Wave propagation in a generalized thermoelastic plate using eigenvalue approach

In the present work, we obtain a dispersion relation for Rayleigh–Lamb wave propagation in a plate of thermoelastic material. For this aim, we consider the theory of generalized thermoelasticity with one relaxation time. The thickness of the plate is taken to be finite and the faces of the plate are assumed to be isothermal and free from stresses. We obtain the analytical solution for the temperature, displacement components, and stresses using an eigenvalue approach. Finally, we derive a dispersion relation for the plate in closed form taking into account isothermal boundary conditions for wave mode propagation. To obtain the phase velocity and attenuation coefficients of propagating wave mode, we use the function iteration numerical scheme to solve the complex dispersion relation. The phase velocity and attenuation coefficients for the first five modes of waves are represented graphically for Lord–Shulman and classical coupled dynamical theories.     

Enhanced Gorilla Troops Optimizer with Deep Learning Enabled Cybersecurity Threat Detection

Recent developments in computer networks and Internet of Things (IoT) have enabled easy access to data. But the government and business sectors face several difficulties in resolving cybersecurity network issues, like novel attacks, hackers, internet criminals, and so on. Presently, malware attacks and software piracy pose serious risks in compromising the security of IoT. They can steal confidential data which results in financial and reputational losses. The advent of machine learning (ML) and deep learning (DL) models has been employed to accomplish security in the IoT cloud environment. This article presents an Enhanced Artificial Gorilla Troops Optimizer with Deep Learning Enabled Cybersecurity Threat Detection (EAGTODL-CTD) in IoT Cloud Networks. The presented EAGTODL-CTD model encompasses the identification of the threats in the IoT cloud environment. The proposed EAGTODL-CTD model mainly focuses on the conversion of input binary files to color images, where the malware can be detected using an image classification problem. The EAGTODL-CTD model pre-processes the input data to transform to a compatible format. For threat detection and classification, cascaded gated recurrent unit (CGRU) model is exploited to determine class labels. Finally, EAGTO approach is employed as a hyperparameter optimizer to tune the CGRU parameters, showing the novelty of our work. The performance evaluation of the EAGTODL-CTD model is assessed on a dataset comprising two class labels namely malignant and benign. The experimental values reported the supremacy of the EAGTODL-CTD model with increased accuracy of 99.47%.     

Improved Ant Lion Optimizer with Deep Learning Driven Arabic Hate Speech Detection

Arabic is the world’s first language, categorized by its rich and complicated grammatical formats. Furthermore, the Arabic morphology can be perplexing because nearly 10,000 roots and 900 patterns were the basis for verbs and nouns. The Arabic language consists of distinct variations utilized in a community and particular situations. Social media sites are a medium for expressing opinions and social phenomena like racism, hatred, offensive language, and all kinds of verbal violence. Such conduct does not impact particular nations, communities, or groups only, extending beyond such areas into people’s everyday lives. This study introduces an Improved Ant Lion Optimizer with Deep Learning Dirven Offensive and Hate Speech Detection (IALODL-OHSD) on Arabic Cross-Corpora. The presented IALODL-OHSD model mainly aims to detect and classify offensive/hate speech expressed on social media. In the IALODL-OHSD model, a three-stage process is performed, namely pre-processing, word embedding, and classification. Primarily, data pre-processing is performed to transform the Arabic social media text into a useful format. In addition, the word2vec word embedding process is utilized to produce word embeddings. The attention-based cascaded long short-term memory (ACLSTM) model is utilized for the classification process. Finally, the IALO algorithm is exploited as a hyperparameter optimizer to boost classifier results. To illustrate a brief result analysis of the IALODL-OHSD model, a detailed set of simulations were performed. The extensive comparison study portrayed the enhanced performance of the IALODL-OHSD model over other approaches.     

