With the proliferation of websites, the security level of password-protected accounts is no longer purely determined by individual ones. Users may register multiple accounts on the same site or across multiple sites, and these passwords from the same users are likely to be the same or similar. As a result, an adversary can compromise the account of a user on a web forum, then guess the accounts of the same user in sensitive accounts, e.g., online banking services, whose accounts could have the same or even stronger passwords. We name this attack as the shadow attack on passwords. To understand the situation, we examined the state of-theart Intra-Site Password Reuses (ISPR) and Cross-Site Password Reuses (CSPR) based on the leaked passwords from the biggest Internet user group (i.e., 668 million members in China).............

[1]. R. Morris and K. Thompson, ―Password security: A case history,‖ Communications of the ACM, vol. 22(11), pp. 594–597, 1979.
[2]. A. Das, J. Bonneau, M. Caesar, N. Borisov, and X. Wang, ―The tangled web of password reuse,‖ in NDSS'2014, 2014.
[3]. D. Florencio and C. Herley, ―A large-scale study of web password habits,‖ in WWW'07 Proceedings of the 16th international conference on World Wide Web, 2007, pp. 657–666.
[4]. CSDN, ―http://www.csdn.net/company/about.html.‖
[5]. Tianya, ―http://help.tianya.cn/about/history/2011/06/02/

Delay tolerant networks (DTNs) are often encountered in military network environments where endto- end connectivity is not guaranteed due to frequent disconnection or delay. This work proposes a provenancebased trust framework, namely PROVEST (provenance-based trust model) that aims to achieve accurate peerto- peer trust assessment and maximize the delivery of correct messages received by destination nodes while minimizing message delay and communication cost under resource-constrained network environments. Provenance refers to the history of ownership of a valued object or information. We leverage the interdependency between trustworthiness of information source and information itself in PROVEST...........

Keywords: Delay tolerant networks, provenance, store-and-forward, trust, trustworthiness

[1]. T. Spyropoulos, R. Rais, T. Turletti, K. Obraczka, and A. Vasilakos, ―Routing for disruption tolerant networks: taxonomy and design,‖ Wireless Networks, vol. 16, no. 8, pp. 2349–2370, 2010.
[2]. I.-R. Chen, F. Bao, M. Chang, and J.-H. Cho, ―Trust management for encounter-based routing in delay tolerant networks,‖ in IEEE Global Telecommunications Conference, Miami, FL, 6-10 Dec. 2010, pp. 1–6.
[3]. ——, ―Dynamic trust management for delay tolerant networks and its application to secure routing,‖ IEEE Transactions on Parallel and Distributed Systems, vol. 25, no. 5, pp. 1200–1210, May 2014.
[4]. P. Buneman, S. Khanna, and W. Tan, ―Why and where: A characterization of data provenance,‖ in Proceedings of International Conference on Database Theory, Springer-Verlag, 2001, pp. 316–330.
[5]. J.-H. Cho, M. Chang, I.-R. Chen, and A. Swami, Trust Management VI, IFIP Advances in Information and Communication Technology. 6th IFIPTM, Surat, India: Springer, 2012, vol. 374, ch. A Provenancebased Trust Model of Delay Tolerant Networks, pp. 52–67.

Email Security has become the forefront of network management and implementation. In distributed environments, electronic mail is the most used network based application. Users able to send e-mail to others, who are connected directly or indirectly to the internet using communications suite, Electronic mail networks have grown in both size and importance in a very short period of time. Electronic mail security describes that the policies and procedures implemented by a network administrator to avoid and keep track of unauthorized access, exploitation, modification, or denial of the network and network resources. Now a day's variety of security mechanisms is implemented. Email security is the collective action used to secure the access and
content of an email account or service. It allows an individual or organization to protect the overall access to one or more email addresses/accounts. This paper delivers two approaches that are used to protect email from unauthenticated access.

