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Images of outside scenes are typically degraded by the cloudy or opaque medium in the atmosphere. Haze, fog, and smoke in atmosphere are such phenomena because of atmospheric absorption and scattering. Due to the smoke or fog in the atmosphere, the irradiance received by the camera from the scene point is attenuated along the line of sight. Smoke and Fog in images can be distinguished based on their physical appearance and density variations. To distinguish these images, features such as SIFT, HOG, LBP features are extracted and are trained using SVM classification model. Smoke and Fog in images can be tested successfully that the image belongs to which class after training the images.

Detection, Fog, HOG, LBP, SIFT, Smoke, SVM

[1].Kaiming He, Jian Sun and Xiaoou Tang, “Single Image Haze Removal Using Dark Channel Prior”, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 33, Issue. 12, pp. 2341-2353, 2011.
[2].Jaswinder Kaur, Prabhpreet Kaur, “Comparative Study on Various Single Image Defogging Techniques”, AEEICB17, 2017.
[3].H.Tian, W.Li, Ogunbona, “Single image smoke detection”, ACCV, 2014.
[4].Hongda Tian, Wanqing Li, O. ogunbona and Lei Wang, “Detection and Separation of Smoke from Single Image Frames”, IEEE Transaction, Vol. 27, Issue. 3, pp. 1164 - 1177, 2017.
[5].Yutong Jiang, Changming Sun, Yu Zhao, “Fog Den sity Estimation and Image Defogging Based on Surrogate Modeling for Optical Depth”, Image Processing, Vol. 26, Issue. 7, pp. 3397-3409, 2017.
[6].Jing-Ming Guo, Jin-yu Syue, “An Efficient Fusion-Based Defogging”, Image Processing, Vol. 26, Issue. 9, pp. 4217-4228, 2017.
[7].Rakesh Asery, Ramesh Kumar Sunkara, Aman Kumar, “Fog Detection using GLCM based Features and SVM”, CASP, pp. 72 - 76, 2016.
[8].Yixuan Yuan, Baopu Li, and Max Q.-H. Meng, “Improved Bag of Feature for Automatic Polyp Detection in Wireless Capsule Endoscopy Images”, Automation Science and Engineering, Vol. 13, Issue. 2, pp. 529-535, 2016.
[9].D. G. Lowe, “Distinctive image features from scale-invariant keypoints”, Int. J. Comput. Vision, Vol. 60, pp. 91–110, 2004.
[10].T. Ojala, M. Pietikainen, and T. Maenpaa, “Multiresolution gray scale and rotation in-variant texture classification with local binary patterns”, IEEE Trans. Pattern Anal. Mach. Intell., Vol. 24, Issue. 7, pp. 971–987, 2002.
[11].Z. Guo, L. Zhang, and D. Zhang, “A completed modeling of local binary pattern operator for texture classification”, IEEE Trans. Image Process., Vol. 19, Issue. 6, pp. 1657–1663, 2010.
[12].N. Dalal and B. Triggs, “Histograms of oriented gradients for human detection”, In Proc. IEEE Comput. Soc. Conf. Comput. Vision Pattern Recogn., pp. 886–893, 2005.
[13].D. G. Lowe, “Object recognition from local scale-invariant features”, In Proc. 7th IEEE Int. Conf. Comput. Vision, pp. 1150–1157, 1999.
[14].T. Ojala, M. Pietikainen,¨ and D. Harwood, “A comparative study of texture measures with classification based on featured distributions”, Pattern Recogn., Vol. 29, pp. 51–59, 1996.
[15].B. Li and M. Q.-H. Meng, “Automatic polyp detection for wireless capsule endoscopy images”, Expert Syst. With Appl., Vol. 39, pp. 10952-10958, 2012.
[16].C. Cortes and V. Vapnik, “Support-vector networks”, Mach. Learn., Vol. 20, pp. 273–297, 1995.
[17].C.-C. Chang and C.-J. Lin, “LIBSVM: A library for support vector machines”, ACM Trans. Intell. Syst. Technol. (TIST), Vol. 2, pp. 27, 2011.
[18].J. Mao, U. Phommasak, S. Watanabe, and H. Shioya, “Detecting Foggy Images and Estimating the Fog Degree Factor”, Journal of Computer Science Systems Biology, Vol. 7, Issue. 6, pp. 226-228, 2014.

Freceena Francis, Maya Mohan, "Smoke and fog Detection in Images", International Journal of Computer Sciences and Engineering, Vol.06, Issue.06, pp.54-57, 2018.

The widespread use of Location Based Systems results in the accumulation of movement trajectory details in a massive scale. These mobility traces are very much useful for the researchers and the developers who needs to develop or invent new mobility management applications or modify the existing ones. But without proper privacy preserving mechanism for the published trajectory details may definitely raises the issue of privacy breach for the user. So before publishing the trajectory details suitable anonymization approach has to be applied. It is also found that the protection of significant points is better than the unnecessary anonymiztion whole trajectory points. This paper proposes a new model, which depicts a model that safeguards the significant points from the malevolent attacks by the help of generalization approach. With this model, the significant location points are hided in a specified size diversified area zone. The analysis shows that this approach is well ahead of the similar approaches used by the researches and provides better privacy and less information loss.

