i-manager Publications

i-manager's Journal on Pattern Recognition (JPR)

Current Issue Vol. 11 Issue 2

Most Cited

Volume 5 Issue 4 December - February 2019

Research Paper

Human Emotion Recognition from Facial Expressions

Ortil Msugh * , Twaki Koko Grace**

*-**Department of Computer Science. FCT College of Education, Abuja, Nigeria.

Msugh, O., & Grace,T. K. (2019). Human Emotion Recognition from Facial Expressions. i-manager’s Journal on Pattern Recognition, 5(4), 1-12. https://doi.org/10.26634/jpr.5.4.15539

Abstract

Emotion recognition remains a potential research area as efforts to make machines to mimic humans in most areas of human life is yet actualized. This paper presents an emotion recognition system, to enhance recognition accuracy for better user experience. Principal Component Analysis (PCA) was implemented via Singular Value Decomposition (SVD) and used for feature extraction process. In classification process, Discrete Hidden Markov Model (HMM) was utilized in a principled manner. Two-dimensional spatial face features were realized by varying quantization levels. The quantization level with the efficient feature description, judged by the highest recognition accuracy was chosen to train the system. The recognition accuracy of the system was studied on two publicly available datasets, namely, JAFFE and Cohn Kanade (CK) datasets. The system showed better performances compared with other state of the art systems.

References

[1]. Arumugam, D., & Purushothaman, S. (2011). Emotion classification using facial expression. International Journal of Advanced Computer Science and Applications, 2(7), 92-98.

[2]. Belhumeur, P. N., Hespanha, J. P., & Kriegman, D. J. (1997). Eigenfaces vs. fisherfaces: Recognition using class specific linear projection. Yale University New Haven United States, 19(7), 711-720.snacks

[3]. Beristain, A., & Graña, M. (2009). Emotion recognition based on the analysis of facial expresions: A survey. New Mathematics and Natural Computation, 5(2), 513-534.

[4]. Bishop, C. M. C. C. M. (2006). Pattern recognition and machine learning. Springer, 4(4).

[5]. Calvo, R. A., & D'Mello, S. (2010). Affect detection: An interdisciplinary review of models, methods, and their applications. IEEE Transactions on Affective Computing, 1(1), 18-37.

[6]. Cohen, I., Sebe, N., Garg, A., Chen, L. S., & Huang, T. S. (2003). Facial expression recognition from video sequences: Temporal and static modeling. Computer Vision and Image Understanding, 91(1-2), 160-187.

[7]. Dailey, M. N., Joyce, C., Lyons, M. J., Kamachi, M., Ishi, H., Gyoba, J., & Cottrell, G. W. (2010). Evidence and a computational explanation of cultural differences in facial expression recognition. Emotion, 10(6), 874.

[8]. Duda, R. O., Hart, P. E., & Stork, D. G. (1998). Pattern Classification, 1(2).

[9]. Eddy, S. R. (1998). Profile hidden Markov models. Bioinformatics (Oxford, England), 14(9), 755-763.

[10]. Happy, S. L., & Routray, A. (2015). Automatic facial expression recognition using features of salient facial patches. IEEE Transactions on Affective Computing, 6(1), 1-12.

[11]. Hsu, C. W., & Chang, C. H. (2008). A Practical Guide to Support Vector Classification, BJU Int., 101(1), 1396 - 1400.

[12]. Huang, M. W., Wang, Z. W., & Ying, Z. L. (2010, October). A new method for facial expression recognition based on sparse representation plus LBP. In Image and rd Signal Processing (CISP), 2010 3 International Congress on (Vol. 4, pp. 1750-1754). IEEE.

[13]. Lyons, M. J., Budynek, J., & Akamatsu, S. (1999). Automatic classification of single facial images. IEEE Transactions on Pattern Analysis and Machine Intelligence, 21(12), 1357-1362.

[14]. Lyons, M., Akamatsu, S., Kamachi, M., & Gyoba, J. (1998, April). Coding facial expressions with gabor wavelets. In Automatic Face and Gesture Recognition, 1998. Proceedings. Third IEEE International Conference on (pp. 200-205). IEEE.

[15]. Nefian, A. V., & Hayes, M. H. (1998, May). Hidden Markov models for face recognition. In Acoustics, Speech and Signal Processing, 1998. Proceedings of the 1998 IEEE International Conference on (Vol. 5, pp. 2721-2724). IEEE.

[16]. Rabiner, L. R., & Juang, B. H. (1986). An introduction to hidden Markov models. IEEE ASSP Magazine, 3(1), 4-16.

[17]. Russell, J. A. (1994). Is there universal recognition of emotion from facial expression? A review of the crosscultural studies. Psychological Bulletin, 115(1), 102.

[18]. Shawe-Taylor, J., & Tutorial, M. L. (2014). Kernel Methods for Pattern Analysis.

[19]. Shlens, J. (2014). A tutorial on principal component analysis. arXiv preprint arXiv:1404.1100.

[20]. Song, F., Guo, Z., & Mei, D. (2010, November). Feature selection using principal component analysis. In System Science, Engineering Design and Manufacturing Informatization (ICSEM), 2010 International Conference on (Vol. 1, pp. 27-30). IEEE.

[21]. Stamp, M. (2015). A Revealing Introduction to Hidden Markov Models.

[22]. Su, H., & Wang, X. (2014, November). Principal Component Analysis in Linear Discriminant Analysis Space for Face Recognition. In 2014 5^th International Conference on Digital Home (ICDH) (pp. 30-34). IEEE.

[23]. Theodoridis, V. & Koutroumbas, K. (2008). Pattern Recogniton (4^th Ed), Academic Press.

[24]. Turk, M. A., & Pentland, A. P. (1991, June). Face recognition using eigenfaces. In Computer Vision and Pattern Recognition, 1991. Proceedings CVPR'91., IEEE Computer Society Conference on (pp. 586-591). IEEE.

[25]. Wang, J., Chen, Y., & Adjouadi, M. (2008, October). A comparative study of multilinear principal component analysis for face recognition. In Applied Imagery Pattern Recognition Workshop, 2008. AIPR'08. 37^th IEEE (pp. 1-6). IEEE.

