Rahim Khan¹, Ihsan Rabbi¹, Umar Farooq¹, Jawad Khan^2,*, Fahad Alturise^3,*

CMC-Computers, Materials & Continua, Vol.84, No.1, pp. 827-842, 2025, DOI:10.32604/cmc.2025.063303 - 09 June 2025

Abstract Leaf disease identification is one of the most promising applications of convolutional neural networks (CNNs). This method represents a significant step towards revolutionizing agriculture by enabling the quick and accurate assessment of plant health. In this study, a CNN model was specifically designed and tested to detect and categorize diseases on fig tree leaves. The researchers utilized a dataset of 3422 images, divided into four classes: healthy, fig rust, fig mosaic, and anthracnose. These diseases can significantly reduce the yield and quality of fig tree fruit. The objective of this research is to develop a… More >

Qiang Guo^1,2,*, Rubo Zhang¹, Bingbing Zhang³

CMC-Computers, Materials & Continua, Vol.84, No.1, pp. 1307-1327, 2025, DOI:10.32604/cmc.2025.063288 - 09 June 2025

Abstract Pedestrian detection has been a hot spot in computer vision over the past decades due to the wide spectrum of promising applications, and the major challenge is false positives that occur during pedestrian detection. The emergence of various Convolutional Neural Network-based detection strategies substantially enhances pedestrian detection accuracy but still does not solve this problem well. This paper deeply analyzes the detection framework of the two-stage CNN detection methods and finds out false positives in detection results are due to its training strategy misclassifying some false proposals, thus weakening the classification capability of the following… More >

Zhigao Zeng¹, Jun Liu¹, Bing Zheng², Shengqiu Yi¹, Xinpan Yuan¹, Qiang Liu^1,*

CMC-Computers, Materials & Continua, Vol.84, No.1, pp. 513-536, 2025, DOI:10.32604/cmc.2025.063257 - 09 June 2025

Abstract To solve the problems of complex lesion region morphology, blurred edges, and limited hardware resources for deploying the recognition model in pneumonia image recognition, an improved EfficientNetV2 pneumonia recognition model based on multiscale attention is proposed. First, the number of main module stacks of the model is reduced to avoid overfitting, while the dilated convolution is introduced in the first convolutional layer to expand the receptive field of the model; second, a redesigned improved mobile inverted bottleneck convolution (IMBConv) module is proposed, in which GSConv is introduced to enhance the model’s attention to inter-channel information,… More >

Tian Zhao^*, Jun Tu^*, Puzheng Quan, Ruisheng Xiong

CMC-Computers, Materials & Continua, Vol.84, No.1, pp. 1351-1377, 2025, DOI:10.32604/cmc.2025.063209 - 09 June 2025

Abstract Aiming at the problems of incomplete characterization of text relations, poor guidance of potential representations, and low quality of model generation in the field of controllable long text generation, this paper proposes a new GSPT-CVAE model (Graph Structured Processing, Single Vector, and Potential Attention Computing Transformer-Based Conditioned Variational Autoencoder model). The model obtains a more comprehensive representation of textual relations by graph-structured processing of the input text, and at the same time obtains a single vector representation by weighted merging of the vector sequences after graph-structured processing to get an effective potential representation. In the… More >

Reem Alkanhel^1,#, Abuzar B. M. Adam^2,#, Samia Allaoua Chelloug¹, Dina S. M. Hassan^1,*, Mohammed Saleh Ali Muthanna³, Ammar Muthanna⁴

CMC-Computers, Materials & Continua, Vol.84, No.1, pp. 607-624, 2025, DOI:10.32604/cmc.2025.063206 - 09 June 2025

Abstract The 3GPP standard defines the requirements for next-generation wireless networks, with particular attention to Ultra-Reliable Low-Latency Communications (URLLC), critical for applications such as Unmanned Aerial Vehicles (UAVs). In this context, Non-Orthogonal Multiple Access (NOMA) has emerged as a promising technique to improve spectrum efficiency and user fairness by allowing multiple users to share the same frequency resources. However, optimizing key parameters–such as beamforming, rate allocation, and UAV trajectory–presents significant challenges due to the nonconvex nature of the problem, especially under stringent URLLC constraints. This paper proposes an advanced deep learning-driven approach to address the resulting… More >

