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Agriculture is the back-bone of country`s economy, where farmer`s source of income widely depends upon farming. During the cultivation of crops, it is required to properly monitor and due to change in atmospheric condition or the loss of soil nutrition these crops get encountered with certain type of diseases. Health monitoring and disease detection on plants is very critical for sustainable agriculture. It is very difficult to monitor the plant diseases manually. It requires tremendous amount of work, expertise in the plant diseases, and also require the excessive processing time. Thus farmer cannot recognize easily because of which they incur loss in production and yield. So here we propose the system where we can detect the disease based on the leaf image and diagnose for proper medication based on the result.

felzenszwalb, Quickshift, color based segmentation, ResNet, LeNet, Estimation

[1] Anksha Rastogi, RitikaArora and ShanuSharma,” Leaf Disease Detection and Grading using ComputerVision Technology &Fuzzy Logic”
[2] RatihKartika Dewi and R. V. Hari Ginardi,”Feature -Extraction for Identificationof Sugarcane rust disease
[3] Yuan Tian,Chunjiang Zhao, Shenglian Lu and XinyuGuo,” SVM- based Multiple Classifier System for Recognition of Wheat Leaf Diseases
[4] SmitaNaikwadi, NiketAmoda,” Advances In Image Processing For Detection Of Plant Diseases”

Sheela N, Harsha B M, Nikhil Shastri, Gurudatt Bhat, "Image Based Plant Leaf Disease Recognition and Estimation System," International Journal of Computer Sciences and Engineering, Vol.7, Issue.6, pp.725-731, 2019.

The pipeline system is the most important part in media transport in order to deliver fluid to another station. The weak maintenance and poor safety will directly lead to financial losses in term of fluid waste, destruction of human race, animals, birds and causing environmental impacts by leaving large amount of unwanted gases in the ecosystem. There are many classifications of techniques to make it easier to show their specific method and application. In this paper we will discuss about the problem faced by Maharashtra Natural gas Ltd (MNGL) and Mahanagar Gas. The motive is to find the leakage is at what percent as these Navratna‟s uses Compressed natural gas (CNG), Liquefied petroleum gas (LPG) and Piped Gas and what is the flow rate of Leakage as well as we can monitor the health of the system. We have developed a system wherein Flow Rate, Temperature, Fire, Gas are the parameters of Inspection. A design is developed using Slave 1 and 2 for demonstration purpose on LCD display also using Internet of Things platform for easy receving data formats, and a html webpage is prepared to read the values in Tabular context. Discoveries dependent on the this IOT strategy can be utilized for near investigation in the future. It‟s the best technique to recognize spill in gas pipelines. More analysis and reproduction should be completed to get the quick consequence of spilling and estimation of their area.

Arduino Uno, IOT, DHT22, Flame sensor, MQ6, Flowsensor

[1] Mahesh P Potadar, Pranav S Salvi, Rvindra B Sathe, Poonam S Chavan. LPG Detection and Automatic Gas cylinder Booking System. IJERMT May-2015 Volume 2, Issue-3.
[2] Sharmad Pasha Thingspeak Based Sensing and Monitoring System for IoT with Matlab Analysis International Journal of New Technology and Research (IJNTR) ISSN: 2454-4116, Volume-2, Issue-6, June 2016 Pages 19-23
[3] E. Jebamalar Leavline, D. Asir Antony Gnana Singh, B.

