Special issue on applied computational intelligence

Computational intelligence techniques are traditionally adopted in several different application domains such as industry, healthcare, decision making, and gaming, to name but a few. Despite this growing diffusion, there are still many possible areas where computational intelligence application is partial or could be extended and improved due to the actual limitations in terms of computational power or strict requirements in terms of assurance of the results. This special issue aims to investigate the impact of the adoption of advanced and innovative computational intelligence techniques in emerging application fields like IoT and big data. This edition of the special issue is focused primarily on industrial and health applications with special emphasis on real-time systems grounded on big data ecosystems. The special issue will bring together researchers on different disciplines from academia and industry with a common objective: go beyond the frontiers of today's applications of computational intelligence techniques. After review, a total of 18 papers have been accepted for publication in this issue. Convolutional neural networks (CNNs) have made a great impact on attaining state-of-the-art results in image task classification. Weight initialization is one of the fundamental steps in formulating a CNN model. It determines the failure or success of the CNN model. In the contribution by Olimov et al., “Weight initialization based-rectified linear unit activation function to improve the performance of a convolutional neural network model,” the authors conduct research based on the mathematical background of different weight initialization strategies to determine the one with better performance.1 To have smooth training, authors expect the activation of each layer of the CNN model to follow the standard normal distribution with mean 0 and standard deviation 1. It prevents gradients from vanishing and leads to more smooth training. However, it was found that even with the appropriate weight initialization technique, a regular rectified linear unit (ReLU) activation function increases the activation mean value. In this article, the authors address this issue by proposing a weight initialization-based (WIB)-ReLU activation function. The proposed method resulted in more smooth training. Moreover, the experiments showed that WIB-ReLU outperforms ReLU, Leaky ReLU, parametric ReLU, and exponential linear unit activation functions and results in up to 20% decrease in loss value and 5% increase in accuracy score on both Fashion-MNIST and CIFAR-10 databases. Popular songs have evolved from vinyl records (voice files) to music videos. Sound and video can be mixed and presented in such a manner that only sound is heard. Scenes from music programs require continuous editing to maximize audience immersion. Music program scene editing is developed through the expertise of a television program director (PD). The PD considers detailed editing methods by analyzing lyrics and the singer's choreography to express a song in a rhythmic video. The contribution by Kim “Bio-signal-processing-based convolutional neural networks model for music program scene editing” presents an algorithm for music program scene editing that determines the size of a shot, evaluating the psychological distance between singer and viewers after measuring the pitch contour of a song.2 In laparoscopic surgery, the smoke generated by operations including electrocautery and laser ablation seriously degrades the quality of endoscopic images. It not only reduces the visibility of the surgery, leading to increased risk of injury but also affects the performance of image processing in computer-assisted surgery such as segmentation, 3D reconstruction, and tracking. Therefore, a desmoking algorithm is required to eliminate smoke in endoscopic images. In the contribution by Lin et al., “A desmoking algorithm for endoscopic images based on improved U-Net model,” the authors study a U-Net model that can eliminate smoke from a laparoscopic image in real-time and preserve the natural appearance of the organ surface.3 Their method is based on the improved U-Net in which the convolutional block attention module is used as an embedded guide mask of the decoder part. The laparoscopic image dataset is provided by the Hamlyn Center, and the Blender software is used to simulate various situations of smoke added to the laparoscopic image for training and testing. For the proposed method, the peak signal-to-noise ratio value is up to 29.27, and the structural similarity index is up to 0.945 over the test images. The experimental results demonstrate that the proposed method achieves a better performance than the other six existing methods, which is applicable for real-time endoscopic image processing. In the last decade, artificial intelligent systems based on neural networks have gradually become the primary source for clinical decision support systems (CDSS) and are being used in diverse areas of medical diagnosis, classification, and prediction. An artificial neural network (ANN) consists of a large number of processing units that perform the computation in a parallel and distributed environment. They learn the pattern from the examples provided to it and then generalize based on the concepts they have learned while training. The contribution by Shafi et al., “Artificial neural networks as clinical decision support systems,” presents a review of the current status of ANN and its variants as CDSS in various medical disciplines.4 The work describes the methods making use of simple ANN and the use of real-time approaches based on big data using ANN in cloud computing environments for various medical applications. Critical analysis of various methods based on smart approaches indicates that feed-forward back propagation ANN performs sufficiently better in the domain of medicines with a high degree of accuracy. Web scraping is the process of extracting data from web pages, and it is an essential part of the generation of datasets. Currently, the field is dominated by capable commercial applications; however, there is always a need for web crawling and web scraping applications for custom projects. Developing fit-for-purpose tools for retrieving and structuring