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The increase of use of Social networking sites in recent years has both pros and consequences. The idea is to have safer use of these Social media sites so as to obtain maximum benefits from them rather than having malicious effects from them. One of the misuses of these social networking sites like Twitter, Facebook, and Instagram is posting of absurd contents over these Social Medias. This content can be extremely harmful as it causes insult, depression, anxiety, peer pressure. This needs to be detected and reported for better use of social media. This measure will make an approach for better use of Internet yard. Hence, this research work aims at Predicting and detecting these harassing comments with the help of Machine learning Algorithms. The idea is to do Sentimental Analysis of the tweets obtained from Twitter, Pre-process them, apply Machine Learning algorithms along with Bag-Of-Words on the tweets and classify the tweets as positive and negative. Tweets classified as negative will help to detect the tweet as bullying or not. The proposed research work uses bag of words approach along with Laplace version of the Naive-Bayes classifier with Laplace function for Detection. For Prediction CART model with Bag-of-Words approach is used. The platform used here is R studio with various packages.

Prediction, Detection, Hectoring, Bag-of-word ,Laplace, Confusion Matrix

[1]. Bello-Orgaz, Gema, Jason J. Jung, and David Camacho. "Social big data: Recent achievements and new challenges." Information Fusion 28 (2016): 45-59.
[2]. Frommholz, Ingo, Haider M. Al-Khateeb et all"On textual analysis and machine learning for cyberstalking detection." Datenbank-Spektrum 16, no. 2 (2016),pp-127-135..
[3]. Al-garadi, Mohammed Ali, Kasturi Dewi Varathan, and Sri Devi Ravana. "Cybercrime detection in online communications: The experimental case of cyberbullying detection in the Twitter network." Computers in Human Behavior 63 (2016), pp. 433-443.
[4]. Chen, Ying, Yilu Zhou et all "Detecting offensive language in social media to protect adolescent online safety." In Privacy, Security, Risk and Trust (PASSAT), 2012 International Conference on and 2012 International Confernece on Social Computing (SocialCom), pp. 71-80. IEEE, 2012..
[5]. Parker, Shawniece L., and Yen-Hung Hu. "Content Mining Techniques for Detecting Cyberbullying in Social Media." Virginia Journal of Science 67, no. 3 (2016),pp 1-8.
[6]. Sheela, L. Jaba. "A Review of Sentiment Analysis in Twitter Data Using Hadoop." International Journal of Database Theory and Application 9, no. 1 (2016): 77-86.
[7]. Zhao, Rui, Anna Zhou, and Kezhi Mao. "Automatic detection of cyberbullying on social networks based on bullying features." In Proceedings of the 17th international conference on distributed computing and networking, p. 43. ACM, 2016.
[8]. Ismail, Mohamed Maher Ben, and Ouiem Bchir. "Insult detection in social network comments using possibilistic based fusion approach." In Computer and Information Science, pp. 15-25. Springer International Publishing, 2015.
[9]. Nagar, Himanshu, Chetna Dabas, and J. P. Gupta. "Navie Bayes and K-Means Hybrid Analysis for Extracting Extremist Tweets", ACM Conference, pp 27-32.

Sakshi Gujral , "Predicting and Detecting Hectoring on Social Media Using Machine Learning," International Journal of Computer Sciences and Engineering, Vol.5, Issue.8, pp.173-176, 2017.

In the modern time of computing, Cloud computing will perform a major technical role in the IT world. Although various cloud service models are there present, Infrastructure as a Service (IAAS) has become the base of the next future Network of Services (NOS). Several advantages of cloud computing attracts the persons and organization to move their data from remote to the cloud. Cloud Providers mostly focus on providing services and provides a lesser focus on data security and privacy which is the main side of cloud computing. Since the single cloud storage does not fulfill the demands of the individuals and organizations, a move towards multi-cloud storage (MCS) has been emerged. In this report presents an overview of motivation of service overloading, attackers, different techniques and its limitations made to protect data security in the cloud storage .In this paper also explains the usual challenges in cloud storage services. This work delivers a superior solution in the service over loading architecture and key thought in the decision making process for the individuals and organizations in the adoption of superior cloud storage service.

