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Adaptation of Federated Explainable Artificial Intelligence for Efficient and Secure E-Healthcare Systems

Computers, Materials & Continua 2024年第3期78卷 3413-3429页

作者： Rabia Abid muhammad rizwan Abdulatif Alabdulatif Abdullah Alnajim Meznah Alamro Mourade Azrour Department of Computer Science Kinnaird College for WomenLahorePakistan College of Science and Engineering University of DerbyDerbyDE221GBUK Department of Computer Science College of ComputerQassim UniversityBuraydah51452Saudi Arabia Department of Information Technology College of ComputerQassim UniversityBuraydah51452Saudi Arabia Department of Information Technology College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh84428Saudi Arabia STI Laboratory IDMS TeamFaculty of Sciences and TechniquesMoulay Ismail University of MeknèsMeknesMorocco

Explainable Artificial Intelligence(XAI)has an advanced feature to enhance the decision-making feature and improve the rule-based technique by using more advanced Machine Learning(ML)and Deep Learning(DL)based *** this paper,we chose e-healthcare systems for efficient decision-making and data classification,especially in data security,data handling,diagnostics,laboratories,and *** Machine Learning(FML)is a new and advanced technology that helps to maintain privacy for Personal Health Records(PHR)and handle a large amount of medical data *** this context,XAI,along with FML,increases efficiency and improves the security of e-healthcare *** experiments show efficient system performance by implementing a federated averaging algorithm on an open-source Federated Learning(FL)*** experimental evaluation demonstrates the accuracy rate by taking epochs size 5,batch size 16,and the number of clients 5,which shows a higher accuracy rate(19,104).We conclude the paper by discussing the existing gaps and future work in an e-healthcare system.

关键词： Artificial intelligence data privacy federated machine learning healthcare system security

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Depression Intensity Classification from Tweets Using Fast Text Based Weighted Soft Voting Ensemble

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Computers, Materials & Continua 2024年第2期78卷 2047-2066页

作者： muhammad rizwan muhammad Faheem Mushtaq Maryam Rafiq Arif Mehmood Isabel de la Torre Diez Monica Gracia Villar Helena Garay Imran Ashraf Department of Artificial Intelligence The Islamia University of BahawalpurBahawalnagar63100Pakistan Department of Information Technology Khwaja Fareed University of Engineering and ITRYKhan64200Pakistan Department of Computer Science&Information Technology The Islamia University of BahawalpurBahawalnagar63100Pakistan Department of Signal Theory and Communications and Telematic Engineering Unviersity of ValladolidPaseo de Belén 15Valladolid47011Spain Faculty of Social Science and Humanities Universidad Europea del Atlántico Isabel Torres 21Santander39011Spain Department of Project Management Universidad Internacional IberoamericanaCampeche24560México Fundación Universitaria Internacional de Colombia Bogotá111611Colombia Universidad Internacional Iberoamericana AreciboPuerto Rico00613USA Universidade Internacional do Cuanza Cuito46703Angola Department of Information and Communication Engineering Yeungnam UniversityGyeongsan38541South Korea

Predicting depression intensity from microblogs and social media posts has numerous benefits and applications,including predicting early psychological disorders and stress in individuals or the general public.A major challenge in predicting depression using social media posts is that the existing studies do not focus on predicting the intensity of depression in social media texts but rather only perform the binary classification of depression and moreover noisy data makes it difficult to predict the true depression in the social media *** study intends to begin by collecting relevant Tweets and generating a corpus of 210000 public tweets using Twitter public application programming interfaces(APIs).A strategy is devised to filter out only depression-related tweets by creating a list of relevant hashtags to reduce noise in the ***,an algorithm is developed to annotate the data into three depression classes:‘Mild,’‘Moderate,’and‘Severe,’based on International Classification of Diseases-10(ICD-10)depression diagnostic *** baseline classifiers are applied to the annotated dataset to get a preliminary idea of classification performance on the *** FastText-based model is applied and fine-tuned with different preprocessing techniques and hyperparameter tuning to produce the tuned model,which significantly increases the depression classification performance to an 84%F1 score and 90%accuracy compared to ***,a FastText-based weighted soft voting ensemble(WSVE)is proposed to boost the model’s performance by combining several other classifiers and assigning weights to individual models according to their individual *** proposed WSVE outperformed all baselines as well as FastText alone,with an F1 of 89%,5%higher than FastText alone,and an accuracy of 93%,3%higher than FastText *** proposed model better captures the contextual features of the relatively small sample class and aids in the detection of

