Mudasir Dilawar^*, Muhammad Shahbaz

CMC-Computers, Materials & Continua, Vol.83, No.3, pp. 5091-5114, 2025, DOI:10.32604/cmc.2025.064090 - 19 May 2025

Abstract In the era of Industry 4.0, condition monitoring has emerged as an effective solution for process industries to optimize their operational efficiency. Condition monitoring helps minimize unplanned downtime, extending equipment lifespan, reducing maintenance costs, and improving production quality and safety. This research focuses on utilizing Bayesian search-based machine learning and deep learning approaches for the condition monitoring of industrial equipment. The study aims to enhance predictive maintenance for industrial equipment by forecasting vibration values based on domain-specific feature engineering. Early prediction of vibration enables proactive interventions to minimize downtime and extend the lifespan of critical… More >

Yogendra P. S. Maravi^*, Nishchol Mishra

CMC-Computers, Materials & Continua, Vol.83, No.3, pp. 5517-5537, 2025, DOI:10.32604/cmc.2025.063964 - 19 May 2025

Abstract The growing demand for international travel has highlighted the critical need for reliable tools to verify travelers’ healthcare status and meet entry requirements. Personal health passports, while essential, face significant challenges related to data silos, privacy protection, and forgery risks in global sharing. To address these issues, this study proposes a blockchain-based solution designed for the secure storage, sharing, and verification of personal health passports. This innovative approach combines on-chain and off-chain storage, leveraging searchable encryption to enhance data security and optimize blockchain storage efficiency. By reducing the storage burden on the blockchain, the system… More >

Ahmet Emre Ergün, Aytuğ Onan^*

CMC-Computers, Materials & Continua, Vol.83, No.3, pp. 4855-4877, 2025, DOI:10.32604/cmc.2025.063826 - 19 May 2025

Abstract Large Language Models (LLMs) have significantly advanced human-computer interaction by improving natural language understanding and generation. However, their vulnerability to adversarial prompts–carefully designed inputs that manipulate model outputs–presents substantial challenges. This paper introduces a classification-based approach to detect adversarial prompts by utilizing both prompt features and prompt response features. Eleven machine learning models were evaluated based on key metrics such as accuracy, precision, recall, and F1-score. The results show that the Convolutional Neural Network–Long Short-Term Memory (CNN-LSTM) cascade model delivers the best performance, especially when using prompt features, achieving an accuracy of over 97% in… More >

Taher Alzahrani^*

CMC-Computers, Materials & Continua, Vol.83, No.3, pp. 4575-4606, 2025, DOI:10.32604/cmc.2025.063448 - 19 May 2025

Abstract The rapid evolution of malware presents a critical cybersecurity challenge, rendering traditional signature-based detection methods ineffective against novel variants. This growing threat affects individuals, organizations, and governments, highlighting the urgent need for robust malware detection mechanisms. Conventional machine learning-based approaches rely on static and dynamic malware analysis and often struggle to detect previously unseen threats due to their dependency on predefined signatures. Although machine learning algorithms (MLAs) offer promising detection capabilities, their reliance on extensive feature engineering limits real-time applicability. Deep learning techniques mitigate this issue by automating feature extraction but may introduce computational overhead,… More >

Arshad Mehmmod^1,#, Komal Batool^1,#, Ahthsham Sajid^2,3, Muhammad Mansoor Alam^2,3, Mazliham MohD Su’ud^3,*, Inam Ullah Khan³

CMC-Computers, Materials & Continua, Vol.83, No.3, pp. 5155-5179, 2025, DOI:10.32604/cmc.2025.062971 - 19 May 2025

Abstract Traditional rule-based Intrusion Detection Systems (IDS) are commonly employed owing to their simple design and ability to detect known threats. Nevertheless, as dynamic network traffic and a new degree of threats exist in IoT environments, these systems do not perform well and have elevated false positive rates—consequently decreasing detection accuracy. In this study, we try to overcome these restrictions by employing fuzzy logic and machine learning to develop an Enhanced Rule-Based Model (ERBM) to classify the packets better and identify intrusions. The ERBM developed for this approach improves data preprocessing and feature selections by utilizing… More >

Junjie Cao^1,2, Zhiyong Yu^2,*, Xiaotao Xu¹, Baohong Zhu³, Jian Yang²

CMC-Computers, Materials & Continua, Vol.83, No.3, pp. 5235-5257, 2025, DOI:10.32604/cmc.2025.062371 - 19 May 2025

Abstract This paper introduces a quantum-enhanced edge computing framework that synergizes quantum-inspired algorithms with advanced machine learning techniques to optimize real-time task offloading in edge computing environments. This innovative approach not only significantly improves the system’s real-time responsiveness and resource utilization efficiency but also addresses critical challenges in Internet of Things (IoT) ecosystems—such as high demand variability, resource allocation uncertainties, and data privacy concerns—through practical solutions. Initially, the framework employs an adaptive adjustment mechanism to dynamically manage task and resource states, complemented by online learning models for precise predictive analytics. Secondly, it accelerates the search for… More >

David Conway, Paolina Centonze^*

Journal of Cyber Security, Vol.7, pp. 71-78, 2025, DOI:10.32604/jcs.2025.063711 - 14 May 2025

Abstract Ransomware is a variant of malicious software that aims to encrypt data or whole systems to lock out the intended users. The attackers then demand a ransom for the decryption key to allow the intended users access to their data or system again. Ransomware attacks have the potential to be used against industries like healthcare and finance, as well as against the public sector, have threatened and forced the operations of key infrastructure used by millions to cease, and extorted millions and millions of dollars from victims. Automated methods have been designed and implemented to More >

Yuliang Zhang^1,2,*, Zezhou Yang¹, Lianghuai Tong^3,*, Yanjuan Zhao⁴, Xiaoqi Jia⁵, Anda Han⁶

FDMP-Fluid Dynamics & Materials Processing, Vol.21, No.4, pp. 891-938, 2025, DOI:10.32604/fdmp.2024.059903 - 06 May 2025

Abstract This paper investigates the start-up and shutdown phases of a five-bladed closed-impeller centrifugal pump through experimental analysis, capturing the temporal evolution of its hydraulic performances. The study also predicts the transient characteristics of the pump under non-rated operating conditions to assess the accuracy of various machine learning methods in forecasting its instantaneous performance. Results indicate that the pump’s transient behavior in power-frequency mode markedly differs from that in frequency-conversion mode. Specifically, the power-frequency mode achieves steady-state values faster and exhibits smaller fluctuations before stabilization compared to the other mode. During the start-up phase, as… More >

Muhammad Izhar^1,*, Khadija Parwez², Saman Iftikhar³, Adeel Ahmad⁴, Shaikhan Bawazeer³, Saima Abdullah⁴

Journal on Artificial Intelligence, Vol.7, pp. 55-83, 2025, DOI:10.32604/jai.2025.062479 - 25 April 2025

Abstract The growing intersection of gynecological cancer diagnosis and cybersecurity vulnerabilities in healthcare necessitates integrated solutions that address both diagnostic accuracy and data protection. With increasing reliance on IoT-enabled medical devices, digital twins, and interconnected healthcare systems, the risk of cyber-physical attacks has escalated significantly. Traditional approaches to machine learning (ML)–based diagnosis often lack real-time threat adaptability and privacy preservation, while cybersecurity frameworks fall short in maintaining clinical relevance. This study introduces HealthSecureNet, a novel Cyber-Integrated Predictive Framework designed to detect gynecological cancer and mitigate cybersecurity threats in real time simultaneously. The proposed model employs a… 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 >