Abinaya, H. Deepika, LPG Gas Leakage Detection and Alert System. International Journal of Electronics Engineering Research. ISSN 0975-6450 Volume 9, Number 7 (2017) pp. 1095-1097
[4] Hitendra Rawat, Ashish Kushwah, Khyati Asthana, Akanksha Shivhare “LPG Gas Leakage Detection & Control System” National Conference on Synergetic Trends in engineering and Technology (STET-2014) International Journal of Engineering and Technical Research ISSN: 2321-0869, Special Issue
[5] Deepthi Miriyampalli, Ponnuri Anil Kumar, Abdul Khadir Shaik, Ravichandra Vipparla, Komalphanindra Potineni Gas Leakage Detection based on IoT using Raspberry Pi International Journal for Research in Applied Science & Engineering Technology (IJRASET)

ISSN: 2321-9653; IC Value: 45.98; SJ Impact Factor: 6.887 Volume 6 Issue II, February 2018- Available at www.ijraset.com
[6] V.Naren1, P.Indrajith², R.Aravind Prabhu³, C S Sundar Ganesh 4 Intelligent Gas Leakage Detection System with IoT Using ESP 8266 Module International Journal of Advanced Research in Electrical,Electronics and Instrumentation Engineering.
[7] Rohan Chandra Pandey, Manish Verma, Lumesh Kumar Sahu, Saurabh Deshmukh INTERNET OF THINGS (IOT) BASED GAS LEAKAGE MONITORING AND ALERTING SYSTEM WITH MQ-6 SENSOR© 2018 IJCRT | Volume 6, Issue 1 January 2018 | ISSN: 2320-2882
[8] T.Soundarya, J. V. Anchitaalagammai, G.DeepaPriya, S.S. Karthick Kumar, “C-Leakage: Cylinder LPG Gas Leakage
Detection for Safety,” IOSR journal of electronics and
Communication Engineering , Vol.9, no. 1,Ver.VI, PP.53 -58, Feb.2014.

Renuka Kishor Kale, S. L. Nalbalwar, S. B. Deosarkar, Sachin Singh, "Leakage Detection in Underground Gas Pipeline," International Journal of Computer Sciences and Engineering, Vol.7, Issue.6, pp.732-736, 2019.

In corporate and government department’s increasingly keeping large size electronic databases, which are accessed using internet or intranet. Important information implement from the data using Privacy data mining methods. While performing data mining steps, there is an inherent danger to the privacy of the data. The valuable data stored in the database should not be accessible to users. Most of the privacy preserving methods are based on reduction in the granularity of the implementing of the data. This ends to loss of information but it improves privacy. Therefore, in PPDM there is a conflict between loss of information and the privacy. Effective Methods are required which do not compromise the security mechanisms. Some of the methods proposed for privacy preserving data mining include randomization method, k-anonymity model, l-diversity and distributed privacy preservation. The k-anonymity model is based on a quasi-identifier, which is a collection of attributes in a database that is the identifier for the entire data. All the data in the database is assumed to be in a set of tables, and each tuple is information of an individual customer. K-anonymity Methods are based on the reduction of granularity in representation of data using pseudo identifiers. Major Methods used for granularity reduction are generalization and suppression. In generalization, the attribute values are converted into a range that reduces the granularity and reduces the risk of identifying individual values. In suppression, value of the attribute is removed completely. These methods introduce loss of detail which may affect the accuracy. This induces the search for anonymization algorithms that achieve the required level of anonymization while incurring a minimization of loss of information. Finding optimal anonymous datasets using generalization or suppression has been proved to be a NP hard problem. Therefore, some standard heuristic search Methods such as Genetic Algorithms (GAs), Particle Swam Optimization (PSO) and Ant Colony Optimization (ACO) can be used to find optimal datasets.

Data mining, Secure Multiparty Computation, Genetic Algorithm, Particle Swam Optimization, Ant Colony Optimization

