Background:Music has proven to be vital in enhancing resilience and promotingwell-being.Previously,the impact of music in sports environments was solely investigated,while this paper applies it to study environments,s...Background:Music has proven to be vital in enhancing resilience and promotingwell-being.Previously,the impact of music in sports environments was solely investigated,while this paper applies it to study environments,standing out as pioneering research.The study consists of a systematic development of a conceptual framework based on theories of Uses and Gratification Expectancy(UGE)and perceived motivation based on music elements.Their components are observed variables influencing students’psychological well-being(as the dependent variable).Resilience is examined as a mediator,influencing the relationships of both observed and dependent variables.The main purpose of this study is to highlight the positive effects of online music consumption on the psychological well-being of students.Methods:Semi-structured qualitative interviews were conducted with eighteen final year creative multimedia undergraduate students belonging to five central region Malaysian universities,especially on their UGE needs,and a similar concept survey instrument with two hundred participants.The interview data were analysed through thematic analysis,while the survey data through descriptive and Partial Least Squares Structural Equation Modeling(PLS-SEM).Results:The results highlight that students gain motivation from online music,which positively affects their psychological well-being(β=0.190,p=0.003,f^(2)=0.037),while resilience significantly affects this relationship(β=0.562,p<0.001,f^(2)=0.461).However,the results also predict a partial relationship between constructs based on UGE with psychological well-being,mediated by resilience,i.e.,AT-UGE(β=0.021,p=0.783,f^(2)=0.000),SIPI-UGE(β=0.228,p=0.004,f^(2)=0.044).Conclusion:The outcome of the study reflected practical,meaningful,and statistically significant results.The majority of the predictors,with the exception of one,i.e.,AT-UGE,displayed a clear positive relation of online music consumption on the Psychological Well-being of students.Future research will explore varying contextual factors impacting online music-related gratifications,motivations,and resilience,along with additional potential mediators and moderators.展开更多
The problem of fake news detection(FND)is becoming increasingly important in the field of natural language processing(NLP)because of the rapid dissemination of misleading information on the web.Large language models(L...The problem of fake news detection(FND)is becoming increasingly important in the field of natural language processing(NLP)because of the rapid dissemination of misleading information on the web.Large language models(LLMs)such as GPT-4.Zero excels in natural language understanding tasks but can still struggle to distinguish between fact and fiction,particularly when applied in the wild.However,a key challenge of existing FND methods is that they only consider unimodal data(e.g.,images),while more detailed multimodal data(e.g.,user behaviour,temporal dynamics)is neglected,and the latter is crucial for full-context understanding.To overcome these limitations,we introduce M3-FND(Multimodal Misinformation Mitigation for False News Detection),a novel methodological framework that integrates LLMs with multimodal data sources to perform context-aware veracity assessments.Our method proposes a hybrid system that combines image-text alignment,user credibility profiling,and temporal pattern recognition,which is also strengthened through a natural feedback loop that provides real-time feedback for correcting downstream errors.We use contextual reinforcement learning to schedule prompt updating and update the classifier threshold based on the latest multimodal input,which enables the model to better adapt to changing misinformation attack strategies.M3-FND is tested on three diverse datasets,FakeNewsNet,Twitter15,andWeibo,which contain both text and visual socialmedia content.Experiments showthatM3-FND significantly outperforms conventional and LLMbased baselines in terms of accuracy,F1-score,and AUC on all benchmarks.Our results indicate the importance of employing multimodal cues and adaptive learning for effective and timely detection of fake news.展开更多
As healthcare systems increasingly embrace digitalization,effective management of electronic health records(EHRs)has emerged as a critical priority,particularly in inpatient settings where data sensitivity and realtim...As healthcare systems increasingly embrace digitalization,effective management of electronic health records(EHRs)has emerged as a critical priority,particularly in inpatient settings where data sensitivity and realtime access are paramount.Traditional EHR systems face significant challenges,including unauthorized access,data breaches,and inefficiencies in tracking follow-up appointments,which heighten the risk of misdiagnosis and medication errors.To address these issues,this research proposes a hybrid blockchain-based solution for securely managing EHRs,specifically designed as a framework for tracking inpatient follow-ups.By integrating QR codeenabled data access with a blockchain architecture,this innovative approach enhances privacy protection,data integrity,and auditing capabilities,while facilitating swift and real-time data retrieval.The architecture adheres to Role-Based Access Control(RBAC)principles and utilizes robust encryption techniques,including SHA-256 and AES-256-CBC,to secure sensitive information.A comprehensive threat model outlines trust boundaries and potential adversaries,complemented by a validated data transmission protocol.Experimental results demonstrate that the framework remains reliable in concurrent access scenarios,highlighting its efficiency and responsiveness in real-world applications.This study emphasizes the necessity for hybrid solutions in managing sensitive medical information and advocates for integrating blockchain technology and QR code innovations into contemporary healthcare systems.展开更多
The exponential growth of Internet of Things(IoT)devices,autonomous systems,and digital services is generating massive volumes of big data,projected to exceed 291 zettabytes by 2027.Conventional cloud computing,despit...The exponential growth of Internet of Things(IoT)devices,autonomous systems,and digital services is generating massive volumes of big data,projected to exceed 291 zettabytes by 2027.Conventional cloud computing,despite its high processing and storage capacity,suffers from increased network latency,network congestion,and high operational costs,making it unsuitable for latency-sensitive applications.Edge computing addresses these issues by processing data near the source but faces scalability challenges and elevated Total Cost of Ownership(TCO).Hybrid solutions,such as fog computing,cloudlets,and Mobile Edge Computing(MEC),attempt to balance cost and performance;however,they still struggle with limited resource sharing and high deployment expenses.This paper proposes Public Edge as a Service(PEaaS),a novel paradigm that utilizes idle resources contributed by universities,enterprises,cellular operators,and individuals under a collaborative service model.By decentralizing computation and enabling multi-tenant resource sharing,PEaaS reduces reliance on centralized cloud infrastructure,minimizes communication costs,and enhances scalability.The proposed framework is evaluated using EdgeCloudSim under varying workloads,for keymetrics such as latency,communication cost,server utilization,and task failure rate.Results reveal that while cloud has a task failure rate rising sharply to 12.3%at 2000 devices,PEaaS maintains a low rate of 2.5%,closely matching edge computing.Furthermore,communication costs remain 25% lower than cloud and latency remains below 0.3,even under peak load.These findings demonstrate that PEaaS achieves near-edge performance with reduced costs and enhanced scalability,offering a sustainable and economically viable solution for next-generation computing environments.展开更多
