Hong Huang¹, Xingxing Zhang^1,*, Ye Lu¹, Ze Li¹, Shaohua Zhou²

CMC-Computers, Materials & Continua, Vol.78, No.3, pp. 3929-3951, 2024, DOI:10.32604/cmc.2024.047918

Abstract While encryption technology safeguards the security of network communications, malicious traffic also uses encryption protocols to obscure its malicious behavior. To address the issues of traditional machine learning methods relying on expert experience and the insufficient representation capabilities of existing deep learning methods for encrypted malicious traffic, we propose an encrypted malicious traffic classification method that integrates global semantic features with local spatiotemporal features, called BERT-based Spatio-Temporal Features Network (BSTFNet). At the packet-level granularity, the model captures the global semantic features of packets through the attention mechanism of the Bidirectional Encoder Representations from Transformers (BERT) model. At the byte-level granularity,… More >

Mohammad Mehdi Sharifi Nevisi¹, Elnaz Bashir², Diego Martín^3,*, Seyedkian Rezvanjou⁴, Farzaneh Shoushtari⁵, Ehsan Ghafourian²

CMC-Computers, Materials & Continua, Vol.78, No.3, pp. 3971-3991, 2024, DOI:10.32604/cmc.2024.047875

Abstract This paper focuses on wireless-powered communication systems, which are increasingly relevant in the Internet of Things (IoT) due to their ability to extend the operational lifetime of devices with limited energy. The main contribution of the paper is a novel approach to minimize the secrecy outage probability (SOP) in these systems. Minimizing SOP is crucial for maintaining the confidentiality and integrity of data, especially in situations where the transmission of sensitive data is critical. Our proposed method harnesses the power of an improved biogeography-based optimization (IBBO) to effectively train a recurrent neural network (RNN). The proposed IBBO introduces an innovative… More >

Ying Su¹, Morgan C. Wang¹, Shuai Liu^2,*

CMC-Computers, Materials & Continua, Vol.78, No.3, pp. 3529-3549, 2024, DOI:10.32604/cmc.2024.047189

Abstract Long-term time series forecasting stands as a crucial research domain within the realm of automated machine learning (AutoML). At present, forecasting, whether rooted in machine learning or statistical learning, typically relies on expert input and necessitates substantial manual involvement. This manual effort spans model development, feature engineering, hyper-parameter tuning, and the intricate construction of time series models. The complexity of these tasks renders complete automation unfeasible, as they inherently demand human intervention at multiple junctures. To surmount these challenges, this article proposes leveraging Long Short-Term Memory, which is the variant of Recurrent Neural Networks, harnessing memory cells and gating mechanisms… More >

Navaneetha Krishnan Muthunambu¹, Senthil Prabakaran², Balasubramanian Prabhu Kavin³, Kishore Senthil Siruvangur⁴, Kavitha Chinnadurai¹, Jehad Ali^5,*

CMC-Computers, Materials & Continua, Vol.78, No.3, pp. 3089-3127, 2024, DOI:10.32604/cmc.2023.043172

Abstract The extensive utilization of the Internet in everyday life can be attributed to the substantial accessibility of online services and the growing significance of the data transmitted via the Internet. Regrettably, this development has expanded the potential targets that hackers might exploit. Without adequate safeguards, data transmitted on the internet is significantly more susceptible to unauthorized access, theft, or alteration. The identification of unauthorised access attempts is a critical component of cybersecurity as it aids in the detection and prevention of malicious attacks. This research paper introduces a novel intrusion detection framework that utilizes Recurrent Neural Networks (RNN) integrated with… More >

Chao Chen^1,2, Yankun Chen³, Haohai Jin⁴, Li Chen^5,*, Zhisong Liu³, Haozhe Sun⁴, Junchi Hong⁴, Haonan Wang⁴, Shiyu Fang⁴, Xin Zhang²

Intelligent Automation & Soft Computing, Vol.39, No.1, pp. 113-114, 2024, DOI:10.32604/iasc.2024.051760

Abstract This article has no abstract. More >

M. S. Vivekanandan¹, T. Jesudas^2,*

Intelligent Automation & Soft Computing, Vol.39, No.1, pp. 109-111, 2024, DOI:10.32604/iasc.2024.051758

Abstract This article has no abstract. More >

Koren Hyogene Kwag^1,*, Ibrahim Kak¹, Ying Li², Walid Khass¹, Alec McKechnie¹, Oksana Nulman¹, Brande Brown¹, Manoj Chhabra¹

Congenital Heart Disease, Vol.19, No.1, pp. 107-121, 2024, DOI:10.32604/chd.2024.045953

Abstract Objective: Accurate measurement of QT interval, the ventricular action potential from depolarization to repolarization, is important for the early detection of Long QT syndrome. The most effective QT correction (QTc) formula has yet to be determined in the pediatric population, although it has intrinsically greater extremes in heart rate (HR) and is more susceptible to errors in measurement. The authors of this study compare six different QTc methods (Bazett, Fridericia, Framingham, Hodges, Rautaharju, and a computer algorithm utilizing the Bazett formula) for consistency against variations in HR and RR interval. Methods: Descriptive Retrospective Study. We included participants from a pediatric… More >

N. Gnanasekaran, C. Balaji^*

Frontiers in Heat and Mass Transfer, Vol.2, No.2, pp. 1-6, 2011, DOI:10.5098/hmt.v2.2.3008

Abstract This paper reports the results of an experimental investigation of transient, turbulent mixed convection in a vertical channel in which one of the walls is heated and the other is adiabatic. The goal is to simultaneously estimate the constants in a Nusselt number correlation whose form is assumed a priori by synergistically marrying the experimental results with repeated numerical calculations that assume guess values of the constants. The convective heat transfer coefficient “h”is replaced by the Nusselt number (Nu) and the constants in the Nusselt number are to be evaluated. From the experimentally obtained temperature time history and the simulated… More >

Yan Wang^*, You Lu, Yuqing Zhou, Zhijian Zhao

CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.3, pp. 2673-2703, 2024, DOI:10.32604/cmes.2023.046743

Abstract Indoor positioning is a key technology in today’s intelligent environments, and it plays a crucial role in many application areas. This paper proposed an unscented Kalman filter (UKF) based on the maximum correntropy criterion (MCC) instead of the minimum mean square error criterion (MMSE). This innovative approach is applied to the loose coupling of the Inertial Navigation System (INS) and Ultra-Wideband (UWB). By introducing the maximum correntropy criterion, the MCCUKF algorithm dynamically adjusts the covariance matrices of the system noise and the measurement noise, thus enhancing its adaptability to diverse environmental localization requirements. Particularly in the presence of non-Gaussian noise,… More >

Yongliang Yang, Linghua Xu^*, Maolin Luo, Xiao Wang, Min Cao

CMC-Computers, Materials & Continua, Vol.78, No.2, pp. 2741-2765, 2024, DOI:10.32604/cmc.2024.046768

Abstract Due to the complex environment of the university laboratory, personnel flow intensive, personnel irregular behavior is easy to cause security risks. Monitoring using mainstream detection algorithms suffers from low detection accuracy and slow speed. Therefore, the current management of personnel behavior mainly relies on institutional constraints, education and training, on-site supervision, etc., which is time-consuming and ineffective. Given the above situation, this paper proposes an improved You Only Look Once version 7 (YOLOv7) to achieve the purpose of quickly detecting irregular behaviors of laboratory personnel while ensuring high detection accuracy. First, to better capture the shape features of the target,… More >