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[4] Singh, M. D., Krishna, P. R., & Saxena, A. (2010, January). A cryptography based privacy preserving solution to mine cloud data. In Proceedings of the Third Annual ACM Bangalore Conference (p. 14). ACM.
[5] Patrick Sharkey, Hongwei Tian, Weining Zhang, and Shouhuai Xu, 2008, Privacy-Preserving Data Mining through Knowledge Model Sharing, Springer-Verlag Berlin Heidelberg, pp. 97– 115, 2008
[6] Pawel Jurczyk, Li Xiong, 2008, Privacy-Preserving Data Publishing for Horizontally Partitioned Databases, CIKM’08, October 26–30USA., ACM 978-1-59593-991- 3/08/10.
[7] Campan, A., & Truta, T. (2009). Data and structural k-anonymity in social networks. Privacy, Security, and Trust in KDD, 33-54.
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[11] Shi, P., Xiong, L., & Fung, B. (2010, October). Anonymizing data with quasi-sensitive attribute values. In Proceedings of the 19th ACM international conference on Information and knowledge management (pp. 1389-1392). ACM.
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[13] P.Samarati, Protecting respondents’ identities in micro data release, IEEE Transactions on Knowledge and Data Engineering 13 (6) (2001) 1010–1027.
[14] Van der Merwe, D., & Engelbrecht, A. P. (2003). Data clustering using particle swarm optimization. In IEEE congress on evolutionary computation (1) (pp. 215–220). New York: IEEE.
[15] Holden, N., & Freitas, A. (2008).A hybrid PSO/ACO algorithm for discovering classification rules in data mining. Journal of Artificial Evolution and Applications, 2008, 11 pages.
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[27] Qing Cao, Tian He, and Tarek Abdelzaher, uCast: Unified Connectionless Multicast for Energy Efficient Content Distribution in Sensor Networks, IEEE Transactions On Parallel And Distributed Systems, Vol. 18, No. 2, February 2007.
[28] Latiff, N.M.A.; Tsimenidis, C.C.; Sharif, B.S., "Performance Comparison of Optimization Algorithms for Clustering in Wireless Sensor Networks," Mobile Adhoc and Sensor Systems, 2007. MASS 2007. IEEE International Conference on , vol., no., pp.1-4, 8-11 Oct. 2007.
[29] Matthew Settles,” An Introduction to Particle Swarm Optimization”, 2005.
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Yuvraj Singh, Pankaj Pratap Singh, Anirudh Kumar Tripathi, Amit kishor, "Study of Incentive Compatible Privacy Preserving Data Analysis," International Journal of Computer Sciences and Engineering, Vol.7, Issue.6, pp.737-741, 2019.

Voting is the right of every eligible citizen. It is the power vested upon the people which allows them to choose a party or a person who will represent them as a part of the government. On one side of the coin, are the people whereas on the other side, are the parties. Every election, a stupendous sum of money is spent by the parties in doing social work, promotion of candidates and many more such fields. Thus, it would be of strategic importance to a party, if they are able to predict the voting outcomes in an area in advance, as it can help them to carry forward their campaign judiciously. In this proposed work, a dataset from Show of Hands is used which contains multiple features, several of them hidden, which were discovered after data analytics. The aim is to correctly predict the party a person is most likely to vote for, in the USA presidential election. For this purpose, first after collecting the data, we perform data cleaning and feature extraction. Next, the data is given as input to our model. The model is trained using multiple machine learning algorithms like Logistic Regression, Support Vector Machine (SVM), Naïve Bayes Classifier and Random Forest. The accuracy of these models is compared and the prediction report is generated.

Voting, Data Analytics, Data Cleaning, Machine Learning

[1] Zolghadr, M. Niaki, S.A. Niaki, “Modelling and Forecasting US Presidential Election using learning algorithms”, International Journal of Industrial Engineering, Vol.14, Issue.3, pp.491-500, 2018.
[2] A. Wakjira, “Predicting voting Affiliation Using Machine Learning Algorithms ”, Metropolia Ammattikorkeakoulu Publisher,2014
[3] P. Kassraie, A. Modirshanechi and H. Aghajan, “Election Vote Share Prediction using a Sentiment-based Fusion of Twitter Data with Google Trends and Online Polls.”, In the proceedings of the 6th International Conference on Data Science, Technology and Applications (DATA 2017),pp.363-370.
[4] P. Salunkhe, S. Deshmukh, “Twitter Based Election Prediction and Analysis ”, International Research Journal of Engineering and Technology (IRJET), Vol.04, Issue.10, pp.539-544, 2017.
[5] Marie Fernandes , "Data Mining: A Comparative Study of its Various Techniques and its Process", International Journal of Scientific Research in Computer Science and Engineering, Vol.5, Issue.1, pp.19-23, 2017.
[6] A. Jenita Jebamalar, "Efficiency of Data Mining Algorithms Used In Agnostic Data Analytics Insight Tools", International Journal of Scientific Research in Network Security and Communication, Vol.6, Issue.6, pp.14-18, 2018.
[7] K. Sree Divya, P. Bhargavi, S. Jyothi, "Machine Learning Algorithms in Big data Analytics", International Journal of Computer Sciences and Engineering, Vol.6, Issue.1, pp.63-70, 2018.
[8] WEKA Manual for Version 3-6-8, The University of Waikato, 2012.

