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Epilepsy is neurological disorders affecting the quality of life by making people worry about future seizure events. Many of other seizure prediction research shows that some seizure prediction results are still need better and reliable prediction algorithm for helping to develop seizure prediction system. Electrocephalography (EEG) can be use for seizure analysis but using better algorithm we can create system that can give an alarm before seizure occur so patient or doctor can take appropriate action to overcome from the risk. In this study, few methods are compared to find better accuracy to find better algorithm. Using well mannered algorithm we can create automated seizure prediction system. Here, algorithms such as SVM (support vector machine), RA (Regression Analysis), and ANN (Artificial Neural Network) are compared. In this paper, we tried to compare the seizure prediction methods for getting more accurate results for the future work of predicting seizure type.

Support vector machine (SVM), artificial neural network (ANN), EEG, EPILAB Tool, Seizure prediction

[1]Cao Xiao, Shouyi Wang,“An Adaptive Pattern Learning Framework to Personalize Online Seizure Prediction”, IEEE Transaction on Big Data, Vol. 10, Issue.10, pp.1-13, 2016.
[2] Chisci. L, Mayino. A Perferi. G, Sciandrone. M,Anile. C, Colicchio. G, Fuggetta. F,“Real-time Epileptic Seizure Prediction Using AR models and Support Vector Machinces”, IEEE transaction on Biomedical Engineering, Vol. 57, Issue. 5 pp. 1-9, 2010
[3] Feldwisch Drentrup. H, Schelter. B, Jachan. M, Nawrath. J, Timmer. J,Schulze-Bonhage. A, “Joining the Benefits: Combining Epileptic Seizure Prediction Methods”, International Epilepsia, Vol. 8, Issue. 51, pp 1-9, 2010
[4] L. Iasemidis, D. Shiau , W. Chaoyalitwongse, P. Sackellares , Pardalos P., J. Pricipe, P. Carney, Prasad A., B. Veeramani, K. Tsakalis, “Adaptive Epileptic Seizure Prediction System”, IEEE Tramsactions on Biomedical Engineering, Vol. 5, Issue. 50, pp.1-12, 2003
[5] L. Iasemidis, D. Shiau, P. Pardalos, W. Chaovalitwongse, K. Narayanana, A. Prasada, K. Tsakalis, P. Carney, J. Sackellares, “Long-term prospective online real-time seizure prediction”, Clinical Neurophysiology, Vol. 3, Issue. 11, 2005.
[6] P. Rajdev, M. Ward, J. Rickus, R. Worth, P. Irazoqui. “Realtime seizure prediction from local field potentials using an adaptive Wiener algorithm” Computers in biology and medicine, Vol. 1, Issue. 40, pp.1-7, 2010
[7] J.D. Bronzino,“Principles of electroencephalography,” The Biomedical Engineering Handbook, Vol. 3, Issue. 14, pp. 1-5, 2006
[8] J. Muthuswamy, N. V. Thakor,“Spectral analysis methods for neurological signals,” J. Neurosci. Methods, vol. 83, pp. 1–14, 1998
[9] N. Hazarika, J. Z. Chen, A. C. Tsoi, A. Sergejew, “Classification of EEG signals using the wavelet transform,” Signal Process., vol. 59, pp. 61–72, 1997.
[10] S. Murali, V. V. Kulish, “Mdeling of Evoked Potential of Electroencephalograms: Anoverview”, Digital signal Process, Vol. 17, pp. 665-674, 2007.
[11] H. Alnashah, Y. Assaf, J. Paul, N. Thakor,“EEG signal Modeling Using Adaptive Markov Process amplitude”, IEEE Transaction Biomedical Engineering, Vol. 51, Issue. 5, pp. 744-751, 2004
[12] A. Y. Kaplan, A. A. Fingelkurts, S. V. Borisoy, B. S. Darkhoysky, “Non-stationary Nature of the Brian Activity as Revealed By EEG/ MEG: Methodological practical and Conceptual Challenges”, Signal Process., Vol. 85, pp. 2190-2212, 2005.
[13]M. F. Harrison, M. G. Frei, I. Osorio, “accumulated energy revisited”, Clinical Neurophysiology, Vol. 3, pp. 527-531, 2005.
[14] F. Mormann, T. Kreuz, C. Rieke, R. G. Andrzejak, A. Kraskov, P. David, C. E. Elger, K. Lehnertz, “Predictability of epileptic seizures”, Clinical Neurophysiology, Vol. 3, pp. 569-587, 2005.
[15] Leon D. Iasemidis, Deng-Shan Shiau, Wanpracha chaovalitwonge, J. Chris Sackellares, Panos M. Pardalos, Jose C., Paul R. Carney, Awadhesh Parasd, Balaji Veermani, Konstantinos Tsakalis, “Adaptive Epileptic Seizure Prediction System”, Vol. 12, pp.1-9, 2005.
[16] K. A. Helini Kulasuriya, M.U.S. Perera, “Forecasting Epileptic Seizure Using EEG Signals, Wavelet transform and Artificial Neural Network”, IEEE, Vol. 22, Issue. 51, pp. 1-8, 2011
[17] Morteza Behnam, Hossein Pourghassem,“Power Complexity Feature-Based Seizure Prediction Using DNN and Firefly BPNN Optimization Algorithm”, IEEE, Vol. 15, PP.1-13, 2015.

Shivangini Patel, Bhavesh Tanawala, Kirti Sharma, "Comparative Pattern Learning Framework for Seizure Prediction," International Journal of Computer Sciences and Engineering, Vol.7, Issue.4, pp.775-779, 2019.

Recently the wireless body area network (WBAN) is an emerging technology in various network applications. A WBAN is composed by implanting sensor nodes on human body and are used to sense the thermal effects on human body. The architecture of WBAN uses some communication technologies to send or receive data among nodes and the communication occur either between node to node, nodes to cluster head or cluster head to sink. This paper presents the survey on different techniques used to implement WBAN and the role in different network applications. The purpose of this paper is to study problems accomplished during the communication and data sensing (power consumption, lifetime, path loss, attenuation).

