Dejene Ejigu Dedefa | Lincoln University of Pennsylvania (original) (raw)

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Papers by Dejene Ejigu Dedefa

2015 IEEE International Black Sea Conference on Communications and Networking (BlackSeaCom), 2015

Lecture Notes in Computer Science, 2015

2012 Fourth International Conference on Intelligent Networking and Collaborative Systems, 2012

ABSTRACT This paper presents an application of evolutionary fuzzy classifier design to a road acc... more ABSTRACT This paper presents an application of evolutionary fuzzy classifier design to a road accident data analysis. A fuzzy classifier evolved by the genetic programming was used to learn the labeling of data in a real world road accident data set. The symbolic classifier was inspected in order to select important features and the relations among them. Selected features provide a feedback for traffic management authorities that can exploit the knowledge to improve road safety and mitigate the severity of traffic accidents.

Neural Network World, 2012

Descriptive analyses of the magnitude and situation of road safety in general and road accidents ... more Descriptive analyses of the magnitude and situation of road safety in general and road accidents in particular is important, but understanding of data quality, factors related with dangerous situations and different interesting patterns in a data is of even greater importance. Under the umbrella of an information architecture research for road safety in developing countries, the objective of this machine learning experimental research is to explore data quality issues, analyze trends and predict the role of road users on possible injury risks. The research employed TreeNet, Classification and Adaptive Regression Trees (CART),

2013 IEEE Symposium on Computational Intelligence in Vehicles and Transportation Systems (CIVTS), 2013

ABSTRACT Traffic accidents represent a major problem threatening peoples lives, health, and prope... more ABSTRACT Traffic accidents represent a major problem threatening peoples lives, health, and property. Traffic behavior and driving in particular is a social and cultural phenomenon that exhibits significant differences across countries and regions. Therefore, traffic models developed in one country might not be suitable for other countries. Similarly, attributes of importance, dependencies, and patterns found in data describing traffic in one region might not be valid for other regions. All this makes traffic accident analysis and modelling a task suitable for data mining and machine learning approaches that develop models based on actual real-world data. In this study, we investigate a data set describing traffic accidents in Ethiopia and use a machine learning method based on artificial evolution and fuzzy systems to mine symbolic description of selected features of the data set.

2011 World Congress on Information and Communication Technologies, 2011

This research tries to view accident data collection and analysis as a system that requires a spe... more This research tries to view accident data collection and analysis as a system that requires a special view towards understanding the whole and making sense out of it for improved decision making in the effort of reducing the problem of road safety. Under the umbrella of an information architecture research for road safety in developing countries, the objective of this machine learning experimental research is to explore and predict the role of road users on possible injury risks. The research employed Classification and Adaptive Regression Trees (CART) and RandomForest approaches. To identify relevant patterns and illustrate the performance of the techniques for the road safety domain, road accident data collected from Addis Ababa Traffic Office is exposed to many sided analyses. Empirical results showed that the models could classify accidents with promising accuracy.

As pervasive computing is at its preliminary stage a number of computing solutions were proposed ... more As pervasive computing is at its preliminary stage a number of computing solutions were proposed and more are still under experimentation. In this paper a four layered generic architecture is proposed with a recommended components so as to support context awareness for pervasive applications. Detail of each layer is described sufficiently to insight the process of context generation from low level contextual data up to high level context reporting scheme in a given pervasive environment. The implementation section of this article describes how the context reasoning component employee different level of reasoning techniques such as knowledge acquisition, context based rule execution and the application of ontology in pervasive computing.

Lecture Notes in Computer Science, 2014

2012 World Congress on Information and Communication Technologies, 2012

ABSTRACT This research tries to bring in an enterprise view to road safety information management... more ABSTRACT This research tries to bring in an enterprise view to road safety information management towards understanding the whole and making sense out of it for improved decision-making in the effort of reducing the problem of road safety. Under the umbrella of an information architecture research for road safety, the objective of this research is to identify the role of collision related factors to the severity of an accident in explaining road safety situations and define or construct road safety information architecture based on existing enterprise frameworks. The research followed a Design Science research paradigm and Zachman Enterprise Framework was used to guide the development of the road safety information architecture. The research also employed classification techniques using Decision tree and Rule induction approaches. Results showed that the architectural representation guided by the selected framework can provide a holistic view to the management of road safety data. Moreover the identified patterns in a form of rule can supplement the previous severity prediction experiments with promising performance.

A thesis for the partial fulfillment of doctorial degree, …, Jan 1, 2007

… baseline data for tsetse area-wide …, Jan 1, 2008

... Authors Leak, SGA; Ejigu, D.; Vreysen, MJB Editors Leak, SGA;Ejigu, D.;Vreysen, MJB Book Coll... more ... Authors Leak, SGA; Ejigu, D.; Vreysen, MJB Editors Leak, SGA;Ejigu, D.;Vreysen, MJB Book Collection of entomological baseline data for tsetse area-wide ... KNOWLEDGE FOR LIFE TechnicalInfo | Links| Subject Map | Privacy Policy | Terms and Conditions ©2011 CABI.

…, Jan 1, 2006

In order to implement reactive and proactive functionalities in a pervasive environment, contextu... more In order to implement reactive and proactive functionalities in a pervasive environment, contextual data must be processed. One of the most important features of the context is the position of the users and the devices. In this paper, we describe a method to determine the position of a WiFi enabled device. The prediction is based on the signal strength of the available access points. The prediction model is built from a database containing the signal strength measured in some known locations. The result is the name of the room/office where the device is localised. We also present a usage scenario, in which the user/device position is used to start proactive actions in our pervasive service environment called PerSE.