Brij Bhooshan Gupta^1,2,3,*, Akshat Gaurav⁴, Razaz Waheeb Attar⁵, Varsha Arya^6,7, Ahmed Alhomoud⁸, Kwok Tai Chui⁹

CMC-Computers, Materials & Continua, Vol.80, No.3, pp. 4895-4916, 2024, DOI:10.32604/cmc.2024.050815

Abstract Phishing attacks present a persistent and evolving threat in the cybersecurity land-scape, necessitating the development of more sophisticated detection methods. Traditional machine learning approaches to phishing detection have relied heavily on feature engineering and have often fallen short in adapting to the dynamically changing patterns of phishing Uniform Resource Locator (URLs). Addressing these challenge, we introduce a framework that integrates the sequential data processing strengths of a Recurrent Neural Network (RNN) with the hyperparameter optimization prowess of the Whale Optimization Algorithm (WOA). Our model capitalizes on an extensive Kaggle dataset, featuring over 11,000 URLs, each More >

Yujin Liu¹, Zhenkai Zhang¹, Li Ma¹, Yan Jia^1,2,*, Weihua Yin³, Zhifeng Liu³

Energy Engineering, Vol.121, No.10, pp. 3019-3035, 2024, DOI:10.32604/ee.2024.052594

Abstract Accurate short-term photovoltaic (PV) power prediction helps to improve the economic efficiency of power stations and is of great significance to the arrangement of grid scheduling plans. In order to improve the accuracy of PV power prediction further, this paper proposes a data cleaning method combining density clustering and support vector machine. It constructs a short-term PV power prediction model based on particle swarm optimization (PSO) optimized Long Short-Term Memory (LSTM) network. Firstly, the input features are determined using Pearson’s correlation coefficient. The feature information is clustered using density-based spatial clustering of applications with noise More >

Yue He¹, Yaxiong You¹, Zhian He¹, Haiying Lu¹, Lei Chen^2,*, Yuqi Jiang², Hongkun Chen²

Energy Engineering, Vol.121, No.10, pp. 2825-2849, 2024, DOI:10.32604/ee.2024.052371

Abstract In recent times, the impact of typhoon disasters on integrated energy active distribution networks (IEADNs) has received increasing attention, particularly, in terms of effective cascading fault path prediction and enhanced fault recovery performance. In this study, we propose a modified ORNL-PSerc-Alaska (OPA) model based on optimal power flow (OPF) calculation to forecast IEADN cascading fault paths. We first established the topology and operational model of the IEADNs, and the typical fault scenario was chosen according to the component fault probability and information entropy. The modified OPA model consisted of two layers: An upper-layer model to More >

Cuiping Li¹, Kunqi Gao¹, Can Chen², Junhui Li^1,*, Xiaoxiao Wang², Yinchi Shao², Xingxu Zhu¹

Energy Engineering, Vol.121, No.10, pp. 2937-2959, 2024, DOI:10.32604/ee.2024.052167

Abstract In recent years, distributed photovoltaics (DPV) has ushered in a good development situation due to the advantages of pollution-free power generation, full utilization of the ground or roof of the installation site, and balancing a large number of loads nearby. However, under the background of a large-scale DPV grid-connected to the county distribution network, an effective analysis method is needed to analyze its impact on the voltage of the distribution network in the early development stage of DPV. Therefore, a DPV orderly grid-connected method based on photovoltaics grid-connected order degree (PGOD) is proposed. This method… More >

Benxin Li^*, Xuanming Chang

Energy Engineering, Vol.121, No.10, pp. 3001-3018, 2024, DOI:10.32604/ee.2024.051332

Abstract The increasingly large number of electric vehicles (EVs) has resulted in a growing concern for EV charging station load prediction for the purpose of comprehensively evaluating the influence of the charging load on distribution networks. To address this issue, an EV charging station load prediction method is proposed in coupled urban transportation and distribution networks. Firstly, a finer dynamic urban transportation network model is formulated considering both nodal and path resistance. Then, a finer EV power consumption model is proposed by considering the influence of traffic congestion and ambient temperature. Thirdly, the Monte Carlo method… More > Graphic Abstract

