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One of the important characteristics of the modern-day world is its high connectivity. While it has brought people closer and made lives easier, it has also paved way for harmful content, such as diseases, rumors, computer viruses, etc., to flow easily and spread even quicker. Therefore, finding the source of such unwanted diffusion processes becomes critical to mitigate the damages and avoid future threats. Consequently, infection source identification in complex networks has become an important problem with wide range of effective and meaningful applications. Researchers, over the years, have produced elegant and efficient solutions for the same. The main aim of this paper is to study the factors affecting locating a source of infection. This study largely focuses on four such factors: topology, graph density, infection probability and infection size. For performance analysis, three well known state-of-art source identification techniques, i.e., Dynamic Age (DA), Reverse Infection (RI) and Minimum Description Length (MDL), are employed. Largescale and extensive experiments conducted on various datasets indicate that all the four factors play critical roles in infection source identification, irrespective of the source identification technique employed.

Infection Source Identification, Information Diffusion, Social Networks, Complex Networks, SI Model

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Syed Shafat Ali, Syed Afzal Murtaza Rizvi, "Factors Influencing Infection Source Identification in Complex Networks: An Empirical Study," International Journal of Computer Sciences and Engineering, Vol.7, Issue.5, pp.1791-1804, 2019.

This present digital world is made up of both secure and insecure data communication. So, it is imperative to secure our data from adversaries. To protect our data while communications, lots of mechanisms are there, but still some vulnerability exists. Steganography is one of the security techniques where we can hide our data inside different mediums like video, image, and audio files. There are also some vulnerable exists. Since it hides the data in the least significant bit [LSB], attacks could be performed easily if the adversary knows some data concealed in the object. In this research, we proposed a new multi-layered security algorithm to reinforce security of steganography along with Cryptography to ensure the privacy and integrity of the data by using the native Operating System batch programming. In this Algorithm, we used several encryption techniques with many undercover keys, did ASCII to Binary conversions, exchange of symbols and Hash function. This Proposed mechanism reinforced the security of steganography by ensuring confidentiality and integrity. It would be a big challenge for the adversary. In this research, we used our coding to perform Steganography and to produce the hash code.

Steganography, Cryptography, Hash function, Plain Text, Cipher Text, LSB, Pixel, Undercover key, Operating System [OS], SHA512

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J. Sebastian Nixon, Mesele Gebre Awgichew, Akalu Assefa Afaro, Fisaha Solomon, Paulos Bogale Wada, Fevan Tafari, "A Proposed Algorithm to Reinforce the Security of Steganograpy in conjunction with Cryptography to Assure Privacy and Integrity of the Communication by using Native OS Batch Programming Technique," International Journal of Computer Sciences and Engineering, Vol.7, Issue.5, pp.1805-1819, 2019.

An intermittent connectivity experience node by paradigm of opportunistic forwarding has been proposed to serve emerging wireless networking applications transmit messages to a distant destination for given delay bound, disjoined parts of the network exchange information by nodes broker to exploit node mobility. Forwarding decision is made by main challenge of opportunistic forwarding relays the best chosen cumulative probability within the delay bound of destination. The Bee Colony algorithm performs a kind of neighborhood search to solve this issue at recent work combined with global search used for both combined optimization and continuous optimization. Choosing initial centroid clusters is the problem for OFPC affects the efficiency of algorithm. The initial centroids for clusters are found to introduce by Bee Colony algorithm. A new opportunistic forwarding scheme is designed after finding the initial centroid, partial centrality with opportunistic forwarding (OFPC), and theoretically quantifies the partial centrality influence on the data forwarding performance using Bee colony optimization. Bee Colony optimization creates multi agent system capable to successfully solved difficult combinatorial optimization problem as the basic idea. Different TTL requirements are epidemic by applying our scheme on three real opportunistic networking scenarios, our extensive evaluations show that our scheme achieves significantly better mean delivery delay and cost compared to the state-of-the-art works, achieve delivery ratios sufficiently closer. The OFPC outperforms other solutions overall with extending shows the evaluation result, especially in terms of mean delivery delay and cost.

