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Search Results (14)
  • Open Access

    ARTICLE

    Information Centric Networking Based Cooperative Caching Framework for 5G Communication Systems

    R. Mahaveerakannan1, Thanarajan Tamilvizhi2,*, Sonia Jenifer Rayen3, Osamah Ibrahim Khalaf4, Habib Hamam5,6,7,8

    CMC-Computers, Materials & Continua, Vol.80, No.3, pp. 3945-3966, 2024, DOI:10.32604/cmc.2024.051611 - 12 September 2024

    Abstract The demands on conventional communication networks are increasing rapidly because of the exponential expansion of connected multimedia content. In light of the data-centric aspect of contemporary communication, the information-centric network (ICN) paradigm offers hope for a solution by emphasizing content retrieval by name instead of location. If 5G networks are to meet the expected data demand surge from expanded connectivity and Internet of Things (IoT) devices, then effective caching solutions will be required to maximize network throughput and minimize the use of resources. Hence, an ICN-based Cooperative Caching (ICN-CoC) technique has been used to select… More >

  • Open Access

    ARTICLE

    Efficient Clustering Network Based on Matrix Factorization

    Jieren Cheng1,3, Jimei Li1,3,*, Faqiang Zeng1,3, Zhicong Tao1,3, Yue Yang2,3

    CMC-Computers, Materials & Continua, Vol.80, No.1, pp. 281-298, 2024, DOI:10.32604/cmc.2024.051816 - 18 July 2024

    Abstract Contrastive learning is a significant research direction in the field of deep learning. However, existing data augmentation methods often lead to issues such as semantic drift in generated views while the complexity of model pre-training limits further improvement in the performance of existing methods. To address these challenges, we propose the Efficient Clustering Network based on Matrix Factorization (ECN-MF). Specifically, we design a batched low-rank Singular Value Decomposition (SVD) algorithm for data augmentation to eliminate redundant information and uncover major patterns of variation and key information in the data. Additionally, we design a Mutual Information-Enhanced More >

  • Open Access

    ARTICLE

    Multidomain Correlation-Based Multidimensional CSI Tensor Generation for Device-Free Wi-Fi Sensing

    Liufeng Du1,*, Shaoru Shang1, Linghua Zhang2, Chong Li1, Jianing Yang3, Xiyan Tian1

    CMES-Computer Modeling in Engineering & Sciences, Vol.138, No.2, pp. 1749-1767, 2024, DOI:10.32604/cmes.2023.030144 - 17 November 2023

    Abstract Due to the fine-grained communication scenarios characterization and stability, Wi-Fi channel state information (CSI) has been increasingly applied to indoor sensing tasks recently. Although spatial variations are explicitly reflected in CSI measurements, the representation differences caused by small contextual changes are easily submerged in the fluctuations of multipath effects, especially in device-free Wi-Fi sensing. Most existing data solutions cannot fully exploit the temporal, spatial, and frequency information carried by CSI, which results in insufficient sensing resolution for indoor scenario changes. As a result, the well-liked machine learning (ML)-based CSI sensing models still struggling with stable More >

  • Open Access

    ARTICLE

    Multi-label Emotion Classification of COVID–19 Tweets with Deep Learning and Topic Modelling

    K. Anuratha1,*, M. Parvathy2

    Computer Systems Science and Engineering, Vol.45, No.3, pp. 3005-3021, 2023, DOI:10.32604/csse.2023.031553 - 21 December 2022

    Abstract The COVID-19 pandemic has become one of the severe diseases in recent years. As it majorly affects the common livelihood of people across the universe, it is essential for administrators and healthcare professionals to be aware of the views of the community so as to monitor the severity of the spread of the outbreak. The public opinions are been shared enormously in microblogging media like twitter and is considered as one of the popular sources to collect public opinions in any topic like politics, sports, entertainment etc., This work presents a combination of Intensity Based… More >

  • Open Access

    ARTICLE

    Prognostic model for prostate cancer based on glycolysis-related genes and non-negative matrix factorization analysis

    ZECHAO LU1,#, FUCAI TANG1,#, HAOBIN ZHOU2,#, ZEGUANG LU3,#, WANYAN CAI4,#, JIAHAO ZHANG5, ZHICHENG TANG6, YONGCHANG LAI1,*, ZHAOHUI HE1,*

    BIOCELL, Vol.47, No.2, pp. 339-350, 2023, DOI:10.32604/biocell.2023.023750 - 18 November 2022

