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Cloud computing provides a flexible and convenient way for data sharing, which brings various benefits for both the society and individuals. But there exists a natural resistance for users to directly outsource the shared data to the cloud server since the data often contain valuable information. Thus, it is necessary to place cryptographically enhanced access control on the shared data. Identity-based encryption is a promising cryptographically primitive to build a practical data sharing system. However, access control is not static. That is, when some user’s authorization is expired, there should be a mechanism that can remove him/her from the system. Consequently, the revoked user cannot access both the previously and subsequently shared data. To this end, we propose a notion called revocable-storage identity-based encryption (RS-IBE), which can provide the forward/backward security of cipher text by introducing the functionalities of user revocation and cipher text update simultaneously. Furthermore, we present a concrete construction of RS-IBE, and prove its security in the defined security model. The performance comparisons indicate that the proposed RS-IBE scheme has advantages in terms of functionality and efficiency, and thus is feasible for a practical and cost-effective data-sharing system. Finally, we provide implementation results of the proposed scheme to demonstrate its practicability.

Cloud computing, data sharing, revocation, Identity-based encryption, ciphertext update, decryption key exposure

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Maadala Chandra Sekhar, Keerthi Kethineni, "Secure Data Sharing in Cloud Computing Using Revocable- Storage Identity-Based Encryption," International Journal of Computer Sciences and Engineering, Vol.6, Issue.7, pp.1094-1107, 2018.

Process mining is an emerging research field of computer science, which utilizes automatically generated event logs in information system. An event log contains time stamped record for events which refer to activities under taken for specific cases. Event log also store additional information regarding event such as resources linked to the activity, or data elements recorded with the event. To allow these events to happen in parallel to the current state of a process may be represented by multiple nodes. Those process model can follow the notation of petri nets and Business process Modelling Notation (BPMN), widely used for their improvement. Process model guidance is an important model feature by which the software process is orchestrated. Process mining techniques have been used in business process domain with no focus on the software engineering processes. These techniques have produced faster results and the ability to check conformance and compliance. This paper discussed the basic concepts, applications, frameworks, types, algorithms and research issues of process mining.

Process Mining(PM), Educaional Process Mining(EPM), Process Mining techniques, Business Process Modelling(BPM), Event Logs.

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[19]. Gabriel M. Veiga and Diogo R. Ferreira, “Understanding Spaghetti Models with Sequence Clustering for ProM”

S. Vijayarani, A. Sakila, R. Ramya, "Process Mining - A Comprehensive Review," International Journal of Computer Sciences and Engineering, Vol.6, Issue.7, pp.1108-1113, 2018.

Image enhancement is one of the characteristic of image processing which is used for different real time application such as medical, scientific, geographical and military etc. In this paper, we mainly focus on the literature study of satellite image data enhancement. The major issues of satellite images are enhancement of gray-scale/colour image, noise, weak colour information, artifacts, distortion, large size, resolution high frequency content etc. To obtain the improved quality of satellite image various image processing techniques has been developed such as DWT, SVD, DT-CWT etc. and different authors also proposed some mechanism or algorithm of the image enhancement. This paper also presents the overview of various image enhancement techniques.