One-step entanglements generation on distant superconducting resonators in the dispersive regime

We present a scalable quantum-bus-based device for generating the entanglement on photons in distant superconducting resonators (SRs). The device is composed of some one-dimensional (1D) SRs \(r_j\) coupled to the quantum bus (another common resonator R) in its different positions assisted by superconducting quantum interferometer devices which are used to tune the coupling strengths between \(r_j\) and R. By using the technique for catching and releasing a photon state in a 1D SR, it can work as an entanglement generator or a node in quantum communication. To demonstrate the performance of this device, we propose a one-step scheme to generate high-fidelity Bell state on photons in two distant SRs. It works in the dispersive regime of \(r_j\) and R, which enables us to extend it to generate high-fidelity multi-Bell states on different resonator pairs simultaneously.     

Linearly implicit predictor–corrector methods for space-fractional reaction–diffusion equations with non-smooth initial data

In this paper, linearly implicit predictor–corrector methods are proposed for solving space-fractional reaction–diffusion equations with non-smooth initial data. The methods are based on Matrix Transfer Technique for spatial discretization and are shown to be unconditionally stable. It is observed that the linearly implicit predictor–corrector method derived by using (1,1)-Padé approximation to matrix exponential function incurs oscillatory behavior for some time steps. These oscillations are due to high frequency components present in the solution and are diminished as the order of the space-fractional derivative decreases (slow diffusion). We present a priori reliability constraint to avoid these unwanted oscillations and generalize the constraints for all $(m,m)$-Padé approximants, $m\in {\mathbb{Z}}^{+}$, to the matrix exponential functions. These time stepping constraints are seen to be dependent on the order of the space-fractional derivative. The linearly implicit predictor–corrector method based on the (0,2)-Padé approximations to the matrix exponential function is shown to be oscillation-free for any time step. Error estimates are obtained for the methods and are theoretically shown to be second-order convergent. Computational complexity of the algorithms is discussed for solving multidimensional space-fractional reaction–diffusion systems. Several numerical experiments are performed to support our theoretical observations and to show the effectiveness, reliability, and efficiency of the methods.     

Extending the theory of planned behavior (TPB) to explain online game playing among Malaysian undergraduate students

As the world moves into the web 2.0 era, everyone can connect virtually, and online game playing has become a trend. Online games are played over computer networks, usually over the Internet. Online games entail a number of advantages, such as the ability to connect to multiplayer games, although single-player online games are also rather popular. This exploratory study focused on modeling the determinants of actual use of online game playing. Many researchers have shown perceived enjoyment and flow experience as important drivers of actual use of online game playing. The theory of planned behavior has been used in this study. Data were collected from 1584 Universiti Sains Malaysia students with different backgrounds using a structured questionnaire. The findings show that perceived enjoyment has the strongest influence on actual use. Other variables found to influence actual usage include the level of perceived behavioral control, subjective norms, attitude, perceived enjoyment, and flow experience. Implications of this research for future researchers will also be discussed. We hope this research will increase researchers’ interest in further development in this sector and that the model will assist the games industry to identify factors that increase actual use by players.     

Improved Multileader Optimization with Shadow Encryption for Medical Images in IoT Environment

Nowadays, security plays an important role in Internet of Things (IoT) environment especially in medical services’ domains like disease prediction and medical data storage. In healthcare sector, huge volumes of data are generated on a daily basis, owing to the involvement of advanced health care devices. In general terms, health care images are highly sensitive to alterations due to which any modifications in its content can result in faulty diagnosis. At the same time, it is also significant to maintain the delicate contents of health care images during reconstruction stage. Therefore, an encryption system is required in order to raise the privacy and security of healthcare data by not leaking any sensitive data. The current study introduces Improved Multileader Optimization with Shadow Image Encryption for Medical Image Security (IMLOSIE-MIS) technique for IoT environment. The aim of the proposed IMLOSIE-MIS model is to accomplish security by generating shadows and encrypting them effectively. To do so, the presented IMLOSIE-MIS model initially generates a set of shadows for every input medical image. Besides, shadow image encryption process takes place with the help of Multileader Optimization (MLO) with Homomorphic Encryption (IMLO-HE) technique, where the optimal keys are generated with the help of MLO algorithm. On the receiver side, decryption process is initially carried out and shadow image reconstruction process is conducted. The experimentation analysis was carried out on medical images and the results inferred that the proposed IMLOSIE-MIS model is an excellent performer compared to other models. The comparison study outcomes demonstrate that IMLOSIE-MIS model is robust and offers high security in IoT-enabled healthcare environment.     

