Georgia Garani^1,*, George Pramantiotis², Francisco Javier Moreno Arboleda³

CMC-Computers, Materials & Continua, Vol.86, No.3, 2026, DOI:10.32604/cmc.2025.071509 - 12 January 2026

Abstract Earthquakes are highly destructive spatio-temporal phenomena whose analysis is essential for disaster preparedness and risk mitigation. Modern seismological research produces vast volumes of heterogeneous data from seismic networks, satellite observations, and geospatial repositories, creating the need for scalable infrastructures capable of integrating and analyzing such data to support intelligent decision-making. Data warehousing technologies provide a robust foundation for this purpose; however, existing earthquake-oriented data warehouses remain limited, often relying on simplified schemas, domain-specific analytics, or cataloguing efforts. This paper presents the design and implementation of a spatio-temporal data warehouse for seismic activity. The framework integrates… More >

Ateeqa Jalal^1,*, Umar Farooq^1,4,5, Ihsan Rabbi^1,4, Afzal Badshah², Aurangzeb Khan^1,4, Muhammad Mansoor Alam^3,4, Mazliham Mohd Su’ud^4,*

CMC-Computers, Materials & Continua, Vol.86, No.2, pp. 1-19, 2026, DOI:10.32604/cmc.2025.068289 - 09 December 2025

Abstract The exponential growth of Internet of Things (IoT) devices, autonomous systems, and digital services is generating massive volumes of big data, projected to exceed 291 zettabytes by 2027. Conventional cloud computing, despite its high processing and storage capacity, suffers from increased network latency, network congestion, and high operational costs, making it unsuitable for latency-sensitive applications. Edge computing addresses these issues by processing data near the source but faces scalability challenges and elevated Total Cost of Ownership (TCO). Hybrid solutions, such as fog computing, cloudlets, and Mobile Edge Computing (MEC), attempt to balance cost and performance;… More >

Ehsan Akbari¹, Tajbakhsh Navid Chakherlou¹, Hamed Tabrizchi^2,3,*, Amir Mosavi^3,4,5,6

CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.1, pp. 305-325, 2025, DOI:10.32604/cmes.2025.068581 - 30 October 2025

Abstract The ability to predict multiaxial fatigue life of Al-Alloy 7075-T6 under complex loading conditions is critical to assessing its durability under complex loading conditions, particularly in aerospace, automotive, and structural applications. This paper presents a physical-informed neural network (PINN) model to predict the fatigue life of Al-Alloy 7075-T6 over a variety of multiaxial stresses. The model integrates the principles of the Geometric Multiaxial Fatigue Life (GMFL) approach, which is a novel fatigue life prediction approach to estimating fatigue life by combining multiple fatigue criteria. The proposed model aims to estimate fatigue damage accumulation by the More >

Canlin Cui¹, Junyu Yao^1,*, Heng Xia^2,*

CMC-Computers, Materials & Continua, Vol.85, No.3, pp. 4275-4306, 2025, DOI:10.32604/cmc.2025.069097 - 23 October 2025

Abstract High-quality data is essential for the success of data-driven learning tasks. The characteristics, precision, and completeness of the datasets critically determine the reliability, interpretability, and effectiveness of subsequent analyzes and applications, such as fault detection, predictive maintenance, and process optimization. However, for many industrial processes, obtaining sufficient high-quality data remains a significant challenge due to high costs, safety concerns, and practical constraints. To overcome these challenges, data augmentation has emerged as a rapidly growing research area, attracting considerable attention across both academia and industry. By expanding datasets, data augmentation techniques improve greater generalization and more… More >

Vijaya Choudhary^1,2,3,*, Paramita Guha^1,2, Giovanni Pau⁴

Journal on Big Data, Vol.7, pp. 1-14, 2025, DOI:10.32604/jbd.2025.065122 - 12 August 2025

Abstract Crop phenotyping plays a critical role in precision agriculture by enabling the accurate assessment of plant traits, supporting improved crop management, breeding programs, and yield optimization. However, cowpea leaves present unique challenges for automated phenotyping due to their diverse shapes, complex vein structures, and variations caused by environmental conditions. This research presents a deep learning-based approach for the classification of cowpea leaf images to support crop phenotyping tasks. Given the limited availability of annotated datasets, data augmentation techniques were employed to artificially expand the original small dataset while preserving essential leaf characteristics. Various image processing More >

