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A Novel Approach to Enhanced Cancelable Multi-Biometrics Personal Identification Based on Incremental Deep Learning

computers, Materials & Continua 2025年第5期83卷 1727-1752页

作者： Ali Batouche Souham Meshoul Hadil Shaiba Mohamed Batouche Department of Computer and Information Sciences Northumbria UniversityLondonE17HTUK Department of Information Technology College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh11671Saudi Arabia Department of Computer Science College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh11671Saudi Arabia

The field of biometric identification has seen significant advancements over the years,with research focusing on enhancing the accuracy and security of these *** of the key developments is the integration of deep learning techniques in biometric ***,despite these advancements,certain challenges *** of the most significant challenges is scalability over growing *** methods either require maintaining and securing a growing database,introducing serious security challenges,or relying on retraining the entiremodelwhen new data is introduced-a process that can be computationally expensive and *** challenge underscores the need for more efficient methods to scale *** this end,we introduce a novel approach that addresses these challenges by integrating multimodal biometrics,cancelable biometrics,and incremental learning *** work is among the first attempts to seamlessly incorporate deep cancelable biometrics with dynamic architectural updates,applied incrementally to the deep learning model as new users are enrolled,achieving high performance with minimal catastrophic *** leveraging a One-Dimensional Convolutional Neural Network(1D-CNN)architecture combined with a hybrid incremental learning approach,our system achieves high recognition accuracy,averaging 98.98% over incrementing datasets,while ensuring user privacy through cancelable templates generated via a pre-trained CNN model and random *** approach demonstrates remarkable adaptability,utilizing the least intrusive biometric traits like facial features and fingerprints,ensuring not only robust performance but also long-term serviceability.

关键词： Incremental learning personal identification cancelablemulti-biometrics pattern recognition security deep learning cyber-attacks transfer learning random projection catastrophic forgetting

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Road Traffic Monitoring from Aerial Images Using Template Matching and Invariant Features

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computers, Materials & Continua 2024年第3期78卷 3683-3701页

作者： Asifa Mehmood Qureshi Naif Al Mudawi Mohammed Alonazi Samia Allaoua Chelloug Jeongmin Park Department of Creative Technologies Air UniversityIslamabad46000Pakistan Department of Computer Science College of Computer Science and Information SystemNajran UniversityNajran55461Saudi Arabia Department of Information Systems College of Computer Engineering and SciencesPrince Sattam bin Abdulaziz UniversityAl-Kharj16273Saudi Arabia Department of Information Technology College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Engineering Tech University of KoreaGyeonggi-do15073South Korea

Road traffic monitoring is an imperative topic widely discussed among *** used to monitor traffic frequently rely on cameras mounted on bridges or ***,aerial images provide the flexibility to use mobile platforms to detect the location and motion of the vehicle over a larger *** this end,different models have shown the ability to recognize and track ***,these methods are not mature enough to produce accurate results in complex road ***,this paper presents an algorithm that combines state-of-the-art techniques for identifying and tracking vehicles in conjunction with image *** extracted frames were converted to grayscale,followed by the application of a georeferencing algorithm to embed coordinate information into the *** masking technique eliminated irrelevant data and reduced the computational cost of the overall monitoring ***,Sobel edge detection combined with Canny edge detection and Hough line transform has been applied for noise *** preprocessing,the blob detection algorithm helped detect the *** of varying sizes have been detected by implementing a dynamic thresholding *** was done on the first image of every ***,to track vehicles,the model of each vehicle was made to find its matches in the succeeding images using the template matching *** further improve the tracking accuracy by incorporating motion information,Scale Invariant Feature Transform(SIFT)features have been used to find the best possible match among multiple *** accuracy rate of 87%for detection and 80%accuracy for tracking in the A1 Motorway Netherland dataset has been *** the Vehicle Aerial Imaging from Drone(VAID)dataset,an accuracy rate of 86%for detection and 78%accuracy for tracking has been achieved.

关键词： Unmanned Aerial Vehicles(UAV) aerial images dataset object detection object tracking data elimination template matching blob detection SIFT VAID

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Deep Learning Based Side-Channel Attack Detection for Mobile Devices Security in 5G Networks

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清华大学学报自然科学版（英文版） 2025年第3期30卷 1012-1026页

作者： Amjed A.Ahmed Mohammad Kamrul Hasan Ali Alqahtani Shayla Islam Bishwajeet Pandey Leila Rzayeva Huda Saleh Abbas Azana Hafizah Mohd Aman Nayef Alqahtani Center for Cyber Security Faculty of Information Science and TechnologyUniversiti Kebangsaan MalaysiaBangi 43600Malaysia Imam Alkadhim University College Department of Computer Techniques EngineeringBaghdad 10066Iraq Center for Cyber Security Faculty of Information Science and TechnologyUniversiti Kebangsaan MalaysiaBangi 43600Malaysia Department of Networks and Communications Engineering College of Computer Science and Information SystemsNajran UniversityNajran 61441Saudi Arabia Institute of Computer Science and Digital Innovation UCSI UniversityKuala Lumpur 56000Malaysia Department of Intelligent Systems and Cyber Security Astana IT UniversityAstana 20000Kazakstan Department of Computer Science College of Computer Science and EngineeringTaibah UniversityMadinah 42353Saudi Arabia Department of Electrical Engineering College of EngineeringKing Faisal UniversityAl-Hofuf 31982Saudi Arabia

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.

