Comparison of Different Machine Learning Algorithms for National Flags Classification

Each country in the world has its own combination of colors, shapes and symbols on their flags. Some of them use an animal figure such as an eagle, some use an object like a boat; some nations prefer religion figures such as a crescent, or a cross. Some questions yet remain and need an answer. What are the factors that determine the flag of a nation? What factors are affecting the color or colors of a national flag? And what are the reasons for existence of symbols on some national flags?In this paper, we worked an analysis on national flags and factors that mostly affects the design of them. In order to find out these factors, we have used feature extraction method, after that we used different machine learning algorithms to predict religion and landmass of the country. We also showed correlations of certain components that are possible to exist on a national flag such as dominant color or colors on a flag, bars or stripes, normal and sacred symbols such as sun, stars, crosses, crescents, and triangles and, finally some specific icons like a boat or an animal figure.This study shows the associations of some characteristics of countries or different nationalities. There are many affected factors and there are very close correlations between these factors. It also includes the classification of national flag data using Multilayer Perceptron, CART and C4.5 algorithms and comparison of these techniques based on accuracy and performance for classification of national flag’s features

Application Of Machine Learning In Healthcare: Analysis On MHEALTH Dataset

The healthcare services in developed and developing countries are critically important. The use of machine learning techniques in healthcare industry has a vital importance and increases rapidly. The corporations in healthcare sector need to take advantage of the machine learning techniques to obtain valuable data that could later be used to diagnose diseases at much earlier stages. In this study, a research is conducted with the purpose of discovering further use of the machine learning techniques in healthcare sector. Research was conducted by analyzing a well-established dataset called MHEALTH, comprising body motion and vital signs recordings for ten volunteers of diverse profile while performing 12 physical activities. Dataset was analyzed using certain classification algorithms such as Multilayer Perceptron and Support Vector Machine, then results from these algorithms were compared to determine the most utile algorithm for analyzing such dataset. Study aims to determine irregularities using data from body motion and vital signs of volunteers, then these findings can be used either to diagnose particular diseases before they occur and avoid them. Results can also be used to monitor movements of ill or elderly people and observe whether they are doing any prohibited movements that would lead them to injuries or further illnesses