Search Results for author: Issam Hammad

Found 8 papers, 0 papers with code

Enhancing Accuracy and Maintainability in Nuclear Plant Data Retrieval: A Function-Calling LLM Approach Over NL-to-SQL

no code implementations10 Jun 2025 Mishca de Costa, Muhammad Anwar, Dave Mercier, Mark Randall, Issam Hammad

Retrieving operational data from nuclear power plants requires exceptional accuracy and transparency due to the criticality of the decisions it supports.

Towards Secure and Private Language Models for Nuclear Power Plants

no code implementations10 Jun 2025 Muhammad Anwar, Mishca de Costa, Issam Hammad, Daniel Lau

This paper introduces a domain-specific Large Language Model for nuclear applications, built from the publicly accessible Essential CANDU textbook.

Language Modeling Language Modelling +2

Managing the Impact of Sensor's Thermal Noise in Machine Learning for Nuclear Applications

no code implementations2 Oct 2023 Issam Hammad

This paper lists some applications for Canada Deuterium Uranium (CANDU) reactors where such sensors are used and therefore can be impacted by the thermal noise issue if machine learning is utilized.

Sensor Fusion

Subtractor-Based CNN Inference Accelerator

no code implementations2 Oct 2023 Victor Gao, Issam Hammad, Kamal El-Sankary, Jason Gu

This paper presents a novel method to boost the performance of CNN inference accelerators by utilizing subtractors.

Using Deep Learning to Automate the Detection of Flaws in Nuclear Fuel Channel UT Scans

no code implementations26 Feb 2021 Issam Hammad, Ryan Simpson, Hippolyte Djonon Tsague, Sarah Hall

The proposed CNN model achieves this target by automatically identifying at least a portion of each flaw where further manual analysis is performed to identify the width, the length, and the type of the flaw.

A Comparative Study on Machine Learning Algorithms for the Control of a Wall Following Robot

no code implementations26 Dec 2019 Issam Hammad, Kamal El-Sankary, Jason Gu

A comparison of the performance of various machine learning models to predict the direction of a wall following robot is presented in this paper.

All BIG-bench Machine Learning +1

Deep Learning Training with Simulated Approximate Multipliers

no code implementations26 Dec 2019 Issam Hammad, Kamal El-Sankary, Jason Gu

The paper demonstrates that using approximate multipliers for CNN training can significantly enhance the performance in terms of speed, power, and area at the cost of a small negative impact on the achieved accuracy.

Deep Learning

Cannot find the paper you are looking for? You can Submit a new open access paper.