Medical imaging is essential in modern health care,allowing accurate diagnosis and effective treatment planning.These images,however,often demonstrate low contrast,noise,and brightness distortion that reduce their dia...Medical imaging is essential in modern health care,allowing accurate diagnosis and effective treatment planning.These images,however,often demonstrate low contrast,noise,and brightness distortion that reduce their diagnostic reliability.This review presents a structured and comprehensive analysis of advanced histogram equalization(HE)-based techniques for medical image enhancement.Our review methodology encompasses:(1)classical HE approaches and related limitations in medical domains;(2)adaptive schemes like Adaptive Histogram Equalization(AHE)and Contrast Limited Adaptive Histogrma Equalization(CLAHE)and their advance variants;(3)brightnesspreserving schemes like BBHE and MMBEBHE and related algorithms;(4)dynamic and recursive histogram equalization methods incorporating DHE and RMSHE;(5)fuzzy logic-based enhancement methodologies addressing uncertainty and noise in medical images;and(6)hybrid optimization methodologies through the application of metaheuristic algorithms(World Cup Optimization,Particle Swarm Optimization,Genetic Algorithms,along with histogram-based methodologies.)There is also a comparative discussion given based on contrast improvement,image brightness preservation,noise management,and computational efficiency.Such advancements have better capabilities of improving image quality,which is more important for improved diagnosis and image analysis.展开更多
With the widespread adoption of digital equipment in intelligent substations,testing digital signals in power systems has become an important role for relay protection test equipment.Testing and calibrating digital si...With the widespread adoption of digital equipment in intelligent substations,testing digital signals in power systems has become an important role for relay protection test equipment.Testing and calibrating digital signals require high accuracy.However,existing methods have low precision,cannot be calibrated at full range for all indexes,and have complex configuration,making them unsuitable for routine calibration work.To solve the above problems,a novel calibration method is designed and implemented using field programmable gate array(FPGA)to achieve accurate input and output time control.Accurate calibration relies on multiple forms of traceability including theoretical value traceability based on waveform comparison,time scale value traceability based on accurate time stamps,and algorithm traceability based on typical algorithms.Compared with other existing methods,the proposed approach reduces the mean absolute error of action time and time measurement by 92.88%,effectively addressing a key industry challenge and offering a valuable reference for further research,application,and standardization.展开更多
Multimodal dialogue systems often fail to maintain coherent reasoning over extended conversations and suffer from hallucination due to limited context modeling capabilities.Current approaches struggle with crossmodal ...Multimodal dialogue systems often fail to maintain coherent reasoning over extended conversations and suffer from hallucination due to limited context modeling capabilities.Current approaches struggle with crossmodal alignment,temporal consistency,and robust handling of noisy or incomplete inputs across multiple modalities.We propose Multi Agent-Chain of Thought(CoT),a novel multi-agent chain-of-thought reasoning framework where specialized agents for text,vision,and speech modalities collaboratively construct shared reasoning traces through inter-agent message passing and consensus voting mechanisms.Our architecture incorporates self-reflection modules,conflict resolution protocols,and dynamic rationale alignment to enhance consistency,factual accuracy,and user engagement.The framework employs a hierarchical attention mechanism with cross-modal fusion and implements adaptive reasoning depth based on dialogue complexity.Comprehensive evaluations on Situated Interactive Multi-Modal Conversations(SIMMC)2.0,VisDial v1.0,and newly introduced challenging scenarios demonstrate statistically significant improvements in grounding accuracy(p<0.01),chain-of-thought interpretability,and robustness to adversarial inputs compared to state-of-the-art monolithic transformer baselines and existing multi-agent approaches.展开更多
This paper investigates the teaching reform of the Program Comprehension and Analysis course in the context of industry-education integration and AI empowerment.To align with the evolving needs of the software industr...This paper investigates the teaching reform of the Program Comprehension and Analysis course in the context of industry-education integration and AI empowerment.To align with the evolving needs of the software industry,the course content has been updated to incorporate AI techniques such as large language models and deep learning.The reform enriches educational resources and introduces innovative instructional approaches.In addition,high-quality practical teaching cases have been developed,and immersive,hands-on learning experiences have been designed based on industrial platforms and real-world applications.These initiatives aim to enhance the practical skills and innovative thinking of professional degree graduate students,fostering high-caliber talent that aligns with industry demands.A survey of 90 graduate students revealed high levels of satisfaction regarding course content,teaching methodology,and skill development.The reform has proven effective in cultivating interdisciplinary professionals with solid foundations in software engineering and AI-driven innovation.展开更多
With the rapid development of artificial intelligence,the intelligence level of software is increasingly improving.Intelligent software,which is widely applied in crucial fields such as autonomous driving,intelligent ...With the rapid development of artificial intelligence,the intelligence level of software is increasingly improving.Intelligent software,which is widely applied in crucial fields such as autonomous driving,intelligent customer service,and medical diagnosis,is constructed based on complex technologies like machine learning and deep learning.Its uncertain behavior and data dependence pose unprecedented challenges to software testing.However,existing software testing courses mainly focus on conventional contents and are unable to meet the requirements of intelligent software testing.Therefore,this work deeply analyzed the relevant technologies of intelligent software testing,including reliability evaluation indicator system,neuron coverage,and test case generation.It also systematically designed an intelligent software testing course,covering teaching objectives,teaching content,teaching methods,and a teaching case.Verified by the practical teaching in four classes,this course has achieved remarkable results,providing practical experience for the reform of software testing courses.展开更多
