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Robust ACO-Based Landmark Matching and Maxillofacial Anomalies Classification

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Intelligent Automation & Soft Computing 2023年第2期35卷 2219-2236页

作者： Dalel Ben ismail Hela Elmannai Souham Meshoul Mohamed Saber Naceur LTSIRS LR20ES06 Institut National des Sciences Appliquées et de Technologie INSATUniversitéde CarthageTunisia Department of Information Technology College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh 11671Saudi Arabia

Imagery assessment is an efﬁcient method for detecting craniofacial anomalies.A cephalometric landmark matching approach may help in orthodontic diagnosis,craniofacial growth assessment and treatment *** landmark matching and anomalies detection helps face the manual labelling lim-itations and optimize preoperative planning of maxillofacial *** aim of this study was to develop an accurate Cephalometric Landmark Matching method as well as an automatic system for anatomical anomalies classiﬁ***,the Active Appearance Model(AAM)was used for the matching *** pro-cess was achieved by the Ant Colony Optimization(ACO)algorithm enriched with proximity ***,the maxillofacial anomalies were classiﬁed using the Support Vector Machine(SVM).The experiments were conducted on X-ray cephalograms of 400 patients where the ground truth was produced by two *** frameworks achieved a landmark matching error(LE)of 0.50±1.04 and a successful landmark matching of 89.47%in the 2 mm and 3 mm range and of 100%in the 4 mm *** classiﬁcation of anomalies achieved an accuracy of 98.75%.Compared to previous work,the proposed approach is simpler and has a comparable range of acceptable matching cost and anomaly classiﬁ*** have also shown that it outperformed the K-nearest neigh-bors(KNN)classiﬁer.

关键词： Maxillofacial anomalies cephalometric landmarks similarity chi-square distance quadratic assignment problem ant colony optimization SVM

Neuroprotective effects of exogenous brain-derived neurotrophic factor on amyloid-beta 1-40-induced retinal degeneration

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Neural Regeneration Research 2023年第2期18卷 382-388页

作者： Mohd Aizuddin Mohd Lazaldin Igor Iezhitsa Renu Agarwal Puneet Agarwal Nafeeza Mohd ismail Department of Biosciences Faculty of ScienceUniversiti Teknologi MalaysiaJohor BahruMalaysia School of Medicine International Medical UniversityKuala LumpurMalaysia Department of Pharmacology and Bioinformatics Volgograd State Medical UniversityVolgogradRussia faculty of Medicine Universiti Teknologi MARASungai Buloh CampusSelangorMalaysia

Amyloid-beta(Aβ)-related alterations,similar to those found in the brains of patients with Alzheimer's disease,have been observed in the retina of patients with *** levels of brain-derived neurotrophic factor(BDNf)are believed to be associated with the neurotoxic effects of Aβ*** investigate the mechanism underlying the neuroprotective effects of BDNf on Aβ_(1-40)-induced retinal injury in Sprague-Dawley rats,we treated rats by intravitreal administration of phosphate-buffered saline(control),Aβ_(1-40)(5 nM),or Aβ_(1-40)(5 nM)combined with BDNf(1μg/mL).We found that intravitreal administration of Aβ_(1-40)induced retinal ganglion cell ***-Gold staining showed a significantly lower number of retinal ganglion cells in the Aβ_(1-40)group than in the control and BDNf *** the Aβ_(1-40)group,low number of RGCs was associated with increased caspase-3 expression and reduced TrkB and ERK1/2 *** abolished Aβ_(1-40)-induced increase in the expression of caspase-3 at the gene and protein levels in the retina and upregulated TrkB and ERK1/2 *** findings suggest that treatment with BDNf prevents RGC apoptosis induced by Aβ_(1-40)by activating the BDNf-TrkB signaling pathway in rats.