[26]. Widanagamaachchi, W. N. (2009). Facial emotion recognition with a neural network approach. University of Colombo.

[27]. Ye, Q., Ye, N., & Yin, T. (2015). Fast orthogonal linear discriminant analysis with application to image classification. Neurocomputing, 158, 216-224.

[28]. Yu, H., & Yang, J. (2001). A direct LDA algorithm for high-dimensional data-with application to face recognition. Pattern Recognition, 34(10), 2067-2070.

[29]. Zhi, R., Flierl, M., Ruan, Q., & Kleijn, W. B. (2011). Graph - Preserving Sparse Non negative Matrix Factorization with Application to Facial Expression Recognition. In IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 14(1), pp. 38-52. doi: 10.1109/TSMCB.2010.2044788

[30]. Žitković, G. (2010). Introduction to Stochastic Processes.

Full Article (HTML)

Pdf

Research Paper

On the Development of a Novel Smell Agent Optimization (SAO) for Optimization Problems

A. T. Salawudeen* , M. B. Mu'azu , Y. A. Sha'aban*, E. A. Adedokun****

-,Department of Computer Engineering, Ahmadu Bello University, Zaria, Nigeria.

Department of Electrical Engineering, University of Hafer Al-Batin, Saudi Arabia.

Salawudeen, A. T., Mu'azu, M. B., Sha'aban, Y. A., & Adedokun, E. A. (2019).On the Development of a Novel Smell Agent Optimization (SAO) For Optimization Problems. i-manager’s Journal on Pattern Recognition, 5(4), 13-26. https://doi.org/10.26634/jpr.5.4.15677

Abstract

This paper presents the development of a new optimization algorithm called the Smell Agent Optimization (SAO). The algorithm uses the phenomenon of smell and the intuitive trailing behavior of an agent to identify a smell source. The developed algorithm has two basic modes used in the optimization process, which are the sniffing mode and trailing mode. In the sniffing mode, the evaporation of smell molecules from a source is modeled and in the trailing mode, the movement of an agent towards the smell molecules is modeled. The performance of SOA was evaluated using 10 benchmark functions and results was compared with PSO, ABC, and GA. Simulation results showed the efficiency of the developed SAO in solving unimodal and multimodal functions.

References

[1]. Abdechiri, M., Meybodi, M. R., & Bahrami, H. (2013). Gases Brownian motion optimization: An algorithm for optimization (GBMO). Applied Soft Computing, 13(5), 2932-2946.

[2]. Amoore, J. E., & Hautala, E. (1983). Odor as an ald to chemical safety: Odor thresholds compared with threshold limit values and volatilities for 214 industrial chemicals in air and water dilution. Journal of Applied Toxicology, 3(6), 272-290.

[3]. Axel, R. (2005). Scents and sensibility: A molecular logic of olfactor y perception (Nobel lecture). Angewandte Chemie International Edition, 44(38), 6110- 6127.

[4]. Black. S. M. (n.d.). Chemistry of Smell (chem 1010) [Online].

[5]. Brand, P., Robertson, H. M., Lin, W., Pothula, R., Klingeman, W. E., Jurat-Fuentes, J. L., & Johnson, B. R. (2018). The origin of the odorant receptor gene family in insects. bioRxiv, 259424.

[6]. Buck, L. B. (2005). Unraveling the sense of smell (Nobel lecture). Angewandte Chemie International Edition, 44(38), 6128-6140.

[7]. Chapman, S., & Cowling, T. G. (1970). The Mathematical Theory of Non-uniform Gases: An Account of the Kinetic Theory of Viscosity, Thermal Conduction and Diffusion in Gases. Cambridge Mathematical Library. Cambridge University Press, 1, 27-52.

[8]. Chen, J., Lang, X., Wang, Y., Li, G., Yang, Z., & Fan, S. (2018). Comparative evaluation of different noncondensable gases on thermal behaviors, kinetics, high pressure properties, and product characteristics of heavy oil. Energy Conversion and Management, 162, 13-25.

[9]. de March, C. A., Topin, J., Bruguera, E., Novikov, G., Ikegami, K., Matsunami, H., & Golebiowski, J. (2018). Odorant receptor 7D4 activation dynamics. Angewandte Chemie, 130(17), 4644-4648.

[10]. Dorigo, M., Maniezzo, V., & Colorni, A. (1996). Ant system: Optimization by a colony of cooperating agents. IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 26(1), 29-41.

[11]. Doty, R. L., Applebaum, S., Zusho, H., & Settle, R. G. (1985). Sex differences in odor identification ability: A crosscultural analysis. Neuropsychologia, 23(5), 667-672.

[12]. Eberhart, R., & Kennedy, J. (1995, October). A new optimizer using particle swarm theory. In Micro Machine and Human Science, 1995. MHS'95. Proceedings of the Sixth International Symposium on (pp. 39-43). IEEE.

[13]. Karaboga, D., & Basturk, B. (2007). A powerful and efficient algorithm for numerical function optimization: Artificial Bee Colony (ABC) algorithm. Journal of Global Optimization, 39(3), 459-471.

[14]. Kumar, S., Moore, C., & Gonzalez, A. (2017). Perfume Chemistry: Analysis of Jasmine Oil by Gas Chromatography - Mass Spectrometry.

[15]. Li, X. L. (2002). An optimizing method based on autonomous animats: fish-swarm algorithm. Systems Engineering-Theory & Practice, 22(11), 32-38.

[16]. Lynch, P. J. (2018). Head anatomy with olfactory nerve. Olfactory bulb labeled in English. Retrieved from: https://commons.wikimedia.org/wiki/File:Head_olfactory _nerve_ _olfactory_bulb_en.png

[17]. Malarvizhi, K., & Kumar, M. (2015, February). Particle swarm optimization tuned BELBIC controller for 8/6 SRM operation. In Electronics and Communication Systems nd (ICECS), 2015 2 International Conference on (pp. 904- 909). IEEE.

