RIYAD F. ALZHRANI¹, LAMA BINOBAID², ABDULAZIZ A. ALORAINI¹, MESHAL S. ALSAHLI¹, AHMED H. BAKHEIT³, HANADI H. ASIRI³, SABRY M. ATTIA², ALI ALHOSHANI², GAMALELDIN I. HARISA^1,*

Oncology Research, Vol.33, No.4, pp. 919-935, 2025, DOI:10.32604/or.2024.054537 - 19 March 2025

Abstract Objective: Prostate cancer (PCA) is the second most widespread cancer among men globally, with a rising mortality rate. Enzyme-responsive lipid nanoparticles (ERLNs) are promising vectors for the selective delivery of anticancer agents to tumor cells. The goal of this study is to fabricate ERLNs for dual delivery of gefitinib (GF) and simvastatin (SV) to PCA cells. Methods: ERLNs loaded with GF and SV (ERLNGFSV) were assembled using bottom-up and top-down techniques. Subsequently, these ERLN cargoes were coated with triacylglycerol, and phospholipids and capped with chitosan (CS). The ERLNGFSV, and CS engineered ERLNGFSV (CERLNGFSV) formulations were… More > Graphic Abstract

Ruhul Amin¹, Md. Jamil Khan¹, Tonway Deb Nath¹, Md. Shamim Reza², Jungpil Shin^3,*

CMC-Computers, Materials & Continua, Vol.82, No.3, pp. 3907-3919, 2025, DOI:10.32604/cmc.2025.061645 - 06 March 2025

Abstract Heart disease includes a multiplicity of medical conditions that affect the structure, blood vessels, and general operation of the heart. Numerous researchers have made progress in correcting and predicting early heart disease, but more remains to be accomplished. The diagnostic accuracy of many current studies is inadequate due to the attempt to predict patients with heart disease using traditional approaches. By using data fusion from several regions of the country, we intend to increase the accuracy of heart disease prediction. A statistical approach that promotes insights triggered by feature interactions to reveal the intricate pattern… More >

Mohamed A.G. Hazber^1,*, Ebrahim Mohammed Senan^2,3, Hezam Saud Alrashidi¹

CMES-Computer Modeling in Engineering & Sciences, Vol.142, No.3, pp. 3229-3254, 2025, DOI:10.32604/cmes.2025.062302 - 03 March 2025

Abstract Hepatitis is an infection that affects the liver through contaminated foods or blood transfusions, and it has many types, from normal to serious. Hepatitis is diagnosed through many blood tests and factors; Artificial Intelligence (AI) techniques have played an important role in early diagnosis and help physicians make decisions. This study evaluated the performance of Machine Learning (ML) algorithms on the hepatitis data set. The dataset contains missing values that have been processed and outliers removed. The dataset was counterbalanced by the Synthetic Minority Over-sampling Technique (SMOTE). The features of the data set were processed… More >

Pieter C. van de Woestijne^1,*, Ingrid M. van Beynum², Kevin M. Veen³, Ad J. J. C Bogers¹

Congenital Heart Disease, Vol.19, No.6, pp. 593-601, 2024, DOI:10.32604/chd.2025.060607 - 27 January 2025

Abstract Background: Construction of a central shunt by connection of hypoplastic true pulmonary arteries to the ascending aorta (AO) can be performed as a first step in staged repair of pulmonary atresia with ventricular septal defect and major aorto-pulmonary collateral arteries (PA-VSD-MAPCAs) intended to promote growth and development of the central pulmonary arteries. Methods: To determine early and intermediate-term growth of true pulmonary arteries after their connection to the AO as a first step in staged repair of PA-VSD-MAPCAs, we reviewed all angiographic studies and CT imaging of patients, treated in our tertiary referral center in… More > Graphic Abstract

Yao Jin^1,2,*, Jie Zhao^1,2, Xiaozhe Tan^1,2, Linghou Miao^1,2, Wenxing Yu^1,2

Digital Engineering and Digital Twin, Vol.2, pp. 131-144, 2024, DOI:10.32604/dedt.2024.048142 - 31 December 2024

Abstract Substation siting is an important foundation and a key task in power system planning. The article is based on a three-dimensional GIS platform combined with an improved BP neural network algorithm and proposes a substation siting method that is more efficient, accurate and provides a better user experience. Firstly, the BP algorithm is enhanced to improve its convergence speed and computational efficiency for a more accurate and reasonable calculation of optimal site selection. Then, a 24-item selection index system with 7 categories is proposed, which provides quantifiable data support and an evaluation basis for substation… More >

