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Fully Automatic segmentation of Gynaecological Abnormality Using a New Viola–Jones Model

Computers, Materials & Continua 2021年第3期66卷 3161-3182页

作者： Ihsan Jasim Hussein M.A.Burhanuddin Mazin Abed Mohammed Mohamed Elhoseny Begonya Garcia-Zapirain Marwah suliman maashi mashael s.maashi BIOCORE Research Group Universiti Teknikal Malaysia MelakaMelaka76100Malaysia Director of UTeM International Centre BIOCORE Research GroupUniversiti Teknikal Malaysia MelakaMelaka76100Malaysia College of Computer science and Information Technology University of AnbarRamadi31001Iraq Department of Computer science College of Computer Information TechnologyAmerican University in the Emirates503000United Arab Emirates eVIDA Laboratory University of DeustoBilbao48007Spain Medical Laboratory science Department King Abdulaziz UniversityJeddah21589Saudi Arabia software Engineering Department College of Computer and Information SciencesKing Saud UniversityRiyadh11451Saudi Arabia

One of the most complex tasks for computer-aided diagnosis(Intelligent decision support system)is the segmentation of ***,this study proposes a new fully automated method for the segmentation of ovarian and breast ultrasound *** main contributions of this research is the development of a novel Viola–James model capable of segmenting the ultrasound images of breast and ovarian cancer *** addition,proposed an approach that can efficiently generate region-of-interest(ROI)and new features that can be used in characterizing lesion *** study uses two databases in training and testing the proposed segmentation *** breast cancer database contains 250 images,while that of the ovarian tumor has 100 images obtained from several hospitals in *** of the experiments showed that the proposed approach demonstrates better performance compared with those of other segmentation methods used for segmenting breast and ovarian ultrasound *** segmentation result of the proposed system compared with the other existing techniques in the breast cancer data set was 78.8%.By contrast,the segmentation result of the proposed system in the ovarian tumor data set was 79.2%.In the classification results,we achieved 95.43%accuracy,92.20%sensitivity,and 97.5%specificity when we used the breast cancer data *** the ovarian tumor data set,we achieved 94.84%accuracy,96.96%sensitivity,and 90.32%specificity.

关键词： Viola-Jones model breast cancer segmentation ovarian tumor ovarian tumor segmentation breast cancer ultrasound images active contour cascade model

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A New Multi-Agent Feature Wrapper Machine Learning Approach for Heart Disease Diagnosis

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Computers, Materials & Continua 2021年第4期67卷 51-71页

作者： Mohamed Elhoseny Mazin Abed Mohammed salama A.Mostafa Karrar Hameed Abdulkareem mashael s.maashi Begonya Garcia-Zapirain Ammar Awad Mutlag Marwah suliman maashi Department of Computer science College of Computer Information TechnologyAmerican University in the Emirates503000United Arab Emirates College of Computer science and Information Technology University of AnbarRamadi31001Iraq Faculty of Computer science and Information Technology Universiti Tun Hussein Onn MalaysiaJohor86400Malaysia College of Agriculture Al-Muthanna UniversitySamawah66001Iraq Department of software Engineering College of Computer and Information SciencesKing Saud UniversityRiyadh11451Saudi Arabia eVIDA Lab University of DeustoBilbao48007Spain Department of Pure science Ministry of EducationGeneral Directorate of CurriculaBaghdadIraq Department of Medical Laboratory science Faculty of Applied Medical ScienceKing Abdulaziz UniversityJeddah21589Saudi Arabia

