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An Implementation of Multiscale Line Detection and Mathematical Morphology for Efficient and Precise Blood Vessel Segmentation in Fundus Images

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Computers, Materials & Continua 2024年第5期79卷 2565-2583页

作者： syed Ayaz Ali shah Aamir shahzad Musaed Alhussein Chuan Meng Goh Khursheed Aurangzeb Tong Boon Tang Muhammad Awais Department of Electrical and Computer Engineering COMSATS University IslamabadAbbottabad CampusAbbottabad22060Pakistan Department of Computer Engineering College of Computer and Information SciencesKing Saud UniversityP.O.Box 51178Riyadh11543Saudi Arabia Faculty of Information and Communication Technology University Tunku Abdul RehmanKampar CampusKampar31900Malaysia Centre for Intelligent Signal and Imaging Research University Teknologi PETRONASBandar Seri IskandarPerak32610Malaysia School of Computing Sciences University of East AngliaNorwichNR47TJUK

Diagnosing various diseases such as glaucoma,age-related macular degeneration,cardiovascular conditions,and diabetic retinopathy involves segmenting retinal blood *** task is particularly challenging when dealing with color fundus images due to issues like non-uniformillumination,low contrast,and variations in vessel appearance,especially in the presence of different ***,the speed of the retinal vessel segmentation system is of utmost *** the surge of now available big data,the speed of the algorithm becomes increasingly important,carrying almost equivalent weightage to the accuracy of the *** address these challenges,we present a novel approach for retinal vessel segmentation,leveraging efficient and robust techniques based on multiscale line detection and mathematical *** algorithm’s performance is evaluated on two publicly available datasets,namely the Digital Retinal Images for Vessel Extraction dataset(DRIVE)and the Structure Analysis of Retina(STARE)*** experimental results demonstrate the effectiveness of our method,withmean accuracy values of 0.9467 forDRIVE and 0.9535 for STARE datasets,aswell as sensitivity values of 0.6952 forDRIVE and 0.6809 for STARE ***,our algorithmexhibits competitive performance with state-of-the-art ***,it operates at an average speed of 3.73 s per image for DRIVE and 3.75 s for STARE *** is worth noting that these results were achieved using Matlab scripts containing multiple *** suggests that the processing time can be further reduced by replacing loops with *** the proposed algorithm can be deployed in real time *** summary,our proposed system strikes a fine balance between swift computation and accuracy that is on par with the best available methods in the field.

关键词： Line detector vessel detection localization mathematical morphology image processing

Enhancing Mild Cognitive Impairment Detection through Efficient Magnetic Resonance Image Analysis

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Computers, Materials & Continua 2024年第8期80卷 2081-2098页

作者： Atif Mehmood Zhonglong Zheng Rizwan Khan Ahmad Al Smadi Farah shahid shahid Iqbal Mutasem K.Alsmadi Yazeed Yasin Ghadi syed Aziz shah Mostafa M.Ibrahim Department of Computer Science and Technology Zhejiang Normal UniversityJinhua321002China Zhejiang Institute of Photoelectronics&Zhejiang Institute for Advanced Light Source Zhejiang Normal UniversityJinhua321004China Department of Data Science and Artificial Intelligence Zarqa UniversityZarqa13100Jordan School of Chemical and Environmental Engineering College of ChemistryChemical Engineering andMaterials ScienceSoochow UniversitySuzhou215123China Department of Management Information Systems College of Applied Studies and Community ServiceImam Abdulrahman Bin Faisal UniversityDammam32244Saudi Arabia Department of Computer Science Al Ain UniversityAbu Dhabi999041United Arab Emirates Department of Electrical Engineering Faculty of EngineeringMinia UniversityMinia61519Egypt Healthcare Technology and Innovation Theme Faculty Research Centre for Intelligent HealthcareCoventry UniversityCoventryCV48BWUK

Neuroimaging has emerged over the last few decades as a crucial tool in diagnosing Alzheimer’s disease(AD).Mild cognitive impairment(MCI)is a condition that falls between the spectrum of normal cognitive function and ***,previous studies have mainly used handcrafted features to classify MCI,AD,and normal control(NC)*** paper focuses on using gray matter(GM)scans obtained through magnetic resonance imaging(MRI)for the diagnosis of individuals with MCI,AD,and *** improve classification performance,we developed two transfer learning strategies with data augmentation(i.e.,shear range,rotation,zoom range,channel shift).The first approach is a deep Siamese network(DSN),and the second approach involves using a cross-domain strategy with customized *** performed experiments on the Alzheimer’s Disease Neuroimaging Initiative(ADNI)dataset to evaluate the performance of our proposed *** experimental results demonstrate superior performance in classifying the three binary classification tasks:NC ***,NC ***,and MCI ***,we achieved a classification accuracy of 97.68%,94.25%,and 92.18%for the three cases,*** study proposes two transfer learning strategies with data augmentation to accurately diagnose MCI,AD,and normal control individuals using GM *** findings provide promising results for future research and clinical applications in the early detection and diagnosis of AD.

