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MANET is a momentary network of nodes without any aid of any infrastructure and network proprietor. Each node in MANET acts as a router or host and moves in dynamic trajectory region according to applied protocol. Recent research work in MANET rise to design various novel protocols based on fundamental protocols proposed by IETF. The route discovery and maintenance feature of MANET routing is essential for its proactive, reactive or hybrid behavior. In this paper, we analyzed comprehensively route discovery and maintenance of DSDV, AODV and IERF ad-hoc routing protocol along with their basic functionality. This will facilitate for finding motive to assessment of competitive MANET routing protocols on performance metrics such as PDR, delay, throughput etc. according to their phenomenal nature. The DSDV protocol is outperformed than AODV and IERF. The IERF protocol is observed superior to AODV due to its key features such as path accumulation, node reliability pair factor and node pruning .

MANET, DSDV, AODV, IERF, NS-2

[1] Varun G. Menon, Sreekal C.S., Vibin Johny, Teenu Tony, and Eldho Alias, ”Performance analysis of traditional topology based routing protocols in mobile ad hoc networks”, The International Journal of Computer Science and Application (TIJCSA ISSN-2278-1080), Vol.2,No.01,March 2013
[2] Anil Kumar Sharma, Neha Bhatia, “”, International Journal of Computational Engineering and Management, Vol.1 2, Page No (100-104), April 2011,
[3] Humaira Nishat, Sake Pothalaiah and Dr. D.Srinivasa Rao,” Performance Evaluation of Routing Protocols in MANETS”, International Journal of Wireless & Mobile Networks (IJWMN) Vol. 3, No. 2 Page No (67-75), April 2011
[4] Boshir Ahmed, Md. Kahlid Ben Islam, Julia Rahman, “Simulation, analysis and performance comparison among different routing protocols for wireless sensor network”, Computer Engineering and intelligent systems, ISSN-2222-1719(Paper) ISSN 2222-2863(online), Vol.2, No.4, 2011
[5] Dr. P. R. Gundalwar, Dr. Vinay Chavan, “Improved Energy and Route Failure Protocol (IERFP): A Framework for MANET Routing”, International Journal of Advanced Research in Computer and Communication Engineering Vol. 4, Issue 10, Page No (303-306), October 2015
[6] www.isi.edu/nsnam/ns/. The Network Simulator, NS2

P. R. Gundalwar and Bhaskar Y. Kathane, "A Comprehensive Analysis on Route Discovery and Maintenance Features of DSDV, AODV and IERF Ad-hoc Routing Protocols," International Journal of Computer Sciences and Engineering, Vol.4, Issue.2, pp.75-78, 2016.

Image Enhancement refers to accentuation or sharpening of image features such as edges, boundaries or contrast so as to get finer details of an image. In this paper a low contrast image enhancement algorithm using Single Scale Retinex (SSR) with undecimated wavelet transform (UWT) has been proposed. The performance of the proposed algorithm is evaluated using a statistical visual contrast measure (VCM). Experimental results obtained from the proposed algorithm gives improvement in terms of the VCM. Paper concludes comparison with existing algorithm and shows that the proposed method performs quite efficiently.

Low Contrast image Enhancement, single scale retinex (SSR), undecimtaed wavelet transform, visual contrast measure

[1] P.Suganya, S.Gayathri, N.Mohanapriya, “Survey on Image Enhancement Techniques”, International Journal of Computer Applications Technology and Research”, Vol. 2 Issue 5, 2013, page. 623-627.
[2] Numan Unaldi, Samil Temel & Suleyman Demirci, “Undecimated Wavelet Based Contrast Enhancement”, International Journal of Computer, Information, Systems and Control Engineering, Vol.7, Issue-9, 2013, page. 571-574.
[3] In- Su Jang, Tae-Hyoung Lee, Wang –Jun Kyung and Yeong –Ho Ha, “ Local Contast Enhancement Based On Adaptive Multiscale Retinex Using Intensity Distribution Of Input Image”, Journal of Imaging Science and Technology, Vol.55, Issue-4, 2011, page. 040502-1-040502-14.
[4] Violeta Bogdanova, “ Image Enhancement Using Retinex Algorithms And Epitomic Representation “, Bulgarian academy of science, cybernetics and information technologies, Vol. 10, Issue-3, Sofia 2010, page. 10-19.
[5] Doo Hyun Choi, Ick Hoon Jang , Mi Hye Kim, Nam Chul Kim, “ Colour Image Enhancement Based On Single-Scale Retinex With A JND Based Non-Linear Filter”, Proceeding of IEEE International symposium on circuits and systems, New Orleans, LA, page. 3948-3951.
[6] Doo Hyun Choi, Ick Hoon Jang , Mi Hye Kim, and Nam Chul Kim, “Color Image Enhancement Using Single-Scale Retinex Based On An Improved Image Formation Model”, 16th European Signal Processing Conference (EUSIPCO 2008), Lausanne, Switzerland, August 2008, page 25-29.
[7] Anish Kumar Vishwakarma, Agya Mishra, Kumar Gaurav, Abhishek Kataria, “Illumination Reduction of Low Contrast Image Enhancement with Homomorphic Filtering Technique”, International Conference on Communication Systems and Netwrk Technologies, 2012, page. 171-173.

