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Association of core promoter mutations of hepatitis B virus and viral load is different in HBeAg(+) and HBeAg(-) patients

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World Journal of Gastroenterology 2011年第6期17卷 708-716页

作者： Andi Utama Marlinang Diarta Siburian Sigit Purwantomo Mariana Destila Bayu Intan Tri Shinta Kurniasih Susan Tai Rino Alvani Gani Laurentius Adrianus Lesmana all sulaiman Wenny Astuti Achwan Soewignjo Soemohardjo Arnelis Nasrul Zubir Julius Syafruddin AR Lelosutan Benyamin Lukito Tantoro Harmono Molecular Epidemiology Division Mochtar Riady Institute for Nanotechnology Universitas Pelita Harapan Lippo Karawaci Tangerang 15810 Indonesia Hepatology Division Department of Intemal Medicine Faculty of Medicine University of Indonesia Jakarta 10430 Indonesia Department of Internal Medicine Mataram General Hospital Mataram 83127 Indonesia Gastroentero-Hepatology Division Department of Internal Medicine Dr. M. Jamil Hospital Padang 25127 Indonesia Gastroentero-Hepatology Division Department of Intemal Medicine Dr. Gatot Soebroto Hospital Jakarta 10410 Indonesia Department of Internal Medicine Siloam Hospital Lippo Village Tangerang 15810 Indonesia Department of Internal Medicine Dr. Moewardi Hospital Surakarta 57126 Indonesia

AIM:To identify the prevalence of hepatitis B e antigen (HBeAg) and to assess the association of hepatitis B virus (HBV) core promoter mutations and viral load in Indonesian ***:Sixty-four patients with chronic hepatitis,65 with liver cirrhosis and 50 with hepatocellular carcinoma were included in this *** and hepatitis B e antibody (HBeAb) tests were performed using enzyme-linked immunosorbent assay and the mutations were analyzed by *** load was measured by real-time polymerase chain ***:Of 179 patients,108 (60.3%) were HBeAg(-) and 86 (79.6%) of these HBeAg(-) patients had been *** A1896 mutation was not found in HBeAg(+) patients,however,this mutation was detected in 70.7% of HBeAg(-) *** mutation was frequently found when HBeAg was not expressed (87.7%),compared to that found in HBeAg seroconverted patients (65.1%).The A1899 mutation was also more prevalent in HBeAg(-) than in HBeAg(+) patients (P=0.004).The T1762/A1764 mutation was frequently found in both HBeAg(+) and HBeAg(-) patients,however,the prevalence of this mutation did not significantly differ among the two groups (P=0.054).In HBeAg(+) patients,the T1762/A1764 mutation was correlated with lower HBV DNA (P < 0.001).The A1899 mutation did not correlate with HBV DNA (P=0.609).In HBeAg(-) patients,the T1762/A1764 mutation alone was not correlated with HBV DNA (P=0.095),however,the presence of either the T1762/A1764 or A1896 mutations was associated with increased HBV DNA (P < 0.001).CONCLUSION:The percentage of HBeAg(-) patients is high in Indonesia,and most of the HBeAg(-) patients had been *** A1896 mutation was most likely the major cause of HBeAg *** T1762/A1764 mutation alone was associated with lower viral loads in HBeAg(+) patients,but not in HBeAg(-) patients.

关键词： Hepatitis B e antibody Hepatitis B e antigen Hepatitis B virus Indonesia Precore/core promoter mutations Viral load

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Hybrid firefly algorithm-neural network for battery remaining useful life estimation

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Clean Energy 2024年第5期8卷 157-166页

作者： Zuriani Mustaffa Mohd Herwan sulaiman Faculty of Computing Universiti Malaysia Pahang Al-Sultan Abdullah(UMPSA)26600 Pekan PahangMalaysia Faculty of Electrical&Electronics Engineering Technology Universiti Malaysia Pahang Al-Sultan Abdullah(UMPSA)26600 Pekan PahangMalaysia

