KrishiCart

Firstly, the company developed an online platform that seeks to connect the farmers to consumers via direct sales and purchase of farm produce. Such a move helps reduce the role of intermediaries and enables the sellers to enjoy fair prices for their products, while at the same time providing consumers with quality products at affordable prices.   It is worth noting that KrishiCart managed to come up with a platform capable of solving the key issues affecting the sector. Issues of communication, exploitation, poor market access, and lack of transparency are some of the key problems being addressed by the new model. The features that have been integrated in the system make the platform more practical and convenient.   Other than the provision of a platform for conducting agricultural trade, KrishiCart also ensures the development of community spirit. Through the use of the platform, farmers are able to stay updated on various developments in the agricultural sector, especially regarding market trends, updates, and better farming approaches. Consumers on the other hand get educated on issues of food quality and genuine farm produce. From the discussed case study, one realizes the enormous potential of technology in ensuring connectivity and transparency in the sector [15].

[1]        1. Siegel RL, Miller KD, Fuchs HE, Jemal A. “Cancer statistics” 2022. CA Cancer J Clin. 2022;72(1):7–33. [2]        Sung H, Ferlay J, Siegel RL, Laversanne M, Soerjomataram I, Jemal A, Bray F. “Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers” in 185 Countries. CA: Cancer J Clin. 2021;71:209–49. [3]         Bostan, I.-S.; Mihaila, M.; Roman, V.; Radu, N.; Neagu, M.T.; Bostan, M.; Mehedintu, C. “Landscape of Endometrial Cancer: Molecular Mechanisms, Biomarkers, and Target Therapy.” Cancers 2024, 16, 2027. https://doi.org/10.3390/ cancers16112027 [4]         Moss, E.L.; Gorsia, D.N.; Collins, A.; Sandhu, P.; Foreman, N.; Gore, A.; Gore, A.; Wood, J.; Kent, C.; Silcock, L.; et al. “Utility of Circulating Tumor DNA for Detection and Monitoring of Endometrial Cancer Recurrence and Progression.” Cancers 2020, 12, 2231. [CrossRef] [PubMed] [5]  Bolivar, A.M.; Luthra, R.; Mehrotra, M.; Chen, W.; Barkoh, B.A.; Hu, P.; Zhang, W.; Broaddus, R.R. “Targeted Next-Generation Sequencing of Endometrial Cancer and Matched Circulating Tumor DNA: Identification of Plasma-Based, Tumor-Associated Mutations in Early Stage Patients.” Mod. Pathol. 2019, 32, 405–414. [CrossRef] [PubMed] [1]    V. Bhardwaj, A. Sharma, S. V. Parambath, I. Gul, X. Zhang, P. E. Lobie, P. Qin, and V. Pandey, "Machine Learning for Endometrial Cancer Prediction and Prognostication," Frontiers in Oncology, vol. 12, no. 852746, pp. 1–13, Jul. 2022, doi: 10.3389/fonc.2022.852746. [2]         Chen, J., et al. (2020). “Machine learning for predicting outcomes in endometrial cancer patients.” AI in Medicine, 107, 101890. [6]  Hueman MT, WangH,Yang CQ,Sheng L, Henson DE, Schwartz AM, et al. “Creating Prognostic Systems for Cancer Patients: A Demonstration Using Breast Cancer.” Cancer Med (2018) 7(8):3611–21. doi: 10.1002/cam4.1629 [7]  Yang CQ, Gardiner L, Wang H, Hueman MT, Chen D. “Creating Prognostic Systems for Well-Differentiated Thyroid Cancer Using Machine Learning.” Front Endocrinol (Lausanne) (2019) 10:288. doi: 10.3389/fendo.2019.00288 [8]  He H, Garcia EA. “Learning From Imbalanced Data.” IEEE Trans Knowledge Data Engineering (2009) 21(9):1263–84. doi: 10.1109/TKDE.2008.239 [9]  Kotsiantis S, Kanellopoulos D, Pintelas P. “Handling Imbalanced Datasets: A Review.” GESTS Int Transactions Comput Sci Eng (2006) 30(1):25-36. [10]  T. G. Dietterich, “Ensemble methods in machine learning,” International Workshop on Multiple Classifier Systems, pp. 1–15, 2000. [11]  Yin F, Shao X, Zhao L, Li X, Zhou J, Cheng Y, et al. “Predicting Prognosis of Endometrioid Endometrial Adenocarcinoma on the Basis of Gene Expression and Clinical Features Using Random Forest.” Oncol Lett