Opportunistic Forwarding with Partial Centrality, Bee Colony algorithm, clustering, opportunistic networking scenarios, Decayed Aggregation Graph

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S. Sivabalan, S. Dhamodharavadhani, R. Rathipriya, "Opportunistic Forward Routing Using Bee Colony Optimization," International Journal of Computer Sciences and Engineering, Vol.7, Issue.5, pp.1820-1827, 2019.

Object-oriented development is a mechanism in which objects provide services to other objects by various means like inheritance, polymorphism, etc. Faults, in object-oriented software, may occur at two levels i.e. object level and interaction level (when one object provides/receives some services from others). A formal representation, of an object-oriented system, may be helpful to understand the behavior of software faults. Faults identification, at earlier stages, may help during the development and testing stages. In this paper, an attempt has been made to model several faults, in an object-oriented system, with the help of Colored Petri Nets. First, a formal representation of object-oriented properties is depicted by Colored Petri Nets. Secondly, various possible faults are modeled using different programming scenarios. The main emphasis was on faults that may arise due to objects and their interactions i.e. inheritance and polymorphism state. Such information may be useful during the testing and maintenance phases of software development.

Object, Faults, Colored Petri Nets, Inheritance, Polymorphism

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Shubham Kaushik, Ratneshwer, "Fault Modelling of an Object-Oriented System using CPN," International Journal of Computer Sciences and Engineering, Vol.7, Issue.5, pp.1828-1845, 2019.

Developers with innovative ideas no longer needs the huge amount of capital investment in hardware or supporting software to deploy their service or the human expense to operate it because of the introduction of Cloud Computing. Cloud Computing is the delivery of computing services such as servers, software, databases, storage, networking, intelligence, analytics etc. over the Internet to facilitate faster innovation, economies of scale and flexible resources. While a lot of research is currently going on in the technology itself, this paper is trying to contain the new and emerging technologies in cloud computing. Here we have tried to bring most important new technologies introduced in Cloud Computing such as data sharing and data security. Finally, we presented some proposals which have connected other areas with cloud computing.

Cloud Computing, Data security, Data sharing, Cloud data sharing, Mobile Cloud Computing

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[10] Li, Ruixuan, et al. "A lightweight secure data sharing scheme for mobile cloud computing." IEEE Transactions on Cloud Computing 6.2 (2018): 344-357.
[11] Xiong, Hu, Hao Zhang, and Jianfei Sun. "Attribute-based privacy-preserving data sharing for dynamic groups in cloud computing." IEEE Systems Journal 99 (2018): 1-22.
[12] Zhang, Yinghui, Axin Wu, and Dong Zheng. "Efficient and privacy-aware attribute-based data sharing in mobile cloud computing." Journal of Ambient Intelligence and Humanized Computing 9.4 (2018): 1039-1048.
[13] Zuo, Cong, et al. "Fine-grained two-factor protection mechanism for data sharing in cloud storage." IEEE Transactions on Information Forensics and Security 13.1 (2018): 186-196.
[14] Jouini, Mouna, and Latifa Ben Arfa Rabai. "A security framework for secure cloud computing environments." Cloud Security: Concepts, Methodologies, Tools, and Applications. IGI Global, 2019. 249-263.
[15] Yang, Li, et al. "A remotely keyed file encryption scheme under mobile cloud computing." Journal of Network and Computer Applications 106 (2018): 90-99.
[16] Langmead, Ben, and Abhinav Nellore. "Cloud computing for genomic data analysis and collaboration." Nature Reviews Genetics 19.4 (2018): 208.
[17] Masala, Giovanni L., Pietro Ruiu, and Enrico Grosso. "Biometric authentication and data security in cloud computing." Computer and Network Security Essentials. Springer, Cham, 2018. 337-353.
[18] Roy, Sandip, et al. "Provably secure fine-grained data access control over multiple cloud servers in mobile cloud computing based healthcare applications." IEEE Transactions on Industrial Informatics 15.1 (2019): 457-468.
[19] Liu, Yi, et al. "Secure and fine-grained access control on e-healthcare records in mobile cloud computing." Future Generation Computer Systems 78 (2018): 1020-1026.
[20] Ning, Zhaolong, et al. "Green and sustainable cloud of things: Enabling collaborative edge computing." IEEE Communications Magazine 57.1 (2019): 72-78.
[21] Nurmi, Daniel, et al. "The eucalyptus open-source cloud-computing system." Proceedings of the 2009 9th IEEE/ACM International Symposium on Cluster Computing and the Grid. IEEE Computer Society, 2009.
[22] Sakshi kathuria,"A Survey on Security Provided by Multi-Clouds in Cloud Computing",International Journal of Scientific Research in Network Security and Communication,Vol.6,Issue.1,pp 23-28
[23] Kaur, U., M. Mahajan, and D. Singh. "A Comparative Analysis of Trust Models in Cloud Computing." International Journal of Scientific Research in Network Security and Communication6.2 (2018): 19-23.

