Due to its superlative physical qualities and its beauty, the diamond is a renowned structure. While the green-colored perimantanes diamondoid is one of a higher diamond structure. Motivated by the structure’s applications and usage, we look into the metric-based parameters of this structure. In this draft, we have discussed metric dimension and their generalizations for the generalized perimantanes diamondoid structure and proved that each parameter depends on the copies of original or base perimantanes diamondoid structure and changes with the parameter n or its number of copies.

The recent explosion of high-rate multimedia transfer has heightened the need for image ciphers that are lossless, payload-grade, and computationally efficient, yet fully structure-adaptive. Traditional block ciphers are effective for byte streams but do not account for spatial dependencies in images, while many chaos-based methods remain ad hoc, lacking strict invertibility and clear keying or synchronization mechanisms. Motivated by these gaps, we present a keyed image cipher that integrates a four-dimensional (4D) chaotic driver with a rule-adaptive variant of Langton's Ant cellular automaton. The scheme performs pixel permutation followed by two mutually inverse decimal diffusions with cross-channel feedback, and a symbolic diffusion stage that encodes bytes into Ant symbols intermingled through three distinct, keystream- and rule-dependent operators. The core novelty is a provably bijective symbolic diffusion with an explicit inverse and a branch-number-style local diffusion lower bound, combined with a linear-time streaming implementation. The cipher passes the NIST SP 800-22 statistical tests on both keystream and ciphertext-derived bitstreams. Experiments on standard test images show NPCR[Formula: see text] UACI[Formula: see text] Shannon entropy [Formula: see text]bits/pixel, negligible adjacent-pixel correlations, high key sensitivity, and strong robustness against 20% salt-and-pepper noise and 20% occlusion. The overall key space is approximately [Formula: see text], indicating high resistance to brute-force attacks.

The Internet of Medical Things (IoMT) has become a pivotal aspect of IoT applications, playing a crucial role in cutting down healthcare expenses, enhancing access to clinical services, and refining operational efficiency within the healthcare domain. An early detection of neurological brain disorders continues to present a formidable challenge. In response, our research endeavors are directed towards the IoMT-based system used for the real-time diagnosis of neurological disorders. In this paper, IoMT-based real-time diagnosis model is developed for neurological brain disorders using systematic deep learning and electroencephalogram (EEG) signal (RT-NeuroDDSM). We first introduce the time domain, channel and spatial attention network (TCSNet) for feature extraction which extracts the high-level time series, channel and spatial features, respectively. TCSNet aims to learn more valuable features from the input data to achieve good classification results. Furthermore, in order to maximize features, we create the modified normative fish swarm (MNFS) feature selection algorithm. Next, the detection of various neurological brain problems, including neuro-typical, epilepsy, and autism spectrum disorder (ASD), is accomplished by applying the hinging hyperplane neural network (HHNN). To verify the performance, we used the publicly accessible EEG datasets from University of Bonn-Germany, CHB-MIT EEG repository, and King Abdul-Aziz University. The RT-NeuroDDSM has an overall classification accuracy of 99.956%, making it 5.471% more in efficiency compared to the existing state-of-the-art model.

The widespread use of the Internet of Things (IoT) technology has both good and bad things about it. There must be a full and reliable security system in place for the Internet of Things so that things can work together in a safe way and intrusions cannot happen. There are now many more ways to keep the Internet of Things safe, thanks to a detecting system. As machine learning and deep learning technologies have become better, a lot of good intrusion detection systems have been made. This kind of study is covered. These two types of security are compared in this study. The current machine-based intrusion detection system is broken down into more detailed categories based on detection technology, data source, architecture, and operating method. These categories are as follows: It is talked about how IoT security will grow in the future and how to understand its intrusion detection system too. In this paper, a cloud-based blockchain security model has been presented for secure data transmission over IoT.

Satellite Internet of Things (IoT) networks based on satellites are becoming increasingly critical for mission-critical applications, including disaster recovery, environmental surveillance, and remote sensing. While becoming more widespread, they are also more vulnerable to various risks, particularly due to the heterogeneous communication technologies they support and the limited computing capacity on each device. When such IoT systems are connected with central HighPerformance Computing (HPC) clouds, particularly by satellite links, new security issues arise, the primary one being the secure transmission of confidential information. To overcome such challenges, this research proposes a new security framework termed DLGAN (Deep Learning-based Generative Adversarial Network), specially designed for satellite-based IoT scenarios. The model leverages the strengths of Convolutional Neural Networks (CNNs) for real-time anomaly detection, combined with Generative Adversarial Networks (GANs) to generate realistic synthetic attack data, thereby addressing the challenge of skewed datasets prevalent in cybersecurity research. Since training GANs may be computationally expensive, the model is optimized to run on an HPC system via the Message Passing Interface (MPI) to enable scalable parallel processing of huge IoT data. Fundamentally, the DLGAN model is based on a generator/discriminator mechanism for effectively distinguishing network traffic as either benign or malicious, with the capability to detect 14 different types of attacks. By harnessingAI-enabled GPUs in the HPC cloud, the system can provide fast and accurate detection while maintaining low computational costs. Experimental evaluations demonstrate that the framework significantly enhances detection accuracy, reduces training time, and scales well with large data volumes, making it highly suitable for real-time security operations. In total, this study highlights how integrating advanced deep learning technologies with HPC-based distributed environments can deliver an efficient and dynamic defense mechanism for contemporary IoT networks. The envisaged solution is unique in its ability to scale, maximize efficiency, and resist attacks while securing satellite-based IoT infrastructures.

Image synthesis based on natural language description has become a research hotspot in edge computing in artificial intelligence. With the help of generative adversarial edge computing networks, the field has made great strides in high-resolution image synthesis. However, there are still some defects in the authenticity of synthetic single-target images. For example, there will be abnormal situations such as “multiple heads” and “multiple mouths” when synthesizing bird graphics. Aiming at such problems, a text generation single-target model SA-AttnGAN based on a self-attention mechanism is proposed. SA-AttnGAN (Attentional Generative Adversarial Network) refines text features into word features and sentence features to improve the semantic alignment of text and images; in the initialization stage of AttnGAN, the self-attention mechanism is used to improve the stability of the text-generated image model; the multistage GAN network is used to superimpose, finally synthesizing high-resolution images. Experimental data show that SA-AttnGAN outperforms other comparable models in terms of Inception Score and Frechet Inception Distance; synthetic image analysis shows that this model can learn background and colour information and correctly capture bird heads and mouths. The structural information of other components is improved, and the AttnGAN model generates incorrect images such as “multiple heads” and “multiple mouths.” Furthermore, SA-AttnGAN is successfully applied to description-based clothing image synthesis with good generalization ability.