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Mobile Ad hoc Network (MANET) is a self-configuring, dynamic, multi-hop and infrastructure less wireless network. Each node in MANET is free to move arbitrarily in any direction. A node can move randomly and may join or leave in network at any time. Each node acts as host and intermediate nodes act as routers by using multi-hop schemes. The adhoc protocols are divided into proactive, hybrid and reactive protocols. In this paper, the Dynamic Source Routing (DSR) protocol is studied. This study focuses on how to reduce the delay while transferring the data using optimized DSR routing protocol. This mechanism is used to reduce the packet loss and time delay and to reduce energy consumption in MANET. This optimizing mechanism helps to choose the best and the shortest path to transfer the data. This mechanism controls routing overhead in MANET.

MANET, QoS, AODV, DSR, Routing, Optimized

[1] YuvrajKumbharey, SuweshShukla and SushilChaturvedi, “Renovated Cluster Based Routing Protocol for MANET”, International Journal of Advanced Computer Reasearch, Vol.3, No.1, pp.206-210, March 2013.
[2] Iftikhar Ahmad, HumairaJabeen and Faisal Riaz, “Improved Quality of Service Protocol for Real Time Traffic in MANET”, International Journal of Computer Networks & Communications, Vol.5, No.4, pp. 75-85, July 2013.
[3] Mamoun Hussein Mamoun, “A Proposed Route Selection Technique in DSR Routing Protocol for MANET”, International Journal of Engineering & Technology, Vol.11, No.2, pp. 10-13. April 2011.
[4] P.Calduwel Newton and L.Arockiam, “A Quality of Service Performance Evaluation Strategy for Delay Classes in General Packet Radio Service”, International Journal of Advanced Science and Technology, Vol.50, pp. 91-98, January 2013.
[5] Rajkumar G and Duraiswamy K, “Time Delay Reduction in MANETs using Improved Fault Tolerant Routing Protocol”, CARE Journal of Applied Research, pp. 22-24, 2012.
[6] Yuchen Fu and Quan Liu, “Research of QoS Routing Algorithm in Ad Hoc Networks based on Reinforcement Learning”, Elektronikairelektrotechnika, Vol.19, No.2, pp. 83-87, 2013.
[7] Kumar Prateek, “MANET- Evaluation of DSDV, AODV and DSR Routing Protocol”, International Journal of Innovations in Engineering and Technology, Vol.2, pp. 99-104, February 2013.
[8] Yang Zhang, et al., “Social-Aware Data Diffusion in Delay Tolerant MANETs”, Network Science CTA. pp. 1-24.
[9] P. Chandra Sekhar, “Impact of Routing Overhead in A Real-Time MANET Environment”, Int.J.Computer Technology & Applications, Vol.4 (2), pp. 257-263. April 2013.
[10] A.Boomarani Malany, et al., “A Neighbor Coverage Based Probabilistic Rebroadcast For Reducing Routing Overhead In Mobile Ad Hoc Networks”, Int. J. Open Problems Compt. Math., Vol. 2, No. 3, pp. 461-468, September 2009.
[11] V.Deeban Chakravarthy, et al., “Dynamic Hello/Timeout Timer Adjustment in Routing Protocols for Reducing Overhead in Manets”, International Journal of Emerging Technology and Advanced Engineering, Vol.3, pp. 302-308, January 2013.
[12] Shoji Kasahara, et al., “Impact of Routing Overhead in A Real-Time MANET Environment”, Preprint submitted to Computer Communications, pp. 1-26, June 2009.
[13] A. Pratapa Reddy, “Multipath Routing Protocol for Reducing Congestion And Delay in MANET”, International Journal of Computer Applications (0975 – 8887), Vol.74-No.18, pp. 1-8, July 2013.
[14] Masaya Ito, Hiroki Nishiyama, and Nei Kato, "A Novel Routing Method for Improving Message Delivery Delay in Hybrid DTN-MANET Networks,"IEEE Global Communications Conference (GLOBECOM), 2013.
[15] Shitalkumar Jain, et al., “A Study of Congestion Aware Adaptive Routing Protocols in MANET”, Computer Engineering and Intelligent Systems, Vol.3, No.4, pp. 64-73. 2012.
[16] S.Santhosh Baboo, “A Hop-by-Hop Congestion-Aware Routing Protocol for Heterogeneous Mobile Ad-hoc Networks”, International Journal of Computer Science and Information Security, Vol. 3, No. 1, pp. 64-73. 2009.
[17] Mehajabeen Fatima, et al., “Route Repair through Route Discovery in MANET”, International Journal of Future Generation Communication and Networking, Vol. 6, No. 6, pp. 169-180, 2013.
[18] Ashish Kumar, “Energy Efficient Routing Protocol Avoiding Route Breaks based on DSR”, International Journal of Computer Applications (0975 – 8887), Vol.44, No.4, pp. 39-44, April 2012.
[19] R. Senthil Kumar, “Minimizing The Route Rediscovery Process In Manet”, Journal of Theoretical and Applied Information Technology, Vol. 61, No. 3, pp. 496-505, March 2014.
[20] Narinderjeet Kaur, et al., “Caching Strategies in MANET Routing Protocols”, International Journal of Scientific and Research Publications, Vol. 2, No. 9, pp. 1-6. September 2012.

P. Revathi, K. Gokul Raj, "An Improved DSR Routing Algorithm to Reduce Delay and Energy Consumption in Mobile Ad Hoc Networks," International Journal of Computer Sciences and Engineering, Vol.9, Issue.3, pp.1-6, 2021.

