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您的检索式：作者名="Azhar Abid"
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| 1 | Exercise tolerance test:a comparison between true positive and fale positive test results显示文摘 | Faisal AW Abid AR Azhar M | | 0，，04： | 1 |
| 2 | 黏弹性Jeffery-Hamel流的磁-微结构分析显示文摘本文通过拉伸/收缩具有独立移动能力大分子的非平行通道,对磁流体的动力学进行数值分析。通过麦克斯韦方法建立了外磁场对黏弹性流体流动影响的数学模型,在经典流体动力动量方程中表现为体力。为了完整描述微观结构现象,利用角动量方程对数学模型进行强化。用凯勒盒有限差分法对所得到的非线性问题进行数值处理。求解如Hartmann数(1≤Ha≤5)、拉伸参数(-4≤C≤4)、旋转参数(3≤K≤9)、Weissenberg数(0.3≤Wi≤0.9)、Reynolds数(50≤Re≤150)等物理量的微分方程形式,并以图表形式表示出来。在所有讨论的情况中,只有发散通道中的角速度随着Hartmann数的增加而增加,这表明微结构旋转是由强磁场激发的。 | Ehtsham AZHAR Abid KAMRAN | 2023 | Journal of Central South University2023，30，6： | 1 |
| 3 | Continuous production of dextran from immobilized cells of Leuconostoc mesenteroides KIBGE HA1 using acrylamide as a support显示文摘 | Qader Shah Ali Ul Aman Afsheen Azhar Abid | 2011 | Indian Journal of Microbiology2011，51，3： | 1 |
| 4 | Multilingual Sentiment Mining System to Prognosticate Governance显示文摘In the age of the internet,social media are connecting us all at the tip of our fingers.People are linkedthrough different social media.The social network,Twitter,allows people to tweet their thoughts on any particular event or a specific political body which provides us with a diverse range of political insights.This paper serves the purpose of text processing of a multilingual dataset including Urdu,English,and Roman Urdu.Explore machine learning solutions for sentiment analysis and train models,collect the data on government from Twitter,apply sentiment analysis,and provide a python library that classifies text sentiment.Training data contained tweets in three languages:English:200k,Urdu:200k and Roman Urdu:11k.Five different classification models are applied to determine sentiments,and eventually,the use of ensemble technique to move forward with the acquired results is explored.The Logistic Regression model performed best with an accuracy of 75%,followed by the Linear Support Vector classifier and Stochastic Gradient Descent model,both having 74%accuracy.Lastly,Multinomial Naïve Bayes and Complement Naïve Bayes models both achieved 73%accuracy. | Muhammad Shahid Bhatti Saman Azhar Abid Sohail Mohammad Hijji Hamna Ayemen Areesha Ramzan | 2022 | Computers, Materials & Continua2022，，4： | 0 |
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