关键词： Alzheimer’s disease mild cognitive impairment normal control transfer learning classification augmentation

Machine learning empowered COVID-19 patient monitoring using non-contact sensing:An extensive review

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Journal of Pharmaceutical Analysis 2022年第2期12卷 193-204页

作者： Umer Saeed syed Yaseen shah Jawad Ahmad Muhammad Ali Imran Qammer H.Abbasi syed Aziz shah Research Centre for Intelligent Healthcare Coventry UniversityCoventryCV15FBUK School of Computing Engineering and Built EnvironmentGlasgow Caledonian UniversityGlasgowG40BAUK School of Computing Edinburgh Napier UniversityEdinburghEH114BNUK James Watt School of Engineering University of GlasgowGlasgowG128QQUK

The severe acute respiratory syndrome coronavirus 2(SARS-CoV-2),which caused the coronavirus disease 2019(COVID-19)pandemic,has affected more than 400 million people *** the recent rise of new Delta and Omicron variants,the efficacy of the vaccines has become an important *** goal of various studies has been to limit the spread of the virus by utilizing wireless sensing technologies to prevent human-to-human interactions,particularly for healthcare *** this paper,we discuss the current literature on invasive/contact and non-invasive/noncontact technologies(including Wi-Fi,radar,and software-defined radio)that have been effectively used to detect,diagnose,and monitor human activities and COVID-19 related symptoms,such as irregular *** addition,we focused on cutting-edge machine learning algorithms(such as generative adversarial networks,random forest,multilayer perceptron,support vector machine,extremely randomized trees,and k-nearest neighbors)and their essential role in intelligent healthcare ***,this study highlights the limitations related to non-invasive techniques and prospective research directions.

关键词： Artificial intelligence Non-invasive healthcare Machine learning Non-contact sensing COVID-19

Fractional Processing Based Adaptive Beamforming Algorithm

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Computers, Materials & Continua 2023年第7期76卷 1065-1084页

作者： syed Asghar Ali shah Tariqullah Jan syed Muslim shah Ruhul Amin Khalil Ahmad Sawalmeh Muhammad Anan Department of Electrical Engineering University of Engineering and TechnologyPeshawar25120Pakistan Department of Electrical Engineering Capital University of Science and TechnologyIslamabad44000Pakistan Department of Computer Science Northern Border UniversityArar91431Saudi Arabia Department of Software Engineering Alfaisal UniversityRiyadh11533Saudi Arabia

Fractional order algorithms have shown promising results in various signal processing applications due to their ability to improve performance without significantly increasing *** goal of this work is to inves-tigate the use of fractional order algorithm in the field of adaptive beam-forming,with a focus on improving performance while keeping complexity *** effectiveness of the algorithm will be studied and evaluated in this *** this paper,a fractional order least mean square(FLMS)algorithm is proposed for adaptive beamforming in wireless applications for effective utilization of *** algorithm aims to improve upon existing beam-forming algorithms,which are inefficient in performance,by offering faster convergence,better accuracy,and comparable computational *** FLMS algorithm uses fractional order gradient in addition to the standard ordered gradient in weight *** derivation of the algorithm is provided and supported by mathematical convergence *** is evaluated through simulations using mean square error(MSE)minimization as a metric and compared with the standard LMS algorithm for various *** results,obtained through Matlab simulations,show that the FLMS algorithm outperforms the standard LMS in terms of convergence speed,beampattern accuracy and scatter *** outperforms LMS in terms of convergence speed by 34%.From this,it can be concluded that FLMS is a better candidate for adaptive beamforming and other signal processing applications.

