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

Abstract: Transformation of Data from the condition of being insignificant to significant has been conceivable because of amazing investigation apparatuses and preparing stages. Organizations have understood the capability of in-formation and are looking a long way ahead from the customary social databases to shapeless semi-organised information produced from contrasting sources. Due to the various gadgets and sensors encircling our biologi-cal system, IOT has turned into a real, as per application of information science, IOT investigation has turned into a gigantic chance to see unimaginable bits of knowledge. Nonetheless, in spite of the different advantages of IOT examination, associations are uncertain about the part which is difficult to understand, for example, security and protection concerns. In here, we talk about the chances and worries of IOT investigation. In addi-tion, we present a nonexclusive information science technique for IOT information investigation stated as Scheme, Analytics and Collect for Internet of Things (PCA-IOT). The presented technique can connected in IOT situations to accomplish information examination for compelling & effective basic leadership..

Key Word: [Internet of things (IOT), Analytics, Big Data, Data Science.]

[1]. Rolf H. Weber. Internet of Things–New security and privacy challenges. Computer Law & Security Review, No. 26, 2010.
[2]. Luigi Atzori, Antonio Iera, Giacomo Morabito. The Internet of Things: A survey. Computer Networks, Vol.54, 2010.
[3]. Mulligan G. The Internet of Things: Here now and coming soon. IEEE Internet Computing, 2010, 14(1): 35- 36.
[4]. Xu Da, Li Wu He, Li Shancang, Internet of things in industries: A survey IEEE Trans. Ind. Inf., 10 (4) (2014), pp.2233-2243.
[5]. P.P. Ray, A survey on Internet of Things architectures, Journal of King Saud University - Computer and Information Sciences, Volume 30, Issue 3, 2018, Pages 291-319.

Abstract: Data dissemination is the process of dispersal or transfer of data to the end-users.The main aim of wireless sensor nodes is to recognize and accommodate data from target domain ,convert the data into usable and desired form and broadcast the information back to its specific sources ,where the underlying application inhabit.In order to perform this task accurately and in an well organized manner we require the development of certain energy efficient routing protocols which sets up different paths in the middle of sensor nodes and the data sink. The Purpose of this paper is to compare various data dissemination protocols and provide detailed information on which protocol is more efficient and why. The paper also provides a detailed comparison between wireless and wired networks.

Keywords:Flat based routing protocols,Hierarchical routing protocols, Wired network,Wireless network.

[1]. Monica Parmar ,SaurabhMishra.'Review of Routing Algorithms in Wireless Sensor Networks'.IOSR Journal of Electronics and Communication Engineering (IOSR-JECE) e-ISSN: 2278-2834,p- ISSN: 2278-8735.Volume 9, Issue 5, Ver. 1 (Sep - Oct. 2014), PP 21-23.
[2]. ShikhaShukla , Meghana K M , Manjunath C R , SantoshNaik.'COMPARISON OF WIRELESS NETWORK OVER WIRED NETWORK AND ITS TYPE' .International Journal of Research - GRANTHAALAYAH.pg 14-20.
[3]. Rajesh Chaudhary ,Dr. Sonia Vatta.'A TUTORIAL OF ROUTING PROTOCOLS IN WIRELESS SENSOR NETWORKS'.RajeshChaudhary et al, International Journal of Computer Science and Mobile Computing, Vol.3 Issue.6, June- 2014, pg. 971-979
[4]. Hassan ECHOUKAIRI , Khalid BOURGBA , Mohammed OUZZIF .'A Survey on Flat Routing Protocols in Wireless Sensor Networks'.
[5]. ReenkamalKaur Gill , PriyaChawla and Monika Sachdeva.'Study of LEACH Routing Protocol for Wireless Sensor Networks '.International Conference on Communication, Computing & Systems (ICCCS–2014) .pg 196-198

Abstract: A novel method is briefed in this paper to verify and confirm whether a suspected computer system is in current date and time settings or not and thereby detecting a Cyber Crime. In this paper, a simplest and powerful methodology is described for collecting indirect clues to fix the computer crime when the chances for direct evidences to prove the crime are remote. A case study in which how the involvement of the digital media viz. a hard disk for sending disputed mail is investigated and by which the proposed method is validated

[1]. Blackstone, W. (1893). Commentaries on the laws of England in Four Books. Philadelphia: J.B. Lippincott Company.
[2]. Boyd, C., Forster, P. (2004). Time and date issues in forensic computing – a case study. Digital Investigation, 1(1), 18-23.
[3]. Carvey, H. (2012). Windows Forensic Analysis Tool Kit: Advanced Analysis Techniques for Windows 7, Third Edition. Waltham, MA: Syngress.
[4]. Casey, E. (2009). Handbook of Digital Forensics and Investigation. Burlington, MA: Academic Press.
[5]. Dumont, R., ESET. (2017). Timestomp. Retrieved on 03.05.2020 from https://attack.mitre.org/techniques/T1099/
[6]. Jaishankar, K. (2011). Cyber Criminology: Exploring Internet Crimes and Criminal Behavior, Boca Raton, FL: CRC Press.

