Browsing by Author "Gizaw, Solomon (PhD)"

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    Amharic Text to Ethiopian Sign Language Translation Model Using Factored Phrase Based Statistical Machine Translation Approach
    (Addis Ababa University, 2021-03-27) Belay, Yoseph; Gizaw, Solomon (PhD)
    Machine translation is a process of natural language translation automation to translate text from one natural language to another natural language. Machine translation is the fastest way to process a vast amount of data and produce usable translations in any language in the world. In this paper, we deal with the design of an Amharic to Ethiopian Sign Language machine translator. Amharic is the official language of Ethiopia. Ethiopian Sign Language is a visual-gestural language used to communicate and interacting by the Ethiopian Deaf community. This study presents a factored Amharic to Ethiopian Sign Language statistical machine translation system composed of three main components. The first component is a neural network-based Amharic part of speech tagger that is used as a preprocessor to factorize the words in the parallel corpora. The second component is a factored statistical machine translator that is used to translate text from Amharic to Ethiopian Sign Language grammatical structure. The third component is a word to Ethiopian Sign Language video clip mapper which takes the translated text as an input and finds matches from the video corpus. We conducted experiments using three different machine translation approaches and compared with the evaluation result of the proposed system. The first experiment is performed using a standard phrased based statistical approach as a baseline model. The second experiment conducted using a factored phrased-based approach. The third experiment carried out by using a neural machine translation approach. Our evaluation's findings demonstrate that the use of factored phrase-based statistical translation approach effectively improves Amharic to EthSL machine translation. Our proposed factored statistical translation achieves a 35.28 BLEU score which outperforms both the baseline standard phrase-based statical machine translation model and the neural machine translation model.
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    Amharic Text to Ethiopian Sign Language Translation Model Using Factored Phrase Based Statistical Machine Translation Approach
    (Addis Ababa University, 3/27/2021) Belay, Yoseph; Gizaw, Solomon (PhD)
    Machine translation is a process of natural language translation automation to translate text from one natural language to another natural language. Machine translation is the fastest way to process a vast amount of data and produce usable translations in any language in the world. In this paper, we deal with the design of an Amharic to Ethiopian Sign Language machine translator. Amharic is the official language of Ethiopia. Ethiopian Sign Language is a visual-gestural language used to communicate and interacting by the Ethiopian Deaf community. This study presents a factored Amharic to Ethiopian Sign Language statistical machine translation system composed of three main components. The first component is a neural network-based Amharic part of speech tagger that is used as a preprocessor to factorize the words in the parallel corpora. The second component is a factored statistical machine translator that is used to translate text from Amharic to Ethiopian Sign Language grammatical structure. The third component is a word to Ethiopian Sign Language video clip mapper which takes the translated text as an input and finds matches from the video corpus. We conducted experiments using three different machine translation approaches and compared with the evaluation result of the proposed system. The first experiment is performed using a standard phrased based statistical approach as a baseline model. The second experiment conducted using a factored phrased-based approach. The third experiment carried out by using a neural machine translation approach. Our evaluation's findings demonstrate that the use of factored phrase-based statistical translation approach effectively improves Amharic to EthSL machine translation. Our proposed factored statistical translation achieves a 35.28 BLEU score which outperforms both the baseline standard phrase-based statical machine translation model and the neural machine translation model.