WBAN, Gateway node, cluster Head, Energy consumption, lifetime

[1] Nitu Choudhary1*, Deepak Kumar 2, “Multilevel Multi-Hop Technique for more than one Forward Node to increase the Stability of Wireless Body Sensor Networks”, International Journal of Computer Sciences and Engineering, Volume-5, Issue-8, 2019.
[2] M. Devapriya1, R. Sudha2, ” A Survey on Wireless Body Sensor Networks for Health care monitoring”, International Journal of Science and Research (IJSR), Volume 3 Issue 9, September 2014.
[3] Arti Sangwan et al,”Wireless Body Sensor Netwroks: a Review”, researchgate, September 2015.
[4] Q. Nadeem et et al, “SIMPLE: Stable increased throughput- multihop protocol for link efficiency in wireless body area networks”, vol1, 2013.
[5] Mehdi Effatparvar1, Mehdi Dehghan2*, Amir Masoud Rahmani1,” Lifetime maximization in wireless body area sensor networks”, Biomedical Research 2017Volume 28 Issue 22, 2017.
[6] Sriyanjana Adhikary et al, “ A New Routing Protocol For Wban To Enhance Energy Consumption And Network Lifetime”.
[7] Sakshi Mehta et al,” Improved Multi-Hop Routing Protocol in Wireless Body Area Networks”, International Journal of Advanced Research in Computer Science and Software Engineering, Volume 5, Issue 7, July 2015.
[8] Xiaochen Lai et al,” A Survey of Body Sensor Networks”,mdpi, 2013.
[9] Garth V. Crosby et al, “Wireless Body Area Networks for Healthcare: A Survey”, International Journal of Ad hoc, Sensor & Ubiquitous Computing (IJASUC) Vol.3, No.3, June 2012
[10] Aslam, M., Nadeem Javaid, A. Rahim, U. Nazir, Ayesha Bibi, and Z. A. Khan. "Survey of extended LEACH-Based clustering routing protocols for wireless sensor networks." IEEE 9th International Conference on Embedded Software and Systems (HPCC-ICESS), pp. 1232-1238, 2012.
[11] Li, Hongjuan, Kai Lin, and Keqiu Li. "Energy-efficient and high-accuracy secure data aggregation in wireless sensor networks." Computer Communications, 2011.
[12] Professor Guang-Zhong Yang, “Body Sensor Networks –Research Challenges and Applications” Imperial College London.

Jatinder Singh, Navjeet Saini, Sandeep Kaur, "Enhancement Over Energy Consumption And Network Lifetime Of Wireless Body Area Network: A Review," International Journal of Computer Sciences and Engineering, Vol.7, Issue.4, pp.780-782, 2019.

Cloud computing and its components make use of their combine efforts to process input request to any cloud architecture. Cloud components such as virtual machine Vm, Cloud Data center DC, Broker request Br and configure a cloud scenario. Cloud computing component make use of request, resource and its utilization analysis to process any of the algorithm. In this paper, An Advance Algorithm named VMERRU (Virtual machine energy resource request utilization) is proposed. The approach also make use of utilizing monitoring of energy , resource usage count, input request requirement and matching requirement of assigning DC, VM to it. Thus an optimal request handling algorithm with parallel computation is proposed. An implementation is performed using CloudSim API cloud analyst simulator and further computation shows the efficiency of proposed algorithm.

Resource Optimization, Cloud Sim, Data Sharing, Virtualization, VMERRU, Parallel Computing, Request Analysis, Cloud Component Scheduling

[1] Bharathy, S. D., & Ramesh, T. (2014). “Securing Data Stored in Clouds Using Privacy Preserving Authenticated Access Control”. Proc. IJCSMC, 3(4), 1069-1074.
[2]. Binbusayyis, A., & Zhang, N. (2015, June). “Decentralized attributebased encryption scheme with scalable revocation for sharing data in public cloud servers”. In Cloud Technologies and Applications (CloudTech), 2015 International Conference on (pp. 1-8). IEEE.
[3]. Chen, J., & Ma, H. (2014, June). “Efficient decentralized attributebased access control for cloud storage with user revocation”. In 2014 IEEE International Conference on Communications (ICC) (pp. 3782- 3787). IEEE.
[4]. Ganeshkumar, M., & Chow, S. S. (2009, November). “Improving privacy and security in multi-authority attribute-based encryption”. In Proceedings of the 16th ACM conference on Computer and communications security (pp. 121-130). ACM.
[5]. Maharajanavar, S. “Anonymous Authentication of Decentralized Access Control of Data Stored in Cloud”. International Journal on Recent and Innovation Trends in Computing and Communication ISSN, 2321- 8169.
[6]. Ruj, S., Stojmenovic, M., & Nayak, A. (2014). “Decentralized access control with anonymous authentication of data stored in clouds”. IEEE transactions on parallel and distributed systems, 25(2), 384-394.
[7]. Vijayalakshmi, A., & Arunapriya, R. (2014). “Authentication of data storage using decentralized access control in clouds”. Journal of Global Research in Computer Science, 5(9).
[8]. Wong, C. K., Gouda, M., & Lam, S. S. (2000). “Secure group communications using key graphs”. IEEE/ACM transactions on networking, 8(1), 16-30.
[9]. P.Shanthi Bala,” Intensification of Educational Cloud Computing and Crisis of Data Security in Public Clouds”,IJCSE Vol. 02, No. 03, 2010, 741-745.
[10]. Sajjan R.S.1*, Vijay Ghorpade2 and Vishvajit Dalimbkar3, “A Survey Paper on Data security in Cloud Computing”, Volume-4, Special Issue-4, June 2016 E-ISSN: 2347-2693.
[11]. Bellovin S. M. and Merritt M. 1992. “Encrypted key exchange Password-based protocols secure against dictionary attacks”. In Research in Security and Privacy. pp. 72-84.
[12]. 1F. Antony Xavier Bronson, S.P. Rajagopalan and ,V. Sai Shanmuga Raja, “A Dynamic Memory Allocation Strategy for Virtual Machines in Cloud Platform”, Volume 119 No. 15 2018, 1423-1444.
[13]. H. T. Dinh, C. Lee, D. Niyato, and P.Wang, “A survey of mobile cloud computing architecture, applications, and approaches”, Wireless communications and Mobile Computing, vol. 13, no. 18, pp. 1587–1611, 2013.
[14]. H. Qi and A. Gani, “Research on mobile cloud computing review, trend and perspective”, Digital Information and Communication Technology and its Applications (DICTAP), Bangkok, Thailand, 2012, pp.195–202.

