Evelyn Anyebe | University of Dundee (original) (raw)
Evelyn Anyebe is a practicing software engineer with interests in data engineering and artificial intelligence. Evelyn graduated from Benue State University Makurdi where obtained a first class in BSc computer science. She also holds an MSc in data engineering from University of Dundee. Evelyn is passionate about creating technology that will improve the quality of life for people of all backgrounds.
Supervisors: Dr Adekunle Adeyelu and Prof Stephen McKenna
Address: Abuja, Nigeria
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Papers by Evelyn Anyebe
NIGERIAN ANNALS OF PURE AND APPLIED SCIENCES, 2019
This study developed and implemented an improved mediator wrapper approach to addressing the chal... more This study developed and implemented an improved mediator wrapper approach to addressing the challenges of integration of semantic heterogeneous databases. It employed Local as View (LaV) paradigm of database integration so as to reduce its cost as well as offer the local sources a degree of independence. The developed model was implemented as web service using JEE and some software development tools with a number of heterogeneous databases as a case study.
Selective classification of skin lesion images and uncertainty estimation is examined to increase... more Selective classification of skin lesion images and uncertainty estimation is examined to increase the adoption of convolutional neural networks(CNNs) in automated skin cancer diagnostic systems. Research on the application of deep learning models to skin cancer diagnosis has shown success as models outperform medical experts [1]. However, concerns on uncertainty in classifiers and difficulty in approximating uncertainty has caused limited adoption of CNNs in Computer-aided diagnostic systems (CADs) in health care. This research propose selective classification to increase confidence in CNN models for skin cancer diagnosis. The methodology is based on SoftMax response(SR), MC dropout and risk-coverage performance evaluation metric. Risk-coverage curves gives physicians and dermatologist information about the expected rate of misclassification by a model. This enable them to measure the reliability of the classifier’s predictions and inform their decision during skin cancer diagnosis....
International Journal of Sciences: Basic and Applied Research, 2020
Selective classification of skin lesion images and uncertainty estimation is examined to increase... more Selective classification of skin lesion images and uncertainty estimation is examined to increase the adoption of convolutional neural networks(CNNs) in automated skin cancer diagnostic systems. Research on the application of deep learning models to skin cancer diagnosis has shown success as models outperform medical experts [1]. However, concerns on uncertainty in classifiers and difficulty in approximating uncertainty has caused limited adoption of CNNs in Computer-aided diagnostic systems (CADs) in health care. This research propose selective classification to increase confidence in CNN models for skin cancer diagnosis. The methodology is based on SoftMax response(SR), MC dropout and risk-coverage performance evaluation metric. Risk-coverage curves gives physicians and dermatologist information about the expected rate of misclassification by a model. This enable them to measure the reliability of the classifier's predictions and inform their decision during skin cancer diagnosis. MC dropout uncertainty estimate was shown to increase accuracy for Melanoma detection by 1.48%. The proposed selective classifier achieved increase melanoma detection. The sensitivity of melanoma increased by 9.91% and 9.73% after selective classification at a coverage of 0.7. This study showed that selective classification and uncertainty estimation can be combined to promote adoption of CNNs in CADs for skin lesions classification.
Nigerian Annals of Pure and Applied Sciences, 2018
This study developed and implemented an improved mediator wrapper approach to addressing the chal... more This study developed and implemented an improved mediator wrapper approach to addressing the challenges of integration of semantic heterogeneous databases. It employed Local as View (LaV) paradigm of database integration so as to reduce its cost as well as offer the local sources a degree of independence. The developed model was implemented as web service using JEE and some software development tools with a number of heterogeneous databases as a case study.
Talks by Evelyn Anyebe
In the early 2000s, there was an explosion in data generated, from the Internet to social network... more In the early 2000s, there was an explosion in data generated, from the Internet to social networks, web servers, sensors and smart devices. The amount and kind of data being stored and transferred was changing but technologies available for data storage and analysis could not keep up with this explosion. This is big data and it was a problem. Hadoop, Map Reduce and Spark represent efforts by toward solving this problem.
NIGERIAN ANNALS OF PURE AND APPLIED SCIENCES, 2019
This study developed and implemented an improved mediator wrapper approach to addressing the chal... more This study developed and implemented an improved mediator wrapper approach to addressing the challenges of integration of semantic heterogeneous databases. It employed Local as View (LaV) paradigm of database integration so as to reduce its cost as well as offer the local sources a degree of independence. The developed model was implemented as web service using JEE and some software development tools with a number of heterogeneous databases as a case study.
Selective classification of skin lesion images and uncertainty estimation is examined to increase... more Selective classification of skin lesion images and uncertainty estimation is examined to increase the adoption of convolutional neural networks(CNNs) in automated skin cancer diagnostic systems. Research on the application of deep learning models to skin cancer diagnosis has shown success as models outperform medical experts [1]. However, concerns on uncertainty in classifiers and difficulty in approximating uncertainty has caused limited adoption of CNNs in Computer-aided diagnostic systems (CADs) in health care. This research propose selective classification to increase confidence in CNN models for skin cancer diagnosis. The methodology is based on SoftMax response(SR), MC dropout and risk-coverage performance evaluation metric. Risk-coverage curves gives physicians and dermatologist information about the expected rate of misclassification by a model. This enable them to measure the reliability of the classifier’s predictions and inform their decision during skin cancer diagnosis....
International Journal of Sciences: Basic and Applied Research, 2020
Selective classification of skin lesion images and uncertainty estimation is examined to increase... more Selective classification of skin lesion images and uncertainty estimation is examined to increase the adoption of convolutional neural networks(CNNs) in automated skin cancer diagnostic systems. Research on the application of deep learning models to skin cancer diagnosis has shown success as models outperform medical experts [1]. However, concerns on uncertainty in classifiers and difficulty in approximating uncertainty has caused limited adoption of CNNs in Computer-aided diagnostic systems (CADs) in health care. This research propose selective classification to increase confidence in CNN models for skin cancer diagnosis. The methodology is based on SoftMax response(SR), MC dropout and risk-coverage performance evaluation metric. Risk-coverage curves gives physicians and dermatologist information about the expected rate of misclassification by a model. This enable them to measure the reliability of the classifier's predictions and inform their decision during skin cancer diagnosis. MC dropout uncertainty estimate was shown to increase accuracy for Melanoma detection by 1.48%. The proposed selective classifier achieved increase melanoma detection. The sensitivity of melanoma increased by 9.91% and 9.73% after selective classification at a coverage of 0.7. This study showed that selective classification and uncertainty estimation can be combined to promote adoption of CNNs in CADs for skin lesions classification.
Nigerian Annals of Pure and Applied Sciences, 2018
This study developed and implemented an improved mediator wrapper approach to addressing the chal... more This study developed and implemented an improved mediator wrapper approach to addressing the challenges of integration of semantic heterogeneous databases. It employed Local as View (LaV) paradigm of database integration so as to reduce its cost as well as offer the local sources a degree of independence. The developed model was implemented as web service using JEE and some software development tools with a number of heterogeneous databases as a case study.
In the early 2000s, there was an explosion in data generated, from the Internet to social network... more In the early 2000s, there was an explosion in data generated, from the Internet to social networks, web servers, sensors and smart devices. The amount and kind of data being stored and transferred was changing but technologies available for data storage and analysis could not keep up with this explosion. This is big data and it was a problem. Hadoop, Map Reduce and Spark represent efforts by toward solving this problem.