Ontology Based Recommender System for Diabetic Patients
Simachew Melaku,
Melkamu Beyene
Issue:
Volume 10, Issue 6, December 2021
Pages:
109-116
Received:
29 September 2021
Accepted:
28 October 2021
Published:
17 November 2021
Abstract: Chronic diseases are a persistent and long-lasting human health conditions that lasts for more than three months. Today the prevalence of chronic non-communicable diseases in Ethiopia increases rapidly because of different reasons like poor nutrition habit, lack of physical activities, drinking alcohols, smoking and life style issues. To overcome this problem different technological applications are developed globally to support both the health professional in diagnosis process and the patients for their self-treatment activities. Ontology helps to create common understanding between human and computers, enable reusability of information, and allows sharing of concepts. Ontology based personalized recommendation model is for diabetic patient in Ethiopian context. We have used design science research methodology in our proposed study. In the development of the proposed model, first we have developed the patient and domain or disease ontology and then the two ontologies needs to integrate in order to develop the required recommendation model. We have used Protégé ontology development tool for the development of the proposed domain or disease and patient ontology. This research discusses how to develop patient and domain or disease ontology and then it also describes how two ontologies need to integrate in order to develop the required recommendation model.
Abstract: Chronic diseases are a persistent and long-lasting human health conditions that lasts for more than three months. Today the prevalence of chronic non-communicable diseases in Ethiopia increases rapidly because of different reasons like poor nutrition habit, lack of physical activities, drinking alcohols, smoking and life style issues. To overcome t...
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Review on Decision Support System for Agrotechnology Transfer (DSSAT) Model
Issue:
Volume 10, Issue 6, December 2021
Pages:
117-124
Received:
21 October 2021
Accepted:
17 November 2021
Published:
24 November 2021
Abstract: Traditional agronomic experiments were conducted at a specific location in time and space, resulting in long, seasonal, time-consuming, and expensive experiments. An international team of scientists has developed a decision support system for the transfer of agrotechnology, which has been used by researchers from around and the world for 15 years. This package incorporates models for over 42 crops (since Version 4.7.5) as well as tools to facilitate effective use of the models. Tools include database management programs for soil, weather, crop management, and experimental data, utilities, and implementation programs. Crop simulation models simulate growth, development, and yield in accordance with soil-plant-atmosphere dynamics. Over the last few years, it has become increasingly difficult to maintain the DSSAT crop models, partly due to the fact that there were different sets of computer code for different crops with little attention to software design at the level of crop models themselves. Thus, the DSSAT crop models have been re-designed and programmed to facilitate more efficient incorporation of new scientific advances, applications, documentation, and maintenance. The basis for the new DSSAT cropping system model (CSM) design is a modular structure in which components separate along scientific discipline lines and are structured to allow easy replacement or addition of modules. In this review paper, I described the approaches used to model the primary scientific components (soil, crop, weather, and management). Besides, the review paper describes the limitations, the future of the DSSAT model, and its importance. The benefits of the new, re-designed DSSAT–CSM will provide considerable opportunities for its development and others in the scientific community for greater cooperation in interdisciplinary research and in the application of knowledge to solve problems in the field, farm, and higher levels.
Abstract: Traditional agronomic experiments were conducted at a specific location in time and space, resulting in long, seasonal, time-consuming, and expensive experiments. An international team of scientists has developed a decision support system for the transfer of agrotechnology, which has been used by researchers from around and the world for 15 years. ...
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Amharic Text Summarization for News Items Posted on Social Media
Abaynew Guadie,
Debela Tesfaye,
Teferi Kebebew
Issue:
Volume 10, Issue 6, December 2021
Pages:
125-138
Received:
25 September 2021
Accepted:
19 October 2021
Published:
24 December 2021
Abstract: This paper introduces Amharic Text Summarization for News Items posted on social media, to summarize the news items posted Amharic texts over a time posted documents from social media on Twitter and Facebook; The main problems of the social media posted texts are that most people would probably read their posted in Amharic texts with duplicate posted documents. However, to find the information the user is looking for she or he will have to find summary posted texts and read important portions of posts as Amharic documents to extract desired information on social media. Summarization is dealing with information overload presenting and posted with a text document for the current time representation of the posted documents to summarize. Our proposed approach has three main components: First, calculate the similarity between each posted document within the two pair of sentences. Second, clustering based on the similarity results of the documents to group them by using Kmeans algorithm. Third, summarizing the clustered posted document individually using TF-IDF algorithms that involve finding statistical ways for the frequent terms to rank the documents. We applied the summarization technique is an extractive summarization approach that is assigned an extract the sentences with highest ranked sentences in the posted documents to form the summaries and the size of the summary can be identified by the user. In the experiment one the highest F-measure score is 87.07% for extraction rate at 30%, in the clustered group of protests posts. The second experiment the highest F-measure score is 84% for extraction rate at 30%, in droughts post groups. In the third experiment the highest F-measure score is 91.37% for extraction rate at 30%, in the sports post groups and also the fourth experiments the highest F-measure score is 93.52% for extraction rate at 30% to generate the summary post texts. If the system to generate the size of summary is increased, the extraction rate also increased in posted texts. For this the evaluation system shown that a very good results to summaries the posted texts on social media.
Abstract: This paper introduces Amharic Text Summarization for News Items posted on social media, to summarize the news items posted Amharic texts over a time posted documents from social media on Twitter and Facebook; The main problems of the social media posted texts are that most people would probably read their posted in Amharic texts with duplicate post...
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