关键词： Adaptive beamforming adaptive array fractional processing least mean square fractional least mean square

An Energy-Efficient 12b 2.56 MS/s SAR ADC Using Successive Scaling of Reference Voltages

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Computers, Materials & Continua 2022年第7期72卷 2127-2139页

作者： Hojin Kang syed Asmat Ali shah HyungWon Kim Department of Electronics Engineering College of Electrical and Computer EngineeringChungbuk National UniversityCheongju28644Korea Department of Electrical and Computer Engineering COMSATS University IslamabadAbbottabad CampusAbbottabad22060Pakistan

This paper presents an energy efficient architecture for successive approximation register(SAR)analog to digital converter(ADC).SAR ADCs with a capacitor array structure have been widely used because of its simple architecture and relatively high ***,conventional SAR ADCs consume relatively high energy due to the large number of capacitors used in the capacitor array and their sizes scaled up along with the number of *** proposed architecture reduces the energy consumption as well as the capacitor size by employing a new array architecture that scales down the reference voltages instead of scaling up the capacitor *** proposed 12-bit SAR ADC is implemented in Complementary Metal Oxide Semiconductor(CMOS)0.13 um library using Cadence Virtuoso design *** results and mathematical model demonstrate the overall energy savings of up to 97.3%compared with conventional SAR ADC,67%compared with the SAR ADC with split capacitor,and 35%compared with the resistor and capacitor(R&C)Hybrid SAR *** ADC achieves an effective number of bits(ENOB)of 11.27 bits and consumes 61.7 uW at sampling rate of 2.56 MS/s,offering an energy consumption of 9.8 fJ per conversion *** proposed SAR ADC offers 95.5%reduction in chip core area compared to conventional architecture,while occupying an active area of 0.088 mm2.

关键词： Low voltage low power successive approximation register analog to digital converter switching energy

Recent advances and emerging trends of rare-earth-ion doped spectral conversion nanomaterials in perovskite solar cells

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Journal of Rare Earths 2022年第11期40卷 1651-1667,I0001页

作者： syed Afaq Ali shah Muhammad Hassan Sayyad Jinghua Sun Zhongyi Guo School of Electrical Engineering&Intelligentization Dongguan University of TechnologyDongguan 523808China Advanced Photovoltaic Research Labs(APRL) Faculty of Engineering SciencesGhulam Ishaq Khan Institute of Engineering Sciences and TechnologyTopiDistrictSwabiKhyberPakhtunkhwa23640Pakistan School of Computer and Information Hefei University of TechnologyHefei 2300o9China

Organic-inorganic lead halide based perovskite solar cells(PSCs)have attracted unprecedented research interest over last *** high performance,combined with merits of low fabrication costs and ease of synthesis make PSCs promising alternate to state of the art silicon(Si)based solar *** r,some inherent shortcomings of PSCs are hindering their market dominance over conventional photovoltaic technologies such as transmission loss of sub-bandgap photons,poor stability and hysteresis ***,use of rare earth(RE)ions doped nanomaterials in PSCs,has been identified as an effective means to address the aforementioned issues by expanding the range of absorption spectra minimizing the non-absorption loss of solar photons,enhancing light scattering and improving operational *** article reviews the recent progress in doping rare-earth(RE)ions in the building blocks of PSCs such as semiconductor electrodes and photoactive perovskite layers,and its use as a separate spectral conversion layer in *** effect of size,shape,constitution and concentration of RE-nanoparticles on the overall performance and device stability will be analyzed in ***,we provide an outlook on the opportunities this newly developed field offers and the critical challenges faced in rationally and effectively using RE-ion-doped nanomaterials in PSCs for better operational stability and enhanced performance.

关键词： Efficiency Electron transport layer Photovoltaics Rare-earth(RE) Perovskite solar cells Up/down conversion

Diffusion Based Channel Gains Estimation in WSN Using Fractional Order Strategies

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Computers, Materials & Continua 2022年第2期70卷 2209-2224页

作者： Nasir Mahmud Khokhar Muhammad Nadeem Majeed syed Muslim shah Department of Computer Engineering University of Engineering and TechnologyTaxila47050Pakistan Punjab University College of Information Technology University of PunjabLahore54000Pakistan Department of Electrical Engineering Capital University of Science and TechnologyIslamabad44000Pakistan

In this study,it is proposed that the diffusion least mean square(LMS)algorithm can be improved by applying the fractional order signal processing *** of Caputo’s fractional derivatives are considered in the optimization of cost *** is suggested to derive a fractional order variant of the diffusion LMS *** applicability is tested for the estimation of channel parameters in a distributed environment consisting of randomly distributed sensors communicating through wireless *** topology of the network is selected such that a smaller number of nodes are *** the network,a random sleep strategy is followed to conserve the transmission power at the *** proposed fractional ordermodified diffusionLMS algorithms are applied in the two configurations of combine-then-adapt and *** average squared error performance of the proposed algorithms along with its traditional counterparts are evaluated for the estimation of the Rayleigh channel *** proof of convergence is provided showing that the addition of the nonlinear term resulting from fractional derivatives helps adjusts the autocorrelation matrix in such a way that the spread of its eigenvalues *** increases the convergence as well as the steady state response even for the larger step *** results are shown for different number of nodes and fractional *** simulation results establish that the accuracy of the proposed scheme is far better than its classical counterparts,therefore,helps better solves the channel gains estimation problem in a distributed wireless *** algorithm has the potential to be applied in other applications related to learning and adaptation.

