Yi Li¹, Wenying Zheng², Yu-Sheng Su^3,4,5,*, Meiqin Tang⁶

CMC-Computers, Materials & Continua, Vol.83, No.2, pp. 2719-2734, 2025, DOI:10.32604/cmc.2025.059949 - 16 April 2025

Abstract AI applications have become ubiquitous, bringing significant convenience to various industries. In e-commerce, AI can enhance product recommendations for individuals and provide businesses with more accurate predictions for market strategy development. However, if the data used for AI applications is damaged or lost, it will inevitably affect the effectiveness of these AI applications. Therefore, it is essential to verify the integrity of e-commerce data. Although existing Provable Data Possession (PDP) protocols can verify the integrity of cloud data, they are not suitable for e-commerce scenarios due to the limited computational capabilities of edge servers, which More >

Yuxin Yan, Huirui Wang, Jingyi Wen, Zerong Lan, Liang Wang^*

CMC-Computers, Materials & Continua, Vol.83, No.2, pp. 3059-3073, 2025, DOI:10.32604/cmc.2024.057949 - 16 April 2025

Abstract The development of artificial intelligence (AI) technologies creates a great chance for the iteration of railway monitoring. This paper proposes a comprehensive method for railway utility pole detection. The framework of this paper on railway systems consists of two parts: point cloud preprocessing and railway utility pole detection. This method overcomes the challenges of dynamic environment adaptability, reliance on lighting conditions, sensitivity to weather and environmental conditions, and visual occlusion issues present in 2D images and videos, which utilize mobile LiDAR (Laser Radar) acquisition devices to obtain point cloud data. Due to factors such as… More >

Yuanhang Zhang¹, Xu An Wang^1,2,*, Weiwei Jiang³, Mingyu Zhou¹, Xiaoxuan Xu¹, Hao Liu¹

CMC-Computers, Materials & Continua, Vol.83, No.1, pp. 1533-1553, 2025, DOI:10.32604/cmc.2025.062910 - 26 March 2025

Abstract Cloud storage, a core component of cloud computing, plays a vital role in the storage and management of data. Electronic Health Records (EHRs), which document users’ health information, are typically stored on cloud servers. However, users’ sensitive data would then become unregulated. In the event of data loss, cloud storage providers might conceal the fact that data has been compromised to protect their reputation and mitigate losses. Ensuring the integrity of data stored in the cloud remains a pressing issue that urgently needs to be addressed. In this paper, we propose a data auditing scheme… More >

Xu Liu¹, Qiujie Li^1,*, Youlin Xu¹, Musaed Alhussein², Khursheed Aurangzeb^2,*, Fa Zhu¹

CMC-Computers, Materials & Continua, Vol.83, No.1, pp. 575-593, 2025, DOI:10.32604/cmc.2025.062445 - 26 March 2025

Abstract The correction of Light Detection and Ranging (LiDAR) intensity data is of great significance for enhancing its application value. However, traditional intensity correction methods based on Terrestrial Laser Scanning (TLS) technology rely on manual site setup to collect intensity training data at different distances and incidence angles, which is noisy and limited in sample quantity, restricting the improvement of model accuracy. To overcome this limitation, this study proposes a fine-grained intensity correction modeling method based on Mobile Laser Scanning (MLS) technology. The method utilizes the continuous scanning characteristics of MLS technology to obtain dense point… More >

Tajinder Kumar¹, Purushottam Sharma^2,*, Xiaochun Cheng^3,*, Sachin Lalar⁴, Shubham Kumar⁵, Sandhya Bansal⁶

CMC-Computers, Materials & Continua, Vol.83, No.1, pp. 719-738, 2025, DOI:10.32604/cmc.2025.060836 - 26 March 2025

Abstract With cloud computing, large chunks of data can be handled at a small cost. However, there are some reservations regarding the security and privacy of cloud data stored. For solving these issues and enhancing cloud computing security, this research provides a Three-Layered Security Access model (TLSA) aligned to an intrusion detection mechanism, access control mechanism, and data encryption system. The TLSA underlines the need for the protection of sensitive data. This proposed approach starts with Layer 1 data encryption using the Advanced Encryption Standard (AES). For data transfer and storage, this encryption guarantees the data’s… More >

