Baligh Al-helali

Baligh Al-helali
Victoria University of Wellington · School of Engineering and Computer Science

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20
Publications
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171
Citations

Publications

Publications (20)
Article
Lack of knowledge is a common consequence of data incompleteness when learning from real-world data. To deal with such a situation, this work utilises transfer learning to re-use knowledge from different (yet related) but complete domains. Due to its powerful feature construction ability, genetic programming is used to construct feature-based trans...
Article
Full-text available
Incompleteness is one of the problematic data quality challenges in real-world machine learning tasks. A large number of studies have been conducted for addressing this challenge. However, most of the existing studies focus on the classification task and only a limited number of studies for symbolic regression with missing values exist. In this wor...
Chapter
Data incompleteness represents a serious issue in real-world applications of machine learning. Imputation methods are algorithms for restoring missing values in the data based on other available entries. Imputation methods have an influence on the learning performance on incomplete data. Therefore, the choice of a right imputation method has an imp...
Chapter
Missing values bring several challenges when learning from real-world data sets. Imputation is a widely adopted approach to estimating missing values. However, it has not been adequately investigated in symbolic regression. When imputing the missing values in an incomplete feature, the other features that are used in the prediction process are call...
Chapter
Symbolic regression via genetic programming has been used successfully for empirical modeling from given data sets. However, real-world data sets might contain missing values. Although there are different approaches to dealing with incomplete data sets for classification, symbolic regression with missing values has been rarely investigated. Similar...
Chapter
Incompleteness is one of the challenging issues in data science. One approach to tackle this issue is using imputation methods to estimate the missing values in incomplete data sets. In spite of the popularity of adopting this approach in several machine learning tasks, it has been rarely investigated in symbolic regression. In this work, a genetic...
Article
Full-text available
Background: An Arabic online text database called Online-KHATT is presented, which addresses the lack of a free benchmarking database of natural Arabic online text. This database consists of natural Arabic online text written without any constraints using digital pen. Objective: The main objective of this work is to build a comprehensive benchmarki...
Article
This article comprehensively surveys Arabic Online Handwriting Recognition (AOHR). We address the challenges posed by online handwriting recognition, including ligatures, dots and diacritic problems, online/offline touching of text, and geometric variations. Then we present a general model of an AOHR system that incorporates the different phases of...
Article
The widely-used PDAs, touch screens, tablet-PCs are alternatives to keyboards with the advantages of being more friendly, easy, and natural. A framework for Arabic online character recognition is developed. The framework integrates the different phases of online Arabic text recognition. The used data poses several challenges such as delayed strokes...
Article
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One of the main disadvantages of fractal image data compression is a loss time in the process of image compression (encoding) and conversion into a system of iterated functions (IFS). In this paper, the idea of the inverse problem of fixed point is introduced. This inverse problem is based on collage theorem which is the cornerstone of the mathemat...

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