Service Overloading, Cloud Security, Data Security, Single and Multi-Cloud Storage

[1]. Bincy Paul and M. Azath, "Survey on Preserving Data Privacy in Cloud", International Journal of Computer Sciences and Engineering, Vol.2, Issue.12, pp.57-61, 2014.
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[4].Venkata Josyula, Malcom Orr, Greg Page, “Cloud omputing:Automating the Virtualized Data Center” Cisco Press 2012.
[5].P. F. Oliveira, L. Lima, T. T. V. Vinhoza, 1. Barros, M. M`edard, "Trusted storage over untrusted networks", IEEE GLOBECOM , Miami, FL.USA, 2010
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[7].Fabian, B., Ermakova, T., & Junghanns, P., “ Collaborative and secure sharing of healthcare data in multi-clouds, Information Systems, 48, 132-150, 2015.
[8]. Thilakanathan, D., Chen, S., Nepal, S., & Calvo, R. A., “ Secure data sharing in the cloud”, In Security, Privacy and Trust in Cloud Systems, Springer Berlin Heidelberg., pp. 45-72 2014.
[9]. Balasaraswathi, V. R., & Manikandan, S., “Enhanced security for multi-cloud storage using cryptographic data splitting with dynamic approach”, In 2014 International Conference on Advanced Communication, Control and Computing Technologies (ICACCCT), pp. 1190- 1194.
[10] P. Ranjima, Sumathi. D , M. Mathew, P. Sivaprakash, "Secure Cloud Storage with Access Control: A Survey", International Journal of Computer Sciences and Engineering, Vol.2, Issue.8, pp.124-126, 2014.
[11]. WANG Liang-Liang, CHEN Ke-Fei, MAO Xian-ping, WANG Yong- Tao, “Efficient and Provably-Secure Certificateless Proxy Re-encryption Scheme for Secure Cloud Data Sharing” Journal of Shanghai Jiaotong University, Vol.19, Issue.4, pp.398-405, 2014.
[12]. Peng Xul, Xiaqi LiU, Zhenguo Sheng, Xuan Shan`, Kai Shuang “SSDS- MC: Slice-based Secure Data Storage in MultiCloud Environment”, 11th EAI International Conference on Heterogeneous Networking for Quality, Reliability, Security and Robustness (QSHINE 2015) pp.304-309.
[13]. Yuuki Kajiura, Shohei Ueno, Atsushi Kanai, Shigeaki Tanimoto, Hiroyuki Sato, “An Approach to Selecting Cloud Services for Data Storage in Heterogeneous-Multicloud Environment with High Availability and Confidentiality”, Autonomous Decentralized Systems (ISADS) IEEE Twelfth International Symposium, pp.205-210, 2015.
[14].Hendrik Graupner, Kennedy Torkura, Philipp Berger, Christoph Meinel “Secure Access Control For Multi-Cloud Resources Local” Computer Networks Conference Workshops (LCN Workshops), 2015 pp 722-729.
[15].Hazila Hasan, Sultan Abdul Halim Muadzam Shah Secured Data Partitioning in Multi Cloud Environment Information And Communication Technologies (]WICT), 2014 pp146-151.
[16]. Xu, L., Wu, X., & Zhang, X., “CL-PRE: A certificate less proxy re-encryption scheme for secure data sharing with public cloud. In Proceedings of the 7th ACM Symposium on Information, Computer and Communications Security, pp. 87-88, 2012.
[17]. Michael O. Rabin, “Efficient Dispersal of Information Security, Load Balancing, and Fault Tolerance”, Journal of Association for Computing Machinery, pp.335-348, 1989.
[18] P. Thakkar, H.K. Mishra, Z. Shaikh, D. Sharma, "Image Encryption and Decryption System Using AES for Secure Transmission", International Journal of Computer Sciences and Engineering, Vol.5, Issue.5, pp.109-114, 2017.
[19]. V.P.Muthukumar and R.Saranya, "A Survey on Security Threats and Attacks in Cloud Computing", International Journal of Computer Sciences and Engineering, Vol.2, Issue.11, pp.120-125, 2014.
[20]. J.-M. Bohli, N. Gruschka, M. Jensen, L. L. Iacono, and N. Marnau, “Security and Privacy-Enhancing Multicloud Architectures,” IEEE Transactions on Dependable and Secure Computing, vol. 10, no. 4, pp. 212–224, Jul. 2013.
[21]. Nitesh Jain, Pradeep Sharma, "A Security Key Management Model for Cloud Environment", International Journal of Scientific Research in Computer Science and Engineering, Vol.5, Issue.1, pp.45-48, 2017.
[22]. N. Deshai, P. Penchala Swamy, G.P. Saradhi Varma, "Enhanced Query Processing Technique Using RASP Effective Services in Cloud", International Journal of Scientific Research in Computer Science and Engineering, Vol.5, Issue.4, pp.75-77, 2017.
[23]. B.Rex Cyril, Dr.S.Britto Ramesh Kumar, “Cloud Computing Data Security Issues, Challenges, Architectures and Methods-A Survey”, International Journal of Engineering and Technology, Vol.2, Issue.4, 2015.

Ramesh Prasad Vishwakarma, Sitendra Tamrakar, Rishi Kumar Sharma, "Data Security and Service Overloading in Cloud Computing –An overview," International Journal of Computer Sciences and Engineering, Vol.5, Issue.8, pp.177-180, 2017.

