Nimra Bari¹, Tahir Saleem², Munam Shah³, Abdulmohsen Algarni⁴, Asma Patel^5,*, Insaf Ullah^6,*

CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.1, pp. 1167-1187, 2025, DOI:10.32604/cmes.2025.070311 - 30 October 2025

Abstract Today, phishing is an online attack designed to obtain sensitive information such as credit card and bank account numbers, passwords, and usernames. We can find several anti-phishing solutions, such as heuristic detection, virtual similarity detection, black and white lists, and machine learning (ML). However, phishing attempts remain a problem, and establishing an effective anti-phishing strategy is a work in progress. Furthermore, while most anti-phishing solutions achieve the highest levels of accuracy on a given dataset, their methods suffer from an increased number of false positives. These methods are ineffective against zero-hour attacks. Phishing sites with… More >

Duc-Dam Nguyen¹, Nguyen Viet Tiep^2,*, Quynh-Anh Thi Bui¹, Hiep Van Le¹, Indra Prakash³, Romulus Costache^4,5,6,7, Manish Pandey^8,9, Binh Thai Pham¹

CMES-Computer Modeling in Engineering & Sciences, Vol.142, No.1, pp. 467-500, 2025, DOI:10.32604/cmes.2024.056576 - 17 December 2024

Abstract This study was aimed to prepare landslide susceptibility maps for the Pithoragarh district in Uttarakhand, India, using advanced ensemble models that combined Radial Basis Function Networks (RBFN) with three ensemble learning techniques: DAGGING (DG), MULTIBOOST (MB), and ADABOOST (AB). This combination resulted in three distinct ensemble models: DG-RBFN, MB-RBFN, and AB-RBFN. Additionally, a traditional weighted method, Information Value (IV), and a benchmark machine learning (ML) model, Multilayer Perceptron Neural Network (MLP), were employed for comparison and validation. The models were developed using ten landslide conditioning factors, which included slope, aspect, elevation, curvature, land cover, geomorphology,… More >

Noor Ullah Bacha¹, Songfeng Lu¹, Attiq Ur Rehman¹, Muhammad Idrees², Yazeed Yasin Ghadi³, Tahani Jaser Alahmadi^4,*

CMC-Computers, Materials & Continua, Vol.81, No.1, pp. 707-748, 2024, DOI:10.32604/cmc.2024.054780 - 15 October 2024

Abstract Cross-Site Scripting (XSS) remains a significant threat to web application security, exploiting vulnerabilities to hijack user sessions and steal sensitive data. Traditional detection methods often fail to keep pace with the evolving sophistication of cyber threats. This paper introduces a novel hybrid ensemble learning framework that leverages a combination of advanced machine learning algorithms—Logistic Regression (LR), Support Vector Machines (SVM), eXtreme Gradient Boosting (XGBoost), Categorical Boosting (CatBoost), and Deep Neural Networks (DNN). Utilizing the XSS-Attacks-2021 dataset, which comprises 460 instances across various real-world traffic-related scenarios, this framework significantly enhances XSS attack detection. Our approach, which… More >

Nisreen Innab¹, Ahmed Abdelgader Fadol Osman², Mohammed Awad Mohammed Ataelfadiel², Marwan Abu-Zanona^3,*, Bassam Mohammad Elzaghmouri⁴, Farah H. Zawaideh⁵, Mouiad Fadeil Alawneh⁶

CMC-Computers, Materials & Continua, Vol.80, No.1, pp. 1325-1345, 2024, DOI:10.32604/cmc.2024.051778 - 18 July 2024

Abstract Phishing, an Internet fraud where individuals are deceived into revealing critical personal and account information, poses a significant risk to both consumers and web-based institutions. Data indicates a persistent rise in phishing attacks. Moreover, these fraudulent schemes are progressively becoming more intricate, thereby rendering them more challenging to identify. Hence, it is imperative to utilize sophisticated algorithms to address this issue. Machine learning is a highly effective approach for identifying and uncovering these harmful behaviors. Machine learning (ML) approaches can identify common characteristics in most phishing assaults. In this paper, we propose an ensemble approach… More >

R. Ani^1,*, O. S. Deepa², B. R. Manju¹

Computer Systems Science and Engineering, Vol.47, No.3, pp. 3033-3048, 2023, DOI:10.32604/csse.2023.033807 - 09 November 2023

