ICT

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Classification Of Malt Barley Seed Using Ensemble Deep Learning Technique
(2023) Dessie Abebe
The primary step in making beer is selecting the malt barley. Every malt house requires that the types of grain be checked before being purchased. Varietal uniformity is essential for the manufacture of high-quality malt. It is challenging to distinguish between different types of malt barley during inspection since it calls for training and experience. To tackle varietal selection difficulties Malt industries have employed and trained experts; even though, those experts do not work effectively due to tiredness, bias, and other factors. Therefore, many researchers are motivated to the development of classification model based on image processing to support experts across the world. Using digital image processing algorithms based on combined morphological, texture, and color features have been investigated to classify different varieties of Ethiopian malt barley.
Detection and Classification of Skin Cancer Using Image Processing and Deep Learning
(2023) Degarege Amare Demessie
Skin cancer is one of the most serious malignancies today, and it kills so many individuals all over the world. It develops in the skin tissue and can harm nearby tissues, result in disability, or even result in death. Furthermore, in today‟s technologically advanced world, it is crucial that machines, not people to fix the issue. One of the finest approaches to address the issues with skin cancer is deep learning. A recent topic of research in contemporary technology that makes use of large data, virtual and augmented reality, and micro services is deep learning
Improved Enset Disease Detection with Image Processing and Convolutional neural network
(2022) Tewokel Asrat
Enset (Ensete ventricosum Cheesman) is a perennial and herbaceous plant which widely distributed and cultivated in southern and southwestern Ethiopia as staple food for more than 20 million people in mixed subsistence farming systems. There are several diseases which tries to decline the yield with quality. However bacterial wilt and mealybug diseases of Enset are widespread in the country‟s major Enset growing regions causing losses of up to 100% destruction of farm fields in extreme cases. This paper looks into the use of deep learning to detect bacterial wilt disease and Enset mealybug, where data is obtained in small amounts and collected under minimally controlled conditions. Data augmentation is employed to get over the limits of the dataset size
Model Development based on Demographic and Psychographic factor to improve performance in case of TVET using Machine Learning
(2023) Habtom Atsebeha Berhe
TVET is important for the growth of human resources and, consequently, for the advancement and prosperity of a community. In this thesis, the factors that significantly affect TVET students' academic achievement and performance were modeled. The academic performance of TVET students has been found to be significantly influenced by a number of factors, including gender, age, monthly family income, study hours, stimulant use during the course of the study, and satisfaction with the area of study placement. According to this study, lower academic accomplishment was associated with a variety of factors, including having a negative opinion of TVET, being female, having a low family income, studying for a shorter amount of time, using stimulants while studying, and not being content with one's field of study.