Ana Hermosilla^1,2,*, Jorge Gallego-Madrid¹, Pedro Martinez-Julia³, Jordi Ortiz⁴, Ved P. Kafle³, Antonio Skarmeta^1,2

CMES-Computer Modeling in Engineering & Sciences, Vol.141, No.2, pp. 1447-1471, 2024, DOI:10.32604/cmes.2024.053379 - 27 September 2024

Abstract As 5th Generation (5G) and Beyond 5G (B5G) networks become increasingly prevalent, ensuring not only network security but also the security and reliability of the applications, the so-called network applications, becomes of paramount importance. This paper introduces a novel integrated model architecture, combining a network application validation framework with an AI-driven reactive system to enhance security in real-time. The proposed model leverages machine learning (ML) and artificial intelligence (AI) to dynamically monitor and respond to security threats, effectively mitigating potential risks before they impact the network infrastructure. This dual approach not only validates the functionality… More >

P. K. A. Chitra¹, S. Appavu alias Balamurugan², S. Geetha³, Seifedine Kadry^4,5,6, Jungeun Kim^7,*, Keejun Han⁸

Computer Systems Science and Engineering, Vol.48, No.5, pp. 1367-1385, 2024, DOI:10.32604/csse.2023.034373 - 13 September 2024

Abstract A generalization of supervised single-label learning based on the assumption that each sample in a dataset may belong to more than one class simultaneously is called multi-label learning. The main objective of this work is to create a novel framework for learning and classifying imbalanced multi-label data. This work proposes a framework of two phases. The imbalanced distribution of the multi-label dataset is addressed through the proposed Borderline MLSMOTE resampling method in phase 1. Later, an adaptive weighted l₂₁ norm regularized (Elastic-net) multi-label logistic regression is used to predict unseen samples in phase 2. The proposed… More >

Mahmoud E. Farfoura^1,*, Ahmad Alkhatib¹, Deema Mohammed Alsekait^2,*, Mohammad Alshinwan^3,7, Sahar A. El-Rahman⁴, Didi Rosiyadi⁵, Diaa Salama AbdElminaam^6,7

CMC-Computers, Materials & Continua, Vol.80, No.3, pp. 4833-4857, 2024, DOI:10.32604/cmc.2024.054849 - 12 September 2024

Abstract The article describes a new method for malware classification, based on a Machine Learning (ML) model architecture specifically designed for malware detection, enabling real-time and accurate malware identification. Using an innovative feature dimensionality reduction technique called the Interpolation-based Feature Dimensionality Reduction Technique (IFDRT), the authors have significantly reduced the feature space while retaining critical information necessary for malware classification. This technique optimizes the model’s performance and reduces computational requirements. The proposed method is demonstrated by applying it to the BODMAS malware dataset, which contains 57,293 malware samples and 77,142 benign samples, each with a 2381-feature… More >

Amna Khatoon¹, Weixing Wang^1,*, Asad Ullah², Limin Li^3,*, Mengfei Wang¹

CMC-Computers, Materials & Continua, Vol.80, No.1, pp. 527-546, 2024, DOI:10.32604/cmc.2024.051147 - 18 July 2024

Abstract Integrating Tiny Machine Learning (TinyML) with edge computing in remotely sensed images enhances the capabilities of road anomaly detection on a broader level. Constrained devices efficiently implement a Binary Neural Network (BNN) for road feature extraction, utilizing quantization and compression through a pruning strategy. The modifications resulted in a 28-fold decrease in memory usage and a 25% enhancement in inference speed while only experiencing a 2.5% decrease in accuracy. It showcases its superiority over conventional detection algorithms in different road image scenarios. Although constrained by computer resources and training datasets, our results indicate opportunities for More >

Xiang Kan¹, Yan Li², Tian Li^1,*, Jiye Zhang¹

FDMP-Fluid Dynamics & Materials Processing, Vol.20, No.5, pp. 1075-1091, 2024, DOI:10.32604/fdmp.2023.044050 - 07 June 2024

Abstract A pantograph serves as a vital device for the collection of electricity in trains. However, its aerodynamic resistance can limit the train’s running speed. As installing fairings around the pantograph is known to effectively reduce the resistance, in this study, different fairing lengths are considered and the related aerodynamic performances of pantograph are assessed. In particular, this is accomplished through numerical simulations based on the k-ω Shear Stress Transport (SST) two-equation turbulence model. The results indicate that the fairing diminishes the direct impact of high-speed airflow on the pantograph, thereby reducing its aerodynamic resistance. However, it More >

