Amel Ksibi^1,*, Mohammed Zakariah², Latifah Almuqren¹, Ala Saleh Alluhaidan¹

CMC-Computers, Materials & Continua, Vol.82, No.3, pp. 4093-4116, 2025, DOI:10.32604/cmc.2025.059615 - 06 March 2025

Abstract The analysis of Android malware shows that this threat is constantly increasing and is a real threat to mobile devices since traditional approaches, such as signature-based detection, are no longer effective due to the continuously advancing level of sophistication. To resolve this problem, efficient and flexible malware detection tools are needed. This work examines the possibility of employing deep CNNs to detect Android malware by transforming network traffic into image data representations. Moreover, the dataset used in this study is the CIC-AndMal2017, which contains 20,000 instances of network traffic across five distinct malware categories: a.… More >

Ali Hasan Dakheel^1,*, Mohammed Raheem Mohammed¹, Zainab Ali Abd Alhuseen¹, Wassan Adnan Hashim^2,3

Intelligent Automation & Soft Computing, Vol.40, pp. 195-220, 2025, DOI:10.32604/iasc.2025.061622 - 28 February 2025

Abstract The prediction of pregnancy-related hazards must be accurate and timely to safeguard mother and fetal health. This study aims to enhance risk prediction in pregnancy with a novel deep learning model based on a Long Short-Term Memory (LSTM) generator, designed to capture temporal relationships in cardiotocography (CTG) data. This methodology integrates CTG signals with demographic characteristics and utilizes preprocessing techniques such as noise reduction, normalization, and segmentation to create high-quality input for the model. It uses convolutional layers to extract spatial information, followed by LSTM layers to model sequences for superior predictive performance. The overall More >

Yuewei Wu¹, Ruiling Fu¹, Tongtong Xing¹, Fulian Yin^1,2,*

CMC-Computers, Materials & Continua, Vol.82, No.1, pp. 751-775, 2025, DOI:10.32604/cmc.2024.057606 - 03 January 2025

Abstract In the Internet era, recommendation systems play a crucial role in helping users find relevant information from large datasets. Class imbalance is known to severely affect data quality, and therefore reduce the performance of recommendation systems. Due to the imbalance, machine learning algorithms tend to classify inputs into the positive (majority) class every time to achieve high prediction accuracy. Imbalance can be categorized such as by features and classes, but most studies consider only class imbalance. In this paper, we propose a recommendation system that can integrate multiple networks to adapt to a large number… More >

Sabrina Meddah^1,2,*, Sid Ahmed Tadjer³, Abdelhakim Idir⁴, Kong Fah Tee^5,6,*, Mohamed Zinelabidine Doghmane¹, Madjid Kidouche¹

Structural Durability & Health Monitoring, Vol.19, No.1, pp. 77-103, 2025, DOI:10.32604/sdhm.2024.053541 - 15 November 2024

Abstract Maintaining the integrity and longevity of structures is essential in many industries, such as aerospace, nuclear, and petroleum. To achieve the cost-effectiveness of large-scale systems in petroleum drilling, a strong emphasis on structural durability and monitoring is required. This study focuses on the mechanical vibrations that occur in rotary drilling systems, which have a substantial impact on the structural integrity of drilling equipment. The study specifically investigates axial, torsional, and lateral vibrations, which might lead to negative consequences such as bit-bounce, chaotic whirling, and high-frequency stick-slip. These events not only hinder the efficiency of drilling… More >

Zewen Zhang¹, Sheng Zhou¹, Chunzheng Cao^1,2,*

CMC-Computers, Materials & Continua, Vol.79, No.2, pp. 2465-2480, 2024, DOI:10.32604/cmc.2024.049605 - 15 May 2024

Abstract The classification of functional data has drawn much attention in recent years. The main challenge is representing infinite-dimensional functional data by finite-dimensional features while utilizing those features to achieve better classification accuracy. In this paper, we propose a mean-variance-based (MV) feature weighting method for classifying functional data or functional curves. In the feature extraction stage, each sample curve is approximated by B-splines to transfer features to the coefficients of the spline basis. After that, a feature weighting approach based on statistical principles is introduced by comprehensively considering the between-class differences and within-class variations of the… More >

Farhan Ullah¹, Xuexia Zhang^1,*, Mansoor Khan², Muhammad Abid^3,*, Abdullah Mohamed⁴