DWT, SVD, Geographical, Image Enhancement, Satlleite data

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[3] Deepali M. Bongulwar “Satellite Image Enhancement”, International Journal of Application or Innovation in Engineering & Management (IJAIEM), Volume 6, Issue 6, June 2017.
[4] Wenkao Yang, Jing Wang and Jing Guo, “A Novel Algorithm for Satellite Images Fusion Based on Compressed Sensing and PCA”, Hindawi Publishing Corporation, Mathematical Problems in Engineering, pp. 1 – 10, Volume 2013.
[5] Lasmika, K. Raveendra, "Improving Quality of Satellite Image by Wavelet Transforming & Morphological Filtering", International Journal of Innovative Research in Science, Engineering and Technology, ISSN: 2319- 8753,Vol. 3, Issue 7, July 2014.
[6] Khaled SAHNOUN, Noureddine BENABADJI “On-Board Satellite Image Compression Using The Fourier Transform And Huffman Coding”
[7] Xue Mei Li “Image Enhancement in the Fractional Fourier Domain” in proceeding IEEE, 2013.
[8] Bhateja, V. “Improvement of masses detection in digital mammograms employing non-linear filtering”, Automation, Computing, Communication, Control and Compressed Sensing (iMac4s), 2013 International Multi-Conference on March 2013, Page(s): 406 – 408.
[9] Zouari, M. “A non linear stretching image enhancement technique for micro-calcification detection”, Advanced Technologies for Signal and Image Processing (ATSIP), 2014 1st International Conference on March 2014, Page(s): 193 – 197.
[10] Vijay Tonder, Vivek Shukla2, Rohit Miri “Improving Quality of Satellite Images Using Enhancement Filters and Fuzzy Based Contrast Enhancement Technique”, International Journal of Innovative Research in Computer and Communication Engineering.2017
[11] Mansing Rathod, Jayashree Khanapuri “Satellite Image Resolution Enhancement Using SWT and DWT with SWT”, International Conference on Nascent Technologies in the Engineering Field (ICNTE-2017) in proceeding of IEEE.
[12] R. Aedla, G. S. Dwarakish and D. V. Reddy, "Satellite image contrast enhancement algorithm based on plateau histogram equalization," Region 10 Symposium, 2014 IEEE, Kuala Lumpur, 2014, pp. 213-218.
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[14] Hasan Demirel and Gholamreza Anbarjafar, “Discrete Wavelet Transform-Based Satellite Image Resolution Enhancement” IEEE Transactions On Geoscience And Remote Sensing, Vol. 49, No. 6, June 2011.
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[16] Mallat, S.G. “Multifrequency channel decompositions of image and wavelet models “, IEEE Trans. Acoust. Speech Signal Process., 1989, 37, (12), pp. 2091–2110
[17] Chan, R.H., C.W. Ho and M. Nikolova “Salt-and pepper noise removal by median-type noise detectors and detail-preserving regularization.
[18] Mohanapriya N, Kalaavathi B “Comparative Study of Different Enhancement Techniques for Medical Images” International Journal of Computer Applications, 2013; 61(20).

Kshipra Singh, Jijo S Nair, "A Literature Review On Satellite Image Data Enhancement Using Digital Image Processing," International Journal of Computer Sciences and Engineering, Vol.6, Issue.7, pp.1114-1119, 2018.

This paper presents the selective block local binary pattern algorithm for human face recognition. Various LBP methods were used to improve the recognition rates. The most important step in face recognition is the feature extraction. Tolerance to illumination variation and computational simplicity is made LBP a very popular method for pattern recognition system. Traditional LBP method is compared with the selective blocks LBP methods by conducting the experiments using ORL and UMIST datasets.

Local Binary Pattern, Selective Block, Pattern Recognition, Feature Extraction, Histogram, Face Recognition