Namita Naik and Agya Mishra, "Low Contrast Image Enhancement Based On Undecimated Wavelet Transform with SSR," International Journal of Computer Sciences and Engineering, Vol.4, Issue.2, pp.79-84, 2016.

Trust management is very crucial aspect in multiple cloud environment .To ensure this Trust-management. Our paper presents C-provider, a trust aware brokering scheme for managing efficient cloud resources (or) services. The C-provider is based on a third party brokering architecture that is proposed to act as a middleware for cloud management and service matching.Our C-provider uses a hybrid and adaptive trust model to compute the overalltrust degree of resources based on the monitored feedbackof the service resources. Also C-provider uses a minimal feedback mechanism that effectively reduces networking issue and improve system efficiency.

Multiple Cloud Computing, Trust-Aware Servicebrokering, Resource Matching, Feedback Aggregation

[1] M. Singhalet al., “Collaboration in multicloud computing environments:Framework and security issues,” Computer, vol. 46, no. 2, pp. 76–84,Feb. 2013.
[2] H. M. Fard, R. Prodan, and T. Fahringer, “A truthful dynamic workflowscheduling mechanism for commercial multicloud environments,” IEEE
Trans. Parallel Distrib. Syst., vol. 24, no. 6, pp. 1203–1212, Jun. 2013.
[3] F. Paraiso, N. Haderer, P. Merle, R. Rouvoy, and L. Seinturier,“A federated multi-cloud PaaSinfrastructure,” in Proc. 5th IEEE Int.Conf. Cloud Comput. (CLOUD), Jun. 2012, pp. 392–399.
[4] P. Jain, D. Rane, and S. Patidar, “A novel cloud bursting brokerageand aggregation (CBBA) algorithm for multi cloud environment,” inProc. 2nd Int. Conf. Adv. Comput. Commun. Technol. (ACCT), Jan. 2012,pp. 383–387.
[5] K. M. Khan and Q. Malluhi, “Establishing trust in cloud computing,”IT Prof., vol. 12, no. 5, pp. 20–27, Sep./Oct. 2010.
[6] K. Hwang and D. Li, “Trusted cloud computing with secure resourcesand data coloring,” IEEE Internet Comput., vol. 14, no. 5, pp. 14–22,Sep./Oct. 2010.
[7] H. Kim, H. Lee, W. Kim, and Y. Kim, “A trust evaluation model forQoS guarantee in cloud systems,” Int. J. Grid Distrib. Comput., vol. 3,no. 1, pp. 1–10, Mar. 2010.
[8] P. D. Manuel, S. ThamaraiSelvi, and M. I. A.-E. Barr, “Trustmanagement system for grid and cloud resources,” in Proc. 1st Int. Conf.Adv. Comput. (ICAC), Dec. 2009, pp. 176–181.
[9] L.-Q. Tian, C. Lin, and Y. Ni, “Evaluation of user behaviortrust in cloud computing,” in Proc. Int. Conf. Comput. Appl. Syst.Modeling (ICCASM), Oct. 2010, pp. V7-576–V7-572.
[10] X. Li and Y. Yang, “Trusted data acquisition mechanism for cloudresource scheduling based on distributed agents,” Chin. Commun., vol. 8,no. 6, pp. 108–116, 2011.
[11] X. Li, H. Ma, F. Zhou, and X. Gui, “Service operator-aware trust schemefor resource matchmaking across multiple clouds,” IEEE Trans. ParallelDistrib. Syst., to be published, doi: 10.1109/TPDS.2014.2321750.
[12] (2014). OPTIMIS.[Online]. Available: http://www.optimis-project.eu/
[13] W. Fan and H. Perros, “A novel trust management frameworkfor multi-cloud environments based on trust service providers,”Knowl.-Based Syst., vol. 70, pp. 392–406, Nov. 2014.
[14] N. Ghosh, S. K. Ghosh, and S. K. Das, “SelCSP: A framework tofacilitate selection of cloud service providers,” IEEE Trans. Cloud
Comput., vol. 3, no. 1, pp. 66–79, Jan./Mar. 2015.
[15] A. Nagarajan and V. Varadharajan, “Dynamic trust enhanced securitymodel for trusted platform based services,” Future Generat.Comput.Syst., vol. 27, no. 5, pp. 564–573, 2011.
[16] S. M. Habib, V. Varadharajan, and M. Muhlhauser, “A trust-awareframework for evaluating security controls of service providers in cloudmarketplaces,” in Proc. 12th IEEE Int. Conf. Trust, Secur., PrivacyComput. Commun., Jul. 2013, pp. 459–468.
[17] T. H. Noor and Q. Z. Sheng, “Trust as a service: A framework fortrust management in cloud environments,” in Web Information System
Engineering (Lecture Notes in Computer Science), vol. 6997. Berlin,Germany: Springer-Verlag, 2011, pp. 314–321.
[18] B. Rochwergeret al., “The RESERVOIR model and architecture foropen federated cloud computing,” IBM J. Res. Develop., vol. 53, no. 4,
pp. 535–545, 2009.
[19] S. A. De Chaves, R. B. Uriarte, and C. B. Westphall, “Towardan architecture for monitoring private clouds,” IEEE Commun. Mag.,vol. 49, no. 12, pp. 130–137, Dec. 2011.LI et al.: C-PROVIDER: A TRUST-AWARE SERVICE BROKERING SCHEME 1415
[20] (2014). RightScale.[Online]. Available: http://www.rightscale.com/
[21] (2014). SpotCloud.[Online]. Available: http://www.spotcloud.com/
[22] (2014). Aeolus. [Online]. Available: http://www.aeolusproject.org/index.html
[23] J. Spring, “Monitoring cloud computing by layer, part 1,” IEEE SecurityPrivacy, vol. 9, no. 2, pp. 66–68, Mar./Apr. 2011.
[24] J. L. Lucas-Simarro, R. Moreno-Vozmediano, R. S. Montero, andI. M. Llorente, “Scheduling strategies for optimal service deploymentacross multiple clouds,” Future Generat. Comput. Syst., vol. 29, no. 6,pp. 1431–1441, Aug. 2013.
[25] K. Hwang, S. Kulkarni, and Y. Hu, “Cloud security with virtualizeddefense and reputation-based trust management,” in Proc. 8th IEEEInt. Conf. Dependable, Autonomic, Secure Comput. (DASC), Dec. 2009,pp. 717–722.

