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E-governance uses Information and communication technology for delivery of services to the citizens. Today the Government of India has adopted e-governance and many projects and services are available online for the easy delivery of the facilities to the general citizens. The e-governance has helped in increasing transparency, reducing corruption, better administration and effective interaction with the Government. Cloud computing is a new technology in which the user pays only for the service which they are using. It is economical, scalable and more secure than the current technology. Realizing this Government is making an effort to incorporate cloud computing in e-governance. This paper explains the importance of cloud computing in e-governance and briefly describes the various projects available online which are using the cloud services. Some of such projects are digilocker, online registration services, etc. Although many of these services are not fully functional but efforts are being made to implement them properly. The Government needs to take proper care while implementing cloud services keeping in mind the proper guidelines and policies so that the confidential, personal and sensitive data is safe and secure.

e-governance, Cloud computing, GI-cloud, IAAS, Digilocker, NPIP

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[4] R.Kumar, “National e-Governance Plan: Vision, Challenges and the Way Forward” Yojana, September 2012.
[5] M.C.Boro “NeGP and India for E-Governance-A Search for its Utility and Challenges”, IJESC, Vol. 7Issue 11, 2017
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[9] A.More, P.Kanungo, “ Use of Cloud Computing for Implementation of e-Governance Services”, IJSRCSE, Vol. 5, Issue 3, pp115-118, June 2017
[10] “Government of India’s GI Cloud (Meghraj) Strategic Direction Paper”, Department of Electronics & Information Technology, April 2013
[11] Government of Inida’s GI-Cloud (MeghRaj) Strategic Direction Paper, Department of Electronics & Information Technology, April 2013
[12] MeghRaj Cloud Initiative, National cloud by NIC, Ministry of Electronics & Information Technology, August 2007
[13] M.Gulati, K.Verma, “Digital locker”, JETIR, Volume 3, Issue 6, June 2016
[14] P.P.Arvind, M.P.Vitthalrao, M.M. Joshi, “DIGILOCKER (Digital Locker – Ambitious Aspect of Digital India Programme)”, International Journal of Mnagement Research, Vol 3, Issue 6, June 2015
[15] J.S.Bharati, A.Garg, “How Useful Is Digital Locker? An Empirical Study In Indian Context”, Indian Journal of Commerce & Management Studies, Volume VII Issue 2(1), May 2016
[16] National Prisons Information Portal, A Digital India Initiative by Government of India
[17] http://vikaspedia.in/e-governance/citizen-services/national-prisons-information-portal
[18] Online Registartion System, Ministry of Electronics & Information Technology, Government of Inida
[19] https://economictimes.indiatimes.com/news/politics-and-nation/about-1-5-lakh-aiims-patients-benfited-from-online-registration-system-government/articleshow/51153642.cms
[20] Forest Survey of India, Ministry of Environmemnt, Forest & Climate Change
[21] https://www.mygov.in/#

Nidhi Srivastava, "MeghRaj – A Cloud Environment for e-governance in India," International Journal of Computer Sciences and Engineering, Vol.6, Issue.11, pp.759-763, 2018.

The novel paradigm of data outsourcing on the cloud is a double-edged sword. On the one hand, it frees data management owners and allows data owners to more easily share their data with targeted users. On the other hand, this poses new challenges in terms of privacy and security. In order to protect the confidentiality of data from the honest but curious cloud service provider, many works have been proposed to support data access control with precise rules. However, so far, no system is able to support both granular access control and the publication of time-sensitive data. In this article, integrating timed cryptography into Cipher text cryptographic encryption (CP-ABE) encryption, we propose a new control of time access and attributes on time-sensitive data for archiving. In the public cloud (called TAFC). Based on the proposed scheme, we also propose an efficient approach to designing policies to access the various access requirements for time-sensitive data. The in-depth analysis of safety and performance shows that the proposed scheme is highly efficient and meets the security requirements for data storage in the public cloud.

