Home / Advanced Search

  • Title/Keywords

  • Author/Affliations

  • Journal

  • Article Type

  • Start Year

  • End Year

Update SearchingClear
  • Articles
  • Online
Search Results (9,683)
  • Open Access


    Correction: Micro-Locational Fine Dust Prediction Utilizing Machine Learning and Deep Learning Models

    Seoyun Kim1,#, Hyerim Yu2,#, Jeewoo Yoon1,3, Eunil Park1,2,*

    Computer Systems Science and Engineering, Vol.48, No.3, pp. 861-861, 2024, DOI:10.32604/csse.2024.053659

    Abstract This article has no abstract. More >

  • Open Access


    Comprehensive Analysis of Gender Classification Accuracy across Varied Geographic Regions through the Application of Deep Learning Algorithms to Speech Signals

    Abhishek Singhal*, Devendra Kumar Sharma

    Computer Systems Science and Engineering, Vol.48, No.3, pp. 609-625, 2024, DOI:10.32604/csse.2023.046730

    Abstract This article presents an exhaustive comparative investigation into the accuracy of gender identification across diverse geographical regions, employing a deep learning classification algorithm for speech signal analysis. In this study, speech samples are categorized for both training and testing purposes based on their geographical origin. Category 1 comprises speech samples from speakers outside of India, whereas Category 2 comprises live-recorded speech samples from Indian speakers. Testing speech samples are likewise classified into four distinct sets, taking into consideration both geographical origin and the language spoken by the speakers. Significantly, the results indicate a noticeable difference in gender identification accuracy among… More >

  • Open Access


    Multimodal Deep Neural Networks for Digitized Document Classification

    Aigerim Baimakhanova1,*, Ainur Zhumadillayeva2, Bigul Mukhametzhanova3, Natalya Glazyrina2, Rozamgul Niyazova2, Nurseit Zhunissov1, Aizhan Sambetbayeva4

    Computer Systems Science and Engineering, Vol.48, No.3, pp. 793-811, 2024, DOI:10.32604/csse.2024.043273

    Abstract As digital technologies have advanced more rapidly, the number of paper documents recently converted into a digital format has exponentially increased. To respond to the urgent need to categorize the growing number of digitized documents, the classification of digitized documents in real time has been identified as the primary goal of our study. A paper classification is the first stage in automating document control and efficient knowledge discovery with no or little human involvement. Artificial intelligence methods such as Deep Learning are now combined with segmentation to study and interpret those traits, which were not conceivable ten years ago. Deep… More >

  • Open Access


    A Systematic Literature Review on Task Allocation and Performance Management Techniques in Cloud Data Center

    Nidhika Chauhan1, Navneet Kaur2, Kamaljit Singh Saini2, Sahil Verma3, Abdulatif Alabdulatif4, Ruba Abu Khurma5,7, Maribel Garcia-Arenas6, Pedro A. Castillo6,*

    Computer Systems Science and Engineering, Vol.48, No.3, pp. 571-608, 2024, DOI:10.32604/csse.2024.042690

    Abstract As cloud computing usage grows, cloud data centers play an increasingly important role. To maximize resource utilization, ensure service quality, and enhance system performance, it is crucial to allocate tasks and manage performance effectively. The purpose of this study is to provide an extensive analysis of task allocation and performance management techniques employed in cloud data centers. The aim is to systematically categorize and organize previous research by identifying the cloud computing methodologies, categories, and gaps. A literature review was conducted, which included the analysis of 463 task allocations and 480 performance management papers. The review revealed three task allocation… More >

  • Open Access


    A Hybrid Machine Learning Framework for Security Intrusion Detection

    Fatimah Mudhhi Alanazi*, Bothina Abdelmeneem Elsobky, Shaimaa Aly Elmorsy

    Computer Systems Science and Engineering, Vol.48, No.3, pp. 835-851, 2024, DOI:10.32604/csse.2024.042401

    Abstract Proliferation of technology, coupled with networking growth, has catapulted cybersecurity to the forefront of modern security concerns. In this landscape, the precise detection of cyberattacks and anomalies within networks is crucial, necessitating the development of efficient intrusion detection systems (IDS). This article introduces a framework utilizing the fusion of fuzzy sets with support vector machines (SVM), named FSVM. The core strategy of FSVM lies in calculating the significance of network features to determine their relative importance. Features with minimal significance are prudently disregarded, a method akin to feature selection. This process not only curtails the computational burden of the classification… More >

  • Open Access


    A New Malicious Code Classification Method for the Security of Financial Software

    Xiaonan Li1,2, Qiang Wang1, Conglai Fan2,3, Wei Zhan1, Mingliang Zhang4,*

    Computer Systems Science and Engineering, Vol.48, No.3, pp. 773-792, 2024, DOI:10.32604/csse.2024.039849