Mukesh Kumar, Mukesh Kumar, Devendra Singh Rathore, "A Survey over Cloud Scheduling Algorithm in Cloud Computing," International Journal of Computer Sciences and Engineering, Vol.7, Issue.4, pp.783-787, 2019.

Classifying traffic in a residential area is always a challenging task in the high-speed network. The analysis and quality of service require more specific network control, which generates network traffic. The existing network has many disadvantages because of that the network was unable to detect the traffic in a network. This survey is based on the machine learning algorithm which will work accordingly to the generated traffic information that will be get from the client for that the boosted classifier contain high accuracy has been generated. So, this network will be used for the classification of the applications like- FTP, Skype, TCP etc. , This type of paper demonstrates that the Machine Learning Algorithm and the use of this algorithm are used to classify network traffic.

Traffic Classification, Computer Networks, C5.0, Machine Learning Algorithms (MLAs), Performance Monitoring

[1]. A. Vlăduţu, D. Comăneci, and C. Dobre, ‘‘Internet traffic classification based on flows’ statistical properties with machine learning,’’ Int. J. Netw. Manage., vol. 27, no. 3, p. e1929, May 2017.
[2]. Y. Yu, J. Long, and Z. Cai, ‘‘Session-based network intrusion detection using a deep learning architecture,’’ in Modeling Decisions for Artificial Intelligence. V. Torra, Y. Narukawa, A. Honda, and S. Inoue, Eds. Cham, Switzerland: Springer, 2017, pp. 144–155.
[3]. D. J. Weller-Fahy, B. J. Borghetti, and A. A. Sodemann, ‘‘A survey of distance and similarity measures used within network intrusion anomaly detection,’’ IEEE Commun. Surveys Tuts., vol. 17, no. 1, pp. 70–91, Jan. 2015.
[4]. M. Ahmed, A. N. Mahmood, and J. Hu, ‘‘A survey of network anomaly detection techniques,’’ J. Netw. Comput. Appl., vol. 60, pp. 19–31, Jan. 2016.
[5]. Y. Li, R. Yu, C. Shahabi, and Y. Liu, “Diffusion convolutional recurrent neural network: Data-driven traffic forecasting,” 2018.
[6]. R. Alvizu, S. Troia, G. Maier, and A. Pattavina, “Matheuristic with machine-learning-based prediction for software-defined mobile metrocore networks,” Journal of Optical Communications and Networking, vol. 9, no. 9, pp. D19–D30, 2017.
[7]. A. Azzouni and et al, “Neutm: A neural network-based framework for traffic matrix prediction in sdn,” CoRR, vol. abs/1710.06799, 2017.
[8]. Y. Liu and et al, “Short-term traffic flow prediction with conv-lstm,” in Wireless Communications and Signal Processing (WCSP), 2017. IEEE, 2017, pp. 1–6.
[9]. M. Wang, Y. Cui, X. Wang, S. Xiao, and J. Jiang, “Machine Learning for Networking: Workflow, Advances and Opportunities,” IEEE Network, vol. 32, no. 2, pp. 92–99, Mar. 2018.
[10]. “Fault Tolerance in TCAM-limited Software Defined Networks,” Computer Networks, vol. 116, no. C, pp. 47–62, Apr. 2017.
[11]. D. Kreutz, F. M. Ramos, P. E. Verissimo, C. E. Rothenberg, S. Azodolmolky, and S. Uhlig, “Software-defined Networking: A Comprehensive Survey,” Proceedings of the IEEE, vol. 103, no. 1, pp. 14–76, 2015
[12]. Y.-D. Lin, H.-Y. Teng, C.-R. Hsu, C.-C. Liao, and Y.-C. Lai, “Fast Failover and Switchover for Link Failures and Congestion in Software Defined Networks,” in IEEE ICC 2016, KL, Malaysia, May 2016.
[13]. I. Butun and S. Morgera, “A Survey of Intrusion Detection Systems in Wireless Sensor Networks,” IEEE, 2014.
[14]. Janice Ca˜nedo, Anthony Skjellum, “Using machine learning to secure IoT systems,” IEEE International Conference on Privacy, Security and Trust (PST), 2016.
[15]. Information on See5/C5.0 - RuleQuest Research Data Mining Tools, 2011. [Online]. Accessible: http://www.rulequest.com/see5-info.html
[16]. Anshul Vishwakarma1* , Amit Khare2, “Vehicle Detection and Tracking for Traffic Surveillance Applications: A Review Paper”, Vol.-6, Issue-7, July 2018 E-ISSN: 2347-2693.
[17]. K. Thyagarajan 1* , N. Vaishnavi 2, “Performance Study on Malicious Program Prediction Using Classification Techniques”, Vol.-6, Issue-5, May 2018 E-ISSN: 2347-2693.

Amit Kumar, Daya Shankar Pandey, Varsha Namdeo, "A Survey on: Finding Network Traffic Classification Methods based on C5.0 Machine Learning Algorithm," International Journal of Computer Sciences and Engineering, Vol.7, Issue.4, pp.788-791, 2019.