data from web services, cloud systems, and big data is a challenging task. Based on empirical studies, some of the challenges include structural issues, formatting/presentation, availability, denial of service, size, and information retrieval problems with browsers. Additionally, the data become inaccessible after the structure/template of the website changes, for example, after the website is updated. Thus, the dataset cannot be updated in the future without manually modifying the parameters of the Web Scraper. In the contribution by Khan et al., “Smart algorithmic-based web crawling and scraping with template auto-update capabilities,” the authors propose an algorithm capable of autocorrecting the template (web scraping parameters) used for locating the target data and dealing with some common empirical problems.5 This is very useful in case there is a need for updating the dataset later, as usually, websites tend to change their pages. Moreover, the authors introduce an implementation of the algorithm via a tool developed for extracting data from the unity asset store. The tool can capture and store data in XML format. The tool extracted a total of 46,785 (40,611 3D and 6174 2D) items, with 35 successful first retries, 11 second retries and 5 fails. As is now well established, the world population is aging rapidly and, according to World Health Organization (WHO), the number of people aged 60 years and older is expected to total 2 billion in 2050. For this reason, an important emerging issue is the definition of a new generation of healthcare platforms capable of monitoring people's quality of life. In the contribution by Bellandi et al., “A design methodology for matching smart health requirements,” the authors propose a new methodology that supports the entire requirements elicitation process starting from the initial phase of gathering the requirements, both clinical, technological, and end-user, up to the choice of the most suitable solution.6 Their proposal provides a new iterative model in the smart healthcare research area. Furthermore, the authors apply their proposal in a real scenario, and report on the end-to-end implementation of the proposed methodology. Nowadays, unmanned aerial vehicles (UAVs) are widely used in many smart systems such as smart logistics, smart agriculture, and environmental monitoring systems. However, the limited computing capability and limited battery lifetime of existing UAVs could significantly impact the quality of service (QoS) of UAV-based smart systems and the quality of experience (QoE) of end-users. Recently, mobile edge computing (MEC), which provisions computing resources close to the mobile end devices, has become a promising solution. However, since a high-speed UAV often flies through the signal range of different edge nodes, the interruption of services in the MEC-based UAV delivery system is a critical issue. A challenging question is when and how to perform dynamic task migration among the edge nodes to ensure service continuity. In the contribution by Li et al., “Energy-aware decision-making for dynamic task migration in MEC-based unmanned aerial vehicle delivery system,” the authors propose an energy-aware decision-making strategy for dynamic task migration named GAD to optimize the UAV energy consumption.7 Given the real-time system status and QoS constraints, and through a dynamic two-tier decision-making mechanism, GAD can efficiently make the task migration decision from four candidate decisions, namely, no migration, data migration only, cold migration, and live migration. Experimental results based on a real-world scenario show that their strategy can outperform other baseline strategies in various metrics, including the flying distances and the energy consumption of UAVs. Location recommending frameworks play a very significant role in prompting users with new places to visit, especially when users are visiting unfamiliar areas. Most of the existing recommender systems do not consider the fact that different users have different behaviors while checking in. Some approaches do not consider the essential factors while providing recommendations. These systems lack adaptability, and hence they provide poor recommendations. In the contribution by Elangovan et al., “Location-based social network recommendations with computational intelligence-based similarity computation and user check-in behavior,” an adaptive approach to provide users with a personalized recommendation has been proposed.8 The authors have considered three features, namely, the user activeness feature, user similarity feature, and the spatial feature. In addition to this, they have also considered the location popularity for a given timeslot. They have divided the users into inactive and active based on the degree of activeness on social networks using fuzzy c-means clustering. They have provided two strategies based on the activeness of the user. A two-dimensional Gaussian kernel density estimation strategy is used for the active user. A one-dimensional power-law function strategy is used for inactive users. Moreover, they have integrated the time-based popularity of the location and probability estimation based on the similarity between the users. To evaluate the proposed model, they have used a large-scale Foursquare dataset. In recent years, deep CNNs have achieved a lot of outstanding results in super-resolution with superior ability. However, the majority of CNNs only use a series of convolution kernels of the same size to extract features. This will cause limited receptive fields. In the contribution by Zhang et al., “Image super-resolution with parallel convolution attention network,” the authors propose a parallel convolution attention network (PCAN) to extract features in an effective way.9 Specifically, a pair of parallel convolutions (PCs) with different kernel sizes is used in one layer in their network, which can extract features within different receptive fields, thereby making full use of the multiscale information. Meanwhile, they apply a channel-spatial attention (CSA) module in each PC block to calculate and fuse channel attention and spatial attention. The obtained attention maps emphasize useful features. Experimental results demonstrate the superiority of their PCAN in comparison with state-of-the-art