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English to Urdu machine translation is still in its infancy. This study illustrates various types of translation divergences and their implication in English to Urdu machine translation. The divergence in English to Urdu machine translation can be thought as representing the translation divergences between Subject-Verb-Object (SVO) class languages to Subject-Object-Verb (SOV) class languages. This study discusses the different types of divergences in English to Urdu machine translation and presents novel computational algorithms to detect and to resolve these divergences in English to Urdu Machine Translation. These algorithms for detection of divergence have been implemented in English to Urdu Machine Translation system, and results have been presented in this paper. The work introduced here is the only one, to the best of our knowledge, which automatically detects and resolves divergences in English to Urdu machine translation.

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Selecting the most miniature possible set of genes from microarray datasets for clinical diagnosis and prediction is one of the most challenging machine learning tasks. A robust gene selection technique is required to identify the most significant subset of genes by removing spurious or non-predictive genes from the original dataset without sacrificing or reducing classification accuracy. Numerous studies have attempted to address this issue by implementing either a filter or a wrapper. Although the filter approaches are computationally efficient, they are completely independent of the induction algorithm. On the other hand, wrapper approaches outperform filter approaches but are computationally more expensive. Therefore, this study proposes an enhanced gene selection method that uses a hybrid technique that combines the Symmetrical Uncertainty (SU) filter and Reference Set Harmony Search Algorithm (RSHSA) wrapper method, known as SU-RSHSA. The framework to develop the proposed SU-RSHSA includes numerous stages: (1) investigate a novel gene selection method based on the HSA and will then determine appropriate values for the HSA’s parameters, (2) enhance the construction process of the initial harmony memory while satisfying the diversity of the solution by embedding a reference set within the HSA (RSHSA), and (3) investigates the effect of integrating Symmetrical Uncertainty (SU) as a filter and RSHSA as a wrapper (SU-RSHSA) to maximize classification accuracy by leveraging their respective advantages. The results demonstrate that the SU-RSHSA outperforms the original HSA and SU-HSA in terms of classification accuracy, a small number of selected relevant genes, and reduced computational time. More importantly, the proposed SU-RSHSA gene selection method effectively generates a small subset of salient genes with high classification accuracy.

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