AccScience Publishing / IJOSI / Volume 7 / Issue 7 / DOI: 10.6977/IJoSI.202309_7(7).0006
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Towards a robust solution to mitigate all content-based filtering draw-backs within a recommendation system

Oumaima Stitini1* ,  Soulaimane Kaloun1 ,  Omar 3 Bencharef1
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1 Computer and system engineering laboratory, Cadi Ayyad University, Marrakesh, Morocco
IJOSI 2023 , 7(7), 89–111; https://doi.org/10.6977/IJoSI.202309_7(7).0006
Submitted: 17 January 2023 | Revised: 25 June 2023 | Accepted: 15 June 2023 | Published: 25 September 2023
© 2023 by the Author(s). Licensee AccScience Publishing, USA. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution -Noncommercial 4.0 International License (CC BY-NC 4.0) ( https://creativecommons.org/licenses/by-nc/4.0/ )
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Abstract

Recommendation systems deliver a method to simplify the user’s desire. Recommendation systems are now commonly used on the Internet. It helps suggestitems in various categories, including e-commerce, medical, education, tourism, and industrial. The electroniccommerce sector has taken a big place in our daily lives as an active research tool, which helps people find what they are looking for. This paper presents a new contribution based on the combination of different algorithms to find a suitable solutionto all the drawbacks of content-based recommender systems. The main contribution of this research lies in how to solve each problem and move on to the next. This paper describes an Ideal Solution Mitigating Content Disadvantages based on Three Phases called ISMCD3P. Experiments show that the algo-rithm can propose an appropriate solution to solve all the problems of content-based filtering. Experimentations oper-ating on real datasets are used to estimate the efficacy of our strategy.

Keywords
Content-based filtering drawbacks
Over-specialization
Limited content
Serendipity
Sparsity
References
[1]

.

[2]
  1. Mistry, P., Maes, P., & Chang, L. (2009). WUW-wear Ur world: a wearable gestural interface. InCHI'09 ex-tended abstracts on Human factors in computing sys-tems(pp. 4111-4116).
  2. Wang, R. Y., & Popović, J. (2009). Real-time hand-tracking with a color glove.ACM transactions on graphics (TOG),28(3), 1-8.
  3. Rautaray, S. S. (2012). Real time hand gesture recogni-tion system for dynamic applications.International Journal of ubicomp (IJU),3(1).
  4. Saxe, D., & Foulds, R. (1996, September). Toward ro-bust skin identification in video images. InProceed-ings of the Second International Conference on Au-tomatic Face and Gesture Recognition(pp. 379-384). IEEE.
  5. Chai, D., & Ngan, K. N. (1998, April). Locating facial region of a head-and-shoulders color image. InPro-ceedings Third IEEE International Conference on Automatic Face and Gesture Recognition(pp. 124-129). IEEE.
  6. Yang, J., Lu, W., & Waibel, A. (1997). Skin-color mod-eling and adaptation. InComputer Vision—ACCV'98: Third Asian Conference on Computer Vision Hong Kong, China, January 8–10, 1998 Pro-ceedings, Volume II 3(pp. 687-694). Springer Berlin Heidelberg.
  7. Yu, C., Wang, X., Huang, H., Shen, J., & Wu, K. (2010, October). Vision-based hand gesture recognition us-ing combinational features. In2010 Sixth Interna-tional Conference on Intelligent Information Hiding and Multimedia Signal Processing(pp. 543-546). IEEE.
  8. Rekha, J., Bhattacharya, J., & Majumder, S. (2011, De-cember). Shape, texture, and local movement hand gesture features for Indian sign language recognition. In3rd international conference on trends in infor-mation sciences & computing (TISC2011)(pp. 30-35). IEEE.
  9. Panwar, M., & Mehra, P. S. (2011, November). Hand gesture recognition for human computer interaction. In2011 International Conference on Image Infor-mation Processing(pp. 1-7). IEEE.
  10. Malima, A. K., Özgür, E., & Çetin, M. (2006). A fast al-gorithm for vision-based hand gesture recognition for robot control.
