iosr-JCE   Volume-22 ~ Issue-3 ~May - Jun. 2020

Abstract: The development of a commercial branch network is very critical to the profitability of the business. The geographical location of a store and its significance can be confirmed by the large differences in the turnover of the stores from the same chain. In the past years, solving an optimal location problem for the establishment of bank, commercial or other branches was based particularly on empirical studies. However, many of these studies did not use quantified methods and therewas no accuracy in the results. In this study, the main problem is divided into smaller problems so that the results can bepresented with precision and greater detail. In this way, it is possible to study and analyze all the individual parameters and data of each smaller sub-problem. This rationalizes the decision-making process, achieving results that are not only useful for making decisionsfor the.....

Keywords:AHP, multicriteria analysis, optimal location, Superdecisions

[1]. Mu, Enrique, Pereyra-Rojas, Milagros.Practical Decision Making, An Introduction to the Analytic Hierarchy Process (AHP) Using Super Decisions. SpringerBriefs in Operations Research. 2017; V.2, 1st ed. Philadelphia: University of Pennsylvania Press; 1992.
Available on: https://www.springer.com/gp/book/9783319338606
[2]. Saaty,Vargas, Models, Methods, Concepts & Applications of the Analytic Hierarchy Process, Second Edition, International Series in Operations Research & Management Science. 2012, V.175. Available on: https://www.springer.com/gp/book/9781461435969
[3]. Domański, Kondrasiuk,Analytic Hierarchy Process — Applications in BankingInnovations in Classification, Data Science, and Information Systems, pp 435-445, 2003 [4]. PodvezkoValentinas, Application of AHP Technique, Journal of Business Economics and Management,2009
[5]. SaatyThomasL.,Decision making — the Analytic Hierarchy and Network Processes (AHP/ANP), Journal of Systems Science and Systems Engineering 13, pages1–35, 2004

Abstract: Indian sign language (ISL) is one of the challenging topic as it is in rudimentary stage of its development, unlike American Sign Languages (ASL). This project aims at classification of Indian sign languages using machine learning models. There has been broad research on ASL and adequate data is available to analyse it. As, India is multi diverse country, there are severalregions and cultures which results in different variations of languages for communication. So, there are very limited standard data sets, which has variations and noises. ISL uses both hands to make gestures instead of one hand unlike ASL. It leads to occlusion of featuresand this is a major barrier for the lack of development in this field. This project aims at helping in then research of this field further by providing a data set of ISL. A data of sign language was created by us for alphabets and numeric. Later, the features will be extracted from the collected segmented data using image pre-processing and Bag of words model. Histograms are generated to map the alphabets with images. In the final step, these features will be fed to supervised models for the classification.

Key Word:Sign language; Gestures; Bag of words; Image processing; Classification; Support Vector Machine; Clustering; Visual words.

[1]. S. C. Agrawal, A. S. Jalal, and C. Bhatnagar. Recognition of indian sign language using feature fusion. In 2012 4th International Conference on Intelligent Human Computer Interaction (IHCI), pages 1–5, 2012.
[2]. Herbert Bay, TinneTuytelaars, and Luc Van Gool. Surf: Speeded up robust features. In AleˇsLeonardis, Horst Bischof, and Axel Pinz, editors, Computer Vision – ECCV 2006, pages 404–417, Berlin, Heidelberg, 2006. Springer Berlin Heidelberg
[3]. J. Canny. A computational approach to edge detection. IEEE Transactions on Pattern Analysis and Machine Intelligence, PAMI-8(6):679–698, 1986.
[4]. K. Dixit and A. S. Jalal. Automatic indian sign language recognition system. In 2013 3rd IEEE International Advance Computing Conference (IACC), pages 883–887, 2013.
[5]. B. Gupta, P. Shukla, and A. Mittal. K-nearest correlated neighbor classification for Indian sign language gesture recognition using feature fusion. In 2016 International Conference on Computer Communication and Informatics (ICCCI), pages 1–5, 2016.

Abstract: Background: Breast cancer is one of the leading causes of cancer death in women around the world. In order to reduce the death rate, the tumors have to be detected at the early stage. The proposed system is a new approach with automatic contouring and texture analysis to aid in the classification of Breast Lesion using Ultrasound. Firstly, the goal of removing the speckle while preserving important information from the lesion boundaries, anisotropic diffusion filtering is applied to the ultrasonic image. A marker-controlled watershed transform is used for image segmentation, automatically extracts the precise contour of breast lesions. 24 Gray Level Co-occurrence Matrix (GLCM) features are extracted from the extracted lesion. Support Vector Machine (SVM) classifier utilizes the selected feature vectors to identify the breast lesion as benign or malignant. A confusion matrix is used to describe the performance of a classification model on a set of test data for which the true values are known. It allows the visualization of the performance of an algorithm.....

Key Word:Ultrasound Images, Gray Level Co-occurrence Matrix (GLCM), Support Vector Machine K-Nearest Neighbor, Confusion matrix.

[1]. H. Cheng, X. Cai, X. Chen, L. Hu, X. Lou, Computer-aided detection and classification of microcalcifications in mammograms: a survey, Pattern Recognition 36 (2003) 2967–2991.
[2]. H. Cheng, X. Shi, R. Min, L. Hu, X. Cai, H. Du, Approaches for automated detection and classification of masses in mammograms, Pattern Recognition 39 (4) (2006) 646–668.
[3]. Breiman, L. "Random forests", Machine Learning Journal Paper, 45, pp.5-32,2001.
[4]. R. Ramani, N.Suthanthira Vanitha, S.Valarmathy, :The Pre-Processing Techniques for Breast Cancer Detection in Mammogram Images", I.J. Image, graphics and signal Processing, vol.5, 47-54, 2013.
[5]. J. Jesneck, J. Lo, J. Baker, Breast mass lesions: computer-aided diagnosis models with mammographic and sonographic descriptors, Radiology 244 (2) (2007) 390–398