D. Singh, N. Jain, P. Jain, P. Kayal, S. Kumawat, N. Batra, PlantDoc: a dataset for visual plant disease detection, inProceedings of the 7th ACM IKDD CoDS and 25th COMAD(2020), pp. 249–253 Google Scholar Dataset.https://www.kaggle.com/datasets/smaranjitghose/corn-or-maize-leaf...
This section tests the Cardiovascular Disease (CVD) classification output of the ICVD-ACOEDL algorithm using a dataset from the Kaggle repository20. There are 629 disease-affected samples and 561 normal samples in the collection. The information about the dataset is given in Table 3. It contains...
Utilizing a comprehensive Kaggle dataset, authors study aims to achieve heightened accuracy in recognizing and segmenting Lyme disease from medical images. Implemented in Python, authors advanced image processing methods demonstrate exceptional performance, reaching an outstanding accuracy of 97.36% after the...
the PlantVillage Kaggle dataset. More specifically, they obtained mAP and accuracy values of 98.10% and 99.97%, respectively, as well as a test time of 0.23 s. Both qualitative and quantitative results confirmed that the presented solution was robust for plant leaf disease detection...
In literature, the Cleveland heart disease dataset is extensively utilized by the researchers15,16. In this regard, Robert et al.17 have used a logistic regression classification algorithm for heart disease detection and obtained an accuracy of 77.1%. Similarly, Wankhade et al.18 have used a ...
approach demonstrates remarkable efficacy, achieving a mean Average Precision (mAP) of 92.3% on a curated dataset, marking an 8.7% point improvement over the baseline method. Additionally, it attains a detection speed of 46.6 frames per second (FPS), adeptly meeting the demands of agricultural ...
Chakradeo, “Real-time plant disease dataset development and detection of plant disease using deep learning,” IEEE Access, 2024 Kamal K, Yin Z, Wu M, Wu Z (2019) Depthwise separable convolution architectures for plant disease classification. Comput Electron Agric 165:104948 Article Google Scholar...
(https://www.kaggle.com/competitions/aptos2019-blindness-detection/data), PAPILA (https://figshare.com/articles/dataset/PAPILA/14798004/1), Glaucoma Fundus (https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/1YRRAC), JSIEC (https://zenodo.org/record/3477553), Retina (...
The present study examines the role of feature selection methods in optimizing machine learning algorithms for predicting heart disease. The Cleveland Heart disease dataset with sixteen feature selection techniques in three categories of filter, wrapper,
(https://www.kaggle.com/competitions/aptos2019-blindness-detection/data), PAPILA (https://figshare.com/articles/dataset/PAPILA/14798004/1), Glaucoma Fundus (https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/1YRRAC), JSIEC (https://zenodo.org/record/3477553), Retina (...