iosr-JCE   Volume-19 ~ Issue-2 ~ March-April 2017

Abstract: Diabetic Retinopathy is a most common diabetic eye disease, which occurs when a blood vessel in the retina change. There are two stages of the disease. The early stage is Non proliferative diabetic retinopathy (NPDR) and later is Proliferative diabetic retinopathy (PDR). In NPDR, various problems may occur, such as macular edema which is swelling in the central retina and retinal ischemia which occurs due to poor blood flow. PDR is the advanced stage of NPDR, new blood vessels starts growing in the retina known as neovascularization............

Keyword: Blood Vessel, Diabetic Retinopathy, Exudates, Fundus Image, Macula and Fovea, NPDR, Optic Disk, PDR.

[1]. Dharitri Deka, Jyoti Prakash Medhi, S. R. Nirmala, ―Detection of macula and fovea for disease analysis in color fundus images,‖ in IEEE 2nd International Conference on Recent Trends in Information System, 2015
[2]. T. Ruba, K. Ramalakshmi, ―Identification and Segmentation of exudates using SVM classifier,‖ IEEE Sponsored 2nd International Conference on Innovation Embedded and Communication Systems, 2015
[3]. Jyothis Jose, Jinsa Kuruvilla, ―Detection of red lesions and hard exudates in color fundus images,‖ Internation Journal of Engineering and Computer Science ISSN: 2319-7242 Volume 3 Issue 10 October, 2014 page No. 8583-8588
[4]. JayakumarLachure, A. V. Deorankar, Sagar Lachure, Swati Gupta, Romit Jadhav, ― Diabetic retinopathy using morphological operations and machine learning,‖ IEEE International Advance Computing Conference, 2015
[5]. Sawmitra Kumar Kuri, ―automatic diabetic retinopathy detection using gabor filter with local entropy thresholding,‖ IEEE 2nd International Conference on Recent Trends in Information System, 2015

Abstract: Multimodal medical image fusion (MIF) is a method ofextracting complementary information fromdifferent sourceimages and combining them into a resultant image. Theintegration of multimodality medical images can providemore complete pathological information for doctors, which greatly helps their diagnosis and treatment.In this paper a survey is carried out over the approaches proposed in earlier for medical image fusion. The complete approaches are categorized into various classes like Morphological methods, knowledge based methods, wavelet based methods, neural network based methods, fuzzy logic based methods, contourlet based methods and the non-subsampled contourlet transform based methods.

Keywords: Medical image fusion, Morphology, Wavelet, contourlet, ANN, fuzzy logic.

[1]. B. Solaiman, R. Debon, F. Pipelier, J. M. Cauvin, and C. Roux, Information fusion: Application to data and model fusion for ultrasound image segmentation,IEEE TBME, vol. 46, no. 10, pp. 1171–1175, 1999.
[2]. B. V. Dasarathy, Editorial: Information fusion in the realm of medical applications-a bibliographic glimpse at its growing appeal, Information Fusion 13 (1) (2012) 1–9.
[3]. F. E. Ali, I. M. El-Dokany, A. A. Saad, and F. E. Abd El-Samie, A curvelet transform approach for the fusion of MR and CT images, Journal of Modern Optics, vol. 57, no. 4, pp. 273–286, 2010.
[4]. L. Yang, B. L. Guo, and W. Ni, Multimodality medical image fusion based on multiscale geometric analysis of contourlet transform, Neuro-computing, vol. 72, no.1–3, pp. 203–211, 2008.
[5]. T. Li and Y. Wang, Multiscaled combination of MR and SPECT images in neuroimaging: a simplex method based variable-weight fusion, Computer Methods and Programs in Biomedicine, vol. 105, no. 1, pp. 31–39, 2012.

Abstract: Motifs template is the input for many bioinformatics systems such codons finding, transcription, transaction, sequential pattern miner, and bioinformatics databases analysis. The size of motifs arranged from one base up to several Mega bases, therefore, the typing errors increase according to the size of motifs. In addition, when the structures motifs are submitted to bioinformatics systems, the specifications of motifs components are required, i.e. the simple motifs, gaps, and the lower bound and upper bound of each gap. The motifs can be of DNA, RNA, or Protein...............