关键词： Depression classification deep learning FastText machine learning

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Heart-Net: AMulti-Modal Deep Learning Approach for Diagnosing Cardiovascular Diseases

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Computers, Materials & Continua 2024年第9期80卷 3967-3990页

作者： DeemaMohammed Alsekait Ahmed Younes Shdefat AymanNabil Asif Nawaz muhammad rizwan Rashid Rana Zohair Ahmed Hanaa Fathi Diaa Salama Abd Elminaam Department of Computer Science and Information Technology Applied CollegePrincess Nourah bint Abdulrahman UniversityRiyadh84428Saudi Arabia College of Engineering and Technology American University of the Middle EastEgaila15453Kuwait Faculty of Computers Science Misr International UniversityCairo11800Egypt University Institute of Information Technology—PMAS Arid Agriculture University Rawalpjndi46000Pakistan Department of Computer Science International Islamic UniversityIslamabad04436Pakistan Jadara Research Center Jadara UniversityIrbid21110Jordan Information System Department Faculty of Computers and Artificial IntelligenceBenha UniversityBanha13511Egypt MEU Research Unit Middle East UniversityAmman11831Jordan

Heart disease remains a leading cause of morbidity and mortality worldwide,highlighting the need for improved diagnostic *** diagnostics face limitations such as reliance on single-modality data and vulnerability to apparatus faults,which can reduce accuracy,especially with poor-quality ***,these methods often require significant time and expertise,making them less accessible in resource-limited *** technologies like artificial intelligence and machine learning offer promising solutions by integrating multi-modality data and enhancing diagnostic precision,ultimately improving patient outcomes and reducing healthcare *** study introduces Heart-Net,a multi-modal deep learning framework designed to enhance heart disease diagnosis by integrating data from Cardiac Magnetic Resonance Imaging(MRI)and Electrocardiogram(ECG).Heart-Net uses a 3D U-Net for MRI analysis and a Temporal Convolutional Graph Neural Network(TCGN)for ECG feature extraction,combining these through an attention mechanism to emphasize relevant *** is performed using Optimized *** approach improves early detection,reduces diagnostic errors,and supports personalized risk assessments and continuous health *** proposed approach results show that Heart-Net significantly outperforms traditional single-modality models,achieving accuracies of 92.56%forHeartnetDataset Ⅰ(HNET-DSⅠ),93.45%forHeartnetDataset Ⅱ(HNET-DSⅡ),and 91.89%for Heartnet Dataset Ⅲ(HNET-DSⅢ),mitigating the impact of apparatus faults and image quality *** findings underscore the potential of Heart-Net to revolutionize heart disease diagnostics and improve clinical outcomes.

关键词： Heart diseases magnetic resonance imaging electrocardiogram deep learning classification

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Security Monitoring and Management for the Network Services in the Orchestration of SDN-NFV Environment Using Machine Learning Techniques

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Computer Systems Science & Engineering 2024年第2期48卷 363-394页

作者： Nasser Alshammari Shumaila Shahzadi Saad Awadh Alanazi Shahid Naseem muhammad Anwar Madallah Alruwaili muhammad rizwan Abid Omar Alruwaili Ahmed Alsayat Fahad Ahmad Department of Computer Science College of Computer and Information SciencesJouf UniversitySakakaAl Jouf72341Saudi Arabia Department of Computer Sciences Kinnaird College forWomenLahorePunjab54700Pakistan Department of Information Sciences Division of Sciences and TechnologyUniversity of EducationLahore54770Pakistan Department of Computer Engineering and Networks College of Computer and Information SciencesJouf UniversitySakaka72341Saudi Arabia Computer Science Florida Polytechnic UniversityLakelandFlorida33805USA Delta3T LahorePunjab54700Pakistan Department of Basic Sciences Common First YearJouf UniversitySakaka72341Saudi Arabia

