Browsing by Author "Lamenew Workshet"

Now showing 1 - 9 of 9
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    Adoption of Electronic Medical Records among Health Professionals at Public Hospitals in Addis Ababa City Administration Health Bureau, Ethiopia.
    (Addis Ababa University, 2012-12) Gebremariam Semere; Lamenew Workshet; Deyassa Negussie
    Introduction: Wellness and health are central to live of all people of age group. Incorporating information communication like Electronic Medical Records on the health care industries is mandatory for the better improvement of patient care and safety, integrated research, for effective planning, monitoring and evaluation of disease etc. Electronic Medical Record implementation in public hospitals in Addis Ababa is on the infant stage not more than three years since its inception.Even though There is discrepancy in adoption among health professionals and is not utilized as needed due to different factors ,most of the public hospitals have implemented it. So identifying the factors which affect the adoption will help to apply proactive measure and correction so as to increase the adoption of EMR among health professionals whom are working at the public hospitals. Objective: This study aimed at identifying the factors that affect the Behavioral Intention and usage behavior of Electronic Medical Record and determine the utilization status among health professionals working in public hospital in Addis Ababa City Administration health Bureau. Method: A Cross-sectional survey was carried out among health professionals working at public hospitals in Addis Ababa using modified theory of unified acceptance and use of technology(UTAUT)model. Four hundred eight health professionals who had training on EMR were interviewed at the five public hospitals. Results: The utilization of EMR among health professions working at the public hospitals was 51.7%permanence expectancy, Effort expectancy, social influence were factors influencing the behavioral intention of health professionals to adopt EMR and Behavioral intention was also significant influencing factor on actual usage behavior. Facilitating condition remains insignificant on the actual usage behavior of EMR among health professionals. Conclusion and Recommendation: The utilization rate of EMR was 51.7%: Having no experience, misunderstanding on the relative advantage, perceiving complexity of the system, inadequate support of the top managers, low behavioral intent were factors associated with the behavioral intention and actual usage of EMR. This study indicates that the necessity of integrating health management information system with the daily health care activities and development of health information policy that can scale the utilization rate.
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    Application of GIS for Urban Planning in Ethiopia with Particular Reference to Abattoir Site Suitability Analysis for Kulito Town: an Exploration
    (Addis Ababa University, 2002-06) Melese Aysheshim; Lamenew Workshet
    In Ethiopia certain urban functions, particularly abattoirs even though their siting is a critical environmental issue (EPA, 2002), are frequently observed being located incompatibly with the surrounding geographic features. As a result, abattoirs pose environmental hazard to their surrounding, and they are also affected by some nearby activities. The implication is that each fiscal year NUPI, which is the chief urban development plan making arm of the Federal Government of Ethiopia, would be confronted with problems of these kind to which appropriate solution should be sought. However, it is admitted that the manual method NUPI is currently using is not sufficient enough to cope with the increasing demand for siting abattoirs. It is rather slow, error prone and laborious. In an attempt to address such problems, the present study specifically explores the potentiality of applying GIS for abattoir site suitability analysis, by taking a selected town of Ethiopia (Kulito) as a case. A model for the analysis has been developed by working closely with knowledgeable experts of NUPI in the problem area as well as reviewing relevant documents. The model was then implemented using Arc View GIS Version 3.1. Based on the comments of NUPI’s experts, the result was proved satisfactory and the method could be used for the designation of suitable abattoir site for other towns with some modifications if necessary
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    Developing Black Box Web Application Penetration Testing Methodology Using Comparative Criteria
    (Addis Ababa University, 2015-06) Gebremedhin Mebrahtu; Lamenew Workshet
    The impact of exploiting web applications can be from stealing confidential information, loss of confidence to a war between countries and civil unrest. Attackers can deface university websites and post offending messages that target to make fighting between communities and civil war. As a result, developers, website and web application owners should take an appropriate measure from being attacked. One of the preventive measures to protect from attacks is to identify the possible vulnerabilities that can lead for attackers to exploit them through a black box penetration testing. To conduct such a test black box assessors need methodologies as without them leads the test to be non-effective and time consuming. That is why different testing methodologies and procedures are being developed. The security tester that may test for the identification of any vulnerability irrespective of the standard, however, hasn’t enough frameworks to compare those testing methodologies. In this research, the set of criteria for selecting and testing black box web application security methodologies was developed and the methodologies was compared based on a set of criteria. Once the strength and weakness of those standards were known, a complete testing methodology based on the current testing methodologies and additional reference was developed. The testing methodology was then tested on a sample of four Ethiopian universities and two intentionally vulnerable. From the research findings it can be concluded that NIST, OWASP, ISACA, ISSAF, and Penetration Testing Framework can be used for black box web application testing. However, they incorporate black box, white box, and gray box testing methodologies within one methodology. Hence, a black box security tester can’t directly use them. More importantly, a black box tester can’t only relay in only one of the testing methodologies as there are areas that each of them don’t cover. As a result a new and complete methodology was developed. Researches such as white box web application security testing, black and white box security testing on other targets besides to web application, risk calculation, knowledge and skill requirement for black and white box security assessors can be further conducted on this area.