Fizza Mansoor¹, Muhammad Affan Alim^2,5,*, Muhammad Taha Jilani³, Muhammad Monsoor Alam^4,5, Mazliham Mohd Su’ud⁵

CMC-Computers, Materials & Continua, Vol.81, No.3, pp. 4603-4624, 2024, DOI:10.32604/cmc.2024.057979 - 19 December 2024

Abstract Software cost estimation is a crucial aspect of software project management, significantly impacting productivity and planning. This research investigates the impact of various feature selection techniques on software cost estimation accuracy using the CoCoMo NASA dataset, which comprises data from 93 unique software projects with 24 attributes. By applying multiple machine learning algorithms alongside three feature selection methods, this study aims to reduce data redundancy and enhance model accuracy. Our findings reveal that the principal component analysis (PCA)-based feature selection technique achieved the highest performance, underscoring the importance of optimal feature selection in improving software More >

MEHRSA JALALIZADEH^1,#, HEVELINE RAYANE MOURA ROESCH^1,#, FERNANDO KORKES², QUOC DIEN-TRINH³, LEONARDO OLIVEIRA REIS^1,4,*

Oncology Research, Vol.32, No.10, pp. 1565-1573, 2024, DOI:10.32604/or.2024.052179 - 18 September 2024

Abstract Objectives: The Brazilian Unified Health System (Sistema Único de Saúde−SUS) is the universal public healthcare system of Brazil that maintains a nationwide database of its patients. Our primary objective was to analyze regional and temporal trends, while our secondary goal was to establish correlations between states’ health economy status and their prostate cancer (PCa) epidemiology. Methods: We analyzed Brazil’s nationwide data on prostate cancer (PCa) incidence, mortality, and care gathered between 2013 and 2021 by the Information Technology Department of SUS (DATA-SUS), updated monthly using the International Classification of Diseases (ICD-10) code. Results: In the period,… More >

Wei Chen^1,*, Xiaogang Wei^2,*, Hongxu Zhang², Wenpeng He²

Journal on Artificial Intelligence, Vol.6, pp. 225-240, 2024, DOI:10.32604/jai.2024.052366 - 16 August 2024

Abstract The Head-Related Transfer Function (HRTF) describes the effects of sound reflection and scattering caused by the environment and the human body when sound signals are transmitted from a source to the human ear. It contains a significant amount of auditory cue information used for sound localization. Consequently, HRTF renders 3D audio accurately in numerous immersive multimedia applications. Because HRTF is high-dimensional, complex, and nonlinear, it is a relatively large and intricate dataset, typically consisting of hundreds of thousands of samples. Storing HRTF requires a significant amount of storage space in practical applications. Based on this, More >

Yizhao Wang^*

CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.3, pp. 3029-3045, 2024, DOI:10.32604/cmes.2024.046270 - 11 March 2024

Abstract Near-fault impulsive ground-shaking is highly destructive to engineering structures, so its accurate identification ground-shaking is a top priority in the engineering field. However, due to the lack of a comprehensive consideration of the ground-shaking characteristics in traditional methods, the generalization and accuracy of the identification process are low. To address these problems, an impulsive ground-shaking identification method combined with deep learning named PCA-LSTM is proposed. Firstly, ground-shaking characteristics were analyzed and ground-shaking the data was annotated using Baker’s method. Secondly, the Principal Component Analysis (PCA) method was used to extract the most relevant features related More >

Xuecheng Yu¹, Yan Huang², Yu Zhang¹, Mingyang Song¹, Zhenhong Jia^1,3,*

CMC-Computers, Materials & Continua, Vol.78, No.1, pp. 473-492, 2024, DOI:10.32604/cmc.2023.044999 - 30 January 2024

Abstract With the increasing dimensionality of network traffic, extracting effective traffic features and improving the identification accuracy of different intrusion traffic have become critical in intrusion detection systems (IDS). However, both unsupervised and semisupervised anomalous traffic detection methods suffer from the drawback of ignoring potential correlations between features, resulting in an analysis that is not an optimal set. Therefore, in order to extract more representative traffic features as well as to improve the accuracy of traffic identification, this paper proposes a feature dimensionality reduction method combining principal component analysis and Hotelling’s T² and a multilayer convolutional bidirectional… More >