ARCHIVES

1. J. M. Alston, ‘‘Reflections on agricultural R&D, productivity, and the data constraint: Unfinished business, unsettled issues,’’ Amer. J.
Agricult. Econ., vol. 100, no. 2, pp. 392–413, Mar. 2018.
2. FAOSTAT FAO. (2018). Food and Agriculture Organization of the United Nations. Rome. [Online]. Available: http://faostat.fao.org.
3. M. F. Neves, ‘‘The food business environment and the role of China and Brazil building a ‘food bridge,’’’ China Agricult. Econ. Rev.,
vol. 2, no. 1, pp. 25–35, Feb. 2010.
4. T. Kasinathan, D. Singaraju, and S. R. Uyyala, ‘‘Insect classification and detection in field crops using modern machine learning
techniques,’’ Inf. Process. Agricult., vol. 8, no. 3, pp. 446–457, Sep. 2021.
5. S. Li, Y. Hong, X. Chen, and X. Liang, ‘‘Present situation and development strategies of peanut production, breeding and seed industry
in Guang-dong,’’ Guangdong Agric. Sci., vol. 47, pp. 78–83, Jan. 2020.
6. M. P. Singh, J. E. Erickson, K. J. Boote, B. L. Tillman, J. W. Jones, and A. H. C. Bruggen, ‘‘Late leaf spot effects on growth,
photosynthesis, and yield in peanut cultivars of differing resistance,’’ Agronomy J., vol. 103, no. 1, pp. 85–91, Jan. 2011.
7. Ravindra Changala, "Brain Tumor Detection and Classification Using Deep Learning Models on MRI Scans", EAI Endorsed
Transactions on Pervasive Health and Technology, Volume 10, March 2024.
8. Ravindra Changala, "Sentiment Analysis in Social Media Using Deep Learning Techniques", International Journal of Intelligent Systems
and Applications in Engineering, 2024, 12(3), 1588–1597.
9. Ravindra Changala, "Integration of IoT and DNN Model to Support the Precision Crop", International Journal of Intelligent Systems
and Applications in Engineering, Vol.12 No.16S (2024).
10. Ravindra Changala, "UI/UX Design for Online Learning Approach by Predictive Student Experience", 7th International Conference on
Electronics, Communication and Aerospace Technology, ICECA 2023 - Proceedings, 2023, pp. 794–799, IEEE Xplore.
11. Ravindra Changala, "Optimization of Irrigation and Herbicides Using Artificial Intelligence in Agriculture", International Journal of
Intelligent Systems and Applications in Engineering, 2023, 11(3), pp. 503–518.
12. H. Qi, B. Zhu, L. Kong, W. Yang, J. Zou, Y. Lan, and L. Zhang, ‘‘Hyper-spectral inversion model of chlorophyll content in peanut leaves,’’
Appl. Sci., vol. 10, no. 7, p. 2259, Mar. 2020.
13. H. Qi, Y. Liang, Q. Ding, and J. Zou, ‘‘Automatic identification of peanut-leaf diseases based on stack ensemble,’’ Appl. Sci., vol. 11,
no. 4, p. 1950, Feb. 2021.
14. C. R. Rahman, P. S. Arko, M. E. Ali, M. A. I. Khan, S. H. Apon, F. Nowrin, and A. Wasif, ‘‘Identification and recognition of Rice diseases
and pests using convolutional neural networks,’’ Biosyst. Eng., vol. 194, pp. 112–120, Jun. 2020.
15. Ravindra Changala, Development of Predictive Model for Medical Domains to Predict Chronic Diseases (Diabetes) Using Machine
Learning Algorithms and Classification Techniques, ARPN Journal of Engineering and Applied Sciences, Volume 14, Issue 6, 2019.
16. Ravindra Changala, “Evaluation and Analysis of Discovered Patterns Using Pattern Classification Methods in Text Mining” in ARPN
Journal of Engineering and Applied Sciences, Volume 13, Issue 11, Pages 3706-3717 with ISSN:1819-6608 in June 2018.
17. Ravindra Changala “A Survey on Development of Pattern Evolving Model for Discovery of Patterns in Text Mining Using Data Mining
Techniques” in Journal of Theoretical and Applied Information Technology, August 2017. Vol.95. No.16, ISSN: 1817-3195, pp.3974-
3987.
18. Y. Lu, S. Yi, N. Zeng, Y. Liu, and Y. Zhang, ‘‘Identification of Rice diseases using deep convolutional neural networks,’’ Neurocomputing, vol. 267, pp. 378–384, Dec. 2017.
19. B. B. Sharma, G. Gupta, P. Vaidya, S. Basheer, F. H. Memon, and R. N. Thakur, ‘‘Internet of Things-based crop classification model
using deep learning for indirect solar drying,’’ Wireless Commun. Mobile Com-put., vol. 2022, pp. 1–11, Jun. 2022.
20. R. M. Rakholia, J. H. Tailor, J. R. Saini, J. Kaur, and H. Pahuja, ‘‘Ground-nuts leaf disease recognition using neural network with progressive resiz-ing,’’ Int. J. Adv. Comput. Sci. Appl., vol. 13, no. 6, pp. 1–6, 2022.
21. Ravindra Changala, Framework for Virtualized Network Functions (VNFs) in Cloud of Things Based on Network Traffic Services,
International Journal on Recent and Innovation Trends in Computing and Communication, ISSN: 2321-8169 Volume 11, Issue 11s,
August 2023.
22. Ravindra Changala, Block Chain and Machine Learning Models to Evaluate Faults in the Smart Manufacturing System, International
Journal of Scientific Research in Science and Technology, Volume 10, Issue 5, ISSN: 2395-6011, Page Number 247-255, SeptemberOctober-2023.
23. M. Konate, J. Sanou, A. Miningou, D. K. Okello, H. Desmae, P. Janila, and R. H. Mumm, ‘‘Past, present and future perspectives on
groundnut breeding in Burkina Faso,’’ Agronomy, vol. 10, no. 5, p. 704, May 2020.
24. A. Tomar, G. Gupta, W. Salehi, C. Vanipriya, N. Kumar, and B. Sharma, ‘‘A review on leaf-based plant disease detection systems using
machine learning,’’ in Proc. Recent Innov. Comput. (ICRIC), vol. 1. Jammu and Kashmir, India: Springer, May 2022, pp. 297–303.
25. Ravindra Changala, Development of CNN Model to Avoid Food Spoiling Level, International Journal of Scientific Research in Computer Science, Engineering and Information Technology, ISSN: 2456-3307, Volume 9, Issue 5, September-October-2023, Page Number 261- 268.
26. Ravindra Changala, A Novel Prediction Model to Analyze Evolutionary Trends and Patterns in Forecasting of Crime Data Using Data
Mining and Big Data Analytics, Mukt Shabd Journal, Volume XI, Issue X, October 2022, ISSN NO: 2347-3150.