Manzheng Yuan^1,2, Kai Yang^2,*

Journal of Cyber Security, Vol.7, pp. 197-219, 2025, DOI:10.32604/jcs.2025.066089 - 07 July 2025

Abstract Currently, network intrusion detection systems (NIDS) face significant challenges in feature redundancy and high computational complexity, which hinder the improvement of detection performance and significantly reduce operational efficiency. To address these issues, this paper proposes an innovative weighted feature selection method combining mutual information and Extreme Gradient Boosting (XGBoost). This method aims to leverage their strengths to identify crucial feature subsets for intrusion detection accurately. Specifically, it first calculates the mutual information scores between features and target variables to evaluate individual discriminatory capabilities of features and uses XGBoost to obtain feature importance scores reflecting their… More >

An-Chao Tsai^*, Chayanon Pookunngern

CMC-Computers, Materials & Continua, Vol.84, No.2, pp. 2455-2471, 2025, DOI:10.32604/cmc.2025.067413 - 03 July 2025

Abstract Food waste presents a major global environmental challenge, contributing to resource depletion, greenhouse gas emissions, and climate change. Black Soldier Fly Larvae (BSFL) offer an eco-friendly solution due to their exceptional ability to decompose organic matter. However, accurately identifying larval instars is critical for optimizing feeding efficiency and downstream applications, as different stages exhibit only subtle visual differences. This study proposes a real-time mobile application for automatic classification of BSFL larval stages. The system distinguishes between early instars (Stages 1–4), suitable for food waste processing and animal feed, and late instars (Stages 5–6), optimal for… More >

Fatima Asiri¹, Wajdan Al Malwi¹, Fahad Masood², Mohammed S. Alshehri³, Tamara Zhukabayeva⁴, Syed Aziz Shah⁵, Jawad Ahmad^6,*

CMC-Computers, Materials & Continua, Vol.84, No.2, pp. 3943-3960, 2025, DOI:10.32604/cmc.2025.066498 - 03 July 2025

Abstract Edge computing (EC) combined with the Internet of Things (IoT) provides a scalable and efficient solution for smart homes. The rapid proliferation of IoT devices poses real-time data processing and security challenges. EC has become a transformative paradigm for addressing these challenges, particularly in intrusion detection and anomaly mitigation. The widespread connectivity of IoT edge networks has exposed them to various security threats, necessitating robust strategies to detect malicious activities. This research presents a privacy-preserving federated anomaly detection framework combined with Bayesian game theory (BGT) and double deep Q-learning (DDQL). The proposed framework integrates BGT… More >

Saha Reno^1,#,*, Koushik Roy^2,#

CMC-Computers, Materials & Continua, Vol.84, No.2, pp. 2061-2119, 2025, DOI:10.32604/cmc.2025.066366 - 03 July 2025

Abstract The blockchain trilemma—balancing decentralization, security, and scalability—remains a critical challenge in distributed ledger technology. Despite significant advancements, achieving all three attributes simultaneously continues to elude most blockchain systems, often forcing trade-offs that limit their real-world applicability. This review paper synthesizes current research efforts aimed at resolving the trilemma, focusing on innovative consensus mechanisms, sharding techniques, layer-2 protocols, and hybrid architectural models. We critically analyze recent breakthroughs, including Directed Acyclic Graph (DAG)-based structures, cross-chain interoperability frameworks, and zero-knowledge proof (ZKP) enhancements, which aim to reconcile scalability with robust security and decentralization. Furthermore, we evaluate the trade-offs More >

Weili Liu¹, Fayi Hao¹, Jiangping Yuan^1,2,*

CMC-Computers, Materials & Continua, Vol.84, No.2, pp. 1951-1979, 2025, DOI:10.32604/cmc.2025.065836 - 03 July 2025

Abstract Additive manufacturing (AM) and Three-dimensional (3D) printing build complex structures layer by layer, greatly expanding design possibilities. Traditional thermoplastics like Polylactic Acid (PLA), Acrylonitrile Butadiene Styrene (ABS), and Polyethylene Terephthalate Glycol (PETG) are widely used in 3D printing, but their non-renewable nature and limited biodegradability have driven research into plant fiber-based materials. These materials, mainly cellulose and lignin, come from sources like wood and agricultural waste, offering renewability, biodegradability, and biocompatibility. This paper reviews recent advances in plant fiber-based materials for 3D printing, covering their development from raw materials to applications. It highlights the sources,… More >

Anees Ara¹, Muhammad Mujahid¹, Amal Al-Rasheed^2,*, Shaha Al-Otaibi², Tanzila Saba¹

