Browsing by Author "Demissie, Solomon"

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    Efficient Practices of Farmers` to Control Striga Hermonthica Weed Infestation on Sorghum: a Case Study of Ensaro District, Central Ethiopia
    (Addis Ababa University, 2018-09-09) Demissie, Solomon; Wondimu, Tigist (PhD)
    The study was conducted in Ensaro District lowland (Kolla) areas found in North Shoa Administrative Zone, Amhara National Regional State with the aims to identify, describe and evaluate the control practices of Striga hermonthica weed infestation on sorghum by farmers of the study area. Data were collected through Participatory Rural Appraisal using semi-structured interview, questionnaire, field observations and group discussion. The different control practices of this weed infestation in the study area were analyzed using descriptive statistics, preference ranking and paired comparison. The more efficient control practices of the study area were combinations of post emerging herbicides, hand weeding and use of inorganic fertilizers. About 68 % of the interviewed member of the local communities and Agricultural extension workers had positive attitude on the effectiveness of the practices. Since the area is highly infested by the Striga hermonthica weed for a long period of time as a result the grain yield of Sorghum doesn’t meet the expectations of the farmers. Although the controlling practices are better solutions than using none, there is still a need to put more effort for irrevocable solution.
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    The Potential for Applying Knowledge Base System for Diagnosis of Acute Respiratory Tract Infections
    (Addis Ababa University, 2010-05-11) Demissie, Solomon; Beshah, Tibebe (Ato)
    Knowledge base systems exercise information technology to acquire and utilize combined human expertise. The technology can be very useful to institutions with clear objectives, rules and problems to provide consistent answers for repetitive decision-making, processes and tasks. Knowledge base systems should be adopted and updated periodically to cater for the new discoveries, and to enhance benefits by addressing the new changes in the clinical diagnostic activities. This research was done to preserve human expert level knowledge on the diagnosis of acute respiratory tract infections so that to make available such expert-knowledge for diagnostic activities. The system, also, could be useful especially in the medical environment where knowledge experts are few, often in scarcity and often soon retire before their expertise is documented. Facts that constituted the global criteria for the knowledgebase were gathered from expert physicians, pharmacists and nurses at the hospital of Dagmawi-minilik and Meshualekia middle-level clinic, Addis Ababa, review of guidelines, manuals, journals of respiratory infections, and online resources. The system uses backward chaining with inference network and decision trees modeling structures basing on facts to draw logical conclusions from the initial states to the final states using respiratory diagnostic functions. For the prototype development, Prolog programming language has been used. The performance of the prototype system is evaluated on qualitative bases. The result is encouraging to design a practical KBS for ARTI diagnosis. Lastly, further studies should be done in artificial intelligence to solve the problem of rare expertise in the diagnosis of respiratory infections.