Singamaneni Krishnapriya^1,2,*, Sukhvinder Singh¹

CMES-Computer Modeling in Engineering & Sciences, Vol.144, No.2, pp. 1849-1881, 2025, DOI:10.32604/cmes.2025.068326 - 31 August 2025

Abstract The explosive expansion of the Internet of Things (IoT) systems has increased the imperative to have strong and robust solutions to cyber Security, especially to curtail Distributed Denial of Service (DDoS) attacks, which can cripple critical infrastructure. The proposed framework presented in the current paper is a new hybrid scheme that induces deep learning-based traffic classification and blockchain-enabled mitigation to make intelligent, decentralized, and real-time DDoS countermeasures in an IoT network. The proposed model fuses the extracted deep features with statistical features and trains them by using traditional machine-learning algorithms, which makes them more accurate… More > Graphic Abstract

Guanhui Li^1,2,#, Jiati Tang^1,#, Qifang Zhang³, Guilin Ou^1,3, Yingchao Lin¹, Liping Chen⁴, Xiang Li⁴, Shengjiang Wu¹, Zhu Ren¹, Zeyu Zhao^1,2, Xuekun Zhang², Benbo Xu^2,*, Xun Liu³, Kesu Wei^1,*

Phyton-International Journal of Experimental Botany, Vol.94, No.8, pp. 2385-2398, 2025, DOI:10.32604/phyton.2025.068213 - 29 August 2025

Abstract To clarify the relationships between the main chemical components in flue-cured tobacco in Guizhou and field meteorological factors during the tobacco growing period, the contributions of meteorological factors to the chemical composition of flue-cured tobacco and related components were explored in this study. The flue-cured tobacco variety Y87 was used as the experimental material, and tobacco samples and meteorological data were collected from seven typical tobacco-growing areas in Guizhou Province. Using a random forest model and canonical correlation analysis, the impact and contribution of the monthly mean temperature, precipitation, and sunshine duration during the field… More >

Brij B. Gupta^1,2,3,4,*, Akshat Gaurav⁵, Wadee Alhalabi⁶, Varsha Arya^7,8, Shavi Bansal^9,10, Ching-Hsien Hsu¹

CMC-Computers, Materials & Continua, Vol.84, No.3, pp. 4755-4772, 2025, DOI:10.32604/cmc.2025.064053 - 30 July 2025

Abstract Detecting cyber attacks in networks connected to the Internet of Things (IoT) is of utmost importance because of the growing vulnerabilities in the smart environment. Conventional models, such as Naive Bayes and support vector machine (SVM), as well as ensemble methods, such as Gradient Boosting and eXtreme gradient boosting (XGBoost), are often plagued by high computational costs, which makes it challenging for them to perform real-time detection. In this regard, we suggested an attack detection approach that integrates Visual Geometry Group 16 (VGG16), Artificial Rabbits Optimizer (ARO), and Random Forest Model to increase detection accuracy… More >

Xiaolong Xiao¹, Xiaoxing Lu^1,*, Ziran Guo¹, Jian Liu¹, Shenglong Wu², Ye Cai²

Energy Engineering, Vol.122, No.8, pp. 3117-3132, 2025, DOI:10.32604/ee.2025.065631 - 24 July 2025

Abstract With the popularization of microgrid construction and the connection of renewable energy sources to the power system, the problem of source and load uncertainty faced by the coordinated operation of multi-microgrid is becoming increasingly prominent, and the accuracy of typical scenario predictions is low. In order to improve the accuracy of scenario prediction under source and load uncertainty, this paper proposes a typical scenario identification model based on random forests and order parameters. Firstly, a method for ordinal parameter identification and quantification is provided for the coordinated operating mode of multi-microgrids, taking into account source-load… More >

Alireza Bahmani^*

Journal on Internet of Things, Vol.7, pp. 1-17, 2025, DOI:10.32604/jiot.2025.060786 - 24 June 2025

Abstract With the development of the Internet of Things (IoT) technology and its widespread integration in various aspects of life, the risks associated with cyberattacks on these systems have increased significantly. Vulnerabilities in IoT devices, stemming from insecure designs and software weaknesses, have made attacks on them more complex and dangerous compared to traditional networks. Conventional intrusion detection systems are not fully capable of identifying and managing these risks in the IoT environment, making research and evaluation of suitable intrusion detection systems for IoT crucial. In this study, deep learning, multi-layer perceptron (MLP), Random Forest (RF),… More >