Manual inspection of onba earing casting defects is not realistic and unreliable,particularly in the case of some micro-level anomalies which lead to major defects on a large scale.To address these challenges,we propo...Manual inspection of onba earing casting defects is not realistic and unreliable,particularly in the case of some micro-level anomalies which lead to major defects on a large scale.To address these challenges,we propose BearFusionNet,an attention-based deep learning architecture with multi-stream,which merges both DenseNet201 and MobileNetV2 for feature extraction with a classification head inspired by VGG19.This hybrid design,figuratively beaming from one layer to another,extracts the enormity of representations on different scales,backed by a prepreprocessing pipeline that brings defect saliency to the fore through contrast adjustment,denoising,and edge detection.The use of multi-head self-attention enhances feature fusion,enabling the model to capture both large and small spatial features.BearFusionNet achieves an accuracy of 99.66%and Cohen’s kappa score of 0.9929 in Kaggle’s Real-life Industrial Casting Defects dataset.Both McNemar’s and Wilcoxon signed-rank statistical tests,as well as fivefold cross-validation,are employed to assess the robustness of our proposed model.To interpret the model,we adopt Grad-Cam visualizations,which are the state of the art standard.Furthermore,we deploy BearFusionNet as a webbased system for near real-time inference(5-6 s per prediction),which enables the quickest yet accurate detection with visual explanations.Overall,BearFusionNet is an interpretable,accurate,and deployable solution that can automatically detect casting defects,leading to significant advances in the innovative industrial environment.展开更多
The rapid growth in available network bandwidth has directly contributed to an exponential increase in mobile data traffic,creating significant challenges for network energy consumption.Also,with the extraordinary gro...The rapid growth in available network bandwidth has directly contributed to an exponential increase in mobile data traffic,creating significant challenges for network energy consumption.Also,with the extraordinary growth of mobile communications,the data traffic has dramatically expanded,which has led to massive grid power consumption and incurred high operating expenditure(OPEX).However,the majority of current network designs struggle to efficientlymanage a massive amount of data using little power,which degrades energy efficiency performance.Thereby,it is necessary to have an efficient mechanism to reduce power consumption when processing large amounts of data in network data centers.Utilizing renewable energy sources to power the Cloud Radio Access Network(C-RAN)greatly reduces the need to purchase energy from the utility grid.In this paper,we propose a bandwidth-aware hybrid energypowered C-RAN that focuses on throughput and energy efficiency(EE)by lowering grid usage,aiming to enhance the EE.This paper examines the energy efficiency,spectral efficiency(SE),and average on-grid energy consumption,dealing with the major challenges of the temporal and spatial nature of traffic and renewable energy generation across various network setups.To assess the effectiveness of the suggested network by changing the transmission bandwidth,a comprehensive simulation has been conducted.The numerical findings support the efficacy of the suggested approach.展开更多
Safeguarding modern networks from cyber intrusions has become increasingly challenging as attackers continually refine their evasion tactics.Although numerousmachine-learning-based intrusion detection systems(IDS)have...Safeguarding modern networks from cyber intrusions has become increasingly challenging as attackers continually refine their evasion tactics.Although numerousmachine-learning-based intrusion detection systems(IDS)have been developed,their effectiveness is often constrained by high dimensionality and redundant features that degrade both accuracy and efficiency.This study introduces a hybrid feature-selection framework that integrates the exploration capability of Prairie Dog Optimization(PDO)with the exploitation behavior of Ant Colony Optimization(ACO).The proposed PDO–ACO algorithm identifies a concise yet discriminative subset of features from the NSLKDD dataset and evaluates them using a Support Vector Machine(SVM)classifier.Experimental analyses reveal that the PDO–ACO model achieves superior detection accuracy of 98%while significantly lowering false alarms and computational overhead.Further validation on the CEC2017 benchmark suite confirms the robustness and adaptability of the hybrid model across diverse optimization landscapes,positioning PDO–ACO as an efficient and scalable approach for intelligent intrusion detection.展开更多
Electrical energy can be harvested from the rotational kinetic energy of moving bodies,consisting of both mechanical and kinetic energy as a potential power source through electromagnetic induction,similar to wind ene...Electrical energy can be harvested from the rotational kinetic energy of moving bodies,consisting of both mechanical and kinetic energy as a potential power source through electromagnetic induction,similar to wind energy applications.In industries,rotational bodies are commonly present in operations,yet this kinetic energy remains untapped.This research explores the energy generation characteristics of two rotational body types,disk-shaped and cylinder-shaped under specific experimental setups.The hardware setup included a direct current(DC)motor driver,power supply,DC generator,mechanical support,and load resistance,while the software setup involved automation testing tools and data logging.Electromagnetic induction was used to harvest energy,and experiments were conducted at room temperature(25℃)with controlled variables like speed and friction.Results showed the disk-shaped body exhibited higher energy efficiency than the cylinder-shaped body,largely due to lower mechanical losses.The disk required only two bearings,while the cylinder required four,resulting in lower bearing losses for the disk.Additionally,the disk experienced only air friction,whereas the cylinder encountered friction from a soft,uneven rubber material,increasing surface contact losses.Under a 40 W resistive load,the disk demonstrated a 17.1%energy loss due to mechanical friction,achieving up to 15.55 J of recycled energy.Conversely,the cylinder body experienced a 48.05%energy loss,delivering only 51.95%of energy to the load.These insights suggest significant potential for designing efficient energy recycling systems in industrial settings,particularly in manufacturing and processing industries where rotational machinery is prevalent.Despite its lower energy density,this system could be beneficially integrated with energy storage solutions,enhancing sustainability in industrial practices.展开更多
Vehicle overtaking poses significant risks and leads to injuries and losses on Malaysia’s roads.In most scenarios,insufficient and untimely information available to drivers for accessing road conditions and their sur...Vehicle overtaking poses significant risks and leads to injuries and losses on Malaysia’s roads.In most scenarios,insufficient and untimely information available to drivers for accessing road conditions and their surrounding environment is the primary factor that causes these incidents.To address these issues,a comprehensive system is required to provide real-time assistance to drivers.Building upon our previous research on a LoRa-based lane change decision-aid system,this study proposes an enhanced Vehicle Overtaking System(VOS).This system utilizes long-range(LoRa)communication for reliable real-time data exchange between vehicles(V2V)and the cloud(V2C).By providing drivers with critical information,including surrounding vehicle movements,through visual and audible warnings,the VOS aims to support vehicle overtaking decisions by calculating the safe distance between vehicles as per the Association of State Highway and Transportation Officials(AASHTO)guidelines.This study also examines the performance of LoRa communication strength and data transmission at various distances using a cloud monitoring tool or dashboard.展开更多
