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Evaluation of particle swarm optimization for strength determination of tropical wood polymer composite Marina Yusoff; Alya Nurizzati Mohd Basir; Norhidayah A Kadir; Shahril Anuar Bahari
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 9, No 2: June 2020
Publisher : Institute of Advanced Engineering and Science

A wood-polymer composite (WPCs) refers to wood-based components that are coupled with polymers to produce a composite material. Obtaining the best strength for the tropical WPCs is still a lack of research mainly for the tropical timber species and require a large consumption of time and cost. This paper highlighted the evaluation of particle swarm optimization (PSO) to assist in finding the optimal value of the composition of tropical WPCs to obtain the best strength that would offer a betterment in a quality product of WPCs. The findings demonstrate that PSO has been shown as a viable and efficient method for WPCs strength. The composition of Sentang, wood sawdust of 50%, HDPE of 49% and 1% coupling agent is demonstrated the best strength for the WPC. The employment of PSO as an assisted tool would give significant benefit to the manufacturer and researcher to determine the composition of material with less cost and time.

Review of anomalous sound event detection approaches Amirul Sadikin Md Affendi; Marina Yusoff
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 8, No 3: September 2019
Publisher : Institute of Advanced Engineering and Science

This paper presents a review of anomalous sound event detection (SED) approaches. SED is becoming more applicable for real-world appliactaions such as security, fire determination or olther emergency alarms. Despite many research outcome previously, further research is required to reduce false positives and improve accurracy. SED approaches are comprehensively organized by methods covering system pipeline components of acoustic descriptors, classification engine, and decision finalization method. The review compares multiple approaches that is applied on a specific dataset. Security relies on anomalous events in order to prevent it one must find these anomalous events. Audio surveillance has become more efficient as that artificial intelligence has stepped up the game. Autonomous SED could be used for early detection and prevention. It is found that the state of the art method viable used in SED using features of log-mel energies in convolutional recurrent neural network (CRNN) with long short term memory (LSTM) with a verification step of thresholding has obtained 93.1% F1 score and 0.1307 ER. It is found that feature extraction of log mel energies are highly reliable method showing promising results on multiple experiments.

A sound event detection based on hybrid convolution neural network and random forest Muhamad Amirul Sadikin Md Afendi; Marina Yusoff
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 11, No 1: March 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v11.i1.pp121-128

Sound event detection (SED) assists in the detainment of intruders. In recent decades, several SED methods such as support vector machine (SVM), K-Means clustering, principal component analysis, and convolution neural network (CNN) on urban sound have been developed. Advanced work on SED in a rare sound event is challenging because it has limited exploration, especially for surveillance in a forest environment. This research provides an alternative method that uses informative features of sound event data from a natural forest environment and evaluates the CNN capabilities of the detection performances. A hybrid CNN and random forest (RF) are proposed to utilize a distinctive sound pattern. The feature extraction involves mel log energies. The detection processes include refinement parameters and post-processing threshold determination to reduce false alarms rate. The proposed CNN-RF and custom CNN-RF models have been validated with three types of sound events. The results of the suggested approach have been compared with wellregarded sound event algorithms. The experiment results demonstrate that the CNN-RF assesses the superiority with remarkable improvement in performance, up to a 0.82 F1 score with a minimum false alarms rate at 10%. The performance shows a functional advantage over previous methods.

Review of single clustering methods Nurshazwani Muhamad Mahfuz; Marina Yusoff; Zakiah Ahmad
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 8, No 3: September 2019
Publisher : Institute of Advanced Engineering and Science

Clustering provides a prime important role as an unsupervised learning method in data analytics to assist many real-world problems such as image segmentation, object recognition or information retrieval. It is often an issue of difficulty for traditional clustering technique due to non-optimal result exist because of the presence of outliers and noise data. This review paper provides a review of single clustering methods that were applied in various domains. The aim is to see the potential suitable applications and aspect of improvement of the methods. Three categories of single clustering methods were suggested, and it would be beneficial to the researcher to see the clustering aspects as well as to determine the requirement for clustering method for an employment based on the state of the art of the previous research findings.

Mel-log energies analysis of authentic audible intrusion activities in a Malaysian forest Amirul Sadikin Md Afendi; Marina Yusoff; Megawati Omar
Bulletin of Electrical Engineering and Informatics Vol 9, No 2: April 2020
Publisher : Institute of Advanced Engineering and Science

Wildlife has been endangered due to illegal activities. This requires more effective surveillance measures. Felling timber and poaching are regular illegal activities but challenging to detect. Hence authorities should resort to modern technologies such as employing autonoumous surveillance to stop them. The Malaysian forest audio data were recorded to lay a foundation in initiating a cheaper and practical approach. Hence this paper reports the collection, processing and analysis of audio data in preparation to develop an autonomous sound event detection system. The recording was an emulation of possible illegal activities in a reserved forest. Sounds of chainsaw and hand hatchet cutting tree trunks were taken. It was found that there was a distinct pattern in the Mel-log energies audio feature of the sound, which could be used to identify illegal activities. Thus, it is believed that a detection through audio is a possible approach to be employed as one of the methods to stop illegal activities in the tropical reserve forests like those in Malaysia.