Heart disease(HD)is a serious widespread life-threatening *** heart of patients with HD fails to pump sufcient amounts of blood to the entire *** the occurrence of HD early and efciently may prevent the manifestation of the debilitating effects of this disease and aid in its effective *** methods for diagnosing HD are sometimes unreliable and insufcient in analyzing the related *** an alternative,noninvasive medical procedures based on machine learning(ML)methods provide reliable HD diagnosis and efcient prediction of HD ***,the existing models of automated ML-based HD diagnostic methods cannot satisfy clinical evaluation criteria because of their inability to recognize anomalies in extracted symptoms represented as classication features from patients with *** this study,we propose an automated heart disease diagnosis(AHDD)system that integrates a binary convolutional neural network(CNN)with a new multi-agent feature wrapper(MAFW)*** MAFW model consists of four software agents that operate a genetic algorithm(GA),a support vector machine(sVM),and Naïve Bayes(NB).The agents instruct the GA to perform a global search on HD features and adjust the weights of sVM and BN during initial classication.A nal tuning to CNN is then performed to ensure that the best set of features are included in HD *** CNN consists of ve layers that categorize patients as healthy or with HD according to the analysis of optimized HD *** evaluate the classication performance of the proposed AHDD system via 12 common ML techniques and conventional CNN models by using across-validation technique and by assessing six evaluation *** AHDD system achieves the highest accuracy of 90.1%,whereas the other ML and conventional CNN models attain only 72.3%–83.8%accuracy on ***,the AHDD system proposed herein has the highest capability to identify patients with *** system can be used by medical

关键词： Heart disease machine learning multi-agent feature wrapper model heart disease diagnosis HD cleveland datasets convolutional neural network

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A Comprehensive Investigation of Machine Learning Feature Extraction and ClassificationMethods for Automated Diagnosis of COVID-19 Based on X-ray Images

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Computers, Materials & Continua 2021年第3期66卷 3289-3310页

作者： Mazin Abed Mohammed Karrar Hameed Abdulkareem Begonya Garcia-Zapirain salama A.Mostafa mashael s.maashi Alaa s.Al-Waisy Mohammed Ahmed subhi Ammar Awad Mutlag Dac-Nhuong Le College of Computer science and Information Technology University of AnbarRamadi31001Iraq College of Agriculture Al-Muthanna UniversitySamawah66001Iraq eVIDA Lab University of DeustoBilbao48007Spain Faculty of Computer science and Information Technology Universiti Tun Hussein Onn MalaysiaJohor86400Malaysia software Engineering Department College of Computer and Information SciencesKing Saud UniversityRiyadh11451Saudi Arabia Faculty of Engineering and Built Environment Universiti Kebangsaan MalaysiaBangi43600Malaysia Pure science Department Ministry of EducationGeneral Directorate of CurriculaBaghdad10Iraq Institute of Research and Development Duy Tan UniversityDa Nang550000Vietnam Faculty of Information Technology Duy Tan UniversityDa Nang550000Vietnam

The quick spread of the CoronavirusDisease(COVID-19)infection around the world considered a real danger for global *** biological structure and symptoms of COVID-19 are similar to other viral chest maladies,which makes it challenging and a big issue to improve approaches for efficient identification of COVID-19 *** this study,an automatic prediction of COVID-19 identification is proposed to automatically discriminate between healthy and COVID-19 infected subjects in X-ray images using two successful moderns are traditional machine learning methods(e.g.,artificial neural network(ANN),support vector machine(sVM),linear kernel and radial basis function(RBF),k-nearest neighbor(k-NN),Decision Tree(DT),andCN2 rule inducer techniques)and deep learningmodels(e.g.,MobileNets V2,ResNet50,GoogleNet,DarkNet andXception).A largeX-ray dataset has been created and developed,namely the COVID-19 ***(400 healthy cases,and 400 COVID cases).To the best of our knowledge,it is currently the largest publicly accessible COVID-19 dataset with the largest number of X-ray images of confirmed COVID-19 infection *** on the results obtained from the experiments,it can be concluded that all the models performed well,deep learning models had achieved the optimum accuracy of 98.8%in ResNet50 *** comparison,in traditional machine learning techniques, the sVM demonstrated the best result for an accuracy of 95% and RBFaccuracy 94% for the prediction of coronavirus disease 2019.