[1]. Attwood T. K., Gisel A., Eriksson N-E., and BongcamRudloff E. "Concepts, Historical Milestones and the Central Place of Bioinformatics in Modern Biology: A European Perspective", Bioinformatics-Trends and Methodologies, 2012.
[2]. PhD. Alla l lapidus, bioinformatics and its applications, SPBAU, SPBSU, ST. Petersburg.
[3]. Dr. Hussein K. Al-Khafaji, a New Approach to Motif Templates Analysis via Compilation Technique, Al-Rafidain University Collage, 2014.
[4]. SeppHochreiter, bioinformatics I sequence analysis and phylogenetics, institute of bioinformatics, Johannes kepler university linz , winter semester 2013/2014.

[5]. J. Mol. Biol., "Crystal Structure of Yeast Phenylalanine Transfer RNA I. Crystallographic Refinement", 123 607, 1978

Abstract: Human activity recognition and object detection are techniques for detecting a human and objects from an image respectively. Which can be greatly useful for medical, security and industrial fields. This paper presents a new approach by combining both the techniques for extra usability and accurate detection of activities. This paper presents the HOG features for detecting the human in an image and SURF features for extraction of matched points from the image................

Keywords: Human detection; Object detection; Activity detection; HOG and SURF fetaures.

[1]. Aarsh Joshi,Chandni Shah,Khyati Jain,Hetal Parmar,Prof. Vaishali R Patel A Review on Human Activity Recognition Using HOG Feature IJSRD
[2]. Chi Qin Lai Soo Siang Teoh A Review on Pedestrian Detection Techniques Based on Histogram of Oriented Gradient Feature 978-1-4799-6428-4/14/$31.00 ©2014 IEEE
[3]. Navneet Dalal ,Bill Triggs "Histogram of Oriented Gradients for Human Detection" Proceedings of the 2005 IEEE computer society
[4]. Narges Gheadi ,Maziar Palhang "New Approach for Human Detection in images Using istogram of Oriented Gradient" 2013 IEEE .
[5]. Qiang Zhu ,Shai Avindan ,Mei-chen Yeh ,and Kwang-Ting Cheng "Fast Human Detection Using a Cascade of Histogram of Oriented Gradients" Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition(CVPR'06)

Abstract: Recently analytical hierarchy process is applied in different fields like selection of business plan, banking industries, technology selection, public transport, and decision in health care. One of the applications of analytical hierarchy process is medical diagnosis which is mostly used in research area. Many researchers are focusing on medical field. This study introduces an approach to find out risk level of the cancer based on analytical hierarchy process (AHP). Cancer is the most frequent cause of death...............

Keywords: Cancer, Multi Criteria Decision Making, Analytical Hierarchy Process, pair comparison

[1]. https://www.cancer.gov
[2]. K.Arutchelvan, Dr.R.Periyasamy ―Cancer Prediction System using Data mining Technique‖ International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056 Volume: 02 Issue: 08 | Nov-2015 www.irjet.net p-ISSN: 2395-0072
[3]. T. Sowmiya, M. Gopi, M. New Begin L.Thomas Robinson ―Optimization of Lung Cancer using Modern data mining techniques.‖ International Journal of Engineering Research ISSN:2319- 6890)(online),2347-5013(print)VolumeNo.3,Issue No.5, pp : 309-3149(2014)
[4]. V.Krishnaiah ―Diagnosis of Lung Cancer Prediction System Using Data Mining Classification Techniques‖ International Journal of Computer Science and Information Technologies, Vol. 4 (1) 2013, 39 – 45 www.ijcsit.Com ISSN: 0975-9646
[5]. Cancer Prevention and Control Retrieved from http://www.cdc.gov/cancer/dcpc/resources/features/worldcancerday/ Retrieved on: 15 November 2013.