Anonymization, Trajectory Publication, Privacy Preservation

[1] Poulis, G., Skiadopoulos, S., Loukides, G., Gkoulalas-Divanis, A.: Apriori-based algorithms for km-anonymizing trajectory data. Transactions on data privacy, 7:2, pp. 165-194 (2014)
[2] Sweeney, L.: Achieving k-anonymity privacy protection using generalization and suppression. International Journal of uncertainty, fuzziness and knowledge-based systems, 10(5), pp. 571-588, 2002.
[3] Machanavajjhala, A., Kifer, D., Gehrke, J., Venkitasubramaniam, M.: l-diversity: Privacy beyond k-anonymity. TKDD, 1(1), 2007.
[4] Li, N., Li, T., Venkatasubramanian, S.: t-closeness: Privacy beyond k-anonymity and l-diversity. In ICDE, pp. 106-11, 2007.
[5] Yarovoy, R., Bonchi, F., Lakshmanan, S., Wang, W.H.: Anonymizing moving objects: How to hide a MOB in a crowd? In:12th Int. Conf. on extending database technology, pp. 72-83, ACM press, New York ,2009.
[6] Huo, Z., Meng, X., Hu, H.,Huang, Y.: You can walk alone: Trajectory privacy preserving through significant stays protection. DASFAA 2012, Part 1, LNCS 7238, pp. 351-366, Springer-Verlag Berlin Heidelberg, 2012.
[7] Zheng, Y., Zhang, L., Xie, X., Ma, W.: Mining interesting locations and travel sequences from GPS trajectories. In: 18th International conference on World Wide Web, pp.791-800, ACM press, New York , 2009
[8] Microsoft Research Geolife, http://research.microsoft.com/en-us/projects/geolife/
[9] Rajesh N, Sajimon Abraham, “Privacy preserved approach for trajectory anonymization through the zone creation for halting points”, International conference on Networls and Advances in Computational Technologies (NetACT17), IEEE explore, pp.229-234, 2017

Rajesh N, Sajimon Abraham, Shyni S Das, "Trajectory Anonymization Through Generalization of Significant Location Points", International Journal of Computer Sciences and Engineering, Vol.06, Issue.06, pp.58-62, 2018.

The emerging digital era generates heaps of computerized information. The greater part of the electronic data in the world today has been created over the last recent couple of years. The velocity of data generation is unimaginable and incomprehensible. People nowadays are commonly using the digital media to express their stand point about a topic. These opinions are analyzed automatically to know whether the client remark is ideal or not good to the said theme. This ought to be possible by Opinion Mining, also called as Sentiment Analysis. The basic chore in Sentiment Analysis is to categorize the orientation of a given review and subsequently identifying whether the sentiment implied is positive, negative or fair. In this paper, the tweets based on the news thread “Whether National Anthem is needed at Cinema theatres?” are analyzed based on the user rating for the opinions. The classifiers like Bernoulli and Multinomial Naive Bayes, Random Forest, k-Nearest Neighbor (k-NN) and Support Vector Machine (SVM) have been used for analyzing the opinions and found that the Random Forest classifier and Multinomial Naïve Bayes classifier is the top rated classifier based on their accuracy values.

Sentiment Analysis, Naive Bayes Classifier, SVM, Random Forest Classifier, KNN Classifier

[1] Khan, Khairullah, Baharum B. Baharudin, and Aurangzeb Khan. "Mining opinion targets from text documents: A review." Journal of Emerging Technologies in Web Intelligence 5.4 (2013): 343-353.
[2] Gupte, Amit, Sourabh Joshi, Pratik Gadgul, Akshay Kadam, and A. Gupte, "Comparative study of classification algorithms used in sentiment analysis.", International Journal of Computer Science and Information Technologies , Vol. 5 (5) : 6261-6264, 2014
[3] Gupta, Ankita, Jyotika Pruthi, and Neha Sahu. "Sentiment Analysis of Tweets using Machine Learning Approach." (2017).
[4] Dey, Lopamudra, Sanjay Chakraborty, Anuraag Biswas, Beepa Bose, and Sweta Tiwari. "Sentiment Analysis of Review Datasets Using Naive Bayes and K-NN Classifier." arXiv preprint arXiv:1610.09982 (2016).
[5] Liu, Bing. "Sentiment analysis and opinion mining." Synthesis lectures on human language technologies 5, no. 1 (2012): 1-167.
[6] Liu, Bing, and Lei Zhang. "A survey of opinion mining and sentiment analysis." In Mining text data, pp. 415-463. Springer US, 2012.
[7] Mohammad, Saif M. "Sentiment analysis: Detecting valence, emotions, and other affectual states from text." In Emotion measurement, pp. 201-237. 2016.
[8] Wagner, Wiebke. "Steven bird, ewan klein and edward loper: Natural language processing with python, analyzing text with the natural language toolkit." Language Resources and Evaluation 44, no. 4 (2010): 421-424.
[9] Palmer, David D. "Tokenisation and sentence segmentation." Handbook of natural language processing (2000): 11-35.
[10] Ye, Qiang, Ziqiong Zhang, and Rob Law. "Sentiment classification of online reviews to travel destinations by supervised machine learning approaches." Expert systems with applications 36.3 (2009): 6527-6535.
[11] Aldoğan, Deniz, and Yusuf Yaslan. "A comparison study on active learning integrated ensemble approaches in sentiment analysis." Computers & Electrical Engineering 57 (2017): 311-323.

