Manzheng Yuan^1,2, Kai Yang^2,*

Journal of Cyber Security, Vol.7, pp. 197-219, 2025, DOI:10.32604/jcs.2025.066089 - 07 July 2025

Abstract Currently, network intrusion detection systems (NIDS) face significant challenges in feature redundancy and high computational complexity, which hinder the improvement of detection performance and significantly reduce operational efficiency. To address these issues, this paper proposes an innovative weighted feature selection method combining mutual information and Extreme Gradient Boosting (XGBoost). This method aims to leverage their strengths to identify crucial feature subsets for intrusion detection accurately. Specifically, it first calculates the mutual information scores between features and target variables to evaluate individual discriminatory capabilities of features and uses XGBoost to obtain feature importance scores reflecting their… More >

Pooja Bidwai^1,2, Shilpa Gite^1,3, Biswajeet Pradhan^4,*, Abdullah Almari⁵

CMC-Computers, Materials & Continua, Vol.84, No.2, pp. 2645-2676, 2025, DOI:10.32604/cmc.2025.061018 - 03 July 2025

Abstract Diabetic Retinopathy (DR) is a critical disorder that affects the retina due to the constant rise in diabetics and remains the major cause of blindness across the world. Early detection and timely treatment are essential to mitigate the effects of DR, such as retinal damage and vision impairment. Several conventional approaches have been proposed to detect DR early and accurately, but they are limited by data imbalance, interpretability, overfitting, convergence time, and other issues. To address these drawbacks and improve DR detection accurately, a distributed Explainable Convolutional Neural network-enabled Light Gradient Boosting Machine (DE-ExLNN) is… More >

Mustafa Turkyilmazoglu^1,2,*, Abdulaziz Alotaibi³

CMES-Computer Modeling in Engineering & Sciences, Vol.143, No.3, pp. 2847-2865, 2025, DOI:10.32604/cmes.2025.068031 - 30 June 2025

Abstract The variable salinity in stored reservoirs connected by a long channel attracts the attention of scientists worldwide, having applications in environmental and geophysical engineering. This study explores the impact of Navier slip conditions on exchange flows within a long channel connecting two large reservoirs of differing salinity. These horizontal density gradients drive the flow. We modify the recent one-dimensional theory, developed to avoid runaway stratification, to account for the presence of uniform slip walls. By adjusting the parameters of the horizontal density gradient based on the slip factor, we resolve analytically various flow regimes ranging… 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 >

Shuqin Zhang¹, Zihao Wang^1,*, Xinyu Su²

CMC-Computers, Materials & Continua, Vol.83, No.3, pp. 5781-5809, 2025, DOI:10.32604/cmc.2025.062080 - 19 May 2025

Abstract The methods of network attacks have become increasingly sophisticated, rendering traditional cybersecurity defense mechanisms insufficient to address novel and complex threats effectively. In recent years, artificial intelligence has achieved significant progress in the field of network security. However, many challenges and issues remain, particularly regarding the interpretability of deep learning and ensemble learning algorithms. To address the challenge of enhancing the interpretability of network attack prediction models, this paper proposes a method that combines Light Gradient Boosting Machine (LGBM) and SHapley Additive exPlanations (SHAP). LGBM is employed to model anomalous fluctuations in various network indicators,… More >

Yiming Rongyang¹, Chengyu Ji¹, Xiangdong Ding^2,*, Jun Gao¹, Jianjian Wei^2,3

Frontiers in Heat and Mass Transfer, Vol.23, No.2, pp. 685-701, 2025, DOI:10.32604/fhmt.2025.063785 - 25 April 2025

Abstract The effect of gradient exhaust strategy and blind plate installation on the inhibition of backflow and thermal stratification in data center cabinets is systematically investigated in this study through numerical methods. The validated Re-Normalization Group (RNG) k-ε turbulence model was used to analyze airflow patterns within cabinet structures equipped with backplane air conditioning. Key findings reveal that server-generated thermal plumes induce hot air accumulation at the cabinet apex, creating a 0.8°C temperature elevation at the top server’s inlet compared to the ideal situation (23°C). Strategic increases in backplane fan exhaust airflow rates reduce server 1’s inlet… More >