关键词： Diffusion strategies distributed channel gains fractional derivatives mean squared error

Apple microbial communities and differences between two main Chinese producing regions

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Food Quality and Safety 2022年第1期6卷 34-44页

作者：沈友明张建一聂继云张惠 syed Asim shah Bacha College of Horticulture Qingdao Agricultural University/Laboratory of Quality&Safety Risk Assessment for Fruit(Qingdao)Ministry of Agriculture and Rural Affairs/National Technology Centre for Whole Process Quality Control of FSEN Horticultural Products(Qingdao)/Qingdao Key Lab of Modern Agriculture Quality and Safety EngineeringQingdaoChina Institute of Pomology Chinese Academy of Agricultural Sciences/Laboratory of Quality&Safety Risk Assessment for Fruit(Xingcheng)Ministry of Agriculture and Rural Affairs/Quality Inspection and Test Center for Fruit and Nursery Stocks(Xingcheng)Ministry of Agriculture and Rural AffairsXingchengChina

Microbes on fresh apples are closely associated with fruit disease,preservation,and quality *** into the microbial commu-nities on apples from different producing regions could reveal the microbial specificity and help disease prevention and quality *** this paper,the apple surface microbes of 44 samples from two main Chinese apple-producing regions,Bohai Bay(BHB)and the Loess Plateau(LP),were investigated by sequencing fungal internal transcribed spacer and bacterial 16s rRNA hypervariable *** and LP apples con-tained significantly different bacterial and fungal *** apples had a higher fungal diversity than LP apples.A total of 102 different fungal and bacterial taxonomies were obtained between apples from the two regions,in which 24 genera were *** apples had higher phytopathogenic fungal genera,such as Tilletiopsis,Acremonium,Candida,and Phoma,indicating the higher phytopathogenic risks of apples from the humid climate of the BHB *** apples contained more bacterial genera identified as gut microbes,indicating the potential risks of contaminating apples with foodborne pathogens in the arid environment of the LP This study highlighted the environment-oriented mi-crobial specificity on apples from two main apple-producing regions,and provided a basis for further investigation.

关键词： Apple regional climate microbiome metagenomics next generation sequencing

Brain Tumor Detection and Segmentation Using RCNN

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Computers, Materials & Continua 2022年第6期71卷 5005-5020页

作者： Maham Khan syed Adnan shah Tenvir Ali Quratulain Aymen Khan Gyu Sang Choi Department of Computer Science University of Engineering&TechnologyTaxila47080Pakistan Faculty of Computing The Islamia University of BahawalpurBahawalpur63100Pakistan Department of Information and Communication Engineering Yeungnam UniversityGyeongbuk38541Korea

Brain tumors are considered as most fatal *** reduce the risk of death,early identification of the disease is *** of the best available methods to evaluate brain tumors is Magnetic resonance Images(MRI).Brain tumor detection and segmentation are tough as brain tumors may vary in size,shape,and *** makes manual detection of brain tumors by exploring MRI a tedious job for radiologists and doctors’.So an automated brain tumor detection and segmentation is *** work suggests a Region-based Convolution Neural Network(RCNN)approach for automated brain tumor identification and segmentation using MR images,which helps solve the difficulties of brain tumor identification efficiently and *** methodology is based on the accurate and efficient selection of tumorous *** reduces computational complexity and *** have validated the designed experimental setup on a standard dataset,BraTS *** used binary evaluation matrices based on Dice Similarity Coefficient(DSC)and Mean Average Precision(mAP).The segmentation results are compared with state-of-the-art methodologies to demonstrate the effectiveness of the proposed *** suggested approach attained an averageDSC of 0.92 andmAP 0.92 for 10 patients,while on the whole dataset,the scores are DSC 0.89 and mAP *** following results clearly show the performance efficiency of the proposed methodology.

关键词： Brain tumor MRI preprocessing image segmentation brain tumor localization medical ML RCNN BraTS 2020 LGG HGG