Urjit Desai, Ameya Dalvi, Atharva Dhuri, "Predicting Voting Outcomes Using Data Analytics and Machine Learning Algorithms," International Journal of Computer Sciences and Engineering, Vol.7, Issue.6, pp.742-745, 2019.

Binarization is very important pre-processing technique for document images which is used to segment the image into foreground and background pixels. Binarization of degraded documents is very challenging due to uneven background, noise, ink dots, degradation of paper ink due to aging etc. Although many binarization techniques are available, but these standard algorithms are sensitive to noise and do not produce good results on different kinds of degradations. The selection of binarization method for a particular degradation is a very tedious job. In this paper, a survey of recent ongoing research efforts in field of image binarization has been carried out. The purpose of this study is to find the research gap in the field of document image binarization.

Binarization, Degraded documents, Thresholding, OCR, Document images

[1] F. Jia, C. Shi, K. He, C. Wang, B. Xiao, “Degraded document image binarization using structural symmetry of strokes”, Pattern Recognition, Vol. 74, pp. 225-240, 2018.
[2] F. Jia, C. Shi, K. He, C. Wang, B. Xiao, “Document image binarization using structural symmetry of strokes”, In the proceedings of 15th International Conference on Frontiers in Handwriting Recognition (ICFHR), Shenzhen, China, pp. 411-416, 2016.
[3] S. Bolan, S. Lu, C.L. Tan, “Robust document image Binraization technique for degraded document images” IEEE Transactions on Image Processing, Vol. 22, Issue 4, pp. 1408-1417, 2013.
[4] S. Mysore, M.K. Gupta, S. Behle, “Complex and degraded color document image binarization”, In the proceedings of 2016 3rd International Conference on Signal Processing and Integrated Networks (SPIN), Noida, India, pp. 157-162, 2016.
[5] J. Bernsen, “Dynamic Thresholding of Gray Level Image”, In the proceedings of International Conference on Pattern Recognition ICPR`86, Berlin, pp. 1251-1255, 1986.
[6] J. Saulva, M. Pietikäinen, “Adaptive document image binarization”, Pattern Recognition, Vol. 33, Issue 2, pp. 225-236, 2000.
[7] O. Boudraa, W.K. Hidouci, D. Michelucci “A robust multi stage technique for image binarization of degraded historical documents” In the proceedings of 5th International Conference on Electrical Engineering (ICEE-B), Boumerdes, pp. 1-6, 2017.
[8] K. Ntirogiannis, B. Gatos, I. Pratikakis “A combined approach for the binarization of handwritten document images” Pattern Recognition Letters, Vol. 35, pp. 3-15, 2014.
[9] J.S. Valverde, R.R. Grigat. “Optimum binarization of technical document images”, In the proceedings of International Conference on Image Processing. Vancouver, Canada, pp. 985-988, 2000.
[10] N. Chaki, S.H. Shaikh, K. Saeed, “A Comprehensive Survey on Image Binarization Techniques” In Exploring Image Binarization Techniques. Vol. 560. Springer, India, pp. 5-15, 2014.
[11] B. Su, S. Lu, C.L. Tan. “Combination of document image binarization techniques”, In the proceedings of International Conference on Document Analysis and Recognition (ICDAR), Beijing, China, pp. 22-26, 2011.
[12] R. Firdousi, S. Parveen. “Local Thresholding Techniques in Image Binarization” International Journal of Engineering and Computer Science, Vol. 3, No. 3, pp. 4062-4065, 2014.
[13] Ø.D. Trier, T. Taxt, “Evaluation of binarization methods for document images”, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 17, Issue 3, pp. 312-315, 1995.
[14] G. Leedham, C. Yan, K. Takru, J.H.N. Tan, L. Mian, “Comparison of some thresholding algorithms for text/background segmentation in difficult document images”, In the proceedings of Seventh International Conference on Document Analysis and Recognition (ICDAR), Edinburgh, UK, pp. 859-864, 2003.
[15] N. Otsu, “A threshold selection method from gray-level histograms”, IEEE transactions on systems, man, and cybernetics, Vol. 9, Issue 1, pp. 62-66, 1979.
[16] B. Gatos, I. Pratikakis, S.J. Perantonis, “Adaptive degraded document image binarization”, Pattern Recognition, Vol. 9, Issue 3, pp. 317-327, 2006.
[17] M.K. Jindal, R.K. Sharma, G.S. Lehal, “A study of different kinds of degradation in printed Gurmukhi script”. In the proceedings of International Conference on Computing: Theory and Applications, (ICCTA`07), Kolkata, India, pp. 538-544, 2007.
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[19] P.K. Sahoo, S. Soltani, A.K.C. Wong, Y.C. Chen, “A survey of thresholding techniques” ,Computer vision, graphics, and image processing, Vol. 41, Issue 2, pp. 233-260, 1988.

