S. N. Garibova^a,b,*, А. I. Isayev^a, S. A. Rzayeva^a, F. N. Mammadov^c

Chalcogenide Letters, Vol.22, No.1, pp. 1-9, 2025, DOI:10.15251/CL.2025.221.1

Abstract The structure particulars of amorphous Ge2Sb2Te5 thermally evaporated on glass substrates, as well as films annealed at temperatures of 500 and 700 K have been studied by the considering of experimentally established facts obtained from X-ray analysis and Raman spectroscopy measurements. The Debye-Scherrer and Williams-Hall methods were applied to the X-ray diffraction data for estimate the size of crystallites, interatomic distances, dislocation density and structure distortion degree. The features of heat treatment effect on numerical values of the above quantities at a given temperatures have been established. The analysis of the spectral distribution of Raman… More >

Hadeel AlQadi¹, Fatimah M. Alghamdi², Hamada H. Hassan³, Mohamed E. Mead⁴, Ahmed Z. Afify^5,*

CMES-Computer Modeling in Engineering & Sciences, Vol.144, No.2, pp. 2029-2061, 2025, DOI:10.32604/cmes.2025.069801 - 31 August 2025

Abstract Parametric survival models are essential for analyzing time-to-event data in fields such as engineering and biomedicine. While the log-logistic distribution is popular for its simplicity and closed-form expressions, it often lacks the flexibility needed to capture complex hazard patterns. In this article, we propose a novel extension of the classical log-logistic distribution, termed the new exponential log-logistic (NExLL) distribution, designed to provide enhanced flexibility in modeling time-to-event data with complex failure behaviors. The NExLL model incorporates a new exponential generator to expand the shape adaptability of the baseline log-logistic distribution, allowing it to capture a… More >

Yuhao Zhang^1,2,3, Peiqiang Zhao^1,2, Xing Chen^1,2, Shaoxuan Zhang⁴, Xinglin Zhang^1,2,*

Structural Durability & Health Monitoring, Vol.19, No.5, pp. 1343-1365, 2025, DOI:10.32604/sdhm.2025.066558 - 05 September 2025

Abstract The structural integrity monitoring of high-density polyethylene (HDPE) geomembranes in landfill containment systems presents a critical engineering challenge due to the material’s vulnerability to mechanical degradation and the complex vibration propagation characteristics in large-scale installations. This study proposes a dual-stream deep learning framework that synergistically integrates raw vibration signal analysis with physics-guided feature extraction to achieve precise rupture detection and localization. The methodology employs a hierarchical neural architecture comprising two parallel branches: a 1D convolutional network processing raw accelerometer signals to capture multi-scale temporal patterns, and a physics-informed branch extracting material-specific resonance features through continuous More >

Jihoon Moon¹, Jiyoung Woo^2,*

CMC-Computers, Materials & Continua, Vol.85, No.1, pp. 1383-1425, 2025, DOI:10.32604/cmc.2025.067784 - 29 August 2025

Abstract Improving early diagnosis of autism spectrum disorder (ASD) in children increasingly relies on predictive models that are reliable and accessible to non-experts. This study aims to develop such models using Python-based tools to improve ASD diagnosis in clinical settings. We performed exploratory data analysis to ensure data quality and identify key patterns in pediatric ASD data. We selected the categorical boosting (CatBoost) algorithm to effectively handle the large number of categorical variables. We used the PyCaret automated machine learning (AutoML) tool to make the models user-friendly for clinicians without extensive machine learning expertise. In addition,… 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 >

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 >