Deepa Mary Mathews, Sajimon Abraham, "Opinion Mining on Twitter Data Using Supervised Machine Learning Algorithms", International Journal of Computer Sciences and Engineering, Vol.06, Issue.06, pp.63-66, 2018.

With the advancement of technology, there is a rapid growth in e-learning. The use of the Internet in the teaching learning process has made significant impact on both teachers and students. Learners in an e-learning environment have different behavioural patterns, attitudes, aptitudes, learning styles, etc. and the learning resources preferred by these learners also vary. Hence, it is a big challenge for 21st century teachers to deliver the courses in an effective way. One solution to solve this problem is to provide an adaptive form of education. Advantage of this adaptive learning system is that it helps to provide apt material according to the interest and knowledge level of the learners or by understanding the learning facts. An adaptive learning system helps to track students’ knowledge, measure progress and provide solution accordingly. With the support of a suitable Learning Management System (LMS) it is possible to administer, track, report and document the delivery of e-learning courses. In this study, Moodle (Modular Object-Oriented Dynamic Learning Environment) LMS is used for managing every aspect of the course such as course creation, conducting online test, etc. In an e-learning environment, learning content accessed by individual learners will be different and they learn subjects at their own speed and interest. It causes the generation of different learning paths. Hence, the objective of the study is to develop a frame work for an adaptive learning system and identify the learning path of the learners based on their activities recorded in Moodle.

Adaptive learning system, E-learning, Learning Management System, Moodle, Learning path

1. C.R. Graham, W. Woodfield, J.B. Harrson, “A Framework For Institutional Adoption and Implementation of Blended Learning in Higher Education”, Internet and Higher Education, Vol. 18, pp.4-14, 2013.
2. A.Norberg, C.D. Dziuban, P.D. Moskal, “A Time-Based Blended Learning Model”, On the Horizon,Vol.19,Issue.3, pp.207-216, 2011.
3. R.C. Raga Jr, J.D. Raga, “Monitoring Class Activity and Predicting Student Performance Using Moodle Action Log Data”, International Journal of Computing Sciences Research, Vol. 1, Issue.3, pp.1-16, 2017.
4. R.R. Estacio, R. C. Raga Jr,”Analysing Students Online Learning Behaviour In Blended Courses Using Moodle”, Asian Association of Open Universities Journal, Vol. 12, Issue.1, pp.52-68, 2017
5. T.Elias “Universal Instructional Design Principles for Moodle” The International Review of Research in Open and Distributed Learning. Vol. 11, Issue.2, pp.110-124, 2010
6. A.Deshpande, P. Pimpare, S. Bhujbal, A. Kommwar,J. Wagh, “Student Performance Analysis, Visualization and Prediction Using Data Mining Techniques”, Imperial Journal of Interdisciplinary Research,Vol.2, Issue.5, pp.115-1118, 2016
7. S.Pawar, S.M. ,“A Proposed System for Adaptive E-Learning Using Ant Colony Optimization” IJSART , Vol. 24, Issue.6, pp.72-76, 2015.
8. K.R. Premlatha,B. Dharani, T.V. Geetha,” Dynamic Learner Profiling and Automatic Learner Classification for Adaptive E-Learning Environment”, Interactive Learning Environments, Vol. 24, Issue.6, pp.1054-1075, 2016.
9. P. Sarkar, C. Kar, “Adaptive E-learning Using Deterministic Finite Automata”, International Journal of Computer Applications, Vol. 97, Issue.21, pp.14-17, 2014.
10. F.Yang,Z.Dong, ” Learning Path Construction in E-learning: What to Learn, how to Learn, and how to Improve”, . Springer Singapore,pp.15-29,2017
11. A.Roy, K.Basu, ”A Comparative Study of Statistical Learning and Adaptive Learning”, arXiv preprint arXiv:1511.07538.
12. O.R. Zaiane, J. Luo, ”Towards Evaluating Learners` Behaviour in a Web-Based Distance Learning Environment”, In the proceedings of the 2001 IEEE International Conference on Advanced Learning Technologies,IEEE, pp.357-360,2001.
13. L.K. Poon, S.C. Kong, M.Y. Wong, T.S.Yau, ” Mining Sequential Patterns of Students’ Access on Learning Management System”, In the proceedings of the 2001 International Conference on Data Mining and Big Data, Springer, Cham, pp.191-198, 2017.
14. A.P. Lopes, ”Learning Management Systems in Higher Education” In the proceedings of the EDULEARN14 Conference , Barcelona, Spain, pp. 5360-5365, 2014.
15. M. Kljun, K.C. Pucihar,F. Solina, ”Persuasive Technologies In M-Learning For Training Professionals: How to Keep Learners Engaged with Adaptive Triggering”, IEEE Transactions on Learning Technologies,2018

Lumy Joseph, Sajimon Abraham, "Identifying the Learning Path of Online Learners in an Adaptive E-Learning Environment", International Journal of Computer Sciences and Engineering, Vol.06, Issue.06, pp.67-73, 2018.

