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All Journal International Journal of Electrical and Computer Engineering IAES International Journal of Artificial Intelligence (IJ-AI)
Heng Siong Lim
Multimedia University
Computer Science & IT Electrical & Electronics Engineering Engineering

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Speech emotion recognition with light gradient boosting decision trees machine Kah Liang Ong; Chin Poo Lee; Heng Siong Lim; Kian Ming Lim
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 4: August 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i4.pp4020-4028

Abstract

Speech emotion recognition aims to identify the emotion expressed in the speech by analyzing the audio signals. In this work, data augmentation is first performed on the audio samples to increase the number of samples for better model learning. The audio samples are comprehensively encoded as the frequency and temporal domain features. In the classification, a light gradient boosting machine is leveraged. The hyperparameter tuning of the light gradient boosting machine is performed to determine the optimal hyperparameter settings. As the speech emotion recognition datasets are imbalanced, the class weights are regulated to be inversely proportional to the sample distribution where minority classes are assigned higher class weights. The experimental results demonstrate that the proposed method outshines the state-of-the-art methods with 84.91% accuracy on the emo-DB dataset, 67.72% on the Ryerson audio-visual database of emotional speech and song (RAVDESS) dataset, and 62.94% on the interactive emotional dyadic motion capture (IEMOCAP) dataset.
Robust UAV localization of ground sensors in urban environments via path loss refinement and geometric selection Ahmed M. A. A. Elngar; Heng Siong Lim; Yee Kit Chan; Yaser Awadh Bakhuraisa; Ida Wahidah
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 1: February 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i1.pp412-428

Abstract

Localizing ground sensors with unmanned aerial vehicles (UAVs) in dense urban environments is challenging because multipath and non-line-of-sight (NLoS) propagation distorts path loss (PL) measurements. This paper proposes a two-stage UAV localization framework that refines PL data and selects geometrically stable waypoint subsets before position estimation. In stage 1, PL samples are spatially smoothed by averaging measurements at neighboring UAV waypoints to reduce localized fluctuations. In stage 2, waypoint subsets are filtered using non-collinearity and non-adjacency constraints, and sensor positions are estimated using weighted least squares (WLS) and particle swarm optimization (PSO), with final estimates averaged across valid subsets. Wireless InSite ray-tracing simulations show that the framework reduces mean absolute error (MAE) from over 150 m to approximately 8.5 m. The proposed approach improves the practicality of UAV-assisted localization for urban internet of things (IoT) sensor deployments.
Co-Authors Ahmed M. A. A. Elngar Chin Poo Lee Ida Wahidah Hamzah Kah Liang Ong Kian Ming Lim Yaser Awadh Bakhuraisa Yee Kit Chan