Wubetu Barud Demilie
@wcu.edu.et
Department of Information Technology
Wachemo University, Hossana, Ethiopia
Wubetu Barud Demilie graduated from Haramaya and Jimma Universities in the field of Information Technology with both BSc and MSc. degrees in the 2013 and 2017 academic years respectively. He has served in the field of education for over 9 years and is currently in service at Wachemo University, in the College of Engineering and Technology, Department of Information Technology, Hossana, Ethiopia. He has held administrative positions at the department, school, and college levels in his career, like the head of the department. He has actively participated in community-based training programs and research works. Accordingly, he has published many research works in different international reputable journals. Currently, he is working as an Assistant Professor position as a researcher and lecturer.
RESEARCH INTERESTS
My research interests are in Natural Language Processing (NLP), Information Retrieval (IR), Machine Learning (ML), Deep Learning (DL), Artificial Intelligence (AI), and Data Mining (DM).
Scopus Publications
Scholar Citations
Scholar h-index
Scholar i10-index
Scopus Publications
Wubetu Barud Demilie
Springer Science and Business Media LLC
AbstractOne of the essential components of human civilization is agriculture. It helps the economy in addition to supplying food. Plant leaves or crops are vulnerable to different diseases during agricultural cultivation. The diseases halt the growth of their respective species. Early and precise detection and classification of the diseases may reduce the chance of additional damage to the plants. The detection and classification of these diseases have become serious problems. Farmers’ typical way of predicting and classifying plant leaf diseases can be boring and erroneous. Problems may arise when attempting to predict the types of diseases manually. The inability to detect and classify plant diseases quickly may result in the destruction of crop plants, resulting in a significant decrease in products. Farmers that use computerized image processing methods in their fields can reduce losses and increase productivity. Numerous techniques have been adopted and applied in the detection and classification of plant diseases based on images of infected leaves or crops. Researchers have made significant progress in the detection and classification of diseases in the past by exploring various techniques. However, improvements are required as a result of reviews, new advancements, and discussions. The use of technology can significantly increase crop production all around the world. Previous research has determined the robustness of deep learning (DL) and machine learning (ML) techniques such as k-means clustering (KMC), naive Bayes (NB), feed-forward neural network (FFNN), support vector machine (SVM), k-nearest neighbor (KNN) classifier, fuzzy logic (FL), genetic algorithm (GA), artificial neural network (ANN), convolutional neural network (CNN), and so on. Here, from the DL and ML techniques that have been included in this particular study, CNNs are often the favored choice for image detection and classification due to their inherent capacity to autonomously acquire pertinent image features and grasp spatial hierarchies. Nevertheless, the selection between conventional ML and DL hinges upon the particular problem, the accessibility of data, and the computational capabilities accessible. Accordingly, in numerous advanced image detection and classification tasks, DL, mainly through CNNs, is preferred when ample data and computational resources are available and show good detection and classification effects on their datasets, but not on other datasets. Finally, in this paper, the author aims to keep future researchers up-to-date with the performances, evaluation metrics, and results of previously used techniques to detect and classify different forms of plant leaf or crop diseases using various image-processing techniques in the artificial intelligence (AI) field.
Wubetu Barud Demilie and Fitsum Gizachew Deriba
Springer Science and Business Media LLC
AbstractA web application is a software system that provides an interface to its users through a web browser on any operating system (OS). Despite their growing popularity, web application security threats have become more diverse, resulting in more severe damage. Malware attacks, particularly SQLI attacks, are common in poorly designed web applications. This vulnerability has been known for more than two decades and is still a source of concern. Accordingly, different techniques have been proposed to counter SQLI attacks. However, the majority of them either fail to cover the entire scope of the problem. The structured query language injection (SQLI) attack is among the most harmful online application attacks and often happens when the attacker(s) alter (modify), remove (delete), read, and copy data from database servers. All facets of security, including confidentiality, data integrity, and data availability, can be impacted by a successful SQLI attack. This paper investigates common SQLI attack forms, mechanisms, and a method of identifying, detecting, and preventing them based on the existence of the SQL query. Here, we have developed a comprehensive framework for detecting and preventing the effectiveness of techniques that address specific issues following the essence of the SQLI attacks by using traditional Navies Bayes (NB), Decision Trees (DT), Support Vectors Machine (SVM), Random Forests (RF), Logistic Regression (LR), and Neural Networks Based on Multilayer Perceptron (MLP), and hybrid approach are used for our study. The machine learning (ML) algorithms were implemented using the Keras library, while the classical methods were implemented using the Tensor Flow-Learn package. For this proposed research work, we gathered 54,306 pieces of data from weblogs, cookies, session usage, and from HTTP (S) request files to train and test our model. The performance evaluation results for training set in metrics such as the hybrid approach (ANN and SVM) perform better accuracies in precision (99.05% and 99.54%), recall (99.65% and 99.61%), f1-score (99.35% and 99.57%), and training set (99.20% and 99.60%) respectively than other ML approaches. However, their training time is too high (i.e., 19.62 and 26.16 s respectively) for NB and RF. Accordingly, the NB technique performs poorly in accuracy, precision, recall, f1-score, training set evaluation metrics, and best in training time. Additionally, the performance evaluation results for test set in metrics such as hybrid approach (ANN and SVM) perform better accuracies in precision (98.87% and 99.20%), recall (99.13% and 99.47%), f1-score (99.00% and 99.33%) and test set (98.70% and 99.40%) respectively than other ML approaches. However, their test time is too high (i.e., 11.76 and 15.33 ms respectively). Accordingly, the NB technique performs poorly in accuracy, precision, recall, f1-score, test set evaluation metrics, and best in training time. Here, among the implemented ML techniques, SVM and ANN are weak learners. The achieved performance evaluation results indicated that the proposed SQLI attack detection and prevention mechanism has been improved over the previously implemented techniques in the theme. Finally, in this paper, we aimed to keep researchers up-to-date, with contributions, and recommendations to the understanding of the intersection between SQLI attacks and prevention in the artificial intelligence (AI) field.
