Multiclass classification has always been challenging in the area of machine learning algorithms. Different publicly available software applications offer various learning algorithms' implementations. paper uses leaf dataset with 30 different plant species with types prepared by Silva et al (2014), and classification is performed using Multilayer Perceptron, Naive Bayes and Support Vector classifiers. Performance of classifiers is compared based on time needed for building the model and classification accuracy.

Neural networks are organized in committees to improve the correctness of the decisions created by artificial neural networks (ANN's). In the classification of human chromosomes, it is accustomed to use multilayer perceptrons with multiple (22-24) outputs. Because of the huge number of synaptic weights to be tuned, these classifiers cannot go beyond a level of 92% overall correctness. In this study we represent a special organized committee of 462 simple perceptrons to improve the rate of correct classification of 22 types of human chromosomes. Each of these simple perceptrons is trained to distinguish between two types of chromosomes. When a new data is entered, the votes of these 462 simple perceptrons and additional 22 dummy perceptrons create a decision matrix of the size 22×22. By a special assembling of these votes we get a higher rate of correct classification of 22 types of human chromosomes.

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.

Abstract-This paper is concerned with automatic segmentation of high resolution digitized metaphases. Firstly using a thresholding technique, a binary image of the cell picture is obtained. This binary image contains the addresses of darker pixels of the gray image of the colored cell picture. Several thousand of random points are assigned from among these addresses, and then using a distance condition, typically 50 pixels, and the number of centers is reduced to near 100. These points are search centers for chromosome segmentation. Algorithm first searches eight pixels surrounding the center. Picks the coordinates of the pixels darker than the gray level 0.9, then passes to one of the pixels recently recorded as dark enough, and repeat the same procedure to the neighbors which are not visited before. If none of the new neighbors are not darker than 0.9, search reaches at the boundaries of the chromosome, and ends. Then we call the pixels of the chromosomes in the colored image from the addresses in the binary counterparts to finish segmentation.

In this paper, a unified algorithm for extracting gray level profiles of Human chromosomes is presented. It is a unified approach since we do not discriminate chromosomes as straight and bended. This is a very helpful procedure which extends the domain of success of most of the previously reported algorithms to highly curved chromosomes. The gray image of the chromosome is thresholded at the gray level 0.9, and the matrix of gray image is transformed into a list of pixel coordinates whose gray level is less than 0.9. To the list of two dimensional points, the most appropriate smooth curve is fitted. Then this smooth curve subdivided into n arcs of equal lengths, and straight lines are drawn that are normal to the curve at the end points of the subdivision. The points of the list are classified into n bins according to their distance to these n straight lines. The average of gray levels of each bin gives the gray levels at the points of the gray level profile of the chromosome. It is seen that the gray level profiles of the bended chromosomes have a high similarity with the straight counterparts.