[1] Bhatt, Ms, et al. "A study of local binary pattern method for facial expression detection." arXiv preprint arXiv:1405.6130 (2014).
[2] Liu, Li, et al. "Extended local binary patterns for texture classification." Image and Vision Computing 30.2 (2012): 86-99.
[3] Huang, Di, et al. "Local binary patterns and its application to facial image analysis: a survey." IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews) 41.6 (2011): 765-781.
[4] Rahim, MdAbdur, et al. "Face recognition using local binary patterns (LBP)." Global Journal of Computer Science and Technology (2013).
[5] Cai, Cheng, and Jianqiao Li. "Cattle face recognition using local binary pattern descriptor." Signal and Information Processing Association Annual Summit and Conference (APSIPA), 2013 Asia-Pacific. IEEE, 2013.
[6] Shyam, Radhey, and YogendraNarain Singh. "Face recognition using augmented local binary pattern and Bray Curtis dissimilarity metric." Signal Processing and Integrated Networks (SPIN), 2015 2nd International Conference on. IEEE, 2015.
[7] Lv, Shiwen, Feipeng Da, and Xing Deng. "A 3D face recognition method using region-based extended local binary pattern." Image Processing (ICIP), 2015 IEEE International Conference on. IEEE, 2015.
[8] Panchal, Pradip, et al. "Pose, illumination and expression invariant face recognition using laplacian of Gaussian and Local Binary Pattern." Engineering (NUiCONE), 2015 5th Nirma University International Conference on. IEEE, 2015.
[9] Dahmouni, Abdellatif, et al. "Face recognition using local binary probabilistic pattern (lbpp) and 2d-dct frequency decomposition." Computer Graphics, Imaging and Visualization (CGiV), 2016 13th International Conference on. IEEE, 2016.
[10] Stekas, Nikolaos, and Dirk van den Heuvel. "Face Recognition Using Local Binary Patterns Histograms (LBPH) on an FPGA-Based System on Chip (SoC)." Parallel and Distributed Processing Symposium Workshops, 2016 IEEE International. IEEE, 2016.
[11] Saleh, Sheikh Ahmed, et al. "An improved face recognition method using Local Binary Pattern method." Intelligent Systems and Control (ISCO), 2017 11th International Conference on. IEEE, 2017.
[12] Olivares-Mercado, Jesus, et al. "Face recognition system for smartphone based on LBP." Biometrics and Forensics (IWBF), 2017 5th International Workshop on. IEEE, 2017.
[13] Yahia, Samah, Yassine Ben Salem, and Mohamed NaceurAbdelkrim. "3D face recognition using local binary pattern and grey level co-occurrence matrix." Sciences and Techniques of Automatic Control and Computer Engineering (STA), 2016 17th International Conference on. IEEE, 2016.
[14] Heisele, Bernd, Thomas Serre, and TomasoPoggio. "A component-based framework for face detection and identification." International Journal of Computer Vision 74.2 (2007): 167-181.
[15] Siddharth, KunwarPankaj, and DakshinaRanjanKisku. "Heterogeneous Face Identification by Fusion of Local Descriptors." Advance Computing Conference (IACC), 2017 IEEE 7th International. IEEE, 2017.
[16] T. Ahonen, A. Hadid, and M. Pietikainen, “Face Description with Local Binary Patterns: Application to Face Recognition,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 28, no. 12,pp. 2037-2041, Dec. 2006.3

Sunil Kumar B. L., SharmilaKumari M., "Selective Block Local Binary Pattern Based Algorithm for Face Recognition," International Journal of Computer Sciences and Engineering, Vol.6, Issue.7, pp.1120-1124, 2018.

The Efficient Process that is analyzing Sentiment is the way of identifying the orientation of opinion in text data. It finds assignment of comments whether it become positive comment or negative comment to perform analysis of review collected from social networking sites. Now a days Use of Social networking sites are going to increase rapidly. In various Micro blogging sites user post their review about any interesting topics about event, about newly launched product, etc. according to that user going to analyze reviews. In this paper we put forward process of analyzing sentiment that is also called as Opinion mining on collected twitter review data set on mobile product based on priority wise selection of feature. By considering this concept we are going to assign polarity to the word which decide polarity of comments and based on that we divide the comments into positive and negative club. For that classification we use machine learning Naive Bayes algorithm and according to that we analyze quality of that product and decide whether to purchase product or not based on selected feature of product.