[18]. Miller, C. C. (1924). The Stokes-Einstein law for diffusion in solution. Proceedings of the Royal Society of London. Series A, Containing Papers of a Mathematical and Physical Character, 106(740), 724-749.

[19]. Momin, J. A. M. I. L., & Yang, X. S. (2013). A literature survey of benchmark functions for global optimization problems. Journal of Mathematical Modelling and Numerical Optimisation, 4(2), 150-194.

[20]. Morrison. J. Human nose can detect 1 trillion odours [Online].

[21]. Passino, K. M. (2002). Biomimicry of bacterial foraging for distributed optimization and control. IEEE Control Systems, 22(3), 52-67.

[22]. Pines, D. (2018). Theory of Quantum Liquids: Normal Fermi Liquids. CRC Press.

[23]. Poole, D. L., & Mackworth, A. K. (2010). Artificial Intelligence: Foundations of Computational Agents. Cambridge University Press.

[24]. Porter, J., Craven, B., Khan, R. M., Chang, S. J., Kang, I., Judkewitz, B., ... & Sobel, N. (2007). Mechanisms of scenttracking in humans. Nature Neuroscience, 10(1), 27.

[25]. Rinaldi, A. (2007). The scent of life: The exquisite complexity of the sense of smell in animals and humans. EMBO reports, 8(7), 629-633.

[26]. Salawudeen, A. T. (2015). Development of an Improved Cultural Artificial Fish Swarm Algorithm with Crossover. Department of Electrical and Computer Engineering, 154.

[27]. Scanes, C. G. (2018). Animal Perception Including Differences With Humans. In Animals and Human Society (pp. 1-11).

[28]. Smell. (n.d). (2017). In Dictionary.com. Retrieved on June 28, 2017, from http://www.dictionar y.com/ browse/smell.

[29]. Stevenson, R. J. (2009). An initial evaluation of the functions of human olfaction. Chemical Senses, 35(1), 3-20.

[30]. Sundaram, K. M., Kumar, R. S., Krishnakumar, C., & Sugavanam, K. R. (2016). Fuzzy logic and firefly algorithm based hybrid system for energy efficient operation of three phase induction motor drives. Indian Journal of Science and Technology, 9(1).

[31]. Tijani, S. A., & Mua'zu, M. B. (2015, November). Stabilization of inverted pendulum system using intelligent Linear Quadratic Regulator controller. In Computational Intelligence (IJCCI), 2015 7^th International Joint Conference on (Vol. 1, pp. 325-333). IEEE.

[32]. Touhara, K. (2014). Odor and Pheromone Molecules, Receptors, and Behavioral Responses. In The Olfactory System (pp. 19-38). Springer, Tokyo.

[33]. Turabieh, H., & Abdullah, S. (2011, January). A hybrid fish swarm optimisation algorithm for solving examination timetabling problems. In International Conference on Learning and Intelligent Optimization (pp. 539-551). Springer, Berlin, Heidelberg.

[34]. Vogt, J. (2017). Quantitative Problem Solving in Physical Chemistry. In Exam Survival Guide: Physical Chemistry (pp. 1-8). Springer, Cham.

[35]. Wysocki, C. J., & Beauchamp, G. K. (1984). Ability to smell androstenone is genetically determined. Proceedings of the National Academy of Sciences, 81(15), 4899-4902.

[36]. Xing, B., & Gao, W. J. (2014). Innovative computational intelligence: A rough guide to 134 clever algorithms (pp. 105-121). Cham, Heidelberg, New York, Dordrecht, London: Springer International Publishing.

[37]. Yang, X. S. (2010). Firefly algorithm, stochastic test functions and design optimisation. arXiv preprint arXiv:1003.1409.

Full Article (HTML)

Pdf

Research Paper

Profiling Inappropriate Users’ Tweets Using Deep Long Short-Term Memory (LSTM) Neural Network

Abubakar Umar* , Sulaimon A. Bashir, Laud Charles Ochei*, Ibrahim A. Adeyanju****

- Department of Computer Science, Federal University of Technology Minna Nigeria.

Robert Gordon University, Aberdeen, UK.

**** Department of Computer Engineering, Federal University, Oye-Ekiti, Nigeria.

Umar, A., Bashir, S. A., Ochei, L.C., & Adeyanju, I. A. (2019). Profiling Inappropriate Users' Tweets Using Deep Long Short-Term Memory (LSTM) Neural Network. i-manager’s Journal on Pattern Recognition, 5(4), 27-43. https://doi.org/10.26634/jpr.5.4.15864

Abstract

In recent times, big Internet companies have come under increased pressure from governments and NGOs to remove inappropriate materials from social media platforms (e.g., Twitter, Facebook, YouTube). A typical example of this problem is the posting of hateful, abusive, and violent tweets on Twitter which has been blamed for inciting hatred, violence and causing societal disturbances. Manual identification of such tweets and the people who post these tweets is very difficult because of the large number of active users and the frequency with which such tweets are posted. Existing approaches for identifying inappropriate tweets have focused on the detection of such tweets without identifying the users who post them. This paper proposes an approach that can automatically identify different types of inappropriate tweets together with the users who post them. The proposed approach is based on a user profiling algorithm that uses a deep Long Short-Term Memory (LSTM) based neural network trained to detect abusive language. With the support of word embedding features learned from the training set, the algorithm is able to classify the tweets of users into different abusive language categories. Thereafter, the user profiling algorithm uses the classes assigned to the tweets of each user to profile each user into different abusive language category. Experiments on the test set show that the deep LSTM-based abusive language detection model reached an accuracy of 89.14% on detecting whether a tweet is bigotry, offensive, racist, extremism-related and neutral. Also, the user profiling algorithm obtained an accuracy of 83.33% in predicting whether a user is a bigot, racist, extremist, uses offensive language and neutral.

References

[1]. Abubakar, U., Bashir, S. A., Abdullahi, M. B., & Adebayo, O. S. (2019). Comparative study of various machine learning algorithms for tweet classification. i-manager's Journal on Computer Science, 6(4), 12-24.