关键词： Fifth Generation(5G)networks smartphone information leakage Side-Channel Attack(SCA) deep learning

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Reliable Task Offloading for 6G-Based IoT Applications

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computers, Materials & Continua 2025年第2期82卷 2255-2274页

作者： Usman Mahmood Malik Muhammad Awais Javed Ahmad Naseem Alvi Mohammed Alkhathami Department of Computer Software Engineering National University of Science and Technology(NUST)Islamabad44000Pakistan Department of Electrical and Computer Engineering COMSATS University IslamabadIslamabad45550Pakistan Information Systems Department College of Computer and Information SciencesImam Mohammad Ibn Saud Islamic University(IMSIU)Riyadh11432Saudi Arabia

Fog computing is a key enabling technology of 6G systems as it provides quick and reliable computing,and data storage services which are required for several 6G *** Intelligence(AI)algorithms will be an integral part of 6G systems and efficient task offloading techniques using fog computing will improve their performance and *** this paper,the focus is on the scenario of Partial Offloading of a Task to Multiple Helpers(POMH)in which larger tasks are divided into smaller subtasks and processed in parallel,hence expediting task ***,using POMH presents challenges such as breaking tasks into subtasks and scaling these subtasks based on many interdependent factors to ensure that all subtasks of a task finish simultaneously,preventing resource ***,applying matching theory to POMH scenarios results in dynamic preference profiles of helping devices due to changing subtask sizes,resulting in a difficult-to-solve,externalities *** paper introduces a novel many-to-one matching-based algorithm,designed to address the externalities problem and optimize resource allocation within POMH ***,we propose a new time-efficient preference profiling technique that further enhances time optimization in POMH *** performance of the proposed technique is thoroughly evaluated in comparison to alternate baseline schemes,revealing many advantages of the proposed *** simulation findings indisputably show that the proposed matching-based offloading technique outperforms existing methodologies in the literature,yielding a remarkable 52 reduction in task latency,particularly under high workloads.

关键词： 6G IoT task offloading fog computing

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RSSI-Based 3D Wireless Sensor Node Localization Using Hybrid T Cell Immune and Lotus Optimization

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computers, Materials & Continua 2024年第12期81卷 4833-4851页

作者： Weiwei Hu Kiran Sree Pokkuluri Rajesh Arunachalam Bander A.Jabr Yasser A.Ali Preethi Palanisamy School of Information Engineering Technology&MediaUniversity of HenanKaifeng475004China Department of Computer Science and Engineering Shri Vishnu Engineering College forWomenBhimavaram534202Andhra PradheshIndia Department of Electronics and Communication Engineering Saveetha School of EngineeringSaveetha Institute of Medical and Technical SciencesThandalam602105ChennaiIndia Department of Computer Engineering College of Computer and Information SciencesKing Saud UniversityRiyadh11543Saudi Arabia Department of Computer Science and Engineering Saveetha School of EngineeringSaveetha Institute of Medical and Technical SciencesThandalam602105ChennaiIndia

Wireless Sensor Network(WSNs)consists of a group of nodes that analyze the information from surrounding *** sensor nodes are responsible for accumulating and exchanging ***,node local-ization is the process of identifying the target node’s *** this research work,a Received Signal Strength Indicator(RSSI)-based optimal node localization approach is proposed to solve the complexities in the conventional node localization ***,the RSSI value is identified using the Deep Neural Network(DNN).The RSSI is conceded as the range-based method and it does not require special hardware for the node localization process,also it consumes a very minimal amount of cost for localizing the nodes in 3D *** position of the anchor nodes is fixed for detecting the location of the ***,the optimal position of the target node is identified using Hybrid T cell Immune with Lotus Effect Optimization algorithm(HTCI-LEO).During the node localization process,the average localization error is minimized,which is the objective of the optimal node *** the regular and irregular surfaces,this hybrid algorithm effectively performs the localization *** suggested hybrid algorithm converges very fast in the three-dimensional(3D)*** accuracy of the proposed node localization process is 94.25%.