Networking,storage,and hardware are just a few of the virtual computing resources that the infrastruc-ture service model offers,depending on what the client needs.One essential aspect of cloud computing that improves ...Networking,storage,and hardware are just a few of the virtual computing resources that the infrastruc-ture service model offers,depending on what the client needs.One essential aspect of cloud computing that improves resource allocation techniques is host load prediction.This difficulty means that hardware resource allocation in cloud computing still results in hosting initialization issues,which add several minutes to response times.To solve this issue and accurately predict cloud capacity,cloud data centers use prediction algorithms.This permits dynamic cloud scalability while maintaining superior service quality.For host prediction,we therefore present a hybrid convolutional neural network long with short-term memory model in this work.First,the suggested hybrid model is input is subjected to the vector auto regression technique.The data in many variables that,prior to analysis,has been filtered to eliminate linear interdependencies.After that,the persisting data are processed and sent into the convolutional neural network layer,which gathers intricate details about the utilization of each virtual machine and central processing unit.The next step involves the use of extended short-term memory,which is suitable for representing the temporal information of irregular trends in time series components.The key to the entire process is that we used the most appropriate activation function for this type of model a scaled polynomial constant unit.Cloud systems require accurate prediction due to the increasing degrees of unpredictability in data centers.Because of this,two actual load traces were used in this study’s assessment of the performance.An example of the load trace is in the typical dispersed system.In comparison to CNN,VAR-GRU,VAR-MLP,ARIMA-LSTM,and other models,the experiment results demonstrate that our suggested approach offers state-of-the-art performance with higher accuracy in both datasets.展开更多
Industrial processes often involve rotating machinery that generates substantial kinetic energy,much of which remains untapped.Harvesting rotational kinetic energy offers a promising solution to reduce energy waste an...Industrial processes often involve rotating machinery that generates substantial kinetic energy,much of which remains untapped.Harvesting rotational kinetic energy offers a promising solution to reduce energy waste and improve energy efficiency in industrial applications.This research investigates the potential of electromagnetic induction for harvesting rotational kinetic energy from industrial machinery.A comparative study was conducted between disk and cylinder-shaped rotational bodies to evaluate their energy efficiency under various load conditions.Experimental results demonstrated that the disk body exhibited higher energy efficiency,primarily due to lower mechanical losses compared to the cylinder body.A power management circuit was developed to regulate and store the harvested energy,integrating voltage,current,and speed sensors along with a charge controller for battery storage.The experimental setup successfully converted rotational kinetic energy into usable electrical power,with the disk achieving up to 16.33 J of recycled energy,outperforming the cylinder.The disk body demonstrated higher energy recovery efficiency compared to the cylinder,particularly under the 40 W resistive load condition.These findings demonstrate the feasibility of implementing energy recycling systems in industrial settings to enhance sustainability,reduce energy consumption,and minimize waste.Future research should focus on optimizing power management systems and improving energy harvesting efficiency to enable wider adoption of energy recycling technologies in various industrial applications.展开更多
Traditional rule-based IntrusionDetection Systems(IDS)are commonly employed owing to their simple design and ability to detect known threats.Nevertheless,as dynamic network traffic and a new degree of threats exist in...Traditional rule-based IntrusionDetection Systems(IDS)are commonly employed owing to their simple design and ability to detect known threats.Nevertheless,as dynamic network traffic and a new degree of threats exist in IoT environments,these systems do not perform well and have elevated false positive rates—consequently decreasing detection accuracy.In this study,we try to overcome these restrictions by employing fuzzy logic and machine learning to develop an Enhanced Rule-Based Model(ERBM)to classify the packets better and identify intrusions.The ERBM developed for this approach improves data preprocessing and feature selections by utilizing fuzzy logic,where three membership functions are created to classify all the network traffic features as low,medium,or high to remain situationally aware of the environment.Such fuzzy logic sets produce adaptive detection rules by reducing data uncertainty.Also,for further classification,machine learning classifiers such as Decision Tree(DT),Random Forest(RF),and Neural Networks(NN)learn complex ways of attacks and make the detection process more precise.A thorough performance evaluation using different metrics,including accuracy,precision,recall,F1 Score,detection rate,and false-positive rate,verifies the supremacy of ERBM over classical IDS.Under extensive experiments,the ERBM enables a remarkable detection rate of 99%with considerably fewer false positives than the conventional models.Integrating the ability for uncertain reasoning with fuzzy logic and an adaptable component via machine learning solutions,the ERBM systemprovides a unique,scalable,data-driven approach to IoT intrusion detection.This research presents a major enhancement initiative in the context of rule-based IDS,introducing improvements in accuracy to evolving IoT threats.展开更多
Vehicle recognition plays a vital role in intelligent transportation systems,law enforcement,access control,and security operations—domains that are becoming increasingly dynamic and complex.Despite advancements,most...Vehicle recognition plays a vital role in intelligent transportation systems,law enforcement,access control,and security operations—domains that are becoming increasingly dynamic and complex.Despite advancements,most existing solutions remain siloed,addressing individual tasks such as vehicle make and model recognition(VMMR),automatic number plate recognition(ANPR),and color classification separately.This fragmented approach limits real-world efficiency,leading to slower processing,reduced accuracy,and increased operational costs,particularly in traffic monitoring and surveillance scenarios.To address these limitations,we present a unified framework that consolidates all three recognition tasks into a single,lightweight system.The framework utilizes MobileNetV2 for efficient VMMR,YOLO(You Only Look Once)for accurate license plate detection,and histogram-based clustering in the HSV color space for precise color identification.Rather than optimizing each module in isolation,our approach emphasizes tight integration,enabling improved performance and reliability.The system also features adaptive image calibration and robust algorithmic enhancements to ensure consistent results under varying environmental conditions.Experimental evaluations demonstrate that the proposedmodel achieves a combined accuracy of 93.3%,outperforming traditional methods and offering practical scalability for deployment in real-world transportation infrastructures.展开更多
Artificial intelligence(AI),particularly deep learning algorithms utilizing convolutional neural networks,plays an increasingly pivotal role in enhancing medical image examination.It demonstrates the potential for imp...Artificial intelligence(AI),particularly deep learning algorithms utilizing convolutional neural networks,plays an increasingly pivotal role in enhancing medical image examination.It demonstrates the potential for improving diagnostic accuracy within dental care.Orthopantomograms(OPGs)are essential in dentistry;however,their manual interpretation is often inconsistent and tedious.To the best of our knowledge,this is the first comprehensive application of YOLOv5m for the simultaneous detection and classification of six distinct dental pathologies using panoramic OPG images.The model was trained and refined on a custom dataset that began with 232 panoramic radiographs and was later expanded to 604 samples.These included annotated subclasses representing Caries,Infection,Impacted Teeth,Fractured Teeth,Broken Crowns,and Healthy conditions.The training was performed using GPU resources alongside tuned hyperparameters of batch size,learning rate schedule,and early stopping tailored for generalization to prevent overfitting.Evaluation on a held-out test set showed strong performance in the detection and localization of various dental pathologies and robust overall accuracy.At an IoU of 0.5,the system obtained a mean precision of 94.22%and recall of 90.42%,with mAP being 93.71%.This research confirms the use of YOLOv5m as a robust,highly efficient AI technology for the analysis of dental pathologies using OPGs,providing a clinically useful solution to enhance workflow efficiency and aid in sustaining consistency in complex multi-dimensional case evaluations.展开更多