关键词： amyloid-beta 1-40 brain-derived neurotrophic factor fluoro-Gold neuroprotection retinal ganglion cells(RGC) retinal toxicity tropomyosin receptor kinase B(TrkB)

Pythagorean Neutrosophic Planar Graphs with an Application in Decision-Making

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Computers, Materials & Continua 2023年第6期75卷 4935-4953页

作者： P.Chellamani D.Ajay Mohammed M.Al-Shamiri Rashad ismail Department of Mathematics Sacred Heart College(Autonomous)Tirupattur635601TamilnaduIndia Department of Mathematics St.Joseph’s College of EngineeringOMRChennai600119TamilnaduIndia Department of Mathematics Faculty of Science and ArtsKing Khalid UniversityMuhayl AssirSaudi Arabia Department of Mathematics and Computer Faculty of ScienceIbb UniversityIbbYemen

Graph theory has a significant impact and is crucial in the structure of many real-life *** simulate uncertainty and ambiguity,many extensions of graph theoretical notions were *** graphs play a vital role in modelling which has the property of non-crossing *** crossing edges benefit,they have some drawbacks,which paved the way for the introduction of planar *** overall purpose of the study is to contribute to the conceptual development of the Pythagorean Neutrosophic *** basic methodology of our research is the incorporation of the analogous concepts of planar graphs in the Pythagorean Neutrosophic *** significant finding of our research is the introduction of Pythagorean Neutrosophic Planar graphs,a conceptual blending of Pythagorean Neutro-sophic and Planar *** idea of Pythagorean Neutrosophic multigraphs and dual graphs are also introduced to deal with the ambiguous *** paper investigates the Pythagorean Neutrosophic planar values,which form the edges of the Pythagorean neutrosophic *** concept of Pythagorean Neutrosophic dual graphs,isomorphism,co-weak and weak isomorphism have also been explored for Pythagorean Neutrosophic planar graphs.A decision-making algorithm was proposed with a numerical illustra-tion by using the Pythagorean Neutrosophic fuzzy graph.

关键词： Pythagorean neutrosophic planar graph planarity value isomorphism dual graphs multigraph

Handling Big Data in Relational Database Management Systems

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Computers, Materials & Continua 2022年第9期72卷 5149-5164页

作者： Kamal ElDahshan Eman Selim Ahmed ismail Ebada Mohamed Abouhawwash Yunyoung Nam Gamal Behery faculty of Science Al-Azhar UniversityCairoEgypt faculty of Computer and Artificial Intelligence Damietta UniversityEgypt Department of Mathematics Faculty of ScienceMansoura UniversityMansoura35516Egypt Department of Computational Mathematics Scienceand Engineering(CMSE)Michigan State UniversityEast LansingMI48824USA Department of Computer Science and Engineering Soonchunhyang UniversityAsan31538Korea

Currently, relational database management systems (RDBMSs)face different challenges in application development due to the massive growthof unstructured and semi-structured data. This introduced new DBMS categories, known as not only structured query language (NoSQL) DBMSs, whichdo not adhere to the relational model. The migration from relational databasesto NoSQL databases is challenging due to the data complexity. This study aimsto enhance the storage performance of RDBMSs in handling a variety of *** paper presents two approaches. The first approach proposes a convenientrepresentation of unstructured data storage. Several extensive experimentswere implemented to assess the efficiency of this approach that could resultin substantial improvements in the RDBMSs storage. The second approachproposes using the JavaScript Object Notation (JSON) format to representmultivalued attributes and many to many (M:N) relationships in relationaldatabases to create a flexible schema and store semi-structured data. Theresults indicate that the proposed approaches outperform similar approachesand improve data storage performance, which helps preserve software stabilityin huge organizations by improving existing software packages whose replacement may be highly costly.

关键词： Big data RDBMS NoSQL DBMSs MongoDB MySQL unstructured data semi-structured data

Optimized Gated Recurrent Unit for Mid-Term Electricity Price forecasting

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Computer Systems Science & Engineering 2022年第11期43卷 817-832页

作者： Rashed Iqbal Hazlie Mokhlis Anis Salwa Mohd Khairuddin Syafiqah ismail Munir Azam Muhammad Department of Electrical Engineering Faculty of EngineeringUniversiti Malaya50603Malaysia Department of Electrical Engineering Main CampusIqra UniversityKarachi75300Pakistan

