Refine your search
Collections
Journals
Year
A B C D E F G H I J K L M N O P Q R S T U V W X Y Z All
Chakraborty, Sayan
- Metaverse in Education Sector: Current Theories, Research, and Future Directions
Abstract Views :76 |
PDF Views:0
Authors
Affiliations
1 Dept. of CSE, Swami Vivekananda University, Kolkata, West Bengal, IN
2 Dept. of CSE, Ideal Institute of Engineering, Kalyani, West Bengal, IN
3 Sayan Chakraborty Dept. of CSE, Swami Vivekananda University, Kolkata, West Bengal, IN
1 Dept. of CSE, Swami Vivekananda University, Kolkata, West Bengal, IN
2 Dept. of CSE, Ideal Institute of Engineering, Kalyani, West Bengal, IN
3 Sayan Chakraborty Dept. of CSE, Swami Vivekananda University, Kolkata, West Bengal, IN
Source
Journal of Mines, Metals and Fuels, Vol 71, No 5 (2023), Pagination: 640-644Abstract
Education methodologies has taken a complete revamp since the declaration of the Covid-19 pandemic across the globe. The teaching and learning methods became dependent on virtual framework during the Covid-19 Pandemic. Metaverse refers to 3D digital space which bridges the gap between real and virtual world with the help of advanced technologies. The current study highlights its origin, features and possible application in the education sector. Metaverse could be an essential tool in the education sector especially in this era. The current work also provides in-depth discussions of metaverse’s features.Keywords
Metaverse, Extended Reality (XR), AI, IoT, AR.References
- A. Tlili, R. Huang, B. Shehata, et al. (2022): “Is Metaverse in education a blessing or a curse: a combined content and bibliometric analysis”. Smart Learn. Environ. 9, pp. 18-24.
- D. M. Barry, N. Ogawa, A. Dharmawansa, H. Kanematsu, Y. Fukumura, T. Shirai, , K. Yajima and T. Kobayashi, (2015): “Evaluation for students’ learning manner using eye blinking system in Metaverse” Procedia Computer Science, 60, 1195–1204.
- K. Chayka, (2021): “Facebook wants us to live in the Metaverse”, Accessed from: https:// www.newyorker.com/culture/infinite-scroll/facebook-wants-us-to-live-in-the-Metaverse
- C. Collins, (2008): “Looking to the future: Higher education in the Metaverse”, Educause Review, 43(5), 51–63.
- A. Parmaxi, (2020): “Virtual reality in language learning: a systematic review and implications for research and practice”, Interactive Learning Environments, 1–13.
- C. Zhang, S. Feng, R. He, Y. Fang, and S. Zhang, (2022): “Gastroenterology in the Metaverse: the dawn of a new era”, Front. Med. 9:904566.
- A. Davis, J. D. Murphy, D. Owens, D. Khazanchi and I. Zigurs, (2009): “Avatars, people, and virtual worlds: Foundations for research in metaverses”, Journal of the Association for Information Systems, 10(2), 90-96.
- J. D. N. Dionisio, W. G., III. Burns, and R. Gilbert, (2013): “3D virtual worlds and the metaverse: Current status and future possibilities”, ACM Computing Surveys(CSUR), 45(3), 1–38.
- H. Duan, J. Li, S. Fan, Z. Lin, X. Wu, and W. Cai, (2021): “Metaverse for social good: A university campus prototype”, In Proceedings of the 29th ACM International Conference on Multimedia, pp. 153–161.
- S. Farjami, R. Taguchi, K. T. Nakahira, R. Nunez Rattia, Y. Fukumura and H. Kanematsu, (2011): “Multilingual problem based learning in Metaverse”, In International conference on knowledge-based and intelligent information and engineering systems, pp. 499–509, Springer, Berlin, Heidelberg.
- K. Getchell, I. Oliver, A. Miller and C. Allison, “Metaverses as a platform for game based learning” (2010): In 2010 24th IEEE International Conference on Advanced Information Networking and Applications, pp. 1195–1202.
- K. Hirsh-Pasek, J. M. Zosh, H. S. Hadani, R. M. Golinkoff, K. Clark, C. Donohue and E. Wartella, (2022): “A whole new world: Education meets the Metaverse”, The Brookings Institution.
- G. J. Hwang and S. Y. Chien, (2022): “Definition, roles, and potential research issues of the metaverse in education: An artificial intelligence perspective”, Computers and Education: Artificial Intelligence, 3, pp. 101-112.
- H. Kanematsu, T. Kobayashi, N. Ogawa, Y. Fukumura, D. M. Barry and H. Nagai, (2012): “Nuclear energy safety project in Metaverse”, Intelligent interactive multimedia: Systems and services, pp. 411–418.
- J. Kemp, and D. Livingstone, (2006): “Putting a Second Life “Metaverse” skin on learning management systems”, In Proceedings of the Second Life education workshop at the Second Life community convention (Vol.20). CA, San Francisco: The University of Paisley, pp. 19-24.