关键词： Coronavirus disease COVID-19 diagnosis machine learning convolutional neural networks resnet50 artificial neural network support vector machine X-ray images feature transfer learning

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Leveraging Gradient-Based Optimizer and Deep Learning for Automated soil Classification Model

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Computers, Materials & Continua 2023年第7期76卷 975-992页

作者： Hadeel Alsolai Mohammed Rizwanullah mashael maashi Mahmoud Othman Amani A.Alneil Amgad Atta Abdelmageed Department of Information systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer and self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia Department of software Engineering College of Computer and Information SciencesKing Saud UniversityP.O.box 103786Riyadh11543Saudi Arabia Department of Computer science Faculty of Computers and Information TechnologyFuture University in EgyptNew Cairo11835Egypt

soil classification is one of the emanating topics and major concerns in many *** the population has been increasing at a rapid pace,the demand for food also increases *** approaches used by agriculturalists are inadequate to satisfy the rising demand,and thus they have hindered soil *** comes a demand for computer-related soil classification methods to support *** study introduces a Gradient-Based Optimizer and Deep Learning(DL)for Automated soil Clas-sification(GBODL-AsC)*** presented GBODL-AsC technique identifies various kinds of soil using DL and computer vision *** the presented GBODL-AsC technique,three major processes are *** the initial stage,the presented GBODL-AsC technique applies the GBO algorithm with the EfficientNet prototype to generate feature *** soil categorization,the GBODL-AsC procedure uses an arithmetic optimization algorithm(AOA)with a Back Propagation Neural Network(BPNN)*** design of GBO and AOA algorithms assist in the proper selection of parameter values for the EfficientNet and BPNN models,*** demonstrate the significant soil classification outcomes of the GBODL-AsC methodology,a wide-ranging simulation analysis is performed on a soil dataset comprising 156 images and five *** simulation values show the betterment of the GBODL-AsC model through other models with maximum precision of 95.64%.

关键词： soil classification earth sciences machine learning parameter optimization metaheuristics

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Cache Memory Design for single Bit Architecture with Different sense Amplifiers

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Computers, Materials & Continua 2022年第11期73卷 2313-2331页

作者： Reeya Agrawal Anjan Kumar salman A.AlQahtani mashael maashi Osamah Ibrahim Khalaf Theyazn H.H.Aldhyani VLsI Center of Excellence GLA UniversityMathura281406India Computer Engineering Department Research Chair of New Emerging Technologies and 5G Networks and BeyondCollege of Computer and Information SciencesKing Saud UniversitySaudi Arabia software Engineering Department King Saud UniversityRiyadh11543Saudi Arabia Al-Nahrain University Al-Nahrain Nano-Renewable Energy Research CenterBaghdadIraq Applied College in Abqaiq King Faisal UniversityAl-Ahsa31982Saudi Arabia

Most modern microprocessors have one or two levels of on-chip caches to make things run faster,but this is not always the *** of the time,these caches are made of static random access memory *** take up a lot of space on the chip and use a lot of electricity.A lot of the time,low power is more important than several *** is true for phones and *** memory design for single bit architecture consists of six transistors static random access memory cell,a circuit of write driver,and sense amplifiers(such as voltage differential sense amplifier,current differential sense amplifier,charge transfer differential sense amplifier,voltage latch sense amplifier,and current latch sense amplifier,all of which are compared on different resistance values in terms of a number of transistors,delay in sensing and consumption of *** conclusion arises that single bit six transistor static random access memory cell voltage differential sense amplifier architecture consumes 11.34μW of power which shows that power is reduced up to 83%,77.75%reduction in the case of the current differential sense amplifier,39.62%in case of charge transfer differential sense amplifier and 50%in case of voltage latch sense amplifier when compared to existing latch sense amplifier ***,power reduction techniques are applied over different blocks of cache memory architecture to optimize *** single-bit six transistors static random access memory cell with forced tack technique and voltage differential sense amplifier with dual sleep technique consumes 8.078μW of power,i.e.,reduce 28%more power that makes single bit six transistor static random access memory cell with forced tack technique and voltage differential sense amplifier with dual sleep technique more energy efficient.