Credit card fraud detection becomes critical due to increase in online transactions. Customers bought products online more often than not. The payment is either through debit or financials. The malicious users may attack the online information and hack credit and debit cards. Detection and prevention mechanisms thus are need of the hour. Researchers work towards achieving immunity against these attacks but perfection yet not achieved. This paper proposes similarity based decision tree approach for financial fraud detection strategy by working on state driven dataset. The objective is to detect the attack at early stage to avoid extravagant situations. The result is presented in the form of classification accuracy, precision and execution time. The result in terms of classification accuracy and execution time is improved by the factor of 10%.

Financial fraud, similarity based decision tree, classification accuracy, precision, execution time

[1] N. Upasani and H. Om, “Evolving fuzzy min-max neural network for outlier detection,” Procedia Comput. Sci., vol. 45, no. C, pp. 753–761, 2015, doi: 10.1016/j.procs.2015.03.148.
[2] D. Wang, B. Chen, and J. Chen, “Credit card fraud detection strategies with consumer incentives,” Omega (United Kingdom), vol. 88, pp. 179–195, Oct. 2019, doi: 10.1016/j.omega.2018.07.001.
[3] R. Saia and S. Carta, “Evaluating financial transactions in the frequency domain for a proactive fraud detection approach,” in ICETE 2017 - Proceedings of the 14th International Joint Conference on e-Business and Telecommunications, vol. 4, pp. 335–342, 2017, doi: 10.5220/0006425803350342.
[4] A. Pumsirirat and L. Yan, “Credit card fraud detection using deep learning based on auto-encoder and restricted Boltzmann machine,” Int. J. Adv. Comput. Sci. Appl., vol. 9, no. 1, pp. 18–25, 2018, doi: 10.14569/IJACSA.2018.090103.
[5] R. Saia, “A discrete wavelet transform approach to fraud detection,” in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 10394 LNCS, pp. 464–474, 2017, doi: 10.1007/978-3-319-64701-2_34.
[6] W. Liu, Z. Wang, X. Liu, N. Zeng, Y. Liu, and F. E. Alsaadi, “A survey of deep neural network architectures and their applications,” Neurocomputing, vol. 234, pp. 11–26, Apr. 2017, doi: 10.1016/j.neucom.2016.12.038.
[7] I. Sohony, R. Pratap, and U. Nambiar, “Ensemble learning for financial fraud detection,” in ACM International Conference Proceeding Series, pp. 289–294, 2018, doi: 10.1145/3152494.3156815.
[8] S. Bhattacharyya, S. Jha, K. Tharakunnel, and J. C. Westland, “Data mining for financial fraud: A comparative study,” Decis. Support Syst., vol. 50, no. 3, pp. 602–613, Feb. 2011, doi: 10.1016/j.dss.2010.08.008.
[9] E. W. T. Ngai, Y. Hu, Y. H. Wong, Y. Chen, and X. Sun, “The application of data mining techniques in financial fraud detection: A classification framework and an academic review of literature,” Decis. Support Syst., vol. 50, no. 3, pp. 559–569, 2011, doi: 10.1016/j.dss.2010.08.006.
[10] F. Carcillo, A. Dal Pozzolo, Y. A. Le Borgne, O. Caelen, Y. Mazzer, and G. Bontempi, “SCARFF: A scalable framework for streaming financial fraud detection with spark,” Inf. Fusion, vol. 41, pp. 182–194, May 2018, doi: 10.1016/j.inffus.2017.09.005.
[11] C. Wang and D. Han, “Credit card fraud forecasting model based on clustering analysis and integrated support vector machine,” Cluster Comput., vol. 22, pp. 13861–13866, Nov. 2019, doi: 10.1007/s10586-018-2118-y.
[12] M. Zamini and G. Montazer, “Credit Card Fraud Detection using autoencoder based clustering,” in 9th International Symposium on Telecommunication: With Emphasis on Information and Communication Technology, IST 2018, pp. 486–491, 2019, doi: 10.1109/ISTEL.2018.8661129.
[13] E. Duman and M. H. Ozcelik, “Detecting financial fraud by genetic algorithm and scatter search,” Expert Syst. Appl., vol. 38, no. 10, pp. 13057–13063, Sep. 2011, doi: 10.1016/j.eswa.2011.04.110.
[14] N. Malini and M. Pushpa, “Analysis on financial fraud identification techniques based on KNN and outlier detection,” in Proceedings of the 3rd IEEE International Conference on Advances in Electrical and Electronics, Information, Communication and Bio-Informatics, AEEICB 2017, pp. 255–258, 2017, doi: 10.1109/AEEICB.2017.7972424.
[15] I. Benchaji, S. Douzi, and B. Elouahidi, “Using Genetic Algorithm to Improve Classification of Imbalanced Datasets for Credit Card Fraud Detection,” in 2018 2nd Cyber Security in Networking Conference, CSNet 2018, 2019, doi: 10.1109/CSNET.2018.8602972.

Shivani, Harjinder Kaur, "Fraud Detection by the Use of Correlation Based Tree Formation Approach," International Journal of Computer Sciences and Engineering, Vol.9, Issue.3, pp.7-12, 2021.

Today’s world cloud computing becoming so popular because of an effective and efficient way to provide computing resources and services to customers on demand. Cloud computing being an information technology paradigm enables access to shared pools of configurable system resources and higher-level services often over the Internet. The objective of providers is to maximize profits by their price schemes, while the main purpose of clients is to have quality of services for a reasonable price. Thus the vital aim is to maximize the profit for service providers get quality of service at best price for the client. Because of cloud computing development, choosing cloud services can be complicated time-consuming for customers. To facilitate cloud service delivery, the authors propose a cloud service broker who provides automated selection of suitable cloud services, assure the best performance, reliability, cost efficiency.

