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Detection of Omicron Caused Pneumonia from Radiology Images Using Convolution Neural Network(CNN)

Computers, Materials & Continua 2023年第2期74卷 3743-3761页

作者： Arfat Ahmad Khan Malik Muhammad Ali Shahid Rab Nawaz Bashir salman iqbal Arshad Shehzad Ahmad Shahid Javeria Maqbool Chitapong Wechtaisong College of Computing Khon Kaen UniverityKhon Kaen40000Thailand Department of Computer Science COMSATS University IslamabadVehari61100Pakistan Department of Petroleum&Gas Engineering University of Engineering&Technology54890Pakistan Electrical Engineering Department SBASSELahore University of Management SciencesLahore54890Pakistan School of Telecommunication Engineering Suranaree University of TechnologyNakhon Ratchasima30000Thailand

COVID-19 disease caused by the SARS-CoV-2 virus has created social and economic disruption across *** ability of the COVID-19 virus to quickly mutate and transfer has created serious concerns across the *** is essential to detectCOVID-19 infection caused by different variants to take preventive measures *** existing method of detection of infections caused by COVID-19 and its variants is costly and *** impacts of theCOVID-19 pandemic in developing countries are very drastic due to the unavailability of medical facilities and infrastructure to handle the *** is the major symptom of COVID-19 *** radiology of the lungs in varies in the case of bacterial pneumonia as compared to COVID-19-caused *** pattern of pneumonia in lungs in radiology images can also be used to identify the cause associated with *** this paper,we propose the methodology of identifying the cause(either due to COVID-19 or other types of infections)of pneumonia from radiology ***,because different variants of COVID-19 lead to different patterns of pneumonia,the proposed methodology identifies pneumonia,the COVID-19 caused pneumonia,and Omicron caused pneumonia from the radiology *** fulfill the above-mentioned tasks,we have used three Convolution Neural Networks(CNNs)at each stage of the proposed *** results unveil that the proposed step-by-step solution enhances the accuracy of pneumonia detection along with finding its cause,despite having a limited dataset.

关键词： COVID-19 pneumonia radiology images omicron convolution neural network(CNN) microscopy

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Enhanced Topic-Aware Summarization Using Statistical Graph Neural Networks

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Computers, Materials & Continua 2024年第8期80卷 3221-3242页

作者： Ayesha Khaliq salman Afsar Awan Fahad Ahmad Muhammad Azam Zia Muhammad Zafar iqbal Department of Computer Science University of Agriculture FaisalabadFaisalabadPunjab37300Pakistan School of Computing Faculty of TechnologyUniversity of PortsmouthSouthseaPortsmouthPO12UPUK Department of Mathematics and Statistics University of Agriculture FaisalabadFaisalabadPunjab37300Pakistan

The rapid expansion of online content and big data has precipitated an urgent need for efficient summarization techniques to swiftly comprehend vast textual documents without compromising their original *** approaches in Extractive Text Summarization(ETS)leverage the modeling of inter-sentence relationships,a task of paramount importance in producing coherent *** study introduces an innovative model that integrates Graph Attention Networks(GATs)with Transformer-based Bidirectional Encoder Representa-tions from Transformers(BERT)and Latent Dirichlet Allocation(LDA),further enhanced by Term Frequency-Inverse Document Frequency(TF-IDF)values,to improve sentence selection by capturing comprehensive topical *** approach constructs a graph with nodes representing sentences,words,and topics,thereby elevating the interconnectivity and enabling a more refined understanding of text *** model is stretched to Multi-Document Summarization(MDS)from Single-Document Summarization,offering significant improvements over existing models such as THGS-GMM and Topic-GraphSum,as demonstrated by empirical evaluations on benchmark news datasets like Cable News Network(CNN)/Daily Mail(DM)and *** results consistently demonstrate superior performance,showcasing the model’s robustness in handling complex summarization tasks across single and multi-document *** research not only advances the integration of BERT and LDA within a GATs but also emphasizes our model’s capacity to effectively manage global information and adapt to diverse summarization challenges.

