These paper is about my android application made for Schools. This app is an online interactive platform for Students, Teachers and Parents to enable digital school and have efficient & continuous communication. This platform enables Teachers, School Admin to digitize process and reduce manual work. This application is easy operate. By using this application parents can be familiar with the school environment. They can know the complete details of syllabus, attendance and mark list. By this project students can get close to school environment. They can get every updates regarding their marks, syllabus, academic plans and fee. All the copyrights will be provided by admin. The main rights are provided to admin he can do everything and can watch everybody's profile.

Keywords –Android Application, Student, Teacher, Parent.

[1]. A.J.Kadam, Aradhana Singh, Komal Jagtap, Srujana Tankala," Mobile Web Based Android Application for College Management
Sysytem", International Journal of Advanced Research in Computer and Communication Engineering Vol. 6, Issue 2, Feb 2017.
[2]. Pallavi Mohadikar, Nasrin Mulani, Afnan Shaikh, Rachna Sable,"College Parent Interaction using Android
Application",International Journal of Computer Science & Network,Volume 4, Issue 1, February 2015.
[3]. Manasi Kawathekar, Kirti Bhate, Pankaj Belgaonkar,"An Android Application for Student Information System", International
Journal of Advanced Research in Computer Engineering & Technology, Volume 4, Issue 9, September 2015.
[4]. Urvashi Konpare, Abhilasha Awachat, Suchita Raut," E-School Android Application ",International Conference on Recent Trends
in Engineering Science & Technology, Volume 5, Issue 1, January 2017.

Email spam or junk e-mail (unwanted e-mail "usually of a commercial nature sent out in bulk") is one of the major problems of the today's Internet, bringing financial damage to companies and annoying individual users. Among the approaches developed to stop spam, filtering is an important and popular one. Common uses for mail filters include organizing incoming email and removal of spam and computer viruses. A less common use is to inspect outgoing email at some companies to ensure that employees comply with appropriate laws. Users might also employ a mail filter to prioritize messages, and to sort them into folders based on subject matter or other criteria. Mail filters.......

Keywords: Spam Filtering, Machine learning, Learning-Based Methods, Classification

[1]. Aladdin Knowledge Systems, Anti-spam white paper,Retrieved December 28, 2011.
[2]. F. Smadja, H. Tumblin, "Automatic spam detection as a text classification task", in: Proc. of Workshop on Operational Text Classification Systems, 2002.
[3]. A. Hassanien, H. Al-Qaheri, "Machine Learning in Spam Management", IEEE TRANS., VOL. X, NO. X, FEB.2009
[4]. P. Cunningham, N. Nowlan, "A Case-Based Approach to Spam Filtering that Can Track Concept Drift", Retrieved December 28, 2011
[5]. M. Sahami, ―Learning Limited Dependence Bayesian Classifiers,‖ Proceedings of the Second International Conference on Knowledge Discovery and Data Mining, The AAAI Press, Menlo Park, 1996, pp. 334-338.

This paper deal with a computer system capable of integrating, storing, analyzing, and displaying geographic information. Currently, the Geographic Information System is not only inadequate to cartography but also related to various data management such as natural resource management, environmental impact assessment, and etc. The study area is Marathawada region selected, the Hydrologic Information System can be developed using the selected data model Arc hydro. The ArcGIS Hydro data model Arc Hydro is a geospatial and temporal data model for surface water resources that operates within ArcGIS and supports hydrologic simulation models. Arc Hydro defines a set of water resources feature classes...........

Keywords:Web-Based GIS; Geographic Information System; Watershed; water resource management; Archydro

[1] Arc Hydro: GIS for Water Resources by David Maidment(ESRI Press)
[2] Comprehensive Terrain Preprocessing Using Arc Hydro Tools: http://www.hydrosys.net
[3] M.B.AbbottandA.W.Minns(1998),"Computationalhydraulics"(2ndEd.)AshgatePress, Aldershot, 557p.
[4] https://www.tutorialspoint.com/asp.net/asp.net_tutorial.pdf
[5] J.G.Arnold R.Srinivasan, R.S.Muttiah, J.R.Williams 1998.Large area hydrologic modeling and assessment- Part 1:"

Distributed Denial of Service (DDoS) attacks are a virulent, relatively new type of attack on the availability of Internet services and resources. DDoS attackers infiltrate large numbers of computers by exploiting software vulnerabilities, to set up DDoS attack networks.These unwitting computer are then invoked to wage a coordinated, large-scale attack against one or more victim systems. As specific counter measures are developed, attackers enhance existing DDoS attack tools, developing new and derivative DDoS techniques and attack tools. Rather than react to new attacks with specific countermeasures, it would be desirable to develop comprehensive DDoS solutions that defend against known and future............