Cloud Computing, Cloud Broker, Quality of Service, Efficiency, Reliability, Profit Maximization

[1]M. Nazir,” Cloud Computing: Overview & Current Research Challenges”, IOSR Journal of Computer Engineering, Vol. 8, issue. 1,pp. 14-22, 2012.
[2]Srinivasan.A, Kalaimani R, “Profit maximization scheme with guaranteed quality of service in cloud computing”, International Journal of Pure and Applied Mathematics, Vol. 119, issue.14, pp. 1307-1316, 2018.
[3]R.Khurana, R.K. Baw, “Quality Based Cloud Service Broker for Optimal Cloud Service Provider Selection”, International Journal of Applied Engineering Research, Vol. 12, no. 18, pp. 7962–7975, 2017.
[4]V. Paulsson V. Emeakaroha J. Morrison and T. Lynn “Cloud Service Brokerage: A Systematic Literature Review Using a Software Development Lifecycle” Proceedings of the Twenty-second Americas Conference on Information Systems, San Diego, 2016.
[5]A. M. Manasrah, T. Smadi and A. Almomani, “A variable service broker routing policy for data center selection in cloud analyst”, J. King Saud University Computer Inf. Sci., vol. 29, pp. 365-377, 2017.
[6]R. Pal1, S. Mishra, P.K.Pathak,“Study on Cost Estimation of Service Delivery in Cloud Computing Environment”, International Journal of Information and Computation Technology, Vol. 4, No. 3, pp. 299-308, 2014.
[7]A.Motwani, R.Chaturvedi, A.Shrivastava, “Profit Based Data Center Service Broker Policy for Cloud Resource Provisioning”, International Journal of Electrical, Electronics Computer Engineering, Vol. 5, Issue. 1, Pages 54-60, 2016.
[8] A. N. Toosi, K. Vanmechelen, K. Ramamohanarao and R. Buyya, “Revenue Maximization with Optimal Capacity Control in Infrastructure as a Service Cloud Markets”, IEEE Transactions on Cloud Computing, vol. 3, pp. 261-274, 2015.
[9] M. Ahmed, M. A. Hossain, “Cloud Computing And Security Issues In The Cloud”, International Journal Of Network Security & Its Applications, vol. 6, pp. 25-36, 2014.
[10]Q. Zhang, Q. Zhu and R. Boutaba, “Dynamic Resource Allocation for Spot Markets in Cloud Computing Environments,” 2011 Fourth IEEE International Conference on Utility and Cloud Computing, Melbourne, VIC, Australia, pp. 178-185, 2011.
[11]S. Yadav, Rakshith H.V., K. Badari Nath, “A Survey on Cloud Computing Providers and Applications”, International Journal of Computer Sciences and Engineering, Vol.8, Issue.5, pp.86-93, 2020.
[12]Smita Sharma, R.P. Singh, “A Critical Survey on: Cloud Security and privacy issues and its associated solutions”, International Journal of Computer Sciences and Engineering, Vol.7, Issue.8, pp.288-296, 2019.

M.S.Namose, A.N. Shinde, C.R. Patil, V.R. Rodage, N.D. Awalekar, "Profit Maximization for Cloud Broker in Cloud Computing," International Journal of Computer Sciences and Engineering, Vol.9, Issue.3, pp.13-17, 2021.

As technology is blooming cloud computing becomes indispensible part of many companies. The users are dependent upon cloud infrastructure as it is widely adopted and used technology. In cloud computing the prime concern is shared storage and it has many security issues. One of these security issues is DDOS attack that can effect business organization which utilizes cloud. This paper describes an approach to handle DDOS attack in cloud systems. In the proposed approach Interpolation between the values are located. In the proposed approach, security attributes gives highest Interpolation and reliability is the next highest Interpolation values. Both of these attributes serve as root nodes. The comparison between these attributes and training data is made to determine the DDOS attack. This means complication of calculations is reduced. Execution time is greatly reduced using this procedure. Results obtained are similar but execution time is reduced. The mechanism of ordering and normalization gives the hierarchical clustering.