- S. Ahuja, B.K. Panigrahi, N. Dey, et al. (2021): “Deep transfer learning-based automated detection of COVID-19 from lung CT scan slices”, Appl Intell51, 571–585.
- C. Bhatt, N. Dey, A.S.Ashour, (2017): “Internet of Things and Big Data Technologies for Next Generation Healthcare”, Volume 23, pp. 1-24.
- S. Ahuja, B.K. Panigrahi, N. Dey, V. Rajinikanth, T.K. Gandhi, (2020): “Deep transfer learning-based automated detection of COVID-19 from lung CT scan slices”, Applied Intelligence, pp. 57-63.
- L. H. Lee, T. Braud, P. Zhou, L. Wang, D. Xu, Z. Lin, A. Kumar, C. Bermejo, and P. Hui, “All one needs to know about Metaverse: A complete survey on technological singularity”, virtual ecosystem, and research agenda, 2021, arXiv preprint arXiv:2110.05352.
- N. G. Narin, (2021): “A content analysis of the metaverse articles. Journal of Metaverse”, 1(1), 17–24.
- S. Park, and S. Kim, (2021): “Identifying world types to deliver gameful experiences for sustainable learning in the Metaverse”, Sustainability, 14(3), 1361-1365.
- A., Siyaev, and G. S. Jo, (2021): “Neuro-symbolic speech understanding in aircraft maintenance metaverse” IEEE Access, 9, 154484–154499. https:// doi.org/10.1109/access.2021.3128616.
- A. Siyaev, and G. S. Jo, (2021): “Towards aircraft maintenance Metaverse using speech interactions with virtual objects in mixed reality”, Sensors, 21(6).
- https://www.gartner.com/en/newsroom/press-releases/ 2022-02-07-gartner-predicts-25-per cent-of-people-will-spend-at-least-one-hour-per-day-in-the-metaverse-by-2026 [Last accessed: 28.11.2022]
- Y. Zhao, J. Jiang, Y. Chen, R. Liu, Y. Yang, X. Xue, et al. (2022): “Metaverse: perspectives from graphics, interactions and visualization”. Visual Informat. 6, 56–67
- Predicting Prices of Cash Crop using Machine Learning
Abstract Views :166 |
PDF Views:0
Authors
Affiliations
1 Department of Industrial Engineering and Management, B.M.S College of Engineering, Bengaluru, IN
2 Department of Operations and IT, ICFAI Business School, Hyderabad, IN
1 Department of Industrial Engineering and Management, B.M.S College of Engineering, Bengaluru, IN
2 Department of Operations and IT, ICFAI Business School, Hyderabad, IN
Source
Journal of Mines, Metals and Fuels, Vol 71, No 6 (2023), Pagination: 804-810Abstract
More than half of the Indian population depends on agriculture as a source of livelihood. But, India’s marginal farmers especially, earn meagre amounts from their harvested yields. This may be attributed partly due to relatively smaller land holdings, and partly due to minimal access to resources that aid with informative price forecasts. In order to alleviate the stress caused by the lack of sound financial planning, this research proposes the utilization of machine learning to predict commodity prices. The solution obtained through such a model would assist farmers in predicting the price and associated estimates can be made with respect to yield, sowing patterns and suitable recommendations for sales. The solution developed in this research is a result of a thorough exploration of the literature in this domain, identification of verified secondary sources for data collection, and proposes a methodology to design a machine learning model that predicts prices for seasonal cash crops specific to the markets of Karnataka. Cotton has been used as the crop of focus in this study. ARIMA and Bayesian ridge regression have been used for predictive analytics, and the results obtained indicate a high correlation between the predicted and actual.Keywords
Agriculture, Cash crop, Price prediction, Machine learning, Regression.References
- Agriculture in India: Industry Overview, Market Size, Role in Development... IBEF. (n.d.). India Brand Equity Foundation. Retrieved June 18. 2022.
- Anubha, Tripathi, K., Kumar, K., & Khandelwal, G. (2021): Onion Price Prediction for the Market of Kayamkulam. Data Analytics and Management, 77– 85.
- Basso, B., & Liu, L. (2019): Seasonal crop yield forecast: Methods, applications, and accuracies. Advances in Agronomy, 154, 201–255. 4. Cenas, P. V. (2017): Forecast of Agricultural Crop Price using Time Series and Kalman Filter Method. Asia Pacific Journal of Multidisciplinary Research, 5(4). 2018.
- Chen, Z., Goh, H. S., Sin, K. L., Lim, K., Chung, N. K. H., & Liew, X. Y. (2021): Automated Agriculture Commodity Price Prediction System with Machine Learning Techniques. Advances in Science, Technology and Engineering Systems Journal, 6(4), 376-384.
- Ghutake, I., Verma, R., Chaudhari, R., & Amarsinh, V. (2021): An intelligent Crop Price Prediction using suitable Machine Learning Algorithm. ITM Web of Conferences. https://doi.org/10.1051/itmconf/ 20214003040.
