Publications by authors named "Taskeed Jabid"
The agricultural sector is a vital component of Bangladesh's economy, but its agri-food supply chain faces signifi-cant inefficiencies primarily due to the involvement of numerous intermediaries. This complexity not only reduces the profits for farmers but also affects the overall transparency and efficiency of the supply chain. This study aims to em-ploy blockchain technology to transform the traditional agri-food supply chain in Bangladesh, focusing on increasing transparency, enhancing efficiency, and improving profitability for farmers, thus potentially bolstering the entire agri-food ecosystem in the country.
View Article and Find Full Text PDFThe utilization of computer vision techniques has significantly enhanced the automation processes across various industries, including textile manufacturing, agriculture, and information technology. Specifically, in the domain of textile manufacturing, these techniques have revolutionized the detection of fiber defects and the quantification of cotton content in fabrics. Traditionally, the assessment of cotton percentages was a labor-intensive and time-consuming process that relied heavily on manual testing methods.
View Article and Find Full Text PDFCompared to other popular research domains, dermatology got less attention among machine learning researchers. One of the main concerns for this problem is an inadequate dataset since collecting samples from the human body is very sensitive. In recent years, arsenic has emerged as a significant issue for dermatologists.
View Article and Find Full Text PDFArticle Synopsis
- Agriculture hasn't fully benefited from machine learning due to a lack of standard datasets.
- The first publicly available dataset of mango leaves has been created, containing 4000 images of 1800 distinct leaves affected by seven diseases.
- This dataset, based on mango leaves from Bangladesh, aims to help improve disease identification and potentially enhance mango yields worldwide.
Article Synopsis
- Eukaryotic promoter prediction is a challenging aspect of computational genomics, crucial for understanding genetic regulatory networks, and requires improved tools due to the growing sequence data.
- A novel method is introduced that utilizes 128 unique DNA motifs and a Support Vector Machine (SVM) to effectively differentiate between promoter and non-promoter sequences across various organisms, achieving high accuracy rates and low false positives.
- The study concludes that using 4-mer frequencies along with machine learning can significantly enhance the identification of RNA polymerase II promoters compared to existing methods.