关键词： Summarization graph attention network bidirectional encoder representations from transformers Latent Dirichlet Allocation term frequency-inverse document frequency

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Measuring Reliability of A Web Portal Based on Testing Profile

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Computers, Materials & Continua 2023年第3期74卷 6641-6663页

作者： Malik Muhammad Ali Shahid Shahida Sulaiman Mohammed Al-Sarem Aqeel Ur Rahman salman iqbal Rab Nawaz Bashir Arfat Ahmad Khan Momen M.Alrawi Rashiq R.Marie Settawit Poochaya COMSATS University Islamabad Vehari Vehari61100Pakistan Universiti Teknologi Malaysia Johor Bahru81310Malaysia College of Computer Science and Engineering Taibah UniversityMedina41477Saudi Arabia Information System Department Sheba Region UniversityMarib14400Yemen Suranaree University of Technology Nakhon Ratchasima30000Thailand Khon Kaen University Khon Kaen40000Thailand Umm Al-Qura University Mecca24382Saudi Arabia Taibah University Medina42353Saudi Arabia

Conventionally,the reliability of a web portal is validated with generalized conventional methods,but they fail to provide the desired ***,we need to include other quality factors that affect reliability such as usability for improving the reliability in addition to the conventional reliability ***,the primary objectives of web portals are to provide interactive integration of multiple functions confirming diverse requirements in an efficient *** this paper,we employ testing profiles tomeasure the reliability through software operational profile,input space profile and usability profile along with qualitative measures of reliability and ***,the case study used for verification is based on aweb application that facilitates information and knowledge sharing among its online *** proposed scheme is compared with the conventional reliability improvement method in terms of failure detection and *** final results unveil that the computation of reliability by using the traditional method(utilizing failure points with the assistance of Mean Time Between Failures(MTBF)and Mean Time To Failure(MTTF)becomes ineffective under certain *** such situations,the proposed scheme helps to compute the reliability in an effective ***,the outcomes of the study provide insight recommendations about the testing and measurement of reliability for Web based software or applications.

关键词： Software usability software reliability web portals software testing software testing profile

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Significance of including lid thickness and particle shape factor in numerical modeling for prediction of particle trap efficiency of invert trap

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Water Science and Engineering 2024年第2期17卷 166-176页

作者： salman Beg Deo Raj Kaushal Department of Civil Engineering Indian Institute of Technology DelhiNew Delhi 110016India

Sediment accumulation on the bed of open sewers and drains reduces hydraulic efficiency and can cause localized *** invert traps installed underneath the bed of open sewers and drains can eliminate sediment build-up by catching sediment *** three-dimensional(3D)computational studies have examined the particle trapping performance of invert traps of different shapes and depths under varied sediment and flow conditions,considering particles as *** two-dimensional and 3D numerical modeling,researchers assumed the lid geometry to be a thin line and a plane,*** this 3D numerical study,the particle trapping efficiency of a slotted irregular hexagonal invert trap fitted at the flume bottom was examined by incorporating the particle shape factor of non-spherical sewage solid particles and the thicknesses of upstream and downstream lids over the trap in the discrete phase model of the ANSYS Fluent 2020 R1 *** volume of fluid(VOF)and the realizable k-turbulence models were used to predict the velocity *** two-dimensional particle image velocimetry(PIV)was used to measure the velocity field inside the invert *** results showed that the thicknesses of upstream and downstream lids affected the velocity field and turbulent kinetic energy at all flow *** joint impact of the particle shape factor and lid thickness on the trap efficiency was *** both the lid thickness and particle shape factor were considered in the numerical modeling,trap efficiencies were underestimated,with relative errors of-8.66%to-0.65%in comparison to the experimental values of Mohsin and Kaushal(2017).They were also lower than the values predicted by Mohsin and Kaushal(2017),which showed an overall overestimation with errors of-2.3%to 17.4%.

