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Investigating the effect of changes in neuronal connectivity on the brain's behavior is of interest in neuroscience studies. Complex network theory is one of the most capable tools to study the effects of these changes on collective brain behavior. By using complex networks, the neural structure, function, and dynamics can be analyzed. In this context, various frameworks can be used to mimic neural networks, among which multi-layer networks are a proper one. Compared to single-layer models, multi-layer networks can provide a more realistic model of the brain due to their high complexity and dimensionality. This paper examines the effect of changes in asymmetry coupling on the behaviors of a multi-layer neuronal network. To this aim, a two-layer network is considered as a minimum model of left and right cerebral hemispheres communicated with the corpus callosum. The chaotic model of Hindmarsh-Rose is taken as the dynamics of the nodes. Only two neurons of each layer connect two layers of the network. In this model, it is assumed that the layers have different coupling strengths, so the effect of each coupling change on network behavior can be analyzed. As a result, the projection of the nodes is plotted for several coupling strengths to investigate how the asymmetry coupling influences the network behaviors. It is observed that although no coexisting attractor is present in the Hindmarsh-Rose model, an asymmetry in couplings causes the emergence of different attractors. The bifurcation diagrams of one node of each layer are presented to show the variation of the dynamics due to coupling changes. For further analysis, the network synchronization is investigated by computing intra-layer and inter-layer errors. Calculating these errors shows that the network can be synchronized only for large enough symmetric coupling.

This work was supported in part by the Ministry of Higher Education under the Fundamental Research Grant Scheme under Grant FRGS/1/2018/ICT04/UTM/01/1; and in part by the Faculty of Informatics and Management, University of Hradec Kralove, through SPEV project under Grant 2102/2022., Phishing has become an increasing concern and captured the attention of end-users as well as security experts. Existing phishing detection techniques still suffer from the de ciency in performance accuracy and inability to detect unknown attacks despite decades of development and improvement. Motivated to solve these problems, many researchers in the cybersecurity domain have shifted their attention to phishing detection that capitalizes on machine learning techniques. Deep learning has emerged as a branch of machine learning that becomes a promising solution for phishing detection in recent years. As a result, this study proposes a taxonomy of deep learning algorithm for phishing detection by examining 81 selected papers using a systematic literature review approach. The paper rst introduces the concept of phishing and deep learning in the context of cybersecurity. Then, taxonomies of phishing detection and deep learning algorithm are provided to classify the existing literature into various categories. Next, taking the proposed taxonomy as a baseline, this study comprehensively reviews the state-of-the-art deep learning techniques and analyzes their advantages as well as disadvantages. Subsequently, the paper discusses various issues that deep learning faces in phishing detection and proposes future research directions to overcome these challenges. Finally, an empirical analysis is conducted to evaluate the performance of various deep learning techniques in a practical context, and to highlight the related issues that motivate researchers in their future works. The results obtained from the empirical experiment showed that the common issues among most of the state-of-the-art deep learning algorithms are manual parameter-tuning, long training time, and de cient detection accuracy., Ministry of Higher Education under the Fundamental Research Grant Scheme FRGS/1/2018/ICT04/UTM/01/1, Faculty of Informatics and Management, University of Hradec Kralove, through SPEV project 2102/2022

Infertility has recently emerged as a severe medical problem. The essential elements in male infertility are sperm morphology, sperm motility, and sperm density. In order to analyze sperm motility, density, and morphology, laboratory experts do a semen analysis. However, it is simple to err when using a subjective interpretation based on laboratory observation. In this work, a computer-aided sperm count estimation approach is suggested to lessen the impact of experts in semen analysis. Object detection techniques concentrating on sperm motility estimate the number of active sperm in the semen. This study provides an overview of other techniques that we can compare. The Visem dataset from the Association for Computing Machinery was used to test the proposed strategy. We created a labelled dataset to prove that our network can detect sperms in images. The best not-super tuned result is mAP 72.15.

The deployment of optical network infrastructure and development of new network services are growing rapidly for beyond 5/6G networks. However, optical networks are vulnerable to several types of security threats, such as single-point failure, wormhole attacks, and Sybil attacks. Since the uptake of e-commerce and e-services has seen an unprecedented surge in recent years, especially during the COVID-19 pandemic, the security of these transactions is essential. Blockchain is one of the most promising solutions because of its decentralized and distributed ledger technology, and has been employed to protect these transactions against such attacks. However, the security of blockchain relies on the computational complexity of certain mathematical functions, and because of the evolution of quantum computers, its security may be breached in real-time in the near future. Therefore, researchers are focusing on combining quantum key distribution (QKD) with blockchain to enhance blockchain network security. This new technology is known as quantum-secured blockchain. This article describes different attacks in optical networks and provides a solution to protect networks against security attacks by employing quantum-secured blockchain in optical networks. It provides a brief overview of blockchain technology with its security loopholes, and focuses on QKD, which makes blockchain technology more robust against quantum attacks. Next, the article provides a broad view of quantum-secured blockchain technology. It presents the network architecture for the future research and development of secure and trusted optical networks using quantum-secured blockchain. The article also highlights some research challenges and opportunities.

