![Open Access](https://i-scholar.in/lib/pkp/templates/images/icons/fulltextgreen.png)
![Restricted Access](https://i-scholar.in/lib/pkp/templates/images/icons/fulltextred.png)
Arabic Dataset for Farmer's Intent Identification toward Developing a Chatbot
A chatbot is an application of artificial intelligence in natural language processing and speech recognition. It is a computer program that imitates humans in making conversations with other people. Chatbots that specialize in a single topic, such as agriculture, are known as domain-specific chatbots. In this paper, we present a dataset for farmer intents. Intent identification is the first step in building a chatbot. The dataset includes five intents (pest or disease identification, irrigation, fertilization, weed identification, and plantation date). The length of the dataset is 720 records. We applied a Multi-Layers Perceptron (MLP) for intent classification. We tried different numbers of neurons per hidden layer and compared between increasing the number of neurons with the fixed number of epochs. The result shows that as the number of neurons in the hidden layers increases, the introduced MLP achieves high accuracy in a small number of epochs. MLP achieves 97% accuracy on the introduced dataset when the number of neurons in each hidden layer is 256 and the number of epochs is 10.
Keywords
Chatbot, Intent Classification, Dataset, Deep Neural Networks.
User
Font Size
Information
- A.Eleni, and L. Moussiades, (2020) "An overview of chatbot technology." In IFIP International Conference on Artificial Intelligence Applications and Innovations, pp. 373-383. Springer, Cham.
- L. Weibo et al, (2017) “A survey of deep neural network architectures and their applications", Neurocomputing, vol. 234, pp 11-26.
- K. Andreas, and F. X. Prenafeta-Boldú, (2018) “Deep learning in agriculture: A survey”, Computers and Electronics in Agriculture, vol. 147, pp. 70-90.
- P.B. Brandtzaeg, and A. Følstad, (2017) “Why people use chatbots”. " In International conference on internet science, pp. 377-392. Springer, Cham.
- K.M. Colby, D.H. Franklin, S. Weber, and H. Kraemer, (1972) “Turing-like insdistinguishability tests for the validation of a computer simulation of paranoid processes”, Artificial Intelligence, vol.3, pp. 199-221.
- E.Adamopoulou and L. Moussiades, ( 2020) "Chatbots: History, technology, and applications", Machine Learning with Applications, Volume 2.
- T. Zemčík, (2019) “A brief history of chatbots. DEStech Transactions on Computer Science and Engineering”, http://dx.doi.org/10.12783/dtcse/aicae2019/31439.
- M. D. G. B. Marietto et al, "Artificial intelligence markup language: a brief tutorial", 2013, arXiv preprint arXiv:1307.3091.
- G. Molnár and Z. Szüts, (2018) "The Role of Chatbots in Formal Education," 2018 IEEE 16th International Symposium on Intelligent Systems and Informatics (SISY), pp. 000197-000202, doi: 10.1109/SISY.2018.8524609.
- Soffar, H., (2019). Apple siri features, use, advantages, disadvantages & using of siri for learning. Science online website: https://www.online-sciences.com/technology/apple-siri-features-use-advantages-disadvantages-using-of-siri-for-learning/. (Retrieved 20 June 2022).
- Watson assistant | ibm cloud. (2020). https://www.ibm.com/cloud/watson-assistant/. (Retrieved 29 February 2020).
- Google now. (2020). https://www.google.co.uk/landing/now/. (Retrieved 29 February 2020).
- Google assistant, your own personal google. (2019). Assistant website: https://assistant. google.com/. (Retrieved 30 August 2019).
- R. Kar, and R. Haldar, (2016) “Applying chatbots to the internet of things: Opportunities and architectural elements”, arXiv preprint arXiv:1611.03799.
- M. Hijjawi, and H. Abusaimeh, (August, 2015) “APPLYING AN ARABIC CONVERSATIONAL AGENT IN THE JORDANIAN E-GOVERNMENT”, International Journal of Computer Science & Information Technology (IJCSIT) Vol. 7, No. 4, pp. 1-10.