Number of unattended level crossing accidents and people involved in train track accidents are increasing after each year as per the rail accidents statistics in previous year . Most cases involve accidents due to the carelessness, sound of train not heard and accidents on curved tracks as train suddenly appear .Possibility of providing manned level crossing at regular intervals of distance is merely possible as lot of infrastructure , human resource and track modification are to be performed . The fatalities can be minimized to a great extend if a vision based signal system can be implemented , which could provide a signal when the train is about to pass and signal stays on until all the compartment pass. As the sense of vision provides much more attention than any other sense. The system can be implemented easily and left unattended with minimal maintenance also the system can be positioned at regular intervals of track with less track modification.

Unmanned Level Crossing, Piezo Vibration sensor, Train, Railway Tracks, Jaywalkers

[1] Vítor Amaral, Francisco Marques, André Lourenço, José Barata, Pedro Santana, “Laser-Based Obstacle Detection at Railway Level Crossings”, Journal of Sensors , vol. 2016, Article ID 1719230, 11 pages, 2016.
[2] Adeolu O Dina, Cornelius O Akanni, Bamidele A Badejo, “Evaluation of Railway Level Crossing Attributes on Accident Causation in Lagos, Nigeria”, Indonesian Journal of Geography, Vol 48, Issue No 2 ,pp. 108-117, (2016)
[3] Satoru Kitamura, Manabu Teramoto, Satoshi Itoya ,“Improvement of Availability of Level Crossing System by Autonomous Decentralized Technology”, IEEE 13th International Symposium on Autonomous Decentralized System (ISADS) , Bangkok , pp. 143-148, 2017.
[4] Fausto Pedro García Márquez, Diego J. Pedregal, Dr Clive Roberts “New methods for the condition monitoring of level crossings”,InternationalJournal of System Sciences, Volume 46 , Issue 5, pp. 878-884, 2015

Mohamed Ameen, "Track Vibration Based Signal System for Unattended Rail Crossing and on Track Signalling," International Journal of Computer Sciences and Engineering, Vol.5, Issue.8, pp.181-183, 2017.

Cognitive radio network is a network which overcomes the deficiency of spectrum in this fast-growing radio network. In this network, secondary users sense the spectrum that is used by primary users and if spectrum is vacant or not utilized then utilize the spectrum with proper management and sharing. For the foremost step sensing, different techniques are proposed but energy detector (ED) is most widely used due to its least sensing time requirement and low complexity. But there are limitations also; its performance degrades due to unknown variations in noise and this uncertainty cause problem of SNR wall and problem in achievement of exact energy threshold. Till now, various approaches have been proposed to accomplish better performance of system under low SNR environment. So, to increase the throughput and efficiency of system double threshold, dynamic threshold, two stage spectrum sensing etc. techniques have been proposed to alleviate above mentioned problems and also mathematical relation between energy threshold, probability of detection and false alarm is considered.

Noise uncertainty, Threshold techniques, SNR wall, Number of samples, receiver operating characteristics (ROC), cooperative spectrum sensing