O. Jamsheela, Mohd Abdul Hameed, "Recent Trends in Cloud Computing," International Journal of Computer Sciences and Engineering, Vol.7, Issue.5, pp.1846-1851, 2019.

Human personality has played a vital role in an individual`s life as well as in the development of an organization. One of the ways to judge human personality is by using standard questionnaires or by analyzing the Curriculum Vitae (CV). Traditionally, recruiters manually shortlist/filters a candidate’s CV as per their requirements. In this paper, we present a system that automates the eligibility check and aptitude evaluation of candidates in a recruitment process. To meet this need an online application is developed for the analysis of aptitude or personality test and candidate’s CV. The system analyzes professional eligibility based on the uploaded CV. The system employs a machine learning approach using TF-IDF Algorithm. The output of our system gives a decision for candidate recommendation. Further, the resulting scores help in evaluating the qualities in the candidates by analyzing the scores obtained in different areas. The graphical analysis of the performance of any candidate makes it easier to evaluate his/her personality and helpful in analyzing the CV properly. Thus, the system provides a helping hand for the recruitment process so that the candidate’s CV will be shortlisted and the fair decision will be made.

Psychometric Analysis, Machine-learning, TF-IDF, Curriculum Vitae Analysis, Personality Evaluation

[1] FoDRA – Nikolaos D. Almalis George A. Tsihrintzis , Aggeliki D. Strati , “A New Content-Based Job Recommendation Algorithm for Job Seeking and Recruiting” , 2016.
[2] Data,Vishnu M Menon Computer Rahul Nath H A, “A Novel Approach to Evaluate and Rank Candidates in A Recruitment Process by Estimating Emotional Intelligence through Social Media”,May 2016.
[3] Manasi Ombhase, Prajakta Gogate, Tejas Patil
“Automated Personality Classification Using Data Mining Techniques” 10.13140/RG.2.2.35949.59363, 2017
[4] Vivian Lai, Kyong Jin Shim, Richard J. Oentaryo, Philips K. Prasetyo, Casey Vu Ee-Peng Lim, David Lo, “Career Mapper: An Automated Resume Evaluation Tool”, 2016.
[5] Janet E. Squires, Leslie Hayduk, Alison M. Hutchinson, Lisa ACranley, Mark Gierl, Greta G. Cummings, Peter G. Norton and Carole A Estabrooks, "A Protocol for Advanced Psychometric Assessment of Surveys", Nursing Research and Practice Volume 2013 (2013), Article ID 156782,20 December 2012.
[6] Automated CV Processing along with Psychometric Analysis in Job Recruiting Process Firoz Ahmed , Mehrin Anannya 2 , Tanvir Rahman 3 , Risala Tasin Khan4 Institute of Information Technology , May 2015.
[7] Mesurado, Belén & J. Mateo, Niño & Valencia, Marshall & Richaud, Maria, “Extraversion: Nature, Development and Implications to Psychological Health and Work Life”, (2014).