- S. Suman and J. Kumar, (2022 ) “Interactive Agricultural Chatbot Based on Deep Learning”, In: Hemanth, D.J., Pelusi, D., Vuppalapati, C. (eds) Intelligent Data Communication Technologies and Internet of Things. Lecture Notes on Data Engineering and Communications Technologies, vol 101. Springer, Singapore. https://doi.org/10.1007/978-981-16-7610-9_70
- L. Kannagi, et al, (2018) "Virtual conversational assistant:„The FARMBOT‟." International journal of engineering technology science and research vol. 5, no. 3, pp. 520-527.
- G.M. Mostaco, I.R.C. De Souza, L.B. Campos, and C.E. Cugnasca, (June 2018) “AgronomoBot: a smart answering Chatbot applied to agricultural sensor networks”, In 14th international conference on precision agriculture, vol. 24, pp. 1-13.
- N. Chandolikar, C. Dale, T. Koli, M. Singh and T. Narkhede, (2022) "Agriculture Assistant Chatbot Using Artificial Neural Network," 2022 International Conference on Advanced Computing Technologies and Applications (ICACTA), 2022, pp. 1-5, doi: 10.1109/ICACTA54488.2022.9753433.
- V. Nayak, P. R Nayak N, Sampoorna, Aishwarya, N.H. Sowmya, (2021) ”Agroxpert - Farmer assistant”,Global Transitions Proceedings,vol. 2, Issue 2, pp. 506-512,
- U. Kiruthika, S. K. S. Raja, V. Balaji and C. J. Raman, (2020) "E-Agriculture for Direct Marketing of Food Crops Using Chatbots," International Conference on Power, Energy, Control and Transmission Systems (ICPECTS), 2020, pp. 1-4, doi: 10.1109/ICPECTS49113.2020.9337024.
- H. Liu, Y. Liu, L. Wong, and LK. Lee, T. Hao, (2020) "A Hybrid Neural Network BERT-Cap Based on Pre-Trained Language Model and Capsule Network for User Intent Classification", Complexity, vol. 2020, Article ID 8858852, 11 pages, https://doi.org/10.1155/2020/8858852
- S.S. Date, (November 2020) “A comprehensive review on intents, intention mining and intention classification”. International Journal of Science and Research, vol. 9, pp. 16–20.
- T. Nguyen, M. Shcherbakov, “Improvement of Intent Classification Using Diacritic Restoration for Text Message in Chatbot” In: Kravets, A.G., Shcherbakov, M., Parygin, D., Groumpos, P.P. (eds) Creativity in Intelligent Technologies and Data Science. CIT&DS 2021. Communications in Computer and Information Science, vol 1448. Springer, Cham. https://doi.org/10.1007/978-3-030-87034-8_9
- Y. Wang, J. Huang, T. He, and X.Tu, (2020) “Dialogue intent classification with character-CNN-BGRU networks”, Multimedia Tools and Applications, vol. 79(7), pp.4553-4572.
- S. Larson, et al, (2019) “An Evaluation Dataset for Intent Classification and Out-of-Scope Prediction”, arXiv abs/1909.02027.
- A. Elsayed, M. Hazman and S. F. Ellakwa, (2019 ) "Integrating expert system with a full-text search to solve growers' problems," 15th International Computer Engineering Conference (ICENCO), 2019, pp. 192-197, doi: 10.1109/ICENCO48310.2019.9027311.
- K. Abainia, S. Ouamour and H. Sayoud, (2017) “A Novel Robust Arabic Light Stemmer”, Journal of Experimental & Theoretical Artificial Intelligence (JETAI'17),vol. 29, No. 3, pp. 557-573.
- Y. Tang, (2016) "TF. Learn: TensorFlow's high-level module for distributed machine learning." arXiv preprint arXiv:1612.04251.
- E. Khadangi and A. Bagheri, (2013) "Comparing MLP, SVM and KNN for predicting trust between users in Facebook," ICCKE 2013, pp. 466-470, doi: 10.1109/ICCKE.2013.6682864.
- Syam, N. and Kaul, R., (2021) “Machine Learning and Artificial Intelligence in Marketing and Sales: Essential Reference for Practitioners and Data Scientists”. Emerald Group Publishing.
![](https://i-scholar.in/public/site/images/abstractview.png)
Abstract Views: 151
![](https://i-scholar.in/public/site/images/pdfview.png)
PDF Views: 89