cloud computing, DDOS attack, Interpolation

[1] K. Kim, M. Erza, A. Harry, and C. Tanuwidjaja, Network Intrusion Detection using Deep Learning A Feature Learning Approach. 2018.
[2] W. Kong, Y. Lei, and J. Ma, “Data security and privacy information challenges in cloud computing,” Int. J. Comput. Sci. Eng., vol. 16, no. 3, pp. 215–218, 2018.
[3] S. A. Repalle, V. R. Kolluru, and 2, “Intrusion Detection System using AI and Machine Learning Algorithm,” Int. Res. J. Eng. Technol., vol. 4, no. 12, pp. 1709–1715, 2017.
[4] Z. Zhou, C. Du, L. Shu, G. Hancke, J. Niu, and H. Ning, “An Energy-Balanced Heuristic for Mobile Sink Scheduling in Hybrid WSNs,” IEEE Trans. Ind. Informatics, vol. 12, no. 1, pp. 28–40, 2016.
[5] Y. Chen, J. Yang, W. Trappe, and R. P. Martin, “Detecting and localizing identity-based attacks in wireless and sensor networks,” IEEE Trans. Veh. Technol., vol. 59, no. 5, pp. 2418–2434, 2010.
[6] S. G. Hersha, Rajendra Patil, “Protocol Specfic Multi Threaded Network Intrusion Detection System(PM-NIDS) for DOS/DDOS attack detection in cloud,” IEEE ACcess, 2018.
[7] S. Moradi, “A distributed method based on mobile agent to detect Sybil attacks in wireless sensor networks,” IEEE Access, pp. 276–280, 2016.
[8] D. Kim and S. An, “PKC-based dos attacks-resistant scheme in wireless sensor networks,” IEEE Sens. J., vol. 16, no. 8, pp. 2217–2218, 2016.
[9] M. Luo, C. Zou, and J. Xu, “An efficient identity-based broadcast signcryption scheme,” J. Softw., vol. 7, no. 2, pp. 366–373, 2012.
[10] S. Sadrhaghighi and I. T. Engineering, “Detect Pollution Attacks in Intra-Session Network Coding,” pp. 7–12, 2016.
[11] P. Sarigiannidis, “Analysing Indirect Sybil Attacks in Randomly Deployed Wireless Sensor Networks,” IEEE Access, pp. 0–5, 2016.
[12] S. Mahajan, “A Mechanism of preventing Sybil Attack in MANET using Bacterial Foraging Optimization,” IEEE Access, pp. 4–8, 2016.

Simmi, Harjinder Kaur, "Correlation Based Mechanism for the Detection of DDOS Attack," International Journal of Computer Sciences and Engineering, Vol.9, Issue.3, pp.18-22, 2021.

In today’s fast era, vechicle accidents are considered to be the main agony faced all over the world that may be due to steep turns present in high altitudes dense population or traffic hazards. Although there are many distinct reasons behind automobile accidents, maximum accidents arise in hilly areas due to uncontrolled speed and ignorance of steep turns by the driver. More often youth just like the pace and when they drive they forget everything around them like vehicles coming from another side, steep turn present in high altitude, sometimes visibility is poor due to pollution and other atmospheric hazards. And if accident occurs, there appears to be a trouble attaining the emergency service at certain locations in mountainous areas or it is difficult to spot the place of accident in time for lack of know-how. As an answer, the integration of internet of things (IoT) and android application technologies can reduce the quantity of accidents and improve the emergency service provider facility for the travellers travelling to hilly areas. In this paper, a clever app is designed that anyone (service provider or traveller) can download and register and get the emergency contact service provider nearby and it will monitor the distance between the car and other vehicle coming from other side using a distance sensor at the steep turns present in high altitudes. It also notifies the traveller with a push message having temperature and weather information so that driver can be warned to keep a track of pace and reduce it gradually.

IOT, Arduino, Sensor, Android, high altitud esteep turns

[1]. Patel K. Utilizing the Emergence of Android Smartphones for Public Welfare by Providing Advance Accident Detection and Remedy by 108 Ambulances. Int. J. Eng. Res. Technol. (IJERT) ; 2:1340–1342, 2013.
[2]. Aloul F., Zualkernan I., Abu-Salma R., Al-Ali H., Al-Merri M. iBump: Smartphone application to detect car accidents. Comput. Electrical Eng.; 43:66–75, 2014. doi: 10.1016/j.compeleceng.2015.03.003. [CrossRef] [Google Scholar]
[3]. Reddy M., Tulasi J. Accident detection depending on the vehicle position and vehicle theft tracking, reporting systems. Int. J. Sci. Eng. Technol. Res. ; 3:2359–2362, 2014. [Google Scholar]
[4] Prabha, R.Sunitha , R.Anitha “Automatic Vehicle Accident Detection and Messaging System Using GSM and GPS Modem” - International Journal of Advanced Research in Electrical, Electronics and Instrumentation Engineering, Vol. 3, Issue 7, July 2014.
[5]. Ali H.M., Alwan Z.S. Car Accident Detection and Notification System Using Smartphone. Int. J. Comput. Sci. Mob. Comput. ;4:620–635, 2015. [Google Scholar]
[6] Vijay Savania, Hardik Agravata and Dhrumil Patela “Alcohol Detection and Accident Prevention of Vehicle” - International Journal of Innovative and Emerging Research in Engineering Volume 2, Issue 3, 2015.
[7] A. Lage and J. C. Correa, “Weather station with cellular communication network,” in 2015 XVI Workshop on Information Processing and Control (RPIC), pp.15, Oct 2015.
[8] Elie Nasr, Elie Kfoury, David Khoury “An IoT Approach to Vehicle Accident Detection, Reporting, and Navigation” – 2016 IEEE International Multidisciplinary Conference on Engineering Technology.
[9] Md. Syedul Amin, Jubayer Jalil, M. B. I. Reaz “Accident Detection and Reporting System using GPS, GPRS and GSM Technology” – IEEE/OSAIAPR International Conference on Informatics, Electronics and Vision.
[10] Khot I., Jadhav M., Desai A., Bangar V. Go Safe: Android application for accident detection and notification. Int. Res. J. Eng. Technol.; 5: 4118–4122, 2018. [Google Scholar]
[11] A. Mohanapriya, N. Saranya, S.P. Kavya, R. Deepak , M. Mahitha ,G. Gobi, “Accident Prevention and Detection System Using Image Processing and IoT,” International Journal of Computer Sciences and Engineering, Vol.7, Issue.3, pp.456-460, 2019.
[12] Dwaipayan Saha, Indrani Mukherjee, Jesmin Roy, Sumanta Chatterjee, "Smart Safety and Accident Prevention System for Mountain Roads", International Journal of Computer Sciences and Engineering, Vol.08, Special Issue.01, pp.16-18, 2020.