[8] M.Balabanovic and Y. Shoham, “Fab: Content-based, Collaborative Recommendation. Communications of the ACM,” vol. 40, no. 3, pp. 66-72, 1997.
[9] Ramezani, M., Bergman, L., Thompson, R., Burke, R. and Mobasher, B., “Selecting and applying recommendation technology”. In Proc. of International Workshop on Recommendation and Collaboration, in Conjunction with 2008 International ACM Conference on Intelligent User Interfaces. Canaria, Canary Islands, Spain, 2008.
[10] Badrul Sarwar, G. Karypis, J. Konstan, and J. Riedl, “Item-Based Collaborative Filtering Recommendation Algorithms,” Proceedings of the 10th International Conference of World Wide Web, pp. 285-295, 2001.
[11] Shereen Albitar, Sebastien Fournier, Bernald Espinasse, An effective tf/idf-based text-to-text semantic similarity measure for text, spinger, pp. 105-114, 2014.
[12] Stephen Robertson,"Understanding inverse document frequency: on theoretical arguments for IDF", Journal of Documentation, Vol. 60 Issue: 5, pp.503-520, 2004.
[13] R.S. Walse, G.D. Kurundkar, P. U. Bhalchandra, "A Review: Design and Development of Novel Techniques for Clustering and Classification of Data", International Journal of Scientific Research in Computer Science and Engineering, Vol.06, Issue.01, pp.19-22, 2018.
[14] Shahzad Qaiser,Ramsha Ali ,International Journal of Computer Applications (0975 – 8887), “Text Mining: Use of TF-IDF to Examine the Relevance of Words to Documents” Volume 181 – No.1, July 2018.
[15] Amit Palve, Rohini D.Sonawane, Amol D. Potgantwar, "Sentiment Analysis of Twitter Streaming Data for Recommendation using, Apache Spark", International Journal of Scientific Research in Network Security and Communication, Vol.5, Issue.3, pp.99-103, 2017.
[16] Huan Liu, Lei Yu, "Towards integrating feature selection algorithms for classification and clustering", IEEE Transaction on Knowledge and Data Engineering, vol. 17, no. 4, pp. 491-502, April 2005.

Jayashree Rout, Sudhir Bagade, Pooja Yede , Nirmiti Patil, "Personality Evaluation and CV Analysis using Machine Learning Algorithm," International Journal of Computer Sciences and Engineering, Vol.7, Issue.5, pp.1852-1857, 2019.

Software quality metrics are part of software metrics focusing primarily on process, product and project quality aspects. Software quality metrics primarily focus on software measurement and its development process. The main objective of software testing is to enhance the quality of software. Many experts in the field of software testing propose various number of metrics. With the help of these we can detect trends and can prevent problems in efficient cost control, quality improvements, time and risk reduction with their probable solutions. Thus, in the global competitive market, it facilitates ensuring and achieving optimal business goals.

Software Testing, Software Testing Metrics, Software Testing Product Metrics, Software Testing Process Metrics

