Context Mining with Machine Learning Approach: Understanding, Sensing, Categorizing, and Analyzing Context Parameters

Article Sidebar

PDF
Published: May 4, 2023
DOI: https://doi.org/10.17762/ijritcc.v11i4.6453
Keywords:
Context, Context awareness, Taxonomy of Context, Internet of Behaviour, Ubiquitous of Computing, User preference

Main Article Content

Pranali G. Chavhan
Department of Computer Engineering, Vishwakarma Institute of Information Technology, Pune, India
Ritesh V. Patil
Department of Computer Engineering, PDEA's College of Engineering, Manjari (Bk.), Pune, India
Parikshit N. Mahalle
Department of Computer Engineering, Vishwakarma Institute of Information Technology, Pune, India

Abstract

Context is a vital concept in various fields, such as linguistics, psychology, and computer science. It refers to the background, environment, or situation in which an event, action, or idea occurs or exists. Categorization of context involves grouping contexts into different types or classes based on shared characteristics. Physical context, social context, cultural context, temporal context, and cognitive context are a few categories under which context can be divided. Each type of context plays a significant role in shaping our understanding and interpretation of events or actions. Understanding and categorizing context is essential for many applications, such as natural language processing, human-computer interaction, and communication studies, as it provides valuable information for interpretation, prediction, and decision-making.


In this paper, we will provide an overview of the concept of context and its categorization, highlighting the importance of context in various fields and applications. We will discuss each type of context and provide examples of how they are used in different fields. Finally, we will conclude by emphasizing the significance of understanding and categorizing context for interpretation, prediction, and decision-making.

Article Details

How to Cite
Chavhan, P. G., Patil, R. V. ., & Mahalle, P. N. (2023). Context Mining with Machine Learning Approach: Understanding, Sensing, Categorizing, and Analyzing Context Parameters. International Journal on Recent and Innovation Trends in Computing and Communication, 11(4), 278–290. https://doi.org/10.17762/ijritcc.v11i4.6453
Issue
Vol. 11 No. 4 (2023): April (2023) Issue
Section
Articles

References

Abowd, G.D., Dey, A.K., Brown, P.J., Davies, N., Smith, M. and Steggles, P., 1999. Towards a better understanding of context and context-awareness. In Handheld and Ubiquitous Computing: First International Symposium, HUC’99 Karlsruhe, Germany, September 27–29, 1999 Proceedings 1 (pp. 304-307). Springer Berlin Heidelberg.

Zhang, X., Cheng, W., Zong, B., Chen, Y., Xu, J., Li, D. and Chen, H., 2020, January. Temporal context-aware representation learning for question routing. In Proceedings of the 13th international conference on web search and data mining (pp. 753-761).

Sarker, I.H. and Kayes, A.S.M., 2020. ABC-RuleMiner: User behavioral rule-based machine learning method for context-aware intelligent services. Journal of Network and Computer Applications, 168, p.102762.

Weizenbaum, E., Torous, J. and Fulford, D., 2020. Cognition in context: understanding the everyday predictors of cognitive performance in a new era of measurement. JMIR mHealth and uHealth, 8(7), p.e14328.

Venkatachalam, P. and Ray, S., 2022. How do context-aware artificial intelligence algorithms used in fitness recommender systems? A literature review and research agenda. International Journal of Information Management Data Insights, 2(2), p.100139.

Sharif, M. and Alesheikh, A.A., 2018. Context?aware movement analytics: implications, taxonomy, and design framework. Wiley Interdisciplinary Reviews: Data mining and knowledge discovery, 8(1), p.e1233.

Augusto, J.C., 2022. Contexts and context-awareness revisited from an intelligent environments perspective. Applied Artificial Intelligence, 36(1), p.2008644.

Christopoulou, S.C., 2022, April. Impacts on context aware systems in evidence-based health informatics: a review. In Healthcare (Vol. 10, No. 4, p. 685). MDPI.

Ponce, V. and Abdulrazak, B., 2022. Context-aware end-user development review. Applied Sciences, 12(1), p.479.