Machine learning, Naive Bayes, opinion mining, Sentiment analysis

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[2] Lopamudra Dey , Sanjay Chakraborty , Anuraag Biswas , Beepa Bose, Sweta Tiwari “Sentiment Analysis of Review Datasets using Naive Bayes? and K-NN Classifier”.
[3] Huma Parveen,Prof. Shikha Pandey “Sentiment Analysis on Twitter Data-set using Naive Bayes Algorithm”2nd International Conference on Applied and Theoretical Computing and Communication Technology (iCATccT),2016 pp 416-419.
[4] Alexander Hogenboom, Daniella Bal,Flavius Frasincar “Exploiting Emoticons in Sentiment Analysis”Journal of Web Engineering, Vol. 0, No. 0 (2013) 000?000 cRinton Press.
[5] Ms. Md. Sania Sultana, Mr. G. V Suresh ,“Opinion Mining on Twitter Data of Movie Reviews using R”.Conference on, 2003, pp. 10?17 vol.
[6] Jiangtao Ren, Sau Dan Lee, Xianlu Chen, Ben Kao, Reynold Cheng and David Cheung, “Naive Bayes Classification of Uncertain Data”, (2009).
[7] Bo Pang and Lillian Lee , Shivakumar Vaithyanathan ,“Thumbs up? Sentiment Classification using Machine Learning Techniques”.
[8] P. Bavithra Matharasi, Dr. A.Senthilrajan, “Sentiment Analysis of Twitter Data using Naive Bayes with Unigram Approach”, (May 2017).
[9] Vishal A. Kharde , S.S. Sonawane , “Sentiment Analysis of Twitter Data: A Survey of Techniques”, (April 2016).
[10] Maneesh Singhal, Ramashankar Sharma, “Optimization of Naive Bayes Data Mining Classification Algorithm”.
[11] Walaa Medhat, Ahmed Hassan , Hoda Korashy, “Sentiment analysis algorithms and applications: A survey”.
[12] Sai Krishna, D., G Akshay Kulkarni and A. Mohan, Kurup,“Sentiment Analysis-Time Variant Analytics”, commerce Websites in India, International Journal of Advanced Research in Computer Science and Software Engineering, 2015.
[13] Chen, X., M. Vorvoreanu and K. Madhavan, “Mining Social Media Data for Understanding Students’ Learning Experiences”IEEE Transactions on Learning Technologies, 2014.
[14] Barbosa, L. and J. Feng, “Robust sentiment detection on twitter from biased and noisy data”In Proc. of Coling, 2010.
[15] Davidov, D., O. Tsur and A. Rappoport, “Enhanced sentiment learning using twitter hashtags and smileys”, In Proceedings of Coling, 2010.
[16] Sayali D. Jadhav, H. P. Channe, “Comparative Study of K-NN, Naive Bayes and Decision Tree Classification Techniques”.

Dipali Bhalekar, Prakash Rokade, "Sentiment Analysis: A Proficient Methodology for Analyzing Review on Product," International Journal of Computer Sciences and Engineering, Vol.6, Issue.7, pp.1125-1128, 2018.

One significant limitation in Multi-Agent Systems development methodologies is lack of proper requirements validation. The authors have tried to implement requirement validation in Multi-Agent Systems using ontologies. Organizational Multi-Agent Systems Engineering is used as the agent development methodology. The aim of this paper is to include an appropriate method for validation of the requirements in Multi-Agent System development. In addition to working as a knowledge base, the authors in this paper have used ontologies to support requirements validation. Requirements validation is performed through rules that ascend from requirements and enforcement of these rules is done through a formal language, Semantic Web Rule Language. Genomic Information Retrieval is taken as case study. The Java Agent Development Environment (JADE) framework is used along with the Protégé 5.2.0 for ontology development. Apache Jena API, OWLAPI and SWRLAPI are used for implementation of the Multi-Agent System.

Multi- Agent System, Protégé, Jena, JADE, OWLAPI, SWRLAPI, agent communication, OWL, RDF