[2]. Agarwal, S., & Sureka, A. (2016). But I Did Not Mean It! Intent Classification of Racist Posts on Tumblr. European Intelligence and Security Infromatics Conference (pp. 124-127). IEEE. doi:10. 17632/hd3b6v659v.2

[3]. Albadi, N., Kurdi, M., & Mishra, S. (2018). Are they our brother? Analysis and detection of religious hate speech in the Arabic twittersphere. International Conference on Advances in Social Networks Analysis and Mining (pp. 69- 76). IEEE.doi:10.1109/ASONAM.2018.8508247

[4]. Alfina, I., Mulia, R., Fanany, M., & Ekanata, Y. (2017). Hate speech detection in the indonesian language: a dataset and preliminary study. International Conference on Advanced Computer Science and Information Systems (ICACSIS) (pp. 233-238). IEEE. doi:10.1109/ ICACSIS.2017.8355039

[5]. Al-Quirishi, M., Aldrees, R., AlRubaian, M., Al- Rakhami, M., Rahman, M. S., & Alamri, A. (2015). A new model for classifying social media users according to their behaviors. World Symposium on Web Applications and Networking (WSWAN) (pp. 1-5). IEEE. doi:10.1109/WSWAN.2015.7209085

[6]. Al-Rfou, R., Alain, G., Almahairi, A., Angermueller, C., Bahdanau, D., Ballas, N.,…..& Zhang, Y., (2016). Theano: A Python framework for fast computation of mathematical expressions. arXiv:1605.02688v1 [cs.SC]

[7]. Aphinyanaphongs, Y., Ray, B., Statnikov, A., & Krebs, P. (2014, August). Text classification for automatic detection of alcohol use-related tweets: A feasibility study. In th Proceedings of the 2014 IEEE 15 International Conference on Information Reuse and Integration (IEEE IRI 2014) (pp. 93-97). IEEE. doi:10.1109/IRI.2014.7051877

[8]. Chikashi, N., Joel, T., Achint, T., Yashar, M., & Yi, C. (2016). Abusive Language Detection in Online User Content. International World Wide Web Conference (pp. 145-153). Association of Computing Machinery (ACM). doi:dx.doi.org/10.1145/ 2872427.2883062

[9]. Cufoglu, A. (2014). User Profiling - A Short Review. International Journal of Computer Applications, 108(3), 1-9. doi:10.5120/18888-0179

[10]. Dey, R., & Salemt, F. M. (2017, August). Gate-variants of Gated Recurrent Unit (GRU) neural networks. In 2017 th IEEE 60 International Midwest Symposium on Circuits and Systems (MWSCAS) (pp. 1597-1600). IEEE. doi:10.1109/MWSCAS.2017.8053243

[11]. Djuric, N., Zhou, J., Morris, R., Grbovic, M., Radosavljevic, V., & Bhamidipati, N. (2015, May). Hate speech detection with comment embeddings. In th Proceedings of the 24 International Conference on World Wide Web (pp. 29-30). ACM. doi:dx.doi.org/10.1145/ 2740908.2742760.

[12]. DL4J (2017, August 12). Deep Learning and Neural Network Glossary. Retrieved March 23, 2018, from https://deeplearning4j.org/cn/glossary

[13]. Fatahillah, N., Suryati, P., & Haryawan, P. (2017). Implementation of Naïve Bayes Classifier Algorithm on Social Media (Twitter) to the Teaching of Indonesian Hate Speech. International Conference on Sustainable Information Engineering and Technology (pp. 128-131). IEEE. doi:10.1109/SIET.2017.8304122

[14]. Ikeda, K., Hattori, G., Ono, C., Asoh, H., & Higashino, T. (2013). Twitter user profiling based on text and community mining for market analysis. Knowledge-Based Systems, 51, 35-47. doi:10.1016/j.knosys.2013.06.020

[15]. Iqbal, M. (2019, February 27). Twitter Revenue and Usage Statistics. Business of Apps, Retrieved March 02, 2019, from http://www.businessofapps.com

[16]. Kang, K., Yoon, C., & Kim, E. Y. (2016, January). Identifying Depressive Users in Twitter Using Multimodal Analysis. In 2016 International Conference on Big Data and Smart Computing (BigComp) (pp. 231-238). IEEE. doi:10.1109/BIGCOMP.2016.7425918

[17]. Keras. (n.d.). The Python Deep Learning Library. Retrieved July 10, 2018, from https://keras.io/

[18]. Lee, W. J., Oh, K. J., Lim, C. G., & Choi, H. J. (2014, February). User profile extraction from Twitter for th personalized news recommendation. In 16 International Conference on Advanced Communication Technology (pp. 779-783). IEEE. doi:10.1109/ICACT.2014.6779068

[19]. Lundeqvist, E., & Svensson, M. (2017). Author Profiling: A Machine Learning approach towards detecting gender, age, and native language of users in social media. UPPSALA University, Department of Information Technology.

[20]. Mikolov, T., Chen, K., Corrado, G., & Dean, J. (2013). Efficient Estimation of Word Represenations in Vector Space. CoRR, abs/1301.3781. doi:https://arxiv.org/abs/ 1301.3781

[21]. Neethu, M. S., & Rajasree, R. (2013, July). Sentiment Analysis in Twitter using Machine Learning Techniques. International Conference on Computing, Communications and Networking Technologies (ICCCNT) (pp. 1-5). IEEE. doi:10.1109/ICCCNT.2013.6726818

[22]. O'Dea, B., Wan, S., Batterham, P. J., Calear, A. L., Paris, C., & Christensen, H. (2015). Detecting suicidality on Twitter. Internet Interventions, 2(2), 183 - 188. doi:10.1016/j.invent.2015.03.005

[23]. Pennington, J., Socher, R., & Manning, C. (2014). Glove: Global vectors for word representation. In Proceedings of the 2014 Conference on Empirical Methods In Natural Language Processing (EMNLP) (pp. 1532-1543).