[1] Aggarwal, K.K., Singh, Y., Kaur, A., and Maihotra, R. (2005), ‘Software Reuse Metrics for Object-Oriented Systems’ , Proceedings of the 2005 Third ACIS Int’l Conference on Software Engineering Research, Management and Applications (SERA ‘05).
[2] Arar, O. F. and Ayan, K. (2016). Deriving thresholds of ¨ software metrics to predict faults on open source software: Replicated case studies. Expert Systems with Applications, 61:106–121.
[3] DeMarco, T. (2013), ‘Controlling Software Projects: Management, Measurement and Estimation.’ ISBN 0-13-171711-1.
[4] Dhavachelvan,P., V.S.K. Uma, Venkatachalapathy G. V. (2006) ‘A new approach in development of distributed framework for automated software testing using agents’ , Volume 19, Issue 4.
[5] Erturk, E. and Sezer, E. A. (2015). A comparison of some soft computing methods for software fault prediction. Expert Systems with Applications, 42(4):1872–1879.
[6] Ghotra, B., McIntosh, S., and Hassan, A. E. (2015). Revisiting the impact of classification techniques on the performance of defect prediction models. In Proceedings of the 37th International Conference on Software Engineering-Volume 1, pages 789–800. IEEE Press.
[7] Honglei, T., Wei, S., and Yanan, Z. (2009). The research on software metrics and software complexity metrics. In Computer Science-Technology and Applications, 2009. IFCSTA’09. International Forum on, volume 1, pages 131–136. IEEE.
[8] Kamei, Y. and Shihab, E. (2016). Defect prediction: Accomplishments and future challenges. In Software Analysis, Evolution, and Reengineering (SANER), 2016 IEEE 23rd International Conference on, volume 5, pages 33–45. IEEE
[9] Karner, C. and Bond, W.P. (2004), ‘Software Engineer Metrics: What do they measure and how do we know?’ Proceeding of the 10th International Software Metrics Symposium, Metrics.
[10] Kumar, L., Misra, S., and Rath, S. K. (2017). An empirical analysis of the effectiveness of software metrics and fault prediction model for identifying faulty classes. Computer Standards & Interfaces, 53:1–32.
[11] Menzies, T., Krishna, R., and Pryor, D. (2015). The promise repository of empirical software engineering data (2015).
[12] Ogasawara, H., Yamada, A. and Kojo, M. (1996) ‘Experiences of software Quality Management Using Metrics through Life cycle’, Proceedings of ICSE.
[13] Paul, C. (2002) ‘Software Testing - A Craftsman’s Approach Second Edition’ CRC Press.
[14] Prakash, P. (2018). ‘Using weighted defects metrics to improve software quality: An analysis and review’. International conference on recent trends and advances in computer science and engineering, LIET, Alwar, Rajasthan, India, 14-15 April, 2018, pages 50-53.
[15] Rathore, S. S. and Kumar, S. (2017). Linear and non-linear heterogeneous ensemble methods to predict the number of faults in software systems. Knowledge-Based Systems, 119:232–256.
[16] Turhan, B., Mısırlı, A. T., and Bener, A. (2013). Empirical evaluation of the effects of mixed project data on learning defect predictors. Information and Software Technology, 55(6):1101–1118.
[17] Yang, X., Lo, D., Xia, X., and Sun, J. (2017). Tlel: A two layer ensemble learning approach for just-in-time defect prediction. Information and Software Technology, 87:206–220.
[18] Zhao, Y., Yang, Y., Lu, H., Liu, J., Leung, H., Wu, Y., Zhou, Y., and Xu, B. (2017). Understanding the value of considering client usage context in package cohesion for fault-proneness prediction. Automated Software Engineering, 24(2):393–453.
[19] Aanchal, Kumar S. (2013). ‘Metrics for Software Components in Object Oriented Environments: A Survey’ International Journal of Scientific Research in Computer Science and Engineering, Volume-1, Issue-2, March-April-2013: pp. 25-29.

Piyush Prakash, Sarvottam Dixit, S. Srinivasan, "Use of Software Metrics on Software Development Projects Life Cycles in OOE to Improve Software Quality," International Journal of Computer Sciences and Engineering, Vol.7, Issue.5, pp.1858-1864, 2019.

Whiteboard has played a vital role in our education system, teachers have been using this prop to deliver the daily knowledge capsules using presentations, slide after slide, which has surely left the dusty old chalkboard at par. This whiteboard also allows presenters the ease of annotating as they continue with the slides by using marker and stuff. But this practice has been revolutionized since the introduction of touch sensitive interactive panel. The classes become more interesting and involvement of students grow stronger, the presenters hold a better control on delivering a good session. But unfortunately, it is not accessible to all due to the cost of these fancy boards, which does not help the mass. Since the usage of whiteboard has emerged as a smart classroom property and adopted by various digitally sound organizations, it is therefore in need of cost effective smarter interactive display. Therefore, in this review, the objective is to identify minimal cost of installation of an interactive whiteboard or means to achieve this ideal system preferably with the help of Computer Vision (a trending perception realistic technology in minimizing cost).