[1] R. P. Borole, P. G. Student, C. Engineering, and N. M. U. Jalgaon, “A Review on Performance Analysis of Energy Detection Technique for Cognitive Radio Over Different Fading Channels,” International journal of innovative research in Science, Engineering and Technology, Vol.4, Issue.8, pp. 7214–7219, 2015.
[2] Q. Liu, J. Gao, and L. Chen, “Optimization of Energy Detection Based Cooperative Spectrum Sensing in Cognitive Radio Networks,” IEEE Communications, Issue. 2009, pp. 0–4, 2010.
[3] G. Vidyadhar Reddy and N. S. Murthy, “Optimization of cooperative spectrum sensing under AWGN and rayleigh channels in cognitive radio network,” 3rd International Conference on Advances in Compting and Communication (ICACC), pp. 126–129, 2013.
[4] D. Teguig, B. Scheers, and V. Le Nir, “Data Fusion Schemes for Cooperative Spectrum Sensing in Cognitive Radio Networks,” Communication and information system conference, Poland, pp. 1–7, 2012.
[5] K. Chabbra, G. Mahendru, and P. Banerjee, “Effect of dynamic threshold & noise uncertainty in energy detection spectrum sensing technique for cognitive radio systems,” 2014 International Conference on Signal Processing and Integrated Networks (SPIN), pp. 377–361, 2014.
[6] C. Korumilli, C. Gadde, and I. Hemalatha, “Performance Analysis of Energy Detection Algorithm in Cognitive Radio,” International Journal of Engineering Research and Applications, Vol. 2, Issue. 4, pp. 1004–1009, 2012.
[7] T. Akram, T. Esemann, and H. Hellbruck, “Cooperative spectrum sensing protocols and evaluation with IEEE 802.15.4 devices,” Physical Commununications, Vol. 19, Issue.1 pp. 93–105, 2016.
[8] C. Technologies, S. Nagar, and S. Nagar, “Comprehensive Analysis of various Energy Detection parameters in Spectrum Sensing for Cognitive Radio systems,” International conference on advances in communication and computing technologies, pp. 1–4, 2014.
[9] a. Mariani, a. Giorgetti, and M. Chiani, “Effects of Noise Power Estimation on Energy Detection for Cognitive Radio Applications,” IEEE Transaction on Communications, Vol. 59, Issue. 12, pp. 3410–3420, 2011.
[10] R. Tandra and A. Sahai, “Walls for Signal Detection,” IEEE journal on Selected Topics of Signal Processing, Vol. 2, Issue. 1, pp. 4–17, 2008.
[11] D. Mercedes, M. Plata, Á. Gabriel, and A. Reátiga, “Evaluation of energy detection for spectrum sensing based on the dynamic selection of detection-threshold,” International conference of electrical engineering research, pp. 135–143, 2012.
[12] Z. Jiang, X. Zhengguang, W. Furong, H. Benxiong, and Z. Bo, “Double Threshold Energy Detection of Cooperative Spectrum Sensing in Cognitive Radio,” 3rd International conference on Cognitive Radio Oriented Wireless Networks Communications (CrownCom), pp. 1–5, 2008.
[13] H. Wang, X. Su, Y. Xu, X. Chen, and J. Wang, “SNR wall and cooperative spectrum sensing in cognitive radio under noise uncertainty,” Journal of Electronics, Vol. 27, Issue. 5, pp. 611–617, 2010.
[14] L. Lu, X. Zhou, U. Onunkwo, and G. Li, “Ten years of research in spectrum sensing and sharing in cognitive radio,” EURASIP Journal on Wireless Communication and Networking, Vol. 28, Issue. 1, p. 1-16, 2012
[15] M. S. Oude Alink, A. B. J. Kokkeler, E. A. M. Klumperink, G. J. M. Smit, and B. Nauta, “Lowering the SNR wall for energy detection using cross-correlation,” IEEE Transaction on Vehicular Technology, Vol. 60, Issue. 8, pp. 3748–3757, 2011.
[16] S. S. Kalamkar, A. Banerjee, and A. K. Gupta, “SNR wall for generalized energy detection under noise uncertainty in cognitive radio,” 19th Asia-Pacific Conference on Communication (APCC), pp. 375–380, 2013.
[17] S. Kalamkar and A. Banerjee, “Improved Double Threshold Energy Detection for Cooperative Spectrum Sensing in Cognitive Radio,” Defence Science Journal, vol. 63, Issue. 1, pp. 34–40, 2013.
[18] V. M. Patil and S. R. Patil, “A survey on spectrum sensing algorithms for cognitive radio,” International Conference on Advances in Human Machine Interactaction (HMI), India, pp. 149–153, 2016.
[19] S. Shrivastava, R. Tiwari, and S. Das, “Dynamic-Double-Threshold Energy Detection Scheme under Noise Varying Environment in Cognitive Radio System,” International Journal of Computer Applications, Vol. 87, Issue. 14, pp. 23–27, 2014.
[20] M. S. Shbat and V. Tuzlukov, “SNR Wall Effect Alleviation by Generalized Detector Employed in Cognitive Radio Networks,” Sensors (Basel)., Vol. 15, pp. 16105–16135, 2015.
[21] S. Atapattu, C. Tellambura, H. Jiang, and N. Rajatheva, “Unified Analysis of Low-SNR Energy Detection and Threshold Selection,” IEEE Transaction on Vehicular Technology, Vol. 64, Issue. 11, pp. 5006-5019, 2014.
[22] P. P. Anaand, “Two Stage Spectrum Sensing for Cognitive Radio Networks using ED and AIC under Noise Uncertainty,” India, 2016.
[23] S. Sanjayjoshi, “Performance Analysis of Two Stage Spectrum,” Inetrnational journal of Innovative Research in Computer and Communication Engineering, Vol.3, Issue.6, pp. 5549–5555, 2015.
[24] M. Sun, C. Zhao, S. Yan, and B. Li, “A Novel Spectrum Sensing for Cognitive Radio Networks with Noise Uncertainty,” IEEE Transaction on Vehicular Technology, Vol. 66, Issue.5, pp. 4424–4429, 2016.
[25] K. Srisomboon, a Prayote, and W. Lee, “Double constraints adaptive energy detection for spectrum sensing in cognitive radio networks,” 8th International Conference Mobile Computing and Ubiquitous Networking, (ICMU), Japan, pp. 76–77, 2015.
[26] R. N. Prashob, a. P. Vinod, and A. K. Krishna, “An adaptive threshold based energy detector for spectrum sensing in cognitive radios at low SNR,” IEEE International Conference on Communication Systems, Singapore, pp. 574–578, 2010.
[27] K. Srisomboon K. Thakulsukanant A. Prayote W. Lee, “Adaptive Two-stage Spectrum Sensing under Noise Uncertainty in Cognitive Radio Networks”, The ECTI Transaction on Electrical Engineering, Electronics & Communication, Vol.14, Issue.1, pp. 21–35, 2016
[28] P. Verma and B. Singh, “Overcoming sensing failure problem in double threshold based cooperative spectrum sensing,” Optik Journal, Vol. 127, Issue. 10, pp. 4200–4204, 2016.
[29] H. Tran and H. Phan, “On Throughput and Quality of Experience in Cognitive Radio Networks,” Wireless Communications and Networking Conference, Qatar, pp. 0–4, 2016.
[30] S. Khanam and A. Kaur, “Enhanced Detection of Cognitive Radio Under Noisy Channels,” International Journal of Computer Science and Engineering, Vol.4, Issue. 10, pp. 116–119, 2016.
[31] N. K. Randhawa and A. S. Buttar, “Sensing of Spectrum Holes in Cognitive Radio Networks : A Survey,” International Journal of Computer Sciences and Engineering, Vol.2, Issue. 8, pp. 28-34, 2014.

