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  • Open Access


    Numerical Assessments on Flow Topology and Heat Transfer Behavior in a Round Tube Inserted with Three Sets of V-Ribs

    Amnart Boonloi1, Withada Jedsadaratanachai2,*

    Frontiers in Heat and Mass Transfer, Vol.21, pp. 81-106, 2023, DOI:10.32604/fhmt.2023.041547

    Abstract Simulation of fluid-flow topology and thermal behavior in a round tube heat exchanger (RTHX) installed by three V-rib sets is reported. The expected phenomena for the rib installation are the generated vortex flow, impinging flow, greater fluid blending and thermal boundary layer disturbance (TBLD). These phenomena are key causes of the augmentation of heat transfer potentiality and thermal efficiency of the RTHX. Effects of rib height (b1 = 0.05D – 0.25D and b2 = 0.05D – 0.25D), rib pitch or rib spacing (P = D, 1.5D and 2D) and fluid directions (positive x (+x flow direction) and negative x (–x… More > Graphic Abstract

    Numerical Assessments on Flow Topology and Heat Transfer Behavior in a Round Tube Inserted with Three Sets of V-Ribs

  • Open Access


    A Novel Method for Determining Tourism Carrying Capacity in a Decision-Making Context Using q−Rung Orthopair Fuzzy Hypersoft Environment

    Salma Khan1, Muhammad Gulistan1, Nasreen Kausar2, Seifedine Kadry3,4,5, Jungeun Kim6,*

    CMES-Computer Modeling in Engineering & Sciences, Vol.138, No.2, pp. 1951-1979, 2024, DOI:10.32604/cmes.2023.030896

    Abstract Tourism is a popular activity that allows individuals to escape their daily routines and explore new destinations for various reasons, including leisure, pleasure, or business. A recent study has proposed a unique mathematical concept called a q−Rung orthopair fuzzy hypersoft set (ROFHS) to enhance the formal representation of human thought processes and evaluate tourism carrying capacity. This approach can capture the imprecision and ambiguity often present in human perception. With the advanced mathematical tools in this field, the study has also incorporated the Einstein aggregation operator and score function into the ROFHS values to support multi-attribute decision-making algorithms. By implementing… More >

  • Open Access


    The Spherical q-Linear Diophantine Fuzzy Multiple-Criteria Group Decision-Making Based on Differential Measure

    Huzaira Razzaque1, Shahzaib Ashraf1,*, Muhammad Naeem2, Yu-Ming Chu3,*

    CMES-Computer Modeling in Engineering & Sciences, Vol.138, No.2, pp. 1925-1950, 2024, DOI:10.32604/cmes.2023.030030

    Abstract Spherical q-linear Diophantine fuzzy sets (Sq-LDFSs) proved more effective for handling uncertainty and vagueness in multi-criteria decision-making (MADM). It does not only cover the data in two variable parameters but is also beneficial for three parametric data. By Pythagorean fuzzy sets, the difference is calculated only between two parameters (membership and non-membership). According to human thoughts, fuzzy data can be found in three parameters (membership uncertainty, and non-membership). So, to make a compromise decision, comparing Sq-LDFSs is essential. Existing measures of different fuzzy sets do, however, can have several flaws that can lead to counterintuitive results. For instance, they treat… More >

  • Open Access


    Terrorism Attack Classification Using Machine Learning: The Effectiveness of Using Textual Features Extracted from GTD Dataset

    Mohammed Abdalsalam1,*, Chunlin Li1, Abdelghani Dahou2, Natalia Kryvinska3

    CMES-Computer Modeling in Engineering & Sciences, Vol.138, No.2, pp. 1427-1467, 2024, DOI:10.32604/cmes.2023.029911

    Abstract One of the biggest dangers to society today is terrorism, where attacks have become one of the most significant risks to international peace and national security. Big data, information analysis, and artificial intelligence (AI) have become the basis for making strategic decisions in many sensitive areas, such as fraud detection, risk management, medical diagnosis, and counter-terrorism. However, there is still a need to assess how terrorist attacks are related, initiated, and detected. For this purpose, we propose a novel framework for classifying and predicting terrorist attacks. The proposed framework posits that neglected text attributes included in the Global Terrorism Database… More >

  • Open Access


    A Novel Incremental Attribute Reduction Algorithm Based on Intuitionistic Fuzzy Partition Distance

    Pham Viet Anh1,3, Nguyen Ngoc Thuy4, Nguyen Long Giang2, Pham Dinh Khanh5, Nguyen The Thuy1,6,*

    Computer Systems Science and Engineering, Vol.47, No.3, pp. 2971-2988, 2023, DOI:10.32604/csse.2023.042068