Wave energy is a promising form of marine renewable energy that offers a sustainable pathway for electricity generation in coastal regions.Despite Malaysia’s extensive coastline,the exploration of wave energy in Sara...Wave energy is a promising form of marine renewable energy that offers a sustainable pathway for electricity generation in coastal regions.Despite Malaysia’s extensive coastline,the exploration of wave energy in Sarawak remains limited due to economic,technical,and environmental challenges that hinder its implementation.Compared to other renewable energy sources,wave energy is underutilized largely because of cost uncertainties and the lack of local performance data.This research aims to identify themost suitable coastal zone in Sarawak that achieves an optimal balance between energy potential,cost-effectiveness,and environmental impact,particularly in relation to infrastructure and regional development.The findings indicate that wave energy generation in Sarawak is technically feasible based on MOGA analysis.Among the studied sites,Bintulu emerged as the most balanced option,with a levelized cost of electricity(LCOE)of 0.778–0.864 USD/kWh and a CO_(2) emission factor as low as 0.019–0.020 CO_(2)/k Wh.Miri,while producing lower emissions than Sematan,recorded a higher LCOE of 1.045 USD/kWh with moderate emissions at 0.029 CO_(2)/kWh.Sematan,characterized by weaker wave conditions and higher installation penalties,resulted in the least favorable outcome,with an LCOE of 3.735 USD/kWh.Bintulu’s strategic location reduces CAPEX requirements,making it the most suitable site for large-scale wave energy deployment in Sarawak.展开更多
This paper delves into effective pathways for transforming course ecosystems from resource provision to knowledge service and competency development through university-enterprise collaboration in co-building knowledge...This paper delves into effective pathways for transforming course ecosystems from resource provision to knowledge service and competency development through university-enterprise collaboration in co-building knowledge graphs and intelligent shared courses.This approach enables personalized,learning-driven teaching.Based on knowledge graphs and integrated teacher-machine-student smart teaching scenarios,it not only innovates autonomous learning environments and human-computer interaction models while optimizing teaching experiences for both instructors and students,but also effectively addresses the issues of students’“scattered,superficial,and fragmented learning”.This establishes the foundation for personalized teaching tailored to individual aptitudes.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
Recognizing human interactions in RGB videos is a critical task in computer vision,with applications in video surveillance.Existing deep learning-based architectures have achieved strong results,but are computationall...Recognizing human interactions in RGB videos is a critical task in computer vision,with applications in video surveillance.Existing deep learning-based architectures have achieved strong results,but are computationally intensive,sensitive to video resolution changes and often fail in crowded scenes.We propose a novel hybrid system that is computationally efficient,robust to degraded video quality and able to filter out irrelevant individuals,making it suitable for real-life use.The system leverages multi-modal handcrafted features for interaction representation and a deep learning classifier for capturing complex dependencies.Using Mask R-CNN and YOLO11-Pose,we extract grayscale silhouettes and keypoint coordinates of interacting individuals,while filtering out irrelevant individuals using a proposed algorithm.From these,we extract silhouette-based features(local ternary pattern and histogram of optical flow)and keypoint-based features(distances,angles and velocities)that capture distinct spatial and temporal information.A Bidirectional Long Short-Term Memory network(BiLSTM)then classifies the interactions.Extensive experiments on the UT Interaction,SBU Kinect Interaction and the ISR-UOL 3D social activity datasets demonstrate that our system achieves competitive accuracy.They also validate the effectiveness of the chosen features and classifier,along with the proposed system’s computational efficiency and robustness to occlusion.展开更多
The concept of Human Activity Recognition(HAR)is integral to applications based on Internet of Things(IoT)-enabled devices,particularly in healthcare,fitness tracking,and smart environments.The streams of data from we...The concept of Human Activity Recognition(HAR)is integral to applications based on Internet of Things(IoT)-enabled devices,particularly in healthcare,fitness tracking,and smart environments.The streams of data from wearable sensors are rich in information,yet their high dimensionality and variability pose a significant challenge to proper classification.To address this problem,this paper proposes hybrid architectures that integrate traditional machine learning models with a deep neural network(DNN)to deliver improved performance and enhanced capabilities for HAR tasks.Multi-sensor HAR data were used to systematically test several hybrid models,including:RF+DNN(Random Forest+Deep Neural Network),XGB+DNN(XGBoost+DNN),GB+DNN(Gradient Boosting+DNN),KNN+DNN(K-Nearest Neighbors+DNN),and DT+DNN(Decision Tree+DNN).The RF+DNN model was the most accurate,achieving a 97.03%score with excellent precision,recall,and F1-score.These findings demonstrate that hybrid machine learning and deep learning systems have a promising future in IoT-based HAR applications.The model provides a novel solution for developing smart and trustworthy monitoring systems that support real-time analytics,patient surveillance,and other IoT applications.展开更多
Recognising human-object interactions(HOI)is a challenging task for traditional machine learning models,including convolutional neural networks(CNNs).Existing models show limited transferability across complex dataset...Recognising human-object interactions(HOI)is a challenging task for traditional machine learning models,including convolutional neural networks(CNNs).Existing models show limited transferability across complex datasets such as D3D-HOI and SYSU 3D HOI.The conventional architecture of CNNs restricts their ability to handle HOI scenarios with high complexity.HOI recognition requires improved feature extraction methods to overcome the current limitations in accuracy and scalability.This work proposes a Novel quantum gate-enabled hybrid CNN(QEH-CNN)for effectiveHOI recognition.Themodel enhancesCNNperformance by integrating quantumcomputing components.The framework begins with bilateral image filtering,followed bymulti-object tracking(MOT)and Felzenszwalb superpixel segmentation.A watershed algorithm refines object boundaries by cleaning merged superpixels.Feature extraction combines a histogram of oriented gradients(HOG),Global Image Statistics for Texture(GIST)descriptors,and a novel 23-joint keypoint extractionmethod using relative joint angles and joint proximitymeasures.A fuzzy optimization process refines the extracted features before feeding them into the QEH-CNNmodel.The proposed model achieves 95.06%accuracy on the 3D-D3D-HOI dataset and 97.29%on the SYSU3DHOI dataset.Theintegration of quantum computing enhances feature optimization,leading to improved accuracy and overall model efficiency.展开更多