The Internet of Things(IoT)ecosystem faces growing security challenges because it is projected to have 76.88 billion devices by 2025 and $1.4 trillion market value by 2027,operating in distributed networks with resour...The Internet of Things(IoT)ecosystem faces growing security challenges because it is projected to have 76.88 billion devices by 2025 and $1.4 trillion market value by 2027,operating in distributed networks with resource limitations and diverse system architectures.The current conventional intrusion detection systems(IDS)face scalability problems and trust-related issues,but blockchain-based solutions face limitations because of their low transaction throughput(Bitcoin:7 TPS(Transactions Per Second),Ethereum:15-30 TPS)and high latency.The research introduces MBID(Multi-Tier Blockchain Intrusion Detection)as a groundbreaking Multi-Tier Blockchain Intrusion Detection System with AI-Enhanced Detection,which solves the problems in huge IoT networks.The MBID system uses a four-tier architecture that includes device,edge,fog,and cloud layers with blockchain implementations and Physics-Informed Neural Networks(PINNs)for edge-based anomaly detection and a dual consensus mechanism that uses Honesty-based Distributed Proof-of-Authority(HDPoA)and Delegated Proof of Stake(DPoS).The system achieves scalability and efficiency through the combination of dynamic sharding and Interplanetary File System(IPFS)integration.Experimental evaluations demonstrate exceptional performance,achieving a detection accuracy of 99.84%,an ultra-low false positive rate of 0.01% with a False Negative Rate of 0.15%,and a near-instantaneous edge detection latency of 0.40 ms.The system demonstrated an aggregate throughput of 214.57 TPS in a 3-shard configuration,providing a clear,evidence-based path for horizontally scaling to support overmillions of devices with exceeding throughput.The proposed architecture represents a significant advancement in blockchain-based security for IoT networks,effectively balancing the trade-offs between scalability,security,and decentralization.展开更多
Multiple Object Tracking(MOT)is essential for applications such as autonomous driving,surveillance,and analytics;However,challenges such as occlusion,low-resolution imaging,and identity switches remain persistent.We p...Multiple Object Tracking(MOT)is essential for applications such as autonomous driving,surveillance,and analytics;However,challenges such as occlusion,low-resolution imaging,and identity switches remain persistent.We propose HAMOT,a hierarchical adaptive multi-object tracker that solves these challenges with a novel,unified framework.Unlike previous methods that rely on isolated components,HAMOT incorporates a Swin Transformer-based Adaptive Enhancement(STAE)module—comprising Scene-Adaptive Transformer Enhancement and Confidence-Adaptive Feature Refinement—to improve detection under low-visibility conditions.The hierarchical DynamicGraphNeuralNetworkwith TemporalAttention(DGNN-TA)models both short-and long-termassociations,and the Adaptive Unscented Kalman Filter with Gated Recurrent Unit(AUKF-GRU)ensures accurate motion prediction.The novel Graph-Based Density-Aware Clustering(GDAC)improves occlusion recovery by adapting to scene density,preserving identity integrity.This integrated approach enables adaptive responses to complex visual scenarios,Achieving exceptional performance across all evaluation metrics,including aHigher Order TrackingAccuracy(HOTA)of 67.05%,a Multiple Object Tracking Accuracy(MOTA)of 82.4%,an ID F1 Score(IDF1)of 83.1%,and a total of 1052 Identity Switches(IDSW)on theMOT17;66.61%HOTA,78.3%MOTA,82.1%IDF1,and a total of 748 IDSWonMOT20;and 66.4%HOTA,92.32%MOTA,and 68.96%IDF1 on DanceTrack.With fixed thresholds,the full HAMOT model(all six components)achieves real-time functionality at 24 FPS on MOT17 using RTX3090,ensuring robustness and scalability for real-world MOT applications.展开更多
Feature selection(FS)plays a crucial role in medical imaging by reducing dimensionality,improving computational efficiency,and enhancing diagnostic accuracy.Traditional FS techniques,including filter,wrapper,and embed...Feature selection(FS)plays a crucial role in medical imaging by reducing dimensionality,improving computational efficiency,and enhancing diagnostic accuracy.Traditional FS techniques,including filter,wrapper,and embedded methods,have been widely used but often struggle with high-dimensional and heterogeneous medical imaging data.Deep learning-based FS methods,particularly Convolutional Neural Networks(CNNs)and autoencoders,have demonstrated superior performance but lack interpretability.Hybrid approaches that combine classical and deep learning techniques have emerged as a promising solution,offering improved accuracy and explainability.Furthermore,integratingmulti-modal imaging data(e.g.,MagneticResonance Imaging(MRI),ComputedTomography(CT),Positron Emission Tomography(PET),and Ultrasound(US))poses additional challenges in FS,necessitating advanced feature fusion strategies.Multi-modal feature fusion combines information fromdifferent imagingmodalities to improve diagnostic accuracy.Recently,quantum computing has gained attention as a revolutionary approach for FS,providing the potential to handle high-dimensional medical data more efficiently.This systematic literature review comprehensively examines classical,Deep Learning(DL),hybrid,and quantum-based FS techniques inmedical imaging.Key outcomes include a structured taxonomy of FS methods,a critical evaluation of their performance across modalities,and identification of core challenges such as computational burden,interpretability,and ethical considerations.Future research directions—such as explainable AI(XAI),federated learning,and quantum-enhanced FS—are also emphasized to bridge the current gaps.This review provides actionable insights for developing scalable,interpretable,and clinically applicable FS methods in the evolving landscape of medical imaging.展开更多
本文提出了光码多分址(CDMA)和光密集波分复用(DWDM)的混合系统,全面研究了四波混频(FWM)的影响。在这个系统中,主要存在两个四波混频问题:包括多址干扰(MAI)和码间干扰(ISI)的帧间四波混频和信道内四波混频。结果表明,综合考虑信道间...本文提出了光码多分址(CDMA)和光密集波分复用(DWDM)的混合系统,全面研究了四波混频(FWM)的影响。在这个系统中,主要存在两个四波混频问题:包括多址干扰(MAI)和码间干扰(ISI)的帧间四波混频和信道内四波混频。结果表明,综合考虑信道间和信道内四波混频的影响,最佳发射功率可选为18 d Bm。当发射功率大于18 d Bm时,混合系统的误码率(BER)将增加。基于此,本文提出了一种电光相位调制器(EOPM)模块,将其放置在波分复用器之后,通过抑制信道内四波混频的影响,同时调制所有波长信号的相位,从而增加混合系统的非线性容限,这极大地改善了基于OOK传输的光学CDMA-DWDM混合系统的性能。此外,由于多对角线(MD)结构具有零互相关特性,通过使用多对角线识别序列码可以减少多址干扰的影响。结果还表明,CDMA技术与色散相结合有助于降低信道间四波混频的影响。此外,识别序列码间隔在减轻码间干扰中起着至关重要的作用,如结果所示,当识别序列码间隔压缩至比特持续时间的25%时,可以避免码间干扰,此时所提出的混合系统的性能最佳。展开更多
The principal component analysis (PCA) is a kind of algorithms in biometrics. It is a statistics technical and used orthogonal transformation to convert a set of observations of possibly correlated variables into a se...The principal component analysis (PCA) is a kind of algorithms in biometrics. It is a statistics technical and used orthogonal transformation to convert a set of observations of possibly correlated variables into a set of values of linearly uncorrelated variables. PCA also is a tool to reduce multidimensional data to lower dimensions while retaining most of the information. It covers standard deviation, covariance, and eigenvectors. This background knowledge is meant to make the PCA section very straightforward, but can be skipped if the concepts are already familiar.展开更多
基金funded by Malaysian Ministry of Higher Education(MOHE)under the Fundamental Research Grant Scheme(FRGS/1/2023/SSI07/MMU/02/3)which is awarded to the Multimedia University.The project is led by the second author.