Hao Li¹, Cunlu Zhao², Jinhui Zhou¹, Jun Zhang³, Hui Wang¹, Yanmei Jiao^1,*, Yugang Zhao^4,5,*

Frontiers in Heat and Mass Transfer, Vol.23, No.2, pp. 507-528, 2025, DOI:10.32604/fhmt.2025.063359 - 25 April 2025

Abstract Based on the rapid advancements in nanomaterials and nanotechnology, the Nanofluidic Reverse Electrodialysis (NRED) has attracted significant attention as an innovative and promising energy conversion strategy for extracting sustainable and clean energy from the salinity gradient energy. However, the scarcity of research investigating the intricate multi-factor coupling effects on the energy conversion performance, especially the trade-offs between ion selectivity and mass transfer in nanochannels, of NRED poses a great challenge to achieving breakthroughs in energy conversion processes. This numerical study innovatively investigates the multi-factor coupling effect of three critical operational factors, including the nanochannel configuration,… More >

Bo Yu^1,2, Yuye Luo³, Luyao Guo⁴, Long Huang^4,*

Frontiers in Heat and Mass Transfer, Vol.23, No.2, pp. 553-575, 2025, DOI:10.32604/fhmt.2025.062851 - 25 April 2025

Abstract This study investigates the flow boiling heat transfer coefficient and pressure gradient of refrigerant R410A in micro-channel flat tubes. Experiments were conducted at saturation temperatures ranging from 25°C to 30°C, mass fluxes between 198 and 305 kg/m²s, and heat fluxes from 9.77 to 20.18 kW/m², yielding 99 sets of local heat transfer coefficient data. The results show that increasing heat flux and mass flux enhances the heat transfer coefficient, although the rate of enhancement decreases with increasing vapor quality. Conversely, higher saturation temperatures slightly reduce the heat transfer coefficient. Additionally, the experimental findings reveal discrepancies in More >

Mohammed Abdelmajeed^1,*, Jiangbin Zheng¹, Ahmed Murtadha¹, Youcef Nafa¹, Mohammed Abaker², Muhammad Pervez Akhter³

CMC-Computers, Materials & Continua, Vol.83, No.2, pp. 3471-3491, 2025, DOI:10.32604/cmc.2025.059870 - 16 April 2025

Abstract Arabic Dialect Identification (DID) is a task in Natural Language Processing (NLP) that involves determining the dialect of a given piece of text in Arabic. The state-of-the-art solutions for DID are built on various deep neural networks that commonly learn the representation of sentences in response to a given dialect. Despite the effectiveness of these solutions, the performance heavily relies on the amount of labeled examples, which is labor-intensive to attain and may not be readily available in real-world scenarios. To alleviate the burden of labeling data, this paper introduces a novel solution that leverages… More >

Rana Muhammad Adnan^1,2, Mo Wang^1,*, Adil Masood³, Ozgur Kisi^4,5,6,*, Shamsuddin Shahid⁷, Mohammad Zounemat-Kermani⁸

CMES-Computer Modeling in Engineering & Sciences, Vol.143, No.1, pp. 1249-1272, 2025, DOI:10.32604/cmes.2025.062339 - 11 April 2025

Abstract Accurate daily suspended sediment load (SSL) prediction is essential for sustainable water resource management, sediment control, and environmental planning. However, SSL prediction is highly complex due to its nonlinear and dynamic nature, making traditional empirical models inadequate. This study proposes a novel hybrid approach, integrating the Adaptive Neuro-Fuzzy Inference System (ANFIS) with the Gradient-Based Optimizer (GBO), to enhance SSL forecasting accuracy. The research compares the performance of ANFIS-GBO with three alternative models: standard ANFIS, ANFIS with Particle Swarm Optimization (ANFIS-PSO), and ANFIS with Grey Wolf Optimization (ANFIS-GWO). Historical SSL and streamflow data from the Bailong… More >