    Abstract The field of finance heavily relies on cybersecurity to safeguard its systems and clients from harmful software. The identification of malevolent code within financial software is vital for protecting both the financial system and individual clients. Nevertheless, present detection models encounter limitations in their ability to identify malevolent code and its variations, all while encompassing a multitude of parameters. To overcome these obstacles, we introduce a lean model for classifying families of malevolent code, formulated on Ghost-DenseNet-SE. This model integrates the Ghost module, DenseNet, and the squeeze-and-excitation (SE) channel domain attention mechanism. It substitutes the standard convolutional layer in DenseNet… More >

  • Open Access


    Multiple Perspective of Multipredictor Mechanism and Multihistogram Modification for High-Fidelity Reversible Data Hiding

    Kai Gao1, Chin-Chen Chang1,*, Chia-Chen Lin2,*

    Computer Systems Science and Engineering, Vol.48, No.3, pp. 813-833, 2024, DOI:10.32604/csse.2024.038308

    Abstract Reversible data hiding is a confidential communication technique that takes advantage of image file characteristics, which allows us to hide sensitive data in image files. In this paper, we propose a novel high-fidelity reversible data hiding scheme. Based on the advantage of the multipredictor mechanism, we combine two effective prediction schemes to improve prediction accuracy. In addition, the multihistogram technique is utilized to further improve the image quality of the stego image. Moreover, a model of the grouped knapsack problem is used to speed up the search for the suitable embedding bin in each sub-histogram. Experimental results show that the… More >

  • Open Access


    Research on Total Electric Field Prediction Method of Ultra-High Voltage Direct Current Transmission Line Based on Stacking Algorithm

    Yinkong Wei1,2, Mucong Wu1,2,*, Wei Wei3, Paulo R. F. Rocha4, Ziyi Cheng1,2, Weifang Yao5

    Computer Systems Science and Engineering, Vol.48, No.3, pp. 723-738, 2024, DOI:10.32604/csse.2023.036062

    Abstract Ultra-high voltage (UHV) transmission lines are an important part of China’s power grid and are often surrounded by a complex electromagnetic environment. The ground total electric field is considered a main electromagnetic environment indicator of UHV transmission lines and is currently employed for reliable long-term operation of the power grid. Yet, the accurate prediction of the ground total electric field remains a technical challenge. In this work, we collected the total electric field data from the Ningdong-Zhejiang ±800 kV UHVDC transmission project, as of the Ling Shao line, and perform an outlier analysis of the total electric field data. We… More >

  • Open Access


    A Multilayer Network Constructed for Herb and Prescription Efficacy Analysis

    Xindi Huang1, Liwei Liang1, Sakirin Tam2, Hao Liang3, Xiong Cai4, Changsong Ding1,5,*

    Computer Systems Science and Engineering, Vol.48, No.3, pp. 691-704, 2024, DOI:10.32604/csse.2022.029970

    Abstract Chinese Medicine (CM) has been widely used as an important avenue for disease prevention and treatment in China especially in the form of CM prescriptions combining sets of herbs to address patients’ symptoms and syndromes. However, the selection and compatibility of herbs are complex and abstract due to intrinsic relationships between herbal properties and their overall functions. Network analysis is applied to demonstrate the complex relationships between individual herbal efficacy and the overall function of CM prescriptions. To illustrate their connections and correlations, prescription function (PF), prescription herb (PH), and herbal efficacy (HE) intra-networks are proposed based on CM theory… More >

  • Open Access


    Investigate the Impact of Dimple Size and Distribution on the Hydrothermal Performance of Dimpled Heat Exchanger Tubes

    Abeer H. Falih*, Basima Salman Khalaf, Basim Freegah

    Frontiers in Heat and Mass Transfer, Vol.22, No.2, pp. 597-613, 2024, DOI:10.32604/fhmt.2024.049812

    Abstract In this study, the primary objective was to enhance the hydrothermal performance of a dimpled tube by addressing areas with low heat transfer compared to other regions. To accomplish this, a comprehensive numerical investigation was conducted using ANSYS Fluent 2022 R1 software, focusing on different diameters of dimples along the pipe’s length and the distribution of dimples in both in-line and staggered arrangements. The simulations utilized the finite element method to address turbulent flow within the tube by solving partial differential equations, encompassing Re numbers spanning from 3000 to 8000. The study specifically examined single-phase flow conditions, with water utilized… More > Graphic Abstract

    Investigate the Impact of Dimple Size and Distribution on the Hydrothermal Performance of Dimpled Heat Exchanger Tubes

Displaying 21-30 on page 3 of 9683. Per Page