Accurately estimating the remaining useful life(RUL)of batteries is crucial for optimizing maintenance,preventing failures,and enhancing reliability,thereby saving costs and *** study introduces a hybrid approach for estimating the RUL of a battery based on the firefly algorithm–neural network(FA–NN)model,in which the FA is employed as an optimizer to fine-tune the network weights and hidden layer biases in the *** performance of the FA–NN is comprehensively compared against two hybrid models,namely the harmony search algorithm(HSA)–NN and cultural algorithm(CA)–NN,as well as a single model,namely the autoregressive integrated moving average(ARIMA).The comparative analysis is based mean absolute error(MAE)and root mean squared error(RMSE).Findings reveal that the FA–NN outperforms the HSA–NN,CA–NN,and ARIMA in both employed metrics,demonstrating su-perior predictive capabilities for estimating the RUL of a ***,the FA–NN achieved a MAE of 2.5371 and a RMSE of 2.9488 compared with the HSA–NN with a MAE of 22.0583 and RMSE of 34.5154,the CA–NN with a MAE of 9.1189 and RMSE of 22.4646,and the ARIMA with a MAE of 494.6275 and RMSE of ***,the FA–NN exhibits significantly smaller maximum errors at 34.3737 compared with the HSA–NN at 490.3125,the CA–NN at 827.0163,and the ARIMA at 1.16e+03,further emphasizing its robust performance in minimizing prediction *** study offers important insights into battery health management,showing that the proposed method is a promising solution for precise RUL predictions.

关键词： battery remaining useful life firefly algorithm neural networks optimization

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Forecasting solar power generation using evolutionary mating algorithm-deep neural networks

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Energy and AI 2024年第2期16卷 346-362页

作者： Mohd Herwan sulaiman Zuriani Mustaffa Faculty of Electrical&Electronics Engineering Technology Universiti Malaysia Pahang Al-Sultan Abdullah(UMPSA)26600 Pekan PahangMalaysia Faculty of Computing Universiti Malaysia Pahang Al-Sultan Abdullah(UMPSA)26600 Pekan PahangMalaysia

This paper proposes an integration of recent metaheuristic algorithm namely Evolutionary Mating Algorithm (EMA) in optimizing the weights and biases of deep neural networks (DNN) for forecasting the solar power generation. The study employs a Feed Forward Neural Network (FFNN) to forecast AC power output using real solar power plant measurements spanning a 34-day period, recorded at 15-minute intervals. The intricate nonlinear relationship between solar irradiation, ambient temperature, and module temperature is captured for accurate prediction. Additionally, the paper conducts a comprehensive comparison with established algorithms, including Differential Evolution (DE-DNN), Barnacles Mating Optimizer (BMO-DNN), Particle Swarm Optimization (PSO-DNN), Harmony Search Algorithm (HSA-DNN), DNN with Adaptive Moment Estimation optimizer (ADAM) and Nonlinear AutoRegressive with eXogenous inputs (NARX). The experimental results distinctly highlight the exceptional performance of EMA-DNN by attaining the lowest Root Mean Squared Error (RMSE) during testing. This contribution not only advances solar power forecasting methodologies but also underscores the potential of merging metaheuristic algorithms with contemporary neural networks for improved accuracy and reliability.

关键词： Deep learning neural networks Evolutionary mating algorithm Feed forward neural networks Metaheuristic Optimizers Solar PV

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State of charge estimation for electric vehicles using random forest

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Green Energy and Intelligent Transportation 2024年第5期3卷 42-51页