Software Defined Network(SDN)and Network Function Virtualization(NFV)technology promote several benefits to network operators,including reduced maintenance costs,increased network operational performance,simplified network lifecycle,and policies *** vulnerabilities try to modify services provided by Network Function Virtualization MANagement and Orchestration(NFV MANO),and malicious attacks in different scenarios disrupt the NFV Orchestrator(NFVO)and Virtualized Infrastructure Manager(VIM)lifecycle management related to network services or individual Virtualized Network Function(VNF).This paper proposes an anomaly detection mechanism that monitors threats in NFV MANO and manages promptly and adaptively to implement and handle security functions in order to enhance the quality of experience for end *** anomaly detector investigates these identified risks and provides secure network *** enables virtual network security functions and identifies anomalies in Kubernetes(a cloud-based platform).For training and testing purpose of the proposed approach,an intrusion-containing dataset is used that hold multiple malicious activities like a Smurf,Neptune,Teardrop,Pod,Land,IPsweep,etc.,categorized as Probing(Prob),Denial of Service(DoS),User to Root(U2R),and Remote to User(R2L)*** anomaly detector is anticipated with the capabilities of a Machine Learning(ML)technique,making use of supervised learning techniques like Logistic Regression(LR),Support Vector Machine(SVM),Random Forest(RF),Naïve Bayes(NB),and Extreme Gradient Boosting(XGBoost).The proposed framework has been evaluated by deploying the identified ML algorithm on a Jupyter notebook in Kubeflow to simulate Kubernetes for validation *** classifier has shown better outcomes(99.90%accuracy)than other classifiers in detecting anomalies/intrusions in the containerized environment.

关键词： Software defined network network function virtualization network function virtualization management and orchestration virtual infrastructure manager virtual network function Kubernetes Kubectl artificial intelligence machine learning

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Effect of fineness of ash on pozzolanic properties and acid resistance of sugarcane bagasse ash replaced cement mortars

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Frontiers of Structural and Civil Engineering 2022年第10期16卷 1287-1300页

作者： Shan E ALI rizwan AZAM muhammad rizwan RIAZ Mohamed ZAWAM Department of Civil Engineering University of Engineering&Technology LahoreLahore 54890Pakistan Faculty of Engineering Cairo UniversityCairo 12613Egypt

This paper addresses the potential use of Sugar Cane Bagasse Ash(SCBA)as a pozzolanic material for partial cement replacement in concrete *** mortars containing SCBA having five different particle size distributions at a replacement rate of 20%by weight were used to study the chemical and physical pozzolanic properties of *** durability of SCBA replaced mortars was also *** with 0%retained on sieve No.325 was used to replace 20%by weight of cement and create mortar specimens that were subjected to sulfuric acid attack of varying concentrations(1%–3%by weight of water).The tested samples were observed to check visual distortion,mass loss,and compressive strength loss at 1,7,14,28,and 56 d of acidic exposure,and the results were compared to those for the control sample,that was lime water cured,at the same *** SCBA sets were found to meet the requirements for pozzolan class N specified by ASTM C *** containing SCBA with 0%or 15%retention produced better compressive strength than the control mortars after 28 ***,X-ray fluorescence and X-ray diffraction analysis showed that the SCBA had favorable chemical properties for a pozzolanic ***,SCBA replaced samples at all ages showed improved resistance against acidic attack relative to that of the control *** deterioration was seen for 3%concentrated *** study’s findings demonstrated that SCBA with an appropriate fineness could be used as a pozzolanic material,consistently with ASTM C 618.