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    Developing Dynamic Bandwidth Allocation Prototype Model for Campus Network Based on Network Traffic Analysis
    (Addis Ababa University, 2011-01) Weldegebriel Hailay; Lamenew Workshet
    Enterprise or campus networks usually impose a set of rules for users to access the network in order to protect network resources and enforce institutional policies (for instance, no sharing of music files or no gaming). This leaves network administrators with the daunting task of identifying the application associated with a traffic flow as early as possible and controlling user’s traffic when needed. Therefore, accurate and early network traffic analysis is an essential step for administrators to detect intrusion, malicious attacks, or forbidden applications. Hence, bandwidth management based on network traffic flow analysis is a researchable area which gives the owner of the research with lots of opportunity to assess the network behaviors. In this work protocol distribution and network traffic load analysis are conducted on different links based on real data. Besides, it is tried to assess the potential application of ntop for measuring the global protocol distribution based on the real data. Moreover, the ingoing and outgoing traffic load is analyzed using MRTG which is a versatile tool for graphing network data and it can run on a Web server. Every five minutes, it reads the inbound and outbound octet counter of the gateway router, and then logs the data to generate daily, weekly, monthly and yearly graphs for web pages. Weblog analysis consists in measuring the usage of relevant traffic activities. Weblog expert tracks web server log file, generating a series of statistics for each host, for operating system, for each browser, for each visitor and soon in the Mekele University (MU) inter campus network as a whole. Based on the traffic analysis at different links and web server of MU, a dynamic bandwidth allocator algorithm is proposed which consists of modules such as administration tool, which provides a graphical interface for configuring bandwidth allocation based on the different bandwidth demand in the intercampus network; policy agent, which implements the configuration and handles communication with the kernel module; kernel module, which implements the packet classifier, packet selector, bandwidth estimator, unique IP address counter, Host DBA and timer. Keywords: network traffic, Dynamic Bandwidth Allocation, Bandwidth Management.
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    Optimal Feature Selection for Network Intrusion Detection: a Data Mining Approach
    (2011-06) Mossie Zewdie; Lamenew Workshet
    The traditional approach in securing computer systems against cyber threats is designing mechanisms such as firewalls, authentication tools, and virtual private networks that create a protective shield almost always with vulnerabilities. This has created Intrusion Detection Systems (IDS) to be developed that complement traditional approaches. However, with the advancement of computer technology, the behavior of intrusions has become complex that makes the work of network security experts hard to analyze and detect intrusions. In order to address these challenges, using data mining techniques have become a possible solution. However, the performance data mining algorithms are affected when no optimized features provided. This is because, complex relationships can be seen as well between the features and intrusion classes contributing to high computational costs in processing tasks, subsequently leads to delays in identifying intrusions. Feature selection is thus important to be conducted in detecting intrusions by allowing the data mining system to focus on what is really important. Researches on data mining have focused on the induction of models with low expected error by totally ignoring the cost that could be incurred during misclassification and feature selection in skewed data distribution between classes. In reality, for many problem domains, the requirement is not merely to predict the most probable class label, since different types of errors carry different costs. For example the cost of allowing unauthorized access can be much greater than that of wrongly denying access to authorized individuals. Similarly the cost of not selecting features that contain unauthorized profiles is much more than probing profiles. Implementing cost sensitive classifiers that involve cost by modifying (direct) and without modifying (indirect) algorithm during model building and feature selection are a rising research interest to handle this problem and attempts have been made. However, little attention has been given to evaluate the performance of direct and indirect cost sensitive classifiers using cost sensitive feature selection approach. In this research, we proposed filter approach to select important features; namely, IGR and CFS to ii illustrate the significance of feature selection in classifying the NSL-KDD intrusion detection dataset. The central idea is the minority class feature sets, those which have low values, can be ranked at the top by gaining high information gain value and correlation percentages, at the same time those score