Lecturer performance system using neural network with Particle Swarm Optimization

Abstract

ABSTRACT:The field of analyzing performance is very important and sensitive in particular when it is related to the performance of lecturers in academic institutions. Locating the weak points of lecturers through a system that provides an early warning to notify or reward the lecturers with warned or punished notices will help them to improve their weaknesses, leads to a better quality in the institutions. The current system has major issues in the higher education at Salahaddin University-Erbil (SUE) in Kurdistan-Iraq. These issues are: first, the assessment of lecturers' activities is conducted traditionally via the Quality Assurance Teams at different departments and colleges at the university, second, the outcomes in some cases of lecturers' performance provoke a low level of acceptance among lectures, as these cases are reflected and viewed by some academic communities as unfair cases, and finally, the current system is not accurate and vigorous. In this paper, Particle Swarm Optimization with Neural Network is used to assess performance of lecturers in more fruitful way and also to enhance the accuracy of recognition system. Different real and novel data sets are collected from SUE. The prepared datasets preprocessed and important features are then fed as input source to the training and testing phases. Particle Swarm Optimization is used to find the best weights and biases in the training phase of the neural network. The best accuracy rate obtained in the test phase is 98.28%. ß

Heart Attack Classification System Using Neural Network Trained with Particle Swarm Optimization

“…The neural network's weights and biases will be optimized using particle swarm optimization (PSO). Secondly, the PSONN is tested with the dataset to see how well it has been trained [10].…”

“…The same dataset was used as we used in this current work. They used three techniques; BPNN was one of them, but in the current work, a PSONN [10] is proposed as well. They used two more techniques, the Fuzzy Inference System (FIS) and Adaptive Neuro-Fuzzy System (ANFIS) as predictors for heart attack.…”

Evaluation of the Hybrid Pedagogic Method in Students’ Progression in Learning Using Neural Network Modelling and Prediction

“…Such scheme is performed based on the supervised machine learning. The NN in this paper is formulated using the earlier works (Zhang et al, 2003;Vandamme et al, 2007;Rashid and Ahmad, 2016;Lau et al, 2019). This paper does not compare the effectiveness of NN performance with other machine learning techniques where it is intended to focus on the evaluation of the hybrid pedagogic approach in students' progressing in learning using NN.…”

A multi hidden recurrent neural network with a modified grey wolf optimizer

“…The study presented in [17] was related to the lecturers' performance. A dynamic and smart system, using both multiple and single soft computing classifier techniques, was utilized for forecasting the lecturers' performance at the College of Engineering, Salahaddin University-Erbil.…”