Cloud storage, Cipher text cryptographic encryption, programmed-release encryption

[1] Z. Qin, H. Xiong, S. Wu, and J. Batamuliza, “A surveyof proxy re-encryption for secure data sharing in cloudcomputing,” IEEE Transactions on Services Computing,Avaliable online, 2016.
[2] F. Armknecht, J.-M. Bohli, G. O. Karame, and F. Youssef,“Transparent data deduplication in the cloud,” inProceedings of the 22nd ACM SIGSAC Conference onComputer and Communications Security, pp. 886–900,ACM, 2015.
[3] R. Masood, M. A. Shibli, Y. Ghazi, A. Kanwal, andA. Ali, “Cloud authorization: exploring techniques and approach towards effective access control framework,”Frontiers of Computer Science, vol. 9, no. 2, pp. 297–321, 2015.
[4] K. Ren, C. Wang, and Q. Wang, “Security challenges for the public cloud,” IEEE Internet Computing, vol. 16,no. 1, pp. 69–73, 2012.
[5] J. Bethencourt, A. Sahai, and B. Waters, “Cipher text policy attribute-based encryption,” in Proceedings of the28th IEEE Symposium on Security and Privacy (S&P’07), pp. 321–334, IEEE, 2007.
[6] Z. Wan, J. Liu, and R. H. Deng, “HASBE: A hierarchical attribute-based solution for flexible and scalable access control in cloud computing,” IEEE Transactions on Information Forensics and Security, vol. 7, no. 2, pp. 743–754,2012.
[7] K. Yang, X. Jia, K. Ren, B. Zhang, and R. Xie, “DACMACS: Effective data access control for multi-authority cloud storage systems,” IEEE Transactions on Information Forensics and Security, vol. 8, no. 11, pp. 1790–1801, 2013.
[8] M. Li, S. Yu, Y. Zheng, K. Ren, and W. Lou, “Scalableand secure sharing of personal health recordsin cloud computing using attribute-based encryption,”IEEE Transactions on Parallel and Distributed Systems,vol. 24, no. 1, pp. 131–143, 2013.
[9] E. Bertino, P. A. Bonatti, and E. Ferrari, “TRBAC: Atemporal role-based access control model,” ACM Transactionson Information and System Security, vol. 4, no. 3,pp. 191–233, 2001.
[10] I. Ray and M. Toahchoodee, “A spatio-temporal rolebasedaccess control model,” in IFIP Annual Conferenceon Data and Applications Security and Privacy, pp. 211–226, Springer, 2007.

Narsimha Banothu, P. Dayaker, P. Bhaskara Reddy , "A Novel Approach for Efficient Data Sharing and Revocation with Data Access control," International Journal of Computer Sciences and Engineering, Vol.6, Issue.11, pp.764-769, 2018.

Sentiment analysis has emerged as a field that has attracted a significant amount of attention since it has a wide variety of applications that could benefit from its results, such as news analytics, marketing, question answering, knowledge management and so on. This area, however, is still early in its development where urgent improvements are required on many issues, particularly on the performance of sentiment classification. Understanding the thoughts of the people is an essential part of the information-gathering behavior. Opinion-rich resources like online review sites and personal blogs have gained immense popularity as they have become easily accessible and are giving new opportunities and posing new challenges as, now people actively use information technology to search out and understand the opinions of others. The flow of interest in the new systems that directly deals with the opinions as a first-class object has given rise to activities in the area of opinion mining and sentiment analysis that work towards the computational analysis of opinions, sentiments and subjectivity in the text. This paper reviews the sentiment analysis methodology and focuses on the techniques to deal with the challenges of sentiment-aware applications. The purpose of this paper is to describe sentiment analysis in detail and to illustrate the method used for it. This survey consists of approaches that work towards enabling opinion-oriented information seeking systems. The main contribution of this paper includes categorization of a number of articles over the years and the illustrations of the recent trends in research in sentiment analysis and its related areas.