Computer vision, Image processing, IWBS, Camera, Infrared light

[1] Alexander Mordvintsev & Abid K. “Image processing in OpenCV”, 2014.
[2] Dalbir Singh, Ridha Omar and Azfar Anuar, “Low Cost Interactive Electronic Whiteboard Using Nintendo Wii Remote”, American Journal of Applied Sciences, Vol.-7 Issue- 11, pp. 1458-1463. 2010.
[3] Cxetin G, Bedi R and Sandler S “Multitouch technologies”. NUI Group, no publisher, 2009
[4] Oliphant, T. E. “Python for Scientific Computing”. Computing in Science & Engineering, Vol.-9, Issue- 3, pp. 10–20, 2007.
[5] Tosuntaş, Ş. B., Karadağ, E., & Orhan, S. “The factors affecting acceptance and use of interactive whiteboard within the scope of FATIH project: A structural equation model based on the Unified Theory of acceptance and use of technology”. Computers & Education, Vol.-81, pp. 169–178. 2015.
[6] Kellerman, A., Gurusinghe, N., Ariyarathna, T., & Gouws, R. “Smart whiteboard for interactive learning”. 2018 IEEE International Conference on Industrial Electronics for Sustainable Energy Systems (IESES). 2018.
[7] Rautaray, S. S., & Agrawal, A. “Vision based hand gesture recognition for human computer interaction: a survey”. Artificial Intelligence Review, Vol.-43(1), pp. 1–54. 2012.
[8] Lee, J. C. “Hacking the Nintendo Wii Remote”. IEEE Pervasive Computing, Vol.-7(3), pp. 39–45. 2008.
[9] Zhang, S., He, W., Yu, Q., & Zheng, X. “Low-cost interactive whiteboard using the Kinect”. International Conference on Image Analysis and Signal Processing. 2012.
[10] Dai, J., & Chung, C.-K. R. “Touchscreen Everywhere: On Transferring a Normal Planar Surface to a Touch-Sensitive Display”. IEEE Transactions on Cybernetics, Vol.-44(8), pp. 1383–1396. 2013.
[11] Şimşek, S., & Durdu, P. O., “Developing an Interactive Learning Environment with Kinect”. Communications in Computer and Information Science, pp. 150–155. 2014
[12] Amano, T., “Projection Center Calibration for a Co-located Projector Camera System”. 2014 IEEE Conference on Computer Vision and Pattern Recognition Workshops. 2014
[13] Yucel, K., Orhan, N., Misirli, G., Bal, G., & Sahin, Y. G. “An improved interactive whiteboard system: A new design and an ergonomic stylus”, 2nd International Conference on Education Technology and Computer. Vol.-3 pp. 148-152. 2010
[14] M.K. Bhuyan, D.R. Neog, M.K. Kar, “Fingertip detection for hand pose recognition”, International Journal of Computer Sciences and Engineering. Vol.-4, Issue-3, pp. 501–511, 2012.
[15] Manne Vamshi Krishna, Gopu Abhishek Reddy, B. Prasanthi and M. Sreevani, “Green Virtual Mouse Using OpenCV”, (IJCSE) International Journal of Computer Sciences and Engineering, Vol.-7, Issue-4, April 2019

Sunny Deep Basumatary, Kishore Kashyap, "Ultra low Cost Interactive Whiteboard using Computer Vision Techniques: A Review," International Journal of Computer Sciences and Engineering, Vol.7, Issue.5, pp.1865-1869, 2019.