  11. Manigandan, M., & Jackin, I. M. (2010, June). Wireless vision based mobile robot control using hand gesture recognition through perceptual color space. In2010 International Conference on Advances in Computer Engineering(pp. 95-99). IEEE.
  12. Fang, Y., Wang, K., Cheng, J., & Lu, H. (2007, July). A real-time hand gesture recognition method. In2007
  13. IEEE International Conference on Multimedia and Expo(pp. 995-998). IEEE.
  14. Dardas, N. H., & Georganas, N. D. (2011). Real-time hand gesture detection and recognition using bag-of-features and support vector machine tech-niques.IEEE Transactions on Instrumentation and Measurement,60(11), 3592-3607.
  15. Rehg, J. M., & Kanade, T. (1994, November). Digiteyes: Vision-based hand tracking for human-computer in-teraction. InProceedings of 1994 IEEE workshop on motion of non-rigid and articulated objects(pp. 16-22). IEEE.
  16. Hsieh, C. T., Yeh, C. H., Hung, K. M., Chen, L. M., & Ke, C. Y. (2012, June). A real time hand gesture recognition system based on DFT and SVM. In2012 8th International Conference on Information Science and Digital Content Technology (ICIDT2012)(Vol. 3, pp. 490-494). IEEE.
  17. Tewari, D., & Srivastava, S. K. (2012). A visual recog-nition of static hand gestures in Indian sign language based on Kohonen self-organizing map algo-rithm.International Journal of Engineering and Ad-vanced Technology (IJEAT),2(2), 165-170.
  18. Ramli, S. (2012, July). GMT feature extraction for rep-resentation of BIM sign language. In2012 IEEE Control and System Graduate Research Collo-quium(pp. 43-48). IEEE.
  19. Utsumi, A., & Ohya, J. (1998, June). Image segmenta-tion for human tracking using sequential-image-based hierarchical adaptation. InProceedings. 1998 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. No. 98CB36231)(pp. 911-916). IEEE..
  20. Blake, A., North, B., & Isard, M. (1998). Learning multi-class dynamics.Advances in neural infor-mation processing systems,11.
  21. Yang, M. H., & Ahuja, N. (1998, October). Detecting human faces in color images. InProceedings 1998 International Conference on Image Processing. ICIP98 (Cat. No. 98CB36269)(Vol. 1, pp. 127-130). IEEE.
  22. Sigal, L., Sclaroff, S., & Athitsos, V. (2004). Skin color-based video segmentation under time-varying illu-mination.IEEE Transactions on Pattern Analysis and Machine Intelligence,26(7), 862-877.
  23. Yoon, H. S., Soh, J., Bae, Y. J., & Yang, H. S. (2001). Hand gesture recognition using combined features of location, angle, and velocity.Pattern recogni-tion,34(7), 1491-1501.
  24. Rahman, M. A., Purnama, I. K. E., & Purnomo, M. H. (2014, August). Simple method of human skin detec-tion using HSV and YCbCr color spaces. In2014 In-ternational Conference on Intelligent Autonomous Agents, Networks and Systems(pp. 58-61). IEEE.
  25. Yu, Y., Bi, S., Mo, Y., & Qiu, W. (2016, June). Real-time gesture recognition system based on Camshift algo-rithm and Haar-like feature. In2016 IEEE Interna-tional Conference on cyber technology in Automa-tion, control, and Intelligent Systems (CYBER)(pp. 337-342). IEEE.
  26. Kaur, S., & Nair, N. (2018, January). Electronic Device Control Using Hand Gesture Recognition System for Differently Abled. In2018 8th International Confer-ence on Cloud Computing, Data Science & Engi-neering (Confluence)(pp. 371-375). IEEE.
  27. Mahmood, M. R., Abdulazeez, A. M., & Orman, Z. (2018, October). Dynamic hand gesture recognition system for Kurdish sign language using two lines of features. In2018 International Conference on Ad-vanced Science and Engineering (ICOASE)(pp. 42-47). IEEE.