文摘Background:Music has proven to be vital in enhancing resilience and promotingwell-being.Previously,the impact of music in sports environments was solely investigated,while this paper applies it to study environments,standing out as pioneering research.The study consists of a systematic development of a conceptual framework based on theories of Uses and Gratification Expectancy(UGE)and perceived motivation based on music elements.Their components are observed variables influencing students’psychological well-being(as the dependent variable).Resilience is examined as a mediator,influencing the relationships of both observed and dependent variables.The main purpose of this study is to highlight the positive effects of online music consumption on the psychological well-being of students.Methods:Semi-structured qualitative interviews were conducted with eighteen final year creative multimedia undergraduate students belonging to five central region Malaysian universities,especially on their UGE needs,and a similar concept survey instrument with two hundred participants.The interview data were analysed through thematic analysis,while the survey data through descriptive and Partial Least Squares Structural Equation Modeling(PLS-SEM).Results:The results highlight that students gain motivation from online music,which positively affects their psychological well-being(β=0.190,p=0.003,f^(2)=0.037),while resilience significantly affects this relationship(β=0.562,p<0.001,f^(2)=0.461).However,the results also predict a partial relationship between constructs based on UGE with psychological well-being,mediated by resilience,i.e.,AT-UGE(β=0.021,p=0.783,f^(2)=0.000),SIPI-UGE(β=0.228,p=0.004,f^(2)=0.044).Conclusion:The outcome of the study reflected practical,meaningful,and statistically significant results.The majority of the predictors,with the exception of one,i.e.,AT-UGE,displayed a clear positive relation of online music consumption on the Psychological Well-being of students.Future research will explore varying contextual factors impacting online music-related gratifications,motivations,and resilience,along with additional potential mediators and moderators.
文摘The problem of fake news detection(FND)is becoming increasingly important in the field of natural language processing(NLP)because of the rapid dissemination of misleading information on the web.Large language models(LLMs)such as GPT-4.Zero excels in natural language understanding tasks but can still struggle to distinguish between fact and fiction,particularly when applied in the wild.However,a key challenge of existing FND methods is that they only consider unimodal data(e.g.,images),while more detailed multimodal data(e.g.,user behaviour,temporal dynamics)is neglected,and the latter is crucial for full-context understanding.To overcome these limitations,we introduce M3-FND(Multimodal Misinformation Mitigation for False News Detection),a novel methodological framework that integrates LLMs with multimodal data sources to perform context-aware veracity assessments.Our method proposes a hybrid system that combines image-text alignment,user credibility profiling,and temporal pattern recognition,which is also strengthened through a natural feedback loop that provides real-time feedback for correcting downstream errors.We use contextual reinforcement learning to schedule prompt updating and update the classifier threshold based on the latest multimodal input,which enables the model to better adapt to changing misinformation attack strategies.M3-FND is tested on three diverse datasets,FakeNewsNet,Twitter15,andWeibo,which contain both text and visual socialmedia content.Experiments showthatM3-FND significantly outperforms conventional and LLMbased baselines in terms of accuracy,F1-score,and AUC on all benchmarks.Our results indicate the importance of employing multimodal cues and adaptive learning for effective and timely detection of fake news.
基金funded by Multimedia University,Cyberjaya,Selangor,Malaysia(Grant Number:PostDoc(MMUI/240029)).
文摘As healthcare systems increasingly embrace digitalization,effective management of electronic health records(EHRs)has emerged as a critical priority,particularly in inpatient settings where data sensitivity and realtime access are paramount.Traditional EHR systems face significant challenges,including unauthorized access,data breaches,and inefficiencies in tracking follow-up appointments,which heighten the risk of misdiagnosis and medication errors.To address these issues,this research proposes a hybrid blockchain-based solution for securely managing EHRs,specifically designed as a framework for tracking inpatient follow-ups.By integrating QR codeenabled data access with a blockchain architecture,this innovative approach enhances privacy protection,data integrity,and auditing capabilities,while facilitating swift and real-time data retrieval.The architecture adheres to Role-Based Access Control(RBAC)principles and utilizes robust encryption techniques,including SHA-256 and AES-256-CBC,to secure sensitive information.A comprehensive threat model outlines trust boundaries and potential adversaries,complemented by a validated data transmission protocol.Experimental results demonstrate that the framework remains reliable in concurrent access scenarios,highlighting its efficiency and responsiveness in real-world applications.This study emphasizes the necessity for hybrid solutions in managing sensitive medical information and advocates for integrating blockchain technology and QR code innovations into contemporary healthcare systems.
文摘The exponential growth of Internet of Things(IoT)devices,autonomous systems,and digital services is generating massive volumes of big data,projected to exceed 291 zettabytes by 2027.Conventional cloud computing,despite its high processing and storage capacity,suffers from increased network latency,network congestion,and high operational costs,making it unsuitable for latency-sensitive applications.Edge computing addresses these issues by processing data near the source but faces scalability challenges and elevated Total Cost of Ownership(TCO).Hybrid solutions,such as fog computing,cloudlets,and Mobile Edge Computing(MEC),attempt to balance cost and performance;however,they still struggle with limited resource sharing and high deployment expenses.This paper proposes Public Edge as a Service(PEaaS),a novel paradigm that utilizes idle resources contributed by universities,enterprises,cellular operators,and individuals under a collaborative service model.By decentralizing computation and enabling multi-tenant resource sharing,PEaaS reduces reliance on centralized cloud infrastructure,minimizes communication costs,and enhances scalability.The proposed framework is evaluated using EdgeCloudSim under varying workloads,for keymetrics such as latency,communication cost,server utilization,and task failure rate.Results reveal that while cloud has a task failure rate rising sharply to 12.3%at 2000 devices,PEaaS maintains a low rate of 2.5%,closely matching edge computing.Furthermore,communication costs remain 25% lower than cloud and latency remains below 0.3,even under peak load.These findings demonstrate that PEaaS achieves near-edge performance with reduced costs and enhanced scalability,offering a sustainable and economically viable solution for next-generation computing environments.