A High-Energy Asymmetric Supercapacitor Based on Tomato-Leaf-Derived Hierarchical Porous Activated Carbon and Electrochemically Deposited Polyaniline Electrodes for Battery-Free Heart-Pulse-Rate Monitoring

A simple and scalable method to fabricate a novel high-energy asymmetric supercapacitor using tomato-leaf-derived hierarchical porous activated carbon (TAC) and electrochemically deposited polyaniline (PANI) for a battery-free heart-pulse-rate monitor is reported. In this study, TAC is prepared by simple pyrolysis, exhibiting nanosheet-type morphology and a high specific surface area of ≈1440 m² g⁻¹, and PANI is electrochemically deposited onto carbon cloth. The TAC- and PANI- based asymmetric supercapacitor demonstrates an electrochemical performance superior to that of symmetric supercapacitors, delivering a high specific capacitance of 248 mF cm⁻² at a current density of 1.0 mA cm⁻². The developed asymmetric supercapacitor shows a high energy density of 270 µWh cm⁻² at a power density of 1400 µW cm⁻², as well as an excellent cyclic stability of ≈95% capacitance retention after 10 000 charging–discharging cycles while maintaining ≈98% Coulombic efficiency. Impressively, the series-connected asymmetric supercapacitors can operate a battery-free heart-pulse-rate monitor extremely efficiently upon solar-panel charging under regular laboratory illumination.     

Dynamic Hand Gesture Recognition Using 3D-CNN and LSTM Networks

Recognition of dynamic hand gestures in real-time is a difficult task because the system can never know when or from where the gesture starts and ends in a video stream. Many researchers have been working on vision-based gesture recognition due to its various applications. This paper proposes a deep learning architecture based on the combination of a 3D Convolutional Neural Network (3D-CNN) and a Long Short-Term Memory (LSTM) network. The proposed architecture extracts spatial-temporal information from video sequences input while avoiding extensive computation. The 3D-CNN is used for the extraction of spectral and spatial features which are then given to the LSTM network through which classification is carried out. The proposed model is a light-weight architecture with only 3.7 million training parameters. The model has been evaluated on 15 classes from the 20BN-jester dataset available publicly. The model was trained on 2000 video-clips per class which were separated into 80% training and 20% validation sets. An accuracy of 99% and 97% was achieved on training and testing data, respectively. We further show that the combination of 3D-CNN with LSTM gives superior results as compared to MobileNetv2 + LSTM.     

Feature Subset Selection with Artificial Intelligence-Based Classification Model for Biomedical Data

Recently, medical data classification becomes a hot research topic among healthcare professionals and research communities, which assist in the disease diagnosis and decision making process. The latest developments of artificial intelligence (AI) approaches paves a way for the design of effective medical data classification models. At the same time, the existence of numerous features in the medical dataset poses a curse of dimensionality problem. For resolving the issues, this article introduces a novel feature subset selection with artificial intelligence based classification model for biomedical data (FSS-AICBD) technique. The FSS-AICBD technique intends to derive a useful set of features and thereby improve the classifier results. Primarily, the FSS-AICBD technique undergoes min-max normalization technique to prevent data complexity. In addition, the information gain (IG) approach is applied for the optimal selection of feature subsets. Also, group search optimizer (GSO) with deep belief network (DBN) model is utilized for biomedical data classification where the hyperparameters of the DBN model can be optimally tuned by the GSO algorithm. The choice of IG and GSO approaches results in promising medical data classification results. The experimental result analysis of the FSS-AICBD technique takes place using different benchmark healthcare datasets. The simulation results reported the enhanced outcomes of the FSS-AICBD technique interms of several measures.