Inventory Control Using Fuzzy Dynamic Programming

There are a variety of efficient approaches to solve crisp inventory models in operations research. In this article a model that uses Bellman and Zadeh’s approach to fuzzy dynamic programming is used. The problem considered is the following: the management of a company wants to close down a certain plant within a definite time interval. Therefore production levels should decrease to zero as smoothly as possible and the stock level at the end of the planning period should be as low as possible. The demand is assumed to be deterministic

Fuzzy C-means Model and Algorithm for Data Clustering

Pattern recognition has become a very important field over the last decade since automation and computerization in many systems has led to large amount of data being stored in the databases.  The primary intention of pattern recognition is to automatically assist humans in analyzing the vast amount of available data and extracting useful knowledge from it. Many algorithms have been developed for many applications, especially for static pattern recognition.  Since the information of these processes can be non-deterministic over the time period, fuzzy approach can be applied to deal with this. In this work, fuzzy approach for optimization techniques in the pattern recognition will be implemented. It will show a fuzzy model for data clustering and feature extraction that best suits for the process of pattern recognition when we deal with non-crisp data

Extracting Gray Level Profiles of Human Chromosomes by Curve Fitting

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

Application of Ensemble Machines of Neural Networks to Chromosome Classification

This work presents approaches to the automatic classification of metaphase chromosomes using several perceptron neural network techniques on neural networks function as committee machines. To represent the banding patterns, only chromosome gray level profiles are exploited. The other inputs to the ensemble machines of the network are the chromosome size and centromeric index. It is shown that, without much effort, the classification performances of the four networks are found to be similar to the ones of a well-developed parametric classifier. Four parallel networks trained for the four different aspects of the data set, the gray level profile vector, Fourier coefficients of gray level profiles, 3D data of chromosome length – centromeric index – total gray levels, and 4D data obtained by the addition of average gray levels. Then the classification results of differently trained neural networks (i.e., experts), are combined by the use of a genuine ensemble-averaging to produce an overall output by the combiner. We discuss the flexibility of the classifier developed, its potential for development, and how it may be improved to suit the current needs in karyotyping

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

Automatic Segmentation of Human Chromosomes

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

Denver Groups Classification of Human Chromosomes Using CANN Teams Supplemented by a Nearest Neighbor Technique CANNT-S

Human chromosomes can be classified into seven Denver groups (A – G) based on the position of the centromere. This classification is an important stage of human chromosome classification, as its output influence the second stage of classification, the correct classification of 24 classes of human chromosomes based on the banding pattern.In this article, the novel artificial neural network committee machines technique (CANNT) developed earlier is supplemented by a nearest neighbor technique, CANNT-S, and the correct classification rate in Denver Groups Classification of Human Chromosomes rose from 96% to a level of 98%

Denver Groups Classification of Human Chromosomes Using CANN Teams

Unbanded human chromosome can be classified into seven Denver Groups (A-G) based on their lengths and the ratio of the length of the shorter arm to the whole length of the chromosome, which is called the centromere index (CI). In this article, the novel artificial neural network committee machines technique (CANNT) developed earlier, is applied to the Denver Groups and the correct classification rate in Denver Groups Classification of Human Chromosomes raised from 96%, to a level of 98%

Human chromosome classification using competitive support vector machine teams

Classification of chromosome is a challenging task and requires very precise autonomous classifier. This paper proposes to employ competing support vector machines (SVMs) placed in a grid. Each agent in cells of the grid is responsible to distinguish two classes. Overall output is determined by simple majority voting of SVMs. Relying same principle as the work by Palalic and Can [17], we compared the results obtained where the algorithms delivers better accuracy

Comparison of expectation-maximization clustering and logistic regression on categorization of planets with known properties

Analysis of the exoplanet data is the top priority of astrophysicists today. With the increasing incoming information there is a need for an efficient and reliable algorithm. The data is taken from exoplanet data explorer which was cross checked and filtered with NASA’s known categorization. These were then sorted into 5 categories: Dwarfs, Terrestrial, Icy, Jovian and Giant planets. This paper compares expectation-maximization clustering algorithm as an unsupervised and logistic regression as a supervised machine learning methodologies. Comparatively, logistic regression outperformed EM, indicating it cannot be used to sort through the incoming data. Further analysis is necessary