Divya Ebenezer Nathaniel, Sonia Panesar, "IoT based Accident Prevention system on High Altitude integrated with Android Application enabled with emergency Service Facility," International Journal of Computer Sciences and Engineering, Vol.9, Issue.3, pp.23-27, 2021.

Aneka is a framework used to implement Platform as a service for cloud computing. Using Aneka features, any complex applications can be easily deployed with benefits either in public or private clouds. Aneka supports all types of cloud providers such as Amazon EC2, Windows Azure and GoGrid. The paper presents how aneka integrates with azure. This integration of both the platforms makes a powerful impact by running more number of compute instances parallel. The advanced features of Aneka platform such as its programming paradigms like programming models, scheduling, Management services, application execution services, accounting and pricing services and dynamic provisioning services. Finally we will determine how aneka works for Amazon EC2 and GoGrid.

Cloud computing, Platform as a service, Aneka, Azure, Cloud application development

[1] Christian Vecchiola, Xingchen Chu, and Rajkumar Buyya, “Aneka: A Software Platform for .NET-based Cloud Computing, High Speed and Large Scale Scientific Computing”, 267-295pp, W. Gentzsch, L. Grandinetti, G. Joubert (Eds.), ISBN: 978 -1-60750-073-5, IOS Press, Amsterdam, Netherlands, 2009.
[2] Rajkumar Buyya, Chee Shin Yeo, Srikumar Venugopal, James Broberg, and Ivona Brandic,” Cloud Computing and Emerging IT Platforms: Vision, Hype, and Reality for Delivering Computing as the 5th Utility, Future Generation Computer Systems”, 25(6):599-616, Elsevier Science, Amsterdam, The Netherlands, June 2009.
[3] David Chappell, “Introducing the Windows Azure Platform.” David Chappell & Associates, October 2010.
[4] Henry Li, Introducing Windows Azure, ISBN: 978-1-4302-2469-3, Apress, 2009.
[5] Sriram Krishnan, “Programming Windows Azure: Programming the Microsoft Cloud”, ISBN: 978-0-596-80197-7, O’REILLY, Sebastopol, CA, USA, May 2010.
[6] Dariusz Rafa? Augustyn and ?ukasz Warcha?, “Cloud Service Solving N-Body Problem Based on Windows Azure Platform”, Proceedings of the 17th Conference on Computer Networks, Communications in Computer and Information ScienceUstron, Poland, June 15-19, 2010.
[7] Wei Lu, Jared Jackson, and Roger Barga, AzureBlast:” A Case Study of Developing Science Applications on the Cloud”, Proceedings of the 19th ACM International Symposium on High Performance Distributed Computing, Chicago, Illinois, USA, June, 2010.
[8] Jie Li, Marty Humphrey, Deb Agarwal, Keith Jackson, Catharine van Ingen and Youngryel Ryu, eScience in the Cloud:” A MODIS Satellite Data Reprojection and Reduction Pipeline in the Windows Azure Platform”, Proceedings of the 24th 30 IEEE International Symposium on Parallel & Distributed Processing (IPDPS 2010), Atlanta, GA, USA, April, 2010.

S. Shabana, E. Susmitha, "A Survey on Aneka Cloud Application and Integration with Windows Azure," International Journal of Computer Sciences and Engineering, Vol.9, Issue.3, pp.28-33, 2021.

Diabetic retinopathy causes the life of eye decay considerably. There are stages associated with the DR. Early detection of DR could lead to the adverse affect of DR to be minimised. Techniques have been devised to tackle and identify the problems of DR at early stage. This paper presents the comprehensive review of techniques such as machine learning and deep learning, used for the purpose of detection of DR and also performs the comparative analysis of parameters used for the same. The proposed algorithm uses MSVM algorithm that discovers more patterns to detect disease accurately. The results will help in predicting quicker and more accurate disease so that it lead timely treatment of the patients.