The attractions in Big Data Analytics made a progress from relational databases to NoSQL databases. A NoSQL structure can be utilized to enhance the distribution of storage and analysis work of data in the world of big data. MongoDB is a type of NoSQL database which represents data as a collection of documents. Ordinary database systems like MySQL can store only organized data in tabular form as rows and columns. As the majority of the data created now is in unstructured or semi structured format, it is difficult for conventional database systems to store or process this data. NoSQL data stores like MongoDB can store this huge data which additionally have very powerful query engines and indexing features. These features made it simple and fast to execute extensive variety of queries including aggregate ones. The aggregation pipeline and map reduce concepts in MongoDB provides support for aggregate operations. This paper primarily makes a comparison of performance of aggregate queries in MySQL and MongoDB. A set of experiments were performed with two datasets of different size in the two databases. The results show that MongoDB performs better in all the cases. The results can be a boost for companies to change the structure of their databases from conventional form to NoSQL.

Relational Databases, NoSQL Databases, MongoDB, MySQL, Aggregation

[1] Ramez Elmasri, Shamkant B. Navathe, “Fundamentals of Database Systems”, Pearson, India, pp. 621-622, 2007.
[2] Mary Femy P.F, Reshma K.R, Surekha Mariam Varghese, “Outcome Analysis Using Neo4j Graph Database”, International Journal on Cybernetics & Informatics, Vol 5, No.2, pp.229-236, 2016.
[3] Dipina Damodaran B, Shirin Salim, Surekha Marium Varghese, “Performance Evaluation of MySQL and MongoDB databases”, International Journal of Cybernetics and Informatics”, Vol.5, No.2, pp. 387-394, 2016
[4] Guoxi Wang, and Jianfeng Tang, “The NoSQL Principles and Basic Application of Cassandra Model”, In the proceeding of the International Conference on Computer Science and Service Systems, Washington, pp.1332-1335, 2012.
[5] Yue Cui, William Perrizo, “Aggregate Function Computation and Iceberg Querying in Vertical databases”, A Thesis submitted to the Graduate Faculty of the North Dakota State University, North Dakota, pp. 10, 2005.
[6] Sanobar Khan, Vanita Mane, “SQL support over MongoDB using metadata”, International Journal of Scientific and Research publications, Vol.3, Issue.10, pp.1-5, 2013.
[7] Seyyed Hamid Aboutorabi, Mehdi Rezapour, Milad Moradi, Nasser Ghadiri, “Performance evaluation of SQL and MongoDB databases for big e-commerce data”, In the proceeding of the International Symposium on Computer Science and Software Engineering (CSSE), Iran,pp.72-78, 2015.
[8] Zhu Wei-ping, Li Ming-xin, Chen Huan, “Using MongoDB to implement textbook management system instead of MySQL”, In the proceeding of the IEEE 3rd International Conference on Communication Software and Networks, Xi’an, China, pp.828-830, 2011.
[9] Enqing Tang, Yushun Fan, “Performance Comparison between Five NoSQL Databases”, In the proceeding of the 7th International Conference on Cloud Computing and Big Data (CCBD), China, pp.105-109, 2016.
[10] https://docs.mongodb.com/manual/

Benymol Jose, Sajimon Abraham, "Analysis of Aggregate Functions in Relational Databases and NoSQL Databases", International Journal of Computer Sciences and Engineering, Vol.06, Issue.06, pp.74-79, 2018.

The huge volumes of data which cannot be manipulated easily by commonly available tools are termed as Big Data. Big Data analytics gives competitive opportunities in designing business plans for Business Analytics. The results are used for taking intelligent business decisions; hence it must be accurate and well-timed. For analytical purpose we use Multiple Linear Regression (MLR) model in the statistical method, a type of Supervised Machine Learning Algorithm. Performance of the particular MLR model with one quantitative dependent attribute and four independent attributes are evaluated using splitting up of the whole data set with Cross-Validation technique. This technique is used to validate the accuracy of model developed from training data with test data to control the problem like over fitting. Here we use Hold-Out Cross Validation method with serial and random partitioning. The data set from UCI machine learning repository are evaluated through simulation methods to check the performance. The model generated in training data are validated with test data, the evaluation shows that the result obtained is a generalized one. The proposed MLR model can be used in the new data set for an accurate result. Here we obtained that the accuracy, measuring with random partitioning is a better method.

