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 >

Maryan Rizinski^1,2,*, Dimitar Trajanov^1,2

CMC-Computers, Materials & Continua, Vol.86, No.1, pp. 1-34, 2026, DOI:10.32604/cmc.2025.069678 - 10 November 2025

Abstract Artificial intelligence (AI) is reshaping financial systems and services, as intelligent AI agents increasingly form the foundation of autonomous, goal-driven systems capable of reasoning, learning, and action. This review synthesizes recent research and developments in the application of AI agents across core financial domains. Specifically, it covers the deployment of agent-based AI in algorithmic trading, fraud detection, credit risk assessment, robo-advisory, and regulatory compliance (RegTech). The review focuses on advanced agent-based methodologies, including reinforcement learning, multi-agent systems, and autonomous decision-making frameworks, particularly those leveraging large language models (LLMs), contrasting these with traditional AI or purely… More >

Junjun Ren¹, Guoqiang Chen², Zheng-Yi Chai³, Dong Yuan^4,*

CMC-Computers, Materials & Continua, Vol.86, No.1, pp. 1-26, 2026, DOI:10.32604/cmc.2025.068795 - 10 November 2025

Abstract Vehicle Edge Computing (VEC) and Cloud Computing (CC) significantly enhance the processing efficiency of delay-sensitive and computation-intensive applications by offloading compute-intensive tasks from resource-constrained onboard devices to nearby Roadside Unit (RSU), thereby achieving lower delay and energy consumption. However, due to the limited storage capacity and energy budget of RSUs, it is challenging to meet the demands of the highly dynamic Internet of Vehicles (IoV) environment. Therefore, determining reasonable service caching and computation offloading strategies is crucial. To address this, this paper proposes a joint service caching scheme for cloud-edge collaborative IoV computation offloading. By… More >

Misbah Anwer^1,*, Ghufran Ahmed¹, Maha Abdelhaq², Raed Alsaqour³, Shahid Hussain⁴, Adnan Akhunzada^5,*

CMC-Computers, Materials & Continua, Vol.86, No.1, pp. 1-15, 2026, DOI:10.32604/cmc.2025.068673 - 10 November 2025

Abstract The exponential growth of the Internet of Things (IoT) has introduced significant security challenges, with zero-day attacks emerging as one of the most critical and challenging threats. Traditional Machine Learning (ML) and Deep Learning (DL) techniques have demonstrated promising early detection capabilities. However, their effectiveness is limited when handling the vast volumes of IoT-generated data due to scalability constraints, high computational costs, and the costly time-intensive process of data labeling. To address these challenges, this study proposes a Federated Learning (FL) framework that leverages collaborative and hybrid supervised learning to enhance cyber threat detection in… More >

Božo Damjanović^*, Pejo Konjatić, Marko Katinić

CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.2, pp. 1735-1753, 2025, DOI:10.32604/cmes.2025.072256 - 26 November 2025

Abstract Ensuring the structural integrity of piping systems is crucial in industrial operations to prevent catastrophic failures and minimize shutdown time. This study investigates a transportation-damaged pipe exposed to high-temperature conditions and cyclic loading, representing a realistic challenge in plant operation. The objective was to evaluate the service life and integrity assessment parameters of the damaged pipe, subjected to 22,000 operational cycles under two daily charge and discharge conditions. The flaw size in the damaged pipe was determined based on a failure assessment procedure, ensuring a conservative and reliable input. The damage was characterized as a… More >

Fenghua Wang¹, Jianguo Gao^1,*, Zhi-Ying Yao², Kenneth Po-Lun Fung³, Cun-Xian Jia², Sheng-Li Cheng¹, Josephine Pui-Hing Wong⁴

International Journal of Mental Health Promotion, Vol.27, No.10, pp. 1497-1514, 2025, DOI:10.32604/ijmhp.2025.069458 - 31 October 2025