  28. Heenaye-Mamode Khan, M., Ittoo, N., & Coder, B. K. (2019). Hand Gestures Categorisation and Recogni-tion. InInformation Systems Design and Intelligent Applications: Proceedings of Fifth International Conference INDIA 2018 Volume 2(pp. 295-304). Springer Singapore.
  29. Rautaray, S. S., & Agrawal, A. (2015). Vision based hand gesture recognition for human computer inter-action: a survey.Artificial intelligence review,43, 1-54.
  30. Rehg, J. M., & Kanade, T. (1995, June). Model-based tracking of self-occluding articulated objects. InPro-ceedings of IEEE International Conference on Com-puter Vision(pp. 612-617). IEEE.
  31. Shimada, N., Shirai, Y., Kuno, Y., & Miura, J. (1998, April). Hand gesture estimation and model refine-ment using monocular camera-ambiguity limitation by inequality constraints. InProceedings Third IEEE International Conference on Automatic Face and Gesture Recognition(pp. 268-273). IEEE.
  32. Wu, Y., & Huang, T. S. (1999, September). Capturing articulated human hand motion: A divide-and-con-quer approach. InProceedings of the seventh IEEE International Conference on computer vision(Vol. 1, pp. 606-611). IEEE.
  33. Wu, Y., Lin, J. Y., & Huang, T. S. (2001, July). Capturing natural hand articulation. InProceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001(Vol. 2, pp. 426-432). IEEE.
  34. Lee, J., & Kunii, T. L. (1995). Model-based analysis of hand posture.IEEE Computer Graphics and Appli-cations,15(5), 77-86.
  35. Heap, T., & Hogg, D. (1996, October). Towards 3D hand tracking using a deformable model. InProceedings of the Second International Conference on Auto-matic Face and Gesture Recognition(pp. 140-145). IEEE.
  36. Cutler, R., & Turk, M. (1998, April). View-based inter-pretation of real-time optical flow for gesture recog-nition. InProceedings Third IEEE International Conference on Automatic Face and Gesture Recog-nition(pp. 416-421). IEEE.
  37. Martin, J., Devin, V., & Crowley, J. L. (1998, April). Ac-tive hand tracking. InProceedings Third IEEE Inter-national Conference on Automatic Face and Gesture Recognition(pp. 573-578). IEEE.
  38. Yuan, Q., Sclaroff, S., & Athitsos, V. (2005, January). Automatic 2D hand tracking in video sequences. In2005 Seventh IEEE Workshops on Applications of Computer Vision (WACV/MOTION'05)-Volume 1(Vol. 1, pp. 250-256). IEEE.
  39. Yang, Q. (2010, June). Chinese sign language recogni-tion based on video sequence appearance modeling. In2010 5th IEEE Conference on Industrial Electron-ics and Applications(pp. 1537-1542). IEEE.
  40. Kim, I. C., & Chien, S. I. (2001). Analysis of 3D hand trajectory gestures using stroke-based composite hidden Markov models.Applied Intelligence,15, 131-143.
  41. Chaudhurya, S., Banerjeeb, S., Ramamoorthya, A., & Vaswania, N. (2000). Recognition of dynamic hand gestures.The Journal of The Pattern Recognition Society. Department of Electrical Engineering and Department of Computer Science Engineering, IIT Delhi.
  42. Bhuyan, M. K., Ghoah, D., & Bora, P. K. (2006, Sep-tember). A framework for hand gesture recognition with applications to sign language. In2006 Annual IEEE India Conference(pp. 1-6). IEEE.
  43. Chen, Q., & Zhu, F. (2018, July). Long term hand track-ing with proposal selection. In2018 IEEE Interna-tional Conference on Multimedia & Expo Work-shops (ICMEW)(pp. 1-6). IEEE.
  44. Guo, J. M., Liu, Y. F., Chang, C. H., & Nguyen, H. S. (2011). Improved hand tracking system.IEEE Transactions on Circuits and Systems for Video Technology,22(5), 693-701.