Big Data Analytics, Multiple Linear Regression, Predictive Analytics, Validation Methods

[1] Kumar, P., & Rathore, D. V. S. (2014). “Efficient capabilities of processing of big data using hadoop map reduce”. International Journal of Advanced Research in Computer and Communication Engineering, 3(6), 7123-6..
[2] Feldman, D., Schmidt, M., & Sohler, C. (2013, January). “Turning big data into tiny data: Constant-size coresets for k-means, pca and projective clustering”. In Proceedings of the Twenty-Fourth Annual ACM-SIAM Symposium on Discrete Algorithms (pp. 1434-1453). Society for Industrial and Applied Mathematics.
[3] Ha, S., Lee, S., & Lee, K. (2014). “Standardization Requirements Analysis on Big Data in Public Sector based on Potential Business Models”. International Journal of Software Engineering and Its Applications, 8(11), 165-172.
[4] Galit Shmueil, “To Explin or Predict?”, Statistical science, vol25 © Institute of Mathematical Science, 2010
[5] Saritha, K., & Abraham, S. (2017, July). “Prediction with partitioning: Big data analytics using regression techniques”. In Networks & Advances in Computational Technologies (NetACT), 2017 International Conference on (pp. 208-214). IEEE.
[6] Dutta, P. S., & Tahbilder, H. (2014). “Prediction of rainfall using data mining technique over Assam”. Indian Journal of Computer Science and Engineering (IJCSE), 5(2), 85-90.
[7] Ahmet A Yildirim, Cem OZdogan, Dan Watson, “ Parallel Data Reduction Techniques for Big Data sets”, Research gate, 2016.
[8] Astrid Scheneider, Gerhard Hommel and Maria Blettner, “Linear Regression Analysis”, 2010; 107(44) 776-82
[9] Chen, C. P., & Zhang, C. Y. (2014). Data-intensive applications, challenges, techniques and technologies: A survey on Big Data. Information Sciences, 275, 314-347
[10] Wang, H., Xu, Z., Fujita, H., & Liu, S. (2016). “Towards felicitous decision making: An overview on challenges and trends of Big Data”. Information Sciences, 367, 747-765.
[11] Bilal, M., Oyedele, L. O., Qadir, J., Munir, K., Ajayi, S. O., Akinade, O. O., ... & Pasha, M. (2016). “Big Data in the construction industry: A review of present status, opportunities, and future trends”, Advanced Engineering Informatics, 30(3), 500-521.
[12] Saritha, K., & Abraham, S. “Big Data Challenges and Issues: Review on Analytic Techniques”. Indian Journal of Computer Science and Engineering (IJCSE) Vol. 8 No. 3 Jun-Jul 2017
[13] https://archive.ics.uci.edu/ml/datasets/Bike+Sharing+Dataset

K. Saritha, Sajimon Abraham, "Statistical Predictabilty in Big Data Analytics with Data Partitioning", International Journal of Computer Sciences and Engineering, Vol.06, Issue.06, pp.80-85, 2018.

The importance of analysing moving object data has increased significantly due to the increased acceptance of context aware devices such as Smartphones, GPS connected gadgets etc. Broad use of wireless context aware devices has accelerated the generation of mobility data in various formats.The vital component of moving object data constitutes of geographical coordinates and time. The analysis of space time points in mobility data gives deep knowledge about the movement pattern of the object. Because of the presence of rich semantic aspects in the moving object data, the mining of context related data requires special methods and attention. There are less number of reported works that primarily focuses on the spatial and temporal behavior of moving objects. This research paper concentrate on the methods of extracting Points of Interests from the moving object trajectories by considering its Spatial and Temporal aspects so as to mine useful knowledge from it. Along with the explicit mobility data the method also considers semantic attributes underlined in the travel trajectory.

Location Based Systems, Moving Objects Clustering, Semantic Trajectory, Spatio Temporal Data mining Clustering