For Big Data processing, analyzing and storing Apache Hadoop is widely adopted as a framework. Hadoop facilitates processing through MapReduce, analyzing using Apache Spark and storage using the Hadoop Distributed File System (HDFS). Hadoop is popular due to its wide applicability and easy to run on commodity hardware functionality. But the installation of Hadoop on single and distributed cluster always remains a headache for the new developers and researchers. In this paper, we present the step by step process to run Hadoop on a single node and also explain how it can be used as a distributed cluster. We have implemented and tested the Hadoop framework using single node and cluster using ten (10) nodes. We have also explained primary keywords to understand the concept of Hadoop.

Apache Hadoop, Hadoop Cluster Configuration, Hadoop Testing, Hadoop Implementation

[1] Forbes Welcome, https://www.forbes.com/sites/gilpress/2014/09/03/12-big-data-definitions-whats-yours/#487d104413ae (Access on March 30, 2019)
[2] Hadoop, http://hadoop.apache.org (Access on March 30, 2019)
[3] Dean, J. and Ghemawat, S., MapReduce: simplified data processing on large clusters. Communications of the ACM, 51(1), pp.107-113 (2008).
[4] Shah A., Padole M. (2019) Performance Analysis of Scheduling Algorithms in Apache Hadoop. In: Shukla R., Agrawal J., Sharma S., Singh Tomer G. (eds) Data, Engineering and Applications. Springer, Singapore
[5] Shvachko, K., Kuang, H., Radia, S. and Chansler, R., 2010, May. The hadoop distributed file system. In MSST (Vol. 10, pp. 1-10).
[6] Vavilapalli, V.K., Murthy, A.C., Douglas, C., Agarwal, S., Konar, M., Evans, R., Graves, T., Lowe, J., Shah, H., Seth, S. and Saha, B., (2013). Apache hadoop yarn: Yet another resource negotiator. In Proceedings of the 4th annual Symposium on Cloud Computing (p.5). ACM.

Ankit Shah, Mamta Padole, "Apache Hadoop: A Guide for Cluster Configuration & Testing," International Journal of Computer Sciences and Engineering, Vol.7, Issue.4, pp.792-796, 2019.

This paper presents the brief study of clustering and data transfer techniques in wireless sensor network. The cluster head selection in clusters depends upon the node energy and probability which is the main concern for enhancement of the life time of a network. Different clustering protocols are designed for better enhancement of the network and reliability of the nodes. This paper gives the brief review on clustering techniques in wireless sensor network.

Wireless Sensor Network, clustering, cluster head, LEACH

[1] Sonia 1*, Deepak Kumar2,” Load Dividing and Reclustering technique to Improve the Reliability of Data In a Network”, International Journal of Computer Sciences and Engineering, Volume-5, Issue-8, 2019.
[2] Ashim Kumar Ghosh1, Anupam Kumar Bairagi2, Dr. M. Abul Kashem3, Md. Rezwan-ul-Islam1, A J M Asraf Uddin1, “ Energy Efficient Zone Division Multihop Hierarchical Clustering Algorithm for Load Balancing in Wireless Sensor Network”, (IJACSA) International Journal of Advanced Computer Science and Applications, Vol. 2, No. 12, December 2011.
[3] I.F. Akyildiz, W. Su*, Y. Sankarasubramaniam, E. Cayirci,” Wireless sensor networks: a survey”, Elsevier, Computer Networks 38, 393–422, 2002.
[4] M. Shanmukhi, 2G. Nagasatish, “LOAD BALANCING USING CLUSTERING IN WSN WITH FUZZY LOGIC TECHNIQUES”, International Journal of Pure and Applied Mathematics Volume 119 No. 14 2018, 61-69.
[5] Shankar Sachdev1, Laxman Yalmar2, Nilesh Gaykhe3, “ Energy Efficient Cluster Based Routing Algorithm in Wireless Sensor Networks”, IJESC, vol. 6 issue 3, 2016.
[6] 1Jaswant Singh Raghuwanshi,2Neelesh Gupta,3Neetu Sharma, ” Energy Efficient Data Communication Approach In Wireless Sensor Networks”, International Journal of Advanced Smart Sensor Network Systems (IJASSN), Vol 4, No.3, July 2014.
[7] Jing, Yang, Li Zetao, and Lin Yi. "An improved routing algorithm based on LEACH for wireless sensor networks." Control and Decision Conference (CCDC), 2013 25th Chinese.IEEE, 2013.
[8] Beiranvand, Zahra, Ahmad Patooghy, and Mahdi Fazeli. "I-LEACH: An efficient routing algorithm to improve performance & to reduce energy consumption in Wireless Sensor Networks." Information and Knowledge Technology (IKT), 2013 5th Conference on. IEEE, 2013.
[9] Liu, Yi, Shan Zhong, Licai You, Bu Lv, and Lin Du. "A Low Energy Uneven Cluster Protocol Design for Wireless Sensor Network." Int`l J. of Communications, Network and System Sciences 5 (2012): 86.
[10] Haneef, Muhammad, Zhou Wenxun, and Zhongliang Deng. "MG-LEACH: Multi group based LEACH an energy efficient routing algorithm for Wireless Sensor Network." Advanced Communication Technology (ICACT), 2012 14th International Conference on. IEEE, 2012.
[11] Aslam, M., Nadeem Javaid, A. Rahim, U. Nazir, Ayesha Bibi, and Z. A. Khan. "Survey of extended LEACH-Based clustering routing protocols for wireless sensor networks."In High Performance Computing and Communication & 2012 IEEE 9th International Conference on Embedded Software and Systems (HPCC-ICESS), 2012 IEEE 14th International Conference on, pp. 1232-1238. IEEE, 2012.
[12] Mr. Santosh.Irappa.Shirol, Ashok Kumar. N, Mr. Kalmesh.M.Waderhatti,”Advanced-LEACH Protocol of Wireless Sensor network”, IJETT - Volume4 Issue6- June 2013.