B. Mahesh kumar and V.Savitha, "A Survey on Emergence of Cloud Computing Using Brokering Services," International Journal of Computer Sciences and Engineering, Vol.4, Issue.2, pp.85-91, 2016.

Heart disease diagnosis is a difficult task which requires utmost accuracy. This accuracy is achieved through knowledge and experience in the field of medicine. This paper describes a heart diagnostic system which analyses several health parameters and medical test results to predict absence or presence of heart disease in terms of artery narrowing in the patient. The proposed system described in the paper is a computer based application which uses the patient information related to the various health parameters which govern the procedure of diagnosis for a heart disease. The system also relies on pre-processing of data. The system described in this paper has been created with a view to assist doctors and medical staff in diagnosis of heart related problems. The application also facilitates self-diagnosis for the common man. This paper deals with the internal functioning of the system which is based on the data mining techniques for classification. Few algorithms for classification based mining and association based mining of data and their comparisons have been incorporated in the paper. Classification is a data mining method used to classify data into pre-defined class labels. For instance, classification can be used to anticipate the weather on a specific day . Famous grouping procedures incorporate decision trees and neural systems.

Classification;Data mining;ID3;Naïve Bayes

[1] NirmalaDevi, M.; Appavu, S.; Swathi, U.V., “An amalgam KNN to predict diabetes mellitus”, Emerging Trends in Computing, Communication and Nanotechnology (ICE-CCN), 2013 International Conference on, pages 691 – 695, 25-26 March 2013.
[2] S.X. Wu, S.F. Liu, M.Q. Li, “The Method of Data Pre-processing in Grey Information Systems”, ControlAutomation, Robotics and Vision, 2006. ICARCV ‘06.9th International conference on, pages 1-4,5-8 Dec. 2006.
[3] Ranganatha S.; Pooja Raj H.R.; Anusha C.;Vinay S.K,, “Medical data mining and analysis for heart disease dataset using classification techniques”, Research & Technology in the Coming Decades (CRT 2013), National Conference on Challenges in, pages 1 – 5, 27-28 Sept. 2013.
[4] Sudhakar, K.; Manimekalai, Dr. M., "Study of Heart Disease Prediction using Data Mining," International Journal of Advanced Research in Computer Science and Software Engineering, Volume 4, Issue 1,ISSN: 2277 128X, pages 1157-1160, January 2014.
[5] D.Lavanya; Dr.K.Usha Rani. "Performance Evaluation of Decision Tree Classifiers on Medical Datasets"International Journal of Computer Applications (0975 – 8887),Volume 26– No.4,pages 1-4, July 2011.
[6] Dr. K. Usha Rani, “Analysis Of Heart Diseases Dataset Using Neural Network Approach”, International Journal of Data Mining & Knowledge Management Process (IJDKP) Vol.1, No.5, September 2011.
[7] Missing values in data mining, “Soft Computing and Intelligent Information Systems”, 25 June 2015.
http://sci2s.ugr.es/MVDM#Imputation%20Methods
[8] Heart disease dataset, “{UCI} Machine Learning Repository”, 9 April 2015.
https://archive.ics.uci.edu/ml/datasets/Heart+Disease

Karthika Jayprakash,Nidhi Kargathra, Pranay Jagtap,Suraj Shridhar and Archana Gupta, "Comparison of Classification Techniques for Heart Health Analysis System," International Journal of Computer Sciences and Engineering, Vol.4, Issue.2, pp.92-95, 2016.

Wireless sensor networks are most vulnerable to identity based attacks in which malicious device is used to create forged MAC addresses of specified authorized client. The identity of a node can be easily verified through cryptographic authentication techniques which are not always possible because it requires key management and other additional infrastructural overhead. In this paper we propose a system for detecting identity based attacks and also searching the actual positions of adversaries which are responsible for the attacks. Firstly we propose OADL (Attack Detection & Localization) model for identity based attack that utilizes correlation of nodes signal's spatial property (i.e spatial information, a physical property of each node) and the average received signal gain of received signal strength (RSS) collected from each wireless sensor nodes. Then we describe the integration of our attack detection model into our real time localization system, which has the ability to locate the actual positions of the attackers through Partitioning AroundMedoids clustering analysis for localization. We are able to show that the actual positions of the attackers that can be located using localization algorithms. Then we make evaluation based on our model through experimentation using both 802.11 network and 802.15.4 network model. Our results will indicate that identity based attack detection can be achieved with high precision in attack detection rate and localization of multiple adversaries.