    Abstract Attribute reduction, also known as feature selection, for decision information systems is one of the most pivotal issues in machine learning and data mining. Approaches based on the rough set theory and some extensions were proved to be efficient for dealing with the problem of attribute reduction. Unfortunately, the intuitionistic fuzzy sets based methods have not received much interest, while these methods are well-known as a very powerful approach to noisy decision tables, i.e., data tables with the low initial classification accuracy. Therefore, this paper provides a novel incremental attribute reduction method to deal more effectively with noisy decision tables,… More >

  • Open Access


    Action Recognition for Multiview Skeleton 3D Data Using NTURGB + D Dataset

    Rosepreet Kaur Bhogal1,*, V. Devendran2

    Computer Systems Science and Engineering, Vol.47, No.3, pp. 2759-2772, 2023, DOI:10.32604/csse.2023.034862

    Abstract Human activity recognition is a recent area of research for researchers. Activity recognition has many applications in smart homes to observe and track toddlers or oldsters for their safety, monitor indoor and outdoor activities, develop Tele immersion systems, or detect abnormal activity recognition. Three dimensions (3D) skeleton data is robust and somehow view-invariant. Due to this, it is one of the popular choices for human action recognition. This paper proposed using a transversal tree from 3D skeleton data to represent videos in a sequence. Further proposed two neural networks: convolutional neural network recurrent neural network_1 (CNN_RNN_1), used to find the… More >

  • Open Access


    The Effect of Lateral Offset Distance on the Aerodynamics and Fuel Economy of Vehicle Queues

    Lili Lei*, Ze Li, Haichao Zhou, Jing Wang, Wei Lin

    FDMP-Fluid Dynamics & Materials Processing, Vol.20, No.1, pp. 147-163, 2024, DOI:10.32604/fdmp.2023.030158

    Abstract The vehicle industry is always in search of breakthrough energy-saving and emission-reduction technologies. In recent years, vehicle intelligence has progressed considerably, and researchers are currently trying to take advantage of these developments. Here we consider the case of many vehicles forming a queue, i.e., vehicles traveling at a predetermined speed and distance apart. While the majority of existing studies on this subject have focused on the influence of the longitudinal vehicle spacing, vehicle speed, and the number of vehicles on aerodynamic drag and fuel economy, this study considers the lateral offset distance of the vehicle queue. The group fuel consumption… More >

  • Open Access


    Linguistic Knowledge Representation in DPoS Consensus Scheme for Blockchain

    Yixia Chen1,2, Mingwei Lin1,2,*

    CMC-Computers, Materials & Continua, Vol.77, No.1, pp. 845-866, 2023, DOI:10.32604/cmc.2023.040970

    Abstract The consensus scheme is an essential component in the real blockchain environment. The Delegated Proof of Stake (DPoS) is a competitive consensus scheme that can decrease energy costs, promote decentralization, and increase efficiency, respectively. However, how to study the knowledge representation of the collective voting information and then select delegates is a new open problem. To ensure the fairness and effectiveness of transactions in the blockchain, in this paper, we propose a novel fine-grained knowledge representation method, which improves the DPoS scheme based on the linguistic term set (LTS) and proportional hesitant fuzzy linguistic term set (PHFLTS). To this end,… More >

  • Open Access


    Action Recognition and Detection Based on Deep Learning: A Comprehensive Summary

    Yong Li1,4, Qiming Liang2,*, Bo Gan3, Xiaolong Cui4

    CMC-Computers, Materials & Continua, Vol.77, No.1, pp. 1-23, 2023, DOI:10.32604/cmc.2023.042494

    Abstract Action recognition and detection is an important research topic in computer vision, which can be divided into action recognition and action detection. At present, the distinction between action recognition and action detection is not clear, and the relevant reviews are not comprehensive. Thus, this paper summarized the action recognition and detection methods and datasets based on deep learning to accurately present the research status in this field. Firstly, according to the way that temporal and spatial features are extracted from the model, the commonly used models of action recognition are divided into the two stream models, the temporal models, the… More >

  • Open Access


    Modified MMS: Minimization Approach for Model Subset Selection

    C. Rajathi, P. Rukmani*

    CMC-Computers, Materials & Continua, Vol.77, No.1, pp. 733-756, 2023, DOI:10.32604/cmc.2023.041507

    Abstract Considering the recent developments in the digital environment, ensuring a higher level of security for networking systems is imperative. Many security approaches are being constantly developed to protect against evolving threats. An ensemble model for the intrusion classification system yielded promising results based on the knowledge of many prior studies. This research work aimed to create a more diverse and effective ensemble model. To this end, selected six classification models, Logistic Regression (LR), Naive Bayes (NB), K-Nearest Neighbor (KNN), Decision Tree (DT), Support Vector Machine (SVM), and Random Forest (RF) from existing study to run as independent models. Once the… More >

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