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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

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)

The Upper Jurassic sediments,marib-shabwa Basin,Yemen:Lithofacies aspects and sequence stratigraphic analysis

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Energy Geoscience 2024年第1期5卷 342-357页

作者： mohammed Albaroot mohamed m.Elhossainy Nabil m.Al-Areeq Emad A.Abdullah Abdelhamid m.salman Department of Applied Geology Faculty of Science and EducationUniversity of Saba RegionMaribYemen Department of Geology Faculty of ScienceKafrelsheikh UniversityKafrelsheikh33516Egypt Department of Geology and Environment Faculty of Applied SciencesThamar UniversityYemen Department of mineral Developing and Oil&Gas Engineering Academy of EngineeringPeoples’Friendship University of Russia(RUDN University)Moscow117198Russia Department of Geology Faculty of ScienceAssiut UniversityAssiut71516Egypt

The present study is devoted to understanding the evolution of the Upper Jurassic sab'atayn Formation in the marib-shabwa Basin,Yemen,through a sequence stratigraphic analysis based on integrating datasets of sedimentology,seismic sections,and well *** sab'atayn Formation(Tithonian age)is represented by a series of clastic and evaporites that were deposited under fluvio-deltaic to prodeltaic *** is divided into four members including Yah(at the base),upwards to seen,Alif,and safir at the *** third-order depositional sequences were determined for the Tithonian succession which were separated by three sequence *** sequences were classified into their systems tracts signifying several sedimentation patterns of progradational,aggradational,and retrogradational parasequence *** first depositional sequence corresponds to the early-middle Tithonian Yah and seen units that can be classified into lowstand,transgressive,and highstand systems *** second sequence comprises the late Tithonian Alif unit that can be subdivided into transgressive and highstand systems *** sandy deposits of the Alif member(highstand deposits)represent the most productive hydrocarbon reservoir in the *** Upper Jurassic sediments in the study area were resulted from a combination of eustatic and tectonic effects.

关键词： sequence stratigraphy sandstone reservoir Well logs Upper Jurassic marib-shabwa Basin Yemen

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

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

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

New Antenna Array Beamforming Techniques Based on Hybrid Convolution/Genetic Algorithm for 5G and Beyond Communications

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Computer modeling in Engineering & sciences 2024年第3期138卷 2749-2767页

作者： shimaa m.Amer Ashraf A.m.Khalaf Amr H.Hussein salman A.Alqahtani mostafa H.Dahshan Hossam m.Kassem Electronics and Communications Engineering Department Higher Institute of Engineering and TechnologyKafr ElsheikhEgypt Electronics and Electrical Communications Engineering Department Faculty of EngineeringMinia UniversityMiniaEgypt Electronics and Electrical Communications Engineering Department Faculty of EngineeringTanta UniversityTantaEgypt Electronics and Electrical Communications Engineering Department Faculty of EngineeringHorus University EgyptNew DamiettaEgypt Department of Computer Engineering College of computer and Information SciencesKing Saud UniversityRiyadh11543Saudi Arabia school of Computing Mathematics and EngineeringCharles Sturt UniversityBathurstNSW2795Australia

side lobe level reduction(sLL)of antenna arrays significantly enhances the signal-to-interference ratio and improves the quality of service(QOs)in recent and future wireless communication systems starting from 5G up to ***,it improves the array gain and directivity,increasing the detection range and angular resolution of radar *** study proposes two highly efficient sLL reduction *** techniques are based on the hybridization between either the single convolution or the double convolution algorithms and the genetic algorithm(GA)to develop the Conv/GA andDConv/GA,*** convolution process determines the element’s excitations while the GA optimizes the element *** m elements linear antenna array(LAA),the convolution of the excitation coefficients vector by itself provides a new vector of excitations of length N=(2m−1).This new vector is divided into three different sets of excitations including the odd excitations,even excitations,and middle excitations of lengths m,m−1,andm,*** the same element spacing as the original LAA is used,it is noticed that the odd and even excitations provide a much lower sLL than that of the LAA but with amuch wider half-power beamwidth(HPBW).While the middle excitations give the same HPBWas the original LAA with a relatively higher *** the increased HPBWof the odd and even excitations,the element spacing is optimized using the ***,the synthesized arrays have the same HPBW as the original LAA with a two-fold reduction in the ***,for extreme sLL reduction,the DConv/GA is *** this technique,the same procedure of the aforementioned Conv/GA technique is performed on the resultant even and odd excitation *** provides a relatively wider HPBWthan the original LAA with about quad-fold reduction in the sLL.

关键词： Array synthesis convolution process genetic algorithm(GA) half power beamwidth(HPBW) linear antenna array(LAA) side lobe level(sLL) quality of service(QOs)

Engineering of efficient visible light photocatalysts:Ti_(1-x+y)Cu_(x)La_(y)O_(2)(x=0.03;y=0,0.005,0.01) compositions

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Journal of Rare Earths 2024年第3期42卷 497-505,I0003页

作者： s.m.Yakout A.m.Youssef Inorganic Chemistry Department National Research Centre(NRC)El Buhouth StDokkiCairo12622Egypt

In dye-photocatalysis, widening the wavelengths absorption range of catalysts as well as aborting the recombination of the photoexcited charge carriers(electrons-holes pairs) are of paramount *** the current study, we report the photocatalytic characteristics of four compositions composed of TiO_(2),Ti_(0.97)Cu_(0.03)O_(2), Ti_(0.965)Cu_(0.03)La_(0.005)O_(2)and Ti_(0.96)Cu_(0.03)La_(0.01)O_(2)powders for two harmful dyes(Congo red and methylene blue). The X-ray diffraction technique verified the configuration of mixed anatase and rutile phases in pure TiO_(2)and Ti_(0.97)Cu_(0.03)O_(2)powders while Ti_(0.965)Cu_(0.03)La_(0.005)O_(2)and Ti_(0.96)Cu_(0.03)La_(0.01)O_(2)powders possess anatase phase only. The changes of the band gap structure and dimensions of the unit cell volume of TiO_(2)confirm the substitution of Ti^(4+)sites by Cu^(2+)and La^(3+)ions. Remarkable widening of wavelengths absorption with formation of long absorption tails were found in TiO_(2)(3.13 e V) through insertion of La^(3+)plus Cu^(2+)ions. Ti_(0.965)Cu_(0.03)La_(0.005)O_(2)and Ti_(0.96)Cu_(0.03)La_(0.01)O_(2)samples exhibit a visible light band gap of 2.3 and 2.7 e V, respectively. Based on sEm images, incorporation of Cu^(2+)and La^(3+)ions influences the surface morphology of TiO_(2). The resulting Ti_(0.97)Cu_(0.03)O_(2), Ti_(0.965)Cu_(0.03)La_(0.005)O_(2)and Ti_(0.96)Cu_(0.03)La_(0.01)O_(2)powders present enhanced photocatalytic activity towards Congo red dye degradation compared to pure one under natural solar spectrum. The best Ti_(0.96)Cu_(0.03)La_(0.01)O_(2)catalyst exhibits superior photocatalytic activity of 96% for 10 ppm CR dye in 20 min as well as above average photocatalytic efficiency of 63% for 30 ppm CR dye in 120 min. As well, it showed acceptable photodegradation activity of 52% for 30 ppm mB solution in 140 min of sunlight irradiation. The heightened photodegradation efficiency of Ti_(0.96)Cu_(0.03)La_(0.01)O_(2)catalyst can be clarified in terms of higher ch

关键词： semiconductor TiO_(2) Rare earths Congo red methylene blue solar spectrum Photodegradation

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