Healthcare networks are transitioning from manual records to electronic health records,but this shift introduces vulnerabilities such as secure communication issues,privacy concerns,and the presence of malicious nodes...Healthcare networks are transitioning from manual records to electronic health records,but this shift introduces vulnerabilities such as secure communication issues,privacy concerns,and the presence of malicious nodes.Existing machine and deep learning-based anomalies detection methods often rely on centralized training,leading to reduced accuracy and potential privacy breaches.Therefore,this study proposes a Blockchain-based-Federated Learning architecture for Malicious Node Detection(BFL-MND)model.It trains models locally within healthcare clusters,sharing only model updates instead of patient data,preserving privacy and improving accuracy.Cloud and edge computing enhance the model’s scalability,while blockchain ensures secure,tamper-proof access to health data.Using the PhysioNet dataset,the proposed model achieves an accuracy of 0.95,F1 score of 0.93,precision of 0.94,and recall of 0.96,outperforming baseline models like random forest(0.88),adaptive boosting(0.90),logistic regression(0.86),perceptron(0.83),and deep neural networks(0.92).展开更多
The rapid evolution of wireless technologies and the advent of 6G networks present new challenges and opportunities for Internet ofThings(IoT)applications,particularly in terms of ultra-reliable,secure,and energyeffic...The rapid evolution of wireless technologies and the advent of 6G networks present new challenges and opportunities for Internet ofThings(IoT)applications,particularly in terms of ultra-reliable,secure,and energyefficient communication.This study explores the integration of Reconfigurable Intelligent Surfaces(RIS)into IoT networks to enhance communication performance.Unlike traditional passive reflector-based approaches,RIS is leveraged as an active optimization tool to improve both backscatter and direct communication modes,addressing critical IoT challenges such as energy efficiency,limited communication range,and double-fading effects in backscatter communication.We propose a novel computational framework that combines RIS functionality with Physical Layer Security(PLS)mechanisms,optimized through the algorithm known as Deep Deterministic Policy Gradient(DDPG).This framework adaptively adapts RIS configurations and transmitter beamforming to reduce key challenges,including imperfect channel state information(CSI)and hardware limitations like quantized RIS phase shifts.By optimizing both RIS settings and beamforming in real-time,our approach outperforms traditional methods by significantly increasing secrecy rates,improving spectral efficiency,and enhancing energy efficiency.Notably,this framework adapts more effectively to the dynamic nature of wireless channels compared to conventional optimization techniques,providing scalable solutions for large-scale RIS deployments.Our results demonstrate substantial improvements in communication performance setting a new benchmark for secure,efficient and scalable 6G communication.This work offers valuable insights for the future of IoT networks,with a focus on computational optimization,high spectral efficiency and energy-aware operations.展开更多
Artificial intelligence(AI)is expanding its roots in medical diagnostics.Various acute and chronic diseases can be identified accurately at the initial level by using AI methods to prevent the progression of health co...Artificial intelligence(AI)is expanding its roots in medical diagnostics.Various acute and chronic diseases can be identified accurately at the initial level by using AI methods to prevent the progression of health complications.Kidney diseases are producing a high impact on global health and medical practitioners are suggested that the diagnosis at earlier stages is one of the foremost approaches to avert chronic kidney disease and renal failure.High blood pressure,diabetes mellitus,and glomerulonephritis are the root causes of kidney disease.Therefore,the present study is proposed a set of multiple techniques such as simulation,modeling,and optimization of intelligent kidney disease prediction(SMOIKD)which is based on computational intelligence approaches.Initially,seven parameters were used for the fuzzy logic system(FLS),and then twenty-five different attributes of the kidney dataset were used for the artificial neural network(ANN)and deep extreme machine learning(DEML).The expert system was proposed with the assistance of medical experts.For the quick and accurate evaluation of the proposed system,Matlab version 2019 was used.The proposed SMOIKD-FLSANN-DEML expert system has shown 94.16%accuracy.Hence this study concluded that SMOIKD-FLS-ANN-DEML system is effective to accurately diagnose kidney disease at initial levels.展开更多
Recently,there has been a notable surge of interest in scientific research regarding spectral images.The potential of these images to revolutionize the digital photography industry,like aerial photography through Unma...Recently,there has been a notable surge of interest in scientific research regarding spectral images.The potential of these images to revolutionize the digital photography industry,like aerial photography through Unmanned Aerial Vehicles(UAVs),has captured considerable attention.One encouraging aspect is their combination with machine learning and deep learning algorithms,which have demonstrated remarkable outcomes in image classification.As a result of this powerful amalgamation,the adoption of spectral images has experienced exponential growth across various domains,with agriculture being one of the prominent beneficiaries.This paper presents an extensive survey encompassing multispectral and hyperspectral images,focusing on their applications for classification challenges in diverse agricultural areas,including plants,grains,fruits,and vegetables.By meticulously examining primary studies,we delve into the specific agricultural domains where multispectral and hyperspectral images have found practical use.Additionally,our attention is directed towards utilizing machine learning techniques for effectively classifying hyperspectral images within the agricultural context.The findings of our investigation reveal that deep learning and support vector machines have emerged as widely employed methods for hyperspectral image classification in agriculture.Nevertheless,we also shed light on the various issues and limitations of working with spectral images.This comprehensive analysis aims to provide valuable insights into the current state of spectral imaging in agriculture and its potential for future advancements.展开更多