Opinion mining; Sentiment analysis; Consumer attitude; Sentiment classification

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[3].J. Jeevanandam, Dr. S. Koteeswaran, “Sentiment Analysis: A Survey of Current Research and Techniques”, International Journal of Innovative Research in Computer and Communication Engineering, Vol. 3, Issue 5, May 2015
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[5].S.J. Ravendra Ratan Singh, “A proposed Novel approach for Sentiment Analysis and opinion Mining”, International Journal of UbiComp (IJU), 5, (.1/2)April 2014 Pp:1-10
[6].D. Yajun, Z. Lizhou , J. Peiquan , “Dimension based Sentiment polarity detection for E-Commerce Reviews”, Advanced Science and Technology Letters Vol.45 (CCA 2014). pp.55-59, http://dx.doi.org/10.14257/ astl.2014.45.11
[7].K. Svetlana, Z. Xiaodan and M. Saif, “Sentiment analysis of short informal texts”, Journal of Artificial Intelligence Research,50:723-762, 2014(b)
[8].B. Pang, L. Lee, “Opinion Mining and Sentiment Analysis”, Foundation and Trends in Information Retrieval. 2(1-2): 1-135.
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[12].F.Salvetti, S. Lewis & C. Reichenbach, “Automatic opinion polarity classification of movie”, Colorado research in linguistics, 2004.
[13].P. Beinik, T. Hastic, & S.Vaithyananthan, “ The sentimental factor: Improving review classification via human-provided information”, In proceedings of the 12th annual meeting on association for computational linguistics, page 263, association for computational linguistics, 2004.
[14].T.Mullen & N. Collier, “Sentiment analysis using support vector machines with diverse information sources”, in EMNLP, Vol 4, pg 412-41,2004
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[16].S. Matsumoto, H.Takamura, & M. Okumara, “Sentiment Classification using word sub-sequences & dependency sub-trees”, In advances in knowledge discovery & data mining, pages 301-311, Springer,2005
[17].B. Luo, J. Zeng & J. Duan, “Emotion space model for classifying opinions in stock message board”, Expert Systems with Applications, 44 , 138–146 , 2016.
[18].S. M Liu, & J.H. Chen, “A multi-label classification based approach for sentiment classification”, Expert Systems with Applications, 42 (3), 1083–1093, 2015.
[19].T. Niu, S. Zhu, L. Pang & A. El Saddik, “Sentiment analysis on multi-view social data” In Multimedia modelling (pp. 15–27). Springer, 2016.
[20].P. Haiyun, C. Erik, H. Amir, “A Review of sentiment analysis research in Chinese language”, Cognitive Computing (2017) 9: 423. https://doi.org/10.1007/S125590-17-9470-8.

Bhumika Pahwa, S. Taruna, Neeti Kasliwal, "Survey on adoption of Sentiment Analysis for studying consumer’s online buying behavior," International Journal of Computer Sciences and Engineering, Vol.6, Issue.11, pp.770-776, 2018.

Demonstrating refactoring sound as for a formal semantics is viewed as a test. Designers compose test cases to check their refactoring implementations. However, it is troublesome and time expending to have a decent test suite since it requires complex sources of info (programs) and a prophet to check whether it is conceivable to apply the transformation. In the event that it is conceivable, the subsequent program must save the perceptible conduct. There are some computerized strategies for testing refactoring motors. In any case, they may have impediments identified with the program generator (comprehensiveness, setup, expressiveness), automation (sorts of prophets, bug classification), time utilization or sorts of refactoring that can be tried. This paper stretches out past system to test refactoring engines. It likewise clarifies the enhancement expressiveness of the program generator for testing more kinds of refactoring`s, such as Extract Function. Moreover, developers simply need to determine the information`s structure in an explanatory dialect. They may likewise set the system to skip some continuous test contributions to enhance performance. This additionally assesses strategy in 18 kinds of refactoring implementations of Java and distinguishes 35 bugs identified with aggregation blunders, behavioral changes, and overly strong conditions. This paper thinks about the effect of the skip on the time utilization and bug detection in this proposed method. By using a skip of 25 in the program generator, it decreases in 96%the times to test the refactoring implementations while missing only 3.9% of the bugs. In almost no time, it finds the principal failure related to aggregation blunder or behavioral change.

Refactoring, overly strong preconditions, automated testing, program generation

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[16] W. Opdyke, “Refactoring Object-Oriented frameworks,” Ph.D. dissertation, the University of Illinois at Urbana-Champaign, 1992.