Identity based attack, localization, OADL model, Received signal strength

[1] John Bellardo and Stefan Savage, “802.11 Denial-ofservice attacks: real vulnerabilities and practical solutions”, Proceedings of the 12th conference on USENIX Security Symposium, Vol. 12, pp. 15-28, 2003.
[2] F. Ferreri, M. Bernaschi and L. Valcamonici, “Access Points Vulnerabilities to Dos Attacks in 802.11 Networks”, IEEE Wireless Communications and Networking Conference, Vol. 1, pp. 634-638, 2004.
[3] Martin Eian, “Fragility of the robust security network: 802.11 denial of service”, Applied Cryptography and Network Security: Lecture Notes in Computer Science, Vol. 5536, pp. 400–416, 2009.
[4] Bing Wu, Jie Wu, E.B. Fernandez and S. Magliveras, “Secure and Efficient Key Management in Mobile Ad Hoc Networks”, Proceedings of IEEE International Symposium on Parallel and Distributed Processing, 2005.
[5] Avishai Wool, “Lightweight Key Management for IEEE 802.11 Wireless Lans With Key Refresh and Host Revocation”, Wireless Networks, Vol. 11, No. 6, pp. 677- 686, 2005.
[6] Mathias Bohge and Wade Trappe, “An Authentication Framework for Hierarchical Ad Hoc Sensor Networks”, Proceedings of the 2nd ACM workshop on Wireless security, pp. 79-87, 2003.
[7] M. Demirbas and Youngwhan Song, “An RSSI-based Scheme for Sybil Attack Detection in Wireless Sensor Networks”, International Symposium on a World of Wireless, Mobile and Multimedia Networks, pp. 564–570, 2006.
[8] Daniel B. Faria and David R. Cheriton, “Detecting identitybased attacks in wireless networks using signalprints”, Proceedings of WiSe’06: ACM Workshop on Wireless Security, pp. 43–52, 2006.
[9] Liang Xiao, L. Greenstein, N. Mandayam, and W. Trappe, “Fingerprints in the Ether: Using the physical layer for wireless authentication”, IEEE International Conference on Communications, pp. 4646–4651, 2007.
[10] Liang Xiao, L. Greenstein, N. Mandayam, and W. Trappe, “A Physical Layer Technique to Enhance Authentication for Mobile Terminals”, IEEE International Conference on Communications, pp. 1520–1524, 2008.
[11] Liang Xiao, L. Greenstein, N. Mandayam, and W. Trappe, “MIMO assisted channel-based authentication in wireless networks” 42nd Annual Conference on Information Sciences and Systems, pp. 642–646, 2008.
[12] Yong Sheng, K. Tan Guanling Chen, D. Kotz and A. Campbell, A, “Detecting 802.11 MAC Layer Spoofing R Maivizhi AND S Matilda: detection and localization of multiple spoofing attackers for mobile wireless networks 1118 Using Received Signal Strength”, IEEE 27th Conference on Computer Communications, pp. 1768–1776, 2008.
[13] Liang Xiao, L. Greenstein, N. Mandayam, and W. Trappe, “Using the physical layer for wireless authentication in time-variant channels”, IEEE Transactions on Wireless Communications, Vol. 7, No. 7, pp. 2571–2579, 2008.