Pritpal Singh, Balwinder Singh, "A Review of Document Image Binarization Techniques," International Journal of Computer Sciences and Engineering, Vol.7, Issue.6, pp.746-749, 2019.

In spite of the fact that the electronic advances have experienced quick improvements as of late, cell phones, for example, PDAs are still similarly powerless rather than work areas as far as computational ability, stockpiling and so on, and are not ready to meet the expanding requests from versatile clients. By incorporating portable figuring and distributed computing, versatile distributed computing (MCC) extraordinarily expands the limit of the portable applications, however it additionally acquires numerous difficulties in distributed computing, e.g., information security and information honesty. In this paper, we use a few cryptographic natives, for example, another composes based intermediary re-encryption to plan a protected and proficient information circulation framework in MCC, which gives information security, information respectability, information verification, and adaptable information appropriation with get to control. Contrasted with customary cloud-based information stockpiling frameworks, our framework is a lightweight and effortlessly deployable answer for portable clients in MCC since no confided in outsiders are included and every versatile client just needs to keep short mystery keys comprising of three gathering components for every single cryptographic activity. At last, we present broad execution examination and exact investigations to exhibit the security, versatility, and productivity of our proposed framework

Distributed System, Mobile Cloud Computing

[1] Zhang, J., Zhang, Z. and Guo, H., 2017. Towards secure data distribution systems in mobile cloud computing. IEEE Trans. Mob. Comput, 16(11), pp.3222-3235.
[2] Dinh, H.T., Lee, C., Niyato, D. and Wang, P., 2013. A survey of mobile cloud computing: architecture, applications, and approaches. Wireless communications and mobile computing, 13(18), pp.1587-1611.
[3] Fernando, N., Loke, S.W. and Rahayu, W., 2013. Mobile cloud computing: A survey. Future generation computer systems, 29(1), pp.84-106.
[4] Rahimi, M.R., Ren, J., Liu, C.H., Vasilakos, A.V. and Venkata subramanian, N., 2014. Mobile cloud computing: A survey, state of art and future directions. Mobile Networks and Applications, 19(2), pp.133-143.
[5] Wang, M., Chen, Y. and Khan, M.J., 2014. Mobile cloud learning for higher education: A case study of Moodle in the cloud. The International Review of Research in Open and Distributed Learning, 15(2).
[6] Rao, N.M., Sasidhar, C. and Kumar, V.S., 2012. Cloud computing through mobile-learning. arXiv preprint arXiv:1204.1594.
[7] Jia, W., Zhu, H., Cao, Z., Wei, L. and Lin, X., 2011, April. SDSM: a secure data service mechanism in mobile cloud computing. In Computer Communications Workshops (INFOCOM WKSHPS), 2011 IEEE Conference on (pp. 1060-1065). IEEE.
[8] Feng, D.G., Zhang, M., Zhang, Y. and Xu, Z., 2011. Study on cloud computing security. Journal of software, 22(1), pp.71-83.