  45. Koh, E., Won, J., & Bae, C. (2009, May). On-premise skin color modeling method for vision-based hand tracking. In2009 IEEE 13th International Sympo-sium on consumer electronics(pp. 908-909). IEEE.
  46. Comaniciu, D., Ramesh, V., & Meer, P. (2003). Kernel-based object tracking.IEEE Transactions on pattern analysis and machine intelligence,25(5), 564-577.
  47. Jepson, A. D., Fleet, D. J., & El-Maraghi, T. F. (2003). Robust online appearance models for visual track-ing.IEEE transactions on pattern analysis and ma-chine intelligence,25(10), 1296-1311.
  48. Zhou, S. K., Chellappa, R., & Moghaddam, B. (2004). Visual tracking and recognition using appearance-adaptive models in particle filters.IEEE Transac-tions on Image Processing,13(11), 1491-1506.
  49. McKenna, S. J., Raja, Y., & Gong, S. (1999). Tracking colour objects using adaptive mixture models.Im-age and vision computing,17(3-4), 225-231.
  50. Binh, N. D., Shuichi, E., & Ejima, T. (2005). Real-time hand tracking and gesture recognition system.Proc. GVIP, 19-21.
  51. Imagawa, K., Lu, S., & Igi, S. (1998, April). Color-based hands tracking system for sign language recognition. InProceedings Third IEEE International Conference on Automatic Face and Gesture Recognition(pp. 462-467). IEEE.
  52. Isard, M., & Blake, A. (1998). CONDENSATION--conditional density propagation for visual track-ing.International journal of computer vision,29(1), 5.
  53. Shan, C., Tan, T., & Wei, Y. (2007). Real-time hand tracking using a mean shift embedded particle fil-ter.Pattern recognition,40(7), 1958-1970.
  54. Weng, S. K., Kuo, C. M., & Tu, S. K. (2006). Video ob-ject tracking using adaptive Kalman filter.Journal of Visual Communication and Image Representa-tion,17(6), 1190-1208.
  55. Zhang, Q. Y., Zhang, M. Y., & Hu, J. Q. (2009). Hand Gesture Contour Tracking Based on Skin Color Probability and State Estimation Model.Journal of Multimedia,4(6).
  56. Zheng, W., & Bhandarkar, S. M. (2009). Face detection and tracking using a boosted adaptive particle fil-ter.Journal of Visual Communication and Image Representation,20(1), 9-27.
  57. Utsumi, A., & Ohya, J. (1999, June). Multiple-hand-ges-ture tracking using multiple cameras. InProceedings. 1999 IEEE Computer Society Conference on Com-puter Vision and Pattern Recognition (Cat. No PR00149)(Vol. 1, pp. 473-478). IEEE.
  58. Peterfreund, N. (1999). Robust tracking of position and velocity with Kalman snakes.IEEE transactions on pattern analysis and machine intelligence,21(6), 564-569.
  59. Mohd Asaari, M. S., Rosdi, B. A., & Suandi, S. A. (2015). Adaptive Kalman Filter Incorporated Eigen-hand (AKFIE) for real-time hand tracking sys-tem.Multimedia Tools and Applications,74, 9231-9257.
  60. Isard, M., & Blake, A. (1998, January). A mixed-state condensation tracker with automatic model-switch-ing. InSixth International Conference on Computer Vision (IEEE Cat. No. 98CH36271)(pp. 107-112). IEEE.
  61. Mammen, J. P., Chaudhuri, S., & Agrawal, T. (2001, September). Simultaneous Tracking of Both Hands by Estimation of Erroneous Observations. InBMVC(pp. 1-10).
  62. Pérez, P., Hue, C., Vermaak, J., & Gangnet, M. (2002). Color-based probabilistic tracking. InComputer Vi-sion—ECCV 2002: 7th European Conference on Computer Vision Copenhagen, Denmark, May 28–31, 2002 Proceedings, Part I 7(pp. 661-675). Springer Berlin Heidelberg.