This paper introduces an innovative approach to addressing a critical challenge in the electric vehicle(EV)industry—the accurate estimation of the state of charge(SOC)of EV batteries under real-world operating *** electric mobility landscape is rapidly evolving,demanding more precise SOC estimation methods to improve range prediction accuracy and battery *** study applies a Random Forest(RF)machine learning algorithm to improve SOC ***,SOC estimation has posed a formidable challenge,particularly in capturing the complex dependencies between various parameters and SOC values during dynamic driving *** methods,including the Extreme Learning Machine(ELM),have exhibited limitations in providing the accuracy and robustness required for practical EV *** contrast,this research introduces the RF model,for SOC estimation approach that excels in real-world *** leveraging decision trees and ensemble learning,the RF model forms resilient relationships between input parameters,such as voltage,current,ambient temperature,and battery temperatures,and SOC *** unique approach empowers the model to deliver precise and consistent SOC estimates across diverse driving *** comparative analyses showcase the superiority of the RF over *** RF model not only outperforms in accuracy but also demonstrates exceptional robustness and reliability,addressing the pressing needs of the EV *** results of this study not only underscore the potential of RF in advancing electric mobility but also suggest a promising integration of the SOC estimation approach into the battery management system of BMW *** integration holds the key to more efficient and dependable electric vehicle operations,marking a significant milestone in the ongoing evolution of EV ***,the RF model demonstrates a lower Root Mean Squared Error(RMSE)of 5.902,8%compared to 6.312,7%for ELM,and a

关键词： Electric vehicles Extreme learning machine Machine learning Random Forest State of charge of battery

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Voids size distribution in gelled waxy crude oil under dynamic and static cooling

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Petroleum Research 2024年第1期9卷 109-114页

作者： Girma T.Chala Shaharin A.sulaiman Department of Mechanical Engineering(Well Engineering) International College of Engineering and ManagementP.O.Box 2511C.P.O Seeb 111MuscatOman Department of Mechanical Engineering Universiti Teknologi PETRONASBandar Seri Iskandar37500TronohPerakMalaysia

Waxy crude oil is known for its high wax contents that can potentially result in gelling following sufficient cooling of the transportation line in the subsea bed at offshore *** gelling over the entire lines requires an accurately predicted restart pressure to restart the clogged and idle ***,the common way of predicting the restart pressure has been reported to result in over-designed and predicted piping *** research findings evidenced the formation of voids which would reduce the restart pressure *** study conducted in this paper is aimed at investigating the voids size distribution in gelled crude oil across and along transportation *** of experiments simulating crude oil transportation during both static and dynamic cooling were *** gelled crude oil below the pour point temperature was then scanned using a Magnetic Resonance Imaging(MRI)system to detect the voids *** resulting voids at each scanning cross-section were quantified,and their distributions were *** was observed that dynamic cooling had minimal impacts on the voids size difference along the pipeline with the difference in voids areas within 10 mm^(2) to be twice and uniform for the entire flow rates ***,voids size in statically cooled waxy crude oil was found to be highly distributed with a maximum of 6 voids size distribution in 10 mm2 *** low-end temperature had the highest size difference while the difference was decreasing with higher end *** study shows that the voids amount in dynamically cooled waxy crude oil could also be estimated with lower numbers of cross-sectional voids ***,the higher cross-sectional voids detection is recommended while estimating voids in statically cooled waxy crude oil.

关键词： Gelled crude oil Voids size distribution Dynamic cooling Static cooling

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Randomization Strategies in Image Steganography Techniques:A Review

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Computers, Materials & Continua 2024年第8期80卷 3139-3171页

作者： AFM Zainul Abadin Rossilawati sulaiman Mohammad Kamrul Hasan Center for Cyber Security Faculty of Information Science and TechnologyUniversiti Kebangsaan Malaysia(UKM)BangiSelangor43600Malaysia Department of Information and Communication Engineering Pabna University of Science and TechnologyPabna6600Bangladesh

Image steganography is one of the prominent technologies in data hiding *** system performance mostly depends on the embedding *** goal is to embed strictly confidential information into images without causing perceptible changes in the original *** randomization strategies in data embedding techniques may utilize random domains,pixels,or region-of-interest for concealing secrets into a cover image,preventing information from being discovered by an *** implementation of an appropriate embedding technique can achieve a fair balance between embedding capability and stego image imperceptibility,but it is challenging.A systematic approach is used with a standard methodology to carry out this *** review concentrates on the critical examination of several embedding strategies,incorporating experimental results with state-of-the-art methods emphasizing the robustness,security,payload capacity,and visual quality metrics of the stego *** fundamental ideas of steganography are presented in this work,along with a unique viewpoint that sets it apart from previous works by highlighting research gaps,important problems,and ***,it offers a discussion of suggested directions for future study to advance and investigate uncharted territory in image steganography.

