One of the basic problems of Computer Science is sorting a list of items. It refers to the arrangement of numerical or alphabetical or character data in statistical order. Bubble, Insertion, Selection, Merge, and Quick sort are most common ones and they all have different performances based on the size of the list to be sorted. As the size of a list increases, some of the sorting algorithm turns to perform better than others and most cases programmers select algorithms that perform well even as the size of the input data increases. As the size of dataset increases, there is always the chance of duplication or some form of redundancies occurring in the list. For example, list of ages of students on a university campus is likely to have majority of them repeating. A new algorithm is proposed which can perform sorting faster than most sorting algorithms in such cases. The improved selection sort algorithm is a modification of the existing selection sort, but here the number of passes needed to sort the list is not solely based on the size of the list, but the number of distinct values in the dataset. This offers a far better performance as compared with the old selection sort in the case where there are redundancies in the list.

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Background When you make a forex transaction, you sell one currency and buy another. If the currency you buy increases against the currency you sell, you profit, and you do this through a broker as a retail trader on the internet using a platform known as meta trader. Only 2% of retail traders can successfully predict currency movement in the forex market, making it one of the most challenging tasks. Machine learning and its derivatives or hybrid models are becoming increasingly popular in market forecasting, which is a rapidly developing field. Objective While the research community has looked into the methodologies used by researchers to forecast the forex market, there is still a need to look into how machine learning and artificial intelligence approaches have been used to predict the forex market and whether there are any areas that can be improved to allow for better predictions. Our objective is to give an overview of machine learning models and their application in the FX market. Method This study provides a Systematic Literature Review (SLR) of machine learning algorithms for FX market forecasting. Our research looks at publications that were published between 2010 and 2021. A total of 60 papers are taken into consideration. We looked at them from two angles: I the design of the evaluation techniques, and (ii) a meta-analysis of the performance of machine learning models utilizing evaluation metrics thus far. Results The results of the analysis suggest that the most commonly utilized assessment metrics are MAE, RMSE, MAPE, and MSE, with EURUSD being the most traded pair on the planet. LSTM and Artificial Neural Network are the most commonly used machine learning algorithms for FX market prediction. The findings also point to many unresolved concerns and difficulties that the scientific community should address in the future. Conclusion Based on our findings, we believe that machine learning approaches in the area of currency prediction still have room for development. Researchers interested in creating more advanced strategies might use the open concerns raised in this work as input.