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| 1 | Effect of temperature and fluid velocity on corrosion mechanism of low carbon steel in presence of 2-hydrazino-4,7-dimethylbenzothiazole in industrial water medium显示文摘 | BADIEA AM MOHANA K N | | 0，，： | 1 |
| 2 | The effect of the sodium benzoate and sodium 4- ( phenylamino ) benzenesulfonate on the corrosion behavior of low carbon steel 显示文摘 | Badiea A Mohammed Kikkeri N Mohana | 2009 | Monatslwfie fur Chemic2009，140，1： | 1 |
| 3 | Effect of sodium nitrite-borax blend on the corrosion rate of low carbon steel in industrial water medium 显示文摘 | Mohana K N Badiea A M | 2008 | Corrosion Science2008，50，10： | 1 |
| 4 | Embodying the ehemieal water treatment in the green ehemistry-A review显示文摘 | Djamel G Badiea G Mohamed W N | 2011 | Desalination2011，271，1： | 1 |
| 5 | Mediating role of brand app trust in the relationship between antecedents and purchase intentions-Iranian B2C mobile apps显示文摘This study investigates the mediating role of brand app trust in the relationship between brand app antecedents and purchase intentions via a brand mobile app.It explores the mediating potential of brand and platform trust in this relationship between antecedents and purchase intentions and also highlights which brand antecedents are useful for directly or indirectly establishing trust towards purchasing via a mobile app in the Iranian mobile commerce market.This study uses survey data collected through an online questionnaire form and applies the partial least squares method to extract its findings.All of the estimation results are found to be unbiased and robust.The results show that brand app trust plays a mediating role in the relationship between the brand app antecedents of word-of-mouth recommendation,subjective norms,perceived image(social antecedents),mobile computing self-efficacy(consumer-based antecedent),and perceived ease of use(system antecedent)and purchase intentions via a brand mobile app in Iran. | Mohammad Mahdi Movahedisaveji Badiea Shaukat | 2020 | Journal of Management Analytics2020，7，1： | 1 |
| 6 | Phishing Websites Detection by Using Optimized Stacking Ensemble Model显示文摘Phishing attacks are security attacks that do not affect only individuals’or organizations’websites but may affect Internet of Things(IoT)devices and net-works.IoT environment is an exposed environment for such attacks.Attackers may use thingbots software for the dispersal of hidden junk emails that are not noticed by users.Machine and deep learning and other methods were used to design detection methods for these attacks.However,there is still a need to enhance detection accuracy.Optimization of an ensemble classification method for phishing website(PW)detection is proposed in this study.A Genetic Algo-rithm(GA)was used for the proposed method optimization by tuning several ensemble Machine Learning(ML)methods parameters,including Random Forest(RF),AdaBoost(AB),XGBoost(XGB),Bagging(BA),GradientBoost(GB),and LightGBM(LGBM).These were accomplished by ranking the optimized classi-fiers to pick out the best classifiers as a base for the proposed method.A PW data-set that is made up of 4898 PWs and 6157 legitimate websites(LWs)was used for this study's experiments.As a result,detection accuracy was enhanced and reached 97.16 percent. | Zeyad Ghaleb Al-Mekhlafi Badiea Abdulkarem Mohammed Mohammed Al-Sarem Faisal Saeed Tawfik Al-Hadhrami Mohammad T.Alshammari Abdulrahman Alreshidi Talal Sarheed Alshammari | 2022 | Computer Systems Science & Engineering2022，41，4： | 0 |
| 7 | Deep Learning and Machine Learning for Early Detection of Stroke and Haemorrhage显示文摘Stroke and cerebral haemorrhage are the second leading causes of death in the world after ischaemic heart disease.In this work,a dataset containing medical,physiological and environmental tests for stroke was used to evaluate the efficacy of machine learning,deep learning and a hybrid technique between deep learning and machine learning on theMagnetic Resonance Imaging(MRI)dataset for cerebral haemorrhage.In the first dataset(medical records),two features,namely,diabetes and obesity,were created on the basis of the values of the