Min-Gyu Kim¹, Hwankuk Kim^2,*

CMES-Computer Modeling in Engineering & Sciences, Vol.143, No.2, pp. 2391-2415, 2025, DOI:10.32604/cmes.2025.063558 - 30 May 2025

Abstract This study proposes an efficient traffic classification model to address the growing threat of distributed denial-of-service (DDoS) attacks in 5th generation technology standard (5G) slicing networks. The proposed method utilizes an ensemble of encoder components from multiple autoencoders to compress and extract latent representations from high-dimensional traffic data. These representations are then used as input for a support vector machine (SVM)-based metadata classifier, enabling precise detection of attack traffic. This architecture is designed to achieve both high detection accuracy and training efficiency, while adapting flexibly to the diverse service requirements and complexity of 5G network… More >

Chiara Innocente^1,*, Matteo Boemio², Gianmarco Lorenzetti², Ilaria Pulito², Diego Romagnoli², Valeria Saponaro², Giorgia Marullo¹, Luca Ulrich¹, Enrico Vezzetti¹

CMES-Computer Modeling in Engineering & Sciences, Vol.143, No.2, pp. 1355-1379, 2025, DOI:10.32604/cmes.2025.063186 - 30 May 2025

Abstract Lip-reading technology, based on visual speech decoding and automatic speech recognition, offers a promising solution to overcoming communication barriers, particularly for individuals with temporary or permanent speech impairments. However, most Visual Speech Recognition (VSR) research has primarily focused on the English language and general-purpose applications, limiting its practical applicability in medical and rehabilitative settings. This study introduces the first Deep Learning (DL) based lip-reading system for the Italian language designed to assist individuals with vocal cord pathologies in daily interactions, facilitating communication for patients recovering from vocal cord surgeries, whether temporarily or permanently impaired. To… More >

Sangmin Kim¹, Byeongcheon Lee¹, Muazzam Maqsood², Jihoon Moon^3,*, Seungmin Rho^4,*

CMES-Computer Modeling in Engineering & Sciences, Vol.143, No.2, pp. 2079-2108, 2025, DOI:10.32604/cmes.2025.061653 - 30 May 2025

Abstract The increased accessibility of social networking services (SNSs) has facilitated communication and information sharing among users. However, it has also heightened concerns about digital safety, particularly for children and adolescents who are increasingly exposed to online grooming crimes. Early and accurate identification of grooming conversations is crucial in preventing long-term harm to victims. However, research on grooming detection in South Korea remains limited, as existing models trained primarily on English text and fail to reflect the unique linguistic features of SNS conversations, leading to inaccurate classifications. To address these issues, this study proposes a novel… More >

Smita Mahajan¹, Shilpa Gite^1,2, Biswajeet Pradhan^3,*, Abdullah Alamri⁴, Shaunak Inamdar⁵, Deva Shriyansh⁵, Akshat Ashish Shah⁵, Shruti Agarwal⁵

CMES-Computer Modeling in Engineering & Sciences, Vol.143, No.2, pp. 2001-2026, 2025, DOI:10.32604/cmes.2025.058456 - 30 May 2025

Abstract The development of generative architectures has resulted in numerous novel deep-learning models that generate images using text inputs. However, humans naturally use speech for visualization prompts. Therefore, this paper proposes an architecture that integrates speech prompts as input to image-generation Generative Adversarial Networks (GANs) model, leveraging Speech-to-Text translation along with the CLIP + VQGAN model. The proposed method involves translating speech prompts into text, which is then used by the Contrastive Language-Image Pretraining (CLIP) + Vector Quantized Generative Adversarial Network (VQGAN) model to generate images. This paper outlines the steps required to implement such a… More >

Muhammad Asif Zahoor Raja¹, Aqsa Zafar Abbasi², Kottakkaran Sooppy Nisar^3,*, Ayesha Rafiq², Muhammad Shoaib⁴

CMES-Computer Modeling in Engineering & Sciences, Vol.143, No.2, pp. 2133-2153, 2025, DOI:10.32604/cmes.2025.058020 - 30 May 2025

