Ran Liu, Yawen Chen, Dong Yang^*, Jingjing Yang^*

CMC-Computers, Materials & Continua, Vol.86, No.2, pp. 1-23, 2026, DOI:10.32604/cmc.2025.070734 - 09 December 2025

Abstract In the field of smart agriculture, accurate and efficient object detection technology is crucial for automated crop management. A particularly challenging task in this domain is small object detection, such as the identification of immature fruits or early stage disease spots. These objects pose significant difficulties due to their small pixel coverage, limited feature information, substantial scale variations, and high susceptibility to complex background interference. These challenges frequently result in inadequate accuracy and robustness in current detection models. This study addresses two critical needs in the cashew cultivation industry—fruit maturity and anthracnose detection—by proposing an… More >

Zhongyun Tang^1,2,3, Hanyi Xu², Haiyang Hu^1,3,*

CMC-Computers, Materials & Continua, Vol.86, No.2, pp. 1-29, 2026, DOI:10.32604/cmc.2025.070616 - 09 December 2025

Abstract With the deep integration of smart manufacturing and IoT technologies, higher demands are placed on the intelligence and real-time performance of industrial equipment fault detection. For industrial fans, base bolt loosening faults are difficult to identify through conventional spectrum analysis, and the extreme scarcity of fault data leads to limited training datasets, making traditional deep learning methods inaccurate in fault identification and incapable of detecting loosening severity. This paper employs Bayesian Learning by training on a small fault dataset collected from the actual operation of axial-flow fans in a factory to obtain posterior distribution. This More >

Xin Ma^1,2, Jin Lei^3,4,*, Chenying Pei⁴, Chunming Wu⁴

CMC-Computers, Materials & Continua, Vol.86, No.2, pp. 1-17, 2026, DOI:10.32604/cmc.2025.069353 - 09 December 2025

Abstract This study proposes a lightweight apple detection method employing cascaded knowledge distillation (KD) to address the critical challenges of excessive parameters and high deployment costs in existing models. We introduce a Lightweight Feature Pyramid Network (LFPN) integrated with Lightweight Downsampling Convolutions (LDConv) to substantially reduce model complexity without compromising accuracy. A Lightweight Multi-channel Attention (LMCA) mechanism is incorporated between the backbone and neck networks to effectively suppress complex background interference in orchard environments. Furthermore, model size is compressed via Group_Slim channel pruning combined with a cascaded distillation strategy. Experimental results demonstrate that the proposed model More >

Jianhua Yang^*, Zhiwei Lv, Changling Huo

CMC-Computers, Materials & Continua, Vol.86, No.1, pp. 1-18, 2026, DOI:10.32604/cmc.2025.068442 - 10 November 2025

Abstract Aiming at the scale adaptation of automatic driving target detection algorithms in low illumination environments and the shortcomings in target occlusion processing, this paper proposes a YOLO-LKSDS automatic driving detection model. Firstly, the Contrast-Limited Adaptive Histogram Equalisation (CLAHE) image enhancement algorithm is improved to increase the image contrast and enhance the detailed features of the target; then, on the basis of the YOLOv5 model, the Kmeans++ clustering algorithm is introduced to obtain a suitable anchor frame, and SPPELAN spatial pyramid pooling is improved to enhance the accuracy and robustness of the model for multi-scale target… More >

Ao Zhang¹, Hongzhen Xu^1,*, Ruxin Liu²

CMC-Computers, Materials & Continua, Vol.85, No.3, pp. 5159-5181, 2025, DOI:10.32604/cmc.2025.067241 - 23 October 2025

Abstract Credit card fraud is one of the primary sources of operational risk in banks, and accurate prediction of fraudulent credit card transactions is essential to minimize banks’ economic losses. Two key issues are faced in credit card fraud detection research, i.e., data category imbalance and data drift. However, the oversampling algorithm used in current research suffers from excessive noise, and the Long Short-Term Memory Network (LSTM) based temporal model suffers from gradient dispersion, which can lead to loss of model performance. To address the above problems, a credit card fraud detection method based on Random… More >

Caixia Chen¹, Pengyu Liu¹, Changhua Wang¹, Yanyan Xie¹, Wei Wang^1,*, Xiaomin Kang^2,*

Journal of Polymer Materials, Vol.42, No.3, pp. 621-660, 2025, DOI:10.32604/jpm.2025.068852 - 30 September 2025

Abstract Due to their high water content, stimulus responsiveness, and biocompatibility, hydrogels, which are functional materials with a three-dimensional network structure, are widely applied in fields such as biomedicine, environmental monitoring, and flexible electronics. This paper provides a systematic review of hydrogel characterization methods and their applications, focusing on primary evaluation techniques for physical properties (e.g., mechanical strength, swelling behavior, and pore structure), chemical properties (e.g., composition, crosslink density, and degradation behavior), biocompatibility, and functional properties (e.g., drug release, environmental stimulus response, and conductivity). It analyzes the challenges currently faced by characterization methods, such as a More >

Kejun Zhang¹, Meijiao Li^1,*, Jiahao Cheng¹, Jun Wang¹, Ying Yang²

CMC-Computers, Materials & Continua, Vol.85, No.1, pp. 1019-1035, 2025, DOI:10.32604/cmc.2025.066532 - 29 August 2025

Abstract With the rapid growth of the Internet and social media, information is widely disseminated in multimodal forms, such as text and images, where discriminatory content can manifest in various ways. Discrimination detection techniques for multilingual and multimodal data can identify potential discriminatory behavior and help foster a more equitable and inclusive cyberspace. However, existing methods often struggle in complex contexts and multilingual environments. To address these challenges, this paper proposes an innovative detection method, using image and multilingual text encoders to separately extract features from different modalities. It continuously updates a historical feature memory bank, More >

Chaimae Hazman^1,2, Azidine Guezzaz², Said Benkirane², Mourade Azrour^3,*, Vinayakumar Ravi⁴, Abdulatif Alabdulatif ⁵

CMC-Computers, Materials & Continua, Vol.85, No.1, pp. 301-329, 2025, DOI:10.32604/cmc.2025.064777 - 29 August 2025

Abstract Attacks are growing more complex and dangerous as network capabilities improve at a rapid pace. Network intrusion detection is usually regarded as an efficient means of dealing with security attacks. Many ways have been presented, utilizing various strategies and focusing on different types of visitors. Anomaly-based network intrusion monitoring is an essential area of intrusion detection investigation and development. Despite extensive research on anomaly-based network detection, there is still a lack of comprehensive literature reviews covering current methodologies and datasets. Despite the substantial research into anomaly-based network intrusion detection algorithms, there is a dearth of More >

Akhila Reddy Yadulla, Mounica Yenugula, Vinay Kumar Kasula^*, Bhargavi Konda, Bala Yashwanth Reddy Thumma

Journal on Internet of Things, Vol.7, pp. 19-48, 2025, DOI:10.32604/jiot.2025.062733 - 11 July 2025

Abstract This study systematically reviews the Internet of Things (IoT) security research based on literature from prominent international cybersecurity conferences over the past five years, including ACM Conference on Computer and Communications Security (ACM CCS), USENIX Security, Network and Distributed System Security Symposium (NDSS), and IEEE Symposium on Security and Privacy (IEEE S&P), along with other high-impact studies. It organizes and analyzes IoT security advancements through the lenses of threats, detection methods, and defense strategies. The foundational architecture of IoT systems is first outlined, followed by categorizing major threats into eight distinct types and analyzing their 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 >