  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 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 %.