Abstract: With an increase usage of credit cards for online purchases as well as regular purchases causes a credit card fraud. In the mode of electronic payment system, fraud transactions are rising on the regular basis. The Modern techniques based on the Data Mining, Genetic Programming etc. has used in detecting fraudulent transactions. Currently; data mining is a popular way to battle frauds because of its effectiveness. Data mining is a well defined procedure that takes data as input and produces output in the forms of models or patterns. In this paper, we focus on Decision Tree technique; basically it provides a system which is supposed to classify a current transaction into fraud or non-fraud using split criteria

Keywords: KDD, SVM, Index Ratio, Gain Ratio, Gini Index .

[1]. Arpita M. Hirudkar and Mrs. S.S Sherekar. "Comparative Analysis of Data Mining Tools and Techniques for Evaluating Performance of Database System". International Journal of Computer Science and Applications, Vol.6, No.2, Apr 2013.
[2]. Data Mining Concepts and Techniques by Jiawei Han, Micheline kamber, Jian pei.
[3]. Kalpana Rangra and Dr.K.L Bansal. "Comparative Study of Data Mining Tools". International Journal of Advanced Research in Computer Science and Software Engineering, Vol.4, Issue 6, June 2014.
[4]. Osama Abu Abbas."Comparisons Between data Clustering Algorithms". The International Arab Journal of Information Technology, Vol.5, No.3, July 2008.
[5]. Sotiris Kotsiantis, Dimitris Kanellopoulos."Association Rules Mining: A Recent Overview". GESTS International Transactions on Computer Science and Engineering, Vol.32 (1), 2006, pp.71-82.

Abstract: An emerging technology, IoT enables us machine to machine communication. For communication in emergency environment, IoT is considered as a strong technology. An emergency alert service offered by Terrestrial Digital Multimedia Broadcasting (T-DMB) is known as Automatic Emergency Alert Service (AEAS). This paper illustrates a consumer transceiver for long range communications of IoT in emergency environments. In order to overcome the drawbacks of AEAS, signaling method is used in proposed system. The IEEE802.11ah is a Wi-Fi protocol as transceiver which is under standardization for low cost and low power services.

Keywords: TDMB, AEAS, IEEE802.11ah, IR sensor, Gas sensor, Xampp, MP Lab.

[1]. M. Song, S. Chang, and Y.-T. Lee, "A wake-up consumer device for enhanced emergency alert service in T-DMB systems," IEEE Trans. Consumer Electron., vol. 61, no. 3, pp. 286-294, Aug. 2015.
[2]. K. Jun and S. Chang, "An interference-aware clustering based on genetic algorithm for cell broadcasting service," IEICE Trans. Fund., vol. e96-a,no. 12, pp. 2740-2744, Dec. 2013.
[3]. Y.-H. Lee, G. Kim, S.-R. Park, Y.-T. Lee, and N. Kim, "An efficient emergency broadcasting signal multiplexing method for supporting the legacy T-DMB receivers in break-in system," IEEE Trans. Consumer Electron., vol. 57, no. 4, pp. 1550-1555, Nov. 2011.
[4]. S. Tarkoma and H. Ailisto, "The internet of things program: the Finnish perspective," IEEE Commun. Mag., vol. 51, no. 3, pp. 10-11, Mar. 2013.
[5]. A. Zanella, N, Bui, A. Castellani, L. Vangelista, and M. Zorzi, "Internet of things for smart cities," IEEE Internet of Things Journal, vol. 1, no. 1, pp. 22-32, Feb. 2014.

Abstract: Web applications are very complex in terms of its design and its code. The dynamic aspects of web applications makes website testing quite challenging. A web application consists of web page as its basic unit. This makes it a page-based web application. This paper focuses on these types of web applications. Based on the web application's navigational graph we extend the MM-path based testing to test page-based web applications. Different test paths are thus obtained

Keywords: Extended MM-path graph, n-tier, Page-based testing Web page,

[1]. R. Chopra. Testing Web Applications: The State of Art and Future Trends.(UK: Cambridge International Science Publishing, 2016).
[2]. http://www.owasp.org/documentation/topten.html

[3]. Mc Cabe et al. Using Cyclomatic Path Analysis to Detect Security Vulnerabilities, 2011.