CMC-Computers, Materials & Continua, Vol.79, No.2, pp. 3373-3395, 2024, DOI:10.32604/cmc.2024.048656 - 15 May 2024

Abstract Accurate wind power forecasting is critical for system integration and stability as renewable energy reliance grows. Traditional approaches frequently struggle with complex data and non-linear connections. This article presents a novel approach for hybrid ensemble learning that is based on rigorous requirements engineering concepts. The approach finds significant parameters influencing forecasting accuracy by evaluating real-time Modern-Era Retrospective Analysis for Research and Applications (MERRA2) data from several European Wind farms using in-depth stakeholder research and requirements elicitation. Ensemble learning is used to develop a robust model, while a temporal convolutional network handles time-series complexities and data… More >

Bhawna Goyal¹, Ayush Dogra², Dawa Chyophel Lepcha¹, Rajesh Singh³, Hemant Sharma⁴, Ahmed Alkhayyat⁵, Manob Jyoti Saikia^6,*

CMC-Computers, Materials & Continua, Vol.78, No.3, pp. 4317-4342, 2024, DOI:10.32604/cmc.2024.047256 - 26 March 2024

Abstract Multimodal medical image fusion has attained immense popularity in recent years due to its robust technology for clinical diagnosis. It fuses multiple images into a single image to improve the quality of images by retaining significant information and aiding diagnostic practitioners in diagnosing and treating many diseases. However, recent image fusion techniques have encountered several challenges, including fusion artifacts, algorithm complexity, and high computing costs. To solve these problems, this study presents a novel medical image fusion strategy by combining the benefits of pixel significance with edge-preserving processing to achieve the best fusion performance. First,… More >

Ahmad Almadhor¹, Chitapong Wechtaisong^2,*, Usman Tariq³, Natalia Kryvinska^4,*, Abdullah Al Hejaili⁵, Uzma Ghulam Mohammad⁶, Mohana Alanazi⁷

Computer Systems Science and Engineering, Vol.48, No.1, pp. 199-216, 2024, DOI:10.32604/csse.2023.039546 - 26 January 2024

Abstract Thermal comfort is an essential component of smart cities that helps to upgrade, analyze, and realize intelligent buildings. It strongly affects human psychological and physiological levels. Residents of buildings suffer stress because of poor thermal comfort. Buildings frequently use Heating, Ventilation, and Air Conditioning (HVAC) systems for temperature control. Better thermal states directly impact people’s productivity and health. This study revealed a human thermal comfort model that makes better predictions of thermal sensation by identifying essential features and employing a tuned Extra Tree classifier, MultiLayer Perceptron (MLP) and Naive Bayes (NB) models. The study employs More >

Rizwan Iqbal^1,*, Rashid Hussain², Sheeraz Arif³, Nadia Mustaqim Ansari⁴, Tayyab Ahmed Shaikh²

CMC-Computers, Materials & Continua, Vol.77, No.2, pp. 1575-1598, 2023, DOI:10.32604/cmc.2023.042432 - 29 November 2023

Abstract Software-Defined Networking (SDN) is a new network technology that uses programming to complement the data plane with a control plane. To enable safe connection, however, numerous security challenges must be addressed. Flooding attacks have been one of the most prominent risks on the internet for decades, and they are now becoming challenging difficulties in SDN networks. To solve these challenges, we proposed a unique firewall application built on multiple levels of packet filtering to provide a flooding attack prevention system and a layer-based packet detection system. This study offers a systematic strategy for wrapping up… More >

Ramiz Gorkem Birdal^*, Ahmet Sertbas

CMC-Computers, Materials & Continua, Vol.76, No.3, pp. 2727-2744, 2023, DOI:10.32604/cmc.2023.032069 - 08 October 2023

Abstract Biometric gait recognition is a lesser-known but emerging and effective biometric recognition method which enables subjects’ walking patterns to be recognized. Existing research in this area has primarily focused on feature analysis through the extraction of individual features, which captures most of the information but fails to capture subtle variations in gait dynamics. Therefore, a novel feature taxonomy and an approach for deriving a relationship between a function of one set of gait features with another set are introduced. The gait features extracted from body halves divided by anatomical planes on vertical, horizontal, and diagonal… More >