[24]. Pitsilis, K. G., Ramampiaro, H., & Langseth, H. (2018). Detecting Offensive Langauge in Tweets using Deep Learning. New York: Cornell University. arXiv:1801.04433v1 [cs.CL]

[25]. Rehurek, R. (2019, April 10). Topic modelling for humans. Retrieved July 10, 2018, from Gensim: https://radimrehurek.com/gensim/

[26]. Rocha, E., Francisco, P. A., Calado, P., & Sofia-Pinto, H. (2011). User Profiling on Twitter. Semantic Web Journal. Retrieved May 12, 2018, from http://www.semantic-webjournal. net

[27]. Sureka, A., & Agarwal, S. (2014, September). Learning to classify hate and extremism promoting tweets. In 2014 IEEE Joint Intelligence and Security Informatics Conference (pp. 320-320). IEEE. doi:10.1109/JISIC.2014.65

[28]. TensorFlow. (n.d.). An end-to-end open source machine learning platform. Retrieved July 10, 2018, from https://www.tensorflow.org/

[29]. Twitter. (n.d.). Filter Realtime Tweets. Retrieved January 20, 2018, from Twitter Developer : https://developer.twitter.com/en/docs/tweets/filterrealtime/ api-reference/post-statuses-filter.html

[30]. Watanabe, H., Bouazizi, M., & Ohtsuki, T. (2018). Hate speech on twitter: a pragmatic approach to collect hateful and offensive expressions and perform hate speech detection. IEEE Access, 6, 13825-13835. doi:10.1109/ACCESS.2018.2806394

[31]. Wikarsa, L., & Thahir, S. N. (2015, November). A text mining application of emotion classifications of Twitter's st users using Naïve Bayes method. In 2015 1 International Conference on Wireless and Telematics (ICWT) (pp. 1-6). IEEE. doi:10.1109/ICWT.2015.7449218

[32]. Zhang, Z., He, Q., Gao, J., & Ni, M. (2018). A deep learning approach for detecting traffic accidents from social media data. Transportation Research Part C: Emerging Technologies, 86, 580-596. doi:10.1016/j.trc.2017.11.027

Full Article (HTML)

Pdf

Research Paper

A Medical Expert System for Predicting the Prevalence of Autoimmune Diabetes Mellitus in Thyroid Patients

Surekha Samsani * , srinivasa rao**

* Department of Computer Science and Engineering, UCEK(A), Jawaharlal Nehru Technological University Kakinada, Andhra Pradesh, India.

** Department of Information Technology, UCEV(A), Jawaharlal Nehru Technological University Kakinada, Andra Pradesh, India.

Samsani,S.,&Suma,G.J. (2019). A Medical Expert System for Predicting the Prevalence of Autoimmune Diabetes Mellitus in Thyroid Patients. i-manager’s Journal on Pattern Recognition, 5(4), 44-50. https://doi.org/10.26634/jpr.5.4.15947

Abstract

Diabetes Mellitus (DM) and Thyroid are the major coexistent autoimmune disorders affecting people globally. Due to prolonged chronic mental stress in the modern lifestyle, Thyroid disorder is affecting all age groups and people with Thyroid disorder have an increased risk of developing DM complications. Because, abnormal Thyroid dysfunction can have dreadful effects on blood glucose control and can affect the course of DM. This paper proposes a Medical Expert system to assist clinicians in predicting the prevalence of developing autoimmune DM more precisely in patients suffering from Thyroid and further helps to investigate better in the line of improving public health. In this work, Fuzzy logic based inference system and unsupervised machine learning algorithms are used to discover associations and dependencies between Thyroid and DM. The inferred knowledge base is used to design Fuzzy based Expert system. To develop a more realistic expert system, blood sample reports of people affected by DM and Thyroid disorder have been collected from various Endocrine centres in Andhra Pradesh, India. Specificity, Sensitivity, Predictive Values, and Likelihood Ratios of the proposed system are promising in support of system functionality

References

[1]. Ambilwade, R., Manza, R., & Kaur, R. (2016). Prediction of Diabetes Mellitus and its Complications using Fuzzy Inference System. International Journal of Emerging Technology and Advanced Engineering, 6(7), 2250-2459.

[2]. Carvajal, D. N., & Rowe, P. C. (2010). Sensitivity, specificity, predictive values, and likelihood ratios. Pediatr Rev., 31(12), 511-513.

[3]. Chaker, L., Ligthart, S., Korevaar, T. I., Hofman, A., Franco, O. H., Peeters, R. P., & Dehghan, A. (2016). Thyroid function and risk of type 2 diabetes: A population-based prospective cohort study. BMC Medicine, 14(1), 150.

[4]. Chandgude, N., & Pawar, S. (2016). Diagnosis of diabetes using fuzzy inference system. In 2016 International Conference on Computing Communication Control and Automation (ICCUBEA), (pp.1-6). IEEE.

[5]. Chaturvedi, K., Patel, R., & Swami, D. K. (2015). Fuzzy C-Means based Inference Mechanism for Association Rule Mining: A Clinical Data Mining Approach. International Journal of Advanced Computer Science and Applications, 6(6), 103-110.

[6]. Choi, B. G., Rha, S. W., Kim, S. W., Kang, J. H., Park, J. Y., & Noh, Y. K. (2019). Machine Learning for the Prediction of New-Onset Diabetes Mellitus during 5-Year Follow-up in Non-Diabetic Patients with Cardiovascular Risks. Yonsei Medical Journal, 60(2), 191-199.

[7]. Duntas, L. H., Orgiazzi, J., & Brabant, G. (2011). The interface between thyroid and diabetes mellitus. Clinical Endocrinology, 75(1), 1-9.

[8]. Dunn, J. C. (1973). A fuzzy relative of the ISODATA process and its use in detecting compact well-separated clusters. Journal of Cybernetics, 3, 32-57.

[9]. Gronich, N., Deftereos, S. N., Lavi, I., Persidis, A. S., Abernethy, D. R., & Rennert, G. (2015). Hypothyroidism is a risk factor for new-onset diabetes: a cohort study. Diabetes Care, 38(9), 1657-1664.