关键词： Invert trap Lid thickness Particle image velocimetry Particle shape factor Turbulent kinetic energy Scanning electron microscope

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A Novel Approach to Energy Optimization:Efficient Path Selection in Wireless Sensor Networks with Hybrid ANN

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Computers, Materials & Continua 2024年第5期79卷 2945-2970页

作者： Muhammad salman Qamar Ihsan ulHaq Amil Daraz Atif MAlamri salman A.AlQahtani Muhammad Fahad Munir Department of Electrical and Computer Engineering International Islamic UniversityIslamabad44000Pakistan School of Information Science and Engineering NingboTech UniversityNingbo315100China Software Engineering Department College of Computer and Information SciencesKing Saud UniversityRiyadh11495Saudi Arabia Computer Engineering Department College of Computer and Information SciencesKing Saud UniversityRiyadh11495Saudi Arabia

In pursuit of enhancing the Wireless Sensor Networks(WSNs)energy efficiency and operational lifespan,this paper delves into the domain of energy-efficient routing ***,the limited energy resources of Sensor Nodes(SNs)are a big challenge for ensuring their efficient and reliable *** data gathering involves the utilization of a mobile sink(MS)to mitigate the energy consumption problem through periodic network *** mobile sink(MS)strategy minimizes energy consumption and latency by visiting the fewest nodes or predetermined locations called rendezvous points(RPs)instead of all cluster heads(CHs).CHs subsequently transmit packets to neighboring *** unique determination of this study is the shortest path to reach *** the mobile sink(MS)concept has emerged as a promising solution to the energy consumption problem in WSNs,caused by multi-hop data collection with static *** this study,we proposed two novel hybrid algorithms,namely“ Reduced k-means based on Artificial Neural Network”(RkM-ANN)and“Delay Bound Reduced kmeans with ANN”(DBRkM-ANN)for designing a fast,efficient,and most proficient MS path depending upon rendezvous points(RPs).The first algorithm optimizes the MS’s latency,while the second considers the designing of delay-bound paths,also defined as the number of paths with delay over bound for the *** methods use a weight function and k-means clustering to choose RPs in a way that maximizes efficiency and guarantees network-wide *** addition,a method of using MS scheduling for efficient data collection is *** simulations and comparisons to several existing algorithms have shown the effectiveness of the suggested methodologies over a wide range of performance indicators.

关键词： Wireless Sensor Networks(WSNs) mobile sink(MS) rendezvous point(RP) machine learning Artificial Neural Networks(ANNs)

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The association between mycotic pneumonia and neonatal septicemia

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Journal of Biosafety and Biosecurity 2024年第3期6卷 137-141页

作者： salman Khalid salman Yasir Mufeed Abdulateef Sawsan Qahtan Taha Al-Quhli Department of Microbiology College of MedicineUniversity of AnbarIraq

Background:Candida species are the fourth most common etiological agents of late-onset infection in the neonatal intensive care unit(NICU)and are responsible for considerable morbidity and ***:From November 2023 to February 2024,we investigated the association of mycotic pneumonia with septicemia in 60 neonates,and their roles of mycotic pneumonia in the morbidity and mortality of neonates in two NICUs in the Al-Ramadi Teaching Hospital for Maternity and ***:All infants in this study had been diagnosed with septicemia and treated with empirical antimicrobial *** early morning nasogastric tube(NG-tube)was used to collect swallowed sputum by suction for culture and sensitivity ***:The average white blood count for the neonates was 8547±5884.5 cells/mm^(2).The mean C-reactive protein was 39.3±26 mg/l,the mean serum albumin was 2.9±0.2 g/dl and the positive bacterial blood culture was 28(46.7%).9(15%)neonates died during the study *** NG-tube culture identified fungal growth in all *** these,49(81.6%)were identified as Candida albicans,6(10%)as Candida tropicalis,and 5(8.3%)as Cryptococcus *** bacterial culture results from the NG-tube samples identified 13(21.6%)patients with gram-positive bacteria and 47(78.3%)with gram-negative ***:We found a prevalence of Candida *** neonates in addition to microbial oxygen tube contamination,indicating a biosafety breach in the neonatal *** infection requires global attention as a probable cause of respiratory failure in neonatal septicemia.

