Dong Hyun Lee¹, Na Kyung Lee², Young Seo Lee^1,2,*

CMC-Computers, Materials & Continua, Vol.86, No.2, pp. 1-17, 2026, DOI:10.32604/cmc.2025.069976 - 09 December 2025

Abstract There has been an increasing emphasis on performing deep neural network (DNN) inference locally on edge devices due to challenges such as network congestion and security concerns. However, as DRAM process technology continues to scale down, the bit-flip errors in the memory of edge devices become more frequent, thereby leading to substantial DNN inference accuracy loss. Though several techniques have been proposed to alleviate the accuracy loss in edge environments, they require complex computations and additional parity bits for error correction, thus resulting in significant performance and storage overheads. In this paper, we propose FeatherGuard,… 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 >

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 - 18 July 2024

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 >

Dibyendu Mukherjee¹, Shivnath Ghosh¹, Souvik Pal^2,*, D. Akila³, N. Z. Jhanjhi⁴, Mehedi Masud⁵, Mohammed A. AlZain⁶

CMC-Computers, Materials & Continua, Vol.72, No.1, pp. 125-140, 2022, DOI:10.32604/cmc.2022.023611 - 24 February 2022

Abstract Numerous Internet of Things (IoT) systems produce massive volumes of information that must be handled and answered in a quite short period. The growing energy usage related to the migration of data into the cloud is one of the biggest problems. Edge computation helps users unload the workload again from cloud near the source of the information that must be handled to save time, increase security, and reduce the congestion of networks. Therefore, in this paper, Optimized Energy Efficient Strategy (OEES) has been proposed for extracting, distributing, evaluating the data on the edge devices. In… More >

Alanoud Alhussain¹, Heba Kurdi^1,*, Lina Altoaimy²

CMC-Computers, Materials & Continua, Vol.59, No.3, pp. 805-815, 2019, DOI:10.32604/cmc.2019.05848

Abstract Edge devices in Internet of Things (IoT) applications can form peers to communicate in peer-to-peer (P2P) networks over P2P protocols. Using P2P networks ensures scalability and removes the need for centralized management. However, due to the open nature of P2P networks, they often suffer from the existence of malicious peers, especially malicious peers that unite in groups to raise each other's ratings. This compromises users' safety and makes them lose their confidence about the files or services they are receiving. To address these challenges, we propose a neural network-based algorithm, which uses the advantages of More >