Diabetic retinopathy, machine learning, deep learning, datasets

[1] P. Adarsh and D. Jeyakumari, “Multiclass svm-based automated diagnosis of diabetic retinopathy,” Int. Conf. Commun. Signal Process., pp. 206–210, 2013.
[2] R. Maher and M. Dhopeshwarkar, “Automatic detection Non-proliferative Diabetic Retinopathy using image processing techniques,” J. Eng. Res. Appl., vol. 6, no. 1, pp. 122–127, 2016.
[3] R. Gargeya and T. Leng, “Automated Identi fi cation of Diabetic Retinopathy Using Deep Learning,” Ophthalmology, pp. 1–8, 2017.
[4] S. M. Student, C. Landran, and G. Kaur, “Review on: Detection of Diabetic Retinopathy using SVM and MDA,” Int. J. Comput. Appl., vol. 117, no. 19, pp. 975–8887, 2015.
[5] I. Ntroduction, “Diabetic Retinopathy Classification using SVM Classifier,” ACM Comput. Surv., vol. 6, no. 7, pp. 7–11, 2017.
[6] S. Shetty, K. B. Kari, and J. A. Rathod, “Detection of Diabetic Retinopathy Using Support Vector Machine ( SVM ),” IEEE 17th Int. Conf. Parallel Distrib. Syst., vol. 23, no. 6, pp. 207–211, 2016.
[7] M. J. Paranjpe and P. M. N. Kakatkar, “Automated Diabetic Retinopathy Severity Classification using Support Vector Machine,” Int. J. Res. Sci. Technol., no. 3, pp. 86–91, 2013.
[8] V. V. Kumari, N. Suriyaharayananm, and C. T. Saranya, “Feature Extraction for Early Detection of Diabetic Retinopathy,” in 2010 International Conference on Recent Trends in Information, Telecommunication and Computing, pp. 359–361, 2010.
[9] V. Ramya, “SVM Based Detection for Diabetic Retinopathy,” Iccad, vol. V, no. I, pp. 11–13, 2018.
[10] R. Gargeya and T. Leng, “Automated Identi fi cation of Diabetic Retinopathy Using Deep Learning,” Ophthalmology, pp. 1–8, 2017.
[11] M. S. Haleem, L. Han, J. Van Hemert, B. Li, and A. Fleming, “Retinal Area Detector from Scanning Laser Ophthalmoscope ( SLO ) Images for Diagnosing Retinal Diseases,” vol. 2194, no. MARCH, 2014.
[12] S. S. Rubini and A. Kunthavai, “Diabetic retinopathy detection based on eigenvalues of the hessian matrix,” Procedia Comput. Sci., vol. 47, no. C, pp. 311–318, 2014.
[13] S. M. Student, C. Landran, and G. Kaur, “Review on: Detection of Diabetic Retinopathy using SVM and MDA,” Int. J. Comput. Appl., vol. 117, no. 19, pp. 975–8887, 2015.
[14] V. Ramya, “SVM Based Detection for Diabetic Retinopathy,” IEEE, vol. V, no. I, pp. 11–13, 2018.
[15] A. Mukhlas and A. Ahmad, “Data Mining Technique?: Towards Supporting Local Co-operative Society in Customer Profiling , Market Analysis and Prototype Construction,” no. May, pp. 109–114, 2016.
[16] R. D. Q. D. Lqglu, D. Lv, D. Frqvlghuhg, I. R. U. Wkh, D. Dqdo, S. Lq, W. K. H. Olwhudwxuh, V. D. U. Pdq, and G. Lq, “A Review of Data Mining and Solar Power Prediction,” vol. 0, pp. 3–7, 2016.
[17] M. Bakon, I. Oliveira, D. Perissin, J. J. Sousa, and J. Papco, “A Data Mining Approach for Multivariate Outlier Detection in Postprocessing of Multitemporal InSAR Results,” vol. 1, pp. 1–8, 2017.
[18] E. E. Brown, “Improving Privacy Preserving Methods to Enhance Data Mining for Correlation Research,” pp. 3–6, 2017.
[19] K. Ahuja, “Data Elimination Based Technique for Mining Frequent Closed Item Set,” pp. 1–4.

Kaveri Devi, Arshdeep Kaur, "MSVM Based Technique Used To Detect Diabetic Retinopathy at Early Stage," International Journal of Computer Sciences and Engineering, Vol.9, Issue.3, pp.34-40, 2021.

Classification is a crucial aspect of machine learning. Multi class Classification has an important role in the classification. It is an on-going research in machine leaning field. In this paper we will come to know about multi class classification algorithms. We will see different algorithms like Decision Tree, SVM, Random Forest, Naive Bayes etc. This is clear cut representation of the basic advantages and limitations regarding different types of classification algorithms and the various measures for implementing results. Nowadays, these types of algorithms are playing a substantial role among different program sequences so as to improve the quality of classification.

Classification, Decision tree, Naive Bayes, SVM

[1]Ms. Prajakta C. Chaudhari, Prof. Dr. S. S. Sane, “Review on Multilabel Classification Algorithms”, IJSRD - International Journal for Scientific Research & Development| Vol. 3, Issue 11, 2016 | ISSN (online): 2321-0613
[2]. Ruchida Sonar, Dr. P.R. Deshmukh “Multiclass Classification: A Review on International Journal of Computer Science and Mobile Computing” , Vol.3 Issue.4, pg. 65-69. April- 2014.
[3]. G. Malik, M. Tarique, “ Machine Learning Techniques For Multi-class Classification International Journal of Advancements in Research & Technology”, Volume 3, Issue 2, February-2014 6ISSN 2278-7763
[4]. Radhika Kotecha, Vijay Ukani, Sanjay Garg.“An empirical analysis of multiclass classification techniques in data mining”, IEEE conference on analysis of multiclass classification techniques, 2375-1282.
[5]. Mahendra Sahare, Hitesh Gupta, “A Review of Multi-Class Classification for Imbalanced Data”, International Journal of Advanced Computer Research (ISSN (print): 2249-7277 ISSN (online): 2277-7970) Volume-2 Number-3 Issue-5, 160, September-2012.
[6]. Bhaskar N. Patel, Satish G. Prajapati and Dr. Kamaljit I. Lakhtaria, “ Efficient Classification of Data Using Decision Tree” Bonfring International Journal of Data Mining, Vol. 2, No. 1, March 2012.
[7]. Mr. Brijain R Patel, Mr. Kushik K Rana , “A Survey on Decision Tree Algorithm”, | Volume 2, Issue 1
[8] Eesha Goel , Er. Abhilasha, “Random Forest: A Review: International Journal of Advanced Research in Computer Science and Software Engineering” , Volume 7, Issue 1, January 2017.
[9] Dr.R. Saravana Kumar2, “A REVIEW OF MULTI-CLASS CLASSIFICATION ALGORITHMS”, Ph.D. Thesis, Rochester Institute of Technology (RIT Scholar Work), (2017) March.
[10] L. Breiman, ?Random Forests,? Machine Learning, vol. 45, no. 1, pp. 5–32, 2001

Johnsymol Joy, Rakhi Krishnan, Ziyad Nazeer, "Different Types of Multi Class Classification Algorithms: A Study," International Journal of Computer Sciences and Engineering, Vol.9, Issue.3, pp.41-44, 2021.