[17] L. Tokuda and D. Batory, “Evolving object-oriented designs with refactorings,” ASE, vol. 8, pp. 89–120, 2001.
[18] A. Garrido and R. Johnson, “Refactoring C with conditional compilation," in ASE, 2003, pp. 323–326.
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[21] P. Borba, A. Sampaio, A. Cavalcanti, and M. Cornelio, “Algebraic reasoning for Object-Oriented programming,” “SCP, vol. 52, pp. 53–100,2004.
[22] L. Silva, A. Sampaio, and Z. Liu, “Laws of Object-Orientation with reference semantics,” in SEFM, 2008, pp. 217–226.
[23] H. Li and S. Thompson, “Testing ErlangRefactorings withQuickCheck,” in IFL, 2008, pp. 19–36.
[24] M. Vakilian and R. E. Johnson, “Alternate refactoring paths reveal usability problems,” in ICSE, 2014, pp. 1–11.
[25] Melina Mongiovi Member, Rohit Gheyi, Gustavo Soares, Márcio Ribeiro, Paulo Borba, "Detecting overly strong preconditions in refactoring engines" IEEE 2017.
[26] Geeta Bagade, Shashank Joshi “Analysis of Aspect-Oriented Systems: Refactorings using AspectJ” International Journal of Computer Sciences and Engineering,Vol.4, Issue .5, pp.76-80, May-2016
[27] Nagaveni, A. Ananda Rao, P. Radhika Raju, “Testing Refactoring Implementations of Object-Oriented Systems”International Journal of Computer Sciences and Engineering, Vol.6 , Issue.7,pp.530-534, Jul-2018

Padakanti Divya, Karanam Madhavi, "Scaling and Testing Refactoring Preconditions in Refactoring Engines," International Journal of Computer Sciences and Engineering, Vol.6, Issue.11, pp.777-783, 2018.

A Wireless Sensor network (WSN) is defined as network of devices that is used to monitor the physical conditions and gather the information from the complex geological range through wireless link. These networks are powered using batteries which are difficult to recharge. In WSN, energy can be consumed usefully or wastefully and toimprove the efficiency of WSN it is necessary to minimize the wastage of energy thereby improving the lifetime of network. Energy conservation is used to minimize the consumption of energy in several intermediate nodes to enhance the lifetime of network. This paper presents a brief overview of architecture of WSN and highlights the area where energy is consumed wastefully and usefully. Some of the techniques which are used for enhancing the network’s lifetime by reducing energy consumption are analyzed in detailed.

WSN (Wireless Sensor Network), Energy conservation, Energy consumption, BS (Base Station), CH (Cluster Head), subsystem, LEACH (Low Energy Adaptive Clustering Hierarchy), Routing, Energy efficiency CSMA (carrier sense multiple access), CSMA/CA (carrier sense multiple access/collision avoidance) etc

