Recent Activities of History of Electrical Engineering Committee (HEE)

Science historians need two major kinds of literary resources, old books, journals, patents, plans and other documents from which to quarry their facts, and critical tools such as histories of science, bibliographies and biographies. Provision of the second category needs positive planning; the first is often itself an accident of local history. Among the factors which have shaped Newcastle upon Tyne may be numbered a Roman river crossing, a Norman castle, mediaeval walls, powerful charters granted by Tudor and Stuart monarchs, a favourable site in a coalfield, and a phenomenal succession of inventive entrepreneurs in mining, chemicals, shipbuilding, and mechanical and electrical engineering. Its scientific and cultural institutions (see Table) are of respectable maturity, and in addition the town possessed by 1815 several chapel and meeting-house libraries, a newsroom and subscription library in the Assembly Rooms together with three circulating libraries run by prominent booksellers. Present resources are concentrated in six organizations, with two more in the near future.

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A Study on Correlation of Subjects on Electrical Engineering Course Using Artificial Neural Network (ANN)

Asian Journal of University Education ◽

10.24191/ajue.v17i2.13391 ◽

2021 ◽

Vol 17 (2) ◽

pp. 144

Author(s):

Fathiah Zakaria ◽

Siti Aishah Che Kar ◽

Rina Abdullah ◽

Syila Izawana Ismail ◽

Nur Idawati Md Enzai

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Electrical Engineering ◽

Electric Circuit ◽

Time Correlation ◽

Ann Model ◽

Core Subject ◽

History Of ◽

Artificial Neural ◽

Artificial Neural Network Ann

Abstract: This paper presents a study of correlation between subjects of Diploma in Electrical Engineering (Electronics/Power) at Universiti Teknologi MARA(UiTM) Cawangan Terengganu using Artificial Neural Network (ANN). The analysis was done to see the effect of mathematical subjects (Pre-calculus and Calculus 1) and core subject (Electric Circuit 1) on Electronics 1. Electronics 1 is found to be a core subject with the history of high failure rate percentage (more than 25%) in previous semesters. This research has been conducted on current final semester students (Semester 5). Seven (7) models of ANN are developed to observe the correlation between the subjects. In order to develop an ANN model, ANN design and parameters need to be chosen to find the best model. In this study, historical data from students’ database were used for training and testing purpose. Total number of datasets used are 58 sets. 70% of the datasets are used for training process and 30% of the datasets are used for testing process. The Regression Coefficient, (R) values from the developed models was observed and analyzed to see the effect of the subject on the performance of students. It can be proven that Electric Circuit 1 has significant correlation with the Electronics 1 subject respected to the highest R value obtained (0.8100). The result obtained proves that student’s understanding on Electric Circuit 1 subject (taken during semester 2) has direct impact on the performance of students on Electronics 1 subject (taken during semester 3). Hence, early preventive measures could be taken by the respective parties. Keywords: Artificial neural network, Diploma in Electrical Engineering, Graduate on time, Correlation.

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