Abeer Alnuaim^*

CMC-Computers, Materials & Continua, Vol.86, No.1, pp. 1-33, 2026, DOI:10.32604/cmc.2025.069110 - 10 November 2025

Abstract The rapid digitalization of urban infrastructure has made smart cities increasingly vulnerable to sophisticated cyber threats. In the evolving landscape of cybersecurity, the efficacy of Intrusion Detection Systems (IDS) is increasingly measured by technical performance, operational usability, and adaptability. This study introduces and rigorously evaluates a Human-Computer Interaction (HCI)-Integrated IDS with the utilization of Convolutional Neural Network (CNN), CNN-Long Short Term Memory (LSTM), and Random Forest (RF) against both a Baseline Machine Learning (ML) and a Traditional IDS model, through an extensive experimental framework encompassing many performance metrics, including detection latency, accuracy, alert prioritization, classification… More >

Ihsan Qadir¹, Usama Naeem², Ahmed Nouman³, Aamir Raza⁴, Jun Wu^1,*

Revue Internationale de Géomatique, Vol.34, pp. 831-853, 2025, DOI:10.32604/rig.2025.069020 - 10 November 2025

Abstract The Jhelum River Basin in Pakistan has experienced recurrent and severe flooding over the past several decades, leading to substantial economic losses, infrastructure damage, and socio-environmental disruptions. This study uses multi-temporal satellite remote sensing data with historical hydrological records to map the spatial and temporal dynamics of major flood events occurring between 1988 and 2019. By utilizing satellite imagery from Landsat 5, Landsat 8, and Sentinel-2, key flood events were analyzed through the application of water indices such as the Normalized Difference Water Index (NDWI) and the Modified NDWI (MNDWI) to delineate flood extents. Historical… More >

Shohreh Moradpour^1,#, Shuai Zhao^2,#, Mojgan Entezari¹, Shamsollah Ayoubi^3,*, Seyed Roohollah Mousavi⁴

Revue Internationale de Géomatique, Vol.34, pp. 809-829, 2025, DOI:10.32604/rig.2025.069538 - 06 November 2025

Abstract Accurate spatial prediction of soil organic carbon (SOC) and soil inorganic carbon (SIC) is vital for land management decisions. This study targets SOC/SIC mapping challenges at the watershed scale in central Iran by addressing environmental heterogeneity through a random forest (RF) model combined with bootstrapping to assess prediction uncertainty. Thirty-eight environmental variables—categorized into climatic, soil physicochemical, topographic, geomorphic, and remote sensing (RS)-based factors—were considered. Variable importance analysis (via) and partial dependence plots (PDP) identified land use, RS indices, and topography as key predictors of SOC. For SIC, soil reflectance (Bands 5 and 7, ETM+), topography, More > Graphic Abstract

Ao Zhang¹, Hongzhen Xu^1,*, Ruxin Liu²

CMC-Computers, Materials & Continua, Vol.85, No.3, pp. 5159-5181, 2025, DOI:10.32604/cmc.2025.067241 - 23 October 2025

Abstract Credit card fraud is one of the primary sources of operational risk in banks, and accurate prediction of fraudulent credit card transactions is essential to minimize banks’ economic losses. Two key issues are faced in credit card fraud detection research, i.e., data category imbalance and data drift. However, the oversampling algorithm used in current research suffers from excessive noise, and the Long Short-Term Memory Network (LSTM) based temporal model suffers from gradient dispersion, which can lead to loss of model performance. To address the above problems, a credit card fraud detection method based on Random… More >

Chih-Yu Liu^1,2, Cheng-Yu Ku^1,2,*, Ming-Han Tsai¹, Jia-Yi You³

CMES-Computer Modeling in Engineering & Sciences, Vol.144, No.3, pp. 2969-2990, 2025, DOI:10.32604/cmes.2025.070663 - 30 September 2025

Abstract Amid growing typhoon risks driven by climate change with projected shifts in precipitation intensity and temperature patterns, Taiwan faces increasing challenges in flood risk. In response, this study proposes a geographic information system (GIS)-based artificial intelligence (AI) model to assess flood susceptibility in Keelung City, integrating geospatial and hydrometeorological data collected during Typhoon Krathon (2024). The model employs the random forest (RF) algorithm, using seven environmental variables excluding average elevation, slope, topographic wetness index (TWI), frequency of cumulative rainfall threshold exceedance, normalized difference vegetation index (NDVI), flow accumulation, and drainage density, with the number of… More >

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 >