Keyward: Application layer DDoS attacks, sketch data structure, intrusion prevention system, cloud computing

[1]. Chenxu Wang , Tony T. N. Miu, Xiapu Luo , and Jinhe Wang, SkyShield: A Sketch-Based Defense SystemAgainst Application Layer DDoS Attacks 2018
[2]. Kiwon Hong, Younjun Kim, Hyungoo Choi, and Jinwoo Park, SDN-Assisted Slow HTTP DDoS AttackDefense Method 2018
[3]. Mais Nijim 1 , Hisham Albataineh 2 , Mohammad Khan 1 , Deepak Rao 1, FastDetict: A Data Mining Engine for Predecting andPreventing DDoS Attacks 2017
[4]. Gaurav Somani, Manoj Singh Gaur, Dheeraj Sanghi, Mauro Conti, Rajkumar Buyya 2017 DDoS attacks in cloud computing: Issues, taxonomy, and future Directions.2017
[5]. Gaurav Somani,Manoj Singh Gaur, Combating DDoSAttacks in the Cloud: Requirements, Trends,and Future Directions,2017

Wireless sensor networks are the networks which can sense, analyze and then communicate the data. Thedata collection at sensor nodes consumes a lot of energy but sensor nodes are energy constraints. Most of the WSNarchitectures consist of stationary nodes which are heavily deployed over a sensing area. In recent times, severalWSN architectures based on mobile elements (MEs) have been proposed. The usage of Mobile elements resulted in anewer mode in order to minimize and provide a energy consumption in wireless sensor networks (WSN). Most ofthem develop mobility to deal with the problem of data collection in WSNs. The scheduling of mobile elements needto address the traverse pattern and also the time of data collection from respective sensor nodes...............

Keywords:Data Collection, Mobile Elements, Power Management, Discovery, Data Communication, Data Forwarding, Motion Control

[1] Mihaela Mitici, Jasper Goseling, Maurits de Graaf, Richard J. Boucherie,"Energy-efficient data collection in wireless sensor networks with time constraints", Performance Evaluation, Volume 102, 2016, Pages 34-52, ISSN 0166-5316, https://doi.org/10.1016/j.peva.2016.06.001.
[2] Rupali Rohankara, C.P.Katti, Sushil Kumar, "Comparison of Energy Efficient Data Collection Techniques in Wireless Sensor Network",3rd International Conference on Recent Trends in Computing 2015, Volume 57, 2015, Pages 146-151, https://doi.org/10.1016/j.procs.2015.07.399.
[3] G.Kannan, T.Sree Renga Raja,"Energy efficient distributed cluster head scheduling scheme for two tiered wireless sensor network", Egyptian Informatics Journal, Volume 16, Issue 2, July 2015, Pages 167-174, https://doi.org/10.1016/j.eij.2015.03.001

Internet most vital information retrieval service is the World Wide Web (WWW). It act as a media for retrieving information related to education, hospital, marketing, games, sports, society,politics,history and Finance etc. The utter volume of networking growth, cyber physical social systems (CPSS) provides huge information and that will leads to the information overload on the web so getting relevant information for achieving the search results and user intention capturing has acquired as a major challenge specifically in personalized websites. Traditional crawlers face complexity...........

Keywords: Cyber Physical Social Systems, PageRank algorithm, Personalized Web search, Web Logs

[1]. X. Wang, L. T. Wang, X. Xie, J. Jin, and M. J. Deen, ``A cloud-edge computing framework for cyber-physical-social services,'' IEEE Commun.Mag., vol. 55, no. 11, pp. 80_85, Nov. 2017.
[2]. X. Wang, L. T. Yang, J. Feng, X. Chen, and M. J. Deen, ``A tensor-based big service framework for enhanced living environments,'' IEEE CloudComput., vol. 3, no. 6, pp. 36_43, Nov./Dec. 2016.
[3]. J. Zeng, L. T.Yang, M. Lin, H. Ning, and J. Ma, ``Asurvey: Cyber-physicalsocial systems and their system-level design methodology,'' FutureGenerat. Comput. Syst., Aug. 2016.
[4]. X. Liu and H. Turtle, ``Real-time user interest modeling for real-time ranking,'' J. Amer. Soc. Inf. Sci. Technol., vol. 64, no. 8, pp. 1557_1576, 2013.
[5]. M. Claypool, P. Le, M. Waseda, and D. Brown. Implicit interest indicators. In Proceedings of Intelligent UserInterfaces 2001, pages 33–40, 2001.