Navneet Kaur, Navjeet Saini, Sandeep Kaur, "The Survey On Data Transfer techniques in Wireless Sensor Networks," International Journal of Computer Sciences and Engineering, Vol.7, Issue.4, pp.797-799, 2019.

The region based energy efficient technique for the data communication among nodes in a wireless sensor network is a best approach to enhance the network life time of a network. The rechargeable node called gateway node placed at the centre of the two region helps to minimize the energy consumption of Cluster heads in respective regions. This paper presents the survey of gateway based energy efficient technique and the communication among region nodes with base station using positive coordinates of the region following the base station.

WSN, gateway node, life time, network region, cluster heads

[1] Preeti Jamwal1, Sonam Mahajan2, “Region Refinement Technique In MGEAR Protocol To Enhancing Sensor Node Life Time”, International Journal of Computer Sciences and Engineering, Volume-5, Issue-8 ,2018.
[2] Nazia anjum, Maood ahmed et al,”, Gateway Based Energy Efficient Routing: GEER”, International Journal of Advance Research, Ideas and Innovations in Technology, Volume3, Issue4, 2017.
[3] Q. Nadeem1, M. B. Rasheed1 et al, “M-GEAR: Gateway-Based Energy-Aware Multi-Hop Routing Protocol for WSNs”, ieee July 2016.
[4] Veena Anand , Deepika Agrawal, Preety Tirkeyb, Sudhakar Pandey, “An energy efficient approach to extend network life time of wireless sensor networks”, Elsevier, Procedia Computer Science, 425 – 430, 2016.
[5] Pallavi Jain1 and Harminder kaur2, ” An Improved Gateway Based Multi Hop Routing Protocol for Wireless Sensor Network”, International Journal of Information & Computation Technology. ISSN 0974-2239 Volume 4, Number 15,pp. 1567-1574, 2014.
[6] Pijus Kumar Pal1, Punyasha Chatterjee2, “ A Survey on TDMA-based MAC Protocols for Wireless Sensor Network”, International Journal of Emerging Technology and Advanced Engineering, Volume 4, Issue 6, June 2014.
[7] Velanati Mohana Gandhi1, M.V.H.Bhaskara Murthy2,M.Lakshmu Naidu3, “ Performance Analysis of Multihop-Gateway Energy Aware Routing (M-Gear) Protocol for Wireless Sensor Networks”, IOSR Journal Of Humanities And Social Science, Volume 21, Issue11,Ver. 9 Nov. 2016.
[8] Shakshi Mehta et al “ Improved Multi-Hop Routing Protocol in Wireless Body Area Networks “, International Journal of Advanced Research in Computer Science and Software Engineering, Volume 5, Issue 7, July 2015.
[9] S. Rani and S.H. Ahmed, Multi-hop Routing in Wireless Sensor Networks, Springer Briefs in Electrical and Computer Engineering.
[10] Jung, W. S., Lim, K. W., Ko, Y. B., & Park, S. J. “A hybrid approach for clustering-based data aggregation in wireless sensor networks”, In Digital Society, IEEE, Third International Conference on, 2009.
[11] Li, Hongjuan, Kai Lin, and Keqiu Li. "Energy-efficient and high-accuracy secure data aggregation in wireless sensor networks." Computer Communications, 2011.
[12] Sujata1, Brijbhushan2,” Energy Efficient PEGASIS Routing Protocol in Wireless Sensor Network”, International Research Journal of Engineering and Technology (IRJET), Volume: 04 Issue: 07 | July-2017.

Amandeep Kaur, Sukhbeer Singh, Neelam Chouhan, "Survey on Region Based M-GEAR Protocol in Wireless Sensor Network," International Journal of Computer Sciences and Engineering, Vol.7, Issue.4, pp.800-802, 2019.

The shift from monolithic to micro services architecture has been remarkable. Micro services have emerged to be an effective and efficient architecture pattern for various applications and have become a preferred choice for efficient and rapid development of distributed applications. Micro services follow a decentralized approach where each service has an individual outcome and communicates with other services. This communication is carried out with the help of a well-designed set of protocols which are known as Application Programming Interfaces (API). It is seen that micro service architecture is used popularly for cloud applications. But apart from cloud applications, micro services along with block chain, internet of things (IoT), machine learning, and other domains have varied use cases. This paper tries to emphasize the importance of the micro services architecture in these domains. We also provide an overview of the research carried out in these fields along with some of the real time use cases of micro services.

