ABSTRACT This study presents the design and optimization of a microstrip monopole antenna for 5G sub‐6 GHz applications, employing a deep learning‐based surrogate model combined with honeybee mating optimization (HBMO). The studied antenna structure employs air via arrays, intended to enhance antenna performance, including improved impedance matching and increased bandwidth. It is important to note that, unlike conventional antennas, the proposed design does not include a fully enclosed metallic cavity similar to a substrate integrated waveguide (SIW) antenna designs. A sensitivity analysis was conducted to assess the impact of these parameters, emphasizing the need for optimal tuning. To generate training and test datasets efficiently, Latin hypercube sampling (LHS) was used. A convolutional neural network (CNN) surrogate model was trained, outperforming other machine learning (ML) algorithms in predictive accuracy and generalization. The proposed CNN‐HBMO framework reduced computational costs by minimizing the need for expensive electromagnetic (EM) simulations, enabling rapid design space exploration. The optimized antenna was fabricated and validated through experimental measurements, achieving 2–3 dBi gain and 𝑆 11 < −10 dB across the 2.7–5.2 GHz band. Compared to existing designs, the proposed antenna offers a compact size (34 × 34 mm) with competitive performance, making it suitable for multi‐band 5G applications.

ABSTRACT This study introduces a novel attention‐guided Discrete Wavelet Transform (DWT)‐based steganography framework, named Attention‐Guided Feature Perturbation (AGFP), which integrates deep visual attention maps with transform‐domain embedding to enhance imperceptibility, robustness, and steganalysis resistance. Unlike recent deep‐learning‐based steganographic systems such as iSCMIS, JARS‐Net, and RMSteg, which achieve high visual fidelity but are susceptible to statistical detection, AGFP perturbs only those wavelet coefficients that are identified as perceptually and statistically stable by attention mechanisms extracted from pre‐trained CNN models (VGG19, ResNet50, AlexNet, and GoogLeNet). The proposed method is evaluated on the USC‐SIPI dataset and the BOSSBase 1.01 benchmark. Experimental results show that AGFP achieves PSNR values between 64.29 and 55.43 dB and SSIM scores between 0.9999 and 0.9989 across varying payloads, indicating consistently high visual quality. While iSCMIS reports slightly higher PSNR and SSIM values, AGFP significantly outperforms all compared methods in bit error rate (BER)—achieving 0.01–0.12, compared to 0.45–0.47 for iSCMIS, 0.31–0.37 for RMSteg, and 0.57–0.75 for JARS‐Net. Furthermore, AGFP attains the lowest RS, SPA, and SRM steganalysis detection scores among both classical and deep‐learning‐based systems. These results confirm that AGFP offers a more balanced and secure steganographic solution, combining high imperceptibility with substantially enhanced robustness and detectability resistance, positioning it as a strong alternative to recent deep‐learning‐based steganographic frameworks.

Abstract The globality of mobbing points to huge influence of economic issues over social and societal aspects in the life dynamics of work. COVID-19 presents a new kind of crisis that transforms these factors and establishes new norms in working life simultaneously. Mobbing is to be defined, in this perspective, as the modifications of situation of work and expectations of workers retraining the boundaries and manifestation of mobbing. This study examines the impact of dislocating mobbing, which is a kind of violence that deteriorates the quality of life for employees as well as workplace productivity, in terms of the new dynamics of mobbing and existing dimensions of mobbing-the COVID-19 perspective. Mixed methods research was carried out through macro-level collection and analysis of tweet data alongside micro-level focus group interviews. While macro findings identified general mobbing dimensions, micro findings revealed more indirect, implicit and specific means of power imbalance. The findings of the research identify emerging gaps in organisational practice regarding diversity and inclusion via the lens of increasing and latent specific power imbalances. In both data analyses, a new dimension of mobbing was identified: the perception of injustice. The emergence of injustice as a new dimension provides a more comprehensive perspective on current practices. The findings of this research are expected to provide valid approaches towards reiteration of existing organisational practices and human resources training.

Abstract SQL injection is one of the important security issues in web applications because it allows an attacker to interact with the application's database. SQL injection attacks can be detected using machine learning algorithms. The effective features should be employed in the training stage to develop an optimal classifier with optimal accuracy. Identifying the most effective features is an NP-complete combinatorial optimization problem. Feature selection is the process of selecting the training dataset's smallest and most effective features. The main objective of this study is to enhance the accuracy, precision, and sensitivity of the SQLi detection method. In this study, an effective method to detect SQL injection attacks has been proposed. In the first stage, a specific training dataset consisting of 13 features was prepared. In the second stage, two different binary versions of the Gray-Wolf algorithm were developed to select the most effective features of the dataset. The created optimal datasets were used by different machine learning algorithms. Creating a new SQLi training dataset with 13 numeric features, developing two different binary versions of the gray wolf optimizer to optimally select the features of the dataset, and creating an effective and efficient classifier to detect SQLi attacks are the main contributions of this study. The results of the conducted tests indicate that the proposed SQL injection detector obtain 99.68% accuracy, 99.40% precision, and 98.72% sensitivity. The proposed method increases the efficiency of attack detection methods by selecting 20% of the most effective features.

Abstract Reverse engineering is used for extracting and understanding software architecture models from source code when the source code is the only available software product. Software module clustering is a reverse engineering method which decomposes software modules into several clusters (subsystems) by using module dependency graph. Finding the best clusters for the modules of software is a multi‐objective and NP‐hard problem; maximizing the cohesion among the modules, minimizing the coupling among different clusters, and maximizing the software modularization quality are considered as the main objectives of software module clustering. Some of these objectives, such as cohesion and coupling, are in contradiction with each other. Simultaneous improvement of all clustering objectives (cohesion, coupling, modularization quality, size, and number of clusters) is the main goal of this study. In this paper, by capitalizing on multi objective genetic algorithm and a new combination of objective functions, we proposed a homogeneous method, namely, Savalan, for clustering software modules. The proposed method generates high‐quality clusters with strong cohesion within clusters and weak connections between clusters for the input source code. The results of conducted experiments on the 14 benchmark programs indicate that simultaneous improvement of all clustering objectives is the main merit of this method. According to the experimental results, the proposed algorithm was able to outperform the previous multi objective methods.

Design and development of legged robots that can navigate effectively and autonomously in a wide range of environments is a challenging problem. At that point it should be noted that nature inspired optimization techniques have been widely studied for autonomous navigation of legged robots. In this study the framework constructed for six-legged walking behavior generation using genetic algorithms is presented and simulation results on Unity are discussed.