Lina Dong, Yeming Dai^*

CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.3, pp. 2913-2956, 2024, DOI:10.32604/cmes.2024.049214

Abstract This study delves into the formation dynamics of alliances within a closed-loop supply chain (CLSC) that encompasses a manufacturer, a retailer, and an e-commerce platform. It leverages Stackelberg game for this exploration, contrasting the equilibrium outcomes of a non-alliance model with those of three differentiated alliance models. The non-alliance model acts as a crucial benchmark, enabling the evaluation of the motivations for various supply chain entities to engage in alliance formations. Our analysis is centered on identifying the most effective alliance strategies and establishing a coordination within these partnerships. We thoroughly investigate the consequences of… More >

Mengdong An¹, Weiyuan Zhong¹, Wei Xu², Xiuli Wang^1,*

FDMP-Fluid Dynamics & Materials Processing, Vol.20, No.6, pp. 1331-1349, 2024, DOI:10.32604/fdmp.2023.046604

Abstract The reactor coolant pump (RCP) rotor seizure accident is defined as a short-time seizure of the RCP rotor. This event typically leads to an abrupt flow decrease in the corresponding loop and an ensuing reactor and turbine trip. The significant reduction of core coolant flow while the reactor is being operated at full load can have very negative consequences. This potentially dangerous event is typically characterized by a complex transient behavior in terms of flow conditions and energy transformation, which need to be analyzed and understood. This study constructed transient flow and rotational speed mathematical More > Graphic Abstract

Jun Wang, Changfu Si, Zhen Wang, Qiang Fu^*

CMC-Computers, Materials & Continua, Vol.79, No.3, pp. 4297-4318, 2024, DOI:10.32604/cmc.2024.050223

Abstract Nowadays, with the rapid development of industrial Internet technology, on the one hand, advanced industrial control systems (ICS) have improved industrial production efficiency. However, there are more and more cyber-attacks targeting industrial control systems. To ensure the security of industrial networks, intrusion detection systems have been widely used in industrial control systems, and deep neural networks have always been an effective method for identifying cyber attacks. Current intrusion detection methods still suffer from low accuracy and a high false alarm rate. Therefore, it is important to build a more efficient intrusion detection model. This paper… More >

Wael M. S. Yafooz^*, Abdullah Alsaeedi

CMC-Computers, Materials & Continua, Vol.79, No.1, pp. 735-759, 2024, DOI:10.32604/cmc.2024.048270

Abstract In the last decade, technical advancements and faster Internet speeds have also led to an increasing number of mobile devices and users. Thus, all contributors to society, whether young or old members, can use these mobile apps. The use of these apps eases our daily lives, and all customers who need any type of service can access it easily, comfortably, and efficiently through mobile apps. Particularly, Saudi Arabia greatly depends on digital services to assist people and visitors. Such mobile devices are used in organizing daily work schedules and services, particularly during two large occasions,… More >

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)… More >

Muhammad Armghan Latif¹, Zohaib Mushtaq², Saad Arif³, Sara Rehman⁴, Muhammad Farrukh Qureshi⁵, Nagwan Abdel Samee⁶, Maali Alabdulhafith^6,*, Yeong Hyeon Gu⁷, Mohammed A. Al-masni⁷

CMC-Computers, Materials & Continua, Vol.78, No.3, pp. 4225-4241, 2024, DOI:10.32604/cmc.2024.047621

Abstract Thyroid disorders represent a significant global health challenge with hypothyroidism and hyperthyroidism as two common conditions arising from dysfunction in the thyroid gland. Accurate and timely diagnosis of these disorders is crucial for effective treatment and patient care. This research introduces a comprehensive approach to improve the accuracy of thyroid disorder diagnosis through the integration of ensemble stacking and advanced feature selection techniques. Sequential forward feature selection, sequential backward feature elimination, and bidirectional feature elimination are investigated in this study. In ensemble learning, random forest, adaptive boosting, and bagging classifiers are employed. The effectiveness of… More >

Yanzhong Sun¹, Xiaoni Du^1,*, Shufen Niu², Xiaodong Yang²

CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.3, pp. 3287-3310, 2024, DOI:10.32604/cmes.2023.043589

Abstract Traditional email systems can only achieve one-way communication, which means only the receiver is allowed to search for emails on the email server. In this paper, we propose a blockchain-based certificateless bidirectional authenticated searchable encryption model for a cloud email system named certificateless authenticated bidirectional searchable encryption (CL-BSE) by combining the storage function of cloud server with the communication function of email server. In the new model, not only can the data receiver search for the relevant content by generating its own trapdoor, but the data owner also can retrieve the content in the same… More >

R. Sujatha, K. Nimala^*

CMC-Computers, Materials & Continua, Vol.78, No.2, pp. 1669-1686, 2024, DOI:10.32604/cmc.2023.046963

Abstract Sentence classification is the process of categorizing a sentence based on the context of the sentence. Sentence categorization requires more semantic highlights than other tasks, such as dependence parsing, which requires more syntactic elements. Most existing strategies focus on the general semantics of a conversation without involving the context of the sentence, recognizing the progress and comparing impacts. An ensemble pre-trained language model was taken up here to classify the conversation sentences from the conversation corpus. The conversational sentences are classified into four categories: information, question, directive, and commission. These classification label sequences are for… More >

Lanyao Zhang¹, Shichao Kan², Yigang Cen³, Xiaoling Chen¹, Linna Zhang^1,*, Yansen Huang^4,5

CMC-Computers, Materials & Continua, Vol.78, No.2, pp. 1631-1648, 2024, DOI:10.32604/cmc.2024.046924

Abstract Unsupervised methods based on density representation have shown their abilities in anomaly detection, but detection performance still needs to be improved. Specifically, approaches using normalizing flows can accurately evaluate sample distributions, mapping normal features to the normal distribution and anomalous features outside it. Consequently, this paper proposes a Normalizing Flow-based Bidirectional Mapping Residual Network (NF-BMR). It utilizes pre-trained Convolutional Neural Networks (CNN) and normalizing flows to construct discriminative source and target domain feature spaces. Additionally, to better learn feature information in both domain spaces, we propose the Bidirectional Mapping Residual Network (BMR), which maps sample… More > Graphic Abstract

Zhenxiang He^*, Xunxi Wang, Chunwei Li

CMC-Computers, Materials & Continua, Vol.78, No.1, pp. 845-871, 2024, DOI:10.32604/cmc.2023.046607

Abstract In the fast-evolving landscape of digital networks, the incidence of network intrusions has escalated alarmingly. Simultaneously, the crucial role of time series data in intrusion detection remains largely underappreciated, with most systems failing to capture the time-bound nuances of network traffic. This leads to compromised detection accuracy and overlooked temporal patterns. Addressing this gap, we introduce a novel SSAE-TCN-BiLSTM (STL) model that integrates time series analysis, significantly enhancing detection capabilities. Our approach reduces feature dimensionality with a Stacked Sparse Autoencoder (SSAE) and extracts temporally relevant features through a Temporal Convolutional Network (TCN) and Bidirectional Long… More >