Abdallah Ghourabi^*

CMC-Computers, Materials & Continua, Vol.80, No.2, pp. 2743-2760, 2024, DOI:10.32604/cmc.2024.053163 - 15 August 2024

Abstract The dominance of Android in the global mobile market and the open development characteristics of this platform have resulted in a significant increase in malware. These malicious applications have become a serious concern to the security of Android systems. To address this problem, researchers have proposed several machine-learning models to detect and classify Android malware based on analyzing features extracted from Android samples. However, most existing studies have focused on the classification task and overlooked the feature selection process, which is crucial to reduce the training time and maintain or improve the classification results. The… More >

Naif Al Mudawi^*

CMC-Computers, Materials & Continua, Vol.79, No.3, pp. 4945-4962, 2024, DOI:10.32604/cmc.2024.048967 - 20 June 2024

Abstract Parkinson’s disease (PD) is a chronic neurological condition that progresses over time. People start to have trouble speaking, writing, walking, or performing other basic skills as dopamine-generating neurons in some brain regions are injured or die. The patient’s symptoms become more severe due to the worsening of their signs over time. In this study, we applied state-of-the-art machine learning algorithms to diagnose Parkinson’s disease and identify related risk factors. The research worked on the publicly available dataset on PD, and the dataset consists of a set of significant characteristics of PD. We aim to apply… More >

Kai Wang¹, Biao He^2,*, Pijush Samui³, Jian Zhou⁴

CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.1, pp. 229-253, 2024, DOI:10.32604/cmes.2024.047569 - 16 April 2024

Abstract Rock bursts represent a formidable challenge in underground engineering, posing substantial risks to both infrastructure and human safety. These sudden and violent failures of rock masses are characterized by the rapid release of accumulated stress within the rock, leading to severe seismic events and structural damage. Therefore, the development of reliable prediction models for rock bursts is paramount to mitigating these hazards. This study aims to propose a tree-based model—a Light Gradient Boosting Machine (LightGBM)—to predict the intensity of rock bursts in underground engineering. 322 actual rock burst cases are collected to constitute an exhaustive… More >

Turke Althobaiti^1,2, Yousef Sanjalawe^3,*, Naeem Ramzan⁴

Computer Systems Science and Engineering, Vol.47, No.1, pp. 453-469, 2023, DOI:10.32604/csse.2023.039207 - 26 May 2023

Abstract Flash Crowd attacks are a form of Distributed Denial of Service (DDoS) attack that is becoming increasingly difficult to detect due to its ability to imitate normal user behavior in Cloud Computing (CC). Botnets are often used by attackers to perform a wide range of DDoS attacks. With advancements in technology, bots are now able to simulate DDoS attacks as flash crowd events, making them difficult to detect. When it comes to application layer DDoS attacks, the Flash Crowd attack that occurs during a Flash Event is viewed as the most intricate issue. This is… More >

Sesikala Bapatla¹, J. Harikiran^2,*

Computer Systems Science and Engineering, Vol.46, No.1, pp. 597-617, 2023, DOI:10.32604/csse.2023.034998 - 20 January 2023

Abstract Diabetes problems can lead to an eye disease called Diabetic Retinopathy (DR), which permanently damages the blood vessels in the retina. If not treated early, DR becomes a significant reason for blindness. To identify the DR and determine the stages, medical tests are very labor-intensive, expensive, and time-consuming. To address the issue, a hybrid deep and machine learning technique-based autonomous diagnostic system is provided in this paper. Our proposal is based on lesion segmentation of the fundus images based on the LuNet network. Then a Refined Attention Pyramid Network (RAPNet) is used for extracting global… More >

Abdulaziz Almazyad, Laila Halman, Alaa Alsaeed^*

CMC-Computers, Materials & Continua, Vol.74, No.3, pp. 4769-4784, 2023, DOI:10.32604/cmc.2023.033382 - 28 December 2022

Abstract The novel Software Defined Networking (SDN) architecture potentially resolves specific challenges arising from rapid internet growth of and the static nature of conventional networks to manage organizational business requirements with distinctive features. Nevertheless, such benefits lead to a more adverse environment entailing network breakdown, systems paralysis, and online banking fraudulence and robbery. As one of the most common and dangerous threats in SDN, probe attack occurs when the attacker scans SDN devices to collect the necessary knowledge on system susceptibilities, which is then manipulated to undermine the entire system. Precision, high performance, and real-time systems… More >

Elias Hossain¹, Mohammed Alshehri², Sultan Almakdi^2,*, Hanan Halawani², Md. Mizanur Rahman³, Wahidur Rahman⁴, Sabila Al Jannat⁵, Nadim Kaysar⁶, Shishir Mia⁴

CMC-Computers, Materials & Continua, Vol.72, No.1, pp. 1713-1746, 2022, DOI:10.32604/cmc.2022.024822 - 24 February 2022

Abstract Diabetes Mellitus is one of the most severe diseases, and many studies have been conducted to anticipate diabetes. This research aimed to develop an intelligent mobile application based on machine learning to determine the diabetic, pre-diabetic, or non-diabetic without the assistance of any physician or medical tests. This study's methodology was classified into two the Diabetes Prediction Approach and the Proposed System Architecture Design. The Diabetes Prediction Approach uses a novel approach, Light Gradient Boosting Machine (LightGBM), to ensure a faster diagnosis. The Proposed System Architecture Design has been combined into seven modules; the Answering… More >

Shuqiang Guo^{1, *}, Baohai Yue¹, Manyang Gao², Xinxin Zhou¹, Bo Wang³

CMC-Computers, Materials & Continua, Vol.64, No.1, pp. 233-251, 2020, DOI:10.32604/cmc.2020.010039 - 20 May 2020

Abstract The recycling of glass bottles can reduce the consumption of resources and contribute to environmental protection. At present, the classification of recycled glass bottles is difficult due to the many differences in specifications and models. This paper proposes a classification algorithm for glass bottles that is divided into two stages, namely the extraction of candidate regions and the classification of classifiers. In the candidate region extraction stage, aiming at the problem of the large time overhead caused by the use of the SIFT (scale-invariant feature transform) descriptor in SS (selective search), an improved feature of… More >

Yong Fang¹, Yunyun Zhang², Cheng Huang^1,*

CMC-Computers, Materials & Continua, Vol.61, No.1, pp. 185-195, 2019, DOI:10.32604/cmc.2019.06144

Abstract In recent years, the rapid development of e-commerce exposes great vulnerabilities in online transactions for fraudsters to exploit. Credit card transactions take a salient role in nowadays’ online transactions for its obvious advantages including discounts and earning credit card points. So credit card fraudulence has become a target of concern. In order to deal with the situation, credit card fraud detection based on machine learning is been studied recently. Yet, it is difficult to detect fraudulent transactions due to data imbalance (normal and fraudulent transactions), for which Smote algorithm is proposed in order to resolve… More >