[1] http://gps-exchange.com/
[2] http://www.geoladders.com/
[3] https://www.gartner.com/reviews/market/horizontal-portals
[4] Spaccapietra, S., Parent, C., Damiani, M. L., de Macedo, J. A., Porto, F.,& Vangenot, C. (2008). A conceptual view on trajectories. Data & knowledge engineering, 65(1), 126-146
[5] Alvares, Luis Otavio, et al. "A model for enriching trajectories with semantic geographical information." Proceedings of the 15th annual ACM international symposium on Advances in geographic information systems. ACM, 2007
[6] Zheng, Y., Xie, X., & Ma, W. Y. (2010). Geolife: A collaborative social networking service among user, location and trajectory. IEEE Data Eng. Bull., 33(2), 32-39.
[7] Siła-Nowicka, K., Vandrol, J., Oshan, T., Long, J. A., Demšar, U., & Fotheringham, A. S. (2016). Analysis of human mobility patterns from GPS trajectories and contextual information. International Journal of Geographical Information Science, 30(5), 881-906.
[8] Alarabi, Louai, Mohamed F. Mokbel, and Mashaal Musleh. "ST-Hadoop: A MapReduce Framework for Spatio-Temporal Data." International Symposium on Spatial and Temporal Databases. Springer, Cham, 2017.
[9] Alvares, L. O., Bogorny, V., Kuijpers, B., de Macedo, J. A. F., Moelans, B., & Vaisman, A. (2007, November). A model for enriching trajectories with semantic geographical information. In Proceedings of the 15th annual ACM international symposium on Advances in geographic information systems (p. 22). ACM.
[10] Palma, A. T., Bogorny, V., Kuijpers, B., & Alvares, L. O. (2008, March). A clustering-based approach for discovering interesting places in trajectories. In Proceedings of the 2008 ACM symposium on Applied computing (pp. 863-868). ACM.
[11] Rocha, J. A. M., Times, V. C., Oliveira, G., Alvares, L. O., & Bogorny, V. (2010, July). DB-SMoT: A direction-based spatio-temporal clustering method. In Intelligent systems (IS), 2010 5th IEEE international conference (pp. 114-119). IEEE.
[12] Bogorny, V., Renso, C., Aquino, A. R., Lucca Siqueira, F., & Alvares, L. O. (2014). Constant–a conceptual data model for semantic trajectories of moving objects. Transactions in GIS, 18(1), 66-88.
[13] Portugal, I., Alencar, P., & Cowan, D. (2017). Developing a Spatial-Temporal Contextual and Semantic Trajectory Clustering Framework. arXiv preprint arXiv:1712.03900.

Nishad A, Sajimon Abraham, Praveen Kumar V.S, "Semantic based Exploration of Interesting Points of moving objects Trajectories", International Journal of Computer Sciences and Engineering, Vol.06, Issue.06, pp.86-90, 2018.

The primary level for the preliminary diagnosis of disease like cancer is the biomedical analysis of microscopic blood sample images. In medical microscopic image analysis, a single image can be evaluated for different types of cells in different phases of maturation. For each cell, the nucleus and cytoplasm might differ in shape, texture, color and density. So it is a challenging problem to automatically segment the cell. In this paper, the various types of white blood segmentation techniques are discussed and the limitations of these methods are also investigated.

Medical image analysis, White blood cell image segmentation

[1] Y. M. Alomari, S. N. H. Sheikh Abdullah, R. ZaharatulAzma, and K. Omar, “Automatic Detection and Quantification of WBCs and RBCs Using Iterative Structured Circle Detection Algorithm,”Comput. Math. Methods Med., vol. 2014, pp. .979-802, Apr. 2014.
[2] F. Scotti, “Robust Segmentation and Measurements Techniques of White Cells in Blood Microscope Images,” presented at the Instrumentation and Measurement Technology Conference, Proceedings of the IEEE, pp. 43–48, 2006.
[3] P. Lorenzo and D. R. Cecilia, “White Blood Cells Identification and Counting from MicroscopicBlood Image,” Int. Scolary Sci. Res. Innov., vol. 7, no. 1, pp. 15–23, 2013.
[4] RozyKumari, NarinderSharma , “A Study on the Different Image Segmentation Technique International”, Journal of Engineering and Innovative Technology (IJEIT) , vol.4, pp.284-289, , July 2014.
[5] P. Maji, A. Mandal, M. Ganguly, and S. Saha, “An automated method for counting and characterizing red blood cells using mathematical morphology,” in Eighth International Conference on Advances in Pattern Recognition (ICAPR), pp. 1–6, 2015.
[6] K. A. Abuhasel, C. Fatichah, and A. M. Iliyasu, “A commixed modified Gram-Schmidt and region growing mechanism for white blood cell image segmentation,” in IEEE 9th International Symposium on Intelligent Signal Processing (WISP), pp. 1–5, 2015
[7] Ritter.NandCooperJ, “Segmentation and border identification of cells in images of peripheral blood smear slides”, Proceedings of the Thirtieth Australasian Conference on Computer Science, vol.62, pp. 161–169, 2007.
[8] Scotti F., “Robust segmentation and measurements techniques of white cells in blood microscope images”, Proceedings of the IEEE instrumentation and measurement technology conference Italy: IEEE Publisher, pp. 43–48, 2006.
[9] J. Hari, A. S. Prasad, and S. K. Rao, “Separation and counting of blood cells using geometrical features and distance transformed watershed,” in 2nd International Conference on Devices, Circuits and Systems (ICDCS), pp. 1–5, 2014.
[10] Piuri V, Scotti F, “Morphological classification of blood leucocytes leucocytes by microscope images”, Proceedings of the IEEE international conference on computational intelligence for measurement systems and applications, 14–16 July. Boston, MA, USA: IEEE Publisher, pp. 103–108, 2004.
[11] Halim NHA, Mashor MY, Hassan R.,” Automatic blasts counting for acute leukemia based on blood samples”, Int J Res Rev ComputSci, vol.2, pp 22-28, 2011.
[12] S. Mohapatra, D. Patra, S. Kumar, S. Satpathy, “Lymphocyte image segmentation using functional link neural architecture for acute leukemia detection,” Biomed. Eng. Lett. 2 100–110, 2012.
[13] P. Lepcha, W. Srisukkham, L. Zhang, and A. Hossain, “Red Blood based disease screening using marker controlled watershed segmentation and post-processing,” in 8th International Conference on Software, Knowledge, Information Management and Applications (SKIMA), pp. 1–7 , 2014.
[14] N. Deb and S. Chakraborty, “A noble technique for detecting anemia through classification of red blood cells in blood smear,” in Recent Advances and Innovations in Engineering (ICRAIE), pp. 1–9, 2014.
[15] Z. Liu, J. Liu, X. Xiao, H. Yuan, X. Li, J. Chang, C. Zheng, “Segmentation of white bloodcells through nucleus mark watershed operations and mean shift clustering” ,Sensors, vol.15, 2011