Changchao Qi¹, Lingdi Fu¹, Ming Wen¹, Hao Qian², Shuai Zhao^1,*

Structural Durability & Health Monitoring, Vol.19, No.4, pp. 1073-1087, 2025, DOI:10.32604/sdhm.2025.061898 - 30 June 2025

Abstract The accuracy and reliability of non-destructive testing (NDT) approaches in detecting interior corrosion problems are critical, yet research in this field is limited. This work describes a novel way to monitor the structural integrity of steel gas pipelines that uses advanced numerical modeling techniques to anticipate fracture development and corrosion effects. The objective is to increase pipeline dependability and safety through more precise, real-time health evaluations. Compared to previous approaches, our solution provides higher accuracy in fault detection and quantification, making it ideal for pipeline integrity monitoring in real-world applications. To solve this issue, statistical… More >

Weiyang Xie^1,2, Cheng Chang^1,2, Ziqin Lai^1,2,*, Sha Liu^1,2, Han Xiao^1,2

FDMP-Fluid Dynamics & Materials Processing, Vol.21, No.6, pp. 1417-1438, 2025, DOI:10.32604/fdmp.2025.060956 - 30 June 2025

Abstract In the context of post-stimulation shale gas wells, the terms “shut-in” and “flowback” refer to two critical phases that occur after hydraulic fracturing (fracking) has been completed. These stages play a crucial role in determining both the well’s initial production performance and its long-term hydrocarbon recovery. By establishing a comprehensive big data analysis platform, the flowback dynamics of over 1000 shale gas wells were analyzed in this work, leading to the development of an index system for evaluating flowback production capacity. Additionally, a shut-in chart was created for wells with different types of post-stimulation fracture More >

Menwa Alshammeri¹, Mamoona Humayun^2,*, Khalid Haseeb³, Ghadah Naif Alwakid¹

CMC-Computers, Materials & Continua, Vol.84, No.1, pp. 433-446, 2025, DOI:10.32604/cmc.2025.065660 - 09 June 2025

Abstract Wireless technologies and the Internet of Things (IoT) are being extensively utilized for advanced development in traditional communication systems. This evolution lowers the cost of the extensive use of sensors, changing the way devices interact and communicate in dynamic and uncertain situations. Such a constantly evolving environment presents enormous challenges to preserving a secure and lightweight IoT system. Therefore, it leads to the design of effective and trusted routing to support sustainable smart cities. This research study proposed a Genetic Algorithm sentiment-enhanced secured optimization model, which combines big data analytics and analysis rules to evaluate… More >

Ali Hamid Farea^1,*, Iman Askerzade^1,2, Omar H. Alhazmi³, Savaş Takan⁴

CMC-Computers, Materials & Continua, Vol.84, No.1, pp. 1457-1484, 2025, DOI:10.32604/cmc.2025.064872 - 09 June 2025

Abstract Feature selection (FS) is a pivotal pre-processing step in developing data-driven models, influencing reliability, performance and optimization. Although existing FS techniques can yield high-performance metrics for certain models, they do not invariably guarantee the extraction of the most critical or impactful features. Prior literature underscores the significance of equitable FS practices and has proposed diverse methodologies for the identification of appropriate features. However, the challenge of discerning the most relevant and influential features persists, particularly in the context of the exponential growth and heterogeneity of big data—a challenge that is increasingly salient in modern artificial… More >

Hanqing Sun¹, Xue Li^2,*, Qiyuan Fan³, Puming Wang³

CMC-Computers, Materials & Continua, Vol.84, No.1, pp. 1659-1679, 2025, DOI:10.32604/cmc.2025.061426 - 09 June 2025

Abstract The era of big data brings new challenges for information network systems (INS), simultaneously offering unprecedented opportunities for advancing intelligent intrusion detection systems. In this work, we propose a data-driven intrusion detection system for Distributed Denial of Service (DDoS) attack detection. The system focuses on intrusion detection from a big data perceptive. As intelligent information processing methods, big data and artificial intelligence have been widely used in information systems. The INS system is an important information system in cyberspace. In advanced INS systems, the network architectures have become more complex. And the smart devices in… More >