Sakshi garg, Muskan Sharma, Sharmelee Thagjam, "Threshold Techniques to Improve Sensing Under Noise Uncertainty in Cognitive Radio Networks," International Journal of Computer Sciences and Engineering, Vol.5, Issue.8, pp.184-189, 2017.

We live in a world where each day tons and tons of data is generated from millions of sources. Companies and organizations thrive for this data in order to acquire valuable information that helps them understanding their customer needs and demands. This valuable insight collaborates in improving the services and products – thus enhancing the overall business and profits. Filtering out such significant information thus requires employing some data mining algorithms. Data mining is a wide area of study that is further developing day by day and is very useful in deriving important information and coherence from large and raw datasets. When we talk about one very well-known field of business, known as Market Basket Analysis – data mining has significantly affected this sector. As the name suggests, it is an analysis of shopper’s basket at a mart. We generally see various items arranged on the shelves in the malls and supermarkets; we also observe certain products recommended to us when we shop online. All of this is worked in the back-end with the help of data-mining algorithms that provide a proper analysis of customer buying patterns and hence it makes the relations and suggests them to the customers, which in turn results in an enhanced sales. Technically, association rule mining and frequent itemset mining is done for such analysis. These algorithms are also used in designing various games and in recommendation systems. In this paper we are thus understanding these algorithms and compare the efficiency of the most common ones on the basis of factors such as time, support and memory consumed.

Data mining, Association rules, Apriori, FP-Growth, Eclat, dEclat

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[3]. Akilandeswari. S and A.V.Senthil Kumar, "A Novel Low Utility Based Infrequent Weighted Itemset Mining Approach Using Frequent Pattern", International Journal of Computer Sciences and Engineering, Vol.3, Issue.7, pp.181-185, 2015.
[4]. Lei Wen, “An efficient algorithm for mining frequent closed itemset,” Fifth World Congress on Intelligent Control and Automation, Vol. 5, pp. 4296-4299, 2004.
[5]. M. J. Zaki and K. Gouda, “Fast vertical mining using diffsets,” Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining - KDD ’03, PP.pp. 326-335, 2003.
[6]. J. Pei, J. Han, H. Lu, S. Nishio, S. Tang, and D. Yang. "H-Mine: Fast and space-preserving frequent pattern mining in large databases", IIE Transactions, Vol.39, Issue 6, pp.593-605, 2007.
[7]. Takeaki Uno, Masashi Kiyomi and Hiroki Arimura, “LCM ver. 2: Efficient Mining Algorithms for Frequent/Closed/Maximal Itemsets”,. Proc. IEEE ICDM Workshop on Frequent Itemset Mining Implementations Brighton, UK, November 1, 2004.
[8]. Bastide, Y., Taouil, R., Pasquier, N., Stumme, G., &Lakhal, L., “Mining frequent patterns with counting inference”, ACM SIGKDD Explorations Newsletter, Vol.2, Issue.2, 66-75, 2000.
[9]. P. Sagar, M. Goyal, "A Review: Comparative Analysis of various Data Mining Techniques", International Journal of Computer Sciences and Engineering, Vol.4, Issue.12, pp.56-60, 2016.
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[12]. M.Manigandan and K. Aravind Kumar, "Secure Multiparty Protocol for Distributed Mining of Association Rules", International Journal of Scientific Research in Computer Science and Engineering, Vol.3, Issue.1, pp.6-10, 2015.
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B. Nigam, A. Nigam, P. Dalal, "Comparative Study of Top 10 Algorithms for Association Rule Mining," International Journal of Computer Sciences and Engineering, Vol.5, Issue.8, pp.190-195, 2017.