Keywords:Authentication, Confidentiality, E-mail Security Enhancements, Cryptographic Algorithms (SHA, DSS…)

[1]. Ms.Supriya and Mrs.Manju Khari, "MANET security breaches: Threat to a Secure communication platform", International Journal on AdHoc Networking Systems
[2]. (IJANS) Vol. 2, No. 2, April 2012
[3]. Satyam Shrivastava, Sonali Jain "A Brief Introduction of Different type of Security Attacks found in Mobile Ad-hoc Network "International Journal of Computer Science & Engineering Technology (IJCSET) Vol.4. No.3. 2013 ISSN : 2229-334
Books:
[4]. William Stallings, Cryptography and Network Security (Principles and Practice, Pearson, Fifth Edition).

Investment decision is a major issue for every individual. The spectrum of investment is extremely wide. Many investment options are available for the investor. People are not aware of best saving scheme for their investment. This paper deals with information from various domain to suggest the investor best investment option for his investment. The decision is based upon various parameters of the investor. A refined algorithm helps the investor to make an effective decision for his investment which suits their requirement. Expert's rules and feature reduction technique have been applied to this data set to convert it into an optimal dataset. Decision tree technique is applied to make investment decisions.

Keywords: Data mining, Decision tree, Expert rules, gain ratio, feature reduction.

[1]. Weka, University of Waikato, New Zealand, http://www.cs.waikato.ac.nz/ml/weka.
[2]. Income Tax and Investment Journal – (AY-2008-09)–by A.N. Agarwal (Income tax expert), Rajesh Agrwal(CA), Sanjay Kulkari (CA), and Dr. Gajanan Patil.- ABC Publication- Nagpur.
[3]. Dr.Binod Kumar Singh" A study on investors‟ attitude towards mutual funds as an investment option", International Journal of Research in Management ISSN 2249-5908 Issue2, Vol. 2 (March-2012).
[4]. S. Saravana Kumar in his article "An Analysis of Investor Preference Towards Equity and Derivatives" published in The Indian journal of commerce, July-September 2010
[5]. Mohammed M Mazid, A B M Shawkat Ali, Kevin S Tickle, "Improved C4.5 Algorithm for Rule Based Classification‟, Recent Advances in Artificial Intelligence, Knowledge Engineering and Data Bases.
[6]. Chotirat "Ann" Ratanamahatana and Dimitrious Gunopulos, "Scaling up the Naïve Bayesian Classifier: Using Decision Tree for Feature Selection‟

Many websites are based on domain specific contents for profile hosting, for example: locations on foursquare, Products in flipkart, amazon and movies in IMDB using the comments post by users on. When user is comment on the particular entity the reference and comparison is made from each and every entities in it. Thus we often mention on entities with comparision makes an object and entity relation ship model that compared to other web pages, in tweets it disambiguate entities of comment by users that is yet to received, many of users reveal their focal linking locations such as land marks, shops, restaurants and mall in tweets, the problem arise in disambiguate with already mention entities of user comments which has not yet received. Fine grained locations make linking of entities easily of location profiles with the gio-coordinates. It is very often to define location profiles.............

[1]. M. Cornolti, P. Ferragina, and M. Ciaramita, "A framework for benchmarking entity-annotation systems," in Proc. 22nd Int. Conf. World Wide Web, 2013, pp. 249–260.
[2]. X. Han and L. Sun, "A generative entity-mention model for linking entities with knowledge base," in Proc. 49th Annu. Meeting Assoc. Comput. Linguistics: Human Language Technol., 2011, pp. 945–954.
[3]. Y. Fang and M.-W. Chang, "Entity linking on microblogs with spatial and temporal signals," Trans. Assoc. Comput. Linguistics, vol. 2, pp. 259–272, 2014.
[4]. W. Shen, J. Wang, and J. Han, "Entity linking with a knowledge base: Issues, techniques, and solutions," IEEE Trans. Knowl. Data Eng., vol. 27, no. 2, pp. 443–460, Feb. 2015.
[5]. F. Hasibi, K. Balog, and S. E. Bratsberg, "Exploiting entity linking in queries for entity retrieval," in Proc ACM Int. Conf. Theory Inf. Retrieval, 2016, pp. 209–218.