Now a day, people face various diseases due to the environmental condition and living habits of them. So prediction of disease at earlier stage becomes important task. But the prediction on the basis of symptoms becomes too difficult for doctor. The correctly prediction of disease is most challenging task. To overcome this problem data mining plays an important and efficient way to predict the disease. Medical science has huge amount of data growth per year. Due to increase amount of data growth in medical and healthcare field the accurate analysis on medical data which has been benefits from early patient care. With the help of disease data, data mining finds hidden pattern information in the large amount of medical data. We have designed the heart disease prediction system............

Keywords: Classification algorithm, machine learning, heart diseases prediction, data mining

[1]. M. Chen, Y. Hao, K. Hwang, L. Wang, and L. Wang,"Disease prediction by machine learning over big data from healthcare communities", ," IEEE Access, vol. 5, no. 1, pp. 8869–8879, 2017.
[2]. B. Qian, X. Wang, N. Cao, H. Li, and Y.-G. Jiang, "A relative similarity based method for interactive patient risk prediction," Springer Data Mining Knowl. Discovery, vol. 29, no. 4, pp. 1070–1093, 2015.
[3]. IM. Chen, Y. Ma, Y. Li, D. Wu, Y. Zhang, and C. Youn, "Wearable 2.0: Enable human-cloud integration in next generation healthcare system," IEEE Common., vol. 55, no. 1, pp. 54–61, Jan. 2017.
[4]. Y. Zhang, M. Qiu, C.-W. Tsai, M. M. Hassan, and A. Alamri, "HealthCPS: Healthcare cyberphysical system assisted by cloud and big data," IEEE Syst. J., vol. 11, no. 1, pp. 88–95, Mar. 2017.
[5]. L. Qiu, K. Gai, and M. Qiu, "Optimal big data sharing approach for telehealth in cloud computing," in Proc. IEEE Int. Conf. Smart Cloud (Smart Cloud), Nov. 2016, pp. 184–189.

In today's world web has gained prominence because of its own just as web development due to which there is a substantially more need of the technique by which we can upgrade the efficiency of findingthe deep-web interface. There is a strategy which surfs the World Wide Web in automatic way known as a web crawler. Deep web databases are remotely distributed, and keep invariablyaltering. To take care of this issue, work done in advance gives two sorts of crawler: generic crawlers and focused crawlers. Focused crawling has drawn a lot of attention from scientists in the past decade. Focused crawler searches the particular term or topic on internet. Vertical search is done very presizely and good searching strategies helps to enhance the accuracy so Best-First search strategy is utilized but...........

Index Terms: Focused Crawler, Genetic algorithm, PageRank, Best First Search, Site ranking algorithm.

[1]. Wei Yan and Li Pan" Designing Focused Crawler Based On Improved Genetic Algorithm", 2018 Tenth International Conference on Advanced Computational Intelligence (ICACI) March 29–31, 2018, Xiamen, China.
[2]. Soumen Chakrabarti, Martin van den Berg 2, Byron Domc, ""Focused crawling: a new approach to topic-specific Web resource discovery", Published by Elsevier Science B.V. All rights reserved in 1999
[3]. Kevin Chen-Chuan Chang, Bin He, Chengkai Li, Mitesh Patel, and Zhen Zhang. Structured databases on the web: Observations and implications. ACM SIGMOD Record, 33(3):61-70, 2004.
[4]. Soumen Chakrabarti, Kunal Punera, and Mallela Subramanyam. Accelerated focused crawling through online relevance feedback. In Proceedings of the 11th international conference on World Wide Web, pages 148-159, 2002.
[5]. Sriram Raghavan and Hector Garcia-Molina. Crawling the hidden web. In Proceedings of the 27th International Conference on Very Large Data Bases, pages 129-138, 2000.