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    Anomaly Based Peer-to-Peer Botnet Detectionusing Fuzzy-Neuronetwork
    (Addis Ababa University, 2020-10-10) Worku, Tewodros; Gizaw, Solomon (PhD)
    Peer-to-Peer (P2P) botnets are considered as one of the most significant contributors to various malicious activities on the Internet. The denial of service attacks, spamming, keylogging, click fraud, traffic sniffing, stealing personal user information, for example credit card numbers, and social security numbers, are some of the illegal activities based on botnets. P2P botnets are networks of infected computing devices, called zombies or bots. These bots are remotely controlled and instructed by malicious entities commonly referred to as Botmasters or hackers. In recent years, lots of researchers have proposed a number of P2P botnet detection models, but due to the evolving nature of botnets, there is still a need for new techniques to identify recent botnets. Due to that, we propose a model that is able to distinguish genuine network traffic from malicious one by analyzing the network flow data using Fuzzy-Neuro Network (FNN). The proposed model has the following components: Feature Extractor, Feature Selector, Dataset Constructor, Preprocessor, Classifier and P2P Botnet Detector. The feature extraction component extracts the network traffic-based feature vectors from the network traffic whereas the feature selection component selects vital features based on their information gain value. The next component which is the dataset constructor is used to convert the comma separated value (CSV) file into sets and help us to split the dataset as training (70%) and testing (30%) sets. Then, the major activities in the preprocessing component are data cleaning, data transformation and data reduction. Finally, the FNN classifier is utilized to classify the network traffic into P2P botnet and normal using the botnet detection module. The feasibility of our proposed model has been validated through experiments using network traffic records acquired from two publicly available P2P botnet datasets Bot-IoT and UNSW-NB15. The datasets include both genuine and malicious network traffic. The evaluation result shows the proposed model is effective in detecting P2P botnets. Based on the evaluation results of our classifier, using Bot-IoT dataset, the model scored 100% for all evaluation metrics. Whereas, using the UNSW-NB15 dataset, the model scored highest classification accuracy of 99.9%, precision of 99.9% and recall of 100% with F-measure rate of 99.9%.
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    Anomaly Based Peer-to-Peer Botnet Detectionusing Fuzzy-Neuronetwork
    (Addis Ababa University, 10/10/2020) Worku, Tewodros; Gizaw, Solomon (PhD)
    Peer-to-Peer (P2P) botnets are considered as one of the most significant contributors to various malicious activities on the Internet. The denial of service attacks, spamming, keylogging, click fraud, traffic sniffing, stealing personal user information, for example credit card numbers, and social security numbers, are some of the illegal activities based on botnets. P2P botnets are networks of infected computing devices, called zombies or bots. These bots are remotely controlled and instructed by malicious entities commonly referred to as Botmasters or hackers. In recent years, lots of researchers have proposed a number of P2P botnet detection models, but due to the evolving nature of botnets, there is still a need for new techniques to identify recent botnets. Due to that, we propose a model that is able to distinguish genuine network traffic from malicious one by analyzing the network flow data using Fuzzy-Neuro Network (FNN). The proposed model has the following components: Feature Extractor, Feature Selector, Dataset Constructor, Preprocessor, Classifier and P2P Botnet Detector. The feature extraction component extracts the network traffic-based feature vectors from the network traffic whereas the feature selection component selects vital features based on their information gain value. The next component which is the dataset constructor is used to convert the comma separated value (CSV) file into sets and help us to split the dataset as training (70%) and testing (30%) sets. Then, the major activities in the preprocessing component are data cleaning, data transformation and data reduction. Finally, the FNN classifier is utilized to classify the network traffic into P2P botnet and normal using the botnet detection module. The feasibility of our proposed model has been validated through experiments using network traffic records acquired from two publicly available P2P botnet datasets Bot-IoT and UNSW-NB15. The datasets include both genuine and malicious network traffic. The evaluation result shows the proposed model is effective in detecting P2P botnets. Based on the evaluation results of our classifier, using Bot-IoT dataset, the model scored 100% for all evaluation metrics. Whereas, using the UNSW-NB15 dataset, the model scored highest classification accuracy of 99.9%, precision of 99.9% and recall of 100% with F-measure rate of 99.9%.