  63. Bhuyan, M. K., Ghosh, D., & Bora, P. K. (2006, June). Feature extraction from 2D gesture trajectory in dy-namic hand gesture recognition. In2006 IEEE Con-ference on Cybernetics and Intelligent Systems(pp. 1-6). IEEE.
  64. Nadgeri, S. M., Sawarkar, S. D., & Gawande, A. D. (2010, November). Hand gesture recognition using CAMSHIFT algorithm. In2010 3rd International Conference on Emerging Trends in Engineering and Technology(pp. 37-41). IEEE.
  65. Bradski, G. R. (1998, October). Real time face and ob-ject tracking as a component of a perceptual user in-terface. InProceedings Fourth IEEE Workshop on Applications of Computer Vision. WACV'98 (Cat. No. 98EX201)(pp. 214-219). IEEE.
  66. Bradski, G., & Kaehler, A. (2008).Learning OpenCV: Computer vision with the OpenCV library. " O'Reilly Media, Inc.".
  67. Wang, X., & Li, X. (2010, December). The study of MovingTarget tracking based on Kalman-CamShift in the video. InThe 2nd International Conference on Information Science and Engineering(pp. 1-4). IEEE.
  68. Huang, S., & Hong, J. (2011, April). Moving object tracking system based on camshift and Kalman filter. In2011 International Conference on Consumer Electronics, Communications and Networks (CECNet)(pp. 1423-1426). IEEE.
  69. Shi, J. (1994, June). Good features to track. In1994 Pro-ceedings of IEEE conference on computer vision and pattern recognition(pp. 593-600). IEEE.
  70. Kolsch, M., & Turk, M. (2004, June). Fast 2D hand tracking with flocks of features and multi-cue inte-gration. In2004 Conference on Computer Vision and Pattern Recognition Workshop(pp. 158-158). IEEE.
  71. Porikli, F., Tuzel, O., & Meer, P. (2006, June). Covari-ance tracking using model update based on means on Riemannian manifolds. InProc. IEEE Conf. on Computer Vision and Pattern Recognition(Vol. 1, pp. 728-735).
  72. Tuzel, O., Porikli, F., & Meer, P. (2006). Region covari-ance: A fast descriptor for detection and classifica-tion. InComputer Vision–ECCV 2006: 9th Euro-pean Conference on Computer Vision, Graz, Austria, May 7-13, 2006. Proceedings, Part II 9(pp. 589-600). Springer Berlin Heidelberg.
  73. Black, M. J., & Jepson, A. D. (1998). Eigentracking: Ro-bust matching and tracking of articulated objects us-ing a view-based representation.International Jour-nal of Computer Vision,26, 63-84.
  74. Xiu, C., Su, X., & Pan, X. (2018, June). Improved target tracking algorithm based on Camshift. In2018 Chi-nese Control and decision conference (CCDC)(pp. 4449-4454). IEEE.
  75. Elmezain, M., Al-Hamadi, A., Appenrodt, J., & Michae-lis, B. (2008, December). A hidden Markov model-based continuous gesture recognition system for hand motion trajectory. In2008 19th International Conference on pattern recognition(pp. 1-4). IEEE.
  76. Kao, C. Y., & Fahn, C. S. (2011). A human-machine in-teraction technique: hand gesture recognition based on hidden Markov models with trajectory of hand motion.Procedia Engineering,15, 3739-3743.
  77. Xu, D., Wu, X., Chen, Y. L., & Xu, Y. (2015). Online dynamic gesture recognition for human robot inter-action.Journal of Intelligent & Robotic Sys-tems,77(3-4), 583-596.
  78. Elmezain, M., Al-Hamadi, A., & Michaelis, B. (2009). Hand gesture recognition based on combined fea-tures extraction.International Journal of Electrical and Computer Engineering,3(12), 2389-2394.
  79. Bhuyan, M. K., Bora, P. K., & Ghosh, D. (2008). Tra-jectory guided recognition of hand gestures having only global motions.World Academy of science, en-gineering, and technology,21, 753-764.
