Henry gas solubility optimization: A novel physics-based algorithm

Manta ray foraging optimization: An effective bio-inspired optimizer for engineering applications

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.

A comprehensive review

Lévy flight distribution: A new metaheuristic algorithm for solving engineering optimization problems

Artificial hummingbird algorithm: A new bio-inspired optimizer with its engineering applications

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.

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A Comprehensive Review of Swarm Optimization Algorithms

A comparative review on mobile robot path planning : classical or meta-heuristic methods?

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.

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Efficientnet-Lite and Hybrid CNN-KNN Implementation for Facial Expression Recognition on Raspberry Pi

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.

Target distance estimation using monocular vision system for mobile robot

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.

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Recent progress of conductive 3D-printed electrodes based upon polymers/carbon nanomaterials using a fused deposition modelling (FDM) method as emerging electrochemical sensing devices

Mohd Nadhir Ab Wahab

Papers

A Comprehensive Review of Swarm Optimization Algorithms

A comparative review on mobile robot path planning : classical or meta-heuristic methods?

Efficientnet-Lite and Hybrid CNN-KNN Implementation for Facial Expression Recognition on Raspberry Pi

Target distance estimation using monocular vision system for mobile robot

Recent progress of conductive 3D-printed electrodes based upon polymers/carbon nanomaterials using a fused deposition modelling (FDM) method as emerging electrochemical sensing devices