27 Feb 2023, JOHOR BAHRU – This online structured course was organized to educate on machine learning background and its applications. Besides that, to educate on the use of machine learning (ML) to categorize environmental sounds and soundscape maps that can assist relevant agencies and industries in decision and policy-making to mitigate noise pollution in smart cities. Also, to monitor and classification of the noise using phyton and MATLAB software.

The registration for this event was open at least one week prior with excellent response. About 112 people registered for this online structured course. Most of the participants are postgraduate students, Ph.D. (58%), Master’s Degree (25.9%) whereas about 15 participants (13.4%) were students who are in their Bachelor’s Degree.

There were two sections for this online structured course where the speaker for the first section was Assoc Prof Dr Usman Ullah Sheikh. He received his PhD degree in 2009 in image processing and computer vision from Universiti Teknologi Malaysia (UTM). His research work is mainly on computer vision, machine learning and embedded systems design. He is currently a Senior Lecturer in UTM.

The second section was conducted by Mr. Ali Othman. He is a lecturer in Dept of Electronics and Telecommunications at the higher college of Science and Technology which belongs to the ministry of Technical & Vocational Education, Souk Al-Jumaa, Tripoli, Libya. He is also a member of the Dept of Telecommunication Software and Systems (Tess) Research Group FEE, UTM. Lastly, he is the chairman of CR Technology system (CRTS group) in the region of the middle east and north Africa WWW.CRTSGROUP.COM.

Based on the feedback from the participants, the course was participated mostly by International (55.4%), followed by locals (44.6%). Most participants are from UTM (90.4%) whereas the others are from UPM, Superior University, Institut Teknologi PLN, UTHM, UKM, and Ahmadu Bello University. Most participants are postgraduate students, Ph.D. (57.8%) and Master’s Degree (26.5%). The others are students from other universities. Most students are from Year 1 to Year 2 (62.7%), followed by Year 3 to Year 4 students (24.1%) and are from the Faculty of Electrical Engineering (63.9%) from UTM Johor Bahru campus (86.7%).