Ever since its outbreak inWuhan,COVID-19 has cloaked the entireworld in a pall of despondency and uncertainty.The present study describes the exploratory analysis of all COVID cases in Saudi Arabia.Besides,the study h...Ever since its outbreak inWuhan,COVID-19 has cloaked the entireworld in a pall of despondency and uncertainty.The present study describes the exploratory analysis of all COVID cases in Saudi Arabia.Besides,the study has executed the forecastingmodel for predicting the possible number of COVID-19 cases in Saudi Arabia till a defined period.Towards this intent,the study analyzed different age groups of patients(child,adult,elderly)who were affected by COVID-19.The analysis was done city-wise and also included the number of recoveries recorded in different cities.Furthermore,the study also discusses the impact of COVID-19 on the economy.For conducting the stated analysis,the authors have created a list of factors that are known to cause the spread of COVID-19.As an effective countermeasure to contain the spread of Coronavirus in Saudi Arabia,this study also proposes to identify the most effective Computer Science technique that can be used by healthcare professionals.For this,the study employs the Fuzzy-Analytic Hierarchy Process integrated with the Technique for Order Performance by Similar to Ideal Solution(F.AHP.TOPSIS).After prioritizing the various Computer Science techniques,the ranking order that was obtained for the different techniques/tools to contain COVID-19 was:A4>A1>A2>A5>A3.Since the Blockchain technique obtained the highest priority,the study recommends that it must be used extensively as an efficacious and accurate means to combat COVID-19.展开更多
Two-dimensional boundary layer flow of an incompressible third grade nanofluid over a stretching surface is investigated.Influence of thermophoresis and Brownian motion is considered in the presence of Newtonian heati...Two-dimensional boundary layer flow of an incompressible third grade nanofluid over a stretching surface is investigated.Influence of thermophoresis and Brownian motion is considered in the presence of Newtonian heating and viscous dissipation.Governing nonlinear problems of velocity, temperature and nanoparticle concentration are solved via homotopic procedure.Convergence is examined graphically and numerically. Results of temperature and nanoparticle concentration are plotted and discussed for various values of material parameters, Prandtl number, Lewis number, Newtonian heating parameter, Eckert number and thermophoresis and Brownian motion parameters. Numerical computations are performed. The results show that the change in temperature and nanoparticle concentration distribution functions is similar when we use higher values of material parameters β1 andβ2. It is seen that the temperature and thermal boundary layer thickness are increasing functions of Newtonian heating parameter γ.An increase in thermophoresis and Brownian motion parameters tends to an enhancement in the temperature.展开更多
The massive technological advancements around the world have created significant challenging competition among companies where each of the companies tries to attract the customers using different techniques. One of th...The massive technological advancements around the world have created significant challenging competition among companies where each of the companies tries to attract the customers using different techniques. One of the recent tech- niques is Augmented Reality (AR). The AR is a new technology which is capable of presenting possibilities that are difficult for other technologies to offer and meet. Nowadays, numerous augmented reality applications have been used in the industry of different kinds and disseminated all over the world. AR will really alter the way individuals view the world. The AR is yet in its initial phases of research and development at different colleges and high-tech institutes. Throughout the last years, AR apps became transportable and generally available on various devices. Besides, AR be- gins to occupy its place in our audio-visual media and to be used in various fields in our life in tangible and exciting ways such as news, sports and is used in many domains in our life such as electronic commerce, promotion, design, and business. In addition, AR is used to facilitate the learning whereas it enables students to access location-specific infor- mation provided through various sources. Such growth and spread of AR applications pushes organizations to compete one another, every one of them exerts its best to gain the customers. This paper provides a comprehensive study of AR including its history, architecture, applications, current challenges and future trends.展开更多
Present analysis discusses the boundary layer flow of Eyring Powell nanofluid past a constantly moving surface under the influence of nonlinear thermal radiation. Heat and mass transfer mechanisms are examined under t...Present analysis discusses the boundary layer flow of Eyring Powell nanofluid past a constantly moving surface under the influence of nonlinear thermal radiation. Heat and mass transfer mechanisms are examined under the physically suitable convective boundary condition. Effects of variable thermal conductivity and chemical reaction are also considered. Series solutions of all involved distributions using Homotopy Analysis method(HAM) are obtained.Impacts of dominating embedded flow parameters are discussed through graphical illustrations. It is observed that thermal radiation parameter shows increasing tendency in relation to temperature profile. However, chemical reaction parameter exhibits decreasing behavior versus concentration distribution.展开更多
The mobile nature of the nodes in a wireless mobile ad-hoc network(MANET) and the error prone link connectivity between nodes pose many challenges. These include frequent route changes, high packet loss, etc. Such pro...The mobile nature of the nodes in a wireless mobile ad-hoc network(MANET) and the error prone link connectivity between nodes pose many challenges. These include frequent route changes, high packet loss, etc. Such problems increase the end-toend delay and decrease the throughput. This paper proposes two adaptive priority packet scheduling algorithms for MANET based on Mamdani and Sugeno fuzzy inference system. The fuzzy systems consist of three input variables: data rate, signal-to-noise ratio(SNR) and queue size. The fuzzy decision system has been optimised to improve its efficiency. Both fuzzy systems were verified using the Matlab fuzzy toolbox and the performance of both algorithms were evaluated using the riverbed modeler(formally known as OPNET modeler). The results were compared to an existing fuzzy scheduler under various network loads, for constant-bit-rate(CBR) and variable-bit-rate(VBR) traffic. The measuring metrics which form the basis for performance evaluation are end-to-end delay, throughput and packet delivery ratio. The proposed Mamdani and Sugeno scheduler perform better than the existing scheduler for CBR traffic. The end-to-end delay for Mamdani and Sugeno scheduler was reduced by an average of 52 % and 54 %, respectively.The performance of the throughput and packet delivery ratio for CBR traffic are very similar to the existing scheduler because of the characteristic of the traffic. The network was also at full capacity. The proposed schedulers also showed a better performance for VBR traffic. The end-to-end delay was reduced by an average of 38 % and 52 %, respectively. Both the throughput and packet delivery ratio(PDR) increased by an average of 53 % and 47 %, respectively. The Mamdani scheduler is more computationally complex than the Sugeno scheduler, even though they both showed similar network performance. Thus, the Sugeno scheduler is more suitable for real-time applications.展开更多
基金funded by the Deanship of Scientific Research(DSR)at King Abdulaziz University,Jeddah,under grant No.(IFPDP-261-22).
