Lei Feng¹, Miao Liu¹, Yexun Yuan¹, Chi Zhang², Peng Geng^1,*

Journal on Internet of Things, Vol.6, pp. 1-16, 2024, DOI:10.32604/jiot.2024.053584

Abstract With the rapid development of electric vehicles, the requirements for charging stations are getting higher and higher. In this study, we constructed a charging station topology network in Nanjing through the Space-L method, mapping charging stations as network nodes and constructing edges through road relationships. The experiment introduced five EV charging recommendation strategies (based on distance, number of fast charging piles, user preference, price, and overall rating) used to simulate disordered charging caused by different user preferences, and the impact of the network dynamic robustness in case of node failure is explored by simulating the… More >

Yunjing Liu^1,, Erhu Zhang^1,2,,*, Jingjing Wang³, Guangfeng Lin², Jinghong Duan⁴

CMC-Computers, Materials & Continua, Vol.80, No.1, pp. 1633-1653, 2024, DOI:10.32604/cmc.2024.052611

Abstract Recovering high-quality inscription images from unknown and complex inscription noisy images is a challenging research issue. Different from natural images, character images pay more attention to stroke information. However, existing models mainly consider pixel-level information while ignoring structural information of the character, such as its edge and glyph, resulting in reconstructed images with mottled local structure and character damage. To solve these problems, we propose a novel generative adversarial network (GAN) framework based on an edge-guided generator and a discriminator constructed by a dual-domain U-Net framework, i.e., EDU-GAN. Unlike existing frameworks, the generator introduces the… More >

Batyrkhan Omarov^1,2,*, Meirzhan Baikuvekov¹, Daniyar Sultan¹, Nurzhan Mukazhanov³, Madina Suleimenova², Maigul Zhekambayeva³

CMC-Computers, Materials & Continua, Vol.80, No.1, pp. 341-359, 2024, DOI:10.32604/cmc.2024.052437

Abstract This research introduces an innovative ensemble approach, combining Deep Residual Networks (ResNets) and Bidirectional Gated Recurrent Units (BiGRU), augmented with an Attention Mechanism, for the classification of heart arrhythmias. The escalating prevalence of cardiovascular diseases necessitates advanced diagnostic tools to enhance accuracy and efficiency. The model leverages the deep hierarchical feature extraction capabilities of ResNets, which are adept at identifying intricate patterns within electrocardiogram (ECG) data, while BiGRU layers capture the temporal dynamics essential for understanding the sequential nature of ECG signals. The integration of an Attention Mechanism refines the model’s focus on critical segments… More >

Chunwei Tian^1,2,3,4, Haoyang Gao^2,3, Pengwei Wang², Bob Zhang^1,*

CMC-Computers, Materials & Continua, Vol.80, No.1, pp. 105-118, 2024, DOI:10.32604/cmc.2024.052097

Abstract Generative adversarial networks (GANs) with gaming abilities have been widely applied in image generation. However, gamistic generators and discriminators may reduce the robustness of the obtained GANs in image generation under varying scenes. Enhancing the relation of hierarchical information in a generation network and enlarging differences of different network architectures can facilitate more structural information to improve the generation effect for image generation. In this paper, we propose an enhanced GAN via improving a generator for image generation (EIGGAN). EIGGAN applies a spatial attention to a generator to extract salient information to enhance the truthfulness… More >

Kenian Wang^1,2,*, Yuan Hong^1,2, Chunxiao Li²

CMC-Computers, Materials & Continua, Vol.80, No.1, pp. 1123-1142, 2024, DOI:10.32604/cmc.2024.052088

Abstract With the exponential increase in information security risks, ensuring the safety of aircraft heavily relies on the accurate performance of risk assessment. However, experts possess a limited understanding of fundamental security elements, such as assets, threats, and vulnerabilities, due to the confidentiality of airborne networks, resulting in cognitive uncertainty. Therefore, the Pythagorean fuzzy Analytic Hierarchy Process (AHP) Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) is proposed to address the expert cognitive uncertainty during information security risk assessment for airborne networks. First, Pythagorean fuzzy AHP is employed to construct an index system… More >

Li Li^*, Qing Zhang, Youran Kong

CMC-Computers, Materials & Continua, Vol.80, No.1, pp. 801-818, 2024, DOI:10.32604/cmc.2024.051916