关键词： Information hiding image steganography randomized embedding techniques payload capacity imperceptibility

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An Effective Hybrid Model of ELM and Enhanced GWO for Estimating Compressive Strength of Metakaolin-Contained Cemented Materials

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Computer Modeling in Engineering & Sciences 2024年第5期139卷 1521-1555页

作者： Abidhan Bardhan Raushan Kumar Singh Mohammed Alatiyyah sulaiman Abdullah Alateyah Department of Civil Engineering National Institute of Technology PatnaPatnaIndia Department of Computer Engineering and Applications GLA UniversityMathuraIndia Department of Computer Science College of Sciences and Humanities-AflajPrince Sattam Bin Abdulaziz UniversityAl-KharjSaudi Arabia Department of Computer Science College of Science and ArtsQassim UniversityUnaizahSaudi Arabia

This research proposes a highly effective soft computing paradigm for estimating the compressive strength(CS)of metakaolin-contained cemented *** proposed approach is a combination of an enhanced grey wolf optimizer(EGWO)and an extreme learning machine(ELM).EGWO is an augmented form of the classic grey wolf optimizer(GWO).Compared to standard GWO,EGWO has a better hunting mechanism and produces an optimal *** EGWO was used to optimize the ELM structure and a hybrid model,ELM-EGWO,was *** train and validate the proposed ELM-EGWO model,a sum of 361 experimental results featuring five influencing factors was *** on sensitivity analysis,three distinct cases of influencing parameters were considered to investigate the effect of influencing factors on predictive *** consequences show that the constructed ELM-EGWO achieved the most accurate precision in both training(RMSE=0.0959)and testing(RMSE=0.0912)*** outcomes of the ELM-EGWO are significantly superior to those of deep neural networks(DNN),k-nearest neighbors(KNN),long short-term memory(LSTM),and other hybrid ELMs constructed with GWO,particle swarm optimization(PSO),harris hawks optimization(HHO),salp swarm algorithm(SSA),marine predators algorithm(MPA),and colony predation algorithm(CPA).The overall results demonstrate that the newly suggested ELM-EGWO has the potential to estimate the CS of metakaolin-contained cemented materials with a high degree of precision and robustness.

关键词： Metakaolin-contained cemented materials compressive strength extreme learning machine grey wolf optimizer swarm intelligence uncertainty analysis artificial intelligence

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Trends in Event Understanding and Caption Generation/Reconstruction in Dense Video:A Review

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Computers, Materials & Continua 2024年第3期78卷 2941-2965页

作者： Ekanayake Mudiyanselage Chulabhaya Lankanatha Ekanayake Abubakar sulaiman Gezawa Yunqi Lei Department of Computer Science Xiamen UniversityXiamen361005China Main Library Wayamba University of Sri LankaKuliyapitiya60200Sri Lanka School of Information Engineering Sanming UniversitySanming365004China

Video description generates natural language sentences that describe the subject,verb,and objects of the targeted *** video description has been used to help visually impaired people to understand the *** is also playing an essential role in devolving human-robot *** dense video description is more difficult when compared with simple Video captioning because of the object’s interactions and event *** learning is changing the shape of computer vision(CV)technologies and natural language processing(NLP).There are hundreds of deep learning models,datasets,and evaluations that can improve the gaps in current *** article filled this gap by evaluating some state-of-the-art approaches,especially focusing on deep learning and machine learning for video caption in a dense *** this article,some classic techniques concerning the existing machine learning were *** provides deep learning models,a detail of benchmark datasets with their respective *** paper reviews various evaluation metrics,including Bilingual EvaluationUnderstudy(BLEU),Metric for Evaluation of Translation with Explicit Ordering(METEOR),WordMover’s Distance(WMD),and Recall-Oriented Understudy for Gisting Evaluation(ROUGE)with their pros and ***,this article listed some future directions and proposed work for context enhancement using key scene extraction with object detection in a particular ***,how to improve the context of video description by analyzing key frames detection through morphological image ***,the paper discusses a novel approach involving sentence reconstruction and context improvement through key frame object detection,which incorporates the fusion of large languagemodels for refining *** ultimate results arise fromenhancing the generated text of the proposedmodel by improving the predicted text and isolating objects using various *** keyframes identify dense ev