Wubetu Barud Demilie and Ayodeji Olalekan Salau
Springer Science and Business Media LLC
AbstractIn this paper, a misspelling detection and correction system was developed for Ethiopian languages (Amharic, Afan Oromo, Tigrinya, Hadiyyisa, Kambatissa, and Awngi). For some of these languages, there have been few works on typo detection and correction systems. However, an effective and all-in-one typo detector and corrector system for Ethiopian languages have yet to be developed. A dictionary-based methodology is used to detect and rectify various forms of misspelling-related issues. The major characteristics of the proposed model can be outlined by presenting suggestions for detected flaws and automatically correcting them utilizing the first suggestion. In addition, the proposed model is evaluated using dictionary-based data sets for all languages. The corpora used were gathered from a variety of sources, including economic, political, social, and related publications, newspapers, and magazines. In this model, the users can perform all spelling-related issues within a single system (all-in-one). That means if the user(s) is (are) working on the Amharic language and then he/she/they can change the language she/he/they prefer(s) without shifting to another graphical user interface (GUI). Here, the users can save time and perform their tasks easily. Similarly, the user(s) can improve their skills in the selected languages accordingly. Finally, precision, recall, and f-measures for each language have been computed following a successful evaluation of the model. The system outperforms an f-measure of 89.57%, 87.57%, 88.31%, 86.83%, 81.83%, and 87.59% for Amharic, Afan Oromo, Tigrinya, Hadiyyisa, Kambatissa, and Awngi languages respectively. Furthermore, recommendations have been provided for future researchers.
Wubetu Barud Demilie and Ayodeji Olalekan Salau
Springer Science and Business Media LLC
AbstractWith the proliferation of social media platforms that provide anonymity, easy access, online community development, and online debate, detecting and tracking hate speech has become a major concern for society, individuals, policymakers, and researchers. Combating hate speech and fake news are the most pressing societal issues. It is difficult to expose false claims before they cause significant harm. Automatic fact or claim verification has recently piqued the interest of various research communities. Despite efforts to use automatic approaches for detection and monitoring, their results are still unsatisfactory, and that requires more research work in the area. Fake news and hate speech messages are any messages on social media platforms that spread negativity in society about sex, caste, religion, politics, race, disability, sexual orientation, and so on. Thus, the type of massage is extremely difficult to detect and combat. This work aims to analyze the optimal approaches for this kind of problem, as well as the relationship between the approaches, dataset type, size, and accuracy. Finally, based on the analysis results of the implemented approaches, deep learning (DL) approaches have been recommended for other Ethiopian languages to increase the performance of all evaluation metrics from different social media platforms. Additionally, as the review results indicate, the combination of DL and machine learning (ML) approaches with a balanced dataset can improve the detection and combating performance of the system.
Wubetu Barud Demilie
Hindawi Limited
Nowadays, there is an abundance of information available from both online and offline sources. For a single topic, we can get more than hundreds of sources containing a wealth of information. The ability to extract or generate a summary of popular content allows users to quickly search for content and obtain preliminary data in the shortest amount of time. Manually extracting useful information from them is a difficult task. Automatic text summarization (ATS) systems are being developed to address this issue. Text summarization is the process of extracting useful information from large documents and compressing it into a summary while retaining all the relevant contents. This review paper provides a broad overview of ATS research works in various Ethiopian languages such as Amharic, Afan Oromo, and Tigrinya using different text summarization approaches. The work has identified the novel and recommended state-of-the-art techniques and methods for future researchers in the area and provides knowledge and useful support to new researchers in this field by providing a concise overview of the various feature extraction methods and classification techniques required for different types of ATS approaches applied to the Ethiopian languages. Finally, different recommendations for future researchers have been forwarded.
Fitsum Deriba
Wydawnictwo SIGMA-NOT, sp. z.o.o.
Wubetu Barud Demilie, Ayodeji Olalekan Salau, and Kiran Kumar Ravulakollu
IEEE
Accurately tagging correct grammar for individual words in a sentence is a critical task for natural language processing applications. Different deep and machine learning-oriented approaches to Part of Speech Tagger (POST) have recently been deployed as promising methods for identifying words in a phrase or sentence. This work presents the detailed concepts of POST research work on the Amharic language. Additionally, a comprehensive comparison of well-known deep and machine learning-oriented approaches was used in the development and implementation of POST for the language. A complete assessment of all published POST research works on the language is presented, together with a discussion of the proposed methods' performance, with a remark. Then, in terms of the recommended methodologies used and their performance evaluation criteria, recent developments and advancements in deep and machine learning oriented parts of speech taggers are described. Finally, we gave future recommendations for study in developing deep and machine learning-oriented POST using the results of the proposed methodologies based on their performances.