关键词： durability cement replacement sugarcane bagasse ash fineness of ash pozzolanic properties mortar acid resistance

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High sensitive pressure sensors based on multiple coating technique

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Chinese Physics B 2020年第2期29卷 442-445页

作者： rizwan Zahoor Chang Liu muhammad rizwan Anwar Fu-Yan Lin An-Qi Hu Xia Guo School of Electronic Engineering State Key Laboratory for Information Photonics and Optical CommunicationsBeijing University of Posts and TelecommunicationsBeijing 100876China

A multi-coating technique of reduced graphene oxide(RGO)was proposed to increase the sensitivity of paper-based pressure *** maximum sensitivity of 17.6 kPa^-1 under the 1.4 kPa was *** electrical sensing mechanism is attributed to the percolation *** paper pressure sensors were applied to monitor the motor vibration,which indicates the potential of mechanical flaw detection by analyzing the waveform difference.

关键词： paper sensor reduced graphene oxide sensitivity

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Numerical Study of Hydrogen Peroxide Thermal Decomposition in a Shock Tube

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Journal of Thermal Science 2017年第3期26卷 235-244页

作者： muhammad rizwan Bhatti Nadeem Ahmed Sheikh Shehryar Manzoor muhammad Mahabat Khan Muzaffar Ali Department of Mechanical Engineering Capital University of Science and Technology Islamabad Pakistan Mechanical Engineering Department University of Engineering and Technology Taxila Pakistan

Hydrogen peroxide(H_2O_2) has its significance during the combustion of heavy hydrocarbons in the internal combustion(IC) engines. Owing to its importance the measurements of H_2O_2 dissociation rate have been reported mostly using the shock tube apparatus. These types of experimental measurements are although quite reliable but require high cost. On the other hand, numerical simulations provide low cost and reliable solutions especially using computation fluid dynamics(CFD) software. In the current study an experimental shock tube flow is modeled using open access platform OpenFOAM to investigate the thermal decomposition of H_2O_2. Using two different convective schemes, limited Linear and upwind, the propagation of shock wave and resultant dissociation reaction are simulated. The results of the simulations are compared with the experimental data. It is observed that the rate constant measured using the simulation data deviates from the experimental results in the low temperature range and approaches the experimental values as the temperature is raised.

关键词： Reaction kinetics Rate constant Numerical simulation Shock wave Reflected shock wave Contact discontinuity Expansion fan Internal flow Compressible flow Gas dynamics

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Status and Social Capital:A Cross-Sectional Quantitative Investigation of Flood Survivors in South Punjab,Pakistan

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International Journal of Disaster Risk Science 2022年第4期13卷 549-560页

作者： muhammad Siddique Akbar muhammad rizwan Safdar Farhan Navid Yousaf Daniel P.Aldrich Institute of Social and Cultural Studies University of the PunjabLahore 054590Pakistan Security and Resilience Studies Program Department of Political ScienceNortheastern UniversityBostonMA 02115USA

A growing body of evidence underscores that social capital mitigates the impact of natural hazards such as *** we know less about the distribution of social ties in developing countries regularly hit by *** study examined the differences between demographic groups in South Punjab,Pakistan affected by the 2010 floods,comparing respondents'levels of social capital measured through proxies such as received social support,perceived community cohesion,accessibility to leadership,and general level of *** carried out univariate and multivariate analyses of factors including gender,education,occupation,landholding,family size,annual income,number of livestock,and home *** data collected in face-to-face interviews with 450 flood survivors,our analysis showed no statistically significant difference in the flood victims'scores on all four scales based on gender and family ***,education,occupation,landholding,annual income,and home stability statistically correlated with levels of social *** with high levels of education,high levels of land,higher home stability,and a government job typically had higher scores in all four measurements of social *** results show strong inequality in the distribution of social capital,with better connections and assistance coming to those who already have better socioeconomic positions in society.

