Evaluating Urdu to Arabic Machine Translation Tools

Ayesha, Maheen Akhter; Noor, Sahar; Ramzan, Muhammad; Khan, Hikmat Ullah

doi:10.14569/ijacsa.2017.081012

Cited by 1 publication

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References 3 publications

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“…These are more complex data learning models with much complex cases. Many researchers use machine learning techniques to solve other problems (Ayesha, Noor, Ramzan, Khan, & Shoaib, 2017;Khan & Daud, 2017; "Using machine learning techniques for subjectivity analysis based on lexical and non-lexical features," n.d.) by using feature selection and other methods (Ishfaq, Khan, & Iqbal, 2016) (Khan & Daud, 2017). These tools are also helpful in classification (Khan, 2017) and information retrieval (A.…”

Section: Introductionmentioning

confidence: 99%

An Empirical Comparative Analysis Using Machine Learning Techniques for Liver Disease Prediction

Alghobiri

Khan

Mahmood

2021

International Journal of Healthcare Information Systems and Informatics

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The human liver is one of the major organs in the body and liver disease can cause many problems in human live. Due to the increase in liver disease, various data mining techniques are proposed by the researchers to predict the liver disease. These techniques are improving day by day in order to predict and diagnose the liver disease in human. In this paper, real-world liver disease dataset is incorporated for diagnosing liver disease in human body. For this purpose, feature selection models are used to select a number of features that best are the most important feature to diagnose the liver disease. After selecting features and splitting data for training and testing, different classification algorithms in terms of naïve Bayes, supervised vector machine, decision tree, k near neighbor and logistic regression models to diagnose the liver disease in human body. The results are cross-validated by tenfold cross validation methods and achieve an accuracy as good as 93%.

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