Arsha P V, Pillai Praveen Thulasidharan, "A Survey Of White Blood Cells Segmentation In Medical Image Analysis", International Journal of Computer Sciences and Engineering, Vol.06, Issue.06, pp.91-94, 2018.

Emotion recognition is a process by which human emotional states can be identified. Most of the present methods make use of visual and audio information’s together. With recent advancements in deep neural networking, there are several methodologies to identify human emotional states. One of the methods that detect the emotional states is based on a multimodal Deep Convolution Neural Network (DCNN), that use both the audio and visual cues in a deep model. BLSTM-RNN is another method which makes use of multimodal features to capture emotions. A much more efficient approach is using a convolutional neural network (CNN) to extract features from the speech, and for the visual modality, the features can be extracted using a deep residual network of 50 layers. To capture contextual information’s a long short-term memory network can be utilized above these two models. Deep belief networks are another method which takes multimodal emotion recognition into account by first learning the features of the audio and video separately; after which it concatenates these two features. Visual features hold more importance in emotion recognition, so ResNet along with SVR for training can be used to predict emotion states effectively.

DCNN, DBN, Residual Network, LSTM, SVR

[1].Y. Wang and L. Guan, " Recognizing human emotional state from audio-visual signals", IEEE Trans. Multimedia., pp:936–946, 2008.
[2].A. Hanjalic and L. Xu, "Affective video content representation and modelling", IEEE Trans. Multimedia., pp: 143–154, 2005.
[3].Y. Cao, Y. Chen, and D. Khosla, "Spiking deep convolutional neural networks for energy-efficient object recognition”, Int. J. Comput. Vis., pp:54–66, 2015
[4].W. Ouyang, X. Wang, X. Zeng, S. Qiu, P. Luo, Y. Tian, H. Li, S. Yang, Z. Wang, C.-C. Loy, et al., ”Deepid-net: Deformable deep convolutional neural networks for object Detection”, In CVPR, 2015.
[5].S. Zhang, S. Zhang, T. Huang, and W. Gao, “Multimodal deep convolutional neural network for audio-visual emotion recognition,” in Proc. Int. Conf. Multimedia Retrieval, pp. 281–284, 2016.
[6].A. Krrizhevsky, I. Sutskever, and G. E. Hinton, "Imagenet classification with deep convolutional neural networks", In NIPS, 2012.
[7].S. Hochreiter and J. Schmidhuber, ”Long short-term memory,” NeuralComput., pp. 1735-1780, 1997.
[8].Xiong, X., De la Torre, F., “Supervised descent method and its applications to face alignment”, in: Proc. of the IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), pp. 532–539, 2013
[9].Farneb¨ack, G, ” Two-frame motion estimation based on polynomial expansion, in: Image Analysis”, in Springer, pp. 363–370, 2003.
[10].Schuster, M., Paliwal, K.K., “Bidirectional recurrent neural networks” IEEE Trans. on Signal Processing 45, pp:2673–2681, 1997.
[11].K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” Proc. Conf. Comput. Vis. Pattern Recognit, pp. 770–778, 2016.
[12].F. Ringeval, A. Sonderegger, J. Sauer, and D. Lalanne, ”Introducing the RECOLA multimodal corpus of remote collaborative and affective interactions”, IEEE Int. Conf. Workshops Automat. Face Gesture Recognit., pp. 1–8, 2013.
[13].Y. Bengio, “Learning deep architectures for AI,” Foundations and Trends in Machine Learning, pp. 1–127, 2009.
[14].J. Ngiam, A. Khosla, M. Kim, J. Nam, H. Lee, and A.Y. Ng, “Multimodal deep learning,” in Proceedings of the 28th International Conference on Machine Learning (ICML), pp. 689–696, 2011
[15]. F. Ringeval et al., “Prediction of asynchronous dimensional emotion ratings from audio visual and physiological data,” Pattern Recognit. Lett., pp. 22–30, 2015.
[16].Panagiotis Tzirakis, George Trigeorgis, Mihalis A. Nicolaou, Bjorn W.Schuller, and Stefanos Zafeiriou, "End-to-End Multimodal Emotion Recognition Using Deep Neural networks", in IEEE Journal of Selected Topics in Signal Processing, pp: 1301 - 1309, 2017.
[17].Y. Kim, H. Lee, and E. M. Provost, “Deep learning for robust feature generation in audiovisual emotion recognition,” in Proc. Int. Conf. Acoust., Speech, Signal Process., pp. 3687–369, 2013
[18].B. Sun, S. Cao, L. Li, J. He, and L. Yu, “Exploring multimodal visual features for continuous affect recognition,” in Proc. 6th Int. Workshop Audio/Visual Emotion Challenge, Amsterdam, pp. 83–88, 2016.

