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| 1 | Detection of hepatitis C virus in the corneas of seropositive donors 显示文摘 | Lee HM Naor J Alhindi R | 2001 | Cornea2001，20，1： | 1 |
| 2 | Photopolymerization shrinkage-stress kinetics in resin-composites:methods development显示文摘 | WATTS D C MAROUF A S ALHINDI A M | | 0，，01： | 1 |
| 3 | Detection of hepatitis C virus in the corneas of sero-positive donors显示文摘 | Lee H M Naor J Alhindi R | 2001 | Cornea2001，20，1： | 1 |
| 4 | Data Mining with Comprehensive Oppositional Based Learning for Rainfall Prediction显示文摘Data mining process involves a number of steps fromdata collection to visualization to identify useful data from massive data set.the same time,the recent advances of machine learning(ML)and deep learning(DL)models can be utilized for effectual rainfall prediction.With this motivation,this article develops a novel comprehensive oppositionalmoth flame optimization with deep learning for rainfall prediction(COMFO-DLRP)Technique.The proposed CMFO-DLRP model mainly intends to predict the rainfall and thereby determine the environmental changes.Primarily,data pre-processing and correlation matrix(CM)based feature selection processes are carried out.In addition,deep belief network(DBN)model is applied for the effective prediction of rainfall data.Moreover,COMFO algorithm was derived by integrating the concepts of comprehensive oppositional based learning(COBL)with traditional MFO algorithm.Finally,the COMFO algorithm is employed for the optimal hyperparameter selection of the DBN model.For demonstrating the improved outcomes of the COMFO-DLRP approach,a sequence of simulations were carried out and the outcomes are assessed under distinct measures.The simulation outcome highlighted the enhanced outcomes of the COMFO-DLRP method on the other techniques. | Mohammad Alamgeer Amal Al-Rasheed Ahmad Alhindi Manar Ahmed Hamza Abdelwahed Motwakel Mohamed I.Eldesouki | 2023 | Computers, Materials & Continua2023，，2： | 0 |
| 5 | Big Data Analytics with Artificial Intelligence Enabled Environmental Air Pollution Monitoring Framework显示文摘Environmental sustainability is the rate of renewable resourceharvesting, pollution control, and non-renewable resource exhaustion. Airpollution is a significant issue confronted by the environment particularlyby highly populated countries like India. Due to increased population, thenumber of vehicles also continues to increase. Each vehicle has its individualemission rate;however, the issue arises when the emission rate crosses thestandard value and the quality of the air gets degraded. Owing to the technological advances in machine learning (ML), it is possible to develop predictionapproaches to monitor and control pollution using real time data. With thedevelopment of the Internet of Things (IoT) and Big Data Analytics (BDA),there is a huge paradigm shift in how environmental data are employed forsustainable cities and societies, especially by applying intelligent algorithms.In this view, this study develops an optimal AI based air quality prediction andclassification (OAI-AQPC) model in big data environment. For handling bigdata from environmental monitoring, Hadoop MapReduce tool is employed.In addition, a predictive model is built using the hybridization of ARIMAand neural network (NN) called ARIMA-NN to predict the pollution level.For improving the performance of the ARIMA-NN algorithm, the parametertuning process takes place using oppositional swallow swarm optimization(OSSO) algorithm. Finally, Adaptive neuro-fuzzy inference system (ANFIS)classifier is used to classify the air quality into pollutant and non-pollutant.A detailed experimental analysis is performed for highlighting the betterprediction performance of the proposed ARIMA-NN method. The obtainedoutcomes pointed out the enhanced outcomes of the proposed OAI-AQPCtechnique over the recent state of art techniques. | Manar Ahmed Hamza Hadil Shaiba Radwa Marzouk Ahmad Alhindi Mashael M.Asiri Ishfaq Yaseen Abdelwahed Motwakel Mohammed Rizwanullah | 2022 | Computers, Materials & Continua2022，，11： | 0 |
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