关键词： Current differential sense amplifier(CDsA) voltage differential sense amplifier(VDsA) voltage latch sense amplifier(VLsA) current latch sense amplifier(CLsA) charge-transfer differential sense amplifier(CTDsA) new emerging technologies

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Clustered single-Board Devices with Docker Container Big stream Processing Architecture

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Computers, Materials & Continua 2022年第12期73卷 5349-5365页

作者： N.Penchalaiah Abeer s.Al-Humaimeedy mashael maashi J.Chinna Babu Osamah Ibrahim Khalaf Theyazn H.H.Aldhyani Department of CsE AITSRajampet516126Andhra PradeshIndia software Engineering Department College of Computer and Information SciencesKing Saud UniversityP.O.Box 103786Riyadh11616Saudi Arabia software Engineering Department King Saud UniversityRiyadh11543Saudi Arabia Department of Electronics and Communication Engineering Annamacharya Institute of Technology and SciencesRajampet516126Andhra PradeshIndia Al-Nahrain University Al-Nahrain Nanorenewable Energy Research CenterBaghdad10072Iraq Applied College in Abqaiq King Faisal UniversityP.O.Box 400Al-Ahsa31982Saudi Arabia

The expanding amounts of information created by Internet of Things(IoT)devices places a strain on cloud computing,which is often used for data analysis and *** paper investigates a different approach based on edge cloud applications,which involves data filtering and processing before being delivered to a backup cloud *** Paper suggest designing and implementing a low cost,low power cluster of single Board Computers(sBC)for this purpose,reducing the amount of data that must be transmitted elsewhere,using Big Data ideas and *** Apache Hadoop and spark Cluster that was used to run a test application was containerized and deployed using a Raspberry Pi cluster and *** obtain system data and analyze the setup’s performance a Prometheusbased stack monitoring and alerting solution in the cloud based market is *** Paper assesses the system’s complexity and demonstrates how containerization can improve fault tolerance and maintenance ease,allowing the suggested solution to be used in *** evaluation of the overall performance is presented to highlight the capabilities and limitations of the suggested architecture,taking into consideration the suggested solution’s resource use in respect to device restrictions.

关键词： Big data edge cloud cluster architecture performance engineering Raspberry pi dockers warm container technology data streaming

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Effect of Eu3+,Bi3+,and Li+ doping on luminescent property of GdNbO4

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Journal of Rare Earths 2024年第1期42卷 66-75页

作者： s.W.Wi Y.s.Lee Department of Physics and Integrative Institute of Basic sciences Soongsil University

We investigated the emission properties of Eu3+-doped GdNbO4-based oxide phosphors,aiming at improving the Eu3+emission by codoping of Bi3+and Li+.Eu3+singly doped,Eu3+and Bi3+doubly doped,and Eu3+,Bi3+,and Li+triply doped GdNbO4were prepared using a high-temperature solid-state reaction *** red-orange emissions from Eu3+in the singly doped samples are significantly improved by the codoping of Bi3+,mainly due to the energy transfer from Bi3+to Eu3+.The additional codoping of Li+is found to increase the Eu3+emission *** improvement might be attributed to the increase in the photoluminescent quantum yield originating from larger grain sizes and better crystallinity.A detailed analysis of the X-ray diffraction pattern and the asymmetric ratio estimated from the photoluminescence spectra show that the local lattice environment around Eu3+in GdNbO4does not change significantly with the codoping of Bi3+and Li+.Our results indicate that Li+doping is a promising way to improve the emission properties of rare-earth ion-doped GdNbO4phosphors.

关键词： Eu 3+ GdNbO 4 Codoping of Li + Photoluminescence sensitizer Rare earths

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First evidence for chiral wobbling of triaxial nuclei

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Nuclear science and Techniques 2024年第7期35卷 4-7页

作者： s.Frauendorf Department of Physics and Astronomy University of Notre DameNotre DameIN 46556USA

Chirality(Greek“handiness”)is a property of many com-plex *** molecules exist in two forms,one being the mirror image of the *** for our hands,it is impossible to make the images identical by a suitable *** two forms are called left-handed and *** have the same binding energy,because the electro-magnetic interaction,which holds the molecule together,does not change under a *** properties that are insensitive to the geometry are also the *** different geometry is the reason why the left-handed form turns the polarization plane of transmitted light in one direction by some angle while the right-handed form turns it in the oppo-site direction by the same ***,the geometrical differences between the two species may have other *** two species of the carvon molecule shown in Fig.1 taste quite differently.