基金funded by Multimedia University,Cyberjaya,Selangor,Malaysia(Grant Number:PostDoc(MMUI/240029)).
文摘Manual inspection of onba earing casting defects is not realistic and unreliable,particularly in the case of some micro-level anomalies which lead to major defects on a large scale.To address these challenges,we propose BearFusionNet,an attention-based deep learning architecture with multi-stream,which merges both DenseNet201 and MobileNetV2 for feature extraction with a classification head inspired by VGG19.This hybrid design,figuratively beaming from one layer to another,extracts the enormity of representations on different scales,backed by a prepreprocessing pipeline that brings defect saliency to the fore through contrast adjustment,denoising,and edge detection.The use of multi-head self-attention enhances feature fusion,enabling the model to capture both large and small spatial features.BearFusionNet achieves an accuracy of 99.66%and Cohen’s kappa score of 0.9929 in Kaggle’s Real-life Industrial Casting Defects dataset.Both McNemar’s and Wilcoxon signed-rank statistical tests,as well as fivefold cross-validation,are employed to assess the robustness of our proposed model.To interpret the model,we adopt Grad-Cam visualizations,which are the state of the art standard.Furthermore,we deploy BearFusionNet as a webbased system for near real-time inference(5-6 s per prediction),which enables the quickest yet accurate detection with visual explanations.Overall,BearFusionNet is an interpretable,accurate,and deployable solution that can automatically detect casting defects,leading to significant advances in the innovative industrial environment.
文摘The rapid growth in available network bandwidth has directly contributed to an exponential increase in mobile data traffic,creating significant challenges for network energy consumption.Also,with the extraordinary growth of mobile communications,the data traffic has dramatically expanded,which has led to massive grid power consumption and incurred high operating expenditure(OPEX).However,the majority of current network designs struggle to efficientlymanage a massive amount of data using little power,which degrades energy efficiency performance.Thereby,it is necessary to have an efficient mechanism to reduce power consumption when processing large amounts of data in network data centers.Utilizing renewable energy sources to power the Cloud Radio Access Network(C-RAN)greatly reduces the need to purchase energy from the utility grid.In this paper,we propose a bandwidth-aware hybrid energypowered C-RAN that focuses on throughput and energy efficiency(EE)by lowering grid usage,aiming to enhance the EE.This paper examines the energy efficiency,spectral efficiency(SE),and average on-grid energy consumption,dealing with the major challenges of the temporal and spatial nature of traffic and renewable energy generation across various network setups.To assess the effectiveness of the suggested network by changing the transmission bandwidth,a comprehensive simulation has been conducted.The numerical findings support the efficacy of the suggested approach.
基金funded by Princess Nourah bint Abdulrahman University Researchers Supporting Project number(PNURSP2026R500)Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia.
文摘Safeguarding modern networks from cyber intrusions has become increasingly challenging as attackers continually refine their evasion tactics.Although numerousmachine-learning-based intrusion detection systems(IDS)have been developed,their effectiveness is often constrained by high dimensionality and redundant features that degrade both accuracy and efficiency.This study introduces a hybrid feature-selection framework that integrates the exploration capability of Prairie Dog Optimization(PDO)with the exploitation behavior of Ant Colony Optimization(ACO).The proposed PDO–ACO algorithm identifies a concise yet discriminative subset of features from the NSLKDD dataset and evaluates them using a Support Vector Machine(SVM)classifier.Experimental analyses reveal that the PDO–ACO model achieves superior detection accuracy of 98%while significantly lowering false alarms and computational overhead.Further validation on the CEC2017 benchmark suite confirms the robustness and adaptability of the hybrid model across diverse optimization landscapes,positioning PDO–ACO as an efficient and scalable approach for intelligent intrusion detection.
基金The APC was funded by Research Management Center, Multimedia University, Malaysia.
文摘Electrical energy can be harvested from the rotational kinetic energy of moving bodies,consisting of both mechanical and kinetic energy as a potential power source through electromagnetic induction,similar to wind energy applications.In industries,rotational bodies are commonly present in operations,yet this kinetic energy remains untapped.This research explores the energy generation characteristics of two rotational body types,disk-shaped and cylinder-shaped under specific experimental setups.The hardware setup included a direct current(DC)motor driver,power supply,DC generator,mechanical support,and load resistance,while the software setup involved automation testing tools and data logging.Electromagnetic induction was used to harvest energy,and experiments were conducted at room temperature(25℃)with controlled variables like speed and friction.Results showed the disk-shaped body exhibited higher energy efficiency than the cylinder-shaped body,largely due to lower mechanical losses.The disk required only two bearings,while the cylinder required four,resulting in lower bearing losses for the disk.Additionally,the disk experienced only air friction,whereas the cylinder encountered friction from a soft,uneven rubber material,increasing surface contact losses.Under a 40 W resistive load,the disk demonstrated a 17.1%energy loss due to mechanical friction,achieving up to 15.55 J of recycled energy.Conversely,the cylinder body experienced a 48.05%energy loss,delivering only 51.95%of energy to the load.These insights suggest significant potential for designing efficient energy recycling systems in industrial settings,particularly in manufacturing and processing industries where rotational machinery is prevalent.Despite its lower energy density,this system could be beneficially integrated with energy storage solutions,enhancing sustainability in industrial practices.
文摘Vehicle overtaking poses significant risks and leads to injuries and losses on Malaysia’s roads.In most scenarios,insufficient and untimely information available to drivers for accessing road conditions and their surrounding environment is the primary factor that causes these incidents.To address these issues,a comprehensive system is required to provide real-time assistance to drivers.Building upon our previous research on a LoRa-based lane change decision-aid system,this study proposes an enhanced Vehicle Overtaking System(VOS).This system utilizes long-range(LoRa)communication for reliable real-time data exchange between vehicles(V2V)and the cloud(V2C).By providing drivers with critical information,including surrounding vehicle movements,through visual and audible warnings,the VOS aims to support vehicle overtaking decisions by calculating the safe distance between vehicles as per the Association of State Highway and Transportation Officials(AASHTO)guidelines.This study also examines the performance of LoRa communication strength and data transmission at various distances using a cloud monitoring tool or dashboard.
基金funded by Multimedia University(Ref:MMU/RMC/PostDoc/NEW/2024/9804).