[1]. Mohamed Elshrkawey, Samiha M. Elsherif, M.ElsayedWahed (2018). An Enhacement Approach for Reducing the Energy Consumption in Wireless Sensor Networks.Journal of King Saud University- Computer and Information Sciences 30 (4) 259-267.
[2]. Deepak, S. (2015). An overview of Wireless Sensor Networks. International Journal of Enhanced Research in Management & Computer Applications, 4 (4), 47-41. ) ISSN: 2319-7471.
[3]. Preeti, B., Deepak, S., & Shamsher, M. (2015). A Review of Routing Protocols in Wireless Sensor Network. International Journal of Enhanced Research in Management & Computer Applications, 4 (5), 34-40. ) ISSN: 2319-7471.
[4]. Rajbir, S., Deepak, S., & Payal. (2017). Performance Evaluation of Routing Protocols in Wireless Sensor Networks. International Journal of Engineering Technology Science and Research, 4 (11), 631-639.
[5]. Sapna, K., Suresh, K., & Deepak, S. (2017). Performance Evaluation of Congestion Control in MANETs using AODV, DSR and ZRP Protocols. International Journal of Advanced Research in Computer Science and Software Engineering (IJARCSSE), 7 (6), 398-403 ISSN: 2277 128X.
[6]. Kirti, M., Suresh, K., & Deepak, S. (2018). Ad-Hoc Wireless Sensor Network Based on IEEE 802.15.4: Theoretical Review. International Journal of Computer Sciences and Engineering , 6 (3), 219-224ISSN 2347-2693.
[7]. Bolaji Omodundi, O.T. Arilogun, J.O. Emuoyibofarhe (2013). A Review if Energy Conservation IN Wireless Sensor Network. Network of Complex System, 3(5).ISSN 2224-610X.
[8]. Bandana Bhatia (2013). Energy Conservation in Wireless Sensor Networks.IOSR Journal of Computer Engineering (IOSR-JCE15(3) 85-89, ISSN 2278-8727.
[9]. Deepak, S., & Suresh, K. (2015). A Comprehensive Review of Routing Protocols in Heterogeneous Wireless Networks. International Journal of Enhanced Research in Management & Computer Applications, 4 (8), 105-121.) ISSN: 2319-7471.
[10]. Payal, Deepak, S., & Suresh, K. (2018). Performance Evaluation of Reactive Routing Protocols Using IEEE 802.15.4 Application in Designed Wireless Sensor Network. International Journal of Computer Sciences and Engineering, 6 (4), 90-96ISSN 2347-2693.
[11]. Reshma I. Tandel (2016) .Leach Protocol in Wireless Sensor Network. International Journal of Computer Science and Technologies, 4. 1894-1896, ISSN 0975-9646.
[12]. Mohamed Elshrkawey, Samiha M. Elsherif,M. ElsayedWahed (2018). An Enhancement Approach for Reducing the Energy Consumption in Wireless Sensor Networks. Journal of King Saud University – Computer and Information Sciences, 259-267, ISSN 1319-1578.
[13]. Mr. Santosh N. Shelke, MR. Sandip R. Shinde( 2013). Energy Saving Techniques in Wireless Sensor Networks. International Journal of Scientific and Engineering Research, 4(4). ISSN 2229-5518.
[14]. SK. Muruganandham, D, Sobya, S. Nallusamy, et.al (2017). Lifetime Expansion of Wireless Sensor using Moderm Routing Algorithm. International Journal of Emerging Trends and Teachnology in Compouter Science, 6(6).ISSN 2278-68956.
[15]. Shio Kumar Singh, M P Singh, D K Singh (2010). A Survey of Energy- Efficient Hierarchical Cluster-based Routing in Wireless Sensor Networks. International Journal of Advanced Networking and Application, 2(2),570-580.

K. Dhull, S. Kumar, A. Ahlawat, S. Dahiya, "Lifetime Enhancement in Wireless Sensor Networks: A Theoretical Review," International Journal of Computer Sciences and Engineering, Vol.6, Issue.11, pp.784-789, 2018.

Breathe is the life source for any being. In our proposed model we take the concept of breathe to gauge the life span of a human being. LMA is an innovative tool which will predict the life span of a human being. This application will use the IoT technology to metric the inhale and exhale of person. Based on the counts of breathe in and breathe out, our system will generate a report which tells us life span of a person from time to time.

Breathe, IOT, Automation, Life measuring, Wi-Fi

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B. V. Subba Rao, J. Sirisha, "An Innovative Life Measuring Application using IOT Technology," International Journal of Computer Sciences and Engineering, Vol.6, Issue.11, pp.790-791, 2018.

Weather forecasting is an important apply for in meteorology and has been one of the most scientifically and technologically difficult problems around the world. Weather prediction approaches are challenged by difficult weather phenomena with incomplete explanation and past data. Weather phenomena have many parameters that are impossible to enumerate and measure. Increasing development on statement systems enabled weather forecast expert systems to combine and divide resources and thus hybrid system has emerged. Even though these improvements on weather forecast, these expert systems can’t be fully reliable since weather forecast is main problem. A predictive Neural Network model and Hidden Markov Model was also residential for the weather prediction program and the outcome compared with real weather data for the predicted periods. The results show that given enough case data, Data Mining techniques can be used for weather forecasting and climate change studies. Data mining is a process that uses a variety of data analysis tools to find out patterns and relationships in data that may be used to create applicable prediction. The proposed ANN and HMM evaluates the presentation of the developed models by applying unusual neurons, hidden layers and transfer functions to predict temperature for 365 days of the year. The criteria used for suitable model selection is mean square error (MSE).