[14] Vladimir Brik, Suman Banerjee, Marco Gruteser and Sangho Oh, “Wireless device identification with radiometric signatures”, Proceedings of the 14th ACM international conference on Mobile computing and networking, pp. 116–127, 2008.
[15] Jie Yang, Yingying Chen, W. Trappe and J. Cheng, “Detection and Localization of Multiple Spoofing Attackers in Wireless Networks”, IEEE Transactions on Parallel and Distributed Systems, Vol. 24, No. 1, pp. 44- 58, 2013.
[16] P. Bahl and V.N. Padmanabhan, “RADAR: An in-Building RF- Based User Location and Tracking System,” Proc. IEEE INFOCOM, vol. 2, Page(s): 775 – 784, 2000
[17] Maurizio A. Spirito, “On the Accuracy of Cellular Mobile Station Location Estimation,” IEEE Transactions On Vehicular Technology,Vol. 50, No. 3, May 2001.
[18] C. Hsu and C. Lin, “A Comparison of Methods for Multiclass Support Vector Machines,” IEEE Trans. Neural Networks, vol. 13, no. 2, pp. 415-425, Mar. 2002
[19] Daniel B. Faria and David R. Cheriton, “DoS and Authentication in Wireless Public Access Networks,” In Proceedings of the First ACM Workshop on Wireless Security (WiSe’02), September 2002
[20] T. Roos, P. Myllymaki, H.Tirri, P. Misikangas, and J.Sievanen, “A probabilistic approach to WLAN user location estimation,” +International Journal of Wireless Information Networks, vol. 9, no. 3, pp.155–164, July 2002.
[21] Mathias Bohge and Wade Trappe, “An Authentication Framework for Hierarchical Ad Hoc Sensor Networks,” IEEE Trans. Ad Hoc Sensor Networks, WiSE’03, September 19, 2003
[22] J. Bellardo and S. Savage, “802.11 Denial-of-Service Attacks: Real Vulnerabilities and Practical Solutions,” Proc. USENIX Security Symp.,pp. 15-28,2003.
[23] F.Ferreri, M.Bernaschi, and L.Valcamonici, “Access Points Vulnerabilities to Dos Attacks in 802.11 Networks,” Proc. IEEE Wireless Comm. And Networking Conf., 2004.
[24] Qing Li and Wade Trappe, “Detecting Spoofing and Anomalous Traffic in Wireless Networks via Forge-
Resistant Relationship,”IEEE Transactions on Information Forensics and Security, Vol. 2, No. 4,December 2007
[25] J. Yang, Y. Chen, and W. Trappe, “Detecting Spoofing Attacks in Mobile Wireless Environments,” Proc. Ann. IEEE Comm. Soc. Conf. Sensor, Mesh and Ad Hoc Comm. and Networks (SECON), 2009.
[26] Yingying Chen, Jie Yang, Wade Trappe and Richard P. Martin, “Detecting and Localizing Identity-Based Attacks in Wireless and Sensor Networks ”IEEE transactions on vehicular technology, vol. 59, no. 5, June 2010

Tuhin Das, "A Study on Identity Based Attack Detection and Localization by the Clustering in Wireless Sensor Network," International Journal of Computer Sciences and Engineering, Vol.4, Issue.2, pp.96-99, 2016.