Chunhao Zhang^1,2, Bin Xie^2,3,*, Zhibin Huo¹

CMC-Computers, Materials & Continua, Vol.84, No.1, pp. 843-860, 2025, DOI:10.32604/cmc.2025.063151 - 09 June 2025

Abstract Time series anomaly detection is crucial in finance, healthcare, and industrial monitoring. However, traditional methods often face challenges when handling time series data, such as limited feature extraction capability, poor temporal dependency handling, and suboptimal real-time performance, sometimes even neglecting the temporal relationships between data. To address these issues and improve anomaly detection performance by better capturing temporal dependencies, we propose an unsupervised time series anomaly detection method, VLT-Anomaly. First, we enhance the Variational Autoencoder (VAE) module by redesigning its network structure to better suit anomaly detection through data reconstruction. We introduce hyperparameters to control… More >

Gang Li^1,#, Zheng Zhou^1,#, Yang Zhang^2,*, Chuanyun Xu², Zihan Ruan¹, Pengfei Lv¹, Ru Wang¹, Xinyu Fan¹, Wei Tan¹

CMC-Computers, Materials & Continua, Vol.84, No.1, pp. 1615-1632, 2025, DOI:10.32604/cmc.2025.063109 - 09 June 2025

Abstract Although conventional object detection methods achieve high accuracy through extensively annotated datasets, acquiring such large-scale labeled data remains challenging and cost-prohibitive in numerous real-world applications. Few-shot object detection presents a new research idea that aims to localize and classify objects in images using only limited annotated examples. However, the inherent challenge in few-shot object detection lies in the insufficient sample diversity to fully characterize the sample feature distribution, which consequently impacts model performance. Inspired by contrastive learning principles, we propose an Implicit Feature Contrastive Learning (IFCL) module to address this limitation and augment feature diversity More >

Mohammad Tabrez Quasim^1,*, Khair Ul Nisa¹, Mohammad Shahid Husain², Abakar Ibraheem Abdalla Aadam¹, Mohammed Waseequ Sheraz¹, Mohammad Zunnun Khan¹

CMC-Computers, Materials & Continua, Vol.84, No.1, pp. 1169-1187, 2025, DOI:10.32604/cmc.2025.062989 - 09 June 2025

Abstract Smart edge computing (SEC) is a novel paradigm for computing that could transfer cloud-based applications to the edge network, supporting computation-intensive services like face detection and natural language processing. A core feature of mobile edge computing, SEC improves user experience and device performance by offloading local activities to edge processors. In this framework, blockchain technology is utilized to ensure secure and trustworthy communication between edge devices and servers, protecting against potential security threats. Additionally, Deep Learning algorithms are employed to analyze resource availability and optimize computation offloading decisions dynamically. IoT applications that require significant resources… More >

Zhenfu Zhang¹, Haiyan Yin², Liudong Zuo³, Pan Lai^1,*

CMC-Computers, Materials & Continua, Vol.84, No.1, pp. 919-936, 2025, DOI:10.32604/cmc.2025.062980 - 09 June 2025

Abstract The knapsack problem is a classical combinatorial optimization problem widely encountered in areas such as logistics, resource allocation, and portfolio optimization. Traditional methods, including dynamic programming (DP) and greedy algorithms, have been effective in solving small problem instances but often struggle with scalability and efficiency as the problem size increases. DP, for instance, has exponential time complexity and can become computationally prohibitive for large problem instances. On the other hand, greedy algorithms offer faster solutions but may not always yield the optimal results, especially when the problem involves complex constraints or large numbers of items.… More >

Seo Yeon Moon¹, Byung Hyun Jo¹, Abir El Azzaoui¹, Sushil Kumar Singh², Jong Hyuk Park^1,*

CMC-Computers, Materials & Continua, Vol.84, No.1, pp. 25-55, 2025, DOI:10.32604/cmc.2025.062966 - 09 June 2025

Abstract With the rapid advancement of ICT and IoT technologies, the integration of Edge and Fog Computing has become essential to meet the increasing demands for real-time data processing and network efficiency. However, these technologies face critical security challenges, exacerbated by the emergence of quantum computing, which threatens traditional encryption methods. The rise in cyber-attacks targeting IoT and Edge/Fog networks underscores the need for robust, quantum-resistant security solutions. To address these challenges, researchers are focusing on Quantum Key Distribution and Post-Quantum Cryptography, which utilize quantum-resistant algorithms and the principles of quantum mechanics to ensure data confidentiality More >