关键词： Neonatal sepsis Mycotic pneumonia Anti-Mycotic therapy NG-tube culture Nosocomial infections

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Enhancing Skin Cancer Diagnosis with Deep Learning:A Hybrid CNN-RNN Approach

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Computers, Materials & Continua 2024年第4期79卷 1497-1519页

作者： Syeda Shamaila Zareen Guangmin Sun Mahwish Kundi Syed Furqan Qadri salman Qadri Faculty of Information Technology Beijing University of TechnologyBeijing100124China Computer Science International Engineering Collage Maynooth UniversityKildareW23 F2H6Irland Research Center for Data Hub and Security Zhejiang LabHangzhou311121China Computer Science Department MNS University of AgricultureMultan59220Pakistan

Skin cancer diagnosis is difficult due to lesion presentation variability. Conventionalmethods struggle to manuallyextract features and capture lesions spatial and temporal variations. This study introduces a deep learning-basedConvolutional and Recurrent Neural Network (CNN-RNN) model with a ResNet-50 architecture which usedas the feature extractor to enhance skin cancer classification. Leveraging synergistic spatial feature extractionand temporal sequence learning, the model demonstrates robust performance on a dataset of 9000 skin lesionphotos from nine cancer types. Using pre-trained ResNet-50 for spatial data extraction and Long Short-TermMemory (LSTM) for temporal dependencies, the model achieves a high average recognition accuracy, surpassingprevious methods. The comprehensive evaluation, including accuracy, precision, recall, and F1-score, underscoresthe model’s competence in categorizing skin cancer types. This research contributes a sophisticated model andvaluable guidance for deep learning-based diagnostics, also this model excels in overcoming spatial and temporalcomplexities, offering a sophisticated solution for dermatological diagnostics research.

关键词： Skin cancer classification deep learning Convolutional Neural Network(CNN) RNN ResNet-50

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Dynamics of a two-group model for assessing the impacts of pre-exposure prophylaxis,testing and risk behaviour change on the spread and control of HIV/AIDS in an MSM population

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Infectious Disease Modelling 2024年第1期9卷 103-127页

作者： Queen Tollett salman Safdar Abba B.Gumel School of Mathematical and Statistical Sciences Arizona State UniversityTempeAZ85287USA Department of Mathematics University of KarachiUniversity Road75270Pakistan Department of Mathematics University of MarylandCollege ParkMD20742USA Department of Mathematics and Applied Mathematics University of PretoriaPretoria0002South Africa

Although much progress has been made in reducing the public health burden of the human immunodeficiency virus(HIV),which causes acquired immunodeficiency syndrome(AIDS),since its emergence in the 1980s(largely due to the large-scale use and availability of potent antiviral therapy,improved diagnostic and intervention and mitigation measures),HIV remains an important public health challenge globally,including in the United *** study is based on the use of mathematical modeling approaches to assess the population-level impact of pre-exposure prophylaxis(PrEP),voluntary testing(to detect undetected HIV-infected individuals),and changes in human behavior(with respect to risk structure),on the spread and control of HIV/AIDS in an MSM(men-who-have sex-with-men)***,a novel two-group mathematical model,which stratifies the total MSM population based on risk(low or high)of acquisition of HIV infection,is *** model undergoes a PrEP-induced backward bifurcation when the control reproduction number of the model is less than one if the efficacy of PrEP to prevent a high-risk susceptible MSM individual from acquiring HIV infection is not perfect(the consequence of which is that,while necessary,having the reproduction number of the model less than one is no longer sufficient for the elimination of the disease in the MSM population).For the case where the efficacy of PrEP is perfect,this study shows that the disease-free equilibrium of the two-group model is globally-asymptotically stable when the associated control reproduction number of the model is less than *** sensitivity analysis was carried out to identify the main parameters of the model that have the highest influence on the value of the control reproduction number of the model(thereby,having the highest influence on the disease burden in the MSM population).Numerical simulations of the model,using a plausible range of parameter values,show that if half of the MSM population co

关键词： HIV/AIDS MS MHigh(low)-risk groups PrEP Behavior change Antiviral treatment Control reproduction number

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Transparent and Accurate COVID-19 Diagnosis:Integrating Explainable AI with Advanced Deep Learning in CT Imaging

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Computer Modeling in Engineering & Sciences 2024年第6期139卷 3101-3123页

作者： Mohammad Mehedi Hassan salman A.AlQahtani Mabrook S.AlRakhami Ahmed Zohier Elhendi Department of Information Systems College of Computer and Information SciencesKing Saud UniversityRiyadh11543Saudi Arabia Department of Computer Engineering College of Computer and Information SciencesKing Saud UniversityRiyadh11543Saudi Arabia Science Technology and Innovation Department King Saud UniversityRiyadh11543Saudi Arabia