Abstract The drug development process takes a long time since it requires sorting through a large number of inactive compounds from a large collection of compounds chosen for study and choosing just the most pertinent compounds that can bind to a disease protein. The use of virtual screening in pharmaceutical research is growing in popularity. During the early phases of medication research and development, it is crucial. Chemical compound searches are now more narrowly targeted. Because the databases contain more and more ligands, this method needs to be quick and exact. Neural network fingerprints were created… More >

Yih Bing Chu^*, Zhi Min Lim, Bryan Keane, Ping Hao Kong, Ahmed Rafat Elkilany, Osama Hisham Abusetta

Journal of Cyber Security, Vol.5, pp. 33-46, 2023, DOI:10.32604/jcs.2023.045422 - 03 November 2023

Abstract The proliferation of digital payment methods facilitated by various online platforms and applications has led to a surge in financial fraud, particularly in credit card transactions. Advanced technologies such as machine learning have been widely employed to enhance the early detection and prevention of losses arising from potentially fraudulent activities. However, a prevalent approach in existing literature involves the use of extensive data sampling and feature selection algorithms as a precursor to subsequent investigations. While sampling techniques can significantly reduce computational time, the resulting dataset relies on generated data and the accuracy of the pre-processing… More >

Omar Reyad^1,2, Hazem Ibrahim Shehata^1,3, Mohamed Esmail Karar^1,4,*

CMC-Computers, Materials & Continua, Vol.76, No.2, pp. 1689-1700, 2023, DOI:10.32604/cmc.2023.039084 - 30 August 2023

Abstract Falling is among the most harmful events older adults may encounter. With the continuous growth of the aging population in many societies, developing effective fall detection mechanisms empowered by machine learning technologies and easily integrable with existing healthcare systems becomes essential. This paper presents a new healthcare Internet of Health Things (IoHT) architecture built around an ensemble machine learning-based fall detection system (FDS) for older people. Compared to deep neural networks, the ensemble multi-stage random forest model allows the extraction of an optimal subset of fall detection features with minimal hyperparameters. The number of cascaded… More >

Mesfer Al Duhayyim^1,*, Sidra Abbas^2,*, Abdullah Al Hejaili³, Natalia Kryvinska⁴, Ahmad Almadhor⁵, Uzma Ghulam Mohammad⁶

Computer Systems Science and Engineering, Vol.47, No.1, pp. 413-429, 2023, DOI:10.32604/csse.2023.037127 - 26 May 2023

Abstract Stroke is a life-threatening disease usually due to blockage of blood or insufficient blood flow to the brain. It has a tremendous impact on every aspect of life since it is the leading global factor of disability and morbidity. Strokes can range from minor to severe (extensive). Thus, early stroke assessment and treatment can enhance survival rates. Manual prediction is extremely time and resource intensive. Automated prediction methods such as Modern Information and Communication Technologies (ICTs), particularly those in Machine Learning (ML) area, are crucial for the early diagnosis and prognosis of stroke. Therefore, this… More >

Sagheer Abbas¹, Shabib Aftab^1,2, Muhammad Adnan Khan^3,4, Taher M. Ghazal^5,6, Hussam Al Hamadi⁷, Chan Yeob Yeun^8,*

CMC-Computers, Materials & Continua, Vol.75, No.3, pp. 6083-6100, 2023, DOI:10.32604/cmc.2023.037933 - 29 April 2023

Abstract The software engineering field has long focused on creating high-quality software despite limited resources. Detecting defects before the testing stage of software development can enable quality assurance engineers to concentrate on problematic modules rather than all the modules. This approach can enhance the quality of the final product while lowering development costs. Identifying defective modules early on can allow for early corrections and ensure the timely delivery of a high-quality product that satisfies customers and instills greater confidence in the development team. This process is known as software defect prediction, and it can improve end-product… More >

Jeonghoon Choi¹, Dongjun Suh^1,*, Marc-Oliver Otto²

CMC-Computers, Materials & Continua, Vol.74, No.2, pp. 2945-2966, 2023, DOI:10.32604/cmc.2023.033417 - 31 October 2022

Abstract Recently, machine learning-based technologies have been developed to automate the classification of wafer map defect patterns during semiconductor manufacturing. The existing approaches used in the wafer map pattern classification include directly learning the image through a convolution neural network and applying the ensemble method after extracting image features. This study aims to classify wafer map defects more effectively and derive robust algorithms even for datasets with insufficient defect patterns. First, the number of defects during the actual process may be limited. Therefore, insufficient data are generated using convolutional auto-encoder (CAE), and the expanded data are More >