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Papers by Abera Asfaw
In the south-west of Ethiopia, enset plant has a major role to play in food security and the abil... more In the south-west of Ethiopia, enset plant has a major role to play in food security and the ability to drought-resistant and multi-purpose in modern lifestyles. The use of traditional fermented kocho has been increased in south Ethiopia as consumers want a healthy dietary food. The microbiology of kocho fermentation ensures the desirable quality of kocho. Existing papers have been organized and reviewed on the dynamics of growth, survival, and biochemical activity of microbes and their importance in the traditional fermentation process of kocho. The quality of kocho depends on the enset processing, fermentation methods, and periods. Few studies have been published on the importance of microorganisms during the kocho fermentation process. In kocho fermentation, LAB is used for the production of metabolites such as acetic acid, propanoic acid, butanoic acid, pentanoic acid, and hexanoic acid. Some of the species of Lactic acid bacteria in kocho fermentation that impart desired protei...
INTERNATIONAL JOURNAL OF COMPUTER ENGINEERING AND TECHNOLOGY
Diabetes mellitus is a common disease caused by a set of metabolic ailments where the sugar stage... more Diabetes mellitus is a common disease caused by a set of metabolic ailments where the sugar stages over drawn-out period is very high. It touches diverse organs of the human body which therefore harm a huge number of the body's system, in precise the blood strains and nerves. Early prediction in such disease can be exact and save human life. To achieve the goal, this research work mainly discovers numerous factors associated to this disease using machine learning techniques. Machine learning methods provide effectual outcome to extract knowledge by building predicting models from diagnostic medical datasets together from the diabetic patients. Quarrying knowledge from such data can be valuable to predict diabetic patients. In this research, six popular used machine learning techniques, namely Random Forest (RF), Logistic Regression (LR), Naive Bayes (NB), C4.5 Decision Tree (DT), K-Nearest Neighbor (KNN), and Support Vector Machine (SVM) are compared in order to get outstanding machine learning techniques to forecast diabetic mellitus. Our new outcome shows that Support Vector Machine (SVM) achieved higher accuracy compared to other machine learning techniques.
Breast cancer is one of the greatest common diseases among women in Africa and worldwide. Accurat... more Breast cancer is one of the greatest common diseases among women in Africa and worldwide. Accurate and early diagnosis is very significant phase in therapy and action. However, it is not an easy one due to some doubts in detection of breast cancer. Machine learning helps us to extract information and knowledge from this the basis of past experiences and detect hard-to-perceive pattern from large and noisy dataset. This paper compares and analysis the performance of machine learning algorithms, namely Decision Tree (DT), Logistic Regression (LR), Naïve Bayes (NB), and K-Nearest Neighbors (KNN) for detecting breast cancer. The data set used for comparison was from UCI Wisconsin original breast cancer data set. The result outcome shows that Logistic Regression performs better and classification accuracy is 96.93%.
In the south-west of Ethiopia, enset plant has a major role to play in food security and the abil... more In the south-west of Ethiopia, enset plant has a major role to play in food security and the ability to drought-resistant and multi-purpose in modern lifestyles. The use of traditional fermented kocho has been increased in south Ethiopia as consumers want a healthy dietary food. The microbiology of kocho fermentation ensures the desirable quality of kocho. Existing papers have been organized and reviewed on the dynamics of growth, survival, and biochemical activity of microbes and their importance in the traditional fermentation process of kocho. The quality of kocho depends on the enset processing, fermentation methods, and periods. Few studies have been published on the importance of microorganisms during the kocho fermentation process. In kocho fermentation, LAB is used for the production of metabolites such as acetic acid, propanoic acid, butanoic acid, pentanoic acid, and hexanoic acid. Some of the species of Lactic acid bacteria in kocho fermentation that impart desired protei...
INTERNATIONAL JOURNAL OF COMPUTER ENGINEERING AND TECHNOLOGY
Diabetes mellitus is a common disease caused by a set of metabolic ailments where the sugar stage... more Diabetes mellitus is a common disease caused by a set of metabolic ailments where the sugar stages over drawn-out period is very high. It touches diverse organs of the human body which therefore harm a huge number of the body's system, in precise the blood strains and nerves. Early prediction in such disease can be exact and save human life. To achieve the goal, this research work mainly discovers numerous factors associated to this disease using machine learning techniques. Machine learning methods provide effectual outcome to extract knowledge by building predicting models from diagnostic medical datasets together from the diabetic patients. Quarrying knowledge from such data can be valuable to predict diabetic patients. In this research, six popular used machine learning techniques, namely Random Forest (RF), Logistic Regression (LR), Naive Bayes (NB), C4.5 Decision Tree (DT), K-Nearest Neighbor (KNN), and Support Vector Machine (SVM) are compared in order to get outstanding machine learning techniques to forecast diabetic mellitus. Our new outcome shows that Support Vector Machine (SVM) achieved higher accuracy compared to other machine learning techniques.
Breast cancer is one of the greatest common diseases among women in Africa and worldwide. Accurat... more Breast cancer is one of the greatest common diseases among women in Africa and worldwide. Accurate and early diagnosis is very significant phase in therapy and action. However, it is not an easy one due to some doubts in detection of breast cancer. Machine learning helps us to extract information and knowledge from this the basis of past experiences and detect hard-to-perceive pattern from large and noisy dataset. This paper compares and analysis the performance of machine learning algorithms, namely Decision Tree (DT), Logistic Regression (LR), Naïve Bayes (NB), and K-Nearest Neighbors (KNN) for detecting breast cancer. The data set used for comparison was from UCI Wisconsin original breast cancer data set. The result outcome shows that Logistic Regression performs better and classification accuracy is 96.93%.