The mobile devices of this generation are head to head with high-end desktops in terms of processing power, memory management schemes, overall usability etc. It won't be surprising to see the industries do away with desktops or even laptops and employ mobile devices given the promise these devices have shown in the last few years. Automation is the need of the hour, and these mobile devices go a long way to help achieve that. These mobile devices can also be employed at places where saving time helps to increase productivity such as maintenance of power plants. Also, an access to real-time data helps the engineer get a much detailed view of the overall working and extent of the problem, if any. Hence to simplify the overall procedure, in this paper, we present a mobile solution to help the Field Engineer automate the task of carrying out the maintenance activity even when connectivity is limited, with the help of mobile device and related technologies which help to transfer data from one end to another.

SAP-PM (Plant Maintenance), Android Application, SQLite, Web Services

[1] Markus Aleksy, Bernd Stieger, Gerhard Vollmar, "Case Study on Utilizing Mobile Applications in Industrial Field Service" , Seventh IEEE International Conference on E-Commerce Technology (CEC'05), ISBN: 978076953755-9 Page No (333-336), July 20-23, 2009.
[2] Welderufael B. Tesfay, Markus Alesky, Karl Anderson, Marko Lehtola, “Mobile Computing Application for Industrial Field Service Engineering: A Case for ABB Service Engineers”, Local Computer Networks Workshops (LCN Workshops), 2013 IEEE 38th Conference on, Sydney, NSW, ISBN: 978147990539-3, Page No (188-193), Oct 21-24, 2013.
[3] How to write a technical paper [Online] Available: www.emcs.org/acstrial/newsletters/summer10/TechPaperWriting

Akash Budholia, Rishit Mehta, Murtaza Godhrawala and Zaheed Shaikh, "Mobile Field Service Engineer Application," International Journal of Computer Sciences and Engineering, Vol.4, Issue.2, pp.100-104, 2016.

The finding of sicknesses utilizing microscopy is basic for medicinal services, and exact forecast. The count of WBC and RBC Cells are very important for the doctor to diagnose various diseases such as anemia, leukemia etc. At present, it requires a tremendous measure of human and financial assets. Hardware solutions like Automated Hematology Counter exits, they are very expensive machines and unaffordable in every hospital laboratory. To overcome these problems, this paper proposes an image processing technique to count the number of red blood & white blood cells in the blood sample image. Our methodology has been to consolidate the all-around created field of advanced imaging, image handling, and manual microscopy to acquire a powerful and minimal effort gadget. We utilize a low cost optical microscope retrofitted with computer controlled imaging and stage positioning modules, and perform MATLAB based image processing on the microscopic images to accomplish the wanted results. The blood cell count that is RBC & WBC count is then used to diagnose the patient as well as detection of abnormalities like leukemia. The "Automated Disease Diagnosis Using Image Microscopy" puts to utilize different parts of Electronics and Telecommunication, essentially Circuit Design and Image Processing for the execution of the undertaking.

Disease Diagnosis; Blood tests; Blood Cell Counting; Malaria Detection; Digital Microscopy; Image Analysis; Malady; RBC; WBC

[1] A. Greenbaum, U. Sikora, and A. Ozcan, “Field-portable wide-field microscopy of dense samples using multi-height pixel super-resolution based lensfree imaging,” Lab on a Chip, 2012.
[2] S.B. Kim, H. Bae, K. Koo, M.R. Dokmeci, A. Ozcan, and A. Khademhosseini, “Lensfree Imaging for Biological Applications,” Journal of the Association for Laboratory Automation, 2012.
[3] S.O. Isikman, W. Bishara and A. Ozcan, “Partially Coherent Lensfree Tomographic Microscopy,” Applied Optics, 2011.
[4] McLaren CE, Brittenham GM and Hasselblad V, “Statistical and graphical evaluation of erythrocyte volume distributions,” Am. J. Physiology, April 1987.
[5] Alberts, B. (2005), "Leukocyte functions and percentage breakdown", Molecular Biology of the Cell, NCBI Bookshelf.
[6] LaFleur-Brooks, M. (2008). “Exploring Medical Language: A Student-Directed Approach,” volume – 7, 2008.
[7] S. H. Ong, Jayasooriah, H. H. Yeow and R. Sinniah, “Decomposition of digital clumps into convex parts by contour tracing and labelling”, Pattern Recognition Letters, volume- 13, No- 11, Page no- 789-795, November 1992.
[8] S. Kumar , S. H. Ong , S. Ranganath , T. C. Ong and F. T. Chew, “A rule-based approach for robust clump splitting”, Pattern Recognition, volume- 39, No- 6, Page no- 1088-1098, June 2006.
[9] R.C. Gonzalez and R. E. Woods, “Digital Image Processing. Prentice-Hall,” Englewood Cliffs, 2002.