low, ranked at the bottom in WEKA tool assuming some of the features can be redundant or contribute little to the detection process. The selected features are experimented repeatedly where features added into the final selected feature set as far as no decrease in performance and then models are constructed on the two algorithms; namely, CS-CM4 (direct) and C4.5 (indirect) using TANAGRA tool. Experiments show that CFS and IGR select below half of the total (41) features with equaled or better performance in most cases. Comparatively, the approach fits more for indirect cost sensitive C4.5 than direct cost sensitive CS-CM4. Generally, the study indicated that CS-CM4 and C4.5 algorithms by far achieved better on the proposed approach with fewer features that require less storage and time to identify new attacks as well as better performance in terms of detection rate, overall classification accuracy, average misclassification cost and false positive rate. Keywords: Cost sensitive feature selection, Cost insensitive feature selection, IGR, CFS.
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    Performance Analysis For Wide Area Network Optimization: The Case Of Addis Ababa University
    (Addis Ababa University, 2014-10-16) Berhanu Negga; Lamenew Workshet
    Wide Area Network (WAN) is one of the most important tool for organizations to run their day to day activities. Today, applications and services that are given using local area network (LAN) are also provided using WAN. However, there is more impact on the WAN performance. Besides, most protocols designed for LAN environments do not perform well over the WAN. The factors affecting the WAN are network availability, bandwidth, network latency, congestion and packet loss. WAN at AAU is no exception. This study is aimed to investigate the effects of different WAN factors using performance analysis tool, with the view to develop a WAN optimization framework that can improve the performance of the AAU network traffic flows over the WAN link. To achieve the objective of the study, an experiment has been conducted using real-time cases that are taken from the AAU WAN environment. The experimentation is conducted through three major phases: network traffic data is collected using Network Performance Monitor (NPM) tool from the AAU WAN environment. The collected data is then analyzed and evaluated to investigate the network performance using metrics such as network availability, response time, and packet loss. Finally, based on the analysis result a WAN optimization framework is developed. In order to develop the WAN optimization framework the researcher followed steps such as Network Condition Recognizer, WAN Bottleneck Determiner, Optimization Solution Provider and Event Reporter. The results of this study indicates that, high response time rate, high packet loss rate, and fluctuating network availability is exhibited in the AAU WAN environment. The WAN optimization framework is developed to solve the real time bottleneck status of the WAN link and apply optimization techniques accordingly. There is also a need to evaluate application performance of the network from the point view of users’ experience. It is therefore recommended for further research.
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    Population Information Support System with Particular Reference to the Requirements of Urban-Regional Planning
    (Addis Ababa University, 1994-05) Lamenew Workshet; Neelameghan A. (Professor)
    In developing countries the demand for reliable and adequate information in general and population information in particular has increased more than ever for purposes of development planning, and for formulating and monitoring population Ethiopia is no exception. From the surveys and interviews carried out for this study. it became evident that establishment of a national population information support system that can collect. organize and process such information to meet users requirements had become crucial to development planning in the country . An attempt has been made to assess users of population information (their information needs. the sources they used. their information seeking behavior, etc.) and the existing support facilities. Interviews, onsite study. reviewing of relevant documents and questionnaire survey were the methods employed for the purpose of data and fact collection . The results of the survey generally indicated that users have problems in timely access to reliable. and adequate To provide a conceptual and structural framework for the establishmnt of a population information support system . the study proposes network and elaborates a population information (POPINET) to be established. providing a better foundation f o r the information support system. Use of GIS in population activities and population project ion support system with special emphasis on urban -regional planning and the development of related databases are proposed and discussed . Recommendations to enhance the level of population information support and recommendations specific to the population information network (POPINET) are presented. -
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    Predicting Tuberculosis Treatment Outcomes using Data Mining Technology.
