Belay Enyew | University of Gondar (original) (raw)

Papers by Belay Enyew

Research paper thumbnail of Detection and classification of gastrointestinal disease using convolutional neural network and SVM

Research paper thumbnail of Employing Machine Learning Techniques for the Malaria Epidemic Prediction in Ethiopia

2018 10th Computer Science and Electronic Engineering (CEEC), 2018

Malaria is one of the leading causes for death in Ethiopia. Even though currently its transmissio... more Malaria is one of the leading causes for death in Ethiopia. Even though currently its transmission decreases with many control efforts, the complexity of the problems is still very severe. Irregular epidemics have high consequences on society in term of morbidity and mortality. Government authorities are also incurring huge cost to control or eliminate the epidemic of malaria. It also costs the country in terms reduced productivity and increased school absenteeism. Accurate and reliable prediction of malarial epidemics is necessary for the health authorities to take the appropriate action for the control of the epidemic. Ethiopia uses a surveillance system to collect incidence and conventional analysis to predict and give an early warning system. However, there is a need of advanced suitable techniques to predict future epidemics.In this paper, we presented a framework which employs machine learning for the Malaria Epidemic Prediction (MEP) in Ethiopia based on the amount of rainfall, relative humidity, mean temperature, elevation and lag malaria cases. The machine learning techniques employed a more accurate opaque box model via a Support Vector Regression (SVR) and a transparent box model via an Adaptive Neuro Fuzzy inference System (ANFIS) to predict the malaria epidemic up to three months ahead. Thus, this framework allows us to gain a relatively high accuracy of prediction besides having transparency allowing us to understand the reasoning behind any prediction. The models were trained, validated and tested using 5 years (2013–2017) of historical climatic, elevation and malaria data from Ethiopia.

Research paper thumbnail of Growth and properties of synthetic AlPO4 crystals

Research paper thumbnail of Employing Data Mining Techniques to Predict Occurrence of Thunderstorm Using Hourly Weather Datasets :In the Case of Gondar Control Zone

International Journal of Engineering Research and, 2018

Research paper thumbnail of Detection and Classification of COVID-19 Disease from X-ray Images Using Convolutional Neural Networks and Histogram of Oriented Gradients

Biomedical Signal Processing and Control

Research paper thumbnail of Employing Data Mining Techniques to Predict Occurrence of Thunderstorm Using Hourly Weather Datasets :In the Case of Gondar Control Zone

Thunderstorms has meaningfully effects on both terminal and en route flights and reduce airspace ... more Thunderstorms has meaningfully effects on both terminal and en route flights and reduce airspace capacity that results delays and have increased substantially en-route congestion. Current technology cannot provide reliable long-term prediction of thunderstorms for aviation operation. The objective of this study was to apply the data mining techniques to predict the occurrence of thunderstorms using 10 years NMA‘s synoptic dataset of Gondar station using design science research method. From collected data sets seven important attributes (cloud amount, cloud type, temperature, pressure, wind speed, rain fall and humidity) was selected from other variables or attributes to build the model. The experiments have been conducted using the six-step hybrid process model using four selected modeling algorithms. After performing an experiment using classification algorithms decision tree and rule induction, the models is evaluated based on their prediction accuracy in classifying the instances...

Research paper thumbnail of Employing Machine Learning Techniques for the Malaria Epidemic Prediction in Ethiopia

