MSc

Mar 1, 2017 - Mar 10, 2019

Mechatronics Engineering

BSc

Sep 1, 2012 - Jun 1, 2016

Mechatronics Engineering

Northern Technical University [Assistant lecturer]

Sep 1, 2019 - Present

• Teaching and Learning: Conducting lectures, seminars, and tutorials; developing course materials; and assessing student performance through assignments and examinations.
• Research: Engaging in scholarly research, contributing to publications, and presenting findings at academic conferences. 
• Student Support: Providing academic guidance and mentorship to students, including supervising projects and dissertations.
• Administrative Duties: Participating in departmental meetings, curriculum development, and contributing to the planning and organization of academic programs.

Hallucinations in GPT-2 Trained Model

Jan 25, 2025

Journal Ingenierie des Systemes d'Information

publisher university of baghdad

DOI 10.31026/j.eng.2019.02.08

This paper analysis the phenomenon of "hallucinations" in text generated by GPT-2 when it produces irrelevant or illogical content. This work has quantified the extent of those hallucinations and look into ways of their mitigation. By using two main techniques: cosine similarity and frequency analysis. These techniques calculate coherency and relevance in the text produced by OpenAI GPT-2 at different training levels. Where a study case was implemented to train the model and ask the questions and retrain the model using these replays. The main findings indicate that this model hallucinates much less at the beginning of learning, with the situation significantly improving as training progresses. Extreme learning does not eliminate all such inadequacies, and more over-training led to more hallucinations. The hallucinated items span from smaller deviations to major content-wise deviations. An inspection reveals some patterns and cues that are predictive of increased output unreliability of the model. This research suggests a stricter training program that involve varied data sets to reduce the rate of hallucinations. More importantly, improve the accuracy of the model by reaching superior levels through the embedding of contextual and factual anchoring systems as well as designing algorithms for higher trigger identification. Other recommendations of the paper include post-generation text evaluation and continuous research to enhance the complexity of the models.

Read Publication

Design of New Hybrid Neural Controller for Nonlinear CSTR System based on Identification

Apr 1, 2019

Journal Journal of Engineering

DOI 10.31026/j.eng.2019.04.06

This paper proposes improving the structure of the neural controller based on the identification model for nonlinear systems. The goal of this work is to employ the structure of the Modified Elman Neural Network (MENN) model into the NARMA-L2 structure instead of Multi-Layer Perceptron (MLP) model in order to construct a new hybrid neural structure that can be used as an identifier model and a nonlinear controller for the SISO linear or nonlinear systems. Two learning algorithms are used to adjust the parameters weight of the hybrid neural structure with its serial-parallel configuration; the first one is supervised learning algorithm based Back Propagation Algorithm (BPA) and the second one is an intelligent algorithm namely Particle Swarm Optimization (PSO) algorithm. The numerical simulation results show that the hybrid NARMA-L2 controller with PSO algorithm is more accurate than BPA in terms of achieving fast learning and adjusting the parameters model with minimum number of iterations, minimum number of neurons in the hybrid network and the smooth output one step ahead prediction controller response for the nonlinear CSTR system without oscillation.

Read Publication

Design of New Hybrid Neural Structure for Modeling and Controlling Nonlinear Systems

Jan 31, 2019

Journal Journal of Engineering

publisher university of baghdad

DOI 10.31026/j.eng.2019.02.08

Issue 2

Volume 25

This paper proposes a new structure of the hybrid neural controller based on the identification model for nonlinear systems. The goal of this work is to employ the structure of the Modified Elman Neural Network (MENN) model into the NARMA-L2 structure instead of Multi-Layer Perceptron (MLP) model in order to construct a new hybrid neural structure that can be used as an identifier model and a nonlinear controller for the SISO linear or nonlinear systems. Weight parameters of the hybrid neural structure with its serial-parallel configuration are adapted by using the Back propagation learning algorithm. The ability of the proposed hybrid neural structure for nonlinear system has achieved a fast learning with minimum number of epoch, minimum number of neurons in the hybrid network, high accuracy in the output without oscillation response as well as useful model for a one step ahead prediction controller for the nonlinear CSTR system that is used in the MATLAB simulation.

Read Publication