Haritha C. V, Pillai Praveen Thulasidharan, "Multimodal Emotion Recognition using Deep Neural Network- A Survey", International Journal of Computer Sciences and Engineering, Vol.06, Issue.06, pp.95-98, 2018.

Ontologies are knowledge representation structures, that models domain knowledge by concepts, instances, rolesand their relationships. Assessment systems can exploit this knowledge by using multiple choice Questions (MCQs). Online assessment systems are mainly using MCQs instead of subjective questions for conducting the tests. Using MCQs for assessments has merits as well as demerits. For assessing wide range of knowledge, MCQs are used. It is because they require very less administrative overhead as well as provide instant feedback to test takers. There are several ontology based MCQ generation approaches proposed by many authors. These approaches generates different kinds of questions, in one approach the stem of all generated question remains the same, another one make use of the semantics of the domain, represented in the form of TBox axioms andABox axioms, to frame interesting MCQs. Some other methods differ in generating distractors for the questions. There are approaches which controls the difficulty level of generated MCQs. This paper gives a literature review and comparison of some of the methods for MCQ generation from ontology.

Multiple Choice Questions, Distractors, Ontology

[1] F. Baader, D. Calvanese, D. L. McGuinness, D. Nardi, and P. F. (eds.) PatelSchneider. ”The Description Logic Handbook: Theory, Implementation and Applications” , Cambridge University Press, second edition, 2007.
[2] Muhammad Aun Abbas, ” A Unified Approach for Dealing with Ontology Mappings and their Defects” , Universite de Bretagne Sud, 2016.
[3] T. Alsubait, B. Parsia, U. Sattler, ” A similarity-based theory of controlling mcq difficulty” , 2013 Second International Conference on e-Learning and e- Technologies in Education (ICEEE), pp. 283-288, 2013.
[4] A. Papasalouros, K. Kanaris, K. Kotis, ” Automatic Generation of Multiple Choice Questions from Domain Ontologies” , International Association for Development of the Information Society, pp. 427-434, 2008.
[5] M. Cubric, M. Tosic, ” Towards automatic generation of e-assessment using semantic web technologies”, Proceedings of the 2010 International Computer Assisted Assessment Conference, ISSN:2045-9432, Vol.1, 2010.
[6] E. Holohan, M. Melia, D. McMullen, C. Pahl, ” Adaptive e-learning content generation based on semantic web technology” , Proceedings of Workshop on Applications of Semantic Web Technologies for e-Learning, Amsterdam, The Netherlands, pp. 29-36, 2005.
[7] M. Tosic, M. Cubric, ” SeMCQ-protege plugin for automatic ontology-driven multiple choice question tests generation” , Proceedings of the 11th International Protege Conference, 2009.
[8] B. Aitko, S. Stankov,M. Rosi, A. GrubiAi, ” Dynamic test generation over ontology-based knowledge representation in authoring shell” , Syst. Appl. 36(4), pp. 8185-8196, 2009.
[9] Ian Horrocks, Peter F.Patel- Schneider, Frank van Harmelen, ”From SHIQ and RDF to OWL: the making of a Web Ontology Language” , Web Semantics: Science, Services and Agents on the World Wide Web, Vol. 1, Issue.1, pp. 7-26, December 2003.
[10] Vinu E.V., Sreenivasa Kumar P. , ” A novel approach to generate MCQs from domain ontology: Considering DL semantics and open-world assumption” , International journal on Web Semantics: Science, Services and Agents on the World Wide Web, 2015.
[11] Tahani Alsubait, Bijan Parsia, Ulrike Sattler, ” Ontology-Based Multiple Choice Question Generation” , German Journal on Artificial Intelligence,Vol.30, Issue.2, pp. 183-188, June2016.
[12] T. Alsubait, B. Parsia, and U. Sattler, ” Measuring similarity in ontologies: How bad is a cheap measure?” ,27th Inernational Workshop on Description Logics (DL-2014), 2014.
[13] T. Alsubait, B. Parsia, U. Sattler, ” Generating multiple choice questions from ontologies: Lessons learnt” , Proceedings of the 11th International Workshop on OWL: Experiences and Directions, Vol.1265, pp. 73-84, 2014.
[14] M. Al-Yahya, ” OntoQue: a question generation engine for educational assesment based on domain ontologies” , Proceedings of the 11th IEEE International Conference on Advanced Learning Technologies (ICALT ’11), Athens, Ga, USA, pp. 393-395, July 2011.

Sahana Serin V. P., Viji Rajendran V., "Automatic Generation of MCQS from Domain Ontology- A Survey", International Journal of Computer Sciences and Engineering, Vol.06, Issue.06, pp.99-102, 2018.