Malaria is one of the leading causes for death in Ethiopia. Even though currently its transmissio... more Malaria is one of the leading causes for death in Ethiopia. Even though currently its transmission decreases with many control efforts, the complexity of the problems is still very severe. Irregular epidemics have high consequences on society in term of morbidity and mortality. Government authorities are also incurring huge cost to control or eliminate the epidemic of malaria. It also costs the country in terms reduced productivity and increased school absenteeism. Accurate and reliable prediction of malarial epidemics is necessary for the health authorities to take the appropriate action for the control of the epidemic. Ethiopia uses a surveillance system to collect incidence and conventional analysis to predict and give an early warning system. However, there is a need of advanced suitable techniques to predict future epidemics.In this paper, we presented a framework which employs machine learning for the Malaria Epidemic Prediction (MEP) in Ethiopia based on the amount of rainfall, relative humidity, mean temperature, elevation and lag malaria cases. The machine learning techniques employed a more accurate opaque box model via a Support Vector Regression (SVR) and a transparent box model via an Adaptive Neuro Fuzzy inference System (ANFIS) to predict the malaria epidemic up to three months ahead. Thus, this framework allows us to gain a relatively high accuracy of prediction besides having transparency allowing us to understand the reasoning behind any prediction. The models were trained, validated and tested using 5 years (2013–2017) of historical climatic, elevation and malaria data from Ethiopia.

Research paper thumbnail of Employing Data Mining Techniques to Predict Occurrence of Thunderstorm Using Hourly Weather Datasets :In the Case of Gondar Control Zone

International Journal of Engineering Research and

Thunderstorms has meaningfully effects on both terminal and en route flights and reduce airspace ... more Thunderstorms has meaningfully effects on both terminal and en route flights and reduce airspace capacity that results delays and have increased substantially en-route congestion. Current technology cannot provide reliable long-term prediction of thunderstorms for aviation operation. The objective of this study was to apply the data mining techniques to predict the occurrence of thunderstorms using 10 years NMA‘s synoptic dataset of Gondar station using design science research method. From collected data sets seven important attributes (cloud amount, cloud type, temperature, pressure, wind speed, rain fall and humidity) was selected from other variables or attributes to build the model. The experiments have been conducted using the six-step hybrid process model using four selected modeling algorithms. After performing an experiment using classification algorithms decision tree and rule induction, the models is evaluated based on their prediction accuracy in classifying the instances of the data set into thundered and non-thundered situations. From those classifier PART is selected by having best classifying accuracy that can classify 10718 or 99.70% instances as correct out of 10750 instances which is processed from Gondar aeronautics and Synoptic station) Keywords— Thunderstorm, synoptic data, Data mining, PART classifier, predictive model.

Research paper thumbnail of Detection and classification of gastrointestinal disease using convolutional neural network and SVM

Research paper thumbnail of Employing Machine Learning Techniques for the Malaria Epidemic Prediction in Ethiopia

2018 10th Computer Science and Electronic Engineering (CEEC), 2018

Malaria is one of the leading causes for death in Ethiopia. Even though currently its transmissio... more Malaria is one of the leading causes for death in Ethiopia. Even though currently its transmission decreases with many control efforts, the complexity of the problems is still very severe. Irregular epidemics have high consequences on society in term of morbidity and mortality. Government authorities are also incurring huge cost to control or eliminate the epidemic of malaria. It also costs the country in terms reduced productivity and increased school absenteeism. Accurate and reliable prediction of malarial epidemics is necessary for the health authorities to take the appropriate action for the control of the epidemic. Ethiopia uses a surveillance system to collect incidence and conventional analysis to predict and give an early warning system. However, there is a need of advanced suitable techniques to predict future epidemics.In this paper, we presented a framework which employs machine learning for the Malaria Epidemic Prediction (MEP) in Ethiopia based on the amount of rainfall, relative humidity, mean temperature, elevation and lag malaria cases. The machine learning techniques employed a more accurate opaque box model via a Support Vector Regression (SVR) and a transparent box model via an Adaptive Neuro Fuzzy inference System (ANFIS) to predict the malaria epidemic up to three months ahead. Thus, this framework allows us to gain a relatively high accuracy of prediction besides having transparency allowing us to understand the reasoning behind any prediction. The models were trained, validated and tested using 5 years (2013–2017) of historical climatic, elevation and malaria data from Ethiopia.

