Mohammad Al-Omari¹, Qasem Abu Al-Haija^2,*

Computer Systems Science and Engineering, Vol.48, No.6, pp. 1537-1555, 2024, DOI:10.32604/csse.2024.056257 - 22 November 2024

Abstract More businesses are deploying powerful Intrusion Detection Systems (IDS) to secure their data and physical assets. Improved cyber-attack detection and prevention in these systems requires machine learning (ML) approaches. This paper examines a cyber-attack prediction system combining feature selection (FS) and ML. Our technique’s foundation was based on Correlation Analysis (CA), Mutual Information (MI), and recursive feature reduction with cross-validation. To optimize the IDS performance, the security features must be carefully selected from multiple-dimensional datasets, and our hybrid FS technique must be extended to validate our methodology using the improved UNSW-NB 15 and TON_IoT datasets. More >

Mohammed A. Alhartomi^1,*, Mohammad F. L. Abdullah², Wafi A. B. Mabrouk², Mohammed S. M. Gismalla³, Ahmed Alzahmi¹, Saeed Alzahrani¹, Mohammad R. Altimania¹, Mohammed S. Alsawat⁴

CMES-Computer Modeling in Engineering & Sciences, Vol.141, No.3, pp. 2087-2105, 2024, DOI:10.32604/cmes.2024.055679 - 31 October 2024

Abstract The demand for broadband data services on high-speed trains is rapidly growing as more people commute between their homes and workplaces. However, current radio frequency (RF) technology cannot adequately meet this demand. In order to address the bandwidth constraint, a technique known as free space optics (FSO) has been proposed. This paper presents a mathematical derivation and formulation of curve track G2T-FSO (Ground-to-train Free Space Optical) model, where the track radius characteristics is 2667 m, divergence angle track is 1.5° for train velocity at V = 250 km/h. Multiple transmitter configurations are proposed to maximize More >

Dattu Ghane^*, Vishnu Wakchaure

Energy Engineering, Vol.121, No.11, pp. 3199-3220, 2024, DOI:10.32604/ee.2024.050952 - 21 October 2024

Abstract Wind energy provides a sustainable solution to the ever-increasing demand for energy. Micro-wind turbines offer a promising solution for low-wind speed, decentralized power generation in urban and remote areas. Earlier researchers have explored the design, development, and performance analysis of a micro-wind turbine system tailored for small-scale renewable energy generation. Researchers have investigated various aspects such as aerodynamic considerations, structural integrity, efficiency optimization to ensure reliable and cost-effective operation, blade design, generator selection, and control strategies to enhance the overall performance of the system. The objective of this paper is to provide a comprehensive design… More >

Tajim Md. Niamat Ullah Akhund^1,2,*, Waleed M. Al-Nuwaiser³

CMC-Computers, Materials & Continua, Vol.80, No.3, pp. 3485-3506, 2024, DOI:10.32604/cmc.2024.054222 - 12 September 2024

Abstract This study explores the impact of hyperparameter optimization on machine learning models for predicting cardiovascular disease using data from an IoST (Internet of Sensing Things) device. Ten distinct machine learning approaches were implemented and systematically evaluated before and after hyperparameter tuning. Significant improvements were observed across various models, with SVM and Neural Networks consistently showing enhanced performance metrics such as F1-Score, recall, and precision. The study underscores the critical role of tailored hyperparameter tuning in optimizing these models, revealing diverse outcomes among algorithms. Decision Trees and Random Forests exhibited stable performance throughout the evaluation. While More >

Abdul Sattar Laghari¹, Mohammad Waqas Chandio¹, Laveet Kumar^2,*, Mamdouh El Haj Assad³

Energy Engineering, Vol.121, No.8, pp. 2023-2038, 2024, DOI:10.32604/ee.2024.051082 - 19 July 2024

Abstract The selection of working fluid significantly impacts the geothermal ORC’s Efficiency. Using a mixture as a working fluid is a strategy to improve the output of geothermal ORC. In the current study, modelling and thermodynamic analysis of ORC, using geothermal as a heat source, is carried out at fixed operating conditions. The model is simulated in the Engineering Equation Solver (EES). An environment-friendly mixture of fluids, i.e., R245fa/R600a, with a suitable mole fraction, is used as the operating fluid. The mixture provided the most convenient results compared to the pure working fluid under fixed operating More >