    Abstract Background: Establishing an appropriate prognostic model for PCa is essential for its effective treatment. Glycolysis is a vital energy-harvesting mechanism for tumors. Developing a prognostic model for PCa based on glycolysis-related genes is novel and has great potential. Methods: First, gene expression and clinical data of PCa patients were downloaded from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO), and glycolysis-related genes were obtained from the Molecular Signatures Database (MSigDB). Gene enrichment analysis was performed to verify that glycolysis functions were enriched in the genes we obtained, which were used in non-negative matrix factorization… More >

  • Open Access

    ARTICLE

    Effective Customer Review Analysis Using Combined Capsule Networks with Matrix Factorization Filtering

    K. Selvasheela1,*, A. M. Abirami2, Abdul Khader Askarunisa3

    Computer Systems Science and Engineering, Vol.44, No.3, pp. 2537-2552, 2023, DOI:10.32604/csse.2023.029148 - 01 August 2022

    Abstract Nowadays, commercial transactions and customer reviews are part of human life and various business applications. The technologies create a great impact on online user reviews and activities, affecting the business process. Customer reviews and ratings are more helpful to the new customer to purchase the product, but the fake reviews completely affect the business. The traditional systems consume maximum time and create complexity while analyzing a large volume of customer information. Therefore, in this work optimized recommendation system is developed for analyzing customer reviews with minimum complexity. Here, Amazon Product Kaggle dataset information is utilized More >

  • Open Access

    ARTICLE

    Evaluating Partitioning Based Clustering Methods for Extended Non-negative Matrix Factorization (NMF)

    Neetika Bhandari1,*, Payal Pahwa2

    Intelligent Automation & Soft Computing, Vol.35, No.2, pp. 2043-2055, 2023, DOI:10.32604/iasc.2023.028368 - 19 July 2022

    Abstract Data is humongous today because of the extensive use of World Wide Web, Social Media and Intelligent Systems. This data can be very important and useful if it is harnessed carefully and correctly. Useful information can be extracted from this massive data using the Data Mining process. The information extracted can be used to make vital decisions in various industries. Clustering is a very popular Data Mining method which divides the data points into different groups such that all similar data points form a part of the same group. Clustering methods are of various types. More >

  • Open Access

    ARTICLE

    Speech Separation Methodology for Hearing Aid

    Joseph Sathiadhas Esra1,*, Y. Sukhi2

    Computer Systems Science and Engineering, Vol.44, No.2, pp. 1659-1678, 2023, DOI:10.32604/csse.2023.025969 - 15 June 2022

    Abstract In the design of hearing aids (HA), the real-time speech-enhancement is done. The digital hearing aids should provide high signal-to-noise ratio, gain improvement and should eliminate feedback. In generic hearing aids the performance towards different frequencies varies and non uniform. Existing noise cancellation and speech separation methods drops the voice magnitude under the noise environment. The performance of the HA for frequency response is non uniform. Existing noise suppression methods reduce the required signal strength also. So, the performance of uniform sub band analysis is poor when hearing aid is concern. In this paper, a More >

  • Open Access

    ARTICLE

    Cold-Start Link Prediction via Weighted Symmetric Nonnegative Matrix Factorization with Graph Regularization

    Minghu Tang1,2,3,*, Wei Yu4, Xiaoming Li4, Xue Chen5, Wenjun Wang3, Zhen Liu6

    Computer Systems Science and Engineering, Vol.43, No.3, pp. 1069-1084, 2022, DOI:10.32604/csse.2022.028841 - 09 May 2022

    Abstract Link prediction has attracted wide attention among interdisciplinary researchers as an important issue in complex network. It aims to predict the missing links in current networks and new links that will appear in future networks. Despite the presence of missing links in the target network of link prediction studies, the network it processes remains macroscopically as a large connected graph. However, the complexity of the real world makes the complex networks abstracted from real systems often contain many isolated nodes. This phenomenon leads to existing link prediction methods not to efficiently implement the prediction of… More >

  • Open Access

    ARTICLE

    Deep Learning Enabled Autoencoder Architecture for Collaborative Filtering Recommendation in IoT Environment

    Thavavel Vaiyapuri*

    CMC-Computers, Materials & Continua, Vol.68, No.1, pp. 487-503, 2021, DOI:10.32604/cmc.2021.015998 - 22 March 2021

    Abstract The era of the Internet of things (IoT) has marked a continued exploration of applications and services that can make people’s lives more convenient than ever before. However, the exploration of IoT services also means that people face unprecedented difficulties in spontaneously selecting the most appropriate services. Thus, there is a paramount need for a recommendation system that can help improve the experience of the users of IoT services to ensure the best quality of service. Most of the existing techniques—including collaborative filtering (CF), which is most widely adopted when building recommendation systems—suffer from rating… More >

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