[9] Qureshi, S.S., Ahmad, T. and Rafique, K., 2011, September. Mobile cloud computing as future for mobile applications-Implementation methods and challenging issues. In Cloud Computing and Intelligence Systems (CCIS), 2011 IEEE International Conference on (pp. 467-471). IEEE.
[10] Huang, D., Xing, T. and Wu, H., 2013. Mobile cloud computing service models: a user-centric approach. Ieee network, 27(5), pp.6-11.
[11] Wang, Y., Chen, R. and Wang, D.C., 2015. A survey of mobile cloud computing applications: perspectives and challenges. Wireless Personal Communications, 80(4), pp.1607-1623.
[12] Raja, C.V., Chitra, K. and Jonafark, M., 2018. A Survey on Mobile Cloud Computing.
[13] Gu, F., Niu, J., Qi, Z. and Atiquzzaman, M., 2018. Partitioning and offloading in smart mobile devices for mobile cloud computing: State of the art and future directions. Journal of Network and Computer Applications.
[14] Skourletopoulos, G., Mavromoustakis, C.X., Mastorakis, G., Batalla, J.M., Dobre, C., Panagiotakis, S. and Pallis, E., 2017. Towards mobile cloud computing in 5G mobile networks: applications, big data services and future opportunities. In Advances in Mobile Cloud Computing and Big Data in the 5G Era (pp. 43-62). Springer.
[15] Li, Y., Gai, K., Qiu, L., Qiu, M. and Zhao, H., 2017. Intelligent cryptography approach for secure distributed big data storage in cloud computing. Information Sciences, 387, pp.103-115.
[16] Sookhak, M., Yu, F.R. and Tang, H., 2017. Secure data sharing for vehicular ad-hoc networks using cloud computing. In Ad Hoc Networks (pp. 306-315). Springer.
[17] Khan, S., Shiraz, M., Boroumand, L., Gani, A. and Khan, M.K., 2017. Towards port-knocking authentication methods for mobile cloud computing. Journal of Network and Computer Applications, 97, pp.66-78.
Mukherjee, M., Matam, R., Shu, L., Maglaras, L., Ferrag, M.A., Choudhury, N. and Kumar, V., 2017. Security and privacy in fog computing: Challenges. IEEE Access, 5, pp.19293-19304

Tejaswini Paka, Sree Divya, "Data Storage Security and Privacy in Mobile Cloud Computing Using Hierarchical Attribute Based Encryption (HABE)," International Journal of Computer Sciences and Engineering, Vol.7, Issue.6, pp.750-754, 2019.

Wireless Sensor network has improved their enhancement in enormous applications like monitoring environment, health application monitoring, military surveillance and also tracking of target that are of real time field. When there is communication between the nodes it affects the network lifetime which results in large consumption of energy. In order to reduce this energy consumption, Data aggregation techniques have been employed which eliminates the unnecessary data that is travelling from source node to sink node. When Wireless Sensor Network is deployed in a hostile environment the sensor node would be susceptible to node failures and also compromised by the adversary, thus it would become critical. There are different data aggregation approaches in wireless sensor network mainly used to increase the consumption of energy and also there are different approaches to preserve the data aggregation mainly used protect the data. It also preserves the various security issues such as data freshness, data integrity, data confidentiality in data aggregation