Yasir Maqsood¹, Syed Muhammad Usman^1,*, Musaed Alhussein², Khursheed Aurangzeb^2,*, Shehzad Khalid³, Muhammad Zubair⁴

CMC-Computers, Materials & Continua, Vol.79, No.2, pp. 2795-2811, 2024, DOI:10.32604/cmc.2024.049410 - 15 May 2024

Abstract Wheat is a critical crop, extensively consumed worldwide, and its production enhancement is essential to meet escalating demand. The presence of diseases like stem rust, leaf rust, yellow rust, and tan spot significantly diminishes wheat yield, making the early and precise identification of these diseases vital for effective disease management. With advancements in deep learning algorithms, researchers have proposed many methods for the automated detection of disease pathogens; however, accurately detecting multiple disease pathogens simultaneously remains a challenge. This challenge arises due to the scarcity of RGB images for multiple diseases, class imbalance in existing… More >

Saeed Seraj¹, Ali Safaa Sadiq^1,*, Omprakash Kaiwartya¹, Mohammad Aljaidi², Alexandros Konios¹, Mohammed Ali³, Mohammed Abazeed³

CMC-Computers, Materials & Continua, Vol.79, No.2, pp. 2123-2149, 2024, DOI:10.32604/cmc.2024.048914 - 15 May 2024

Abstract Mobile banking security has witnessed significant R&D attention from both financial institutions and academia. This is due to the growing number of mobile baking applications and their reachability and usefulness to society. However, these applications are also attractive prey for cybercriminals, who use a variety of malware to steal personal banking information. Related literature in mobile banking security requires many permissions that are not necessary for the application’s intended security functionality. In this context, this paper presents a novel efficient permission identification approach for securing mobile banking (MoBShield) to detect and prevent malware. A permission-based… More >

Shalu¹, Twinkle Acharya¹, Dhwanilnath Gharekhan^1,*, Dipak Samal²

Revue Internationale de Géomatique, Vol.33, pp. 111-134, 2024, DOI:10.32604/rig.2024.051788 - 15 May 2024

Abstract Seismic vulnerability modeling plays a crucial role in seismic risk assessment, aiding decision-makers in pinpointing areas and structures most prone to earthquake damage. While machine learning (ML) algorithms and Geographic Information Systems (GIS) have emerged as promising tools for seismic vulnerability modeling, there remains a notable gap in comprehensive geospatial studies focused on India. Previous studies in seismic vulnerability modeling have primarily focused on specific regions or countries, often overlooking the unique challenges and characteristics of India. In this study, we introduce a novel approach to seismic vulnerability modeling, leveraging ML and GIS to address… More >

Sapiah Sakri¹, Shakila Basheer¹, Zuhaira Muhammad Zain¹, Nurul Halimatul Asmak Ismail^2,*, Dua’ Abdellatef Nassar¹, Manal Abdullah Alohali¹, Mais Ayman Alharaki¹

CMC-Computers, Materials & Continua, Vol.79, No.1, pp. 1157-1185, 2024, DOI:10.32604/cmc.2024.048051 - 25 April 2024

Abstract Background: Sepsis, a potentially fatal inflammatory disease triggered by infection, carries significant health implications worldwide. Timely detection is crucial as sepsis can rapidly escalate if left undetected. Recent advancements in deep learning (DL) offer powerful tools to address this challenge. Aim: Thus, this study proposed a hybrid CNNBDLSTM, a combination of a convolutional neural network (CNN) with a bi-directional long short-term memory (BDLSTM) model to predict sepsis onset. Implementing the proposed model provides a robust framework that capitalizes on the complementary strengths of both architectures, resulting in more accurate and timelier predictions. Method: The sepsis prediction… More >

Shabana Ramzan¹, Yazeed Yasin Ghadi², Hanan Aljuaid³, Aqsa Mahmood^1,*, Basharat Ali⁴

CMC-Computers, Materials & Continua, Vol.79, No.1, pp. 183-199, 2024, DOI:10.32604/cmc.2024.047603 - 25 April 2024

Abstract Traditional farming procedures are time-consuming and expensive as based on manual labor. Farmers have no proper knowledge to select which crop is suitable to grow according to the environmental factors and soil characteristics. This is the main reason for the low yield of crops and the economic crisis in the agricultural sector of the different countries. The use of modern technologies such as the Internet of Things (IoT), machine learning, and ensemble learning can facilitate farmers to observe different factors such as soil electrical conductivity (EC), and environmental factors like temperature to improve crop yield.… More >