corresponding features.The t-Distributed Stochastic Neighbour Embedding algorithm was applied to represent the high-dimensional dataset in a low-dimensional data space.Meanwhile,the Recursive Feature Elimination algorithm(RFE)was applied to rank the features according to priority and their correlation to the target feature and to remove the unimportant features.The features are fed into the various classification algorithms,namely,Support Vector Machine(SVM),K Nearest Neighbours(KNN),Decision Tree,Random Forest,and Multilayer Perceptron.All algorithms achieved superior results.The Random Forest algorithm achieved the best performance amongst the algorithms;it reached an overall accuracy of 99%.This algorithm classified stroke cases with Precision,Recall and F1 score of 98%,100%and 99%,respectively.In the second dataset,the MRI image dataset was evaluated by using the AlexNet model and AlexNet+SVM hybrid technique.The hybrid model AlexNet+SVM performed is better than the AlexNet model;it reached accuracy,sensitivity,specificity and Area Under the Curve(AUC)of 99.9%,100%,99.80%and 99.86%,respectively. | Zeyad Ghaleb Al-Mekhlafi Ebrahim Mohammed Senan Taha H.Rassem Badiea Abdulkarem Mohammed Nasrin M.Makbol Adwan Alownie Alanazi Tariq S.Almurayziq Fuad A.Ghaleb | 2022 | Computers, Materials & Continua2022，，7： | 0 |
| 8 | Individual and combination approaches to forecasting hierarchical time series with correlated data:an empirical study显示文摘Hierarchical time series arise in manufacturing and service industries when the products or services have the hierarchical structure,and top-down and bottomup methods are commonly used to forecast the hierarchical time series.One of the critical factors that affect the performance of the two methods is the correlation between the data series.This study attempts to resolve the problem and shows that the top-down method performs better when data have high positive correlation compared to high negative correlation and combination of forecasting methods may be the best solution when there is no evidence of the correlationship.We conduct the computational experiments using 240 monthly data series from the‘Industrial’category of the M3-Competition and test twelve combination methods for the hierarchical data series.The results show that the regression-based,VAR-COV and the Rank-based methods perform better compared to the other methods. | Hakeem-Ur Rehman Guohua Wan Azmat Ullah Badiea Shaukat Antai | 2019 | Journal of Management Analytics2019，6，3： | 0 |
| 9 | Effects of 12 weeks of back-squat training program on jump performances and bone markers in female students显示文摘Background:The training program promoted improvements of jump abilities throughout the musculoskeletal system including bone markers.The aim of this study is to examine both the acute and chronic response of bone markers to resistance training program.Methods:Ten female students(age:18±0.7 years,body mass:63±3.6 kg;height:164±5.2 cm)participated in this study.They were recruited for a back-squat training program for 12 weeks,two days/week.The full-back squat protocol consisted of 3–5 sets×3–8 repetitions at 45–55%one repetition maximum.Testing sessions included a 5 jump test(5JT),standing long jump(SLJ),drop jump(DJ),and vertical jump(VJ).Results:Substantial improvements in all testing jumps(5JT:∆10%;P=0.000;ES=1.72;SLJ:∆7%;P=0.000;ES=1.33;DJ:∆11%;P=0.000;ES=0.72;VJ:∆20%;P=0.000;ES=1.84)were found during post program in comparison to pre-program results.Moreover,a significant change(P≤0.05)of bone markers during post-exercise compared to pre-exercise either before or after the training program.Only collagen type I carboxy-terminal peptide(CICP)levels elevated after the training program(pre-exercise only)compared to former levels.Conclusion:12 weeks of back-squat training program resulted greater acute improvements of jump abilities with adaptation in all musculoskeletal system including bone formation. | Badiea Sharaif Nidhal Jebabli Saber Abdellaoui Jed Mohamed Tijani Jihen Khalfoun Mohanad Omar Abderraouf Ben Abderrahman | 2022 | TMR Non-Drug Therapy2022，5，3： | 0 |
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