International Journal of Computational Intelligence and Applications

In order to prevent the spread of respiratory viruses like COVID-19 and influenza, an effective protection method is to wear facial masks in densely populated areas. This has led to a growing need for smart services that automatically detect facial masks and replace manual reminding. To address this challenging task and to contribute towards health safety, this paper introduces MedNetV2 system, which is an efficient deep learning-based facial mask detector with a low computational cost. In comparison with existing systems, the main specificities of the proposed system are: (1) the adoption of an effective deep learning-based framework to deal with both the large scale diversity and position variations of masked faces involved in natural scenes, (2) the interaction between face localization and facial mask detection modules to achieve the overall system goal, (3) the lightweight design and the real-time response well suited for real-world scenarios. Extensive experiments on public dataset and real-world video streaming are carried out to validate quantitatively and visually the effectiveness of the proposed system. Promising results, in terms of detection accuracy as well as time response, are achieved when compared it with other state-of-the-art systems.

Speckle noise (SN) is one of the major types of noise that frequently occurs in different coherent imaging systems such as medical imaging, Synthetic Aperture Radar (SAR) and active Radar images. SAR is a powerful imaging technology that generates fine-resolution images and monitors the earth’s surface in order to identify its physical properties. The satellite images captured by SAR are mainly affected by SN, which reduces the quality of images and complicates the image representation. Therefore, removing SN from SAR images is one of the major challenges and needs significant attention. The proposed study introduces an optimal Machine Learning (ML) classifier named Kernel Support Vector Machine-Improved Aquila Optimization (KSVM-IAO) for reducing SN in SAR images. This study uses a two-step process called filtering and enhanced despeckling to minimize the consequence of speckle suppression. In the first step, different imaging filters, namely Improved Lee Filter (ILF), Improved Frost Filter (IFF), Improved Kuan Filter (IKF) and Improved Boxcar Filter (IBF), are utilized to remove the SN in SAR images. Next, the denoised image is fed to the second stage, which makes use of an optimized KSVM-IAO classifier to obtain an enhanced despeckle image. The hyperparameters of KSVM are tuned using the IAO algorithm, which reduces overfitting issues and increases accuracy. MATLAB is the simulation tool used for the analysis of SAR images. The simulation outcomes reveal that the proposed KSVM-IAO method obtained excellent SN removal while preserving the edges and fine details with low computational complexity.

The sine cosine algorithm (SCA) and multi-verse optimizer (MVO) are the recognized optimization strategies frequently employed in numerous scientific areas. However, both SCA and MVO grapple with optimizing the transition between the exploitation and exploration mechanisms. Furthermore, MVO exhibits constraints in its exploitation capabilities. To tackle these limitations, this paper introduces a hybrid model termed SMVO, combining the advantages of both SCA and MVO. This hybrid approach seeks to harmonize exploitation and exploration stages by leveraging the unique advantages of each parent algorithm. The efficacy of SMVO was assessed using 23 benchmark test functions, revealing its competitive performance against not only SCA and MVO but also the ant lion optimization (ALO) and the dragonfly algorithm (DA). Additionally, SMVO’s applicability was further validated by successfully addressing three distinct engineering optimization challenges, underscoring its stability and promise as a global optimization tool.

A novel ensemble machine learning approach for predicting energy consumption in smart appliances is presented in this paper. The main objective is minimizing the number of necessary sensors and improving the prediction accuracy at the same time. The proposed method combines the Fuzzy C-Means method with a multi-stream deep neural network to achieve this goal. The method focuses on prediction accuracy and aims to extract the minimum number of essential features from the dataset corresponding to the required sensors. These selected features are then scatter-reduced and homogenized into subsets. Each subset is used to train a cluster-specific deep neural network designed exclusively for that subset. The final prediction is obtained by computing the fuzzy-weighted sum of these cluster-specific network outputs. Numerical results show that the proposed prediction method outperforms conventional methods in terms of root mean square error and mean absolute percentage error criteria, despite using fewer sensors. This improvement can be attributed to the reduced dataset scatter, which improves the learning speed and model performance. Furthermore, the fuzzy combination of the outputs improves the final prediction accuracy. Overall, the proposed approach provides a more cost-effective and accurate solution for predicting the energy consumed by smart appliances.