[1] Scott Deloach, O-MaSE: A customisable approach to designing and building complex, adaptive multi-agent systems, https://www.researchgate.net, 2010.
[2] Beydoun, G. & Low, G. Complex Intell. Syst. (2016) 2: 235. https://doi.org/10.1007/s40747-016-0025-5
[3] Kieth Decker, DECAF, A Multi-Agent System for Automated Genomic Annotation, Kluwer Academic Publishers, 7,7-27, 2003.
[4] Fabio, Bellifemine, Giovanni Caire, Dominic Greenwood, Developing Multi Agent Systems with JADE, Wiley, 2007.
[5] Katia P. Sycara, Martin van Velsen, Massimo Paolucci, Joseph Andrew Giampapa, The RETSINA MAS infrastructure, Research Gate, July 2003.
[6] Kalliopi Kravari, Efstratios Kontopoulos and Nick Bassiliades, EMERALD: A Multi-Agent System for Knowledge-based Reasoning Interoperability in the Semantic Web, Research Gate, May 4-7, 2010.
[7] Gaurav Kant Shankhdhar, Manuj Darbari, Building Custom, Adaptive and Heterogeneous Multi-Agent Systems for Semantic Information Retrieval Using Organizational-Multi-Agent Systems Engineering, O-MaSE, IEEE Explore, ISBN: 978-1-5090-3480-2, 2016.
[8] Atul Verma , Narendra Jha, Verified Message Exchange in Providing Security for Cloud Computing in Heterogeneous and Dynamic Environment, International Journal of Applied Information Systems, 2017.
[9] Gaurav Kant, Manuj Darbari, Change Management in Semantic Web Services in Legal Domain using FSM & XXM Publication: IJAIS volume9/number1/751-1359, 2015.
[10] Sumit Kumar Mishra, V.K. Singh, Ontology Development For Wheat Information System Description: Publication: IJRET-International Journal of Research in Engineering and Technology 2015, V0l4/I05.
[11] Gaurav Kant, Manuj Darbari, Introducing Two Level Verification Model for Reduction of Uncertainty of Message Exchange in Inter Agent Communication in Organizational-Multi-Agent Systems Engineering, O-MaSE, IOSR Journal of Computer Engineering (IOSR-JCE), http://www.iosrjournals.org/iosr-jce/pages/19(4)Version-2.html, 2017
[12] Nasserine Hamrouni, Verification and validation for MAS APN, 6th International Conference on Sciences of Electronics Technologies of Information and Telecommunications (SETIT), 2012.
[13] Gaurav Kant Shankhdhar and M Darbari. Article: Legal Semantic Web- A Recommendation System. International Journal of Applied Information Systems 7(3):21-27, May 2014. Published by Foundation of Computer Science, New York, USA.
[14] Regulated Open Multi-Agent Systems (ROMAS), A Multi-Agent Approach for Designing Normative Open Systems, Springer, 2015.
[15] Jogannagari M.R., Kothari P.R, “The complexity of Validation Testing in Component Based Software Engineering”, International Journal of Computer Science and Engineering, IJCSE,Volume 5, Issue 12, 2017.
[16] Yagyasen, Diwakar, and Manuj Darbari. (2014) "Application of Semantic Web and Petri Calculus in Changing Business Scenario." Modern Trends and Techniques in Computer Science. Springer International Publishing, 2014. 517-528.
[17] K. Laxmi Pradeep, K. Madhavi, "Approaches for Efficient Learning Software Models: A Survey", International Journal of Computer Sciences and Engineering, Vol.6, Issue.1, pp.108-113, 2018.

G.K. Shankhdhar, M. Darbari, "Implementation of Validation of Requirements in Agent Development by means of Ontology," International Journal of Computer Sciences and Engineering, Vol.6, Issue.7, pp.1129-1135, 2018.

The next generation sequencing(NGS) technology is playing an increasingly prominent role in capturing DNA and RNA sequencing by producing high-throughput sequences (HTS). The major challenge with HTS is the complexity and difficulty of data quality control (QC). Only a high quality data is capable for accurate diagnosis of the disease. For accurate diagnosis the data that needs to be analysed must be appropriate and correct. To fulfill this requirement, computer scientists have implemented the algorithms in easy to use manner that become convenient tools for biological research. The raw sequence generated by the NGS technologies is first cleaned and then moved further for clinical analysis. The step of cleaning includes removal of short sequences and trimming of inappropriate headers. This paper compares some popular, open source tools used for cleaning the captured sequences.