Haoping Yao¹, Xinyu Zhong², Chunxia He^1,*

Journal of Renewable Materials, Vol.12, No.5, pp. 993-1006, 2024, DOI:10.32604/jrm.2024.047758 - 17 July 2024

Abstract To make full use of plant shell fibers (rice husk, walnut shell, chestnut shell), three kinds of wood-plastic composites of plant shell fibers and polyvinyl chloride (PVC) were prepared. X-ray diffraction analysis was carried out on three kinds of plant shell fibers to test their crystallinity. The aging process of the composites was conducted under 2 different conditions. One was artificial seawater immersion and xenon lamp irradiation, and the other one was deionized water spray and xenon lamp irradiation. The mechanical properties (tensile strength, flexural strength, impact strength), changes in color, water absorption, Fourier transform… More >

Xiaofeng Yang^1,3,4, Wei Wang^1,3,4,*, Yujie Shen^2,4, Changning Liu^1,3,4, Tianyi Zhang^1,4

CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.2, pp. 1485-1506, 2024, DOI:10.32604/cmes.2024.049837 - 20 May 2024

Abstract This paper addresses the impact of vertical vibration negative effects, unbalanced radial forces generated by the static eccentricity of the hub motor, and road excitation on the suspension performance of Hub Motor Driven Vehicle (HMDV). A dynamic inertial suspension based on Active Disturbance Rejection Control (ADRC) is proposed, combining the vertical dynamic characteristics of dynamic inertial suspension with the features of ADRC, which distinguishes between internal and external disturbances and arranges the transition process. Firstly, a simulation model of the static eccentricity of the hub motor is established to simulate the unbalanced radial electromagnetic force… More > Graphic Abstract

Adil Hussain^1,*, Ayesha Aslam²

Journal on Artificial Intelligence, Vol.6, pp. 129-152, 2024, DOI:10.32604/jai.2024.050277 - 21 May 2024

Abstract The diagnosis and prognosis of cardiovascular diseases are critical medical responsibilities that assist cardiologists in correctly classifying patients and treating them accordingly. The utilization of machine learning in the medical domain has witnessed a notable surge due to its ability to discern patterns from vast amounts of data. Machine learning algorithms that can categorize cases of cardiovascular illness may help doctors reduce the number of wrong diagnoses. This research investigates the efficacy of different machine learning algorithms in predicting cardiovascular disease in accordance with risk factors. This study utilizes a variety of machine learning models, More >

Amal Hkiri^1,*, Mouna Karmani¹, Omar Ben Bahri², Ahmed Mohammed Murayr², Fawaz Hassan Alasmari², Mohsen Machhout¹

CMC-Computers, Materials & Continua, Vol.78, No.1, pp. 227-247, 2024, DOI:10.32604/cmc.2023.047087 - 30 January 2024

Abstract The RPL (IPv6 Routing Protocol for Low-Power and Lossy Networks) protocol is essential for efficient communication within the Internet of Things (IoT) ecosystem. Despite its significance, RPL’s susceptibility to attacks remains a concern. This paper presents a comprehensive simulation-based analysis of the RPL protocol’s vulnerability to the decreased rank attack in both static and mobile network environments. We employ the Random Direction Mobility Model (RDM) for mobile scenarios within the Cooja simulator. Our systematic evaluation focuses on critical performance metrics, including Packet Delivery Ratio (PDR), Average End to End Delay (AE2ED), throughput, Expected Transmission Count More >

Ayesha Aslam¹, Adil Hussain^2,*

Journal on Artificial Intelligence, Vol.6, pp. 1-21, 2024, DOI:10.32604/jai.2024.047226 - 31 January 2024

Abstract With the increased accessibility of global trade information, transaction fraud has become a major worry in global banking and commerce security. The incidence and magnitude of transaction fraud are increasing daily, resulting in significant financial losses for both customers and financial professionals. With improvements in data mining and machine learning in computer science, the capacity to detect transaction fraud is becoming increasingly attainable. The primary goal of this research is to undertake a comparative examination of cutting-edge machine-learning algorithms developed to detect credit card fraud. The research looks at the efficacy of these machine learning… More >