  80. Bhuyan, M. K., Ajay Kumar, D., MacDorman, K. F., & Iwahori, Y. (2014). A novel set of features for con-tinuous hand gesture recognition.Journal on Multi-modal User Interfaces,8, 333-343.
  81. Li, K., Zhou, Z., & Lee, C. H. (2016). Sign transition modeling and a scalable solution to continuous sign language recognition for real-world applica-tions.ACM Transactions on Accessible Computing (TACCESS),8(2), 1-23.
  82. Rubine, D. (1991). Specifying gestures by exam-ple.ACM SIGGRAPH computer graphics,25(4), 329-337.
  83. Signer, B., Norrie, M. C., & Kurmann, U. (2011). iGes-ture: A Java framework for the development and de-ployment of stoke-based online Gesture recognition algorithms.Technical Report/ETH Zurich, Depart-ment of Computer Science,561.
  84. Premaratne, P., Yang, S., Vial, P., & Ifthikar, Z. (2017). Centroid tracking based dynamic hand gesture recognition using discrete Hidden Markov Mod-els.Neurocomputing,228, 79-83.
  85. Cen, M., & Jung, C. (2017). Complex form of local ori-entation plane for visual object tracking.IEEE Ac-cess,5, 21597-21604.
  86. Tang, J., Cheng, H., Zhao, Y., & Guo, H. (2018). Struc-tured dynamic time warping for continuous hand tra-jectory gesture recognition.Pattern Recognition,80, 21-31.
  87. Singla, A., Roy, P. P., & Dogra, D. P. (2019). Visual ren-dering of shapes on 2D display devices guided by hand gestures.Displays,57, 18-33.
  88. Misra, S., & Laskar, R. H. (2019). Development of a hi-erarchical dynamic keyboard character recognition system using trajectory features and scale-invariant holistic modeling of characters.Journal of Ambient Intelligence and Humanized Computing,10(12), 4901-4923.
  89. Rabiner, L., & Juang, B. (1986). An introduction to hid-den Markov models.IEEE assp magazine,3(1), 4-16.
  90. Charniak, E. (1993). Statistical language learning MIT Press.Google Scholar Google Scholar Digital Li-brary Digital Library.
  91. Chen, F. S., Fu, C. M., & Huang, C. L. (2003). Hand gesture recognition using a real-time tracking method and hidden Markov models.Image and vi-sion computing,21(8), 745-758.
  92. Lee, H. K., & Kim, J. H. (1999). An HMM-based thresh-old model approach for gesture recognition.IEEE Transactions on pattern analysis and machine intel-ligence,21(10), 961-973.
  93. Marcel, S., Bernier, O., Viallet, J. E., & Collobert, D. (2000, March). Hand gesture recognition using in-put-output hidden Markov models. Inproceedings fourth IEEE International Conference on automatic face and gesture recognition (Cat. No. PR00580)(pp. 456-461). IEEE.
  94. Just, A., & Marcel, S. (2009). A comparative study of two state-of-the-art sequence processing techniques for hand gesture recognition.Computer Vision and Image Understanding,113(4), 532-543.
  95. Yang, R., & Sarkar, S. (2006, August). Detecting coar-ticulation in sign language using conditional random fields. In18th International conference on pattern recognition (ICPR'06)(Vol. 2, pp. 108-112). IEEE.
  96. Beh, J., Han, D., & Ko, H. (2014). Rule-based trajectory segmentation for modeling hand motion trajec-tory.Pattern Recognition,47(4), 1586-1601.
  97. Siddiqi, S. M., Gordon, G. J., & Moore, A. W. (2007, March). Fast state discovery for HMM model selection and learning. InArtificial Intelligence and Statistics(pp. 492-499). PMLR.
  98. Ulas, A., & Yildiz, O. T. (2009, December). An incre-mental model selection algorithm based on cross-validation for finding the architecture of a hidden Markov model on hand gesture data sets. In2009 In-ternational Conference on Machine Learning and Applications(pp. 170-177). IEEE.