The wireless sensor networks (WSN) are said to be one of the popular networks in using all-inclusive applications like lots of applications in environment monitoring, military applications, health care monitoring, habitat monitoring, etc. .These networks are structured in many or more number of sensor nodes. The Deployment of nodes in these networks are not secure which may cause to security attacks. In this paper, different types of attacks and network layer attacks are discussed in wireless sensor networks and how to enhance and detect the attacks from the WSN by using some of the methods to resolve the problem.

Wireless Sensor Networks,Attacks,Network Layer

[1] Chelli P, “ Security Issues in Wireless Sensor Networks: Attacks and Countermeasures,” Proceedings of the World Congress on Engineering 2015, Vol. 1, WCE 2015, July 13 3, 2015, London, U.K.
[2] Kanchan Kaushal and Taranvir Kaur, “A Survey on Attacks of WSN and their Security Mechanisms,” International Journal of Computer Applications, Volume 118, No. 18, May 2015.
[3] Sahabul Alam and Debashis De , “ Analysis of security threats in wireless sensor network,” International Journal of Wireless & Mobile Networks (IJWMN), Vol. 6, No. 2, April 2014.
[4] Naser Alajmi , “ Wireless Sensor Networks Attacks and Solutions,” (IJCSIS) International Journal of Computer Science and Information Security, Vol. 12, No. 7, July 2014.
[5] J. Steffi Agino Priyanka, S. Tephillah and A . M. Balamurugan, “Attacks and countermeasures in WSN,” International Journal of Electronics & Communication (IIJEC), Volume 2, Issue 1, January 2014, ISSN 2321-5984.
[6] Jaydip Sen , A Survey on Wireless Sensor Network Security, Vol. 1,No. 2, August 2009, International Journal of Communication Networks and Information Security (IJCNIS)
[7] G.Anand, Dr.H.G.Chandrakanth, Dr.M.N.Giriprasad, SECURITY THREATS & ISSUES IN WIRELESS SENSOR NETWORKS International Journal of Engineering Research and Applications(IJERA) ISSN: 2248-9622 www.ijera.com Vol. 2, Issue 1,Jan-Feb2012,
[8] Manju Gupta and C. Ram Gupta, Security Issues In Wireless Sensor Network, Vol. 2, No. 2, pp. 355-358, July-December 2011, IJCSC.
[9] Priyanka K. Shah and Kajal V. Shukla Secure Data Aggregation Issues in Wireless Sensor Network: A Survey, Journal of Information and Communication Technologies, ISSN 2047-3168, Volume 2, Issue 1, January 2012.
[10] Modares, H, Kuala Lumpur Salleh, R. Moravejosharieh. Overview of Security Issues in Wireless Sensor Networks Computational Intelligence, Modelling and Simulation (CIMSiM), 2011 Third International Conference on 20-22 Sept. 2011.
[11] Rajeshwar Singh1, Singh D.K and Lalan Kumar, A review on security issues in wireless sensor network, ISSN: 0976-8742 & EISSN:0976-8750, Vol. 1, Issue 1, PP-01-07, 2010, JISCE.
[12] T.Kavitha, D.Sridharan Security Vulnerabilities In Wireless Sensor Networks: A Survey, Journal of Information Assurance and Security 5, 031-044, 2010
[13] M. Gruteser, G. Schelle, A. Jain, R. Han, and D. Grunwald, “Privacy-aware location sensor networks”, In Proceedings of the 9th USENIX Workshop on Hot Topics in Operating Systems, (HotOS IX), 2003.
[14] A. Perrig, J. Stankovic, and D. Wagner, “Security in wireless sensor networks”, Communications of ACM , Vol 47, No. 6, pp. 53-57, 2004
[15] E. Shi and A. Perrig, “Designing secure sensor networks”, Wireless Communication Magazine, Vol. 11, No. 6, pp. 38-43, December 2004.
[16] Jaspreet Kaur, Tavleen Kaur”A Comparative Study of Techniques Used in Detection and Prevention of Black Hole Attack inWireless Sensor Networks”,ijreset, Vol. 2 Issue III, March 2014.
[17] Yunzhou Zhang1,2, Xiaohua Zhang1, Zeyu Wang1, Honglei Liu College of Information Science and Engineering , Shenyang SIASUNG Robot & Automation Northeastern University Company, Ltd. Shenyang, P.R.China “Virtual Edge Based Coverage Hole Detection Algorithm in Wireless Sensor Networks” IEEE 2013.
[18] Mohammad Wazid, Student Member, IEEE, Avita Katal, Student Member, IEEE and R H Goudar “TBESP Algorithm for Wireless Sensor Network under Black hole Attack” 2013.
[19] Nishant Sharma1, Upinderpal Singh” Various Approaches to Detect Wormhole Attack in Wireless Sensor Networks” IJCSMC, Vol. 3, Issue. 2, pg.29 – 33, February 2014.
[20] Y. C. Hu, A. Perrig and D. B. Johnson, "Packet Leashes: A Defense against Wormhole Attacks in Wireless Networks", in 22nd Annual Joint Conference of the IEEE Computer and Communications Societies (INFOCOM), pp. 1976-1986, 2003.
[21] Vinay Soni, Pratik Modi, Vishvash Chaudhri,” Detecting Sinkhole Attack in Wireless Sensor Network”, Volume 2, Issue 2, February 2013.
[22] D.Sheela, Naveen kumar. C and Dr. G.Mahadevan; “A Non Cryptographic Method of Sinkhole Attack Detection in Wireless Sensor Networks” IEEE-International Conference on Recent Trends in Information Technology,ICRTIT, pp. 527-532, 2011.
[23] S.Sharmila and Dr G Umamaheswari; “Detection of sinkhole Attack in Wireless Sensor Networks using Message Digest Algorithms” International Conference on Process Automation, Control and Computing (PACC), pp. 1-6, 2011.
[24] Abirami.K, Santhi.B,” Sybil attack in Wireless Sensor Network”,IJET.
[25] Yingying chen, “Detecting and localizing identity-based attacks in wireless sensor network”,IEEE Journal, June 2010.