Real Time interloping endurance system in divergent wireless sensor networks is proposed in this study, to minimize the redundancy and maximize the tolerance life time through increasing mean time to repair on nodes and analyzing Mean time Between Failures. Multipath routing helps in successful completion of the task, but identifying the malicious nodes between divergent networks and surpassing them is often energy consuming and time-delayed. In order to overcome this, a real time system was formulated by identifying the Mean time to repair and the mean time between failures in multipath routing. When there are more than two nodes in the divergent network are identified as malicious, the Mean Time to Repair is applied on the specific network so as to rely on the particular network's tolerance and once the decrease in mean............

Keywords: Interloping detection, interloping endurance, Redundancy minimization, secure transmission

[1]. S. Bo, L. Osborne, X. Yang, and S. Guizani, "Intrusion detection techniques in mobile ad hoc and wireless sensor networks," IEEE Wireless Commun. Mag., vol. 14, no. 5, pp. 560–563, 2007.
[2]. A. P. R. da Silva, M. H. T. Martins, B. P. S. Rocha, A. A. F. Loureiro, L.B. Ruiz, and H. C. Wong, "Decentralized intrusion detection in wireless sensor networks," in Proc. 2005 ACM Workshop Quality Service Security Wireless Mobile Netw.
[3]. Y. Zhou, Y. Fang, and Y. Zhang, "Securing wireless sensor networks: a survey," IEEE Commun. Surveys & Tutorials, vol. 10, no. 3, pp. 6–28, 2008 .
[4]. Philipp Hurni and Torsten Braun "Energy-Efficient Multi-Path Routing in Wireless Sensor Networks" 7th International Conference, September 10-12, 2008.
[5]. Jing Deng, Richard Han, Shivakant Mishra "INSENS: Intrusion-Tolerant Routing in Wireless Sensor Networks" University of Colorado, Department of Computer Science Technical Report CU-CS-939-02.

Web clustering is one of the familiar used techniques in the context of Web mining, which is to aggregate Web objects, such as Web pages or users session, into a number of object groups by measuring the mutual vector distance. Clustering can be performed upon these two types of Web objects, which results in clustering Web users or Web pages. The resulting Web user session groups are considered as representatives of user navigational behavior patterns, while Web page clusters are used for generating task-oriented functionality aggregations of Web organizations. The mined usage knowledge in terms of Web usage patterns and page aggregates can be utilized to improve Web site structure designs

Keywords - Web clustering, web mining, vector distance, web objects.

[1]. Zhang, Y., J.X. Yu, and J. Hou, Web Communities: Analysis and Construction. 2006, Berlin Heidelberg: Springer.
[2]. Ghani, R. and A. Fano. Building Recommender Systems Using a Knowledge Base of Product Semantics. in Proceedings of the Workshop on Recommendation and Personalization in E-Commerce, at the 2nd International Conference on Adaptive Hypermedia and Adaptive Web Based Systems (AH2002). 2002, p. 11-19, Malaga, Spain.
[3]. Chakrabarti, S., et al. The Structure of Broad Topics on the Web. in Proceeding of 11th International World Wide Web Conference. 2002, p. 251 - 262, Honolulu, Hawaii, USA.
[4]. Büchner, A.G. and M.D. Mulvenna, Discovering Internet Marketing Intelligence through Online Analytical Web Usage Mining. SIGMOD Record, 1998. 27(4): p. 54-61.
[5]. Chang, G., et al., eds. Mining the World Wide Web: An Information Search Approach. The Information Retrieval. Vol. 10. 2001, KAP.

Autism spectrum disorder (ASD) has become one of the most prevalent mental disorders over the last few years and its prevalence is still growing every year. It is a serious developmental disorder that afflicts children and that is more common than childhood cancer. The disorder is characterized by a wide variety of possible symptoms such as developmental disabilities, extreme withdrawal, lack of social behaviour, severe language and attention deficits, and repetitive behaviours. The symptoms intensity ranges from almost unnoticeable to very severe. Because of this wide variety of symptoms and intensity, therapy needs to be individualized for every person. On the other hand, the most recent application of virtual reality is the interface to e-learning applications, which is also known as virtual reality based e-learning tool. The potential of virtual reality tool is demonstrated by its ability to facilitate learning processes while avoiding many problems characterizing traditional or conventional teaching learning methods.

