International Journal of Innovative Science and Modern Engineering

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EISSN : 2319-6386
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Paulmakesh A., Gizachew Markos Makebo
International Journal of Innovative Science and Modern Engineering, Volume 7, pp 9-14; doi:10.35940/ijisme.b1281.037221

Abstract:
Due to high material consumption, infrastructure construction is rapidly becoming a serious problem in this world, particularly in developing countries. Concrete is produced in greater amounts, as a result of this aggregate shortage The focus of this research was to see if ash can substitute for fine aggregates in concrete The sample was collected from the Ayka Addis mill pond ash.The experimental laboratory of this research work was used to conduct tests on gradation, specific gravity, unit weight, moisture content, silt content, and water absorption, as well as workability, density, and compressive strength. The workability of each mix was measured before the concrete was cast, and the slum was 25 to 50mm. Furthermore, for accurate testing of density and compressive strength, cubes (150mm x 150mm x 150mm) of 7% to 30% pond and 5% to 30% of ash density, which were of concrete, were prepared. Up to 10% of the total mix, the concrete was found to have compressive strength of 34.75 N/sq.m.For this reason, fine aggregate at 10% replacement is the best aggregate to use.
International Journal of Innovative Science and Modern Engineering, Volume 7, pp 1-8; doi:10.35940/ijisme.b1280.037221

Abstract:
Contending with Non-Technical Losses (NTL) is a major problem for electricity utility companies. Hence providing a lasting solution to this menace motivates this and many more research work in the electricity sector in recent times. Non-technical losses are classed under losses incurred by the electricity utility companies in terms of energy used but not billed due to activities of users or malfunction of metering equipment. This paper therefore is aimed at proffering a solution to this problem by first detecting such loopholes via the analysis of consumers’ consumption pattern leveraging Machine learning (ML) techniques. Support vector machine classifier was chosen and used for classifying the customers’ energy consumption data, training the system and also for performing predictive analysis for the given dataset after a careful survey of a number of machine learning classifiers. A classification accuracy (and subsequently, class prediction) of 79.46% % was achieved using this technique. It has been shown, through this research work, that fraud detection in Electricity monitoring, and hence a solution to non-technical losses can be achieved using the right combinations of Machine Learning techniques in conjunction with AMI technology.
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