Wireless Sensor Network, Data Aggregation, Network lifetime, Energy consumption

[1] Taochun Wang, Xiaolin Qin, and Liang Liu, “An Energy-Efficient and Scalable Secure Data Aggregation for Wireless Sensor Networks”, International Journal of Distributed SensorNetworks, Hindawi, 2013.
[2] Shirshu Varma, Uma Shanker Tiwary, “Data Aggregation in Cluster based Wireless Sensor Networks”, Proceedings of the First International Conference on Intelligent Human Computer Interaction, Springer, 2009, pp 391-400.
[3] Priyanka B.Gaikwad, Manisha R.Dhage,”Survey on Secure Data Aggregation in Wireless Sensor Network”,Internationa lConference on Computing Communication Control and Automation,2015.
[4] Raja Waseem Anwar, Majid Bakhtiari, Anazida Zainal, Abdul Hanan Abdullah and Kashif Naseer Qureshi,, “Security Issues and Attacks in Wireless Sensor Network”, World Applied Sciences Journal, Volume 30, Issue 10, pp. 1224-1227,November 14.
[5] S. Madden et al., “TAG: a Tiny Aggregation Service for Adhoc Sensor Networks”, OSDI 2002, Boston, MA,December 2015.
[6] Adrian Perrig, Robert Szewczyk, Victor Wen, David Culler, J. D. Tygar, “SPINS: Security Protocols for Sensor Networks,” in ACM journal of Wireless Network.
[7] V.Akila , Dr T.Sheela , "Preserving Data and Key Privacy in Data Aggregation in Wireless Sensor Networks” Second International Conference on Computing and Communication Technology,2017.
[8] Joyce Jose, M Prince and Josna Jose, “PEPPDA: Power Efficient Privacy Preserving Data Aggregation for Wireless Sensor Networks” IEEE International Conference on Emerging Trends in Computing, Communication and Nanotechnology,2013.
[9] A.S.Poornima , B.B.Amberker. “ SEEDA : Secure End-to End DataAggregation in Wireless SensorNetworks" IEEE Seventh International Conference on Wireless and Optical Communication Networks 2010.
[10] H.S.Annapurna, M.Siddappa, “Secure Data Aggregation with Fault Tolerance in Wireless Sensor Networks”, IEEE International Conference on Emerging, Research in Electronics and Computer science Technology 2015.
[11] Riker, André, Eduardo Cerqueira, Marilia Curado, and Edmundo Monteiro, ”A two-tier adaptive data aggregation approach for M2M group communication”, IEEE Sensor Journal 16,no.3(2016).
[12] Kiran Maraiya, Kamal Kant, Nitin Gupta, “Wireless Sensor Network: A Review on Data Aggregation” ”, International Journal of Scientific & Engineering Research, Volume 2, Issue 4, 2011.
[13] P.Raghu Vamsi,Krishna Kant,“Secure data aggregation and Instrution detection in Wireless Sensor Networks”, IEEE International Conference on Signal Processing and Communication,2015.
[14] V.Vaidehi,R.Kayalvizhi,N.Chandra Sekar, “Secure Data Aggregation in Wireless Sensor Networks,”IEEE International Conference on Computing Sustainable Global Development,2015.
[15] Mohamed Ben Haj Frej, Khaled Elleithy, “Secure Data Aggregation Model (SDAM) in Wireless Sensor Networks,” IEEE 14th International Conference on Machine Learning and Applications,2015.

Harsha K.M, Divya James, "A Survey on Privacy Preserve Methods in Data Aggregation," International Journal of Computer Sciences and Engineering, Vol.7, Issue.6, pp.755-760, 2019.

Educational system is facing the challenges to improve the quality of education and to make managerial decisions to achieve the quality. Student’s academic performance is the reflection of previous academic background. Performance prediction helps educational institute to support decision making procedures and to formulate better management plans. This performance record is important for the educational institution because they can learn from this to improve their quality by knowing the performance of the student. Educational data mining analyze these data and extract information from it. We can determine the status of student’s academic performance. For achieving this we can use techniques like decision tree, classification, data clustering and neural network and so on. In this paper, we will predict student’s semester wise performance.