- Israeli Trade and Economic Mission in India. (2020, May 11): Small and Marginal farmers in India – Difficulties and Solutions. India - Israel Trade & Economic Office, Embassy of Israel. Retrieved June 18, 2022, https://itrade.gov.il/india/2016/08/29/smallandmarginal- farmers-in-india-difficulties-andsolutions/
- Jain, A., Marvaniya, S., Godbole, S., & Munigala, V. (2020): A Framework for Crop Price Forecasting in Emerging Economies by Analyzing the Quality of Time-series Data. arXiv. Org. https://doi.org/ 10.48550/arXiv.2009.04171
- K., M., Swamy P.S., D., B.R., J., & N.N., N. (2013): Resource Use Efficiency of Bt Cotton and Non-Bt Cotton in Haveri District of Karnataka. International Journal of Agriculture and Food Science Technology, 4(3), 253–258.
- Kalichkin, V. K., Alsova, O. K., & Yu Maksimovich, K. (2021): Application of the decision tree method for predicting the yield of spring wheat. IOP Conference Series: Earth and Environmental Science, 839(3). https://doi.org/10.1088/1755-1315/839/ 3/032042
- Kanwal, S. (2022, February 14): Agriculture in India - statistics & facts. Statista. Retrieved June 18, 2022, from https://www.statista.com/ topics/4868/ agricultural-sector-inindia/#dossierKeyfigures
- Kumar, G. R. (2017, January 24): Focusing on major problems of marginal farmers. The Hans India. Retrieved June 18, 2022, from https:// www.thehansindia.com/posts/index/Hans/2017-01-23/ Focusing-on-majorproblems-of-marginal-farmers/ 275349?infinitescroll=1
- Ma, W., Nowocin, K., Marathe, N., & Chen, G. H. (2019): An interpretable produce price forecasting system for small and marginal farmers in India using collaborative filtering and adaptive nearest neighbours. Proceedings of the Tenth International Conference on Information and Communication Technologies and Development, 6, 1–11. https:// doi.org/10.1145/3287098.3287100
- Mgale, Y. J., Yan, Y., & Timothy, S. (2021): A Comparative Study of ARIMA and Holt-Winters Exponential Smoothing Models for Rice Price Forecasting in Tanzania. OALib, 08(05), 1–9.
- NITI, Integrated Research and Action for Development. (2007, October): Extension of MSP: Fiscal and Welfare Implications. A Study for the Planning Commission. Retrieved June 18, 2022, from https://www.niti.gov.in/planningcommission.gov.in/ docs/reports/sereport/ser/ser_msp.pdf
- Sabu, K.M. and Kumar, T.M. (2020): Predictive analytics in Agriculture: Forecasting prices of Arecanuts in Kerala. Procedia Computer Science, 171, 699–708.
- Global Hunger Index Scores by 2021 GHI Rank. (n.d.). Global Hunger Index (GHI) – Peer Reviewed Annual Publication Designed to Comprehensively Measure and Track Hunger at the Global, Regional, and Country Levels. Retrieved June 18, 2022
- India at a glance FAO in India Food and Agriculture Organization of the United Nations.
- Tutorial point (n.d). Scikit Learn - Bayesian Ridge Regression. Scikit Learn. https:// www.tutorialspoint.com/scikit_learn/scikit_learn_ bayesian_ridge_regression.htm
- VIT University, Vellore, Tamil Nadu, India. (2020): Crop Value Forecasting using Decision Tree Regressor and Models. European Journal of Molecular & Clinical Medicine, 07(02).
- Vohra, A., Pandey, N., & Khatri, S. (2019): Decision Making Support System for Prediction of Prices in Agricultural Commodity. 2019 Amity International Conference on Artificial Intelligence (AICAI). https:/ /doi.org/10.1109/aicai.2019.8701273
- What is Marginal Farmer, IGI Global. (n.d.). IGI Global. Retrieved June 18, 2022, from https://www.igiglobal. com/dictionary/marginal-farmer/68592
- Scikit learn - Bayesian Ridge regression. Tutorials Point. (n.d.). Retrieved June 30, 2022, https:// www.tutorialspoint.com/scikit_learn/scikit_learn_ bayesian_ridge_regression.htm
- Lasso vs Ridge vs elastic net: ML. GeeksforGeeks. (2022, February 11). Retrieved June 30, 2022, from https://www.geeksforgeeks.org/lasso-vs-ridge-vselastic- net-ml/
- Wikipedia contributors. (2006, November 22). Kharif crop. Wikipedia. https://en.wikipedia.org/wiki/ Kharif_crop 30, 2022, from https://www. geeksforgeeks.org/lasso-vs-ridge-vs-elastic-net-ml/
- Wikipedia contributors. (2006, November 22). Kharif crop. Wikipedia. https://en.wikipedia.org/wiki/ Kharif_crop