文摘Medical imaging is essential in modern health care,allowing accurate diagnosis and effective treatment planning.These images,however,often demonstrate low contrast,noise,and brightness distortion that reduce their diagnostic reliability.This review presents a structured and comprehensive analysis of advanced histogram equalization(HE)-based techniques for medical image enhancement.Our review methodology encompasses:(1)classical HE approaches and related limitations in medical domains;(2)adaptive schemes like Adaptive Histogram Equalization(AHE)and Contrast Limited Adaptive Histogrma Equalization(CLAHE)and their advance variants;(3)brightnesspreserving schemes like BBHE and MMBEBHE and related algorithms;(4)dynamic and recursive histogram equalization methods incorporating DHE and RMSHE;(5)fuzzy logic-based enhancement methodologies addressing uncertainty and noise in medical images;and(6)hybrid optimization methodologies through the application of metaheuristic algorithms(World Cup Optimization,Particle Swarm Optimization,Genetic Algorithms,along with histogram-based methodologies.)There is also a comparative discussion given based on contrast improvement,image brightness preservation,noise management,and computational efficiency.Such advancements have better capabilities of improving image quality,which is more important for improved diagnosis and image analysis.
基金supported by the Key Technologies R&D Program of Henan Province(No.242102211065)Postgraduate Education Reform and Quality Im-provement Project of Henan Province(No.YJS2025GZZ36).
文摘With the widespread adoption of digital equipment in intelligent substations,testing digital signals in power systems has become an important role for relay protection test equipment.Testing and calibrating digital signals require high accuracy.However,existing methods have low precision,cannot be calibrated at full range for all indexes,and have complex configuration,making them unsuitable for routine calibration work.To solve the above problems,a novel calibration method is designed and implemented using field programmable gate array(FPGA)to achieve accurate input and output time control.Accurate calibration relies on multiple forms of traceability including theoretical value traceability based on waveform comparison,time scale value traceability based on accurate time stamps,and algorithm traceability based on typical algorithms.Compared with other existing methods,the proposed approach reduces the mean absolute error of action time and time measurement by 92.88%,effectively addressing a key industry challenge and offering a valuable reference for further research,application,and standardization.
文摘Multimodal dialogue systems often fail to maintain coherent reasoning over extended conversations and suffer from hallucination due to limited context modeling capabilities.Current approaches struggle with crossmodal alignment,temporal consistency,and robust handling of noisy or incomplete inputs across multiple modalities.We propose Multi Agent-Chain of Thought(CoT),a novel multi-agent chain-of-thought reasoning framework where specialized agents for text,vision,and speech modalities collaboratively construct shared reasoning traces through inter-agent message passing and consensus voting mechanisms.Our architecture incorporates self-reflection modules,conflict resolution protocols,and dynamic rationale alignment to enhance consistency,factual accuracy,and user engagement.The framework employs a hierarchical attention mechanism with cross-modal fusion and implements adaptive reasoning depth based on dialogue complexity.Comprehensive evaluations on Situated Interactive Multi-Modal Conversations(SIMMC)2.0,VisDial v1.0,and newly introduced challenging scenarios demonstrate statistically significant improvements in grounding accuracy(p<0.01),chain-of-thought interpretability,and robustness to adversarial inputs compared to state-of-the-art monolithic transformer baselines and existing multi-agent approaches.
基金supported by Project of Higher Education Teaching Reform Research in Heilongjiang Province(Graduate Education)(Grant No.SJGYY2024030).
文摘This paper investigates the teaching reform of the Program Comprehension and Analysis course in the context of industry-education integration and AI empowerment.To align with the evolving needs of the software industry,the course content has been updated to incorporate AI techniques such as large language models and deep learning.The reform enriches educational resources and introduces innovative instructional approaches.In addition,high-quality practical teaching cases have been developed,and immersive,hands-on learning experiences have been designed based on industrial platforms and real-world applications.These initiatives aim to enhance the practical skills and innovative thinking of professional degree graduate students,fostering high-caliber talent that aligns with industry demands.A survey of 90 graduate students revealed high levels of satisfaction regarding course content,teaching methodology,and skill development.The reform has proven effective in cultivating interdisciplinary professionals with solid foundations in software engineering and AI-driven innovation.
基金Computer Basic Education Teaching Research Project of Association of Fundamental Computing Education in Chinese Universities(Nos.2025-AFCEC-527 and 2024-AFCEC-088)Research on the Reform of Public Course Teaching at Nantong College of Science and Technology(No.2024JGG015).