ANN, HMM, weather predication, regression, training, testing, Numerical Weather Forecasting etc

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[6] S.Meganathan, M. Poornima and A. Sumathi, “Proximity measures in clustering on patient diagnosis system”, International Journal of Applied Engineering Research, Vol.9(22), 12133-12139, 2014.
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Unnati Acharya, G.J.Sahani, "Mutual city/state weather forecasting by ANN and HMM – Survey," International Journal of Computer Sciences and Engineering, Vol.6, Issue.11, pp.792-796, 2018.

At present there is a huge need of system which uses both physiological and facial data to detect human behavioral lie, thus this survey is based on getting insight for developing a machine learning based technique using facial and physiological data for detection of human behavior. The purpose of this survey is to identify various physiological sensors and their parameters along with sensing data, also to know whether physiological signals are robust and can be controlled by human being or not. It also reviews about various machine learning techniques for face recognition system and presented the most effective face recognition system in our survey. By getting significant understanding of physiological data and facial data with their classification rate it becomes possible to deduce a machine learning based algorithm using facial and physiological data for detection of human behavioral lie. This survey compiled the work done by various author to provide the precise information about the machine learning techniques, physiological sensors, face recognition system for human behavioral lie.

Machine learning techniques, Physiological Sensors, Face Recognition, Emotion Recognition, Lie Detection

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Bishan Lal Thakur, Divyansh Thakur, Payal Pandey, "A Survey based on Machine Learning Approaches for Detection of Human Behavioural Lie using physiological sensors and Face Recognition System," International Journal of Computer Sciences and Engineering, Vol.6, Issue.11, pp.797-806, 2018.

The main objective of the present day eHealthcare is the integrated, interfaced health information systems or networks that should be able to gather, parse, and ensemble the different parts of the medical record pertained to a patient without involving any risk of linking those of another patient. Identifying the multiple fragmented pieces of a specific individual patient data existing in disparate distributed systems for the purposes of exchange remains a challenge though appears to be easy to reach. Many of the countries adopting HL7 family of standards prescribe the Social Security Number or an equivalent or any demographic data of a patient as the Unique Patient Identifier as a data element in the PID division of the EHR. Tagging the distributed data sets with mere numbering needs additional information to confirm the identity of an individual patient and involves many risks. Aadhaar Number concept proves to be a best fix as the UPI. The present paper discusses the related facets to the concept of UPI and the relevance of the Indian Aadhaar Number.

Health Level 7; EHR; Unique Patient Identifier; Master Patient Index; Aadhaar Number

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P. Palanivelrajan, P. Alli, "Unique Patient Identifier for National Ehealthcare Service Delivery: The Indian Approach," International Journal of Computer Sciences and Engineering, Vol.6, Issue.11, pp.807-810, 2018.

Orthogonal Frequency Division Multiplexing (OFDM) is a FDM scheme that has the ability to cope with harsh channel conditions without using complex equalization filters. With the introduction of guard band, OFDM offers better orthogonality for the transmission channels affected by high frequency attenuations in copper wire, frequency selective fading and Narrow-Band (NB) interference due to multipath propagation. Each subcarrier is modulated employing a digital modulation scheme such as Quadrature Amplitude Modulation (QAM) with lower symbol rate in order to achieve higher data rates in comparison to single carrier modulation scheme for the given bandwidth. We have studied QAM-OFDM based optical networks in order to obtain higher data rates with lower Bit Error Rate (BER). Variants of OFDM have also been discussed along with its advantages and limitations to achieve the desired optimum performance in the optical networks. This paper presents a detailed study of M-ary QAM OFDM schemes and methods used to overcome effects of Inter Symbol Interference (ISI) and Inter Carrier Interference (ICI) with optimum utilization of system bandwidth.

OFDM, ISI, ICI, QAM, Fast Fourier Transform (FFT), Inverse Fast Fourier Transform (IFFT), Discrete Fourier Transform (DFT), Peak to Average Power Ratio (PAPR), NB, Quality of Service (QoS), BER

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A. Dhingra, S. Kumar, Payal, D. Sharma, S. Dahiya, "A Comprehensive Review of QAM-OFDM Optical Networks," International Journal of Computer Sciences and Engineering, Vol.6, Issue.11, pp.811-817, 2018.