Malware is the mainly severe threats now a days many attack were launched using open platform android With the increasing significance of malware in Internetattacks, much research has concentrated on developing techniques to collect, study, and mitigate malicious Without confusion , it is compulsory to collect and study malware found on the mobiles . However, it is even more important to develop improvement and revealing techniques based on the insights gain from the study work. Thus developing malware finding approach that is both effective and well-organized, and thus, can be used to replace or set off traditional malware detection methods by using signature, permission as well user feedback methods.

Keywords- malware, filter ,signature

[1] http://www.importantindia.com/24012/importance-of-mobile-phones-in-our-daily-life/
[2] https://www.sophos.com/en-us/security-news-trends/security-trends/malware-goes-mobile.aspx
[3] https://recombu.com/mobile/article/what-is-android-and-what-is-an-android-phone_M12615.html
[4] https://www.dreamstime.com/photos-images/android-logo.html
[5] M. Piercy, "Embedded devices next on the virus target list," in Electronics Systems and Software, vol. 2, no. 6, pp. 42-43, Dec. 2004- Jan. 2004

When it comes to storing data, cloud storage is rapidly turning into the procedure for choice. Cloud storage isquickly becoming the strategy for decision. Putting away files remotely instead of by locally boasts an array of preferencesfor both home and professional clients. Cloud storage means "the storage of data online in the cloud", However, the cloudstorage is not completely trusted. Whether the data put away on cloud are in place or not turns into a significant concernof the clients also access control becomes a difficult job, particularly when we share data on cloud servers. To tacklethis issue Time and Attribute Factors Combined Access Control for Time-Sensitive Data............

Key Words: Attribute Based Encryption, Cloud computing, Time and Attribute Factors Combined Access Control

[1]. Jin Li, Jingwei Li, Xiaofeng Chen, ChunfuJia, and Wenjing Lou, "Identity-Based Encryption with Outsourced Revocation in Cloud Computing", in IEEE transactions on computers, vol. 64, no. 2, february 2015.
[2]. W. Aiello, S. Lodha, and R. Ostrovsky, "Fast digital identity revocation," In Advances in Cryptology (CRYPTO98). New York, NY, USA:Springer, 1998, pp. 137-152.
[3]. A. Boldyreva, V. Goyal, and V. Kumar, "Identity-based encryption with efficient revocation," in Proc. 15thACMConf. Comput. Commun.Security (CCS08), 2008, pp. 417-426.
[4]. D. Boneh and M. Franklin, "Identity-based encryption from the Weilpairing," in Advances in Cryptology (CRYPTO "01), J. Kilian, Ed.Berlin, Germany: Springer, 2001, vol. 2139, pp. 213-229.
[5]. A. Sahai and B. Waters, "Fuzzy identity-based encryption," in Advances in Cryptology (EUROCRYPT‟05), R. Cramer, Ed. Berlin, Germany: Springer, 2005, vol. 3494, pp. 557-557.

As connected objects creating Internet of Things (IoT), a large extents of data are generated every moment.These data are difficult to handle using traditional structure of database leads to provide object databases(NoSQL approach) due to their high performance, flexibility in scaling, and high availability. In pattern matching of object databases, embeded librabrary viz. objectDatabase++ provide effective way large number of in build index designes which gives faster access to variety of datatypes from spatial B+ Tree and pattern matching to IoT biometric applications.We compare five of the most popular NoSQL databases namely, Redis, Cassandra, MongoDb, Couchbase and Neo4jwith IoT data management requirements, in NoSQL database for IoT applications.

Keyword: NoSQL,Redis, Cassandra, MongoDb, Couchbase and Neo4j,objectDatabase++

[1]. Grolinger K, Higashino WA, Tiwari A, Capretz MA. Data management in cloud environments: NoSQL and NewSQL data stores. Journal of Cloud Computing: Advances, Systems and Applications. 2013;2(1):1.
[2]. Hecht R, Jablonski S, editors. Nosql evaluation. International conference on cloud and service computing; 2011: IEEE.
[3]. UnQL http://unql.sqlite.org/index.html/timeline.
[4]. Carra D, Michiardi P, editors. Memory partitioning in memcached: An experimental performance analysis. 2014

With growing reach of internet, today various organizations are using internet for storing, processing, transmitting their sensitive information and data. Destruction, theft, denial of such information leads to huge financial losses to organizations. In today's world cyber security is not only important for organizations but also for ordinary people who uses internet. This paper describes various types of cyber attacks and recent instances of these attacks. It also describes emerging technologies in the field of cyber security. This paper addresses the need of strengthening and securing the space.