关键词： Flood survivors Pakistan Social capital South Punjab

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Improvements in methodology to determine feedback reactivity coefficients

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Nuclear Science and Techniques 2019年第4期30卷 91-104页

作者： Faisal Qayyum muhammad rizwan Ali Awais Zahur R.Khan Department of Nuclear Engineering Pakistan Institute of Engineering and Applied Sciences (PIEAS) Department of Nuclear Engineering Pakistan Institute of Engineering and Applied Sciences(PIEAS)

The reactivity of a nuclear reactor is the most important safety and operating parameter. Due to short reactor period, the Light Water Reactor(LWR) designs require the compensations of rapid unfavorable reactivity increases. The increase in fuel or moderator temperature leads to compensate the reactivity jumps as inherent safety characteristics. The safe and reliable reactor operation requires the accurate assessment of these reactivity changes. This paper highlights the improvements in the methodology to determine the feedback reactivity changes in IAEA MTR benchmark. This method incorporates the reactivity effects of fuel temperature in moderator regions and vice versa. For this purpose, a detailed 3D model of the IAEA 10 MW MTR benchmark reactor is developed employing OpenMC computer code. OpenMC is a probabilistic computer code for neutronic calculations. This work uses temperature-dependent JEFF 3.2 cross-sectional library. The model is validated against the reference results of eigenvalues for control rods(inserted and in fully withdrawn position), control rod reactivity worth, averaged thermal flux in the central flux trap, and power fraction for each fuel element at beginning of life. The validated model is applied to simulate the feedback reactivity coefficients against the conventional reference results. In order to improve the methodology, the effect of the moderator temperature and void on fuel is incorporated to obtain a more realistic value of the fuel temperature ***, the moderator temperature coefficient and void coefficient are improved by incorporating the coupling effects of fuel temperature on moderator. This methodology can be applied to improve the LWR designs.

关键词： OpenMC MTR benchmark Reactivity feedback coefficients Fuel temperature coefficient Moderator temperature coefficient Void coefficient of reactivity

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Non-negligible factors in low-pressure sprinkler irrigation:droplet impact angle and shear stress

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Journal of Arid Land 2022年第11期14卷 1293-1316页

作者： HUI Xin ZHENG Yudong muhammad rizwan Shoukat TAN Haibin YAN Haijun College of Water Resources and Civil Engineering China Agricultural UniversityBeijing 100083China Engineering Research Center of Agricultural Water-Saving and Water Resources Ministry of EducationBeijing 100083China Semi-arid Agriculture Engineering&Technology Research Center of China Shijiazhuang 050051China

Droplet shear stress is considered as an important indicator that reflects soil erosion in sprinkler irrigation more accurately than kinetic energy,and the effect of droplet impact angle on the shear stress cannot be *** this study,radial distribution of droplet impact angles,velocities,and shear stresses were investigated using a two-dimensional video disdrometer with three types of low-pressure sprinkler(Nelson D3000,R3000,and Komet KPT)under two operating pressures(103 and 138 kPa)and three nozzle diameters(3.97,5.95,and 7.94 mm).Furthermore,the relationships among these characteristical parameters of droplet were analyzed,and their influencing factors were comprehensively *** various types of sprinkler,operating pressures,and nozzle diameters,the smaller impact angles and larger velocities of droplets were found to occur closer to the sprinkler,resulting in relatively low droplet shear *** increase in distance from the sprinkler caused the droplet impact angle to decrease and velocity to increase,which contributed to a significant increase in the shear stress that reached the peak value at the end of the ***,the end of the jet was the most prone to soil erosion in the radial direction,and the soil erosion in sprinkler irrigation could not only be attributed to the droplet kinetic energy,but also needed to be combined with the analysis of its shear *** comparing the radial distributions of average droplet shear stresses among the three types of sprinklers,D3000 exhibited the largest value(26.94-3313.51 N/m^(2)),followed by R3000(33.34-2650.80 N/m^(2)),and KPT(16.15-2485.69 N/m^(2)).From the perspective of minimizing the risk of soil erosion,KPT sprinkler was more suitable for low-pressure sprinkler irrigation than D3000 and R3000 *** addition to selecting the appropriate sprinkler type to reduce the droplet shear stress,a suitable sprinkler spacing could also provide acceptable results,because the distance

关键词： center pivot irrigation system water droplet universal model soil erosion water-saving irrigation

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