Abstract Being a nonlinear operator, fractional derivatives can affect the enforcement of existence at any given time. As a result, the memory effect has an impact on all nonlinear processes modeled by fractional order differential equations (FODEs). The goal of this study is to increase the fractional model of the TB virus’s (FMTBV) accuracy. Stochastic solvers have never been used to solve FMTBV previously. The Bayesian regularized artificial (BRA) method and neural networks (NNs), often referred to as BRA-NNs, were used to solve the FMTBV model. Each scenario features five occurrences that each reflect a different… More >

Jiran Zhu¹, Silin He¹, Chun Chen^2,*, Li Zhou², Hongqing Li¹, Di Zhang¹, Fenglin Hua¹, Tianhao Zhu²

Energy Engineering, Vol.122, No.6, pp. 2465-2483, 2025, DOI:10.32604/ee.2025.065453 - 29 May 2025

Abstract In the islanded operation of distribution networks, due to the mismatch of line impedance at the inverter output, conventional droop control leads to inaccurate power sharing according to capacity, resulting in voltage and frequency fluctuations under minor external disturbances. To address this issue, this paper introduces an enhanced scheme for power sharing and voltage-frequency control. First, to solve the power distribution problem, we propose an adaptive virtual impedance control based on multi-agent consensus, which allows for precise active and reactive power allocation without requiring feeder impedance knowledge. Moreover, a novel consensus-based voltage and frequency control 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 >

Zhebin Sun¹, Wei Wang¹, Mingxuan Du², Tao Liang¹, Yang Liu¹, Hailong Fan³, Cuiping Li², Xingxu Zhu², Junhui Li^2,*

Energy Engineering, Vol.122, No.6, pp. 2155-2175, 2025, DOI:10.32604/ee.2025.062218 - 29 May 2025

Abstract Responding to the stochasticity and uncertainty in the power height of distributed photovoltaic power generation. This paper presents a distributed photovoltaic ultra-short-term power forecasting method based on Variational Mode Decomposition (VMD) and Channel Attention Mechanism. First, Pearson’s correlation coefficient was utilized to filter out the meteorological factors that had a high impact on historical power. Second, the distributed PV power data were decomposed into a relatively smooth power series with different fluctuation patterns using variational modal decomposition (VMD). Finally, the reconstructed distributed PV power as well as other features are input into the combined CNN-SENet-BiLSTM… More >

Huanan Yu, Chunhe Ye, Shiqiang Li^*, He Wang, Jing Bian, Jinling Li

Energy Engineering, Vol.122, No.6, pp. 2417-2448, 2025, DOI:10.32604/ee.2025.061214 - 29 May 2025

Abstract With the increasing integration of large-scale distributed energy resources into the grid, traditional distribution network optimization and dispatch methods struggle to address the challenges posed by both generation and load. Accounting for these issues, this paper proposes a multi-timescale coordinated optimization dispatch method for distribution networks. First, the probability box theory was employed to determine the uncertainty intervals of generation and load forecasts, based on which, the requirements for flexibility dispatch and capacity constraints of the grid were calculated and analyzed. Subsequently, a multi-timescale optimization framework was constructed, incorporating the generation and load forecast uncertainties. More >

Ganesh Wakte^1,*, Mukesh Kumar², Mohammad Aljaidi³, Ramesh Kumar⁴, Manish Kumar Singla⁴

Energy Engineering, Vol.122, No.6, pp. 2519-2537, 2025, DOI:10.32604/ee.2025.060658 - 29 May 2025

Abstract Nodal pricing is a critical mechanism in electricity markets, utilized to determine the cost of power transmission to various nodes within a distribution network. As power systems evolve to incorporate higher levels of renewable energy and face increasing demand fluctuations, traditional nodal pricing models often fall short to meet these new challenges. This research introduces a novel enhanced nodal pricing mechanism for distribution networks, integrating advanced optimization techniques and hybrid models to overcome these limitations. The primary objective is to develop a model that not only improves pricing accuracy but also enhances operational efficiency and… More > Graphic Abstract