Rice production has faced numerous challenges in recent years, and traditional methods are still being used to detect rice diseases. This research project developed an automated rice blast disease diagnosis technique based on deep learning, image processing, and transfer learning with pre-trained models such as Inception V3, VGG16, VGG19, and ResNet50. The public dataset consists of 2000 images; about 1200 images belong to the leaf blast class, and 800 to the healthy leaf class. The modified connection-skipping ResNet 50 had the highest accuracy of 99.75% with a loss rate of 0.33, while the other models achieved 98.16%, 98.47%, and 98.56%, respectively. Furthermore, ResNet 50 achieved a validation accuracy of 99.69%, precision of 99.50%, F1-score of 99.70, and AUC of 99.83%. In conclusion, the study demonstrated a superior performance and disease prediction using the Gradio web application.
We developed a computational model to explore the hypothesis that regulatory instructions are context dependent and conveyed through specific 'codes' in human genomic DNA. We provide examples of correlation of computational predictions to reported mapped DNase I hypersensitive segments in the HOXA locus in human chromosome 7. The examples show that statistically significant 9-mers from promoter regions may occur in sequences near and upstream of transcription initiation sites, in intronic regions, and within intergenic regions. Additionally, a subset of 9-mers from coding sequences appears frequently, as clusters, in regulatory regions dispersed in noncoding regions in genomic DNA. The results suggest that the computational model has the potential of decoding regulatory instructions to discover candidate transcription factor binding sites and to discover candidate epigenetic signals that appear in both coding and regulatory regions of genes.
Discovery of lexical characteristics of specific sequence motifs in human genomic DNA can help with predicting and classifying regulatory cis elements according to the genes they control. In lexical models, some "words" may serve as downstream targets of signaling systems, whereas other "words" may specify sequences that selectively control the expression of a subset of genes to produce the various cell types and tissues. To discover lexical features of potential regulatory "words," we have created a database of 9-mers derived from the promoter regions of a subset of human protein-coding genes. This report describes the procedure for extracting information from that database through the web.
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