Keywords- AUTISM spectrum disorder, virtual reality, e-learning tool

[1]. J. Bishop, "The Internet for educating individuals with social impairments," Journal of Computer Assisted Learning, vol. 19, pp. 546-556, 2003.
[2]. [5] M. Dauphin, E. M. Kinney, and R. Stromer, "Using Video-Enhanced Activity Schedules and Matrix Training to Teach Sociodramatic Play to a Child with Autism," Journal of Positive Behavior Interventions, vol. 6, pp. 238-250, 2004.
[3]. T. Miller, G. Leroy, J. Huang, S. Chuang, and M. H. Charlop-Christy, "Using a Digital Library of Images for Communication: Comparison of a Card-Based System to PDA Software," presented at First International Conference on Design Science Research in Information Systems and Technology, Claremont, CA, 2006.
[4]. G. Rajendran and P. Mitchell, "Computer mediated interaction in Asperger's syndrome: the Bubble Dialogue program," Computers and Education, vol. 35, pp. 189-207, 2000.
[5]. Happe, F. & Frith, U. (1996). The neuropsychology of autism. Brain - A jnl of Neurology, 119(4):1377-1400 Hardy, C., Ogden, J., Newman, J. & Cooper, S. (2002) Autism and ICT: A Guide for Teachers and Parents. London, UK: David Fulton Publishers Ltd.

Artificial intelligence has the powerful ability to acquire and analyze large volumes of data and provide decisions for action. E-commerce is now adopting this technology to identify patterns based on browsing, purchase history, credit checks, account information etc. This data collected then form the basis of creating customized recommendations for each customer. Google and Microsoft are already investing into new AI initiatives. Many e-commerce businesses have started implementing different forms of AI to better understand their customers, and provide an enhanced customer experience. This paper highlights the role of artificial intelligence in e-commerce and its application in different areas of e-commerce.

Keywords- Artificial Intelligence, E-Commerce, Machine Learning

[1]. Avneet Pannu, "Artificial Intelligence and its Application in Different Areas", International Journal of Engineering and Innovative Technology (IJEIT) Volume 4, Issue 10, April 2015
[2]. Dheeraj Kapoor, R. K. Gupta," Software Cost Estimation using Artificial Intelligence Technique" International Journal of Research and Development in Applied Science and Engineering (IJRDASE), Volume 9, Issue 1, February 2016
[3]. Mausami Sahu, "Plagiarism Detection Using Artificial
[4]. Intelligence" International Journal of Scientific & Technology Research, Volume 5, Issue 04, April 2016
[5]. Ashish, A.Dongare, Prof.R.D. Ghongade, "Artificial Intelligence Based Bank Cheque Signature Verification System" International
Research Journal of Engineering and Technology (IRJET) Volume 03, Issue 01, Jan-2016 Siddharth Gupta, Deep Borkar, Chevelyn
De Mello

Project scheduling plays an important role in research field. Project scheduling has raised noticeable attention within the last decades as a widely used discipline, applicable to many different real world areas. Software project scheduling is one of the most important scheduling areas faced by software project management team. Both software engineering and software management are very necessary for a successful project. To complete the software project within a listed time limit, allocate a start and end date that determine the milestones and outcomes of the tasks, determine which tasks are depend on another task to complete its operation, save time, build consistency, enhance visibility scheduling is very essential.............

Keywords- Project Scheduling, Planning, Controlling, Project management, Software project.

[1]. Survey paper for Software Project Scheduling and Staffing Problem NANDKISHOR PATIL1, KEDAR SAWANT2, PRATIK WARADE3, YOGESH SHINDE4 BE IT, SAE, Pune, India1, 2, 3,
[2]. A Review of various Software Project Scheduling techniques Ramandeep Kaur M-Phil Student, Guru Kashi University, Talwandi Sabo(Punjab) Gillraman532
[3]. A Brief Review on Integrated Planning of the Project Scheduling and Material Procurement Problem Babak H Tabrizi* Department of Industrial Engineering, University of Tehran, Iran
[4]. Software Engineering "Software Reliability, Testing and quality assurance" Nasib Singh Gill
[5]. Lavagnon A. Ika Amadou Diallo and Denis Thuillier, "Project management in the international development industry" (2009)

Data mining is becoming a invasive knowledge in activities as varied as using historical data to forecast the success of a marketing process looking for patterns in monetary contact to discover banned activities or analyzing genome sequences From this perception it was just a material of time for the control to reach the important area of computer safety This book presents a collection of investigate efforts on the use of data mining in computer safety.