关键词： turns Chiral mirror

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Tribological behavior of lubricant-impregnated porous polyimide

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Friction 2024年第4期12卷 711-725页

作者： Jinbang LI Ningning ZHOU Janet s.s.WONG school of Mechanical Engineering and Mechanics Ningbo UniversityNingbo 315211China Beijing Key Laboratory of Long-life Technology of Precise Rotation and Transmission Mechanisms Beijing Institute of Control EngineeringBeijing 100029China Department of Mechanical Engineering Imperial College LondonLondon SW72AZUK

Porous materials impregnated with lubricants can be used in conditions where limited lubricant is *** this work,three porous polyimides(PPI)with different densities were ***(PAO)impregnated PPI(iPPI)discs were rubbed against steel and sapphire *** operando observations of the iPPI-sapphire contacts show that oil is released under an applied load,forming a meniscus around *** at the outlet is created at high sliding *** amount of released oil increases with increasing PPI *** moduli,E*,estimated based on the actual contact size show that trapped oil in iPPIs contributes to load *** higher speeds,tribological rehydration of the contact occurs in low density iPPI,with that E*rises with *** high density PPIs,high speeds give a constantly high E*which is limited by the viscoelastic properties of the PPI network and possibly the rate of oil *** of iPPI-steel contacts is governed by the mechanical properties of the PPI,the flow of the lubricant,and the roughness of the PPI *** low-and medium-density(highly porous,high roughness)PPIs,large amount of oil is released,and lubrication is mainly via lubricant restricted in the contact in the pores and possibly tribological *** high density(low porosity)PPI,with lower roughness,hydrodynamic lubrication is achieved which gives the lowest *** results show that polymeric porous materials for effective lubrication require the optimization of its surface roughness,stiffness,oil flow,and oil retentions.

关键词： polyimide porous material friction mechanism polymer limited lubrication

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Enhancing fatigue performance of AZ31 magnesium alloy components fabricated by cold metal transfer-based wire arc directed energy deposition through LPB

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Journal of Magnesium and Alloys 2024年第4期12卷 1638-1662页

作者： shambhu Kumar Manjhi srikanth Bontha A.s.s.Balan Department of Mechanical Engineering National Institute of Technology KarnatakaSurathkalSrinivasanagarMangaloreKarnataka 575025India

Cold Metal Transfer-Based Wire Arc Directed Energy Deposition(CMT-WA-DED)presents a promising avenue for the rapid fabrication of components crucial to automotive,shipbuilding,and aerospace ***,the susceptibility to fatigue of CMT-WA-DED-produced AZ31 Mg alloy components has impeded their widespread adoption for critical load-bearing *** this study,a comprehensive investigation into the fatigue behaviour of WA-DED-fabricated AZ31 Mg alloy has been carried out and compared to commercially available wrought AZ31 *** findings indicate that the as-deposited parts exhibit a lower fatigue life than wrought Mg alloy,primarily due to poor surface finish,tensile residual stress,porosity,and coarse grain microstructure inherent in the WA-DED *** Plasticity Burnishing(LPB)treatment is applied to mitigate these issues,which induce significant plastic deformation on the *** treatment resulted in a remarkable improvement of fatigue life by 42%,accompanied by a reduction in surface roughness,grain refinement and enhancement of compressive residual stress ***,during cyclic deformation,WA-DED specimens exhibited higher plasticity and dislocation density compared to both wrought and WA-DED+LPB specimens.A higher fraction of Low Angle Grain Boundaries(LAGBs)in WA-DED specimens contributed to multiple crack initiation sites and convoluted crack paths,ultimately leading to premature *** contrast,wrought and WA-DED+LPB specimens displayed a higher percentage of High Angle Grain Boundaries(HAGBs),which hindered dislocation movement and resulted in fewer crack initiation sites and less complex crack paths,thereby extending fatigue *** findings underscore the effectiveness of LPB as a post-processing technique to enhance the fatigue performance of WA-DED-fabricated AZ31 Mg alloy *** study highlights the importance of LPB surface treatment on AZ31 Mg components produced by CMT-WA-DED to remove surface d

关键词： Wire arc additive manufacturing AZ31 Mg alloy Low plasticity burnishing Low cycle fatigue test strain amplitude

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