Micro services, application domains

[1] Lewis and M. Fowler. “Microservices”. [Online]
[2] Completing the Netflix Cloud Migrationh [Online]
[3] Service-Oriented Architecture: Scaling the Uber Engineering Codebase As We Grow
[4] C. Pahl, P. Jamshidi, “Microservices: A Systematic Mapping Study” In International Conference onCloud Computing and Services Science, 2016.
[5] M. Viggiato,R. Terra, H. Rocha, M.T. Valente, E. Figueiredo, “Microservices in Practice: A Survey Study”, arXiv: 1808.04836v1, 14 Aug 2018
[6] N. Dragoni, S. Giallorenzo, A. L. Lafuente, M. Mazzara, F. Montesi, R. Mustafin, and L. Safina, “Microservices: yesterday, today, and tomorrow”. arXiv:1606.04036v1, 13 Jun 2016
[7] P. D. Francesco, P. Lago, I. Malavolta, “Research on Architecting Microservices: trends Focus and Potential for Industrial Adoption",
IEEE International Conference on Software Architecture (ICSA), pp.21-30, 2017
[8] N. Alshuqayran, N. Ali, R. Evans, "A systematic mapping study in microservice architecture", Proc. SOCA 2016, pp. 44-51, November 2016
[9] D. Taibi, V. Lenarduzzi, C. Pahl, “Architectural Patterns for Microservices: A Systematic Mapping Study” 8th International Conference on Cloud Computing and Services Science, CLOSER 2018.
[10] T. Cerny, M. Donahoo, M. Trnka, “Contextual understanding of microservice architecture: current and future directions”, ACM SIGAPP Applied Computing Review, 2018
[11] G. Campeanu, "A mapping study on microservice architectures of internet of things and cloud computing solutions", 2018 7th Mediterranean Conference on Embedded Computing (MECO), pp. 1-4, June 2018
[12] D. A. Monteiro, R. Hazin, & A. Lima, F. Ferraz,A. Washington, "Survey on Microservice Architecture -Security, Privacy and Standardization on Cloud Computing Environment", Icsea 2017, At Athenas, Greek, 2017
[13] P.D. Francesco, P. Lago and I. Malavolta. “Migrating Towards Microservice Architectures: An Industrial Survey.” 2018 IEEE International Conference on Software Architecture (ICSA), 2018: 29-2909.
[14] J. Ghofrani, D. Lübke, “Challenges of Microservices Architecture: A Survey on the State of the Practice.” ZEUS (2018).
[15] R. Heinrich , A. Hoorn , H. Knoche , F. Li , L. Lwakatare , C. Pahl , S. Schulte , J. Wettinger,”Performance Engineering for Microservices: Research Challenges and Directions”, Proceedings of the 8th ACM/SPEC on International Conference on Performance Engineering Companion, , L`Aquila, Italy April 22-26, 2017.
[16] K. Brown,B. Woolf,”Implementation Patterns of Microservices Architectures”,Proceedings of the 23rd Conference on Pattern Languages of Programs,USA, 2016.
[17] https://docs.microsoft.com/en-us/dotnet/standard/microservices-architecture/architect-microservice-container-applications/communication-in-microservice-architecture[Online]
[18] P. Krivic, P. Skocir ,M. Kusek, G. Jezic,” Microservices as Agents in IoT Systems.”, In: Jezic G., Kusek M., Chen-Burger YH., Howlett R., Jain L. (eds) Agent and Multi-Agent Systems: Technology and Applications. KES-AMSTA 2017. Smart Innovation, Systems and Technologies, vol 74. Springer, Cham,2017.
[19] R. Xu & S. Nikouei,Y. Chen,E. Blasch, A. Aved, “BlendMAS: A BLockchain-ENabled Decentralized Microservices Architecture for Smart Public Safety”,arXiv:1902.10567,Feb 2019.
[20] D. Nagothu, R. Xu,S. Nikouei, & Y.Chen ,”A Microservice-enabled Architecture for Smart Surveillance using Blockchain Technology”,Jul 2018.
[21] A. Khaleq, I. Ra, “Cloud-based Disaster Management as a Service: A Microservice Approach for Hurricane Twitter Data Analysis”, 2018 IEEE Global Humanitarian Technology Conference(GHTC), USA, 2018
[22] S. K. Datta, M. I. Khan, L. Codeca, B. Denis, J. H¨arri, C. Bonnet, "Iot and microservices based testbed for connected car services", 2018 IEEE 19th International Symposium on A World of Wireless Mobile and Multimedia Networks (WoWMoM), pp. 14-19, June 2018.
[23] G. Cherradi,A. Bouziri, A. Boulmakoul,K. Zeitouni,,”An Atmospheric Dispersion Modeling Microservice for HazMat Transportation”,Procedia Computer Science. 130,pp. 526-532,2018.
[24] M.A. Jarwar,S. Ali,I. Chong, “Exploring Web Objects enabled Data-Driven Microservices for E-HealthService Provision in IoT Environment.”, In Proceedings of the 2018 International Conference on Informationand Communication Technology Convergence (ICTC), Jeju, South Korea, pp. 112–117, 17–19 October 2018.
[25] A.M. Del Esposte, F. Kon, F. M. Costa, N. Lago,“InterSCity: A Scalable Microservice-based Open Source Platform for Smart Cities.” In: Proceedings of the 6th International Conference on Smart Cities and Green ICT Systems,2017.
[26] S. Benedict, "Revenue oriented air quality prediction microservices for smart cities," 2017 International Conference on Advances in Computing, Communications and Informatics (ICACCI), India, 2017.
[27] R. Hill,D. Shadija,M. Rezai,"Enabling Community Health Care with Microservices", 2017 IEEE International Conference on Ubiquitous Computing and Communications, China, 2017.
[28] J. Lotz,A. Vogelsang,O. Benderius,C. Berger,”Microservice Architectures for Advanced Driver Assistance Systems: A Case-Study”, arXiv:1902.09140,Feb 2019.
[29] K. Kravari,N. Bassiliades,”A Rule-Based eCommerce Methodology for the IoT Using Trustworthy Intelligent Agents and Microservices”, In: Benzmüller C., Ricca F., Parent X., Roman D. (eds) Rules and Reasoning. RuleML+RR 2018. Lecture Notes in Computer Science, vol 11092,pp. 302-309,2018.
[30] K. Khanda, D. Salikhov,K. Gusmanov, M. Mazzara, N. Mavridis, "Microservice-Based IoT for Smart Buildings", 31st Intern. Conf. on Advanced Information Networking and Applications Workshops (WAINA), pp. 302-308, 2017.
[31] H. Lin,J. Zhao,Y. Jiao,J. Cao,H .Ouyang, B. Yuan and G. Xiong, “Research on designing an integrated electric power marketing information system based on microapplications and microservices architecture.” 2018 IEEE 3rd International Conference on Cloud Computing and Big Data Analysis (ICCCBDA),600-607,2018.
[32] C. Xia,Y. Zhang, L. Wang, S. Coleman and Y. Liu, “Microservice-based cloud robotics system for intelligent space.” Robotics and Autonomous Systems,vol. 110, pp. 139-150,2018.
[33] P. Yugopuspito,F. Panduwinata,S.Sutrisno, “Microservices architecture: Case on the migration of reservation-based parking system.” 2017 IEEE 17th International Conference on Communication Technology (ICCT),pp. 1827-1831,2017.
[34] M. Ciavotta,M. Alge,S. Menato, D. Rovere,P. Pedrazzoli,”A Microservice-based Middleware for the Digital Factory”,Procedia Manufacturing, vol. 11, pp. 931-938,2017.
[35] X. Wang,S. Wang,Z. Hao,X. Zhang,,”Research on the Construction of Regional Credit Bank Platform Based on Microservices”,In: 2018 10th International Conference on Measuring Technology and Mechatronics Automation (ICMTMA), China, 2018.
[36] M.O. Pahl, M. Loipfinger, "Machine learning as a reusable microservice", NOMS 2018 - 2018 IEEE/IFIP Network Operations and Management Symposium,2018: 2374-9709.
[37] V. D. Le, M. M. Neff, R. V Stewart, R. Kelley, E. Fritzinger, S. M. Dascalu, F. C. Harris, "Micro service-based architecture for the NRDC", Proc. IEEE International Conference on Industrial Informatics (INDIN), pp. 1659-1664, 2015.
[38] L. F. Herrera-Quintero, J. C. Vega-Alfonso, K. B. A. Banse, E. C. Zambrano, "Smart its sensor for the transportation planning based on iot approaches using serverless and microservices architecture", IEEE Intelligent Transportation Systems Magazine, vol. 10, no. 2, 2018.

