Study on Spell Checking System using Levenshtein Distance Algorithm

Authors: Thi Thi Soe, Zarni Sann

Abstract-Natural Language Processing (NLP) is one of the most important research area carried out in the world of Artificial Intelligence (AI). NLP supports the tasks of a portion of AI such as spell checking, machine translation, automatic text summarization, and information extraction, so on. Spell checking application presents valid suggestions to the user based on each mistake they encounter in the user’s document. The user then either makes a selection from a list of suggestions or accepts the current word as valid. Spell-checking program is often integrated with word processing that checks for the correct spelling of words in a document. Each word is compared against a dictionary of correctly spelt words. The user can usually add words to the spellchecker’s dictionary in order to customize it to his or her needs. In this paper, the system is intended to develop a spell checker application program by using Levenshtein Distance algorithm.

Keyword- Artificial Intelligence, Spell checker, Levenstein distance.

References-

[1] B.Loghman, Q.Z.Behrang, “CloniZER Spell Checker Adaptive, Language Independent Spell Checker” AIML 05 Conference, 19-21 December 2005.

[2] U.Z.Naushad and K.Mumit “A Comprehensive Bangla Spelling Checker”, Center for Research on Bangla Language Processing, BRAC, University, Bangladesh.

[3] Levenshtein, Vladimir I. “Binary codes capable of correcting detections, insertions, and reversals”, February 1996.

Video Anomaly Detection using Ensemble Learning

Authors: Prof.Vina Lomte, Durgesh Pahurkar, Siddheshwar Patil, Siddharth Patil, Satish Singh

Abstract- The creation of various technologies main objective is to improve our society and to maintain peace in our society. In this paper we try to focus on one of the problem that our society faces, that is various crimes and anomalies that lead to creation of tension in the society. Anomaly detection is a method of identifying an abnormal activity through the live surveillance video. Our proposed system uses Sparse dictionary and auto- encoders for detecting anomaly activities. The model uses Bayes classifier to detect the type of abnormal activity occurred in live surveillance. Ensemble learning is used to enhance the system by combining decisions of Sparse Dictionary and auto-encoders (with convolutional LSTM).

Keywords: Ensemble learning, Sparse Dictionary, auto- encoders, anomaly detection.

References-

[1] Chong Y.S., Tay Y.H. (2017) Abnormal Event Detection in Videos Using Spatiotemporal Autoencoder. In: Cong F., Leung A., Wei Q. (eds) Advances in Neural Networks - ISNN 2017. ISNN 2017. Lecture Notes in Computer Science, vol 10262. Springer, Cham

[2] Sultani, Waqas et al. Real-World Anomaly Detection in Surveillance Videos. 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition (2018): 6479-6488.

[3] Luo, Zhaohui et al. Real-time detection algorithm of abnormal behavior in crowds based on Gaussian mixture model. 2017 12th International Conference on Computer Science and Education (ICCSE) (2017): 183- 187.

[4] Masoudirad, S. Maryam and Jawad Hadadnia. Anomaly detection in video using two-part sparse dictionary in 170 FPS. 2017 3rd Interna- tional Conference on Pattern Recognition and Image Analysis (IPRIA) (2017): 133-139.

[5] Sabokrou, Mohammad Fathy, Mahmood Mojtaba, H Klette, Reinhard. (2015). Real-Time Anomaly Detection and Localization in Crowded Scenes. 10.1109/CVPRW.2015.7301284.

[6] Gajjar, Vandit et al. Human Detection and Tracking for Video Surveil- lance: A Cognitive Science Approach. 2017 IEEE International Confer- ence on Computer Vision Workshops (ICCVW) (2017): 2805-2809.

[7] Lin, Ying-Lung et al. Using Machine Learning to Assist Crime Pre- vention. 2017 6th IIAI International Congress on Advanced Applied Informatics (IIAI-AAI) (2017): 1029-1030.

[8] Arandjelovi, Relja Gronat, Petr Torii, Akihiko Pajdla, Tomas Sivic, Josef. (2015). NetVLAD: CNN architecture for weakly supervised place recognition.

[9] Hasan, M., Choi, J., Neumann, J., Roy-Chowdhury, A.K., Davis, L.S.: Learning temporal regularity in video sequences. In: 2016 IEEE Confer- ence on Computer Vision and Pattern Recognition (CVPR). pp. 733-742 (June 2016)

[10] Gao, Yuan et al. Violence detection using Oriented VIolent Flows. Image Vision Comput. 48-49 (2016): 37-41.

[11] Cheng, Kai-Wen et al. Gaussian Process Regression-Based Video Anomaly Detection and Localization With Hierarchical Feature Rep- resentation. IEEE Transactions on Image Processing 24 (2015): 5288- 5301.

[12] A. Sodemann, M. P. Ross, and B. J. Borghetti, A review of anomaly detection in automated surveillance, Systems, Man, and Cybernetics, Part C: Applications and Reviews, IEEE Transactions on, vol. 42, no. 6, pp. 1257-1272, 2012.

[13] Kratz, Louis and Ko Nishino. Anomaly detection in extremely crowded scenes using spatio-temporal motion pattern models. 2009 IEEE Confer- ence on Computer Vision and Pattern Recognition (2009): 1446-1453.

[14] S. Yi, H. Li, and X. Wang, Understanding pedestrian behaviors from stationary crowd groups, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 34883496, June 2015.

[15] R. A. A. Rupasinghe, S. G. M. P. Senanayake, D. A. Padmasiri, M. P. B. Ekanayake, G. M. R. I. Godaliyadda, and J. V. Wijayakulasooriya, Modes of clustering for motion pattern analysis in video surveillance, in 2016 IEEE International Conference on Information and Automation for Sustainability (ICIAfS), Dec 2016, pp. 16.

[16] S. Andrews, I. Tsochantaridis, and T. Hofmann. Support vector machines for multiple-instance learning. In NIPS, pages 577584, Cambridge, MA, USA, 2002. MIT Press.

[17] R. Arandjelovic, P. Gronat, A. Torii, T. Pajdla, and J. Sivic. NetVLAD: CNN architecture for weakly supervised place recognition. In CVPR, 2016.

[18] A. Gordo, J. Almazan, J. Revaud, and D. Larlus. Deep image retrieval: Learning global representations for image search. In ECCV, 2016.

[19] M. J. Roshtkhari, and M. D. Levine, An on-line, real-time learning method for detecting anomalies in videos using spatiotemporal com- positions, Computer Vision and Image Understanding, vol. 117, no. 10, pp. 1436-1452, 2013.

[20] W. Hu, T. Tan, L. Wang, and S. Maybank, A survey on visual surveil- lance of object motion and behaviors, Systems, Man, and Cybernetics, Part C: Applications and Reviews, IEEE Transactions on, vol. 34, no. 3, pp. 334-352, 2004.

[21] https://iwringer.wordpress.com/2015/11/17/anomaly-detection-concepts- and-techniques/