文摘Networking,storage,and hardware are just a few of the virtual computing resources that the infrastruc-ture service model offers,depending on what the client needs.One essential aspect of cloud computing that improves resource allocation techniques is host load prediction.This difficulty means that hardware resource allocation in cloud computing still results in hosting initialization issues,which add several minutes to response times.To solve this issue and accurately predict cloud capacity,cloud data centers use prediction algorithms.This permits dynamic cloud scalability while maintaining superior service quality.For host prediction,we therefore present a hybrid convolutional neural network long with short-term memory model in this work.First,the suggested hybrid model is input is subjected to the vector auto regression technique.The data in many variables that,prior to analysis,has been filtered to eliminate linear interdependencies.After that,the persisting data are processed and sent into the convolutional neural network layer,which gathers intricate details about the utilization of each virtual machine and central processing unit.The next step involves the use of extended short-term memory,which is suitable for representing the temporal information of irregular trends in time series components.The key to the entire process is that we used the most appropriate activation function for this type of model a scaled polynomial constant unit.Cloud systems require accurate prediction due to the increasing degrees of unpredictability in data centers.Because of this,two actual load traces were used in this study’s assessment of the performance.An example of the load trace is in the typical dispersed system.In comparison to CNN,VAR-GRU,VAR-MLP,ARIMA-LSTM,and other models,the experiment results demonstrate that our suggested approach offers state-of-the-art performance with higher accuracy in both datasets.
文摘Industrial processes often involve rotating machinery that generates substantial kinetic energy,much of which remains untapped.Harvesting rotational kinetic energy offers a promising solution to reduce energy waste and improve energy efficiency in industrial applications.This research investigates the potential of electromagnetic induction for harvesting rotational kinetic energy from industrial machinery.A comparative study was conducted between disk and cylinder-shaped rotational bodies to evaluate their energy efficiency under various load conditions.Experimental results demonstrated that the disk body exhibited higher energy efficiency,primarily due to lower mechanical losses compared to the cylinder body.A power management circuit was developed to regulate and store the harvested energy,integrating voltage,current,and speed sensors along with a charge controller for battery storage.The experimental setup successfully converted rotational kinetic energy into usable electrical power,with the disk achieving up to 16.33 J of recycled energy,outperforming the cylinder.The disk body demonstrated higher energy recovery efficiency compared to the cylinder,particularly under the 40 W resistive load condition.These findings demonstrate the feasibility of implementing energy recycling systems in industrial settings to enhance sustainability,reduce energy consumption,and minimize waste.Future research should focus on optimizing power management systems and improving energy harvesting efficiency to enable wider adoption of energy recycling technologies in various industrial applications.
基金A research grant from the Multimedia University,Malaysia supports this work。
文摘Traditional rule-based IntrusionDetection Systems(IDS)are commonly employed owing to their simple design and ability to detect known threats.Nevertheless,as dynamic network traffic and a new degree of threats exist in IoT environments,these systems do not perform well and have elevated false positive rates—consequently decreasing detection accuracy.In this study,we try to overcome these restrictions by employing fuzzy logic and machine learning to develop an Enhanced Rule-Based Model(ERBM)to classify the packets better and identify intrusions.The ERBM developed for this approach improves data preprocessing and feature selections by utilizing fuzzy logic,where three membership functions are created to classify all the network traffic features as low,medium,or high to remain situationally aware of the environment.Such fuzzy logic sets produce adaptive detection rules by reducing data uncertainty.Also,for further classification,machine learning classifiers such as Decision Tree(DT),Random Forest(RF),and Neural Networks(NN)learn complex ways of attacks and make the detection process more precise.A thorough performance evaluation using different metrics,including accuracy,precision,recall,F1 Score,detection rate,and false-positive rate,verifies the supremacy of ERBM over classical IDS.Under extensive experiments,the ERBM enables a remarkable detection rate of 99%with considerably fewer false positives than the conventional models.Integrating the ability for uncertain reasoning with fuzzy logic and an adaptable component via machine learning solutions,the ERBM systemprovides a unique,scalable,data-driven approach to IoT intrusion detection.This research presents a major enhancement initiative in the context of rule-based IDS,introducing improvements in accuracy to evolving IoT threats.
基金supported in part by Multimedia University Research Fellow under Grant MMUI/250008in part by Telekom Research and Development Sdn Bhd under Grant RDTC/241149.
文摘Vehicle recognition plays a vital role in intelligent transportation systems,law enforcement,access control,and security operations—domains that are becoming increasingly dynamic and complex.Despite advancements,most existing solutions remain siloed,addressing individual tasks such as vehicle make and model recognition(VMMR),automatic number plate recognition(ANPR),and color classification separately.This fragmented approach limits real-world efficiency,leading to slower processing,reduced accuracy,and increased operational costs,particularly in traffic monitoring and surveillance scenarios.To address these limitations,we present a unified framework that consolidates all three recognition tasks into a single,lightweight system.The framework utilizes MobileNetV2 for efficient VMMR,YOLO(You Only Look Once)for accurate license plate detection,and histogram-based clustering in the HSV color space for precise color identification.Rather than optimizing each module in isolation,our approach emphasizes tight integration,enabling improved performance and reliability.The system also features adaptive image calibration and robust algorithmic enhancements to ensure consistent results under varying environmental conditions.Experimental evaluations demonstrate that the proposedmodel achieves a combined accuracy of 93.3%,outperforming traditional methods and offering practical scalability for deployment in real-world transportation infrastructures.
基金funding from the Princess Nourah bint Abdulrahman University Researchers Supporting Project(PNURSP2025R195)the University of Bisha through its Fast-Track Research Support Program.
文摘Artificial intelligence(AI),particularly deep learning algorithms utilizing convolutional neural networks,plays an increasingly pivotal role in enhancing medical image examination.It demonstrates the potential for improving diagnostic accuracy within dental care.Orthopantomograms(OPGs)are essential in dentistry;however,their manual interpretation is often inconsistent and tedious.To the best of our knowledge,this is the first comprehensive application of YOLOv5m for the simultaneous detection and classification of six distinct dental pathologies using panoramic OPG images.The model was trained and refined on a custom dataset that began with 232 panoramic radiographs and was later expanded to 604 samples.These included annotated subclasses representing Caries,Infection,Impacted Teeth,Fractured Teeth,Broken Crowns,and Healthy conditions.The training was performed using GPU resources alongside tuned hyperparameters of batch size,learning rate schedule,and early stopping tailored for generalization to prevent overfitting.Evaluation on a held-out test set showed strong performance in the detection and localization of various dental pathologies and robust overall accuracy.At an IoU of 0.5,the system obtained a mean precision of 94.22%and recall of 90.42%,with mAP being 93.71%.This research confirms the use of YOLOv5m as a robust,highly efficient AI technology for the analysis of dental pathologies using OPGs,providing a clinically useful solution to enhance workflow efficiency and aid in sustaining consistency in complex multi-dimensional case evaluations.
基金supported in part by Multimedia University under the Research Fellow Grant MMUI/250008in part by Telekom Research&Development Sdn Bhd underGrantRDTC/241149Princess Nourah bint Abdulrahman University Researchers Supporting Project number(PNURSP2025R140),Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia.