Nupura Torvekar, Pravin S. Game, "Microservices and It`s Applications : An Overview," International Journal of Computer Sciences and Engineering, Vol.7, Issue.4, pp.803-809, 2019.

Fruit disease detection and classification plays vital role in agriculture area and separation of disease and non-diseased fruits take more time. In this paper, we propose quad tree method to detect the diseased region from the fruit to facilitate effective classification. To detect diseased region we explored to check homogeneity of the sub-tree image pixel of quad tree. Subsequently, the diseased area is used for classification using deep learning approach. In deep learning six hidden layers are used. Further, we have collected 1000 samples of diseased and 1000 samples of non-diseased images from the 20 fruits class to conduct extensive experiment on both detection and classification. In experimentation, we compared the proposed method results with SVM and KNN classifier, the proposed method results shows that compared to SVM and KNN classifiers deep learning gives better results.

Deep Learning, Detection, Quad tree, Sementaion, Classification

[1] S R Dubey and A S Jalal, "Automatic Fruit Disease Classifiction using Images", Computer Vision and Image Processing in Intelligent Systems and Multimedia Technologies, 2014.
[2] J D Pujari, R Yakkundimath, A S Byadgi, "Grading and Classification of Anthranose Fungal Disease Fruits based on Statistical Texture Features", International Journal of AdvancedScience and Technology, Vol. 52, 2013.
[3] M S Hossian, M Al-Hammadi, and G Muhammad, "Automatic Fruit Classification using Deep Learning for Industrial Applications", IEEE Transactions on Industrial Informaics, Vol. 15, Issue. 2, pp.1027-1034, 2019.
[4] S K Behera, L Jena, A K Rath and P K Sthy, "Disease Classification and Grading of Orange using Machine Learning and Fuzzy Logic", 2018 Internaional Conference on Communication and Signal Processing (ICCSP), pp.0678-0682, 2018.
[5] A S M Shafi, Md. B Rahman and M M Rahman, "Fruit Disease Recogniton and Automatic Classification using MSVM with Multiple Features ", Internaional Journal of Computer Apllications, 181(10), pp.12-15, 2018.
[6] Dakshayini Patil, "Fruit Disease Detection using Image Processing Techniques", International Journal for Research in Engineering Application and Mangement(IJREAM), 2018.
[7] S A Gaikwad, K S Deore and M K Waykar, "Fruit Disease Detection and Classification", Internaional Journal of Engineering and Technology (IRJET), Vol.4, Issue.2, 2017.
[8] S Panda and P K Sethy, "Post-Harvest Grading for Carica Papaya Fruit using Image Segmentation and Soft Computing", International Journal for Advance Research in Computer Science, Vol.8, 2017.
[9] A S Nadarajan and A Thamizhar, "Detection of Bacterial Disease in Alphonso Mango using Image Processing", International Jornal for Advanced Research Trends in Engineering and Technology (IJARTET), Vol.4 Isuue.6, 2017.
[10] S Varughese, N Shinde, S Yadav and J Sisodia,"Learning-Based Fruit Disease using Image Processing", International Journal of Innovative and Emerging Research in Engineering, Vol.3, Issue.2, 2016.
[11] S T Khot, P Supriya, M Gitanjali and L Vidya, "Pomegranate Disease Detection using Image Processing Techniques ", International Journal of Advanced Research in Electrical and Electronics and Instrumentation Engineering, Vol.5, Issue.4, 2016.
[12] B J Samajpati and S D Degadwala, "Hybrid Approach for Apple Fruit Diseases Detection and Classification using Random Forest Classifier", 2016 International Con ference on communication and signal Processing (ICCSP), pp. 1015-1019, 2016.
[13] AAwate, D Deshmankar, G Amrutkar, U Bagul and S Sonavane, " Fruit Disease Detection using Color, Texture Analysis and ANN", 2015 International Conference on Green Computing and Internet Things (ICGCIoT) pp. 970-975, 2015.
[14] M Dhakate and A B Ingole, "Dignosis of Pomegranante plant Diseases using Neural Networks", 2015 Fifth National Conference on Computer Vision and Pattern Recogniton, Image Processing and Graphics (NCVPRIPG), pp. 1- 4, 2015.
[15] S H Mohana and C J Prabhakar,"Automatic Detection of Surface Defects onCitrus Fruit Based on Computer Vision Techniques", Iternational Journal of image, Graphics and Signal Processing,Vol.7, Issue. 9, pp. 11-19, 2015.
[16] A Mizushima and R Lu, "An Image Segmentation method for Apple Sorting and Grading using Support Vector Machine and Otsu`s Method", Computers and Electronics in Agriculture, Elsevier, Vol. 94, pp. 29-37, 2013.
[17] M Jhuria, A Kumar, R Borse,"Image Processing for Smart Farming: Detection of Disease and Fruit Grading", 2013 IEEE second International Conference on Image Information Processing(ICIIP), pp. 521-526, 2013.
[18] S R dubey and A S Jalal, "Detectuion and Classification of Apple Fruit Disease using Complete Local Binary Patterns", 2012 Third International Conference on Computer and Communication Technology, pp. 346-3351, 2012.
[19] D G Kim, T F Burks, J Qin and D M Bulanon, "Classification of Grape Fruit Peel Diseases using Color Texture Features", International Journal of Agriculture and Biological Engineering, pp. 41-50, 2009.
[20] J Blasco, N Aleixos and E Molt,"Machine Vision System for Automatic Quality Grading of Fruit", Biosystems Engineering, Elsevier, Vol. 85, Isuue. 4, pp. 415-423, 2003.
[21] V Leemans, H Magein and F Destain, "Defect Segemenation on Jonagold apples using colour vision and Bayesian calssification method", Computers and Ectronics in Agriculture, Elsevier, Vol. 23, Issue. 1, pp. 43-53.
[22] Z F Muhsin, A Rehman, A Altameem, A Saba and M Uddin, "Improved Quadtree Image Segmentation approach to region informmation", The Imaging Science Journal, Vol. 62, Issue.1, 2014.
[23] K S Raghunandan, B M Chethan Kumar, G Hemantha Kumar and C Sunil,"Convolution Neural Network Based Deep Features for Text Recognition in Multi-type Images", 2018 International Conference on Advance Computing, Communications and Informatics(ICACCI), Banglore, pp. 502-507, 2018.
[24] K N Ranjit, H K Chethan and C Naveen, "Identification and Classification of Fruit Diseases", International Journal of Engineering Research and Appliction, Vol. 6, Isuue. 7, 2016.