Electricity price forecasting(EPf)is important for energy system operations and management which include strategic bidding,generation scheduling,optimum storage reserves scheduling and systems ***,accurate EPf is crucial for the purpose of bidding strategies and minimizing the risk for market participants in the competitive electricity ***,accurate time-series prediction of electricity price is very challenging due to complex nonlinearity in the trend of electricity *** work proposes a mid-term forecasting model based on the demand and price data,renewable and non-renewable energy supplies,the seasonality and peak and off-peak hours of working and nonworking *** optimized Gated Recurrent Unit(GRU)which incorporates Bagged Regression Tree(BTE)is developed in the Recurrent Neural Network(RNN)architecture for the mid-term *** layer is employed to optimize the hyperparameters of the heterogeneous GRU with the aim to improve the model’s performance,error reduction and predict the *** this work,the proposed framework is assessed using electricity market data of five major economical states in Australia by using electricity market data from August 2020 to May *** results showed significant improvement when adopting the proposed prediction framework compared to previous works in forecasting the electricity price.

关键词： Deep learning energy management machine learning prediction

A Deep Two-State Gated Recurrent Unit for Particulate Matter (PM_(2.5)) Concentration forecasting

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Computers, Materials & Continua 2022年第5期71卷 3051-3068页

作者： Muhammad Zulqarnain Rozaida Ghazali Habib Shah Lokman Hakim ismail Abdullah Alsheddy Maqsood Mahmud faculty of Computer Science and Information Technology Universiti Tun Hussein Onn MalaysiaBatu PahatJohorMalaysia College of Computer Science King Khalid UniversityAbhaSaudi Arabia College of Computer and Information Sciences Imam Mohammad Ibn Saud Islamic UniversityRiyadhSaudi Arabia Department of Management Information Systems College of Business AdministrationImam Abdulrahman Bin Faisal University31441DammamSaudi Arabia

Air pollution is a significant problem in modern societies since it has a serious impact on human health and the *** Matter(PM_(2.5))is a type of air pollution that contains of interrupted elements with a diameter less than or equal to 2.5 *** risk assessment and epidemiological investigations,a better knowledge of the spatiotemporal variation of PM_(2.5) concentration in a constant space-time area is *** spatiotemporal interpolation approaches commonly relying on robust presumption by limiting interpolation algorithms to those with explicit and basic mathematical expression,ignoring a plethora of hidden but crucial manipulating *** advanced deep learning approaches have been proposed to forecast Particulate Matter(PM_(2.5)).Recurrent neural network(RNN)is one of the popular deep learning architectures which is widely employed in PM_(2.5) concentration *** this research,we proposed a Two-State Gated Recurrent Unit(TS-GRU)for monitoring and estimating the PM_(2.5) concentration forecasting *** proposed algorithm is capable of considering both spatial and temporal hidden affecting elements *** tested our model using data from daily PM_(2.5) dimensions taken in the contactual southeast area of the United States in *** the studies,three evaluation matrices were utilized to compare the overall performance of each algorithm:Mean Absolute Error(MAE),Root Mean Square Error(RMSE),and Mean Absolute Percentage Error(MAPE).The experimental results revealed that our proposed TS-GRU model outperformed compared to the other deep learning approaches in terms of forecasting performance.

关键词： Deep learning PM_(2.5)forecasting air pollution two-state GRU

Software Defect Prediction Harnessing on Multi 1-Dimensional Convolutional Neural Network Structure

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Computers, Materials & Continua 2022年第4期71卷 1521-1546页

作者： Zuhaira Muhammad Zain Sapiah Sakri Nurul Halimatul Asmak ismail Reza M.Parizi Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadhSaudi Arabia Department of Computer Science and Information Technology College of CommunityPrincess Nourah bint Abdulrahman UniversityRiyadhSaudi Arabia College of Computing and Software Engineering Kennesaw State UniversityMariettaGA30060USA