Analysis of the brain activity to external stimulation is an important area of research in biomedical engineering. In this paper, for the first time, we analyzed the brain reaction to visual stimuli with different frequencies using three complexity methods. For this purpose, we utilized fractal theory, sample entropy, and approximate entropy to study the variations of the complexity EEG signals while subjects received visual stimuli at 7, 9, 11, and 13 Hz. The results showed that, in general, by moving from 9 Hz to 13 Hz stimuli, the complexity of EEG signals increases, except in the case of 11 Hz stimulus. The statistical analysis also supported the results of the analysis. The conducted analysis in this research can be performed in the case of other types of external stimuli to study how the brain reacts in different conditions.

In this research, we investigated the effect of changes in walking speed on variations of the complexity of electromyogram (EMG) signals recorded from the right and left legs of subjects. We specifically employed fractal theory and approximate entropy to analyze the changes in the complexity of EMG signals recorded from 13 subjects walked at 1.0, 1.5, 2.0, 2.5, 3.0, 3.5, and 4.0 km/h on a flat surface. The results showed that by increasing of walking speed, the complexity of EMG signals decreases. The statistical analysis also indicated the significant effect of variations in walking speed on the variations of the complexity of EMG signals. This method analysis can be applied to other physiological signals of humans (e.g. electroencephalogram (EEG) signals) to investigate the effect of walking speed on other organs’ activations (e.g. brain).

In this research, we apply complexity-based techniques to study the activations of the brain while the subjects perform different types of locomotion, including walking, jogging, and running. Therefore, we can study the effect of locomotion speed (or toughness level) on brain’s reactions. For this purpose, we analyzed the fractal dimension and approximate entropy of electroencephalogram (EEG) signals recorded from subjects while they walked, jogged, and ran for 20 s in the case of each activity. The analysis of 21 recorded samples showed that the complexity of EEG signals increases by increasing the locomotion speed. This result indicates a higher level of processing in the brain while the subjects perform a harder task. This analysis can be extended to the case of other physiological signals to study the effect of the level of exercise on different organs’ activations.

This paper proposes a novel local feature descriptor coined as a local instant-and-center-symmetric neighbor-based pattern of the extrema-images (LINPE) to detect breast abnormalities in thermal breast images. It is a hybrid descriptor that combines two different feature descriptors: one is the inverse-probability difference extrema (IpDE), and another is the local instant and center-symmetric neighbor-based pattern (LICsNP). IpDE is developed to compute the intensity-inhomogeneity-invariant feature-based image of the breast thermogram. Besides, the LICsNP is intended to capture the local microstructure pattern information in the IpDE image. A new paradigm, named Broad Learning (BL) network, is introduced here as a classifier to differentiate the healthy and sick breast thermograms efficiently. The efficacy of the proposed system is quantitatively validated on the images of DMR-IR and DBT-TU-JU databases. Extensive experimentation on these databases with an average accuracy of 96.90% and 94%, respectively, justifies proposed system's superiority in the differentiation of healthy and sick breast thermograms over the other related existing state-of-the-art methods. The proposed system also performs consistently in the presence of noise and rotational changes.

Nanotechnology is gaining significant attention, with numerous biomedical applications. Silver in wound dressings, copper oxide and silver in antibacterial preparations, and zinc oxide nanoparticles as a food and cosmetic ingredient are common examples. However, adverse effects of nanoparticles in humans and the environment from extended exposure at varied concentrations have yet to be established. One of the drawbacks of employing nanoparticles is their tendency to cause oxidative stress, a significant public health concern with life-threatening consequences. Cardiovascular, renal, and respiratory problems and diabetes are among the oxidative stress-related disorders. In this context, phytoantioxidant functionalized nanoparticles could be a novel and effective alternative. In addition to performing their intended function, they can protect against oxidative damage. This review was designed by searching through various websites, books, and articles found in PubMed, Science Direct, and Google Scholar. To begin with, oxidative stress, its related diseases, and the mechanistic basis of oxidative damage caused by nanoparticles are discussed. One of the main mechanisms of action of nanoparticles was unearthed to be oxidative stress, which limits their use in humans. Secondly, the role of phytoantioxidant functionalized nanoparticles in oxidative damage prevention is critically discussed. The parameters for the characterization of nanoparticles were also discussed. The majority of silver, gold, iron, zinc oxide, and copper nanoparticles produced utilizing various plant extracts were active free radical scavengers. This potential is linked to several surface fabricated phytoconstituents, such as flavonoids and phenols. These phytoantioxidant functionalized nanoparticles could be a better alternative to nanoparticles prepared by other existing approaches.