Wuyi Zhu¹, Meng Zou¹, Wenji Liu¹, Linlin Cui^2,*

Revue Internationale de Géomatique, Vol.34, pp. 363-377, 2025, DOI:10.32604/rig.2025.065708 - 14 July 2025

Abstract Exploring the spatiotemporal changes in Fractional Vegetation Coverage (FVC) helps to more accurately understand the quality of the ecological environment, which is of great significance for regional ecological protection and sustainable economic development. The study takes the Chengdu-Chongqing urban agglomeration as the research area, analyzes the characteristics and trends of vegetation cover changes from 2000 to 2020 using the Google Earth Engine cloud platform, and explores its driving factors based on the enhanced regression tree model. The results show that: (1) From 2000 to 2020, the annual FVC of the Chengdu-Chongqing urban agglomeration showed a… More >

Jitendra Kumar Samriya¹, Virendra Singh², Gourav Bathla³, Meena Malik⁴, Varsha Arya^5,6, Wadee Alhalabi⁷, Brij B. Gupta^8,9,10,11,*

CMC-Computers, Materials & Continua, Vol.84, No.2, pp. 3893-3910, 2025, DOI:10.32604/cmc.2025.063242 - 03 July 2025

Abstract The integration of the Internet of Things (IoT) into healthcare systems improves patient care, boosts operational efficiency, and contributes to cost-effective healthcare delivery. However, overcoming several associated challenges, such as data security, interoperability, and ethical concerns, is crucial to realizing the full potential of IoT in healthcare. Real-time anomaly detection plays a key role in protecting patient data and maintaining device integrity amidst the additional security risks posed by interconnected systems. In this context, this paper presents a novel method for healthcare data privacy analysis. The technique is based on the identification of anomalies in… More >

Francisco J. Soler Mora^1,*, Adrián Peidró Vidal¹, Marc Fabregat-Jaén¹, Luis Payá Castelló^1,2, Óscar Reinoso García ^1,2

CMES-Computer Modeling in Engineering & Sciences, Vol.143, No.3, pp. 3167-3195, 2025, DOI:10.32604/cmes.2025.064510 - 30 June 2025

Abstract Reticular structures are the basis of major infrastructure projects, including bridges, electrical pylons and airports. However, inspecting and maintaining these structures is both expensive and hazardous, traditionally requiring human involvement. While some research has been conducted in this field of study, most efforts focus on faults identification through images or the design of robotic platforms, often neglecting the autonomous navigation of robots through the structure. This study addresses this limitation by proposing methods to detect navigable surfaces in truss structures, thereby enhancing the autonomous capabilities of climbing robots to navigate through these environments. The paper… More >

Yuanyuan Ma¹, Cunzhi Hou¹, Ang Chen¹, Jinghui Zhang¹, Ruixia Jin², Ruixiang Li^3,*

CMC-Computers, Materials & Continua, Vol.84, No.1, pp. 1437-1456, 2025, DOI:10.32604/cmc.2025.064523 - 09 June 2025

Abstract Identifying cloud IP usage scenarios is critical for cybersecurity applications, yet existing machine learning methods rely heavily on numerous features, resulting in high complexity and low interpretability. To address these issues, this paper proposes an approach to identify cloud IPs from the perspective of network attributes. We employ data mining and crowdsourced collection strategies to gather IP addresses from various usage scenarios, which including cloud IPs and non-cloud IPs. On this basis, we establish a cloud IP identification feature set that includes attributes such as Autonomous System Number (ASN) and organization information. By analyzing the… More >

Ronglei Hu¹, Chuce He^1,2, Sihui Liu¹, Dong Yao¹, Xiuying Li¹, Xiaoyi Duan^1,*

CMC-Computers, Materials & Continua, Vol.84, No.1, pp. 937-956, 2025, DOI:10.32604/cmc.2025.062542 - 09 June 2025

Abstract Ciphertext data retrieval in cloud databases suffers from some critical limitations, such as inadequate security measures, disorganized key management practices, and insufficient retrieval access control capabilities. To address these problems, this paper proposes an enhanced Fully Homomorphic Encryption (FHE) algorithm based on an improved DGHV algorithm, coupled with an optimized ciphertext retrieval scheme. Our specific contributions are outlined as follows: First, we employ an authorization code to verify the user’s retrieval authority and perform hierarchical access control on cloud storage data. Second, a triple-key encryption mechanism, which separates the data encryption key, retrieval authorization key, More >