  99. Bishop, C. M. (1995).Neural networks for pattern recognition. Oxford University Press.
  100. Haykin, S. (2009).Neural networks and learning ma-chines, 3/E. Pearson Education India.
  101. Bamwenda, J., & Özerdem, M. S. (2019). Static hand gesture recognition system using artificial neural networks and support vector machine.Dicle Üniver-sitesi Mühendislik Fakültesi Mühendislik Dergisi,10(2), 561-568.
  102. Yang, M. H., Ahuja, N., & Tabb, M. (2002). Extraction of 2D motion trajectories and its application to hand gesture recognition.IEEE Transactions on pattern analysis and machine intelligence,24(8), 1061-1074.
  103. Yang, M. H., & Ahuja, N. (1998, June). Extraction and classification of visual motion patterns for hand ges-ture recognition. InProceedings. 1998 IEEE Com-puter Society Conference on Computer Vision and Pattern Recognition (Cat. No. 98CB36231)(pp. 892-897). IEEE.
  104. Hsu, C. W., & Lin, C. J. (2002). A comparison of meth-ods for multiclass support vector machines.IEEE Transactions on Neural Networks,13(2), 415-425.
  105. Suykens, J. A., & Vandewalle, J. (1999). Least squares support vector machine classifiers.Neural pro-cessing letters,9, 293-300.
  106. Gopalan, R., & Dariush, B. (2009, October). Toward a vision based hand gesture interface for robotic grasp-ing. In2009 IEEE/RSJ International Conference on Intelligent Robots and Systems(pp. 1452-1459). IEEE.
  107. Dominio, F., Donadeo, M., & Zanuttigh, P. (2014). Combining multiple depth-based descriptors for hand gesture recognition.Pattern Recognition Let-ters,50, 101-111.
  108. Thirumuruganathan, S. (2010). A detailed introduction to K-nearest neighbor (k-NN) algorithm.Retrieved March,20, 2012.
  109. Ge, S. S., Yang, Y., & Lee, T. H. (2008). Hand gesture recognition and tracking based on distributed locally linear embedding.Image and Vision Compu-ting,26(12), 1607-1620.
  110. Oka, K., Sato, Y., & Koike, H. (2002). Real-time finger-tip tracking and gesture recognition.IEEE Computer Graphics and Applications,22(6), 64-71.
  111. Davis, J., & Shah, M. (1994). Recognizing hand ges-tures. InComputer Vision—ECCV'94: Third Euro-pean Conference on Computer Vision Stockholm, Sweden, May 2–6, 1994 Proceedings, Volume I 3(pp. 331-340). Springer Berlin Heidelberg.
  112. Yeasin, M., & Chaudhuri, S. (2000). Visual understand-ing of dynamic hand gestures.Pattern Recogni-tion,33(11), 1805-1817.
  113. Hong, P., Turk, M., & Huang, T. S. (2000, March). Ges-ture modeling and recognition using finite state ma-chines. InProceedings Fourth IEEE International Conference on Automatic Face and Gesture Recog-nition (Cat. No. PR00580)(pp. 410-415). IEEE.
  114. Suk, H. I., Sin, B. K., & Lee, S. W. (2010). Hand gesture recognition based on dynamic Bayesian network framework.Pattern recognition,43(9), 3059-3072.
  115. Thai, L. H., Hai, T. S., & Thuy, N. T. (2012). Image clas-sification using support vector machine and artificial neural network.International Journal of Information Technology and Computer Science,4(5), 32-38.
  116. Kang, S., & Park, S. (2009). A fusion neural network classifier for image classification.Pattern Recogni-tion Letters,30(9), 789-793.
  117. Dinh, T. B., Dang, V. B., Duong, D. A., Nguyen, T. T., & Le, D. D. (2006, February). Hand gesture classifi-cation using boosted cascade of classifiers. In2006 International Conference on Research, Innovation, and Vision for the Future(pp. 139-144). IEEE.