Ogbuabor, G.O., Augusto, J.C., Moseley, R. and van Wyk, A., 2022. Context-aware system for cardiac condition monitoring and management: a survey. Behaviour & Information Technology, 41(4), pp.759-776.

Ogbuabor, G.O., Augusto, J.C., Moseley, R. and van Wyk, A., 2022. Context-aware system for cardiac condition monitoring and management: a survey. Behaviour & Information Technology, 41(4), pp.759-776.

Wang, C., Tee, M., Roy, A.E., Fardin, M.A., Srichokchatchawan, W., Habib, H.A., Tran, B.X., Hussain, S., Hoang, M.T., Le, X.T. and Ma, W., 2021. The impact of COVID-19 pandemic on physical and mental health of Asians: A study of seven middle-income countries in Asia. PloS one, 16(2), p.e0246824.

Kavitha, D. and Ravikumar, S., 2021. IOT and context?aware learning?based optimal neural network model for real?time health monitoring. Transactions on Emerging Telecommunications Technologies, 32(1), p.e4132.

Li, D., Xu, T., Zhou, P., He, W., Hao, Y., Zheng, Y. and Chen, E., 2021. Social context-aware person search in videos via multi-modal cues. ACM Transactions on Information Systems (TOIS), 40(3), pp.1-25.

Farahbakhsh, F., Shahidinejad, A. and Ghobaei-Arani, M., 2021. Context?aware computation offloading for mobile edge computing. Journal of Ambient Intelligence and Humanized Computing, pp.1-13.

Chavhan, P.G., Chavhan, G.H., Shafi, P.M. and Mahalle, P.N., 2021. Context Awareness Computing System—A Survey. In Proceedings of International Conference on Communication and Artificial Intelligence: ICCAI 2020 (pp. 423-429). Springer Singapore.

Wu, X., Xu, D., Duan, L. and Luo, J., 2011, June. Action recognition using context and appearance distribution features. In CVPR 2011 (pp. 489-496). IEEE.

Miranda, L., Viterbo, J. and Bernardini, F., 2022. A survey on the use of machine learning methods in context-aware middlewares for human activity recognition. Artificial Intelligence Review, pp.1-32.

Carrera-Rivera, A., Larrinaga, F. and Lasa, G., 2022. Context-awareness for the design of Smart-product service systems: Literature review. Computers in Industry, 142, p.103730.

Tuyen, N.T.V. and Celiktutan, O., 2022, March. Context-Aware Human Behaviour Forecasting in Dyadic Interactions. In Understanding Social Behavior in Dyadic and Small Group Interactions (pp. 88-106). PMLR.

Khanpara, P., Lavingia, K., Trivedi, R., Tanwar, S., Verma, A. and Sharma, R., 2023. A context?aware internet of things?driven security scheme for smart homes. Security and Privacy, 6(1), p.e269.

Xue, Q., Gao, K., Xing, Y., Lu, J. and Qu, X., 2023. A context-aware framework for risky driving behavior evaluation based on trajectory data. IEEE Intelligent Transportation Systems Magazine, 15(1).

Kawsar, F., Fujinami, K., Nakajima, T., Park, J.H. and Yeo, S.S., 2010. A portable toolkit for supporting end-user personalization and control in context-aware applications. Multimedia Tools and Applications, 47, pp.409-432.

Coppola, P., Mea, V.D., Di Gaspero, L., Lomuscio, R., Mischis, D., Mizzaro, S., Nazzi, E., Scagnetto, I. and Vassena, L., 2010. AI techniques in a context-aware ubiquitous environment. Pervasive computing: innovations in intelligent multimedia and applications, pp.157-180.

Sarker, I.H., Abushark, Y.B. and Khan, A.I., 2020. Contextpca: Predicting context-aware smartphone apps usage based on machine learning techniques. Symmetry, 12(4), p.499.

Sliwa, B., Liebig, T., Falkenberg, R., Pillmann, J. and Wietfeld, C., 2018, June. Efficient machine-type communication using multi-metric context-awareness for cars used as mobile sensors in upcoming 5G networks. In 2018 IEEE 87th Vehicular Technology Conference (VTC Spring) (pp. 1-6). IEEE.