Sreebha Bhaskaran^*, Supriya Muthuraman

CMC-Computers, Materials & Continua, Vol.83, No.3, pp. 5037-5070, 2025, DOI:10.32604/cmc.2025.062657 - 19 May 2025

Abstract Efficient resource provisioning, allocation, and computation offloading are critical to realizing low-latency, scalable, and energy-efficient applications in cloud, fog, and edge computing. Despite its importance, integrating Software Defined Networks (SDN) for enhancing resource orchestration, task scheduling, and traffic management remains a relatively underexplored area with significant innovation potential. This paper provides a comprehensive review of existing mechanisms, categorizing resource provisioning approaches into static, dynamic, and user-centric models, while examining applications across domains such as IoT, healthcare, and autonomous systems. The survey highlights challenges such as scalability, interoperability, and security in managing dynamic and heterogeneous infrastructures. More >

Shrabani Sutradhar^1,2, Rajesh Bose³, Sudipta Majumder¹, Arfat Ahmad Khan^4,*, Sandip Roy³, Fasee Ullah⁵, Deepak Prashar^6,7

CMC-Computers, Materials & Continua, Vol.83, No.3, pp. 3975-4029, 2025, DOI:10.32604/cmc.2025.061965 - 19 May 2025

Abstract Cloud-based setups are intertwined with the Internet of Things and advanced, and technologies such as blockchain revolutionize conventional healthcare infrastructure. This digitization has major advantages, mainly enhancing the security barriers of the green tree infrastructure. In this study, we conducted a systematic review of over 150 articles that focused exclusively on blockchain-based healthcare systems, security vulnerabilities, cyberattacks, and system limitations. In addition, we considered several solutions proposed by thousands of researchers worldwide. Our results mostly delineate sustained threats and security concerns in blockchain-based medical health infrastructures for data management, transmission, and processing. Here, we describe… More >

Daoming Wei¹, Fangyan Ge², Bopeng Zhang¹, Zhiqiang Zhao³, Dequan Li^3,*, Lizong Xi⁴, Jinrong Hu^5,*, Xin Wang⁶

CMC-Computers, Materials & Continua, Vol.83, No.3, pp. 5729-5746, 2025, DOI:10.32604/cmc.2025.061402 - 19 May 2025

Abstract Accurate cloud classification plays a crucial role in aviation safety, climate monitoring, and localized weather forecasting. Current research has been focusing on machine learning techniques, particularly deep learning based model, for the types identification. However, traditional approaches such as convolutional neural networks (CNNs) encounter difficulties in capturing global contextual information. In addition, they are computationally expensive, which restricts their usability in resource-limited environments. To tackle these issues, we present the Cloud Vision Transformer (CloudViT), a lightweight model that integrates CNNs with Transformers. The integration enables an effective balance between local and global feature extraction. To… More >

Sheik Mohideen Shah¹, Meganathan Selvamani¹, Mahesh Thyluru Ramakrishna^2,*, Surbhi Bhatia Khan^3,4,5, Shakila Basheer⁶, Wajdan Al Malwi⁷, Mohammad Tabrez Quasim⁸

CMC-Computers, Materials & Continua, Vol.83, No.2, pp. 2907-2926, 2025, DOI:10.32604/cmc.2025.063809 - 16 April 2025

Abstract Efficient energy management is a cornerstone of advancing cognitive cities, where AI, IoT, and cloud computing seamlessly integrate to meet escalating global energy demands. Within this context, the ability to forecast electricity consumption with precision is vital, particularly in residential settings where usage patterns are highly variable and complex. This study presents an innovative approach to energy consumption forecasting using a bidirectional Long Short-Term Memory (LSTM) network. Leveraging a dataset containing over two million multivariate, time-series observations collected from a single household over nearly four years, our model addresses the limitations of traditional time-series forecasting… More >

Xuejing Li^*

CMC-Computers, Materials & Continua, Vol.83, No.2, pp. 1667-1681, 2025, DOI:10.32604/cmc.2025.062161 - 16 April 2025

Abstract Few-shot point cloud 3D object detection (FS3D) aims to identify and locate objects of novel classes within point clouds using knowledge acquired from annotated base classes and a minimal number of samples from the novel classes. Due to imbalanced training data, existing FS3D methods based on fully supervised learning can lead to overfitting toward base classes, which impairs the network’s ability to generalize knowledge learned from base classes to novel classes and also prevents the network from extracting distinctive foreground and background representations for novel class objects. To address these issues, this thesis proposes a… More >