    (Addis Ababa University, 2013-05) Kiflom Samson; Mekonnen Alemayhu; Lamenew Workshet
    Background: Tuberculosis is the second most common causes of death throughout the world next to HIV/AIDS. Ethiopia is also among the high burden countries. Though the disease has been a cause of death for millions of people around the globe, it is curable. Prediction of treatment outcome of TB patients using data mining techniques help the effort to stop TB-health problem. Objective: The objective of this research was to prepare a predictive model for TB treatment outcomes that assist clinical decisions in connection with TB treatment. Method: The six steps Ciso et al Hybrid Model were used. A total of 6332 instances were collected from five health centers of Addis Ababa City Government that provide tuberculosis treatment. A pre-processed the data was fed in to data mining tools with selected classification algorithms. These algorithms were J48, Naïve Bayes, SMO and PART. Accuracy and Area under ROC were the metrics used to compare models generated by the algorithms. Result: After successive experiments using the four algorithms, PART algorithm revealed best performance. An accuracy of 81.32% and area under ROC=0.89. The algorithm generated five rules for the three treatment outcomes and the rules were found to be interesting for experts. The rules contain the following predictor variables for treatment outcome: HIV Status, Sex, Age,Initial Weight with second month weight and Patient Category. Conclusion: The findings from the research indicated that for the tuberculosis dataset with class imbalance PART found to be the best learner algorithm and most importantly clinical decisions such as diagnosis, prognosis and resource allocation can be supported by data mining techniques.
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    Selection of Data Mining Algorithm for Masked Feature Network Intrusion Detection on Real World Data With Missing Value: The Case of Ethiopian Institutes of Agricultural Research
    (Addis Ababa University, 2018-01-03) Admkie Kassahun; Lamenew Workshet
    . Intrusion detection has become a critical component of network administration due to the vast number of attacks persistently threating our computer system. As network attacks have increased in number and severity over the past few years, intrusion detection system (IDS) is increasingly becoming a critical component to secure the network. Due to large volumes of security audit data as well as complex and dynamic properties of intrusion behaviors, optimizing performance of IDS becomes an important open problem that is receiving more and more attention from the research community. Recently there has been much interest in applying data mining to computer network intrusion detection. Many methods have been developed to secure computer networks and communication over the Internet. However, none of the existing methods developed by different researches have an accuracy of detecting attacks with high detection rate and low false alarm rate. Moreover intruders can also chat the system by masking their some features to attack the system. The other thing is most deal with single detection approach with high number of features which is challenging and time consuming to implement. This thesis work is devoted to solve those problems of Ethiopian Institute of Agricultural Research (EIAR) using intrusion detection system architecture that is based on semi-supervised collective classification algorithm of meta.Filtered Collective Classifier that can promptly detect and classify attacks, whether they are known or never seen before, even they mask their some features by using missing value dataset. The data set in this study is taken from EIAR data center. After taking the data, it has been preprocessed. In the preprocessing activities, removing outliers and resolving inconsistencies tasks are taken place. The researcher has taken the dataset initially had 25192 records but after the preprocessing stage, it was reduced to 28 attributes and 12596 records which are labeled as Normal, DOS, U2R, Probe and R2L. For supervised modeling, the 6965 records are taken. For building a predictive model for intrusion detection semi-supervised collective classification meta.Filtered Collective Classifier and ordinary J48 decision tree algorithms have been tested as a classification approach by using unlabeled class with missing value and with no missing value dataset. xvii The model that was created using the Semi-Supervised meta.Filtered Collective Classifier parameters with fully Training/Test set showed the best classification accuracy of 96.2% by using the first dataset, with missing value and the ordinary J48 tree with its default 10-fold cross validation showed better performance of 100% accuracy by using the second dataset, with no missing value to classify the new instances as Normal, DOS, U2R, Probe and R2L classes. The findings of this study have shown that the data mining methods generates interesting rules that are crucial for intrusion detection in the networking industry. Future research directions are forwarded to come up an applicable system in the area of the study.