Knots timber detection and classification with C-Support Vector Machine Fakhira Iwani Muhammad Redzuan; Marina Yusoff
Bulletin of Electrical Engineering and Informatics Vol 8, No 1: March 2019
Publisher : Institute of Advanced Engineering and Science

Timber knots recognition is of prime importance to further determine the timber grade. The recognition is normally based on the human expert’s eyes in which can lead to some flaws based on human limitations and weaknesses. The use of X-ray can cause emits radiation and can be dangerous to the workers. This paper addresses the employment of computational methods for knot detection. A pre-processing and feature extraction methods include contrast stretching, median blur and thresholding, gray scale and local binary pattern were used. More than 400 datasets of knot images of the tropical timbers, namely Acacia and Hevea Brasiliensis have been tested using C-support vector machine as a knot classifier. The findings demonstrate different performances for three types of kernel. Linear kernel function outperformed both radial basis function and polynomial kernel functions for Acacia and Hevea Brasiliensis species. Both species classifications using linear kernel have managed to achieve a promising accuracy. Knots classification with the used of support vector machine has shown a promising result to improve the classifier and test with different types of tropical timbers.

Knots timber detection and classification with C-Support Vector Machine Fakhira Iwani Muhammad Redzuan; Marina Yusoff
Bulletin of Electrical Engineering and Informatics Vol 8, No 1: March 2019
Publisher : Institute of Advanced Engineering and Science

Timber knots recognition is of prime importance to further determine the timber grade. The recognition is normally based on the human expert’s eyes in which can lead to some flaws based on human limitations and weaknesses. The use of X-ray can cause emits radiation and can be dangerous to the workers. This paper addresses the employment of computational methods for knot detection. A pre-processing and feature extraction methods include contrast stretching, median blur and thresholding, gray scale and local binary pattern were used. More than 400 datasets of knot images of the tropical timbers, namely Acacia and Hevea Brasiliensis have been tested using C-support vector machine as a knot classifier. The findings demonstrate different performances for three types of kernel. Linear kernel function outperformed both radial basis function and polynomial kernel functions for Acacia and Hevea Brasiliensis species. Both species classifications using linear kernel have managed to achieve a promising accuracy. Knots classification with the used of support vector machine has shown a promising result to improve the classifier and test with different types of tropical timbers.

Knots timber detection and classification with C-Support Vector Machine Fakhira Iwani Muhammad Redzuan; Marina Yusoff
Bulletin of Electrical Engineering and Informatics Vol 8, No 1: March 2019
Publisher : Institute of Advanced Engineering and Science

Timber knots recognition is of prime importance to further determine the timber grade. The recognition is normally based on the human expert’s eyes in which can lead to some flaws based on human limitations and weaknesses. The use of X-ray can cause emits radiation and can be dangerous to the workers. This paper addresses the employment of computational methods for knot detection. A pre-processing and feature extraction methods include contrast stretching, median blur and thresholding, gray scale and local binary pattern were used. More than 400 datasets of knot images of the tropical timbers, namely Acacia and Hevea Brasiliensis have been tested using C-support vector machine as a knot classifier. The findings demonstrate different performances for three types of kernel. Linear kernel function outperformed both radial basis function and polynomial kernel functions for Acacia and Hevea Brasiliensis species. Both species classifications using linear kernel have managed to achieve a promising accuracy. Knots classification with the used of support vector machine has shown a promising result to improve the classifier and test with different types of tropical timbers.

Hybrid backpropagation neural network-particle swarm optimization for seismic damage building prediction Marina Yusoff; Faris Mohd Najib; Rozaina Ismail
Indonesian Journal of Electrical Engineering and Computer Science Vol 14, No 1: April 2019
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v14.i1.pp360-367

The evaluation of the vulnerability of buildings to earthquakes is of prime importance to ensure a good plan can be generated for the disaster preparedness to civilians. Most of the attempts are directed in calculating the damage index of buildings to determine and predict the vulnerability to certain scales of earthquakes. Most of the solutions used are traditional methods which are time consuming and complex. Some of initiatives have proven that the artificial neural network methods have the potential in solving earthquakes prediction problems. However, these methods have limitations in terms of suffering from local optima, premature convergence and overfitting. To overcome this challenging issue, this paper introduces a new solution to the prediction on the seismic damage index of buildings with the application of hybrid back propagation neural network and particle swarm optimization (BPNN-PSO) method. The prediction was based on damage indices of 35 buildings around Malaysia. The BPNN-PSO demonstrated a better result of 89% accuracy compared to the traditional backpropagation neural network with only 84%. The capability of PSO supports fast convergence method has shown good effort to improve the processing time and accuracy of the results.

Genetic Algorithm with Elitist-Tournament for Clashes-Free Slots of Lecturer Timetabling Problem Marina Yusoff; Anis Amalina Othman
Indonesian Journal of Electrical Engineering and Computer Science Vol 12, No 1: October 2018
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v12.i1.pp303-309

Genetic algorithm (GA) approach is one of an evolutionary optimization technique relies on natural selection. The employment of GA still popular and it was applied to many real-world problems, especially in many combinatorial optimization solutions. Lecturer Timetabling Problem (LTP) has been researched for a few decades and produced good solutions. Although, some of LTP offers good results, the criteria and constraints of each LTP however are different from other universities. The LTP appears to be a tiresome job to the scheduler that involves scheduling of students, classes, lecturers and rooms at specific time-slots while satisfying all the necessary requirements to build a feasible timetable. This paper addresses the employment and evaluation of GA to overcome the biggest challenge in LTP to find clashes-free slots for lecturer based on a case study in the Faculty of Computer and Mathematical Sciences, University Technologi MARA, Malaysia. Hence, the performance of the GA is evaluated based on selection, mutation and crossover using different number of population size. A comparison of performance between simple GA with Tournament Selection scheme combined with Elitism (TE) and a GA with Tournament (T) selection scheme. The findings demonstrate that the embedded penalty measures and elitism composition provide good performance that satisfies all the constraints in producing timetables to lecturers.

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