In the current landscape of the COVID-19 pandemic,the utilization of deep learning in medical imaging,especially in chest computed tomography(CT)scan analysis for virus detection,has become increasingly *** its potential,deep learning’s“black box”nature has been a major impediment to its broader acceptance in clinical environments,where transparency in decision-making is *** bridge this gap,our research integrates Explainable AI(XAI)techniques,specifically the Local Interpretable Model-Agnostic Explanations(LIME)method,with advanced deep learning *** integration forms a sophisticated and transparent framework for COVID-19 identification,enhancing the capability of standard Convolutional Neural Network(CNN)models through transfer learning and data *** approach leverages the refined DenseNet201 architecture for superior feature extraction and employs data augmentation strategies to foster robust model *** pivotal element of our methodology is the use of LIME,which demystifies the AI decision-making process,providing clinicians with clear,interpretable insights into the AI’s *** unique combination of an optimized Deep Neural Network(DNN)with LIME not only elevates the precision in detecting COVID-19 cases but also equips healthcare professionals with a deeper understanding of the diagnostic *** method,validated on the SARS-COV-2 CT-Scan dataset,demonstrates exceptional diagnostic accuracy,with performance metrics that reinforce its potential for seamless integration into modern healthcare *** innovative approach marks a significant advancement in creating explainable and trustworthy AI tools for medical decisionmaking in the ongoing battle against COVID-19.

关键词： Explainable AI COVID-19 CT images deep learning

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DNBP-CCA:A Novel Approach to Enhancing Heterogeneous Data Traffic and Reliable Data Transmission for Body Area Network

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Computers, Materials & Continua 2024年第5期79卷 2851-2878页

作者： Abdulwadood Alawadhi Mohd.Hasbullah Omar Abdullah Almogahed Noradila Nordin salman A.Alqahtani Atif M.Alamri InterNetWorks Research Laboratory School of ComputingUniversiti Utara MalaysiaSintok06010Malaysia Faculty of Computer Science and Information Technology Universiti Tun Hussein Onn MalaysiaParit RajaJohor86400Malaysia School of Games&Creative Technology University for the Creative ArtsFarnhamGU97DSUK Computer Engineering Department College of Computer and Information SciencesKing Saud UniversityRiyadh11543Saudi Arabia Software Engineering Department College of Computer and Information SciencesKing Saud UniversityRiyadh11543Saudi Arabia

The increased adoption of Internet of Medical Things (IoMT) technologies has resulted in the widespread use ofBody Area Networks (BANs) in medical and non-medical domains. However, the performance of IEEE 802.15.4-based BANs is impacted by challenges related to heterogeneous data traffic requirements among nodes, includingcontention during finite backoff periods, association delays, and traffic channel access through clear channelassessment (CCA) algorithms. These challenges lead to increased packet collisions, queuing delays, retransmissions,and the neglect of critical traffic, thereby hindering performance indicators such as throughput, packet deliveryratio, packet drop rate, and packet delay. Therefore, we propose Dynamic Next Backoff Period and Clear ChannelAssessment (DNBP-CCA) schemes to address these issues. The DNBP-CCA schemes leverage a combination ofthe Dynamic Next Backoff Period (DNBP) scheme and the Dynamic Next Clear Channel Assessment (DNCCA)scheme. The DNBP scheme employs a fuzzy Takagi, Sugeno, and Kang (TSK) model’s inference system toquantitatively analyze backoff exponent, channel clearance, collision ratio, and data rate as input parameters. Onthe other hand, the DNCCA scheme dynamically adapts the CCA process based on requested data transmission tothe coordinator, considering input parameters such as buffer status ratio and acknowledgement ratio. As a result,simulations demonstrate that our proposed schemes are better than some existing representative approaches andenhance data transmission, reduce node collisions, improve average throughput, and packet delivery ratio, anddecrease average packet drop rate and packet delay.

关键词： Internet of Medical Things body area networks backoff period tsk fuzzy model clear channel assessment media access control

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