Hussein Alaa Al-Kabbi^1,2, Mohammad-Reza Feizi-Derakhshi^1,*, Saeed Pashazadeh³

Intelligent Automation & Soft Computing, Vol.39, No.4, pp. 665-682, 2024, DOI:10.32604/iasc.2024.050452

Abstract SMS spam poses a significant challenge to maintaining user privacy and security. Recently, spammers have employed fraudulent writing styles to bypass spam detection systems. This paper introduces a novel two-level detection system that utilizes deep learning techniques for effective spam identification to address the challenge of sophisticated SMS spam. The system comprises five steps, beginning with the preprocessing of SMS data. RoBERTa word embedding is then applied to convert text into a numerical format for deep learning analysis. Feature extraction is performed using a Convolutional Neural Network (CNN) for word-level analysis and a Bidirectional Long… More >

Haibo Wang¹, Hongwei Gao^1,*, Pai Jiang¹, Matthieu De Mari², Panzer Gu³, Yinsheng Liu¹

Journal on Internet of Things, Vol.6, pp. 17-41, 2024, DOI:10.32604/jiot.2024.054791

Abstract In the 6G Internet of Things (IoT) paradigm, unprecedented challenges will be raised to provide massive connectivity, ultra-low latency, and energy efficiency for ultra-dense IoT devices. To address these challenges, we explore the non-orthogonal multiple access (NOMA) based grant-free random access (GFRA) schemes in the cellular uplink to support massive IoT devices with high spectrum efficiency and low access latency. In particular, we focus on optimizing the backoff strategy of each device when transmitting time-sensitive data samples to a multiple-input multiple-output (MIMO)-enabled base station subject to energy constraints. To cope with the dynamic varied channel… More >

V. G. Saranya^*, S. Karthik

CMES-Computer Modeling in Engineering & Sciences, Vol.141, No.1, pp. 127-150, 2024, DOI:10.32604/cmes.2024.053825

Abstract Wireless Sensor Networks (WSNs) are a collection of sensor nodes distributed in space and connected through wireless communication. The sensor nodes gather and store data about the real world around them. However, the nodes that are dependent on batteries will ultimately suffer an energy loss with time, which affects the lifetime of the network. This research proposes to achieve its primary goal by reducing energy consumption and increasing the network’s lifetime and stability. The present technique employs the hybrid Mayfly Optimization Algorithm-Enhanced Ant Colony Optimization (MFOA-EACO), where the Mayfly Optimization Algorithm (MFOA) is used to… More >

Sheng Xiang¹, Junhao Ma¹, Qunli Shang¹, Xianbao Wang^1,*, Defu Chen^1,2

CMES-Computer Modeling in Engineering & Sciences, Vol.141, No.1, pp. 713-731, 2024, DOI:10.32604/cmes.2024.052759

Abstract Effective small object detection is crucial in various applications including urban intelligent transportation and pedestrian detection. However, small objects are difficult to detect accurately because they contain less information. Many current methods, particularly those based on Feature Pyramid Network (FPN), address this challenge by leveraging multi-scale feature fusion. However, existing FPN-based methods often suffer from inadequate feature fusion due to varying resolutions across different layers, leading to suboptimal small object detection. To address this problem, we propose the Two-layer Attention Feature Pyramid Network (TA-FPN), featuring two key modules: the Two-layer Attention Module (TAM) and the… More > Graphic Abstract

Meng Ma^1,2,*, Liu Fu^1,2, Xu Guo³, Zhi Zhai^1,2

CMES-Computer Modeling in Engineering & Sciences, Vol.141, No.1, pp. 385-399, 2024, DOI:10.32604/cmes.2024.052585

Abstract Partial Differential Equation (PDE) is among the most fundamental tools employed to model dynamic systems. Existing PDE modeling methods are typically derived from established knowledge and known phenomena, which are time-consuming and labor-intensive. Recently, discovering governing PDEs from collected actual data via Physics Informed Neural Networks (PINNs) provides a more efficient way to analyze fresh dynamic systems and establish PED models. This study proposes Sequentially Threshold Least Squares-Lasso (STLasso), a module constructed by incorporating Lasso regression into the Sequentially Threshold Least Squares (STLS) algorithm, which can complete sparse regression of PDE coefficients with the constraints More >