on three publicly available datasets: OULAD, HID, and Adult, across multiple anonymisation levels. The results indicate that higher anonymisation levels do not always proportionally enhance privacy. While stronger generalisation improves k-anonymity, l-diversity and t-closeness vary significantly across datasets. Uniqueness-only and uniformity-only risk decreased with anonymisation, whereas correlation-only risk remained high. Meanwhile, Markov Model risk consistently remained high, indicating little to no improvement regardless of the anonymisation level. Scalability analysis revealed that conventional metrics and Uniqueness-only risk incurred minimal computational overhead, remaining independent of dataset size. However, correlation-only and uniformity-only risk required significantly more processing time, while Markov Model risk incurred the highest computational cost. Despite this, all metrics remained unaffected by the number of quasi-identifiers, except t-closeness, which scaled linearly beyond a certain threshold. A usability evaluation comparing SCORR with the freely available ARX Tool showed that SCORR reduced the number of user interactions required for risk analysis by 59.38%, offering a more streamlined and efficient process. These results confirm SCORR’s effectiveness in helping data custodians balance privacy protection and data utility, advancing privacy risk assessment beyond existing tools
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