Akshay Bhanushali, Ashwin Katale, Kuldeep Bandal, Vivek Barsopiya and Manish Potey, "Automated Disease Diagnosis Using Image Microscopy," International Journal of Computer Sciences and Engineering, Vol.4, Issue.2, pp.105-109, 2016.

Bluetooth Special Interest group designed the version 4.0+ of the Bluetooth device termed as Bluetooth Low Energy (BLE). It is a wireless personal area network technology. Bluetooth Low Energy (BLE) is a newly emerged technology targeting low-power, low-cost wireless communications within medium or short range. BLE has extended the already wide acceptance of Bluetooth and is an ideal choice for a variety of sensor-based products, as well as ubiquitous mobile devices [1]. This group aimed at novel applications in fitness, security, healthcare and home entertainment industries. Bluetooth Low Energy (BLE) is also called as Bluetooth Smart device. This device is application friendly.lt is built for the Internet of Things (IoT). BLE is the version 4.0+ of the Bluetooth specification.

Bluetooth Low Energy(BLE), Android, Bluetooth, Proximity

[1] Maria Varsamou & Theodore Antonakopoulos, "A bluetooth smart analyzer in iBeacon networks", International Conference on Consumer Electronics - Berlin (ICCE-Berlin), INSPEC Accession Number: 14917193, Page No (288 - 292), 7-10 Sept. 2014.
[2] Louay Bassbouss , Görkem Güçlü & Stephan Steglich, "Towards a remote launch mechanism of TV companion applications using iBeacon ", IEEE 3rd Global Conference on Consumer Electronics (GCCE), NSPEC Accession Number: 14904692, Page No (538 - 539 ), 7-10 Oct. 2014.
[3] Cheolhoon Kim & Sungwon Lee , “A research on Beacon code architecture extension using category and code Beacon structure” , International Conference on Information and Communication Technology Convergence (ICTC), INSPEC Accession Number: 14833413 , Page No (187 - 188), 22-24 Oct. 2014.
WEB REFERENCES
[4] http://ibeaconmodules.us/blogs/news/14702963-tutorial-swift-based-ibeacon-app-development-with-corelocation-on-apple-ios-7-8
[5] http://www.beaconsandwich.com/what-is-ibeacon.html
[6] http://developer.android.com/reference/android/database/sqlite/SQLiteDatabase.html
[7] http://redbearlab.com/blenano/
[8] https://mobstac.quora.com/What-are-the-limitations-of-beacons-And-what%E2%80%99s-the-way-around

Jigar Kothari, Trupti Shah, Bhavin Nagaria, Apurv Choubey and SaiDeepthi Pabba, "Automated Real Time In-Store Retail Marketing Using Beacon," International Journal of Computer Sciences and Engineering, Vol.4, Issue.2, pp.110-113, 2016.

Cloud computing had become the most popular and powerful platform for scientific applications. Green cloud computing also share large scale of resources, storage, knowledge for the scientific researches. Issues in implementation of the Scheduling of job which was the most challenging issues in the green cloud computing area .Some of the implementation is been done in the area of the green cloud computing. This paper will focus on the implement the pre-emptive part of the proposed algorithm of the Hybrid Approach to Round Robin and Priority Based Scheduling Algorithm in green cloud computing.

Cloud;Green Cloud;Round Robin;Priority Based

[1] Gaganjot Kaur and Sugandhi Midha ,’A Preemptive Priority Based Job Scheduling Algorithm in Green Cloud Computing’,2016.
[2] D. Kliazovich, P. Bouvry, and Samee U. Khan,’ DENS: Data Center Energy-Efficient Network-Aware Scheduling ‘,IEEE/ACM International Conference on Green Computing and Communications (GreenCom), Hangzhou, China, 2010
[3] Domagoj Jakobovi ́c et al, Evolving priority scheduling heuristics with genetic programming, Applied Soft Computing (2012) 2781–2789
[4] Shamsollah Ghanbari,Mohamed Othman,’ A Priority based Job Scheduling Algorithm in Cloud Computing’,’Science Direct’,2012.
[5]. Chengpeng Wu ,Junfeng Yao , Songjie "Cloud computing and its key techniques,“ Electronic and Mechanical Engineering and Information Technology (EMEIT), 2011 International Conference on , vol no. 1, p.320-324, 12-14 Aug. 2011(IEEE).
[6]. Xiaonian Wu, Mengqing Deng , Runlian Zhang , Bing Zeng,Shengyuan Zhou “A task Scheduling algorithm based on QOSdriven”International conference on Information Technology and quantitative Management(ITQM) ,p 1162-1169 , ELSEVIER(2013).
[7]. Huankai Chen, Frank Wang, Na Helian, Gbola Akanmu “User-Priority Guided min-Min Scheduling Algorithm for Load Balancing in cloud computing” National conference on Parallel computing technologies , p 1-8, 21-23 Feb 2013(IEEE)
[8]. Buyya Raj kumar, “Cloud Computing: Principles and Paradigms”, Wiley, 2011 .