[10]. Hage, M., Zantout, M. S., & Azar, S. T. (2011). Thyroid disorders and diabetes mellitus. Journal of Thyroid Research, 2011, Article ID 439463. http://dx.doi.org/10.4061/2011/439463

[11]. Han, J., Pei, J., & Yin, Y. (2000). Mining frequent patterns without candidate generation. In ACM Sigmod record, 29(2),1-12.

[12]. Harrar, S. (2019). Low Thyroid Hormone Raises Risk of Type2 Diabetes, Retrieved from https://www.endo crineweb.com/news/diabetes/55372-low-thyroid- hormone- raises-risk-type-2-diabetes

[13]. Jayanthi, N., Babu, B. V., & Rao, N. S. (2017). Survey on clinical prediction models for diabetes prediction. Journal of Big Data, 4(1), 26. DOI: 10.1186/s40537-017- 0082-7

[14]. Johnson, J. L., & Duick, D. S. (2002). Diabetes and thyroid disease: A likely combination. Diabetes Spectrum, 15(3), 140-142.

[15]. Kost, R., Littenberg, B., & Chen, E. S. (2012). Exploring generalized association rule mining for disease co- occurrences. In AMIA Annual Symposium Proceedings, (pp.1284-1293).

[16]. Li, Y., Li, H., & Yao, H. (2018). Analysis and Study of Diabetes Follow-Up Data using a Data-Mining-Based Approach in New Urban Area of Urumqi, Xinjiang, China, 2016-2017. Computational and Mathematical Methods in Medicine.

[17]. Norman, A. (2018). Your future doctor may not be human. This is the rise of AI in Medicine. Futurism–SciFi Visions. Çevrimiçi (Erişim, 1 Şubat 2018), Retrieved from https://futurism. com/ai-medicine-doctor.

[18]. Patricia, W. (August, 2017). Thyroid Disordrs and Diabetes. Retrieved from https://www.diabetesself management.com/about-diabetes/general-diabetes- information/ thyroid-disorders-and-diabetes/

[19]. Sneha, N., & Gangil, T. (2019). Analysis of diabetes mellitus for early prediction using optimal features selection. Journal of Big Data, 6(1), 13

[20]. Wang, C. (2015). A study of membership functions on mamdani-type fuzzy inference system for industrial decision-making. Theses and Dissertations. Paper 1665.

[21]. Zadeh, L. A. (1965). Fuzzy sets. Information and control, 8(3), 338-353.

[22]. Zheng, T., Xie, W., Xu, L., He, X., Zhang, Y., You, M., Yang,G., & Chen, Y. (2017). A machine learning-based framework to identify type 2 diabetes through electronic health records. International Journal of Medical Informatics, 97, 120-127.

Full Article (HTML)

Pdf

Research Paper

Separation, Classification and Expert Mapping of Old Grantha Documents Symbols

Lalit Prakash Saxena*

* Research Scientist, Applied Research Section, Combo Consultancy, Obra UP India.

Saxena, L. P. (2019). Separation, Classification and Expert Mapping of Old Grantha Documents Symbols. i-manager’s Journal on Pattern Recognition, 5(4), 51-67. https://doi.org/10.26634/jpr.5.4.16108

Abstract

This paper attempts to decipher old documents using symbol to script mapping scheme. Symbols are confined to documents either as isolated notations or handwritten texts with a number of not able features. This paper describes a method to separate and classify handwritten non-cursive symbols in Grantha script. This work uses statistical correlation coefficient method for separation and classification, without the recognition of the symbols. The Grantha script symbols mapping model comprises of selection, separation, preprocessing, classification, and finally mapping. The proposed model employs bounding box algorithm for locating the symbols. The algorithm selects the symbols and excludes the non-symbol components to an extent possible. For experiments, 135 Grantha script document images of varying deteriorating complexities were used. The resulting symbol classification rate (i.e., the proportion of symbols automatically classified) was obtained near to 80%, aiding in mapping to a predetermined mapping scheme.

References

[1]. Arica, N., & Yarman-Vural, F. T. (2001). An overview of character recognition focused on off-line handwriting. IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews), 31(2), 216-233. https://doi.org/10.1109/5326.941845

[2]. Baumann, S., Ali, M. B. H., Dengel, A., Jager, T., Malburg, M., Weigel, A., & Wenzel, C. (1997, August). Message extraction from printed documents-a complete solution. In Proceedings of the Fourth International Conference on Document Analysis and Recognition. Vol(2), 1055-1059. IEEE. https://doi.org/10.1109/ ICDAR.1997.620670

[3]. Casey, R. G., & Lecolinet, E. (1996). A survey of methods and strategies in character segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 18(7), 690-706 https://doi.org/10.1109/ 34.506792

[4]. Chan, R. H., Ho, C. W., & Nikolova, M. (2005). Salt-and- pepper noise removal by median-type noise detectors and detail-preserving regularization. IEEE Transactions on Image Processing, 14(10),1479. https://doi.org/10.1109/ TIP.2005.852196

[5]. Chaudhuri, B. B., Pal, U., & Mitra, M. (2002). Automatic recognition of printed Oriya script. Sadhana, 27(1), 23-34 https://doi.org/10.1007/BF02703310

[6]. Chou, K. S., Fan, K. C., & Fan, T. I. (1997). Peripheral and global features for use in coarse classification of Chinese characters. Pattern Recognition, 30(3), 483-489. https://doi.org/10.1016/S0031-3203(96)00090-8

[7]. Das, N., Sarkar, R., Basu, S., Saha, P. K., Kundu, M., & Nasipuri, M. (2015). Handwritten Bangla character recognition using a soft computing paradigm embedded in two pass approach. Pattern Recognition, 48 (6), 2054 - 2071. https://doi.org/10.1016/j.patcog.2014.12.011

[8]. Duda, R. O., Hart, P. E., & Stork, D. G. (2012). Pattern Classification (Vol. 2). John Wiley & Sons.