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    Computational Modeling and Analysis of Traffic Crash and Traffic Volume in Addis – Adama Expressway
    (Addis Ababa University, 2021-04-26) Murad, Ayana; Gizaw, Solomon (PhD)
    Transportation has a major contribution in the development of the human civilization. The accessibility of highway transportation has given many focal points that contribute to a high standard of living. However, many issues related to the highway mode of transportation exist. These issues incorporate highway related accidents, parking troubles, clog, natural risks (carbon emissions, clamor contamination, etc.) and delay. To solve these problems building expressways is one of the solutions. Even though building express way is a good solution for solving problems related to highway traffic, Data collected from Ethiopia Toll Road Enterprise indicated that, on average, about 417 road crashes were reported since September 2014 to February 2016 that leads around 672 traffic accidents. Road traffic crashes (RTCs) are globally acknowledged as increasing threat to society, because they can affect many lives when they result in severe injury or fatality. Ethiopia is among the leading countries in road traffic accident. The recent road safety record of Addis Ababa- Adama expressway is also alarming the severity of the situation and calling for an integral effort of all pertinent stakeholders to reverse the trend. In this research we modeled traffic crashes and traffic volumes in Addis – Adama express way with ordinary differential equation using interpolation methods (i.e. newton DVD and Lagrange interpolations). We solved the ordinary differential equations we got after modeling using Euler method and Runge – Kutta method. We observed if there is any relation between traffic crash and traffic volume. We analyzed the traffic crash data parameters i.e. vehicle type, vehicle type with weekdays and direction to observe the factors causing traffic accident. The data we used for modelling and analyzing is collected from ETRE. Using the mathematical model of traffic crash, we were able to predict 2020 number of traffic crash. The finding shows that traffic crash and traffic volume have linear relationship. From the analysis we observed that Small automobiles are causing the highest traffic crash, the highest number of traffic crash occurred in Friday, and vehicles heading to mojo are causing the highest number of traffic crash. Therefore, ETRE should take restrict monitoring on small automobiles, vehicles heading to mojo, and in weekends.
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    Hate Speech Detection Framework from Social Media Content the Case of Afaan Oromoo Language
    (Addis Ababa University, 2021-12-02) Guta, Lata; Gizaw, Solomon (PhD)
    Hate Speech on social media has unfortunately become a common occurrence in the Ethiopia online community largely due to advances in mobile computing and the Internet. The connectivity and availability of social media platforms in the world allow people to Interact and interchange experiences easily. However, the anonymity and flexibility afforded by the Internet have made it easy for users to communicate aggressively. Hate Speech affects the society in many aspects, such as affecting the mental health of targeted audiences, affects social interaction, leads to violence and distraction of properties. Identifying a text that containing Hate Speech regularly is difficult task for humans, it is tedious and time consuming. To solve the newly emerged Hate Speech propagation in social media sites, recent studies employed different Machine learning algorithms and feature engineering techniques to detect Hate Speech messages automatically. In case of Afaan Oromoo language there is a work on Sentiment Analysis of Afaan Oromo using Machine learning Approach.but it is not in case of Hate and neutral classification rather oponions. In this research, a new Afaan Oromoo Hate Speech dataset from Facebook social media that are labeled into binary classes. TF-IDF, N-gram and word2ve feature are used as a feature for the Machine learning models. We evaluate the models using 80% for training and 20% for testing purpose by using train-test split with accuracy, precession, recall, and f1-score performance metrics were used to compare the models. The model based on LSVM with TF-IDF combination with N-gram achieves slightly better performance than the other models. Support Vector Machine(SVM) algorithm achieve the highest accuracy of 96% which is promised result.
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    Hate Speech Detection Framework from Social Media Content the Case of Afaan Oromoo Language
    (Addis Ababa University, 12/2/2021) Guta, Lata; Gizaw, Solomon (PhD)
    Hate Speech on social media has unfortunately become a common occurrence in the Ethiopia online community largely due to advances in mobile computing and the Internet. The connectivity and availability of social media platforms in the world allow people to Interact and interchange experiences easily. However, the anonymity and flexibility afforded by the Internet have made it easy for users to communicate aggressively. Hate Speech affects the society in many aspects, such as affecting the mental health of targeted audiences, affects social interaction, leads to violence and distraction of properties. Identifying a text that containing Hate Speech regularly is difficult task for humans, it is tedious and time consuming. To solve the newly emerged Hate Speech propagation in social media sites, recent studies employed different Machine learning algorithms and feature engineering techniques to detect Hate Speech messages automatically. In case of Afaan Oromoo language there is a work on Sentiment Analysis of Afaan Oromo using Machine learning Approach.but it is not in case of Hate and neutral classification rather oponions. In this research, a new Afaan Oromoo Hate Speech dataset from Facebook social media that are labeled into binary classes. TF-IDF, N-gram and word2ve feature are used as a feature for the Machine learning models. We evaluate the models using 80% for training and 20% for testing purpose by using train-test split with accuracy, precession, recall, and f1-score performance metrics were used to compare the models. The model based on LSVM with TF-IDF combination with N-gram achieves slightly better performance than the other models. Support Vector Machine(SVM) algorithm achieve the highest accuracy of 96% which is promised result.