文摘With the rapid development of artificial intelligence,the intelligence level of software is increasingly improving.Intelligent software,which is widely applied in crucial fields such as autonomous driving,intelligent customer service,and medical diagnosis,is constructed based on complex technologies like machine learning and deep learning.Its uncertain behavior and data dependence pose unprecedented challenges to software testing.However,existing software testing courses mainly focus on conventional contents and are unable to meet the requirements of intelligent software testing.Therefore,this work deeply analyzed the relevant technologies of intelligent software testing,including reliability evaluation indicator system,neuron coverage,and test case generation.It also systematically designed an intelligent software testing course,covering teaching objectives,teaching content,teaching methods,and a teaching case.Verified by the practical teaching in four classes,this course has achieved remarkable results,providing practical experience for the reform of software testing courses.
基金supported by Swinburne University of Technology Sarawak Campus and Birmingham City University.
文摘Wave energy is a promising form of marine renewable energy that offers a sustainable pathway for electricity generation in coastal regions.Despite Malaysia’s extensive coastline,the exploration of wave energy in Sarawak remains limited due to economic,technical,and environmental challenges that hinder its implementation.Compared to other renewable energy sources,wave energy is underutilized largely because of cost uncertainties and the lack of local performance data.This research aims to identify themost suitable coastal zone in Sarawak that achieves an optimal balance between energy potential,cost-effectiveness,and environmental impact,particularly in relation to infrastructure and regional development.The findings indicate that wave energy generation in Sarawak is technically feasible based on MOGA analysis.Among the studied sites,Bintulu emerged as the most balanced option,with a levelized cost of electricity(LCOE)of 0.778–0.864 USD/kWh and a CO_(2) emission factor as low as 0.019–0.020 CO_(2)/k Wh.Miri,while producing lower emissions than Sematan,recorded a higher LCOE of 1.045 USD/kWh with moderate emissions at 0.029 CO_(2)/kWh.Sematan,characterized by weaker wave conditions and higher installation penalties,resulted in the least favorable outcome,with an LCOE of 3.735 USD/kWh.Bintulu’s strategic location reduces CAPEX requirements,making it the most suitable site for large-scale wave energy deployment in Sarawak.
基金supported by Harbin Institute of Technology High-level Teaching Achievement Award(National Level)Cultivation Project(256709).
文摘This paper delves into effective pathways for transforming course ecosystems from resource provision to knowledge service and competency development through university-enterprise collaboration in co-building knowledge graphs and intelligent shared courses.This approach enables personalized,learning-driven teaching.Based on knowledge graphs and integrated teacher-machine-student smart teaching scenarios,it not only innovates autonomous learning environments and human-computer interaction models while optimizing teaching experiences for both instructors and students,but also effectively addresses the issues of students’“scattered,superficial,and fragmented learning”.This establishes the foundation for personalized teaching tailored to individual aptitudes.
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supported and funded by Princess Nourah bint Abdulrahman University Researchers Supporting Project number(PNURSP2025R410),Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia.
文摘Recognizing human interactions in RGB videos is a critical task in computer vision,with applications in video surveillance.Existing deep learning-based architectures have achieved strong results,but are computationally intensive,sensitive to video resolution changes and often fail in crowded scenes.We propose a novel hybrid system that is computationally efficient,robust to degraded video quality and able to filter out irrelevant individuals,making it suitable for real-life use.The system leverages multi-modal handcrafted features for interaction representation and a deep learning classifier for capturing complex dependencies.Using Mask R-CNN and YOLO11-Pose,we extract grayscale silhouettes and keypoint coordinates of interacting individuals,while filtering out irrelevant individuals using a proposed algorithm.From these,we extract silhouette-based features(local ternary pattern and histogram of optical flow)and keypoint-based features(distances,angles and velocities)that capture distinct spatial and temporal information.A Bidirectional Long Short-Term Memory network(BiLSTM)then classifies the interactions.Extensive experiments on the UT Interaction,SBU Kinect Interaction and the ISR-UOL 3D social activity datasets demonstrate that our system achieves competitive accuracy.They also validate the effectiveness of the chosen features and classifier,along with the proposed system’s computational efficiency and robustness to occlusion.
基金supported by Princess Nourah bint Abdulrahman University Researchers Supporting Project number(PNURSP2026R909)Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia.
文摘The concept of Human Activity Recognition(HAR)is integral to applications based on Internet of Things(IoT)-enabled devices,particularly in healthcare,fitness tracking,and smart environments.The streams of data from wearable sensors are rich in information,yet their high dimensionality and variability pose a significant challenge to proper classification.To address this problem,this paper proposes hybrid architectures that integrate traditional machine learning models with a deep neural network(DNN)to deliver improved performance and enhanced capabilities for HAR tasks.Multi-sensor HAR data were used to systematically test several hybrid models,including:RF+DNN(Random Forest+Deep Neural Network),XGB+DNN(XGBoost+DNN),GB+DNN(Gradient Boosting+DNN),KNN+DNN(K-Nearest Neighbors+DNN),and DT+DNN(Decision Tree+DNN).The RF+DNN model was the most accurate,achieving a 97.03%score with excellent precision,recall,and F1-score.These findings demonstrate that hybrid machine learning and deep learning systems have a promising future in IoT-based HAR applications.The model provides a novel solution for developing smart and trustworthy monitoring systems that support real-time analytics,patient surveillance,and other IoT applications.
基金supported and funded by Princess Nourah bint Abdulrahman University Researchers Supporting Project number(PNURSP2025R410),Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia.