Research paper thumbnail of Growth and properties of synthetic AlPO4 crystals

Research paper thumbnail of Employing Data Mining Techniques to Predict Occurrence of Thunderstorm Using Hourly Weather Datasets :In the Case of Gondar Control Zone

International Journal of Engineering Research and, 2018

Research paper thumbnail of Detection and Classification of COVID-19 Disease from X-ray Images Using Convolutional Neural Networks and Histogram of Oriented Gradients

Biomedical Signal Processing and Control

Research paper thumbnail of Employing Data Mining Techniques to Predict Occurrence of Thunderstorm Using Hourly Weather Datasets :In the Case of Gondar Control Zone

Thunderstorms has meaningfully effects on both terminal and en route flights and reduce airspace ... more Thunderstorms has meaningfully effects on both terminal and en route flights and reduce airspace capacity that results delays and have increased substantially en-route congestion. Current technology cannot provide reliable long-term prediction of thunderstorms for aviation operation. The objective of this study was to apply the data mining techniques to predict the occurrence of thunderstorms using 10 years NMA‘s synoptic dataset of Gondar station using design science research method. From collected data sets seven important attributes (cloud amount, cloud type, temperature, pressure, wind speed, rain fall and humidity) was selected from other variables or attributes to build the model. The experiments have been conducted using the six-step hybrid process model using four selected modeling algorithms. After performing an experiment using classification algorithms decision tree and rule induction, the models is evaluated based on their prediction accuracy in classifying the instances...

Research paper thumbnail of Employing Machine Learning Techniques for the Malaria Epidemic Prediction in Ethiopia

Malaria is one of the leading causes for death in Ethiopia. Even though currently its transmissio... more Malaria is one of the leading causes for death in Ethiopia. Even though currently its transmission decreases with many control efforts, the complexity of the problems is still very severe. Irregular epidemics have high consequences on society in term of morbidity and mortality. Government authorities are also incurring huge cost to control or eliminate the epidemic of malaria. It also costs the country in terms reduced productivity and increased school absenteeism. Accurate and reliable prediction of malarial epidemics is necessary for the health authorities to take the appropriate action for the control of the epidemic. Ethiopia uses a surveillance system to collect incidence and conventional analysis to predict and give an early warning system. However, there is a need of advanced suitable techniques to predict future epidemics.In this paper, we presented a framework which employs machine learning for the Malaria Epidemic Prediction (MEP) in Ethiopia based on the amount of rainfall, relative humidity, mean temperature, elevation and lag malaria cases. The machine learning techniques employed a more accurate opaque box model via a Support Vector Regression (SVR) and a transparent box model via an Adaptive Neuro Fuzzy inference System (ANFIS) to predict the malaria epidemic up to three months ahead. Thus, this framework allows us to gain a relatively high accuracy of prediction besides having transparency allowing us to understand the reasoning behind any prediction. The models were trained, validated and tested using 5 years (2013–2017) of historical climatic, elevation and malaria data from Ethiopia.

Research paper thumbnail of Employing Data Mining Techniques to Predict Occurrence of Thunderstorm Using Hourly Weather Datasets :In the Case of Gondar Control Zone

International Journal of Engineering Research and

Thunderstorms has meaningfully effects on both terminal and en route flights and reduce airspace ... more Thunderstorms has meaningfully effects on both terminal and en route flights and reduce airspace capacity that results delays and have increased substantially en-route congestion. Current technology cannot provide reliable long-term prediction of thunderstorms for aviation operation. The objective of this study was to apply the data mining techniques to predict the occurrence of thunderstorms using 10 years NMA‘s synoptic dataset of Gondar station using design science research method. From collected data sets seven important attributes (cloud amount, cloud type, temperature, pressure, wind speed, rain fall and humidity) was selected from other variables or attributes to build the model. The experiments have been conducted using the six-step hybrid process model using four selected modeling algorithms. After performing an experiment using classification algorithms decision tree and rule induction, the models is evaluated based on their prediction accuracy in classifying the instances of the data set into thundered and non-thundered situations. From those classifier PART is selected by having best classifying accuracy that can classify 10718 or 99.70% instances as correct out of 10750 instances which is processed from Gondar aeronautics and Synoptic station) Keywords— Thunderstorm, synoptic data, Data mining, PART classifier, predictive model.