Precision medicine is an emerging approach for disease treatment and prevention that considers individual variability in genes, environment, and lifestyle for each person. Big data analytics (BDA) using cutting-edge technologies helps to design models that can diagnose, treat and predict diseases. In Ethiopia, healthcare service delivery faces many challenges specifically in relation to prescribing the right medicine to the right patient at the right time. Thus, patients face challenges ranging from staying on treatment plans longer, and then leaving treatment, and finally dying of complications. Therefore, the aim of this paper is to explore the trends, challenges, and opportunities of applying BDA in precision medicine globally and take lessons for Ethiopia through a systematic literature review of 19 peer reviewed articles from five databases. The findings indicated that cancer in general, epilepsy, and systemic diseases altogether are areas currently getting big attention. The challenges are attributed to the nature of health data, failure in collaboration for data sharing, ethical and legal issues, interoperability of systems, poor knowledge skills and culture, and poor infrastructure. Development of modern technologies, experimental technologies and methods, cloud computing, Internet of Things, social networks and Ethiopia’s government initiative to promote private technological firms could be an …

Text summary has become a vital and more popular domain to preserve and highlight the core purpose of textual information as the amount of online information and resource texts has grown. Text summarization is the task of extracting key information from a text document. Text summarizing research in Afaan Oromoo is still rare and hasn't been thoroughly assessed. This study's primary goal was to evaluate the performance and method of extractive models on automatic extractive text summarization for Afaan Oromoo. Automatic Text summarization approaches can be classified as extractive or abstractive. Automatic abstractive text summarization was not included in this study. Existing automatic extractive text summarizing algorithms take key sentences from the source manuscript and provide a summary without changing the data. This paper examined and assessed some studies on the Afaan Oromoo Language Text Summarization system with a focus on methods and performance. In addition, the automatic extractive text summarization domain's challenges in Afaan Oromoo’s were also discussed. This paper used a systematic literature review method to examine the most recent literature in automatic extractive text summarization as it relates to the Afaan Oromoo language. We used a search engine, Google Scholar, ResearchGate, CiteseerX, peer-reviewed papers, and Academia to …

Knowledge is awareness or familiarity gained by experiences of facts, data, and situations. Knowledge management includes techniques and processes to represent, store, search, integrate, and analyze knowledge that is available in digital form. Ontology is a formal explicit specification of a shared conceptualization of a domain of interest and it is a building block of the semantic web and formal description of knowledge. Ontologies capture the structure and knowledge about some domain of interest by describing the concepts in the domain and also the relationships that hold between those concepts. Even though Ethiopia has potential tourist destinations, the country is not benefited from its resources due to misperception about image of the country; lack of promoting the potential tourism resources of the country to the world; problems with sharing, searching and retrieval of tourist information. Thus, the country is forced to accept smaller number of tourists and not getting the benefits it deserves. The objective of this paper is to build ontology for Ethiopian Tourism so that it makes Ethiopian tourism destinations visible to international visitors. We use OWL language implemented in Protégé with other ontology development activities proposed in METHONTOLOGY to build Ethiopian tourism ontology. We also use OWL …

This paper will report on a research to develop Speaker Independent, Continuous Speech Recognizer for Kafi Noonoo (Afro-Asiatic language that belongs to North Omotic sub family in Ethiopia) using Hidden Markov Modeling technique. The portable and open source toolkit called Hidden Markov Model (HMM) Toolkit is used to perform the experiment. The development of HMM based Automatic Speech Recognition (ASR) requires both text and speech corpus for training and testing the HMM. In order to have a model that incorporates different features of the language, we included the different dialects of Kafi Noonoo in the corpus and then prepared the training and test corpus from the scratch, and after preprocessing we have sampled and performed feature extraction using Mel Frequency Cepstral Coefficients (MFCC) feature extraction technique.