Illumina, FASTQ, FASTA, tag removal, single end, paired end

[1]https://twistbioscience.com/company/blog/twistbioscienceexomesequencing4dataanalysis
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[6] Alexis Criscuolo and Sylvain Brisse. ALIENTRIMMER: A tool to quickly and accurately trim off multiple short
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[7] Alexis Criscuolo. AlienTrimmer User Guide. [Version 0.2.1] September 2012
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K.S.Mehta, D.S.Mehta, V.Dahiya, "A Comparative Study of Computational Tools for Biological Sequence Cleaning and Analysis," International Journal of Computer Sciences and Engineering, Vol.6, Issue.7, pp.1136-1140, 2018.

‘Software Testing’ is a demanding action on behalf of numerous projects on software engineering. Amongst all the five core technological action areas of the ‘software engineering lifecycle’, it is the main which still fronts considerable confronts. Software Testing necessitates an adequate amount of sources as well as finances on the way to attain the objective productively. But most of the organizations face the challenges to provide enough resources to test their software in distributed environment, with different loading level. This leads to severe problem when the software deployed into different client environment and varying user load. ‘Cloud computing’ is one of the important up-coming proficiency. This releases innovative access for ‘software testing’. Presented paper examines the ‘software testing’ in the platform of cloud computing and this comprises of models of testing of cloud, latest investigation on the same, marketable tools as well as investigative concerns.

Marketable Tools, Software testing, Cloud Computing, Cloud Testing, Software Engineering Life cycle etc

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Manish Sharma, H.P. Singh, Vibhakar Pathak, "Critical Software Testing Using Cloud Computing Tools," International Journal of Computer Sciences and Engineering, Vol.6, Issue.7, pp.1141-1146, 2018.

In this Internet and Smart devices era, unlimited access to online references, learning materials and archives are available. These millennial learners are smart learners. Thus the use of ICT for learning has also taken a leap from classrooms, physical laboratories, Power Point Presentation, Projectors to Smart, Collaborative, Virtual, Visual and 24 x 7 access to Learning. In academic environment, not only learning and evaluation but also reinforced learning is also needed. The learners experience mandates teachers to be creating contents and provide a innovative learning. The Learning and Content Management System (LCMS) not only supports the needs of both the teachers and e-learners community but gives a seamless synchronization between them to enhance the experience of both. In this paper MOODLE, a free and Open Source Software is used as a tool for creating an LCMS for an Engineering course (Computer Architecture). MOODLE’s features were exploited to bring in the best of the requirements of Content Creation, Course Scheduling, Course Registration, Creative Learning, Quiz, Assignments, Blogs, Assessments and Analysis. It is found that MOODLE is a rich platform for LCMS and is well suited for academic environment LCMS.

LMS, CMS, Moodle, E-learning, Moodle Approach, LCMS, Interactive Learning

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Abhinaw Anand, Sumathy Eswaran, "CASE STUDY: MOODLE Approach to Learning and Content Management System (LCMS)," International Journal of Computer Sciences and Engineering, Vol.6, Issue.7, pp.1147-1152, 2018.

VANETs privacy and security have attracted a lot of attention over the last couple of years. VANETs are being used to boost road safety and empower a wide variety of services like internet, Emergency Message etc. The Vehicular Ad-hoc Network is a collection of smart devices with their vehicle interface. It can play a significant role in the routing process and provide better Security. An increased number of vehicles can raise a number of accidents. That can be part of life loss. So it is the need for smart vehicles that can establish interpersonal communication and warn each other for safety and security. On the other hand, many forms of attacks against VANETs have emerged recently that attempt to compromise the security of VANET networks. Such security attacks on VANETs might cause harmful results. Therefore, making VANETs security has become a key objective for VANET designers. To modify and deploy secure VANET infrastructures remains a significant challenge. The authors portray the different routing protocol by using Octopus diagram, number of attacks and its solution in VANET. With the help of safety and road traffic info among vehicles and related network attacks which improve security under possible attacks in VANETs.

VANET, Attacks, Security, Routing Protocol, Authentication, RSU, and DSRC

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