PERTANIKA JOURNAL OF SOCIAL SCIENCES AND HUMANITIES

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• Ahmad, A., Saraswat, D., Aggarwal, V., Etienne, A., & Hancock, B. (2021). Performance of deep learning models for classifying and detecting common weeds in corn and soybean production systems. Computers and Electronics in Agriculture, 184, Article 106081. https://doi.org/10.1016/j.compag.2021.106081

• Alfaras, M., Soriano, M. C., & Ortín, S. (2019). A fast machine learning model for ECG-based heartbeat classification and arrhythmia detection. Frontiers in Physics, 7, Article 103. https://doi.org/10.3389/fphy.2019.00103

• Al-Timemy, A. H., Khushaba, R. N., Mosa, Z. M., & Escudero, J. (2021). An efficient mixture of deep and machine learning models for covid-19 and tuberculosis detection using x-ray images in resource-limited settings. In D. Oliva, S. A. Hassan & A. Mohamed (Eds.) Artificial Intelligence for COVID-19 (Vol. 358, pp. 77-100). Springer. https://doi.org/10.1007/978-3-030-69744-0_6

• Aravind, K. R., & Raja, P. (2020). Automated disease classification in (Selected) agricultural crops using transfer learning. Automatika, 61(2), 260-272. https://doi.org/10.1080/00051144.2020.1728911

• Asad, M. H., & Bais, A. (2020). Weed detection in canola fields using maximum likelihood classification and deep convolutional neural network. Information Processing in Agriculture, 7(4), 535-545. https://doi.org/10.1016/j.inpa.2019.12.002

• Badage, A. (2018). Crop disease detection using machine learning: Indian agriculture. International Research Journal of Engineering and Technology (IRJET), 5(9), 866-869.

• Chandra, S., Gourisaria, M. K., GM, H., Konar, D., Gao, X., Wang, T., & Xu, M. (2022). Prolificacy assessment of spermatozoan via state-of-the-art deep learning frameworks. IEEE Access, 10, 13715-13727. https://10.1109/ACCESS.2022.3146334

• Chen, L., & Yuan, Y. (2018). Agricultural disease image dataset for disease identification based on machine learning. In J. Li, X. Meng, Y. Zhang, W. Cui & Du, Z. (Eds.), International Conference on Big Scientific Data Management (Vol. 11473, pp. 263-274). Springer. https://doi.org/10.1007/978-3-030-28061-1_26

• Ferreira, A. D. S., Freitas, D. M., da Silva, G. G., Pistori, H., & Folhes, M. T. (2017). Weed detection in soybean crops using ConvNets. Computers and Electronics in Agriculture, 143, 314-324. https://doi.org/10.1016/j.compag.2017.10.027

• Etienne, A., & Saraswat, D. (2019). Machine learning approaches to automate weed detection by UAV-based sensors. In Autonomous Air and Ground Sensing Systems for Agricultural Optimization and Phenotyping IV, International Society for Optics and Photonics (Vol. 11008, Article 110080R). SPIE Digital Library. https://doi.org/10.1117/12.2520536

• Gourisaria, M. K., Harshvardhan, G. M., Agrawal, R., Patra, S. S., Rautaray, S. S., & Pandey, M. (2021). Arrhythmia detection using deep belief network extracted features from ECG signals. International Journal of E-Health and Medical Communications (IJEHMC), 12(6), 1-24. https://doi.org/10.4018/ijehmc.20211101.oa9

• Harshvardhan, G. M., Sahu, A., Gourisaria, M. K., Singh, P. K., Hong, W. C., Singh, V., & Balabantaray, B. K. (2022). On the dynamics and feasibility of transferred inference for diagnosis of invasive ductal carcinoma: A perspective. IEEE Access, 10, 30870-30889. https://doi.org/10.1109/ACCESS.2022.3159700

• Khalajzadeh, H., Mansouri, M., & Teshnehlab, M. (2014). Face recognition using a convolutional neural network and simple logistic classifier. In V. Snášel, P. Krömer, M. Köppen & G. Schaefer (Eds.), Soft Computing in Industrial Applications (Vol. 223, pp. 197-207). Springer. https://doi.org/10.1007/978-3-319-00930-8_18

• Rajagopalan, N., Narasimhan, V., Vinjimoor, S. K., & Aiyer, J. (2021). Retracted article: Deep CNN framework for retinal disease diagnosis using optical coherence tomography images. Journal of Ambient Intelligence and Humanized Computing, 12, 7569-7580. https://doi.org/10.1007/s12652-020-02460-7

• Sannigrahi, A., Singh, V., Gourisaria, M. K., & Srivastava, R. (2021). Diagnosis of skin cancer using feature engineering techniques. In 2021 3rd International Conference on Advances in Computing, Communication Control and Networking (ICAC3N) (pp. 405-411). IEEE Publishing. https://doi.org/10.1109/ICAC3N53548.2021.9725420

• Sarah, S., Singh, V., Gourisaria, M. K., & Singh, P. K. (2021). Retinal disease detection using CNN through optical coherence tomography images. In 2021 5th International Conference on Information Systems and Computer Networks (ISCON) (pp. 1-7). IEEE Publishing. https://doi.org/10.1109/ISCON52037.2021.9702480

• Singh, V., Gourisaria, M. K., GM, H., Rautaray, S. S., Pandey, M., Sahni, M., Leon-Castro, & Espinoza-Audelo, L. F. (2022a). Diagnosis of intracranial tumors via the selective CNN data modeling technique. Applied Sciences, 12(6), Article 2900. https://doi.org/10.3390/app12062900

• Singh, V., Gourisaria, M. K., GM, H., & Singh, V. (2022b). Mycobacterium tuberculosis detection using CNN ranking approach. In T. K. Gandhi, D. Konar, B. Sen & Sharma, K. (Eds.), Advanced Computational Paradigms and Hybrid Intelligent Computing, (Vol. 1373, pp. 583-596). Springer. https://doi.org/10.1007/978-981-16-4369-9_56

• Soystats. (2020). International: World soybean production. The American Soybean Association. http://soystats.com/international-world-soybean-production/

• Tang, J., Wang, D., Zhang, Z., He, L., Xin, J., & Xu, Y., (2017). Weed identification based on K-means feature learning combined with the convolutional neural network. Computers and Electronics in Agriculture, 135, 63-70. https://doi.org/10.1016/j.compag.2017.01.001

• Sivakumar, A. N. V., Li, J., Scott, S., Psota, E., Jhala, A. J., Luck, J. D., & Shi, Y. (2020). Comparison of object detection and patch-based classification deep learning models on mid-to late-season weed detection in UAV imagery. Remote Sensing, 12(13), Article 2136. https://doi.org/10.3390/rs12132136

• Yu, J., Sharpe, S. M., Schumann, A. W., & Boyd, N. S. (2019). Deep learning for image-based weed detection in turfgrass. European Journal of Agronomy, 104, 78-84. https://doi.org/10.1016/j.eja.2019.01.004