关键词： Video description video to text video caption sentence reconstruction

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Credit Card Fraud Detection Using Improved Deep Learning Models

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Computers, Materials & Continua 2024年第1期78卷 1049-1069页

作者： Sumaya S.sulaiman Ibraheem Nadher Sarab M.Hameed Computer Science Department Al-Mustansiriya UniversityBaghdad10001Iraq Computer Science Department University of BaghdadBaghdad10001Iraq Faculty of Basic Education Al-Mustansiriya UniversityBaghdad10001Iraq

Fraud of credit cards is a major issue for financial organizations and *** fraudulent actions become more complex,a demand for better fraud detection systems is *** learning approaches have shown promise in several fields,including detecting credit card ***,the efficacy of these models is heavily dependent on the careful selection of appropriate *** paper introduces models that integrate deep learning models with hyperparameter tuning techniques to learn the patterns and relationships within credit card transaction data,thereby improving fraud *** deep learning models:AutoEncoder(AE),Convolution Neural Network(CNN),and Long Short-Term Memory(LSTM)are proposed to investigate how hyperparameter adjustment impacts the efficacy of deep learning models used to identify credit card *** experiments conducted on a European credit card fraud dataset using different hyperparameters and three deep learning models demonstrate that the proposed models achieve a tradeoff between detection rate and precision,leading these models to be effective in accurately predicting credit card *** results demonstrate that LSTM significantly outperformed AE and CNN in terms of accuracy(99.2%),detection rate(93.3%),and area under the curve(96.3%).These proposed models have surpassed those of existing studies and are expected to make a significant contribution to the field of credit card fraud detection.

关键词： Card fraud detection hyperparameter tuning deep learning autoencoder convolution neural network long short-term memory resampling

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On the interaction phenomena to the nonlinear generalized Hietarinta-type equation

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Journal of Ocean Engineering and Science 2024年第1期9卷 89-97页

作者： Usman Younas T.A.sulaiman Jingli Ren A.Yusuf State Key Laboratory of Power Grid Environmental Protection/Henan Academy of Big Data Zhengzhou UniversityZhengzhou 450001China Faculty of Science Federal University DutseJigawaNigeria Department of Computer Engineering Biruni University IstanbulTurkey

In this paper,we describe the nonlinear behavior of a generalized fourth-order Hietarinta-type equa-tion for dispersive waves in(2+1)*** various wave formations are retrieved by using Hirota’s bilinear method(HBM)and various test function *** Hirota method is a widely used and robust mathematical tool for finding soliton solutions of nonlinear partial differential equa-tions(NLPDEs)in a variety of disciplines like mathematical physics,nonlinear dynamics,oceanography,engineering sciences,and others requires bilinearization of nonlinear *** different wave structures in the forms of new breather,lump-periodic,rogue waves,and two-wave solutions are *** addi-tion,the physical behavior of the acquired solutions is illustrated in three-dimensional,two-dimensional,density,and contour profiles by the assistance of suitable *** on the obtained results,we can assert that the employed methodology is straightforward,dynamic,highly efficient,and will serve as a valuable tool for discussing complex issues in a diversity of domains specifically ocean and coastal *** have also made an important first step in understanding the structure and physical be-havior of complex structures with our findings *** believe this research is timely and relevant to a wide range of engineering *** results obtained are useful for comprehending the fundamental scenarios of nonlinear sciences.

关键词： Hirota’s bilinear method Lump-periodic solutions Breather waves Rouge waves Multi-waves

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