[9]. Fan, K. C., Wang, L. S., & Tu, Y. T. (1998). Classification of machine-printed and handwritten texts using character block layout variance. Pattern Recognition, 31(9), 1275-1284. https://doi.org/10.1016/S0031- 3203(97)00143-X

[10]. Fujisawa, H. (2008). Forty years of research in character and document recognition - an industrial perspective. Pattern Recognition, 41(8), 2435-2446. https://doi.org/10.1016/j.patcog.2008.03.015

[11]. Gatos, B., Pratikakis, I., & Perantonis, S. J. (2006). Adaptive degraded document image binarization. Pattern Recognition, 39 (3), 317-327. https://doi.org/10.1016/j.patcog.2005.09.010

[12]. Gonzalez, R. C., & Woods, R. E. (2002). Digital Image Processing. Publishing House of Electronics Industry, 141(7).

[13]. Haralick, R. M., & Shapiro, L. G. (1992). Computer and Robot Vision (Vol.1, pp.28-48) Reading: Addisonwesley.

[14]. Hu, J., Yu, D., & Yan, H. (1998, August). Structural boundary feature extraction for printed character recognition. In Joint IAPR International Workshops on Statistical Techniques in Pattern Recognition (SPR) and Structural and Syntactic Pattern Recognition (SSPR) (pp. 500 - 507). Springer, Berlin, Heidelberg. https://doi.org/10.1007/ BFb0033272

[15]. Johnson, S. C. (1967). Hierarchical clustering schemes. Psychometrika, 32 (3), 241-254. https://doi.org/10.1007/BF02289588

[16]. Jung, K., Kim, K. I., & Jain, A. K. (2004). Text information extraction in images and video: A survey. Pattern Recognition, 37(5), 977-997. https://doi.org/ 10.1016/j.patcog.2003.10.012

[17]. Kumar, D. U. (2009). Traditional writing system in southern India - palm leaf manuscripts. Design Thoughts, 7, 2-7.

[18]. Kumar, M., Sharma, R. K., & Jindal, M. K. (2014). Efficient feature extraction techniques for offline handwritten Gurumukhi character recognition. National Academy Science Letters, 37 (4), 381-391. https://doi.org/10.1007/s40009-014-0253-4

[19]. Lam, L., Lee, S. W., & Suen, C. Y. (1992). Thinning methodologies-a comprehensive sur vey. IEEE Transactions on Pattern Analysis and Machine Intelligence, 14 (9), 869 - 885. https://doi.org/10.1109/34.161346

[20]. Lehal, G. S., & Singh, C. (1999). Feature extraction and classification for OCR of Gurmukhi script. Vivek- Bombay, 12(2), 2-12.

[21]. Liu, C. L. (2006, September). Handwritten chinese character recognition: Effects of shape normalization and feature extraction. In Summit on Arabic and Chinese Handwriting Recognition (pp. 104-128). Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-78199- 8_7

[22]. MacQueen, J. (1967, June). Some methods for classification and analysis of multivariate observations. In Proceedings of the Fifth Berkeley Symposium on Mathematical Statistics and Probability (Vol. 1, No. 14, pp. 281-297).

[23]. Mezghani, N., Mitiche, A., & Cheriet, M. (2008). Bayes classification of online arabic characters by Gibbs modeling of class conditional densities. IEEE Transactions on Pattern Analysis and Machine Intelligence, 30(7), 1121-1131. https://doi.org/10.1109/TPAMI.2007.70753

[24]. Mizukami, Y. (1998). A handwritten chinese character recognition system using hierarchical displacement extraction based on directional features. Pattern Recognition Letters, 19 (7), 595-604. https://doi.org/10.1016/S0167-8655(98)00034-8

[25]. Mori, S., Suen, C. Y., & Yamamoto, K. (1992). Historical review of OCR research and development. Proceedings of the IEEE, 80 (7), 1029 - 1058. https://doi.org/10.1109/5.156468

[26]. Nixon, M. S., & Aguado, A. S.(2008). Feature Extraction and Image Processing (2^nd Ed).Academic Press,ISBN 978-0-12-372538-7, (2 ed.). Elsevier Ltd., London, UK.

[27]. Pal, U., & Chaudhuri, B. B. (2004). Indian script character recognition: A survey. Pattern Recognition, 37 (9), 1887 - 1899. https://doi.org/10.1016/j.patcog.2004.02.003

[28]. Plamondon, R., & Srihari, S. N. (2000). Online and offline handwriting recognition: A comprehensive survey. IEEE Transactions on Pattern Analysis and Machine Intelligence, 22(1), 63-84. https://doi.org/10.1109/ 34.824821

[29]. Rocha, J., & Pavlidis, T. (1995). Character recognition without segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 17(9), 903- 909. https://doi.org/10.1109/34.406657

[30]. Russ, J. C. (2007). The Image Processing Handbook, CRC. Boca Raton, FL.

[31]. Sauvola, J., & Pietikäinen, M. (2000). Adaptive document image binarization. Pattern Recognition, 33(2), 225-236. https://doi.org/10.1016/S0031-3203(99)00055-2

[32]. Saxena, L. P. (2014). An effective binarization method for readability improvement of stain-affected (degraded) palm leaf and other types of manuscripts. Current Science, 489-496.

[33]. Serra, J. (1982). Image Analysis and Mathematical Morphology, (Vol.1).

[34]. Shelke, S., & Apte, S. (2015, January). A fuzzy based classification scheme for unconstrained handwritten Devanagari character recognition. In 2015 International Conference On Communication, Information & Computing Technology (ICCICT) (pp. 1-6). IEEE. https://doi.org/10.1109/ICCICT.2015.7045738

[35]. Sonka, M., Hlavac, V., & Boyle, R. (2007). Image Processing, Analysis, and Machine Vision. Thomson- Engineering.

[36]. Srinivasan, S., Joseph, J. V. M., & Harikumar, P. (2012). Indus script deciphered: the method of semblance at work. Current Science, 268-281.