Ranjit K N, Raghunandan K S, Naveen C, Chethan H K, Sunil C, "Deep Features Based Approach for Fruit Disease Detection and Classification," International Journal of Computer Sciences and Engineering, Vol.7, Issue.4, pp.810-817, 2019.

Text classification is the task of allocating the documents into one or more number of predefined categories. In general, this technique is used in the field of information retrieval, text summarization and, text extraction. To perform the classification task, transformation of text into feature vectors is the important stage. The main advantage of this transformation is to discover the most significant words from the document. This process is also known as word embedding, which is used to represent the meaning of words into vector format. The word embedding’s are employed in a high dimensional space where the embeddings of similar or related words are adjacent to each other. This main aim of this research work is to classify the text documents based on their contents. In order to achieve this task, in this research work the different word embedding algorithms are used to represent documents. The performance measures are Precision, recall, f-measure and accuracy.

Text Classification, Document Representation, Word Embedding, Word2Vec, GloVe, WordRank

[1]. Korde, V., & Mahender, C. N. (2012). Text classification and classifiers: A survey. International Journal of Artificial Intelligence & Applications, 3(2), 85.
[2]. Jon Ezeiza Alvarez. (2017). A review of word embedding and document similarity algorithms applied to academic text
[3]. Liu, Q., Huang, H., Gao, Y., Wei, X., Tian, Y., & Liu, L. (2018, August). Task-oriented word embedding for text classification. In Proceedings of the 27th International Conference on Computational Linguistics (pp. 2023-2032).
[4]. Yu, L. C., Wang, J., Lai, K. R., & Zhang, X. (2017, September). Refining word embeddings for sentiment analysis. In Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing (pp. 534-539).
[5]. Li, L., Qin, B., & Liu, T. (2017). Contradiction detection with contradiction-specific word embedding. Algorithms, 10(2), 59.
[6]. Bengio, Y., Ducharme, R., Vincent, P., & Jauvin, C. (2003). A neural probabilistic language model. Journal of machine learning research, 3(Feb), 1137-1155.
[7]. Bollegala, D., Alsuhaibani, M., Maehara, T., & Kawarabayashi, K. I. (2016, March). Joint word representation learning using a corpus and a semantic lexicon. In Thirtieth AAAI Conference on Artificial Intelligence.
[8]. Faruqui, M., Dodge, J., Jauhar, S. K., Dyer, C., Hovy, E., & Smith, N. A. (2014). Retrofitting word vectors to semantic lexicons. arXiv preprint arXiv:1411.4166.
[9]. Diaz, F., Mitra, B., & Craswell, N. (2016). Query expansion with locally-trained word embeddings. arXiv preprint arXiv:1605.07891.
[10]. Zamani, H., & Croft, W. B. (2017, August). Relevance-based word embedding. In Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval (pp. 505-514). ACM.
[11]. Uysal, A. K., & Gunal, S. (2014). The impact of preprocessing on text classification. Information Processing & Management, 50(1), 104-112.
[12]. Bollegala, D., Yoshida, Y., & Kawarabayashi, K. I. (2018, April). Using k-way Co-occurrences for Learning Word Embeddings. In Thirty-Second AAAI Conference on Artificial Intelligence.
[13]. Pennington, J., Socher, R., & Manning, C. (2014). Glove: Global vectors for word representation. In Proceedings of the 2014 conference on empirical methods in natural language processing (EMNLP) (pp. 1532-1543).
[14]. Dutta, D. (2018). A Review of Different Word Embeddings for Sentiment Classification using Deep Learning. arXiv preprint arXiv:1807.02471.
[15]. Mandelbaum, A., & Shalev, A. (2016). Word embeddings and their use in sentence classification tasks. arXiv preprint arXiv:1610.08229.
[16]. Rosander, O., & Ahlstrand, J. (2018). Email Classification with Machine Learning and Word Embeddings for Improved Customer Support.

R. Janani, S. Vijayarani, "Text Classification: A Comparative Analysis of Word Embedding Algorithms," International Journal of Computer Sciences and Engineering, Vol.7, Issue.4, pp.818-822, 2019.