文摘The Internet of Things(IoT)ecosystem faces growing security challenges because it is projected to have 76.88 billion devices by 2025 and $1.4 trillion market value by 2027,operating in distributed networks with resource limitations and diverse system architectures.The current conventional intrusion detection systems(IDS)face scalability problems and trust-related issues,but blockchain-based solutions face limitations because of their low transaction throughput(Bitcoin:7 TPS(Transactions Per Second),Ethereum:15-30 TPS)and high latency.The research introduces MBID(Multi-Tier Blockchain Intrusion Detection)as a groundbreaking Multi-Tier Blockchain Intrusion Detection System with AI-Enhanced Detection,which solves the problems in huge IoT networks.The MBID system uses a four-tier architecture that includes device,edge,fog,and cloud layers with blockchain implementations and Physics-Informed Neural Networks(PINNs)for edge-based anomaly detection and a dual consensus mechanism that uses Honesty-based Distributed Proof-of-Authority(HDPoA)and Delegated Proof of Stake(DPoS).The system achieves scalability and efficiency through the combination of dynamic sharding and Interplanetary File System(IPFS)integration.Experimental evaluations demonstrate exceptional performance,achieving a detection accuracy of 99.84%,an ultra-low false positive rate of 0.01% with a False Negative Rate of 0.15%,and a near-instantaneous edge detection latency of 0.40 ms.The system demonstrated an aggregate throughput of 214.57 TPS in a 3-shard configuration,providing a clear,evidence-based path for horizontally scaling to support overmillions of devices with exceeding throughput.The proposed architecture represents a significant advancement in blockchain-based security for IoT networks,effectively balancing the trade-offs between scalability,security,and decentralization.
基金supported in part by Multimedia University under the Research Fellow Grant MMUI/250008in part by Telekom Research&Development Sdn Bhd under Grants RDTC/241149 and RDTC/231095+1 种基金Princess Nourah bint Abdulrahman University Researchers Supporting Project number(PNURSP2025R140)Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia.
文摘Multiple Object Tracking(MOT)is essential for applications such as autonomous driving,surveillance,and analytics;However,challenges such as occlusion,low-resolution imaging,and identity switches remain persistent.We propose HAMOT,a hierarchical adaptive multi-object tracker that solves these challenges with a novel,unified framework.Unlike previous methods that rely on isolated components,HAMOT incorporates a Swin Transformer-based Adaptive Enhancement(STAE)module—comprising Scene-Adaptive Transformer Enhancement and Confidence-Adaptive Feature Refinement—to improve detection under low-visibility conditions.The hierarchical DynamicGraphNeuralNetworkwith TemporalAttention(DGNN-TA)models both short-and long-termassociations,and the Adaptive Unscented Kalman Filter with Gated Recurrent Unit(AUKF-GRU)ensures accurate motion prediction.The novel Graph-Based Density-Aware Clustering(GDAC)improves occlusion recovery by adapting to scene density,preserving identity integrity.This integrated approach enables adaptive responses to complex visual scenarios,Achieving exceptional performance across all evaluation metrics,including aHigher Order TrackingAccuracy(HOTA)of 67.05%,a Multiple Object Tracking Accuracy(MOTA)of 82.4%,an ID F1 Score(IDF1)of 83.1%,and a total of 1052 Identity Switches(IDSW)on theMOT17;66.61%HOTA,78.3%MOTA,82.1%IDF1,and a total of 748 IDSWonMOT20;and 66.4%HOTA,92.32%MOTA,and 68.96%IDF1 on DanceTrack.With fixed thresholds,the full HAMOT model(all six components)achieves real-time functionality at 24 FPS on MOT17 using RTX3090,ensuring robustness and scalability for real-world MOT applications.
文摘Feature selection(FS)plays a crucial role in medical imaging by reducing dimensionality,improving computational efficiency,and enhancing diagnostic accuracy.Traditional FS techniques,including filter,wrapper,and embedded methods,have been widely used but often struggle with high-dimensional and heterogeneous medical imaging data.Deep learning-based FS methods,particularly Convolutional Neural Networks(CNNs)and autoencoders,have demonstrated superior performance but lack interpretability.Hybrid approaches that combine classical and deep learning techniques have emerged as a promising solution,offering improved accuracy and explainability.Furthermore,integratingmulti-modal imaging data(e.g.,MagneticResonance Imaging(MRI),ComputedTomography(CT),Positron Emission Tomography(PET),and Ultrasound(US))poses additional challenges in FS,necessitating advanced feature fusion strategies.Multi-modal feature fusion combines information fromdifferent imagingmodalities to improve diagnostic accuracy.Recently,quantum computing has gained attention as a revolutionary approach for FS,providing the potential to handle high-dimensional medical data more efficiently.This systematic literature review comprehensively examines classical,Deep Learning(DL),hybrid,and quantum-based FS techniques inmedical imaging.Key outcomes include a structured taxonomy of FS methods,a critical evaluation of their performance across modalities,and identification of core challenges such as computational burden,interpretability,and ethical considerations.Future research directions—such as explainable AI(XAI),federated learning,and quantum-enhanced FS—are also emphasized to bridge the current gaps.This review provides actionable insights for developing scalable,interpretable,and clinically applicable FS methods in the evolving landscape of medical imaging.
基金Supported by Multimedia University(Malaysia),project SAP ID(MMUI/160092)
文摘本文提出了光码多分址(CDMA)和光密集波分复用(DWDM)的混合系统,全面研究了四波混频(FWM)的影响。在这个系统中,主要存在两个四波混频问题:包括多址干扰(MAI)和码间干扰(ISI)的帧间四波混频和信道内四波混频。结果表明,综合考虑信道间和信道内四波混频的影响,最佳发射功率可选为18 d Bm。当发射功率大于18 d Bm时,混合系统的误码率(BER)将增加。基于此,本文提出了一种电光相位调制器(EOPM)模块,将其放置在波分复用器之后,通过抑制信道内四波混频的影响,同时调制所有波长信号的相位,从而增加混合系统的非线性容限,这极大地改善了基于OOK传输的光学CDMA-DWDM混合系统的性能。此外,由于多对角线(MD)结构具有零互相关特性,通过使用多对角线识别序列码可以减少多址干扰的影响。结果还表明,CDMA技术与色散相结合有助于降低信道间四波混频的影响。此外,识别序列码间隔在减轻码间干扰中起着至关重要的作用,如结果所示,当识别序列码间隔压缩至比特持续时间的25%时,可以避免码间干扰,此时所提出的混合系统的性能最佳。
文摘The principal component analysis (PCA) is a kind of algorithms in biometrics. It is a statistics technical and used orthogonal transformation to convert a set of observations of possibly correlated variables into a set of values of linearly uncorrelated variables. PCA also is a tool to reduce multidimensional data to lower dimensions while retaining most of the information. It covers standard deviation, covariance, and eigenvectors. This background knowledge is meant to make the PCA section very straightforward, but can be skipped if the concepts are already familiar.