Internet of Things (IoT) is an innovative paradigm that considers pervasive presence of variety of things. In particular, IoT consists of a large number of tiny devices which are deployed to sense the data. The sensed data has the potential to add a new dimension to human life. It is one of the latest technologies which provide connectivity to anyone, anywhere at any time. As voluminous numbers of devices are going to be connected and the devices are mostly battery connected, energy plays a vital role in IoT networks. Moreover, routing becomes a challenging task. The authors propose a framework to sustain the remaining energy of individual devices in IoT networks.

Energy, Routing, Framework, IoT

[1]. S. Santiago, L. Arockiam, “Energy Efficiency in Internet of Things: An Overview”, International Journal of Recent Trends in Engineering & Research (IJRTER), Vol.2, pp.475-482, 2016.
[2]. Zhu Chunsheng, Victor CM Leung, Lei Shu, Edith C-H Ngai, “Green internet of things for smart world”, IEEE Access, Vol.3, pp. 2151-2162, 2015.
[3]. Lamaazi, Hanane, Nabil Benamar, “RPL enhancement using a new objective function based on combined metrics”, Wireless Communications and Mobile Computing Conference (IWCMC), IEEE, pp. 1459-1464, 2017.
[4]. Ok Dudu, Furqan Ahmed, Piergiuseppe Di Marco, Roman Chirikov and Cicek Cavdar, “Energy aware routing for Internet of Things with heterogeneous devices”, International Symposium on Personal, Indoor, and Mobile Radio Communications (PIMRC), IEEE, pp. 1-5, 2017.
[5]. Risteska Stojkoska, Biljana, Kire Trivodaliev and Danco Davcev, “Internet of Things framework for home care systems”, Wireless Communications and Mobile Computing, Vol. 2017, pp. 1-8, 2017.
[6]. Nguyen T D, Khan J Y and Ngo D T, “An effective energy-harvesting-aware routing algorithm for WSN-based IoT applications”, International Conference on Communications (ICC), pp. 1-6, 2017.
[7]. Sousa, Natanael, Jose VV Sobral, Joel JPC Rodrigues, Ricardo AL Rabêlo, and Petar Solic, “ERAOF: A new RPL protocol objective function for Internet of Things applications”, 2nd International Multidisciplinary Conference on Computer and Energy Science (SpliTech), IEEE, pp. 1-5, 2017.
[8]. Sarwesh P, N Shekar V Shet and K Chandrasekaran, “Energy efficient network architecture for IoT applications”, International Conference on Green Computing and Internet of Things (ICGCIoT), IEEE, pp. 784-789, 2015.
[9]. Hassan El Alami and Abdellah Najid, “Energy-Efficient Fuzzy Logic Cluster Head Selection in Wireless Sensor Networks”, IEEE, pp. 1-6, 2016.
[10]. Kaur, Navroop and Sandeep K Sood, “An energy-efficient architecture for the Internet of Things (IoT)”, IEEE Systems Journal, Vol. 11, Issue 2, pp. 796-805, 2017.
[11]. Santiago, S. and Arockiam, L., “ELT-EAPR: Expected Life Time of Energy Aware Parent Routing for IoT Networks”, International Journal of Pure and Applied Mathematics, Vol. 118, Issue 8, pp.243-249, 2018.
[12]. Santiago, S., A. Kumar, and L. Arockiam, "EALBA: Energy Aware Load Balancing Algorithm for IoT Networks", In Proceedings of the 2018 International Conference on Mechatronic Systems and Robots, ACM, pp. 46-50, 2018.
[13]. Santiago. S, Arockiam. L, “E2TBR: Energy-Efficient Transmission Based Routing for IoT Networks,” IJCSEITR, Vol.7, pp.93-100, 2017.
[14]. BJ.Hubert Shanthan, Dr.L.Arockiam, “LAHUBMAX Priority Based Meta Task Scheduling Algorithm in multicloud”, International Journal of Computer Sciences

S. Santiago, L. Arockiam, "Energy Aware Routing Framework for Internet of Things," International Journal of Computer Sciences and Engineering, Vol.7, Issue.5, pp.1870-1872, 2019.