关键词： Sensor node localization received signal strength indicator 3D wireless sensor network deep neural network average localization error and hybrid T cell immune with lotus effect optimization algorithm

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Fitness Sharing Chaotic Particle Swarm Optimization (FSCPSO): A Metaheuristic Approach for Allocating Dynamic Virtual Machine (VM) in Fog Computing Architecture

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computers, Materials & Continua 2024年第8期80卷 2557-2578页

作者： Prasanna Kumar Kannughatta Ranganna Siddesh Gaddadevara Matt Chin-Ling Chen Ananda Babu Jayachandra Yong-Yuan Deng Department of Computer Science&Engineering Ramaiah Institute of TechnologyBangalore560054India Department of Artificial Intelligence&Data Science Ramaiah Institute of TechnologyBangalore560054India School of Information Engineering Changchun Sci-Tech UniversityChangchun130600China Department of Computer Science and Information Engineering Chaoyang University of TechnologyTaichung41349Taiwan Department of Information Science and Engineering Malnad College of EngineeringHassan573202India

In recent decades,fog computing has played a vital role in executing parallel computational tasks,specifically,scientific workflow *** cloud data centers,fog computing takes more time to run workflow ***,it is essential to develop effective models for Virtual Machine(VM)allocation and task scheduling in fog computing *** task scheduling,VM migration,and allocation,altogether optimize the use of computational resources across different fog *** process ensures that the tasks are executed with minimal energy consumption,which reduces the chances of resource *** this manuscript,the proposed framework comprises two phases:(i)effective task scheduling using a fractional selectivity approach and(ii)VM allocation by proposing an algorithm by the name of Fitness Sharing Chaotic Particle Swarm Optimization(FSCPSO).The proposed FSCPSO algorithm integrates the concepts of chaos theory and fitness sharing that effectively balance both global exploration and local *** balance enables the use of a wide range of solutions that leads to minimal total cost and makespan,in comparison to other traditional optimization *** FSCPSO algorithm’s performance is analyzed using six evaluation measures namely,Load Balancing Level(LBL),Average Resource Utilization(ARU),total cost,makespan,energy consumption,and response *** relation to the conventional optimization algorithms,the FSCPSO algorithm achieves a higher LBL of 39.12%,ARU of 58.15%,a minimal total cost of 1175,and a makespan of 85.87 ms,particularly when evaluated for 50 tasks.

关键词： Fog computing fractional selectivity approach particle swarm optimization algorithm task scheduling virtual machine allocation

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An End-to-End Transformer-Based Automatic Speech Recognition for Qur’an Reciters

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computers, Materials & Continua 2023年第2期74卷 3471-3487页

作者： Mohammed Hadwan Hamzah A.Alsayadi Salah AL-Hagree Department of Information Technology College of ComputerQassim UniversityBuraydah51452Saudi Arabia Department of Computer Science College of Applied SciencesTaiz UniversityTaiz6803Yemen Computer Science Department Faculty of Computer and Information SciencesAin Shams UniversityCairo11566Egypt Computer Science Department Faculty of SciencesIbb UniversityYemen Department of Computer Sciences&Information Ibb UniversityYemen

The attention-based encoder-decoder technique,known as the trans-former,is used to enhance the performance of end-to-end automatic speech recognition(ASR).This research focuses on applying ASR end-toend transformer-based models for the Arabic language,as the researchers’community pays little attention to *** Muslims Holy Qur’an book is written using Arabic diacritized *** this paper,an end-to-end transformer model to building a robust Qur’an *** is *** acoustic model was built using the transformer-based model as deep learning by the PyTorch framework.A multi-head attention mechanism is utilized to represent the encoder and decoder in the acoustic *** filter bank is used for feature *** build a language model(LM),the Recurrent Neural Network(RNN)and Long short-term memory(LSTM)were used to train an n-gram word-based *** a part of this research,a new dataset of Qur’an verses and their associated transcripts were collected and processed for training and evaluating the proposed model,consisting of 10 h *** recitations performed by 60 *** experimental results showed that the proposed end-to-end transformer-based model achieved a significant low character error rate(CER)of 1.98%and a word error rate(WER)of 6.16%.We have achieved state-of-the-art end-to-end transformer-based recognition for Qur’an reciters.