[4]. Hamzeh Al Shaar and Ramzi Haraty, Modeling and Automated Black Box Regression Testing of Web Applications, Journal of Theoretical and Applied Information Technology, 1182-1198.

[5]. Danny Roest, Ali Mesbah and Arie van Deursen, Regression Testing Ajax Applications: Coping with Dynamism, 2010, pp 127-136..

Abstract: Whenever law enforcement agency tries to gather information on mobs and public safety, it became increasingly clear that the primary factor in assuring a safe and comfortable environment for the large mob is the planning for their management. There is a considerable emphasis in this paper on Mob management planning and implementation because the police and other law enforcement agencies believe that it is the key to providing safe events in tense situations. Mob management must take into account all the elements of an event especially the type of events (circus, sporting, theatrical, concert, rally, parade, etc.), characteristics of the facility, size and demeanor of the mob, methods of entrance.............

Keywords: Mob Management, drone, helmet, surveillance, disaster management, Crowd Management, Public Safety in India.

[1]. http://www.business-standard.com/article/specials/story-in-numbers-stampedes-make-life-cheap-in-india-116102400038_1.html
[2]. https://www.police.ucsb.edu/files/docs/Crowd-Management-Intervention-and-Control.pdf
[3]. http://ndma.gov.in/images/pdf/managingcrowdsguide.pdf
[4]. http://www.respect.org.in/img/topics/org-1476680801_org-1436865040_Crowd%20Management%20&%20Public%20Safety%20in%20India-%20A%20New%20Policy%20Intiative.pdf

Abstract: Software reliability prediction is very challenging in maintenance phase as well as in the starting phases of software development. In the past few years many software reliability models have been proposed for assessing reliability of software but developing accurate reliability prediction models is difficult due to the recurrent or frequent changes in data in the domain of software engineering. As a result, the software reliability prediction models built on one dataset show a significant decrease in their accuracy when they are used with new data. The objective of this paper is to introduce a new approach that optimizes the accuracy of software reliability predictive models when used with raw data..............

Keywords: Software Reliability, Bio-inspired Computing, Ant Colony Optimization technique

[1]. R. K. Mohanty, V. Ravi, and M. R. Patra, "Hybrid intelligent Systems for predicting Software reliability," Elsevier, Applied Soft Computing, vol. 13, No. 1, pp. 189-200, 2013.
[2]. R. K. Mohanty, V. Ravi, and M. R. Patra, "Application of Machine learning Techniques to Predict software reliability," International Journal of Applied Evolutionary Computation, vol. 1, No.3, pp. 70-86, 2010.
[3]. K. Cai, C. Yuan, and M. L. Zhang, "A critical review on software reliability modeling," Reliability engineering and Systems Safety, vol. 32, pp. 357-371, 1991.
[4]. T. Dohi, Y, Nishio, and S. Osaki, "Optional software release scheduling based on artificial neural networks," Annals of Software engineering, vol. 8, pp. 167-185, 1999.
[5]. N. Karunanithi, Y. K. Malaiya, and D. Whitley, "The scaling problem in neural networks for software reliability prediction," In Proceedings of the Third International IEEE Symposium of Software Reliability Engineering, Los Alamitos, CA, pp. 76- 82, 1992a

Abstract: Android "Naugat" (codenamed Android N in development) is the seventh major version of Android Operating System called Android 7.0. It was first released as a Android Beta Program build on March 9 , 2016 with factory images for current Nexus devices, which allows supported devices to be upgraded directly to the Android Nougat beta via over-the-air update. Nougat is introduced as notable changes to the operating system and its development platform also it includes the ability to display multiple apps on-screen at once in a split-screen view with the support for inline replies to notifications, as well as an OpenJDK-based Java environment and support for the Vulkan graphics rendering API, and "seamless" system updates on supported devices.

Keywords: Jellybean, kitkat, lollipop, marshmallow, naugat

[1]. Ali Waqas (September 28, 2011). "Android 2.3.6 Gingerbread Update For Nexus S Available (Wi-Fi And Tethering Fix)". Addictive Tips. Retrieved December 20, 2011. "Introducing #Android Nougat. Thank you, world, for all your sweet name ideas! #Android Reveal". Twitter. June 30, 2016. Retrieved June 30, 2016.