Abstract Background: Limited mental health literacy (MHL) among university service providers is a significant obstacle to effective psychological support. Developing and systematically assessing evidence-based interventions is an urgent priority, particularly in low- and middle-income countries (LMICs). This study aimed to evaluate the effectiveness of the Acceptance & Commitment to Empowerment: Linking Youths AND ‘Xin’ (Hearts) (ACE-LYNX) intervention in reducing stigma, improving psychological well-being, and enhancing the MHL and empowerment practices of university mental health providers in China. Methods: A total of 124 trained providers participated in this longitudinal study. Quantitative data were collected at baseline, immediately… More >

Muhammad Adeen Jamal¹, Bilal Khan^2,*, Sameed Ur Rehman¹, Wahab Khan¹

Journal on Artificial Intelligence, Vol.7, pp. 417-435, 2025, DOI:10.32604/jai.2025.070762 - 20 October 2025

Abstract The development of artificial intelligence (AI) has sparked the invention of chatbots, which are intelligent conversational agents. These chatbots have the potential to completely transform how people interact while enhancing user experience. This study explores the building along with its execution of a chatbot for customer service support at a university reception using recurrent neural networks (RNNs). To increase user requests, the accuracy of the information, and overall satisfaction with the service, it evaluates machine learning models including RNN, XLNet, and Bidirectional Encoder Representations from Transformers (BERT). In this research project, data were gathered from… More >

Beth Steinberg^1,*, Yulia Mulugeta¹, Catherine Quatman-Yates², Maeghan Williams², Anvitha Gogineni¹, Maryanna Klatt¹

International Journal of Mental Health Promotion, Vol.27, No.9, pp. 1237-1264, 2025, DOI:10.32604/ijmhp.2025.067232 - 30 September 2025

Abstract Background: Community-based first responders face high levels of workplace stressors that can profoundly impact their physical and mental health. Mindfulness-based interventions have shown promise in decreasing stress and increasing psychological resilience; however, implementation is difficult due to unpredictability of the job, department culture, and generational preferences. The objective of this qualitative study was to identify and enhance understanding of the specific needs and potential barriers and facilitators for the implementation of mindfulness-based programming for community-based first responders. Methods: A phenomenological qualitative study design was used to gain insights into the lived experiences of first responders… More >

Victor Otieno Mony^*, Anselemo Peters Ikoha, Roselida O. Maroko

Journal of Cyber Security, Vol.7, pp. 395-415, 2025, DOI:10.32604/jcs.2025.070952 - 30 September 2025

Abstract The widespread adoption of Cloud-Based Services has significantly increased the surface area for cyber threats, particularly targeting authentication mechanisms, which remain among the most vulnerable components of cloud security. This study aimed to address these challenges by developing and evaluating an Intelligent Zero Trust Architecture model tailored to mitigate authentication-related threats in Cloud-Based Services environments. Data was sourced from public repositories, including Kaggle and the National Institute for Standards and Technology MITRE Corporation’s Adversarial Tactics, Techniques, & Common Knowledge (ATT&CK) framework. The study utilized two trust signals: Behavioral targeting system users and Contextual targeting system… More >

Reza Nadimi^1,*, Maryam Hashemi², Koji Tokimatsu³

Journal on Internet of Things, Vol.7, pp. 49-69, 2025, DOI:10.32604/jiot.2025.070822 - 30 September 2025

Abstract Existing Internet of Things (IoT) systems that rely on Amazon Web Services (AWS) often encounter inefficiencies in data retrieval and high operational costs, especially when using DynamoDB for large-scale sensor data. These limitations hinder the scalability and responsiveness of applications such as remote energy monitoring systems. This research focuses on designing and developing an Arduino-based IoT system aimed at optimizing data transmission costs by concentrating on these services. The proposed method employs AWS Lambda functions with Amazon Relational Database Service (RDS) to facilitate the transmission of data collected from temperature and humidity sensors to the… More >