Keywords - Scan Detection; Virus exposure; Anomaly disclosure; Shelter

[1]. Data Mining for Security Applications : Bhavani Thuraisingham, Latifur Khan, Mohammad M. Masud, Kevin W. Hamlen
[2]. Rakesh Agrawal, Tomasz Imieliski, and Arun Swami. Mining association rules between sets of items in large databases. In Proceedings of the 1993 ACM SIGMOD international conference on Management of data.
[3]. Daniel Barbara and Sushil Jajodia, editors. Applications of Data Mining in Computer Security. Kluwer Academic Publishers
[4]. Markus M. Breunig, Hans-Peter Kriegel, Raymond T. Ng, and J Sander. Lof: identifying density-based local outliers. In Proceedings of the 2000 ACM SIG-MOD international conference on Management of data, pages
[5]. Varun Chandola and Vipin Kumar. Summarization {compressing data into an informative representation. In Fifth IEEE International Conference on Data Mining, pages.

Wireless Sensor Networks (WSNs) are formed by a very large number of modest power-constrained wireless sensor nodes; it can identify and monitor the changes around them by self. WSNs are implemented in various fields, which include the medical field, army and manufacturing applications. Nodes of WSN are restricted to energy consumption, data storage, and computational power. Routing plays an important role in WSN. Choosing of routing path is considered as important because the choosing of lengthy or congested routing path leads to loss of energy. This paper aims to propose a new routing protocol namely enhanced load balancing routing protocol (ELBRP), which will balance the load in WSN in order to avoid the congestion and reducing the energy consumption............

Keywords- Energy, Load Balancing, Sensor, Routing, Wireless.

[1]. Sofiane Moad, Morten Tranberg Hansen, Raja Jurdak, Branislav Kusy, Nizar Bouabdallah, Load Balancing Metric with Diversity for Energy Efficient Routing in Wireless Sensor Networks, Procedia Computer Science, Volume 5, 2011, Pages 804-811.
[2]. Prasenjit Chanak, Indrajit Banerjee, Hafizur Rahaman, Load management scheme for energy holes reduction in wireless sensor networks, Computers & Electrical Engineering, Volume 48, 2015, Pages 343-357.
[3]. Germán A. Montoya, Yezid Donoso, Energy Load Balancing Strategy to Extend Lifetime in Wireless Sensor Networks, Procedia Computer Science, Volume 17, 2013, Pages 395-402.
[4]. B. Baranidharan, B. Santhi, DUCF: Distributed load balancing Unequal Clustering in wireless sensor networks using Fuzzy approach, Applied Soft Computing, Volume 40, 2016, Pages 495-506.
[5]. Pratyay Kuila, Prasanta K. Jana, Energy Efficient Load-Balanced Clustering Algorithm for Wireless Sensor Networks, Procedia Technology, Volume 6, 2012, Pages 771-777.

Internet of Things (IoT) can be seen as a pervasive network of networks: numerous heterogeneous entities both physical and virtual interconnected with any other entity or entities through unique addressing schemes, interacting with each other to provide/request all kinds of services. Given the enormous number of connected devices that are potentially vulnerable, highly significant risks emerge around the issues of security, privacy, and governance; calling into question the whole future of IoT. During the data exchange, it is mandatory to secure the messages between sender and receiver to handle the malicious human based attacks. The main problem during Fingerprint based approaches is the computational overhead..........

Keywords- Confidentiality, Complex Numbers, Digital Fingerprint, Internet of Things.