Detection and correction of style errors in a language plays an important role in development of language related resources like Machine Translation, Grammar checking, Natural Language Interfaces etc. Style errors may include various types of errors like Missing sentence ender, Wrong sentence ender, Error due to missing comma, Repeated/duplicate word Error, error due to missing conjunction etc.. Though considerable work has been done in the area for English and related languages, but the Indian Language scenario presents a relatively more complex and uphill task. In this paper, author has presented an algorithm for detection of various style errors present in Punjabi language. Author tested his algorithm using three different kinds of data sets and it is observed that the algorithm performs better for wrong sentence ender and duplicate word type error as compare to missing conjunction type style error.

Style error, grammar checker, Punjabi Language processing, NLP

[1]. Duni Chander. 1964., “Punjabi Bhasha da Viakaran (Punjabi)”, Punjab University Publication Bureau, Chandigarh, India.
[2]. Daniel Naber. 2003., “A Rule-Based Style and Grammar Checker”, Diplomarbeit Technische Fakultät, Universität Bielefeld, Germany. (http://www.danielnaber.de/languagetool/download/sty le_and_grammar_checker.pdf (1/10/2007))
[3]. Harjeet S. Gill and Henry A. Gleason, Jr. “A Reference Grammar of Punjabi”, Publication Bureau, Punjabi University, Patiala, India. 1986.
[4]. Joginder S. Puar., “The Punjabi verb form and function”, Publication Bureau, Punjabi University, Patiala, India, 1990.
[5]. Md. Jahangir Alam, Naushad UzZaman, and Mumit Khan. 2006. “N-gram based Statistical Grammar Checker for Bangla and English”, In Proc. of ninth International Conference on Computer and Information Technology (ICCIT 2006), Dhaka, Bangladesh.
[6]. K. Kukich (1992) "Techniques for automatically correcting words in text", ACM Computing Surveys. 24(4): 377-439.
[7]. P. Kundu and B.B. Chaudhuri, "Error Pattern in Bangla Text". International Journal of Dravidian Linguistics, Vol.28, Issue.2, pp.49-88, 1999.
[8]. K.W. Church and W.A. Gale, "Probability scoring for spelling correction", Statistical Computing, Vol.1, Issue.1, pp.93-103, 1991.
[9]. F.J. Damerau, "A technique for computer detection and correction of spelling errors", Commun. ACM. Vol.7, Issue.3, pp.171-176, 1964.
[10]. Morris, Robert and Cherry, Lorinda L, “Computer detection of typographical errors”, IEEE Trans Professional Communication, vol. PC-18, no.1, pp54-64, March 1975.
[11]. Pollock, J. J., Zamora, A, “Collection and characterization of spelling errors in scientific and scholarly text”, J. Amer. Soc. Inf. Sci. Vol.34, Issue.1, pp.51-58, 1983.
[12]. Yannakoudakis, E. J., Fawthrop, D., “An intelligent spelling corrector”, Inf. Process. Manage. 19, 12, 101-108, 1983.
[13]. Yannakoudakis, E.J. and Fawthrop, D, “An intelligent spelling error corrector”, Information Processing and Management, vol.19, no.2, pp.101-108, 1983.
[14]. Wagner, Robert A. and Fischer, Michael J, “The string-to-string correction problem”, Journal of the A.C.M., vol.21, no.1, pp168-173, January 1974.
[15]. R.E. Gorin, "SPELL: A spelling checking and correction program", Online documentation for the DEC-10 computer, 1971.
[16]. Durham, I, Lamb, D.A, and Saxe, J.B, “Spelling correction in user interfaces”, Communications of the A.C.M., vol.26, no.10, pp764-773, October 1983.
[17]. M.D. Kernighan, K.W. Church, and W.A. Gale., “A spelling correction program based on a noisy channel model”, In Proceedings of the Thirteenth International Conference on Computational Linguistics, pp.205-210, 1990.
[18]. Gale and Church, William A. Gale and Kenneth W. Church., “A program for aligning sentences in bilingual corpora”, In Proceedings of the 29th Meeting of the ACL, pages 177-184. Association for Computational Linguistics, 1991.
[19]. Roger Mitton, “Spelling checkers, spelling correctors and the misspellings of poor spellers”, Information Processing and Management: an International Journal, Vol..23 Issue.5, pp.495-505, Sept. 1987.

S. K. Sharma, "Detection and Correction of Style Errors Present in Punjabi Sentences," International Journal of Computer Sciences and Engineering, Vol.5, Issue.8, pp.196-199, 2017.

Research on various aspects of the multimedia learning materials such as animation, sound, graphics, interactive video and its impacts on learning has been conducted. For the development of any comprehensible educational system, the learning materials and methodology play a very important role. The students’ inner psychology is an integral part of any learning system aiming at personalized information material delivery. Personalized information material delivery can be described as a process of building the learning materials according to the students’ personal inner psychology, his/her behavioural aspects, goals, likes and dislikes. The inner psychology of a student is generally represented in the form of his/her intelligent quotient, emotional quotient, personality, stress and attitude quotient. The research works differ in the way they represent the student psychology, how they update the student’s inner feeling and the teaching–learning strategies they adapt for providing the personalized information learning materials.