Garima Malik and Gaganjot Kaur, "Hybrid Approach to Round Robin and Priority Based Scheduling Algorithm," International Journal of Computer Sciences and Engineering, Vol.4, Issue.2, pp.114-116, 2016.

This paper discusses the application of multimedia technologies in web learning environment. It presents the suitable classification algorithm based on regression analysis to predict the learners’ needs and performance. Predicting educational outcome and monitoring the progress of students in a web-based learning setting is a difficult task. But, there are possibilities to improve the system based on individual learning pattern in the web learning environment. In this paper, regression algorithm is implemented to predict student performance at the end of the semester. The results can be used to improve the educators’ perception to reform the syllabus, thereby escalating the probability of a higher score by covering students. Higher learning institutes contribution reserve learning courses during the web can use this replica to identify which area of their itinerary can be improved by data mining technology to accomplish and enhance the performance of the student. By attempting to study the new algorithm this, research paper is a need of the hour. The main aim of this research paper is to study about the newly proposed regression algorithm and its’ efficiency. It discusses the multimedia elements and their effects in the web learning system.

Multimedia technologies, Classification, Regression algorithms, Data Mining, Performance, Web Learning system

[1]. Dr. Mai NEO et al, Technology Acceptance of Healthcare E-Learning Modules: A Study of Korean and Malaysian Students’ Perceptions, TOJET: The Turkish Online Journal of Educational Technology – April 2015, volume 14 issue 2.
[2]. Mai NEO et al, (2008), Students' perceptions of interactive multimedia-mediated web-based learning: A Malaysian perspective Proceedings facility Melbourne 2008: Full paper: Neo, Neo & Yap, pp. 658-666.
[3]. Gomez, J.: Conceptual Modeling of Device- Independent Web Applications. J. IEEE Multimedia. 8, 26--39 (2001)
[4]. Conte, T., Massollar J., Mendes E., Travassos, G. H.: Usability Evaluation Based on Web Design Perspectives. In Empirical Software Engineering and Measurement, ESEM, First International Symposium. pp. 146--155 (2007)
[5]. Moreno, R., & Mayer, R. E. Interactive multimodal learning environments. Educational Psychology Review, (2007). 19, 309-326.
[6]. Lloyd P. Rieber, Computers, Graphics, & Learning The University of Georgia — Athens, (2000), pp. 50-125.
[7]. Moreno, R. Learning with high-tech and multimedia environments. Current Directions in Psychological Science, 2006. 15, 63–67
[8]. Sorden, S. D. A cognitive approach to instructional design for multimedia learning. Information Science Journal, 8, 263-279. (2005).
[9]. Cochrane, T.: Mobilising Learning: A Primer for Utilising Wireless Palm Devices to Facilitate a Collaborative Learning Environment. In ASCILITE Conference, pp. 147-157. Brisbane (2005)
[10]. Ivory, M. Y., Megraw, R.: Evolution of Web Site Design Patterns. ACM Trans. Inf. Syst. 23, 463—497 (2005)
[11]. Baharun, N., Porter A.: Teaching Statistics Using a Blended Approach: Integrating Technology-based Resources. In ASCILITE Conference, pp. 40--48. Auckland, New Zealand (2009).
[12]. Mayer, R. E. (2002). Cognitive Theory and the Design of Multimedia Instruction: An Example of the Two‐Way Street between Cognition and Instruction. New directions for teaching and learning, 2002(89), 55-71.
[13]. Um, E. The effect of positive emotions on cognitive processes in multimedia-based learning (Doctoral dissertation). Retrieved from Dissertations & Theses Database. (2008).

J. Mary Perpetual Succour and L. Jayasimman, "Enhancing Web Learning System Using Multimedia and Regression Algorithm," International Journal of Computer Sciences and Engineering, Vol.4, Issue.2, pp.117-120, 2016.