  118. Burger, T., Aran, O., Urankar, A., Caplier, A., & Akarun, L. (2008). A Dempster-Shafer theory based combi-nation of classifiers for hand gesture recognition. InComputer Vision and Computer Graphics. Theory and Applications: International Conference VISIGRAPP 2007, Barcelona, Spain, March 8-11, 2007. Revised Selected Papers(pp. 137-150). Springer Berlin Heidelberg.
  119. Ng, C. W., & Ranganath, S. (2002). Real-time gesture recognition system and application.Image and Vi-sion Computing,20(13-14), 993-1007.
  120. Wang, G. W., Zhang, C., & Zhuang, J. (2012). An appli-cation of classifier combination methods in hand gesture recognition.Mathematical Problems in En-gineering,2012.
  121. Corradini, A. (2002, May). Real-time gesture recogni-tion by means of hybrid recognizers. InGesture and Sign Language in Human-Computer Interaction: In-ternational Gesture Workshop, GW 2001 London, UK, April 18–20, 2001 Revised Papers(pp. 34-47). Berlin, Heidelberg: Springer Berlin Heidelberg.
  122. Sridevi, P., Islam, T., Debnath, U., Nazia, N. A., Chakraborty, R., & Shahnaz, C. (2018, December). Sign Language recognition for Speech and Hearing Impaired by Image processing in Matlab. In2018 IEEE Region 10 Humanitarian Technology Confer-ence (R10-HTC)(pp. 1-4). IEEE.
  123. Singha, J., Misra, S., & Laskar, R. H. (2016). Effect of variation in gesticulation pattern in dynamic hand gesture recognition system.Neurocomputing,208, 269-280.
  124. Mohammed, A. A., Minhas, R., Wu, Q. J., & Sid-Ahmed, M. A. (2011). Human face recognition based on mul-tidimensional PCA and extreme learning ma-chines.Pattern recognition,44(10-11), 2588-2597.
  125. Liu, H., Yu, L., Wang, W., & Sun, F. (2016). Extreme learning machine for time sequence classifica-tion.Neurocomputing,174, 322-330.
  126. Chen, X., & Koskela, M. (2015). Skeleton-based action recognition with extreme learning machines.Neuro-computing,149, 387-396.
  127. Yang, W., Liu, Y., Zhang, Q., & Zheng, Y. (2019). Com-parative object similarity learning-based robust vis-ual tracking.IEEE Access,7, 50466-50475.
  128. Wu, X. Y. (2020). A hand gesture recognition algorithm based on DC-CNN.Multimedia Tools and Applica-tions,79(13-14), 9193-9205.
  129. Singha, J., & Laskar, R. H. (2016). Recognition of global hand gestures using self co-articulation infor-mation and classifier fusion.Journal on Multimodal User Interfaces,10(1), 77-93.
  130. Benitez-Garcia, G., Prudente-Tixteco, L., Castro-Ma-drid, L. C., Toscano-Medina, R., Olivares-Mercado, J., Sanchez-Perez, G., & Villalba, L. J. G. (2021). Improving real-time hand gesture recognition with semantic segmentation.Sensors,21(2), 356.
  131. Shanthakumar, V. A., Peng, C., Hansberger, J., Cao, L., Meacham, S., & Blakely, V. (2020). Design and eval-uation of a hand gesture recognition approach for real-time interactions.Multimedia Tools and Appli-cations,79, 17707-17730.
  132. Yadav, K. S., Misra, S., Khan, T., Bhuyan, M. K., & Las-kar, R. H. (2020). Segregation of meaningful strokes, a pre‐requisite for self co‐articulation removal in iso-lated dynamic gestures.IET Image Pro-cessing,15(5), 1166-1178.
  133. Choudhury, A., Talukdar, A. K., Sarma, K. K., & Bhuyan, M. K. (2021). An adaptive thresholding-based movement epenthesis detection technique us-ing hybrid feature set for continuous fingerspelling recognition.SN Computer Science,2, 1-21.
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International Journal of Systematic Innovation, Electronic ISSN: 2077-8767 Print ISSN: 2077-7973, Published by AccScience Publishing
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