CMC-Computers, Materials & Continua, Vol.84, No.2, pp. 2717-2731, 2025, DOI:10.32604/cmc.2025.065566 - 03 July 2025

Abstract DeepSeek Chinese artificial intelligence (AI) open-source model, has gained a lot of attention due to its economical training and efficient inference. DeepSeek, a model trained on large-scale reinforcement learning without supervised fine-tuning as a preliminary step, demonstrates remarkable reasoning capabilities of performing a wide range of tasks. DeepSeek is a prominent AI-driven chatbot that assists individuals in learning and enhances responses by generating insightful solutions to inquiries. Users possess divergent viewpoints regarding advanced models like DeepSeek, posting both their merits and shortcomings across several social media platforms. This research presents a new framework for predicting… More >

Pengfei Wang^1,2,3, Jiwu Sun², Lu Lu^1,4, Hongchen Li¹, Hongzhe Liu², Cheng Xu², Yongqiang Liu^1,*

CMC-Computers, Materials & Continua, Vol.84, No.2, pp. 2883-2903, 2025, DOI:10.32604/cmc.2025.065267 - 03 July 2025

Abstract Flooding and heavy rainfall under extreme weather conditions pose significant challenges to target detection algorithms. Traditional methods often struggle to address issues such as image blurring, dynamic noise interference, and variations in target scale. Conventional neural network (CNN)-based target detection approaches face notable limitations in such adverse weather scenarios, primarily due to the fixed geometric sampling structures that hinder adaptability to complex backgrounds and dynamically changing object appearances. To address these challenges, this paper proposes an optimized YOLOv9 model incorporating an improved deformable convolutional network (DCN) enhanced with a multi-scale dilated attention (MSDA) mechanism. Specifically,… More >

Mingyang Zhao^1,2,*, Xiaojun Li^1,3, Miao Li^1,2, Bangbang Mu^1,2

CMC-Computers, Materials & Continua, Vol.84, No.2, pp. 2827-2846, 2025, DOI:10.32604/cmc.2025.065251 - 03 July 2025

Abstract The control of gas extraction in coal mines relies on the effectiveness of gas extraction. The main method of gas extraction is to drive drill pipes into the coal seam through a drilling rig and use technologies such as hydraulic fracturing to pre-extract gas in the drill holes. Therefore, the real-time detection of the drill pipe status is closely related to the effectiveness of gas extraction. To achieve fast and accurate identification of drill pipes, we propose FSS-YOLO, which is a lightweight drill pipe detection method based on YOLOv8n-obb. This method first introduces the FasterBlock… More >

Zhunruo Feng¹, Ruomeng Shi², Yuhan Jiang³, Yiming Han¹, Zeyang Ma¹, Yuheng Ren^4,*

CMC-Computers, Materials & Continua, Vol.84, No.2, pp. 3559-3575, 2025, DOI:10.32604/cmc.2025.065152 - 03 July 2025

Abstract In this study, we propose Space-to-Depth and You Only Look Once Version 7 (SPD-YOLOv7), an accurate and efficient method for detecting pests in maize crops, addressing challenges such as small pest sizes, blurred images, low resolution, and significant species variation across different growth stages. To improve the model’s ability to generalize and its robustness, we incorporate target background analysis, data augmentation, and processing techniques like Gaussian noise and brightness adjustment. In target detection, increasing the depth of the neural network can lead to the loss of small target information. To overcome this, we introduce the… More >

Maheen Anwar¹, Saima Farhan¹, Yasin Ul Haq², Waqar Azeem³, Muhammad Ilyas⁴, Razvan Cristian Voicu^5,*, Muhammad Hassan Tanveer⁵

CMC-Computers, Materials & Continua, Vol.84, No.2, pp. 3477-3502, 2025, DOI:10.32604/cmc.2025.065141 - 03 July 2025

Abstract Glaucoma, a chronic eye disease affecting millions worldwide, poses a substantial threat to eyesight and can result in permanent vision loss if left untreated. Manual identification of glaucoma is a complicated and time-consuming practice requiring specialized expertise and results may be subjective. To address these challenges, this research proposes a computer-aided diagnosis (CAD) approach using Artificial Intelligence (AI) techniques for binary and multiclass classification of glaucoma stages. An ensemble fusion mechanism that combines the outputs of three pre-trained convolutional neural network (ConvNet) models–ResNet-50, VGG-16, and InceptionV3 is utilized in this paper. This fusion technique enhances… More >