Xixi Yan¹, Jing Zhang^2,*, Pengyu Cheng²

CMC-Computers, Materials & Continua, Vol.83, No.1, pp. 1399-1414, 2025, DOI:10.32604/cmc.2025.059969 - 26 March 2025

Abstract Conditional proxy re-encryption (CPRE) is an effective cryptographic primitive language that enhances the access control mechanism and makes the delegation of decryption permissions more granular, but most of the attribute-based conditional proxy re-encryption (AB-CPRE) schemes proposed so far do not take into account the importance of user attributes. A weighted attribute-based conditional proxy re-encryption (WAB-CPRE) scheme is thus designed to provide more precise decryption rights delegation. By introducing the concept of weight attributes, the quantity of system attributes managed by the server is reduced greatly. At the same time, a weighted tree structure is constructed… More >

Arif Ullah¹, Siti Fatimah Abdul Razak^2,*, Sumendra Yogarayan², Md Shohel Sayeed²

CMC-Computers, Materials & Continua, Vol.82, No.3, pp. 5185-5204, 2025, DOI:10.32604/cmc.2025.059355 - 06 March 2025

Abstract Networking, storage, and hardware are just a few of the virtual computing resources that the infrastructure service model offers, depending on what the client needs. One essential aspect of cloud computing that improves resource allocation techniques is host load prediction. This difficulty means that hardware resource allocation in cloud computing still results in hosting initialization issues, which add several minutes to response times. To solve this issue and accurately predict cloud capacity, cloud data centers use prediction algorithms. This permits dynamic cloud scalability while maintaining superior service quality. For host prediction, we therefore present a… More >

Yawar Abbas¹, Aisha Ahmed Alarfaj², Ebtisam Abdullah Alabdulqader³, Asaad Algarni⁴, Ahmad Jalal^1,5, Hui Liu^6,*

CMC-Computers, Materials & Continua, Vol.82, No.3, pp. 4759-4776, 2025, DOI:10.32604/cmc.2025.059224 - 06 March 2025

Abstract Human Activity Recognition (HAR) in drone-captured videos has become popular because of the interest in various fields such as video surveillance, sports analysis, and human-robot interaction. However, recognizing actions from such videos poses the following challenges: variations of human motion, the complexity of backdrops, motion blurs, occlusions, and restricted camera angles. This research presents a human activity recognition system to address these challenges by working with drones’ red-green-blue (RGB) videos. The first step in the proposed system involves partitioning videos into frames and then using bilateral filtering to improve the quality of object foregrounds while… More >

Ferzat Anka¹, Ghanshyam G. Tejani^2,3,*, Sunil Kumar Sharma⁴, Mohammed Baljon⁵

CMES-Computer Modeling in Engineering & Sciences, Vol.142, No.3, pp. 2691-2724, 2025, DOI:10.32604/cmes.2025.061522 - 03 March 2025

Abstract Due to the intense data flow in expanding Internet of Things (IoT) applications, a heavy processing cost and workload on the fog-cloud side become inevitable. One of the most critical challenges is optimal task scheduling. Since this is an NP-hard problem type, a metaheuristic approach can be a good option. This study introduces a novel enhancement to the Artificial Rabbits Optimization (ARO) algorithm by integrating Chaotic maps and Levy flight strategies (CLARO). This dual approach addresses the limitations of standard ARO in terms of population diversity and convergence speed. It is designed for task scheduling… More >

Ayman Noor^*

Computer Systems Science and Engineering, Vol.49, pp. 259-286, 2025, DOI:10.32604/csse.2025.060387 - 21 February 2025

Abstract Precisely forecasting the performance of Deep Learning (DL) models, particularly in critical areas such as Uniform Resource Locator (URL)-based threat detection, aids in improving systems developed for difficult tasks. In cybersecurity, recognizing harmful URLs is vital to lowering risks associated with phishing, malware, and other online-based attacks. Since it directly affects the model’s capacity to differentiate between benign and harmful URLs, finding the optimum mix of hyperparameters in DL models is a significant difficulty. Two commonly used architectures for sequential and spatial data processing, Long Short-Term Memory (LSTM)/Gated Recurrent Unit (GRU) and Convolutional Neural Network… More >