ASSOCIATE PROF. DR. ANIS SALWA BINTI MOHD KHAIRUDDIN
Department of Electrical Engineering
Faculty of Engineering
anissalwa@um.edu.myView CV | |
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Scopus Link | |
Biography | |
Associate Professor Dr. Anis Salwa Mohd Khairuddin is a Chartered Engineer registered under the Engineering Council, U.K. She is also a member of IEEE Computational Intelligence Society (CIS). In addition, she is the Focal point for Sub-Committee on Microelectronics and Information Technology (SCMIT) representing Malaysia in ASEAN COSTI (from year 2020 until current). The Committee on Science, Technology & Innovation (COSTI) is responsible for operationalizing and translating the APASTI strategic thrusts into specific actions. As for administrative responsibilities, she was the Final year Project (FYP) Coordinator for 5 consecutive years (from 2015/2016 till 2019/2020). Consecutively, after being the FYP coordinator, she has been appointed as the Outcome-Based Education (OBE) coordinator since 2020/2021 until current. Outcome-Based Education (OBE) is one of the requirements for Malaysia to become a full member of the Washington Accord (WA), an international agreement to mutually recognize Bachelor degrees in the field of engineering. She is currently the Head of Centre of Intelligent Systems for Emerging Technology (CISET). Her research expertise lies in pattern recognition methods, focusing on the application of artificial intelligence by developing computationally intelligent algorithms for spatial and temporal signal processing. She works on the application of machine learning methods to solve various pattern recognition problems in the areas of precision agriculture, smart manufacturing, and energy management. Researcher ORCID : https://orcid.org/0000-0002-9873-4779 Researcher profile : https://umexpert.um.edu.my/anissalwa |
Publication
Finance
Project Title | Progress | Status |
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Intelligent Monitoring Systems based on Computer Vision Methods |
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on going |
Sustainable And Smart Food Packaging System |
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on going |
Intelligent Price Forecasting System For Optimal Energy Market |
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end |
This information is generated from Research Grant Management System |
Model-based impending lithium battery terminal voltage collapse detection via data-driven and machine learning approaches
An optimised YOLOv4 deep learning model for efficient malarial cell detection in thin blood smear images
An automatic garbage detection using optimized YOLO model
Improved feature extraction network in lightweight YOLOv7 model for real-time vehicle detection on low-cost hardware
Real-Time KenalKayu System with YOLOv3
Vehicles Trajectories Analysis Using Piecewise-Segment Dynamic Time Warping (PSDTW)
Smart Innovation, Systems and Technologies
YOLO-based Network Fusion for Riverine Floating Debris Monitoring System
Defect Severity Classification of Complex Composites using CWT and CNN
Automated grading of Citrus Suhuiensis fruit using deep learning method
Real-Time Tropical Wood Species Recognition System with YOLOv3