Abstract Due to the diversity and unpredictability of changes in malicious code, studying the traceability of variant families remains challenging. In this paper, we propose a GAN-EfficientNetV2-based method for tracing families of malicious code variants. This method leverages the similarity in layouts and textures between images of malicious code variants from the same source and their original family of malicious code images. The method includes a lightweight classifier and a simulator. The classifier utilizes the enhanced EfficientNetV2 to categorize malicious code images and can be easily deployed on mobile, embedded, and other devices. The simulator utilizes… More >

Pablo Fernández Saura^1,*, José M. Bernabé Murcia¹, Emilio García de la Calera Molina¹, Alejandro Molina Zarca², Jorge Bernal Bernabé¹, Antonio F. Skarmeta Gómez¹

CMC-Computers, Materials & Continua, Vol.80, No.1, pp. 163-193, 2024, DOI:10.32604/cmc.2024.051307

Abstract The management of network intelligence in Beyond 5G (B5G) networks encompasses the complex challenges of scalability, dynamicity, interoperability, privacy, and security. These are essential steps towards achieving the realization of truly ubiquitous Artificial Intelligence (AI)-based analytics, empowering seamless integration across the entire Continuum (Edge, Fog, Core, Cloud). This paper introduces a Federated Network Intelligence Orchestration approach aimed at scalable and automated Federated Learning (FL)-based anomaly detection in B5G networks. By leveraging a horizontal Federated learning approach based on the FedAvg aggregation algorithm, which employs a deep autoencoder model trained on non-anomalous traffic samples to recognize… More >

Amna Khatoon¹, Weixing Wang^1,*, Asad Ullah², Limin Li^3,*, Mengfei Wang¹

CMC-Computers, Materials & Continua, Vol.80, No.1, pp. 527-546, 2024, DOI:10.32604/cmc.2024.051147

Abstract Integrating Tiny Machine Learning (TinyML) with edge computing in remotely sensed images enhances the capabilities of road anomaly detection on a broader level. Constrained devices efficiently implement a Binary Neural Network (BNN) for road feature extraction, utilizing quantization and compression through a pruning strategy. The modifications resulted in a 28-fold decrease in memory usage and a 25% enhancement in inference speed while only experiencing a 2.5% decrease in accuracy. It showcases its superiority over conventional detection algorithms in different road image scenarios. Although constrained by computer resources and training datasets, our results indicate opportunities for More >

Saad Akbar^1,2, Humera Azam¹, Sulaiman Sulmi Almutairi^3,*, Omar Alqahtani⁴, Habib Shah⁴, Aliya Aleryani⁴

CMC-Computers, Materials & Continua, Vol.80, No.1, pp. 1075-1104, 2024, DOI:10.32604/cmc.2024.050913

Abstract The prompt spread of COVID-19 has emphasized the necessity for effective and precise diagnostic tools. In this article, a hybrid approach in terms of datasets as well as the methodology by utilizing a previously unexplored dataset obtained from a private hospital for detecting COVID-19, pneumonia, and normal conditions in chest X-ray images (CXIs) is proposed coupled with Explainable Artificial Intelligence (XAI). Our study leverages less preprocessing with pre-trained cutting-edge models like InceptionV3, VGG16, and VGG19 that excel in the task of feature extraction. The methodology is further enhanced by the inclusion of the t-SNE (t-Distributed… More >

Deema Mohammed Alsekait^1,*, Asif Nawaz², Ayman Nabil³, Mehwish Bukhari², Diaa Salama AbdElminaam^3,4,5,6,*

Computer Systems Science and Engineering, Vol.48, No.4, pp. 1031-1054, 2024, DOI:10.32604/csse.2024.051847

Abstract The article describes a novel method for sentiment analysis and requirement elicitation from social media feedback, leveraging advanced machine learning techniques. This innovative approach automates the extraction and classification of user requirements by analyzing sentiment in data gathered from social media platforms such as Twitter and Facebook. Utilizing APIs (Application Programming Interface) for data collection and Graph-based Neural Networks (GNN) for feature extraction, the proposed model efficiently processes and analyzes large volumes of unstructured user-generated content. The preprocessing pipeline includes data cleaning, normalization, and tokenization, ensuring high-quality input for the sentiment analysis model. By classifying… More >