[37]. Vamvakas, G., Gatos, B., & Perantoni, S. J. (2009, July). A novel feature extraction and classification Methodology for the recognition of historical documents. In Proceedings of the 2009 10^thInternational Conference on Document Analysis and Recognition (pp. 491-495). IEEE Computer Society. https://doi.ieeecomputer society.org/10.1109/ICDAR.2009.223

[38]. Vamvakas, G., Gatos, B., Petridis, S., & Stamatopoulos, N. (2007, September). An efficient feature extraction and dimensionality reduction scheme for isolated greek handwritten character recognition. In Ninth International Conference on Document Analysis and Recognition (ICDAR 2007) (Vol. 2, pp. 1073-1077). IEEE. https://doi.org/10.1109/ICDAR.2007.4377080

[39]. Van Rijsbergen, C. J. (1979). Information Retrieval nd (2 Ed.). Newton, MA.

[40]. Venugopalan, K. (1983). A Primer in Grantha Characters (2^nd Ed). St. Peter, Minn: James H. Nye.

[41]. Wakahara, T., & Yamashita, Y. (2012, September). k- NN Classification of handwritten characters via accelerated GAT correlation. In 2012 International Conference on Frontiers in Handwriting Recognition (ICFHR 2012) (pp. 143-148). IEEE. https://doi.org/ 10.1016/j.patcog.2013.05.005

[42]. Wang, K., & Kangas, J. A. (2003). Character location in scene images from digital camera. Pattern Recognition, 36(10), 2287-2299. https://doi.org/ 10.1016/S0031-3203(03)00082-7

[43]. Wen, M. G., Fan, K. C., & Han, C. C. (2004). Classification of chinese characters using pseudo skeleton features. J. Inf. Sci. Eng., 20(5), 903-922.

[44]. Yin, F., Wang, Q. F., & Liu, C. L. (2013). Transcript mapping for handwritten chinese documents by integrating character recognition model and geometric context. Pattern Recognition, 46(10), 2807-2818. https://doi.org/10.1016/j.patcog.2013.03.013

i-manager's Journal on Pattern Recognition (JPR)

Current Issue Vol. 11 Issue 2

Most Read

Most Cited

Volume 5 Issue 4 December - February 2019

Human Emotion Recognition from Facial Expressions

Ortil Msugh * , Twaki Koko Grace**

*-**Department of Computer Science. FCT College of Education, Abuja, Nigeria.

Msugh, O., & Grace,T. K. (2019). Human Emotion Recognition from Facial Expressions. i-manager’s Journal on Pattern Recognition, 5(4), 1-12. https://doi.org/10.26634/jpr.5.4.15539

Abstract

References

Full Article (HTML)

Pdf

On the Development of a Novel Smell Agent Optimization (SAO) for Optimization Problems

A. T. Salawudeen* , M. B. Mu'azu **, Y. A. Sha'aban***, E. A. Adedokun****

*-**,****Department of Computer Engineering, Ahmadu Bello University, Zaria, Nigeria. *** Department of Electrical Engineering, University of Hafer Al-Batin, Saudi Arabia.

Salawudeen, A. T., Mu'azu, M. B., Sha'aban, Y. A., & Adedokun, E. A. (2019).On the Development of a Novel Smell Agent Optimization (SAO) For Optimization Problems. i-manager’s Journal on Pattern Recognition, 5(4), 13-26. https://doi.org/10.26634/jpr.5.4.15677

Abstract

References

Full Article (HTML)

Pdf

Profiling Inappropriate Users’ Tweets Using Deep Long Short-Term Memory (LSTM) Neural Network

Abubakar Umar* , Sulaimon A. Bashir**, Laud Charles Ochei***, Ibrahim A. Adeyanju****

*-** Department of Computer Science, Federal University of Technology Minna Nigeria. *** Robert Gordon University, Aberdeen, UK. **** Department of Computer Engineering, Federal University, Oye-Ekiti, Nigeria.

Umar, A., Bashir, S. A., Ochei, L.C., & Adeyanju, I. A. (2019). Profiling Inappropriate Users' Tweets Using Deep Long Short-Term Memory (LSTM) Neural Network. i-manager’s Journal on Pattern Recognition, 5(4), 27-43. https://doi.org/10.26634/jpr.5.4.15864

Abstract

References

Full Article (HTML)

Pdf

A Medical Expert System for Predicting the Prevalence of Autoimmune Diabetes Mellitus in Thyroid Patients

Surekha Samsani * , srinivasa rao**

* Department of Computer Science and Engineering, UCEK(A), Jawaharlal Nehru Technological University Kakinada, Andhra Pradesh, India. ** Department of Information Technology, UCEV(A), Jawaharlal Nehru Technological University Kakinada, Andra Pradesh, India.

Samsani,S.,&Suma,G.J. (2019). A Medical Expert System for Predicting the Prevalence of Autoimmune Diabetes Mellitus in Thyroid Patients. i-manager’s Journal on Pattern Recognition, 5(4), 44-50. https://doi.org/10.26634/jpr.5.4.15947

Abstract

References

Full Article (HTML)

Pdf

Separation, Classification and Expert Mapping of Old Grantha Documents Symbols

Lalit Prakash Saxena*

* Research Scientist, Applied Research Section, Combo Consultancy, Obra UP India.

Saxena, L. P. (2019). Separation, Classification and Expert Mapping of Old Grantha Documents Symbols. i-manager’s Journal on Pattern Recognition, 5(4), 51-67. https://doi.org/10.26634/jpr.5.4.16108

Abstract

References

Full Article (HTML)

Pdf

A. T. Salawudeen* , M. B. Mu'azu , Y. A. Sha'aban*, E. A. Adedokun****

-,Department of Computer Engineering, Ahmadu Bello University, Zaria, Nigeria.

Department of Electrical Engineering, University of Hafer Al-Batin, Saudi Arabia.

Abubakar Umar* , Sulaimon A. Bashir, Laud Charles Ochei*, Ibrahim A. Adeyanju****

- Department of Computer Science, Federal University of Technology Minna Nigeria.

Robert Gordon University, Aberdeen, UK.

**** Department of Computer Engineering, Federal University, Oye-Ekiti, Nigeria.

* Department of Computer Science and Engineering, UCEK(A), Jawaharlal Nehru Technological University Kakinada, Andhra Pradesh, India.

** Department of Information Technology, UCEV(A), Jawaharlal Nehru Technological University Kakinada, Andra Pradesh, India.