ICT Tools, Memory and Learning, Multimedia and Animation, Stress, attitude

[1] Ann McNamara et al, “Perception in graphics, visualization, virtual environments and animation”, ACM ISBN: 978-1-4503-1135-9, 2011.
[2] Riaza et al, “Using 3-D Animation in Multimedia learning for memory management concepts”, IEEE –International conference on Signal Processing, 2009.
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T. Saravanan, N. Nagadeepa, "Understanding the Learners’ Intelligence, Stress and Attitude to Learning on Worldwide Knowledge, Research, and Experience," International Journal of Computer Sciences and Engineering, Vol.5, Issue.8, pp.200-205, 2017.

To overcome the drawback of underutilisation of the spectrum in wireless communication field, the Cognitive Radio (CR) technology came into existence, which permits the unlicensed or the Secondary Users (SU) to opportunistically use the available licensed spectrum when the licensed or the Primary User (PU) is not in use. The unlicensed user should not disrupt the working of the licensed user. To reduce the problem of shadowing and fading in CR, cooperative sensing was introduced in which many Cognitive Radio Users (CR Users) collectively report their decisions or data to the Fusion Center (FC) and it makes the final decision regarding the absence or presence of PU. In cooperative sensing, larger overhead is observed. Hence, clustering is one of the methods which reduces overhead. Clustering is a topology management system, in which the nodes are organized into logical groups known as clusters. It not only boosts the performance of the network but also achieves network scalability and stability, supports cooperative tasks, reduces the bandwidth requirement. This paper reviews the numerous clustering schemes, analyzes their characteristics and studies their performances.

Cognitive radio, Cooperative spectrum sensing (CSS), Clustering, Cluster head, Fusion centre, Probability of detection, Probability of false alarm

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Muskan Sharma, Sakshi Garg, Sharmelee Thangjam, "Clustering in Cognitive Radio Networks: A Review," International Journal of Computer Sciences and Engineering, Vol.5, Issue.8, pp.206-210, 2017.

The problem that has occurred as a result of the increased connection between the device and the system is creating information at an exponential rate that it is becoming increasingly difficult for a possible solution for processing. Therefore, creating a platform for such advanced level data processing, which increase the level of hardware and software with bright data. In order to improve the efficiency of the Hadoop Cluster in large data collection and analysis, we have proposed an algorithm system that meets the needs of protected discrimination data in Hadoop Clusters and improves performance and efficiency. The proposed paper aims to find out the effectiveness of the new algorithm, compare, consultation, and find out the best solution for improving the big data scenario is a competitive approach. The map reduction techniques from Hadoop will help maintain a close watch on the underlying or discriminatory Hadoop clusters with insights of results as expected from the luminosity.

Big data, hadoop, heterogeneous clusters, map reduce, throughput, latency

[1] Zhuo Liu, “Efficient Storage Design and Query Scheduling for Improving Big Data Retrieval and Analytics”, Dissertation, Auburn University, Alabama 2015.
[2] Zongben Xu, Yong Shi, “Exploring Big Data Analysis: Fundamental Scientific Problems”, Springer Ann. Data. Sci., Vol. 2, Issue. 4, pp 363–372, December 2015.
[3] F.G. Tinetti, I. Real, R. Jaramillo, and D. Barry, “Hadoop Scalability and Performance Testing in Heterogeneous Clusters”, In the Proceedings of the 2015 International Conference on Parallel and Distributed Processing Techniques and Applications (PDPTA-2015), Part of WORLDCOMP’15 pp.441-446, 2015.
[4] K. Kamtekar, under the guidance of R. Jain “Performance Modeling of Big Data”, Washington University in St. Louis, pp. 1-9, June 2015.
[5] F.H. Liu, Y.R. Liou, H.F. Lo, K.C. Chang and W.T. Lee, “The Comprehensive Performance Rating for Hadoop Clusters on Cloud Computing Platform”, International Journal of Information and Electronics Engineering, Vol. 4, No. 6, pp.480-484, November 2014.
[6] T.K. Das, P.M. Kumar, “BIG Data Analytics: A Framework for Unstructured Data Analysis”, International Journal of Engineering and Technology (IJET), ISSN: 0975-4024, Vol. 5 No. 1, pp.153-156, Feb-Mar 2013.
[7] F. Novacescu, “Big Data in High Performance Scientific Computing”, International Journal of Analele Universităţii "Eftimie Murgu", published by the "Eftimie Murgu" University of Resita, ANUL XX, NR. 1, pp.207-216, 2013, ISSN 1453 - 7397.
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[9] J. Xie, S. Yin, X. Ruan, Z. Ding, Y. Tian, J. Majors, A. Manzanares, and X. Qin, “Improving MapReduce Performance through Data Placement in Heterogeneous Hadoop Clusters”, Proceedings of the 19th International Heterogeneity in Computing Workshop, Atlanta, Georgia, pp.1-9, April 2010.