关键词： Attention-based encoder-decoder recurrent neural network long short-term memory qur’an reciters recognition diacritized arabic text

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Design of a Lightweight Compressed Video Stream-Based Patient Activity Monitoring System

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computers, Materials & Continua 2024年第1期78卷 1253-1274页

作者： Sangeeta Yadav Preeti Gulia Nasib Singh Gill Piyush Kumar Shukla Arfat Ahmad Khan Sultan Alharby Ahmed Alhussen Mohd Anul Haq Department of Computer Science&Applications Maharshi Dayanand UniversityRohtak124001India University Institute of Technology Rajiv Gandhi Proudyogiki VishwavidyalayaBhopal462033India Department of Computer Science College of ComputingKhon Kaen UniversityKhon Kaen40002Thailand Department of Computer Science College of Computer and Information SciencesMajmaah UniversityAl Majmaah11952Kingdom of Saudi Arabia Department of Computer Engineering College of Computer and Information SciencesMajmaah UniversityAl Majmaah11952Kingdom of Saudi Arabia

Inpatient falls from beds in hospitals are a common *** falls may result in severe *** problem can be addressed by continuous monitoring of patients using *** advancements in deep learning-based video analytics have made this task of fall detection more effective and *** with fall detection,monitoring of different activities of the patients is also of significant concern to assess the improvement in their *** computation-intensive models are required to monitor every action of the patient *** requirement limits the applicability of such ***,to keep the model lightweight,the already designed fall detection networks can be extended to monitor the general activities of the patients along with the fall *** by the same notion,we propose a novel,lightweight,and efficient patient activity monitoring system that broadly classifies the patients’activities into fall,activity,and rest classes based on their *** whole network comprises three sub-networks,namely a Convolutional Neural Networks(CNN)based video compression network,a Lightweight Pose Network(LPN)and a Residual Network(ResNet)Mixer block-based activity recognition *** compression network compresses the video streams using deep learning networks for efficient storage and retrieval;after that,LPN estimates human ***,the activity recognition network classifies the patients’activities based on their *** proposed system shows an overall accuracy of approx.99.7% over a standard dataset with 99.63% fall detection accuracy and efficiently monitors different events,which may help monitor the falls and improve the inpatients’health.

关键词： Fall detection activity recognition human pose estimation accuracy

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HDEL: a hierarchical deep ensemble approach for text-based emotion detection

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Multimedia Tools and Applications 2025年第4期84卷 1799-1820页

作者： Vora, Shivani Mehta, Rupa G. Information Technology Department C.K.Pithawala College of Engineering and Technology Gujarat Surat India Department of Computer Science and Engineering SVNIT Gujarat Surat India

Emotion detection from social media data plays a crucial role in studying societal emotions concerning different events, aiding in predicting the reactions of specific social groups. However, it is complex to automatically extract implicit emotional information from noisy social media text data. This study introduces the Hierarchical Deep Ensemble Learning (HDEL) system to identify emotions in text data. The proposed HDEL model utilizes BiLSTM (Bidirectional Long Short-Term Memory), CNN (Convolutional Neural Network), BiGRU (Bidirectional Gated Recurrent Unit), and RCNN (Recurrent Convolutional Neural Network) in the first level of its hierarchy. The predicted probabilities of the four models are embedded with input data to prepare the intermediate hybrid data. This hybrid data is fed to the next layer of the proposed system, which utilizes a Random Forest (RF) algorithm to predict the emotion. The proposed approach is tested using three emotion datasets: the WASSA-2017 Emotion Intensity (EmoInt) dataset, the International Survey on Emotion Antecedents and Reactions (ISEAR) dataset, and the CrowdFlower (CF) dataset. EmoInt and ISEAR are clean and balanced, while CF is noisy and imbalanced. The results are compared with various state-of- the-art Machine Learning models. The outperforming results depict the superiority of the proposed approach. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Learning systems

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An Efficient Framework for Utilizing Underloaded Servers in Compute Cloud

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computer Systems science & engineering 2023年第1期44卷 143-156页

作者： M.Hema S.Kanaga Suba Raja Department of Information Technology Easwari Engineering CollegeChennai600089India Department of Computer Science and Engineering Easwari Engineering CollegeChennai600089India

In cloud data centers,the consolidation of workload is one of the phases during which the available hosts are allocated *** phenomenon ensures that the least possible number of hosts is used without compromise in meeting the Service Level Agreement(SLA).To consolidate the workloads,the hosts are segregated into three categories:normal hosts,under-loaded hosts,and over-loaded hosts based on their *** is to be noted that the identification of an extensively used host or underloaded host is challenging to ***-old values were proposed in the literature to detect this *** current study aims to improve the existing methods that choose the underloaded hosts,get rid of Virtual Machines(VMs)from them,andfinally place them in some other *** researcher proposes a Host Resource Utilization Aware(HRUAA)Algorithm to detect those underloaded and place its virtual machines on different hosts in a vibrant Cloud *** mechanism presented in this study is contrasted with existing mechanisms *** results attained from the study estab-lish that numerous hosts can be shut down,while at the same time,the user's workload requirement can also be *** proposed method is energy-efficient in workload consolidation,saves cost and time,and leverages active hosts.

关键词： Workload consolidation energy consumption under load server resource utilization

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