Zainab, A., S. Refaat, S. and Bouhali, O., 2020. Ensemble-based spam detection in smart home IoT devices time series data using machine learning techniques. Information, 11(7), p.344.

Lee, K.C., Kim, J.H., Lee, J.H. and Lee, K.M., 2007, April. Implementation of ontology based context-awareness framework for ubiquitous environment. In 2007 International Conference on Multimedia and Ubiquitous Engineering (MUE'07) (pp. 278-282). IEEE.

Viswanathan, H., Chen, B. and Pompili, D., 2012. Research challenges in computation, communication, and context awareness for ubiquitous healthcare. IEEE Communications Magazine, 50(5), pp.92-99.

TA?KIN, D. and Yazar, S., 2020. A Long-range context-aware platform design for rural monitoring with IoT In precision agriculture. International Journal of Computers Communications & Control, 15(2).

Arcelus, A., Goubran, R., Sveistrup, H., Bilodeau, M. and Knoefel, F., 2010, April. Context-aware smart home monitoring through pressure measurement sequences. In 2010 IEEE International Workshop on Medical Measurements and Applications (pp. 32-37). IEEE.

Degha, H.E., Laallam, F.Z. and Said, B., 2019. Intelligent context-awareness system for energy efficiency in smart building based on ontology. Sustainable computing: informatics and systems, 21, pp.212-233.

Yurur, O., Liu, C.H. and Moreno, W., 2014. A survey of context-aware middleware designs for human activity recognition. IEEE Communications Magazine, 52(6), pp.24-31.

Rathi, S.R. and Deshpande, Y.D. (2022), "Course complexity in engineering education using E-learner's affective-state prediction", Kybernetes, Vol. ahead-of-print No. ahead-of-print. https://doi.org/10.1108/K-09-2021-0806

S. R. Rathi and Y. D. Deshpande, "Embedding Affect Awareness into Online Learning Environment using Deep Neural Network," 2019 5th International Conference On Computing, Communication, Control And Automation (ICCUBEA), Pune, India, 2019, pp. 1-6, doi: 10.1109/ICCUBEA47591.2019.9128811.

S. R. Rathi and V. K. Kolekar, "Trust Model for Computing Security of Cloud," 2018 Fourth International Conference on Computing Communication Control and Automation (ICCUBEA), Pune, India, 2018, pp. 1-5, doi: 10.1109/ICCUBEA.2018.8697881.

S. Rathi, Y. Deshpande, S. Nagaral, A. Narkhede, R. Sajwani and V. Takalikar, "Analysis of User’s Learning Styles and Academic Emotions through Web Usage Mining," 2021 International Conference on Emerging Smart Computing and Informatics (ESCI), Pune, India, 2021, pp. 159-164, doi: 10.1109/ESCI50559.2021.9397037.

Kalyankar, P.A., Mulani, A.O., Thigale, S.P., Chavhan, P.G. and Jadhav, M.M., 2022. Scalable face image retrieval using AESC technique. Journal Of Algebraic Statistics, 13(3), pp.173-176.

Chavhan, P.G., Chavhan, G.H., Shafi, P.M. and Mahalle, P.N., 2021. Context Awareness Computing System—A Survey. In Proceedings of International Conference on Communication and Artificial Intelligence: ICCAI 2020 (pp. 423-429). Springer Singapore.

Tashildar, A., Shah, N., Gala, R., Giri, T. and Chavhan, P., 2020. Application development using flutter. International Research Journal of Modernization in Engineering Technology and Science, 2(8), pp.1262-1266.

Kayande, S., Darekar, P., Bhajbhuje, Y., Barudwale, M., Chavhan, P.G. and Patil, S.B., 2022. Recommendation Systems for Community Commerce. Mathematical Statistician and Engineering Applications, 71(4), pp.8971-8986.

Chavhan, M.P.G., Rathi, M.S.R., Borawake, M.V., Godse, M.A., Daphal, M.P., Dalavi, M.H. and Nikam, M.D., Systematic literature of Online Beauty Industry Service Management System.