P. Dadheech, D. Goyal, S. Srivastava, "Performance Improvement of Heterogeneous Hadoop Clusters Using MapReduce For Big Data," International Journal of Computer Sciences and Engineering, Vol.5, Issue.8, pp.211-214, 2017.

Over the past few years, the popularity of social networking sites (SNS) has increased immensely. In the internet age, these social networking sites like Facebook, Twitter, Orkut, LinkedIn, MySpace, etc not only offer the scope of connecting also offers the scope of getting updates at any possible hour of the day. Online networks provide significant advantages both to the individuals and in business sectors. Many users provide information about themselves on social network which can be searched and hacked by the strangers. Thus, it raises privacy and security issues. Unfortunately many users are not aware of this.This paper elaborates Privacy issues concerned with social networking and proposes a framework to deal with. In this paper, study is made on how the current privacy plays on social network sites, how personal information is being influenced by internet and social network, and also how the privacy become a risk and how to employ security awareness to avoid privacy risk.

Social networking, Privacy, Security, Internet, Hacking

[1]. V. Raghunatha Reddy, C.V. Madhusudan Reddy, M. Ebenezar, “A Study on Anti-Phishing Techniques”, International Journal of Computer Sciences and Engineering,Volume-4 , Issue-1 , Page no. 30-36, Jan-2016
[2]. L. A. Cutillo and R. Molva , "Safebook: A Privacy-Preserving Online Social Network Leveraging on Real-Life Trust", IEEE, pp. 94-101, 2009.
[3]. A. Srivastava and G. Geethakumari, "A Framework to Customize Privacy Settings of Online Social Network Users," IEEE Recent Advances in Intelligent Computational Systems (RAICS), pp. 187-192, 2013.
[4]. A. Dhami, N. Agarwal, T. K. Chakraborty, B. P. Singh and J. Minj, "Impact of trust, security and privacy concerns in social networking: An exploratory study to understand the pattern of information revelation in Facebook," 3rd IEEE International Advance Computing Conference (IACC), pp. 465-469, 2013.
[5]. W. Binden, M. Jormae, Z. Zain and J. Ibrahim, "Employing Information Security Awareness to Minimize Over-Exposure of Average Internet User on Social Networks," International Journal of Scientific and Research Publications, vol. 4, no. 1, pp. 1-6, 2014.
[6]. X. Chen and S. Shi, "A Literature Review of Privacy Research on Social Network Sites," International Conference on Multimedia Information Networking and Security, pp. 93-97, 2009.
[7]. F. Raji, A. Miri and M. D. Jazi, "Preserving Privacy in Online Social Networks," Springer-Verlag Berlin Heidelberg 2012, pp. 1-13, 2012.
[8]. J. Ge, J. Peng and Z. Chen, "Your Privacy Information are Leaking When You Surfing on the Social Networks: A Survey of the degree of online self-disclosure (DOSD)," IEEE 13th Int’l Conf. on Cognitive Informatics & Cognitive Computing (ICCI*CC’14), pp. 329-336, 2014.
[9]. W. Binden, M. Jormae, Z. Zain and J. Ibrahim, "Employing Information Security Awareness to Minimize Over-Exposure of Average Internet User on Social Networks," International Journal of Scientific and Research Publications, vol. 4, no. 1, pp. 1-6, 2014.
[10]. X. Chen and S. Shi, "A Literature Review of Privacy Research on Social Network Sites," International Conference on Multimedia Information Networking and Security, pp. 93-97, 2009.
[11]. F. Raji, A. Miri and M. D. Jazi, "Preserving Privacy in Online Social Networks," Springer-Verlag Berlin Heidelberg 2012, pp. 1-13, 2012.
[12]. J. Ge, J. Peng and Z. Chen, "Your Privacy Information are Leaking When You Surfing on the Social Networks: A Survey of the degree of online self-disclosure (DOSD)," IEEE 13th Int’l Conf. on Cognitive Informatics & Cognitive Computing (ICCI*CC’14), pp. 329-336, 2014.
[13]. Maritza Johnson, Serge Egelman, and Steven M Bellovin., Facebook and privacy: it’s complicated”, In Proceedings of the Eighth Symposium on Usable Privacy and Security, pp.9. ACM, 2012.
[14]. Abdullah Al Hasib., “Threats of online social networks”, IJCSNS International Journal of Computer Science and Network Security,Vol.9, pp..288–93, 2009.
[15]. Balachander Krishnamurthy and Craig E Wills., “Characterizing privacy in online social networks”, In Proceedings of the workshop on Online social networks, pp.37-42. ACM, 2008.

Uma Maheswari, S. Balaji , "Privacy Preservation on Online Social Networking Issues and Challenges," International Journal of Computer Sciences and Engineering, Vol.5, Issue.8, pp.215-217, 2017.