methods. Embedded systems and wireless sensor networks (WSNs) are found today in increasingly critical areas of applications. They have become integrated and embedded in nearly all aspects of everyday life, including manufacturing, healthcare, education, critical infrastructure, and entertainment. The number of connected devices continues to grow, and due to the insecure nature of these devices, the amount of risk continues to grow as well. These risks, however, can be mitigated with the creation and adoption of WSN security standards developed to create an environment of safety, security, and confidence in the technology. Designing the security policy for WSNs requires asking some preliminary questions. These questions are particularly important in the case of WSNs because their use is highly decentralized. Blockchain's ability to govern decentralized networks make it especially suitable for designing a self-managing system on WSN devices. The contribution by Lazrag et al., “Efficient and secure routing protocol based on Blockchain approach for wireless sensor networks,” proposes a routing protocol that uses Blockchain technology to offer a shared memory between the network's nodes.10 The simulation results have shown that this solution could be applicable and could resolve the issues cited above. Deep learning-based neural networks and their variants have gained popularity due to their inherent flexibility to handle unforeseen events, especially when a chaotic time series of big data needs to be dealt with. There are innumerable applications that benefit from the interest in computational intelligent approaches, including but not limited to robotics, healthcare, transport, industrial, decision making, and gaming. The contribution by Shafi et al., “A computationally intelligent neural network-based nonlinear autoregressive exogenous balancing approach for real-time processing in industrial applications using big data,” attempts to investigate the effectiveness of using a neural nonlinear autoregressive with exogenous inputs (NARX) controller in an emerging application field of balancing systems like an inverted pendulum (IP) using big data.11 This article's aim has been to control an IP cart system by designing a neural NARX controller, and the focus is primarily on real-time processing in industrial applications grounded on big data ecosystems. In the proposed work, an IP system is mathematically modeled and first controlled utilizing a combination of classical proportional-integral-derivative (PID) controllers for cart and pendulum. Second, a chaotic time series of input–output data are obtained and are used to train two NARX controllers for cart and pendulum, respectively. Both the controllers are designed as single-input single-output systems with one layer each at input and output with a suitable number of hidden layers and neurons. Performance comparison of the NARX system behavior with a PID controller indicates that the NARX controllers successfully adapt to two different kinds of unknown inputs and effectively stabilize the system. Simulation results confirm that NARX controllers follow the training parameters and exhibit superior performance and overall system stability than PID control. Experimental results demonstrate the effectiveness of the approach. In the contribution by Ryu et al., “Effect of virtual error sensor location for active sound quality control in a car cabin,” a virtual error microphone-based active sound quality control (VEM-ASQC) algorithm is proposed to make engine sound quality better inside a car cabin.12 In a single-input and single-output system, if a VEM is installed at either the left or right ear, the sound pressure level (SPL) at the other ear can fluctuate, and this leads to the excessive SPL difference between the two ears when previous methods were applied. In this article, an uncomplicated method that can make engine sounds at both left and right ears track the target engine sound levels with fewer errors and which reduces the left–right tracking error (TE) (in SPL) difference was investigated. The suggested VEM-ASQC algorithm was implemented in an actual car for a real-time control experiment, and target profiles of nine engine orders were designed to control the sound quality actively. The control experiment showed that the proposed approach reduces the errors to track the target levels and the left–right TE differences more compared to those of previous methods. Wild boar (Sus scrofa) is a destructive species of swine. They spread diseases, represent a threat to native species, and destroy natural habitats by destabilizing river banks, thus reducing water flow. The monitoring of populations of wild boars is central to the execution and evaluation of methods to control them. To address this issue, in the contribution by Júnior et al., “Wild boar recognition using convolutional neural networks,” they retrain and apply four CNNs (AlexNet, VGG-16, Inception-v3, and ResNet-50) to classify different species of “bush pigs” in real-world footage: two native species of the Brazilian fauna, collared peccary (Pecari tajacu) and white-lipped peccary (Tayassu pecari), and one invasive species, wild boar (S. scrofa).13 Results show that CNNs can be used to classify animals with very similar behavior and appearance and that ResNet-50 outperforms all compared CNNs in terms of accuracy (98.33%) and the lowest probability of false positives (i.e., native species classified as wild boar). The International Telecommunications Union defines in its IMT-2020 recommendations three types of use of 5G services: massive machine-type communications (mMTC), enhanced mobile broadband (eMBB), and ultra-reliable low latency communications (uRLLC). The mMTC service allows a considerable number of machines and devices to communicate while guaranteeing a good quality of service. The eMBB service allows very high data throughput, even at the cell border. The uRLLC service is used for ultra-reliable communication for critical needs requiring very low latency. These services are provided separately in a given cell. However, the number of connected objects is starting to increase rapidly as well as the bit rates and energy consumption. The 5G network must make it possible to provide access to a vast number of users of its