文摘Recognising human-object interactions(HOI)is a challenging task for traditional machine learning models,including convolutional neural networks(CNNs).Existing models show limited transferability across complex datasets such as D3D-HOI and SYSU 3D HOI.The conventional architecture of CNNs restricts their ability to handle HOI scenarios with high complexity.HOI recognition requires improved feature extraction methods to overcome the current limitations in accuracy and scalability.This work proposes a Novel quantum gate-enabled hybrid CNN(QEH-CNN)for effectiveHOI recognition.Themodel enhancesCNNperformance by integrating quantumcomputing components.The framework begins with bilateral image filtering,followed bymulti-object tracking(MOT)and Felzenszwalb superpixel segmentation.A watershed algorithm refines object boundaries by cleaning merged superpixels.Feature extraction combines a histogram of oriented gradients(HOG),Global Image Statistics for Texture(GIST)descriptors,and a novel 23-joint keypoint extractionmethod using relative joint angles and joint proximitymeasures.A fuzzy optimization process refines the extracted features before feeding them into the QEH-CNNmodel.The proposed model achieves 95.06%accuracy on the 3D-D3D-HOI dataset and 97.29%on the SYSU3DHOI dataset.Theintegration of quantum computing enhances feature optimization,leading to improved accuracy and overall model efficiency.
基金funded by the Northern Border University,Arar,KSA,under the project number“NBU-FFR-2025-3555-07”.
文摘Healthcare networks are transitioning from manual records to electronic health records,but this shift introduces vulnerabilities such as secure communication issues,privacy concerns,and the presence of malicious nodes.Existing machine and deep learning-based anomalies detection methods often rely on centralized training,leading to reduced accuracy and potential privacy breaches.Therefore,this study proposes a Blockchain-based-Federated Learning architecture for Malicious Node Detection(BFL-MND)model.It trains models locally within healthcare clusters,sharing only model updates instead of patient data,preserving privacy and improving accuracy.Cloud and edge computing enhance the model’s scalability,while blockchain ensures secure,tamper-proof access to health data.Using the PhysioNet dataset,the proposed model achieves an accuracy of 0.95,F1 score of 0.93,precision of 0.94,and recall of 0.96,outperforming baseline models like random forest(0.88),adaptive boosting(0.90),logistic regression(0.86),perceptron(0.83),and deep neural networks(0.92).
基金funded by the deanship of scientific research(DSR),King Abdukaziz University,Jeddah,under grant No.(G-1436-611-225)。
文摘The rapid evolution of wireless technologies and the advent of 6G networks present new challenges and opportunities for Internet ofThings(IoT)applications,particularly in terms of ultra-reliable,secure,and energyefficient communication.This study explores the integration of Reconfigurable Intelligent Surfaces(RIS)into IoT networks to enhance communication performance.Unlike traditional passive reflector-based approaches,RIS is leveraged as an active optimization tool to improve both backscatter and direct communication modes,addressing critical IoT challenges such as energy efficiency,limited communication range,and double-fading effects in backscatter communication.We propose a novel computational framework that combines RIS functionality with Physical Layer Security(PLS)mechanisms,optimized through the algorithm known as Deep Deterministic Policy Gradient(DDPG).This framework adaptively adapts RIS configurations and transmitter beamforming to reduce key challenges,including imperfect channel state information(CSI)and hardware limitations like quantized RIS phase shifts.By optimizing both RIS settings and beamforming in real-time,our approach outperforms traditional methods by significantly increasing secrecy rates,improving spectral efficiency,and enhancing energy efficiency.Notably,this framework adapts more effectively to the dynamic nature of wireless channels compared to conventional optimization techniques,providing scalable solutions for large-scale RIS deployments.Our results demonstrate substantial improvements in communication performance setting a new benchmark for secure,efficient and scalable 6G communication.This work offers valuable insights for the future of IoT networks,with a focus on computational optimization,high spectral efficiency and energy-aware operations.
文摘Artificial intelligence(AI)is expanding its roots in medical diagnostics.Various acute and chronic diseases can be identified accurately at the initial level by using AI methods to prevent the progression of health complications.Kidney diseases are producing a high impact on global health and medical practitioners are suggested that the diagnosis at earlier stages is one of the foremost approaches to avert chronic kidney disease and renal failure.High blood pressure,diabetes mellitus,and glomerulonephritis are the root causes of kidney disease.Therefore,the present study is proposed a set of multiple techniques such as simulation,modeling,and optimization of intelligent kidney disease prediction(SMOIKD)which is based on computational intelligence approaches.Initially,seven parameters were used for the fuzzy logic system(FLS),and then twenty-five different attributes of the kidney dataset were used for the artificial neural network(ANN)and deep extreme machine learning(DEML).The expert system was proposed with the assistance of medical experts.For the quick and accurate evaluation of the proposed system,Matlab version 2019 was used.The proposed SMOIKD-FLSANN-DEML expert system has shown 94.16%accuracy.Hence this study concluded that SMOIKD-FLS-ANN-DEML system is effective to accurately diagnose kidney disease at initial levels.
文摘Recently,there has been a notable surge of interest in scientific research regarding spectral images.The potential of these images to revolutionize the digital photography industry,like aerial photography through Unmanned Aerial Vehicles(UAVs),has captured considerable attention.One encouraging aspect is their combination with machine learning and deep learning algorithms,which have demonstrated remarkable outcomes in image classification.As a result of this powerful amalgamation,the adoption of spectral images has experienced exponential growth across various domains,with agriculture being one of the prominent beneficiaries.This paper presents an extensive survey encompassing multispectral and hyperspectral images,focusing on their applications for classification challenges in diverse agricultural areas,including plants,grains,fruits,and vegetables.By meticulously examining primary studies,we delve into the specific agricultural domains where multispectral and hyperspectral images have found practical use.Additionally,our attention is directed towards utilizing machine learning techniques for effectively classifying hyperspectral images within the agricultural context.The findings of our investigation reveal that deep learning and support vector machines have emerged as widely employed methods for hyperspectral image classification in agriculture.Nevertheless,we also shed light on the various issues and limitations of working with spectral images.This comprehensive analysis aims to provide valuable insights into the current state of spectral imaging in agriculture and its potential for future advancements.