Educational Data Mining, Neural Network, Normalization, Knowledge Discovery in Databases, Prediction, Backpropagation algorithm

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Shankar G. Mundhe, Shital Y. Gaikwad, "Performance Prediction In Educational Data Mining using Neural Network," International Journal of Computer Sciences and Engineering, Vol.7, Issue.6, pp.761-764, 2019.

Communication methods and medium of our daily life has been changed by the new emerging Technologies. We can go back in our past and look how communication has got so easier over the years. We are using different types of communication tools with outstanding outputs that we could only imagine some years ago. To communicate with someone in society today, we have so many options present like we can do chat on social media websites, text them, email, or using the traditional approach with new features of calling to a person. Artificial Intelligence is the most popular and advanced technology which we are using now a days in every field of work. The Artificial Intelligence concept and features is used by most of the search engines, websites, smart phones, and websites and smart phone OS to improve their performance. The text to speech and Speech Recognition system made by Google is a very unique feature which helps every type of persons in making effective communication. In this paper we portray a problem statement on Artificial Intelligence Tools usage on a single Classroom of students where each of them are of different countries and admitted in a single course in a university and if any teacher delivers a lectures in his or her class in a single language.

Search Engines, Artificial Intelligence, Text-to-Speech, Google Assistance, Natural Language Processing (NLP).

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[2]. Stefan A. D. Popenici and Sharon Kerr, Exploring the impact of artificial intelligence on teaching and learning in higher education.
Available at: https://telrp.springeropen.com/articles/10.1186/s41039-017-0062-8
[3]. Bernard Marr ,"How Is AI Used In Education -- Real World Examples Of Today And A Peek Into The Future", https://www.forbes.com/sites/bernardmarr/2018/07/25/how-is-ai-used-in-education-real-world-examples-of-today-and-a-peek-into-the-future/#1035cd5d586e
[4]. TUOMI I., CABRERA GIRALDEZ Marcelino, VUORIKARI Riina, PUNIE Yves, "The Impact of Artificial Intelligence on Learning, Teaching, and Education", EUR - Scientific and Technical Research Reports, Publications Office of the European Union, ISBN: 978-92-79-97257-7 (online), ISSN:1831-9424 (online), DOI:10.2760/12297 (online)
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Saurabh Jha, Priti Mishra, "A Survey on the Use of Artificial Intelligence Technology and Google Tools for Multilingual Students Sitting and Studying in a Common Classroom," International Journal of Computer Sciences and Engineering, Vol.7, Issue.6, pp.765-768, 2019.

This paper focuses on the identification and modeling of the pilot plant of the department. For this work, four loops of pilot plant are considered. Level loop, flow loop, cascade loop and temperature loop. Transfer function for level loop is derived by using process reaction curve and the flow loop transfer function is derived using dynamics of Control valve and specifications of process plant components. Cascade Loop is the series combination of flow and level loops. The temperature system is nonlinear in nature due to three variables initial temperature, input flow and heat supplied by heater. Hence different control schemes are considered to control the various conditions i.e. batch process, continuous Process with constant input and output flow. Also Feedback linearization is used for as a unique control scheme. Pilot plant being a multi loop system, there is interaction between the loops such as level and temperature loop. The interaction is eliminated with the help of decoupler and relative gain array to obtain non-interacting level and temperature loops such that the temperature can be controlled without affecting the level parameter of the system.

Mathematical Model, Pilot plant,Feedback Linearization, Relative gain array, Decoupler

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Saurabh U. Mohite, Abhishek P. Patil, Shreyas D. Patil, D. N. Pawar, "Mathematical Modeling and Simulation of Multi Loop Pilot Plant," International Journal of Computer Sciences and Engineering, Vol.7, Issue.6, pp.769-774, 2019.