Abstract: In this paper we suggest and proposed a new conceptual method layout of image super resolution (SR) restoration with SRCNN and Opencv combination. The objective of (SR) super resolution is to recuperate from a low-resolution input a high resolution image. We will also use the Library of Open Source Computer Vision. Open CV. Originally developed by Intel, Opencv is used for many applications for real time computer vision. (In this specific project, were going to use it to pre-and post our images). We must transform our images between the RGB frequently, BGR and the color spaces of Ycrcb. This is important due to the training of the SRCNN network in the Ycrcb color space on the luminance (Y) channel.

Key Word:Super-Resolution; Deep Convolutional NeuralNetworks; Machine Learning; Regularization;Patch-Extraction; Non-Linear Mapping; Image reconstruction

[1] https://en.wikipedia.org/wiki/Convolutional_neural_network
[2] T Bluche: transcription and segmentation of Joint line for end to end handwritten paragraph recognition. (2016)
[3] A. Roumy, and M. L. and M. Bevilacqua: Morel Lowcomplexity single image to super resolution on nonnegative neighbor embedding. 2012
[4] Loy, C.C., and Dong, C: SRCNN, Learning a deep convolutional network for image super resolution (2014).
[5] D-Y. Yeung. and H. Chang:, Super resolution through neighbor embedding, 2004.

Abstract: In this study a Convolutional Neural Network (CNN) model was proposed for the prediction of customer churn in a telecommunication industry. Many supervised machine learning models have been built and used for predicting customer churn in past researches. However, in thebuilding of these models, there is need for human intervention to carry out attributeselectionwhich is very tedious, time-consuming, tailored to specific datasetsand often result to attribute selection problems. This study proposed a convolutional neural network model for predicting customer churning behavior and to also get rid of human attribute selectionand its problems. Two datasets were created from the fourteen thousand data instances that were gotten from one of......

Key Word:Convolutional Neural Network, Customer Churn, Attribute selection, Multi-layer Perceptron

[1]. A., Saran Kumar; D., Chandrakala, "A Survey on Customer Churn Prediction Using Machine Learning Techniques," International Journal of Computer Applications , Vols. 154 - No. 10, pp. 0975 - 8887, November 2016.
[2]. V., Umayaparvathi, K., Iyakutti;, "Attribute Selection and Customer Churn Prediction - Deep Learning Approach," 2016.
[3]. A. Boateng and O. O. Owusu, "Mobile Number Portabilty: On the Switching Trends among Subscribers within Telecommunication Industry in a Ghanian City," 2013.
[4]. C. B. Bhattacharya, "When Customers are Members: Customer Retention in Paid Membership Contexts," Journal of the Academy of Marketing Science, 1998.
[5]. Zhang, Yongbin; Liang, Ronghue; Zheng, Yeli Li Yanying; Berry, Michael, "Behavior Based Telecommunication Churn Prediction with Neural Network Approach," International Symposium on Computer & Society, 2011

Abstract: Thyroid nodules, which are abnormal cell growth that forms a lump in the thyroid gland occurs in more than 50% of the adult population. While ultrasonography is a commonly used for diagnosis because it is non-invasive, non-radioactive, relatively inexpensive and widely available, the usual visual interpretation results in subjective interpretation, inter-observer variability and it's a time-consuming process. In addition, different stages of malignancy may not be detected, even in existing Computer Aided Diagnosis (CAD) systems. Therefore, there is need for CAD systems that can classify thyroid nodules into multiple stages of malignancies. In this work, we developed a classification system that classifies ultrasonic thyroid images into Thyroid Imaging Reporting and Data System (TIRADS) classes using convolutional neural network. Histogram equalization and adaptive filtering were used to improve....

Key Word:Deep Learning, TIRADS, Computer aided diagnosis system, Transfer Learning, Convolutional Neural Network

[1]. Dhaygude,P., Maharashtra , P. and Handore, S.M A Review of Thyroid Disorder DetectionUsing Medical Images, International Journal on Recent and Innovation Trends in Computing and Communication, 2014, 2 (12) :4194 – 4197
[2]. Popoveniuc, G., and Jonklaas, J. Thyroid nodules. The Medical clinics of North America, 2012, 96(2), 329–349.
[3]. Kwak J., Han K., Yoon J., Moon H., Son E., Park S., Jung H., Choi J., Kim B. and Kim E. Thyroid imaging reporting and data system for US features of nodules: a step in establishing better stratification of cancer risk, Radiology, 2011, 260 (3): 892–899.
[4]. Gharib, H Fine-Needle Aspiration Biopsy of Thyroid Nodules: Advantages, Limitations, andEffect , Mayo Clinic Proceedings, 1994, 69 (1) :44 – 49
[5]. Misiakos, E. P., Margari, N., Meristoudis, C., Machairas, N.,Schizas, D., Petropoulos, K., Machairas, A Cytopathologic diagnosis of fine needle aspiration biopsies of thyroid nodules. World Journal of Clinical Cases, 2016, 4(2):38–48.