文摘Ever since its outbreak inWuhan,COVID-19 has cloaked the entireworld in a pall of despondency and uncertainty.The present study describes the exploratory analysis of all COVID cases in Saudi Arabia.Besides,the study has executed the forecastingmodel for predicting the possible number of COVID-19 cases in Saudi Arabia till a defined period.Towards this intent,the study analyzed different age groups of patients(child,adult,elderly)who were affected by COVID-19.The analysis was done city-wise and also included the number of recoveries recorded in different cities.Furthermore,the study also discusses the impact of COVID-19 on the economy.For conducting the stated analysis,the authors have created a list of factors that are known to cause the spread of COVID-19.As an effective countermeasure to contain the spread of Coronavirus in Saudi Arabia,this study also proposes to identify the most effective Computer Science technique that can be used by healthcare professionals.For this,the study employs the Fuzzy-Analytic Hierarchy Process integrated with the Technique for Order Performance by Similar to Ideal Solution(F.AHP.TOPSIS).After prioritizing the various Computer Science techniques,the ranking order that was obtained for the different techniques/tools to contain COVID-19 was:A4>A1>A2>A5>A3.Since the Blockchain technique obtained the highest priority,the study recommends that it must be used extensively as an efficacious and accurate means to combat COVID-19.
基金funded by the Deanship of Scientific Research (DSR), King Abdulaziz University (KAU), under Grant No. 37-130-35-HiCi
文摘Two-dimensional boundary layer flow of an incompressible third grade nanofluid over a stretching surface is investigated.Influence of thermophoresis and Brownian motion is considered in the presence of Newtonian heating and viscous dissipation.Governing nonlinear problems of velocity, temperature and nanoparticle concentration are solved via homotopic procedure.Convergence is examined graphically and numerically. Results of temperature and nanoparticle concentration are plotted and discussed for various values of material parameters, Prandtl number, Lewis number, Newtonian heating parameter, Eckert number and thermophoresis and Brownian motion parameters. Numerical computations are performed. The results show that the change in temperature and nanoparticle concentration distribution functions is similar when we use higher values of material parameters β1 andβ2. It is seen that the temperature and thermal boundary layer thickness are increasing functions of Newtonian heating parameter γ.An increase in thermophoresis and Brownian motion parameters tends to an enhancement in the temperature.
文摘The massive technological advancements around the world have created significant challenging competition among companies where each of the companies tries to attract the customers using different techniques. One of the recent tech- niques is Augmented Reality (AR). The AR is a new technology which is capable of presenting possibilities that are difficult for other technologies to offer and meet. Nowadays, numerous augmented reality applications have been used in the industry of different kinds and disseminated all over the world. AR will really alter the way individuals view the world. The AR is yet in its initial phases of research and development at different colleges and high-tech institutes. Throughout the last years, AR apps became transportable and generally available on various devices. Besides, AR be- gins to occupy its place in our audio-visual media and to be used in various fields in our life in tangible and exciting ways such as news, sports and is used in many domains in our life such as electronic commerce, promotion, design, and business. In addition, AR is used to facilitate the learning whereas it enables students to access location-specific infor- mation provided through various sources. Such growth and spread of AR applications pushes organizations to compete one another, every one of them exerts its best to gain the customers. This paper provides a comprehensive study of AR including its history, architecture, applications, current challenges and future trends.
基金Supported by the World Class 300 Project(No.S2367878)of the SMBA(Korea)
文摘Present analysis discusses the boundary layer flow of Eyring Powell nanofluid past a constantly moving surface under the influence of nonlinear thermal radiation. Heat and mass transfer mechanisms are examined under the physically suitable convective boundary condition. Effects of variable thermal conductivity and chemical reaction are also considered. Series solutions of all involved distributions using Homotopy Analysis method(HAM) are obtained.Impacts of dominating embedded flow parameters are discussed through graphical illustrations. It is observed that thermal radiation parameter shows increasing tendency in relation to temperature profile. However, chemical reaction parameter exhibits decreasing behavior versus concentration distribution.
文摘The mobile nature of the nodes in a wireless mobile ad-hoc network(MANET) and the error prone link connectivity between nodes pose many challenges. These include frequent route changes, high packet loss, etc. Such problems increase the end-toend delay and decrease the throughput. This paper proposes two adaptive priority packet scheduling algorithms for MANET based on Mamdani and Sugeno fuzzy inference system. The fuzzy systems consist of three input variables: data rate, signal-to-noise ratio(SNR) and queue size. The fuzzy decision system has been optimised to improve its efficiency. Both fuzzy systems were verified using the Matlab fuzzy toolbox and the performance of both algorithms were evaluated using the riverbed modeler(formally known as OPNET modeler). The results were compared to an existing fuzzy scheduler under various network loads, for constant-bit-rate(CBR) and variable-bit-rate(VBR) traffic. The measuring metrics which form the basis for performance evaluation are end-to-end delay, throughput and packet delivery ratio. The proposed Mamdani and Sugeno scheduler perform better than the existing scheduler for CBR traffic. The end-to-end delay for Mamdani and Sugeno scheduler was reduced by an average of 52 % and 54 %, respectively.The performance of the throughput and packet delivery ratio for CBR traffic are very similar to the existing scheduler because of the characteristic of the traffic. The network was also at full capacity. The proposed schedulers also showed a better performance for VBR traffic. The end-to-end delay was reduced by an average of 38 % and 52 %, respectively. Both the throughput and packet delivery ratio(PDR) increased by an average of 53 % and 47 %, respectively. The Mamdani scheduler is more computationally complex than the Sugeno scheduler, even though they both showed similar network performance. Thus, the Sugeno scheduler is more suitable for real-time applications.