Kun Li¹, Tianfu Li^2,*, Xiuwei Wang¹, Qingchun Meng¹, Zhenjie Wang¹, Jinyang Luo^1,2, Zhaohui Wang¹, Yuedong Yao²

Energy Engineering, Vol.122, No.6, pp. 2215-2238, 2025, DOI:10.32604/ee.2025.062876 - 29 May 2025

Abstract CO₂ flooding for enhanced oil recovery (EOR) not only enables underground carbon storage but also plays a critical role in tertiary oil recovery. However, its displacement efficiency is constrained by whether CO₂ and crude oil achieve miscibility, necessitating precise prediction of the minimum miscibility pressure (MMP) for CO₂-oil systems. Traditional methods, such as experimental measurements and empirical correlations, face challenges including time-consuming procedures and limited applicability. In contrast, artificial intelligence (AI) algorithms have emerged as superior alternatives due to their efficiency, broad applicability, and high prediction accuracy. This study employs four AI algorithms—Random Forest Regression (RFR), Genetic… More >

Faisal Ghazi Beshaw¹, Thamir Hassan Atyia², Mohd Fadzli Mohd Salleh¹, Mohamad Khairi Ishak³, Abdul Sattar Din^1,*

CMC-Computers, Materials & Continua, Vol.83, No.2, pp. 3553-3583, 2025, DOI:10.32604/cmc.2025.061400 - 16 April 2025

Abstract The significance of precise energy usage forecasts has been highlighted by the increasing need for sustainability and energy efficiency across a range of industries. In order to improve the precision and openness of energy consumption projections, this study investigates the combination of machine learning (ML) methods with Shapley additive explanations (SHAP) values. The study evaluates three distinct models: the first is a Linear Regressor, the second is a Support Vector Regressor, and the third is a Decision Tree Regressor, which was scaled up to a Random Forest Regressor/Additions made were the third one which was… More >

Gökhan Şahin^1,*, Ihsan Levent², Gültekin Işık², Wilfried van Sark¹, Sabir Rustemli³

CMES-Computer Modeling in Engineering & Sciences, Vol.143, No.1, pp. 1215-1248, 2025, DOI:10.32604/cmes.2025.063193 - 11 April 2025

Abstract This research investigates the influence of indoor and outdoor factors on photovoltaic (PV) power generation at Utrecht University to accurately predict PV system performance by identifying critical impact factors and improving renewable energy efficiency. To predict plant efficiency, nineteen variables are analyzed, consisting of nine indoor photovoltaic panel characteristics (Open Circuit Voltage (Voc), Short Circuit Current (Isc), Maximum Power (Pmpp), Maximum Voltage (Umpp), Maximum Current (Impp), Filling Factor (FF), Parallel Resistance (Rp), Series Resistance (Rs), Module Temperature) and ten environmental factors (Air Temperature, Air Humidity, Dew Point, Air Pressure, Irradiation, Irradiation Propagation, Wind Speed, Wind… More >

Chen Chen^1,2, Fengqin Liu¹, Jianan Li², Bo Wang², Yuanming Xiao², Tao Sun¹, Guoying Zhou^2,*

Phyton-International Journal of Experimental Botany, Vol.94, No.3, pp. 973-986, 2025, DOI:10.32604/phyton.2025.062421 - 31 March 2025

Abstract Anisodus tanguticus (Maxim.) Pascher, a medicinal plant in the Solanaceae family, is widely distributed across the Qinghai-Tibet Plateau. Its medicinal properties, particularly the tropane alkaloids, are influenced by various ecological factors, but the underlying ecological mechanisms remain poorly understood. This study aimed to investigate how key environmental variables influence both the morphological traits and tropane alkaloid contents of A. tanguticus, with the goal of providing data to support the sustainable cultivation and management of this species. We collected samples from 71 sites across its natural habitat, analyzing the effects of factors such as soil nutrients, altitude, and… More >

Ravi Veerabhadrappa^*, Poornima Athikatte Sampigerayappa

Computer Systems Science and Engineering, Vol.49, pp. 333-351, 2025, DOI:10.32604/csse.2025.061285 - 19 March 2025

Abstract In recent years, the analysis of encrypted network traffic has gained momentum due to the widespread use of Transport Layer Security and Quick UDP Internet Connections protocols, which complicate and prolong the analysis process. Classification models face challenges in understanding and classifying unknown traffic because of issues related to interpret ability and the representation of traffic data. To tackle these complexities, multi-modal representation learning can be employed to extract meaningful features and represent them in a lower-dimensional latent space. Recently, auto-encoder-based multi-modal representation techniques have shown superior performance in representing network traffic. By combining the… More >