Keywords: cyber security, cyber attacks, cyber crime, malware, blockchain, context aware security, vdn

[1]. "Defining Malware: FAQ". technet.microsoft.com. Retrieved 10 September 2009.
[2]. "An Undirected Attack Against Critical Infrastructure" (PDF). United States Computer Emergency Readiness Team(Us-cert.gov). Retrieved 28 September 2014.
[3]. Young, A.; M. Yung (1996). Cryptovirology: extortion-based security threats and countermeasures. IEEE Symposium on Security and Privacy. pp. 129–140. doi:10.1109/SECPRI.1996.502676. ISBN 0-8186-7417-2.
[4]. Jack Schofield (28 July 2016). "How can I remove a ransomware infection?". The Guardian. Retrieved 28 July 2016.
[5]. "Exploit on Amnesty pages tricks AV software". The H online. Heinz Heise. 20 April 2011. Retrieved 8 January 2011.

The energy inside the Wireless Sensor Nodes are having low memory and least battery life. Due to this the main energy is utilized during sending of the data. Some part of energy is utilized in processing the data due this problem, we need a process by which the energy consumption can be manage. In this will consider the cost of sending as well as processing. So, we will use short distance path as well as compression of the data to reduce the power consumption. To designed and implemented it, a dynamic trust-aware routing framework for dynamic WSNs. trustworthy and energy-efficient route. Most importantly, It proves effective against those harmful attacks developed out of identity deception; the resilience of it is verified through extensive evaluation with both simulation under various scenarios

Keywords: Wireless Sensor Network, Energy optimization, compression, efficiency.

[1]. R.-C. Chen et al., "Using ambient intelligence to extend network lifetime in wireless sensor networks", Springer, Received: 19 June 2015 / Accepted: 31 August 2015, Published online: 30 sept 2015.
[2]. Suraj Sharma and Sanjay Kumar Jena, "Cluster based Multipath Routing Protocol for Wireless Sensor Networks", ACM SIGCOMM Computer Communication Review, Volume 45, Number 2, April 2015.
[3]. Shubhanjali Sharma, Vinesh Jain, M.C. Govil,"Base Station Controlled and Energy Efficient Centralized Hierarchical Routing Protocol", 2015 IEEE International Symposium on Nanoelectronic and Information Systems (iNIS), IEEE, 21-23 Dec. 2015.
[4]. Abdul Rehman Khan, Nitin Rakesh, Abhay BansaP and Dev Kumar Chaudhary,"Comparative Study ofWSN Protocols", 2015 Third International Conference on Image Infonnation Processing, IEEE,2015.
[5]. Shaikh, F. K., & Zeadally, S. (2016). Energy harvesting in wireless sensor networks: A comprehensive review. Renewable and Sustainable Energy Reviews, 55, 1041–1054..

Cloud computing offers utility-oriented IT services to millions of users concurrently. Cloud computing is acting at leading role in world's technical industry .Cloud Computing demand growing drastically, which has imposed cloud service provider to make certain proper resource utilization with less cost and less energy consumption. In paper present ,Tasks are scheduled in such a way to reduce the actual CPU time and mostly system execution time or unlike consolidation problems initiate in cloud computing related to the, task ,VM and server consolidation. Thes problems develop to be challenging for resource utilization in cloud computing. Scheduling of independent.........

Keywords: Cloud computing; Shortest Job First Scheduling; Load Balancing; Resource utilization; Makespan; Energy-efficient

[1]. Ekta Rani , Harpreet Kaur "Study on Fundamental Usage of cloudsim simulator and Algorithm of Resource Allocation in cloud Computing "July 3-5, 2017, IIT Delhi, Delhi, India.
[2]. Sonam Seth1, ipur Singh2 "Dynamic heterogeneous shortest job first (DHSJF): a task scheduling approach for heterogeneous cloud computing systems" April 2018.
[3]. Mokhtar A. Alworafi , Atyaf Dhari , Asma A. Al-Hashmi An Improved SJF Scheduling Algorithm in Cloud Computing Environment 2016 International Conference
[4]. Rajat Pugaliya *1, Prof. Madhu B R "Algorithm for task Consolidation in cloud computing" Pugaliya et. al., (Iss.5): May 2018 .
[5]. D.ChitraDeviandV. "Load Balancing in Cloud Computing Environment Using Improved Weighted Round Robin Algorithm for Nonpreemptive Dependent Tasks" RhymendUthariaraj( 2015).