Developing successful software with no defects is one of the main goals of software *** order to provide a software project with the anticipated software quality,the prediction of software defects plays a vital *** learning,and particularly deep learning,have been advocated for predicting software defects,however both suffer from inadequate accuracy,overfitting,and complicated *** this paper,we aim to address such issues in predicting software *** propose a novel structure of 1-Dimensional Convolutional Neural Network(1D-CNN),a deep learning architecture to extract useful knowledge,identifying and modelling the knowledge in the data sequence,reduce overfitting,and finally,predict whether the units of code are defects *** design large-scale empirical studies to reveal the proposed model’s effectiveness by comparing four established traditional machine learning baseline models and four state-of-the-art baselines in software defect prediction based on the NASA *** experimental results demonstrate that in terms of f-measure,an optimal and modest 1DCNN with a dropout layer outperforms baseline and state-of-the-art models by 66.79%and 23.88%,respectively,in ways that minimize overfitting and improving prediction performance for software *** to the results,1D-CNN seems to be successful in predicting software defects and may be applied and adopted for a practical problem in software ***,in turn,could lead to saving software development resources and producing more reliable software.

关键词： Defects software defect prediction deep learning convolutional neural network machine learning

Effect of Sulfur fertilization on Productivity and Grain Zinc Yield of Rice Grown under Low and Adequate Soil Zinc Applications

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Rice science 2023年第6期30卷 632-640页

作者： Kankunlanach KHAMPUANG Nanthana CHAIWONG Atilla YAZICI Baris DEMIRER ismail CAKMAK Chanakan PROM-U-THAI Agronomy Division Department of Plant and Soil SciencesFaculty of AgricultureChiang Mai UniversityChiang Mai 50200Thailand field and Renewable Energy Crops Research Institute Department of AgricultureBangkok 10900Thailand faculty of Engineering and Natural Sciences Sabanci UniversityIstanbul 34956Turkey Lanna Rice Research Center Chiang Mai UniversityChiang Mai 50200Thailand

This study aimed to investigate the responses in rice(Oryza sativa *** 97)production and grain zinc(Zn)accumulation to combined Zn and sulfur(S)*** experiment was designed as a factorial experiment with two Zn and three S concentrations applied to the soil in a completely randomized design with four *** plants were grown under greenhouse conditions at low(0.25 mg/kg)and adequate(5 mg/kg)Zn rates combined with S(CaSO_(4)·2H_(2)O)application(low,2.5 mg/kg;moderate,10 mg/kg,and adequate,50 mg/kg).The lowest rate of S at adequate soil Zn treatment increased grain yield by 68%compared with the same S rate at low Zn *** with the adequate S rate at low Zn and adequate Zn supply produced the highest grain yield,with increases of 247%and 143%compared with low S rate at low Zn and adequate Zn supply,*** concentration of grain Zn and S responded differently to the applied S rates depending on the soil Zn *** highest grain Zn concentration,reaching 41.5 mg/kg,was observed when adequate Zn was supplied at the low S ***,the adequate S rate at the low soil Zn conditions yielded the highest grain S *** total grain Zn uptake per plant showed particular increases in grain Zn yield when adequate S rates were applied,showing increases of 208%and 111%compared with low S rate under low and adequate soil Zn conditions,*** results indicated that the synergistic application of soil Zn and S improves grain production and grain Zn *** results highlight the importance of total grain Zn yield in addition to grain Zn concentration,especially under the growth conditions where grain yield shows particular increases as grain Zn is diluted due to increased grain yield by increasing S fertilization.

关键词： grain rice sulfur zinc deficiency

ANN-based empirical modelling of pile behaviour under static compressive loading

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frontiers of Structural and Civil Engineering 2018年第4期12卷 594-608页

作者： Abdussamad ismail Department of Civil Engineering Bayero UniversityKanoNigeria

Artificial neural networks have been widely used over the past two decades to successfully develop empirical models for a variety of geotechnical problems. In this paper, an empirical model based on the product-unit neural network (PUNN) is developed to predict the load-deformation behaviour of piles based SPT values of the supporting soil. Other parameters used as inputs include particle grading, pile geometry, method of installation as well as the elastic modulus of the pile material. The model is trained using full-scale pile loading tests data retrieved from fHWA deep foundations database. from the results obtained, it is observed that the proposed model gives a better simulation of pile load-deformation curves compared to the fleming’s hyperbolic model and t-z approach.

关键词： piles in compression load-deformation behaviour product-unit neural network