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Author

Mohd Nadhir Ab Wahab

Bio: Mohd Nadhir Ab Wahab is an academic researcher from Universiti Sains Malaysia. The author has contributed to research in topics: Computer science & Monocular vision. The author has an hindex of 5, co-authored 15 publications receiving 350 citations. Previous affiliations of Mohd Nadhir Ab Wahab include Universiti Malaysia Perlis & University of Salford.

Papers
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Journal ArticleDOI
18 May 2015-PLOS ONE
TL;DR: In this paper, the authors provide an in-depth survey of well-known swarm optimization algorithms and compare them with each other comprehensively through experiments conducted using thirty wellknown benchmark functions and a number of statistical tests are then carried out to determine the significant performances.
Abstract: Many swarm optimization algorithms have been introduced since the early 60’s, Evolutionary Programming to the most recent, Grey Wolf Optimization. All of these algorithms have demonstrated their potential to solve many optimization problems. This paper provides an in-depth survey of well-known optimization algorithms. Selected algorithms are briefly explained and compared with each other comprehensively through experiments conducted using thirty well-known benchmark functions. Their advantages and disadvantages are also discussed. A number of statistical tests are then carried out to determine the significant performances. The results indicate the overall advantage of Differential Evolution (DE) and is closely followed by Particle Swarm Optimization (PSO), compared with other considered approaches.

382 citations

Journal ArticleDOI
TL;DR: Dijkstra ’s Algorithm (DA) is considered a benchmark solution and Constricted Particle Swarm Optimization (CPSO) is found performing better than other meta-heuristic approaches in unknown environments.

92 citations

Journal ArticleDOI
TL;DR: In this article, a hybrid CNN-KNN model was proposed for facial expression recognition on the Raspberry Pi 4, where CNN was used for feature extraction and KNN was used to perform expression recognition.
Abstract: Facial expression recognition (FER) is the task of determining a person’s current emotion. It plays an important role in healthcare, marketing, and counselling. With the advancement in deep learning algorithms like Convolutional Neural Network (CNN), the system’s accuracy is improving. A hybrid CNN and k-Nearest Neighbour (KNN) model can improve FER’s accuracy. This paper presents a hybrid CNN-KNN model for FER on the Raspberry Pi 4, where we use CNN for feature extraction. Subsequently, the KNN performs expression recognition. We use the transfer learning technique to build our system with an EfficientNet-Lite model. The hybrid model we propose replaces the Softmax layer in the EfficientNet with the KNN. We train our model using the FER-2013 dataset and compare its performance with different architectures trained on the same dataset. We perform optimization on the Fully Connected layer, loss function, loss optimizer, optimizer learning rate, class weights, and KNN distance function with the k-value. Despite running on the Raspberry Pi hardware with very limited processing power, low memory capacity, and small storage capacity, our proposed model achieves a similar accuracy of 75.26% (with a slight improvement of 0.06%) to the state-of-the-art’s Ensemble of 8 CNN model.

33 citations

Proceedings ArticleDOI
15 Nov 2011
TL;DR: The main topic explains method used in monocular vision system, which is Hough transforms and how this method operates and the basic mathematical morphology which is implemented in Hough transform for image processing is shown.
Abstract: Mobile robot with vision could be useful for many applications and purposes. However, the vision system needs to be robust, effective, robust and fast to achieve on its goal. Somehow it needs stereo vision system to estimate the depth of the object. In this paper, a monocular vision system is introduced to the mobile robot to enhance their capabilities for calculating the distance or depth approximately. The main topic explains method used in monocular vision system, which is Hough transforms and how this method operates. This paper also shows the basic mathematical morphology which is implemented in Hough transform for image processing.

31 citations

Journal ArticleDOI
TL;DR: In this paper, the authors focus on the most outstanding studies that have been published since 2018 and provide insight into how the research community could improve the electrochemical performance of 3D-printed electrodes to significantly increase the sensitivity of the 3D printed electrodes as electrode sensing devices.
Abstract: 3D-printing or additive manufacturing is presently an emerging technology in the fourth industrial revolution that promises to reshape traditional manufacturing processes. The electrochemistry field can undoubtedly take advantage of this technology to fabricate electrodes to create a new generation of electrode sensor devices that could replace conventionally manufactured electrodes; glassy carbon, screen-printed carbon and carbon composite electrodes. In the electrochemistry research area, studies to date show that there is a demand for electrically 3D printable conductive polymer/carbon nanomaterial filaments where these materials can be printed out through an extrusion process based upon the fused deposition modelling (FDM) method. FDM could be used to manufacture novel electrochemical 3D printed electrode sensing devices for electrochemical sensor and biosensor applications. This is due to the FDM method being the most affordable 3D printing technique since conductive and non-conductive thermoplastic filaments are commercially available. Therefore, in this minireview, we focus on only the most outstanding studies that have been published since 2018. We believe this to be a highly-valuable research area to the scientific community, both in academia and industry, to enable novel ideas, materials, designs and methods relating to electroanalytical sensing devices to be generated. This approach has the potential to create a new generation of electrochemical sensing devices based upon additive manufacturing. This minireview also provides insight into how the research community could improve the electrochemical performance of 3D-printed electrodes to significantly increase the sensitivity of the 3D-printed electrodes as electrode sensing devices.

30 citations


Cited by
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Journal ArticleDOI
TL;DR: A novel metaheuristic algorithm named Henry gas solubility optimization (HGSO), which mimics the behavior governed by Henry’s law to solve challenging optimization problems, provides competitive and superior results compared to other algorithms when solving challenging optimize problems.

602 citations

Journal ArticleDOI
TL;DR: The comparison results on the benchmark functions suggest that MRFO is far superior to its competitors, and the real-world engineering applications show the merits of this algorithm in tackling challenging problems in terms of computational cost and solution precision.

519 citations

Journal ArticleDOI
01 Dec 2016
TL;DR: The comprehensive review of Krill Herd Algorithm as applied to different domain is presented, which covers the applications, modifications, and hybridizations of the KH algorithms.
Abstract: Graphical abstractDisplay Omitted HighlightsThe comprehensive review of Krill Herd Algorithm as applied to different domain is presented.The review covers the applications, modifications and hybridizations of the KH algorithms.It provides future research directions across different areas. Krill Herd (KH) algorithm is a class of nature-inspired algorithm, which simulates the herding behavior of krill individuals. It has been successfully utilized to tackle many optimization problems in different domains and found to be very efficient. As a result, the studies has expanded significantly in the last 3 years. This paper presents the extensive (not exhaustive) review of KH algorithm in the area of applications, modifications, and hybridizations across these fields. The description of how KH algorithm was used in the approaches for solving these kinds of problems and further research directions are also discussed.

449 citations

Journal ArticleDOI
TL;DR: The statistical simulation results revealed that the LFD algorithm provides better results with superior performance in most tests compared to several well-known metaheuristic algorithms such as simulated annealing (SA), differential evolution (DE), particle swarm optimization (PSO), elephant herding optimization (EHO), the genetic algorithm (GA), moth-flame optimization algorithm (MFO), whale optimization algorithm

248 citations

Journal ArticleDOI
TL;DR: In this paper, a bio-inspired optimization algorithm called artificial hummingbird algorithm (AHA) is proposed to solve optimization problems, which simulates the special flight skills and intelligent foraging strategies of hummingbirds in nature.

195 citations