Parvathy K., S. Rajalakshmi, "A Review on Network Layer Attacks in Wireless Sensor Networks," International Journal of Computer Sciences and Engineering, Vol.9, Issue.3, pp.45-48, 2021.

The Data Centric Security (DCS) approach is talked about in detail. This approach is the central one utilized as a part of this postulation for upgrading cloud computing security and privacy. The Paper begins with looking into and ordering conceivable security solutions, in light of the DCS ideas in the cloud computing model. 3, at that point expands on these, to shape the applied structure for DCS implementations proposed in this exploration. [6] The normal advantages of applying the DCS way to deal with the cloud computing environments are talked about in. The extent of the application of the DCS way to deal with the cloud computing model for this proposal is recognized in the fundamental security necessities of applying the DCS way to deal with this extension are additionally cleared up. In addition, the accessible innovations that can be utilized to accomplish these necessities are looked into in that section. In light of such audits, appropriateness of these advancements is evaluated to determine a novel solution, which is among the fundamental contributions of this examination. Finally, the outline of this part is quickly introduced. [9]

Communication network, 3G, mobile core network, Broadband Router, HNB

[1] J. Somorovsky, M. Heiderich, M. Jensen, J. Schwenk, N. Gruschka, and L. Lo Iacono, ,(2011)“All your clouds are belong to us: security analysis of cloud management interfaces,” in Proceedings of the 3rd ACM workshop on Cloud computing security workshop, CCSW ’11, (New York, NY, USA), pp. 3–14, ACM, 2011.
[2] T. Ristenpart, E. Tromer, H. Shacham, and S. Savage, (2009) “Hey, you, get off of my cloud: exploring information leakage in third-party compute clouds,” in Proceedings of the 16th ACM conference on Computer and communications security, CCS ’09, (New York, NY, USA), pp. 199–212, ACM, 2009.
[3] D. Molnar and S. Schechter, (2010) “Self hosting vs . cloud hosting, : Accounting for the security impact of hosting in the cloud,” in Workshop of the economics of cloud security, pp. 1–18, 2010.
[4] Y. Chen, V. Paxson, and R. Katz, (2010) “The hybrex model for confidentiality and privacy in cloud computing,” Technical Report UCB/EECS-2010-5, EECS Department, University of California, Berkeley, January 2010.
[5] N. Santos, K. P. Gummadi, and R. Rodrigues, ( 20009) “Towards trusted cloud computing,” in Proceedings of the 2009 conference on Hot topics in cloud computing, HotCloud’09, (Berkeley, CA, USA), USENIX Association, 2009.
[6] D. Kuhlmann, R. Landfermann, H. V. Ramasamy, M. Schunter, G. Ramunno, and D. Vernizzi, (2006) “An open trusted computing architecture – secure virtual machines enabling user-defined policy enforcement,” Work, pp. 1–14, 2006.
[7] N. Pohlmann and H. Reimer, (2008) “Trusted computing - eine einfA˜ 1 4 hrung,” in Trusted Computing (N. Pohlmann and H. Reimer, eds.), pp. 3–12, Vieweg+Teubner, 2008. 10.1007/978-3-8348-9452- 6 1.
[8] M. Nauman, S. Khan, X. Zhang, and J.-P. Seifert,(2010) “Beyond kernel-level integrity measurement: Enabling remote attestation for the android platform,” in Trust and Trustworthy Computing (A. Acquisti, S. Smith, and A.-R. Sadeghi, eds.), vol. 6101 of Lecture Notes in Computer Science, pp. 1–15, Springer Berlin / Heidelberg, 2010. 10.1007/978-3-642-13869-01.
[9] I. Bente, G. Dreo, B. Hellmann, S. Heuser, J. Vieweg, J. von Helden, and J. Westhuis,(2011) “Towards permission-based attestation for the android platform,” in Trust and Trustworthy Computing (J. McCune, B. Balacheff, A. Perrig, A.-R. Sadeghi, A. Sasse, and Y. Beres, eds.), vol. 6740 of Lecture Notes in Computer Science, pp. 108–115, Springer Berlin / Heidelberg, 2011. 10.1007/978-3-642- 21599-58.
[10] R. Neisse, D. Holling, and A. Pretschner, (2011) “Implementing trust in cloud infrastructures,” in Cluster, Cloud and Grid Computing (CCGrid), 2011 11th IEEE/ACM International Symposium on, pp. 524 –533, may 2011.
[11] B. Parno,(2008) “Bootstrapping trust in a ”trusted” platform,” in Proceedings of the 3rd conference on Hot topics in security, (Berkeley, CA, USA), pp. 9:1–9:6, USENIX Association, 2008.

Thade Lakshmi Devi, S. Krishna Mohan Rao, "Data Centric Security Approach For Cloud Computing," International Journal of Computer Sciences and Engineering, Vol.9, Issue.3, pp.49-52, 2021.