[1]. J. Lin, W. Yu, N. Zhang, X. Yang, H. Zhang, W. Zhao,"A Survey on Internet of Things: Architecture, Enabling Technologies, Security and Privacy, and Applications", IEEE Internet of Things Journal, Vol. 4 No. 5, pp. 1125-1142, 2017.
[2]. X. Luo, J. Liu, D. Zhang, and X. Chang, "A large-scale web QoS prediction scheme for the industrial Internet of Things based on a kernel machine learning algorithm," Computer Networks, Vol. 101, pp. 81-89, 2016.
[3]. W. Zhao, R. Lun, C. Gordon, A. M. Fofana, D. D. Espy, A. Reinthal, B. Ekelman, G. D. Goodman, J. E. Niederriter, and X. Luo, "A human-centered activity tracking service: Towards a healthier workplace," IEEE Transactions on Human-Machine Systems, Vol. 47, No. 3, pp. 343-355, 2017.
[4]. H. Lin, T. Zong, and Y. Yeh, "A DL Based Short strong Designated Verifier Fingerprint Scheme with Low Computation" Journal of Information Science and Engineering, Vol. 27, pp. 451-463, 2011.
[5]. Andrew Chi-Chih Yao ; Yunlei Zhao,"Online based Fingerprints for Low-Power Devices", IEEE Transactions on Information Forensics and Security, Vol. 8, No. 2, pp. 283-294, 2013.

Brain computer interface also known as the Brain Machine interface (BMI) is direct communication between brain and computer devices without the any muscle reaction.BCI researchaimsto provide communication ability to the people who are totally paralyzed or suffer neurological neuromuscular disorders
like amyotrophic lateral sclerosis, brain stem stroke or spinal cord injury. The efficient classification of motor imagery movements for disabled people can lead toexact design of Brain Computer Interface (BCI). In this paper data are taken from the UCI data repository BCI competition III dataset 1. Statistical featuredescriptors such as mean, maximum, minimum and standard deviation from each channel in every trial are extracted to reduce the dimensionality. Finally multilayer perceptron and J48 classification algorithms are applied to compare the efficiency of the proposed model.

Keywords- Brain Computer Interface, Multi Layer Perceptron, J48, Statistical Features.

[1]. Jonathan R. Wolpaw, NielsBirbaumer, Dennis J. McFarland, GertPfurtscheller, Theresa M. Vaughan," Brain-computer interface for communication and control", Clinical Neurophysiology (113),(2002),pp767–791.
[2]. H.H. Alwasiti, I. Aris, & A. Jantan, "Brain Computer Interface Design and Applications: Challenges and Future," World Applied Sciences Journal, 11(7) (2010), pp. 819-825.
[3]. L. F Nicolas-Alonso, & J. Gomez-Gil, "Brain computer interfaces, a review," Sensors, 12(2) (2012),pp. 1211-1279,.
[4]. Aswinseshadri K and ThulasiBai V "Genetic Algorithm Based Feature Selection for the Classification of Electrocardiogram Features in Brain Computer Interface"International Journal of Control Theory and Applications ISSN: 0974-5572.pp 352-364.
[5]. SaadatNasehi. HosseinPourghassem" A Novel Effective Feature Selection Algorithm based on S-PCA and Wavelet Transform Features in EEG Signal Classification ",In Communication Software and Networks (ICCSN), (2011) IEEE 3rd International Conference ,IEEE, pp-114-117.

Now the world is rallying digitized. To bring development in innovative world, we are ongoing on in a new concept known as the big data. Almost eighty to ninety percent of professions that are moving today seek a new and better approach to remain ambitious and profitable. To do this, big data leads them in a path that stopovers ahead of the curves. Thus big data is an advance that helps people to make their life more convenient, profitable and compatible one. Big data plays a major role in planning important vital and operational plans and instrument them. Apart from profession people also use big data for many other reasons............

[1]. Neelam Singh, Neha Garg, Varsha Mittal, Data – insights, motivation and challenges, Volume 4, Issue 12, December-2013, 2172, ISSN 2229-5518 2013.
[2]. Karthik Kambatlaa, Giorgos Kollias b, Vipin Kumarc, Ananth Gramaa, Trends in big data Analytics, (2014) 74 2561–2573
[3]. Francis X. "On the Origin(s) and Development of the Term \Big Data"_ Francis X., 2012
[4]. Venkata narasimha inukollu1, sailaja arsi1 and srinivasa rao ravuri3 Security issues associated with big data in cloud computing Vol.6, No.3, May 2014
[5]. Matzat1, Ulf-Dietrich Reips2,3 1 Eindhoven "Big Data" 2012, 7 (1), 1–5 ISSN 1662-5544.