Twitter is a miniaturized scale blogging service in which individuals share and talk about their contemplations and perspectives in 140 characters without being obliged by space and time. A huge number of tweets are produced every day on diverse issues. Social researchers network have distinguished a few connections and measurements that actuate homophily. Assessments or feelings towards various issues have been seen as a key measurement which describes human conduct. Individuals typically express their assumptions towards different issues. Diverse people from various strolls of social life may impart same insight towards different issues. At the point when these people constitute a gathering, such gatherings can be advantageously named same wavelength groups or gatherings. That is, same wavelength groups will be bunches framed on the premise of conclusions and suppositions of comparable tint towards different issues by various people. Such same wave length groups crucially associate the people in an important and intentional organization.

Twitter, blogging, data, Politics

[1]. Balahur, A., Steinberger, R., Goot, E. V. D., Pouliquen, B., &Kabadjov, M. “Opinion mining on newspaper quotations” Web Intelligence and Intelligent Agent Technologies, 2009. WI-IAT`09. IEEE/WIC/ACM International Joint Conferences on (Vol. 3, pp. 523-526). IET.
[2]. David, MB, Andrew, YN & Michael IJ 2003, Latent Dirichlet Allocation , Journal of Machine Learning Research, vol. 3, pp.993-1022.
[3]. Sriram, B, David, F & Murat D 2010, Short Text Classification in Twitter to Improve Information Filtering ACM SIGIR, Geneva, Switzerland, pp. 841-842.
[4]. Bermingham and A. F. Smeaton, “ On using Twitter to monitor political sentiment and predict election results,” in n: Sentiment Analysis where AI meets Psychology (SAAIP) Workshop at the International Joint Conference for Natural Language Processing (IJCNLP), Chiang Mai, Thailand, 2011.
[5]. Potts, Christopher 2011, On the negativity of negation. In Proceedings of Semantics and Linguistic Theory 20, Ithaca, NY, CLC Publications, pp. 636-659.
[6]. Cambria, E & Hussain, A 2013, Sentic Computing: Techniques, Tools and applications Springer briefs in cognitive computation.
[7]. Pang, B & Lee, L 2008, ‘opinion mining and sentiment analysis’ Foundations and Trends in Information Retrieval, vol. 2, no. 1, pp. 1 135.
[8]. Abbasi, MA, Chai, S, liu, H & Sagoo, K 2012, Real -world behavior analysis through a social media : 5th International Conference on Social Computing, Behavioral-Cultural Modeling & Prediction (SBP 2012), USA, pp. 18-26.
[9]. Michal Skuza,Andrzej Romanowski, "Sentiment Analysis of Twitter Data within Big Data Distributed Environment for Stock Prediction", Computer Science and Information Systems pp. 1349– 1354, 2015 F230 ACSIS, Vol.5.
[10].Venkata Sasank Pagolu, Kamal Nayan Reddy Challa, Ganapati Panda,Babita Majhi, "Sentiment Analysis of Twitter Data for Predicting Stock Market Movements", International conference on Signal Processing, Communication, Power and Embedded System (SCOPES), 2016.
[11].Tina Ding, Vanessa Fang, Daniel Zuo, "Stock Market Prediction based on Time Series Data and Market Sentiment", 2012.
[12]. Phillip Tichaona Sumbureru, "Analysis of Tweets for Prediction of Indian Stock Markets", International Journal of Science and Research (IJSR), Volume 4 Issue 8, August 2015.
[13]. Rishabh Soni, K. James Mathai, "Improved Twitter Sentiment Prediction through „Cluster-then-Predict Model‟", International Journal of Computer Science and Network, Volume 4, Issue 4, August 2015
[14]. Linhao Zhang, "Sentiment Analysis on Twitter with Stock Price and Significant Keyword Correlation", April 16, 2013.

Atul Agrawal, Omprakesh Singh, "Large Scale Short Text Analysis to Recognize Categories," International Journal of Computer Sciences and Engineering, Vol.7, Issue.5, pp.1873-1877, 2019.