different service categories. Call admission control (CAC) techniques focus more on availability in terms of bit rate and coverage. In the contribution by Slalmi et al., “5G NB-IoT: Efficient network call admission control in cellular networks,” the authors suggest an algorithm for modeling CAC in an area served by the three categories of services in a 5G access network, mainly based on minimum energy consumption.14 This technique will allow connected objects that consume low energy to connect to the network with an adequate quality of service and enable the development of the Internet of Things. Secret sharing schemes are being widely used to distribute a secret between various participants so that an authorized subset of participants belonging to appropriate access structures can reconstruct this secret. However, a dealer might get corrupted by adversaries and may influence this secret sharing or the reconstruction process. Verifiable secret sharing (VSS) overcomes this issue by adding a verifiability protocol to the original secret sharing scheme. The contribution by Koikara et al., “Publicly verifiable threshold secret sharing based on three-dimensional-cellular automata” proposes a computationally secure publicly verifiable secret sharing scheme based on three-dimensional cellular automata (3D-CA).15 Unlike the more widely used linear secret sharing schemes or secret sharing schemes based on the Chinese remainder theorem, their proposed scheme performs the secret-sharing using 3D-CA. The secret is considered one of the initial configurations of the 3D-CA, and the following configurations are devised to be the shares distributed among the participants. Update mechanisms and various rules make it hard for an adversary to corrupt or duplicate a share. To make it even more efficient, they have added a verifiability layer such that a dealer posts a public share and private share to each shareholder. The verifiability layer reduces the interaction between dealers and participants and hence increases the security. The randomness of the shares has been calculated using the National Institute of Standards and Technology statistical test suite. Imaging systems with different imaging sensors are widely applied to the surveillance field, military field, and medical field. Particularly, infrared imaging sensors can acquire thermal radiation emitted by different objects but lack textural details, and visible imaging sensors can capture abundant textural information but suffer from loss of scene information under poor weather conditions. The fusion of infrared and visible images can synthesize a new image with complementary information of the source images. In the contribution by Zhu et al., “Multiscale channel attention network for infrared and visible image fusion,” the authors present a deep learning method with encoder–decoder architecture for infrared and visible image fusion.16 First, multiscale channel attention blocks are introduced to extract features at different scales, which can preserve more meaningful information and enhance the important information. Secondly, they utilize the improved fusion strategy based on visual saliency to fuse feature maps. Lastly, the fusion result is restored via a reconstruction network. In comparison with other state-of-the-art approaches, their experimental results achieve appealing performance on visual effects and objective assessments. Successful implementation of demand response (DR) always depends on the proper policy and their empowered technologies. The contribution by Saeed et al., “Intelligent implementation of residential demand response using multiagent system and deep neural networks,” proposed an intelligent multiagent system to idealize the residential DR in a distributed network.17 In their model, the primary stakeholders (smart homes and retailers) are demonstrated as a multifunctional intelligent agent. Home agents (HAs) are able to predict and schedule the energy load, and retailer agents (RAs) predict wholesale market price, and sell energy to HAs. Both HAs and RAs are modeled to predict real-time pricing. Deep neural networks, that is, long short-term memory network and hybrid CNN-LSTM, are used to predict the electricity load and energy price. Simulation results present good accuracy. The proposed work is compared with the existing model w.r.t. RMSE, MSE, and MAE. The comparison shows their model outperformed the existing models. The contribution by Kim, “Assessing engagement levels in a non-face-to-face learning environment through facial expression analysis,” proposes a method for analyzing the level of student engagement in a non-face-to-face learning situation by processing facial expressions.18 In previous studies, the level of learning engagement was determined through biosignals, such as galvanic skin response feedback. The method proposed in this study assesses engagement levels in an individual education situation by processing emotions revealed through a student's facial expressions in real-time. The emotional state revealed through facial expressions can be a useful indicator for assessing the level of immersion in learning. The algorithm proposed in this study is effective because teachers can teach while simultaneously monitoring students' engagement. The articles presented in this special issue provide insights into fields related to applied computational intelligence, including models, performance evaluation and improvements, and application developments. We hope the readers can benefit from the insights of these papers and contribute to these rapidly growing areas. We also hope that this special issue would shed light on major developments in the area of Concurrency and Computation: Practice and Experience and attract the attention of the scientific community to pursue further investigations leading to the rapid implementation of these technologies. We would like to express our appreciation to all the authors for their informative contributions and the reviewers for their support and constructive critiques in making this special issue possible. Finally, we would like to express our sincere gratitude to Professor David W. Walker, the Editor in Chief, for providing us with this unique opportunity to present our works in Concurrency and Computation: Practice and Experience.