Batik is one of Indonesia’s most famous and commonly encountered cultural heritage sites. Batik is a type of Indonesian specialty cloth where the pattern is drawn on the cloth using wax. The number of batik pattern varieties produced by Indonesia introduces challenges in recognizing and differentiating between batik patterns. Various previous works have been done to research how to use computer vision to recognize and differentiate between batik patterns. Some of the methods used in previous works involve convolutional neural networks; previous studies use pre-trained models or manually design the model, which may not be suitable for recognizing batik patterns. This study focuses on developing a convolutional neural network model optimized and designed automatically using genetic algorithms using more unique images than previous works. Genetic algorithms have been proven in previous works from other fields to produce better models compared to pre-trained and manually designed models. The experiment results show that a model design using genetic algorithms outperforms pre-trained models by a significant margin. The model automatically achieved an accuracy of 0.8654 with a parameter ∼1% of the VGG-19 model, whereas the VGG-19 model achieved an accuracy of 0.7596.

Healthcare insurance fraud has become a major problem worldwide in recent decades, resulting in significant financial losses for every affected country. Traditional fraud detection methods, however, often fall short as they primarily focus on analyzing data from the current period, thereby neglecting valuable historical information. In our study, we introduce a novel approach inspired by the financial concept of “credit” to detect fraudulent activities in various domains, such as healthcare insurance, credit card, and online retail transactions. Our approach aims to build a credit evaluation model (CEM) that can distinguish between fraudulent and normal activities by analyzing their historical records. We acknowledge that numerous fraud detection methods have been proposed, but they often struggle to detect edge cases, which limits their practical effectiveness. To address this challenge, our proposed CEM employs a time interval-aware long short-term memory (LSTM) algorithm to assist fraud detection. Furthermore, we propose an innovative approach that transforms traditional binary classification into a multi-classification problem, which improves the model’s ability to handle diverse fraudulent activities. We conducted experiments to evaluate the effectiveness of our proposed approach and model, comparing them against baseline algorithms and recently proposed methods. The results indicate that our approach outperforms the others, demonstrating its potential for practical use in detecting fraudulent activities across various domains.

Realistic co-speech gestures are important to anthropomorphize ECAs, as nonverbal behavior improves expressiveness of their speech greatly. However, the existing approaches to generating co-speech gestures with sufficient details (including fingers, etc.) in 3D scenarios are indeed rare. Additionally, they hardly address the problem of abnormal gestures, temporal–spatial coherence and diversity of gesture sequences comprehensively. To handle abnormal gesture issues, we put forward an angle conversion method to remove body part length from the original in-the-wild video dataset via transferring coordinates of human upper body key points into relative deflection angles and pitch angles. We also propose a neural network called HARP with encoder–decoder architecture to transfer MFCC featured speech audio into aforementioned angles on the basis of CNN and LSTM. The angles then can be rendered as corresponding co-speech gestures. Compared with the other latest approaches, the co-speech gestures generated by HARP are proved to be almost as good as the real person, i.e., they have strong temporal–spatial coherence, diversity, persuasiveness and credibility. Our approach puts finer control on co-speech gestures than most of the existing works by handling all key points of the human upper body. It is more feasible for industrial application, since HARP can be adaptive to any human upper body model. All related code and evidence videos of HARP can be accessed at https://github.com/drrobincroft/HARP.

Image denoising is a fundamental problem in computer vision (CV). Vision Transformer (ViT) is an improvement in CV after convolutional neural networks (CNNs). Recently, it has been demonstrated that the RepVGG network of the structural re-parameterization performs well in image tasks. Experiments indicate that the ViT-based uniformer transformer network has successfully balanced local and global information. As image denoising tasks require more local and global information about the image, a novel image denoising model named structural Re-parameterization Uniformer Transformer-UNet (Rep-UUNet) is proposed in this paper. The model is structural and re-parameterized the structure of the Uniformer Transformer network and uses the UNet skip connections to reconstruct the output image. To complete image downstream tasks, the RepVGG model which utilizes the image local information is used. Indicators such as PSNR, SSIM and others are used to assess the performance of image denoising. Experimental results demonstrate that our Rep-UUNet network model outperforms other five models.