[2]. Elgin, Ben (August 17, 2005). "Google Buys Android for Its Mobile Arsenal". Bloomberg Business week. Bloomberg. Archived from the original on February 24, 2011. Retrieved February 20, 2012.android architecture figure and explanation from ―TECHBLOGON.COM‖site given.

[3]. Amadeo, Ron (March 10, 2016). "Surprise! The Android N Developer Preview is out right now". Ars Technica. Condé Nast. Retrieved July 1, 2016..

[4]. Burke, Dave (April 13, 2016). "Android N Developer Preview 2, out today!" Android Developers blog. Google Inc. Retrieved June 5, 2016

[5]. Savov, Vlad (May 18, 2016). "Google details Android N features ahead of late summer release". The Verge. Vox Media. Retrieved July 1, 2016. Kastrenakes, Jacob (May 18, 2016). "Google's latest Android N beta is meant for everyone". The Verge. Vox Media. Retrieved July 1, 2016

Abstract: This study aims to figure out one of the current challenges to aviation is reducing the congestion in major airports. The problem statement describes an important issue for system planners, airport operators and management who are dealing with various aspect of congestion at airport. At the same time resource optimiza-tion is a primary concern for all segments in the system. The proposed model offers a specialized Multi Criteria Decision making model for smooth and manageable passenger flow process at airport. It also focus on passen-ger operation and minimum service time by forecasting various factors that affect to down the performance, efficient use of resources. The proposed model is able to serve dynamic solution by forecasting the undesirable condition as per different timeframe.

Keywords: Decision variables, Multi-Criteria Decision Making, ranking, timeframe

[1]. Matthias Ehrgott and XavierGandibleux "Multiple Criteria Optimization: State of the Art Annotated Bibliographic Surveys"
[2]. Jaromir Siroky, Pavlina Hlavsova 2014. Optimizing process of check-in and security check at airport terminals. Number 4, Volume IX, December.
[3]. D Nagesh Kumar, IISc Optimization Methods: M1L1. [4]. Ishikawa, Kaoru (1976). Guide to Quality Control. Asian Productivity Organization. ISBN 92-833-1036-5.
[5]. Da Silva de Santis SH, Marcicano JPP, Cunto G, Figueiredo RA (2016) .Use of Quality Tools for Problem Analysis (FMEA and Ishikawa Diagram) in a Small Textile Business Textile Sci Eng 6: 258. doi:10.4172/2165-8064.1000258.

Abstract: Industry is the production of goods or related service within an economy. The major source of revenue of a group or company is the indicator of its relevant industry. The right location is often critical to the success of a business. Location is particularly important for retail businesses. Poor location decisions are difficult and expensive to overcome. If we are buying an established business it is still necessary to check the suitability of the location because it is reflected in the price paid for goodwill. there, for The general public's demand of Bangladesh for safe business is rising promptly with the improvement of the living standard. However, the allocation of limited and unbalanced...............

Keywords: Multiple criteria decision analysis (MCDA), uncertainty, belief rule base (BRB), evidential reasoning (ER), and intelligent decision system (IDS)

[1] M Sonmez, G. Graham and J. B. Yang and G D Holt, ―Applying evidential reasoning to pre-qualifying construction contractors‖, Journal of Management in Engineering, Vol.18, No.3, pp.111-119, 2002.
[2] J. B. Yang, ―Rule and utility based evidential reasoning approach for multiple attribute decision analysis under uncertainty‖, European Journal of Operational Research, Vol. 131, No.1, pp.31-61, 2001.
[3] Y. M. Wang, J. B. Yang and D. L. Xu, ―Environmental Impact Assessment Using the Evidential Reasoning Approach‖, European Journal of Operational Research, Vol.174, No.3, pp.1885-1913, 2006.
[4] Lisa M. (2008). The Sage encyclopedia of qualitative research methods. Los Angeles, Calif.: Sage Publications. ISBN 1-4129-4163-6.
[5] http://www.pearson.ch/1449/9780273722595/An-Introduction-to-Geographical.aspx