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  • Open Access


    A Machine Learning Approach to User Profiling for Data Annotation of Online Behavior

    Moona Kanwal1,2,*, Najeed A. Khan1, Aftab A. Khan3

    CMC-Computers, Materials & Continua, Vol.78, No.2, pp. 2419-2440, 2024, DOI:10.32604/cmc.2024.047223

    Abstract The user’s intent to seek online information has been an active area of research in user profiling. User profiling considers user characteristics, behaviors, activities, and preferences to sketch user intentions, interests, and motivations. Determining user characteristics can help capture implicit and explicit preferences and intentions for effective user-centric and customized content presentation. The user’s complete online experience in seeking information is a blend of activities such as searching, verifying, and sharing it on social platforms. However, a combination of multiple behaviors in profiling users has yet to be considered. This research takes a novel approach and explores user intent types… More >

  • Open Access


    Weighted Forwarding in Graph Convolution Networks for Recommendation Information Systems

    Sang-min Lee, Namgi Kim*

    CMC-Computers, Materials & Continua, Vol.78, No.2, pp. 1897-1914, 2024, DOI:10.32604/cmc.2023.046346

    Abstract Recommendation Information Systems (RIS) are pivotal in helping users in swiftly locating desired content from the vast amount of information available on the Internet. Graph Convolution Network (GCN) algorithms have been employed to implement the RIS efficiently. However, the GCN algorithm faces limitations in terms of performance enhancement owing to the due to the embedding value-vanishing problem that occurs during the learning process. To address this issue, we propose a Weighted Forwarding method using the GCN (WF-GCN) algorithm. The proposed method involves multiplying the embedding results with different weights for each hop layer during graph learning. By applying the WF-GCN… More >

  • Open Access


    Deep Learning Model for News Quality Evaluation Based on Explicit and Implicit Information

    Guohui Song1,2, Yongbin Wang1,*, Jianfei Li1, Hongbin Hu1

    Intelligent Automation & Soft Computing, Vol.38, No.3, pp. 275-295, 2023, DOI:10.32604/iasc.2023.041873

    Abstract Recommending high-quality news to users is vital in improving user stickiness and news platforms’ reputation. However, existing news quality evaluation methods, such as clickbait detection and popularity prediction, are challenging to reflect news quality comprehensively and concisely. This paper defines news quality as the ability of news articles to elicit clicks and comments from users, which represents whether the news article can attract widespread attention and discussion. Based on the above definition, this paper first presents a straightforward method to measure news quality based on the comments and clicks of news and defines four news quality indicators. Then, the dataset… More >

  • Open Access


    LC-NPLA: Label and Community Information-Based Network Presentation Learning Algorithm

    Shihu Liu, Chunsheng Yang*, Yingjie Liu

    Intelligent Automation & Soft Computing, Vol.38, No.3, pp. 203-223, 2023, DOI:10.32604/iasc.2023.040818

    Abstract Many network presentation learning algorithms (NPLA) have originated from the process of the random walk between nodes in recent years. Despite these algorithms can obtain great embedding results, there may be also some limitations. For instance, only the structural information of nodes is considered when these kinds of algorithms are constructed. Aiming at this issue, a label and community information-based network presentation learning algorithm (LC-NPLA) is proposed in this paper. First of all, by using the community information and the label information of nodes, the first-order neighbors of nodes are reconstructed. In the next, the random walk strategy is improved… More >

  • Open Access


    News Modeling and Retrieving Information: Data-Driven Approach

    Elias Hossain1, Abdullah Alshahrani2, Wahidur Rahman3,*

    Intelligent Automation & Soft Computing, Vol.38, No.2, pp. 109-123, 2023, DOI:10.32604/iasc.2022.029511

    Abstract This paper aims to develop Machine Learning algorithms to classify electronic articles related to this phenomenon by retrieving information and topic modelling. The Methodology of this study is categorized into three phases: the Text Classification Approach (TCA), the Proposed Algorithms Interpretation (PAI), and finally, Information Retrieval Approach (IRA). The TCA reflects the text preprocessing pipeline called a clean corpus. The Global Vectors for Word Representation (Glove) pre-trained model, FastText, Term Frequency-Inverse Document Frequency (TF-IDF), and Bag-of-Words (BOW) for extracting the features have been interpreted in this research. The PAI manifests the Bidirectional Long Short-Term Memory (Bi-LSTM) and Convolutional Neural Network… More >

  • Open Access


    Recommendation Method for Contrastive Enhancement of Neighborhood Information

    Hairong Wang, Beijing Zhou*, Lisi Zhang, He Ma

    CMC-Computers, Materials & Continua, Vol.78, No.1, pp. 453-472, 2024, DOI:10.32604/cmc.2023.046560

    Abstract Knowledge graph can assist in improving recommendation performance and is widely applied in various personalized recommendation domains. However, existing knowledge-aware recommendation methods face challenges such as weak user-item interaction supervisory signals and noise in the knowledge graph. To tackle these issues, this paper proposes a neighbor information contrast-enhanced recommendation method by adding subtle noise to construct contrast views and employing contrastive learning to strengthen supervisory signals and reduce knowledge noise. Specifically, first, this paper adopts heterogeneous propagation and knowledge-aware attention networks to obtain multi-order neighbor embedding of users and items, mining the high-order neighbor information of users and items. Next,… More >

  • Open Access


    A Joint Entity Relation Extraction Model Based on Relation Semantic Template Automatically Constructed

    Wei Liu, Meijuan Yin*, Jialong Zhang, Lunchong Cui

    CMC-Computers, Materials & Continua, Vol.78, No.1, pp. 975-997, 2024, DOI:10.32604/cmc.2023.046475

    Abstract The joint entity relation extraction model which integrates the semantic information of relation is favored by relevant researchers because of its effectiveness in solving the overlapping of entities, and the method of defining the semantic template of relation manually is particularly prominent in the extraction effect because it can obtain the deep semantic information of relation. However, this method has some problems, such as relying on expert experience and poor portability. Inspired by the rule-based entity relation extraction method, this paper proposes a joint entity relation extraction model based on a relation semantic template automatically constructed, which is abbreviated as… More >

  • Open Access


    A Weighted Multi-Layer Analytics Based Model for Emoji Recommendation

    Amira M. Idrees1,*, Abdul Lateef Marzouq Al-Solami2

    CMC-Computers, Materials & Continua, Vol.78, No.1, pp. 1115-1133, 2024, DOI:10.32604/cmc.2023.046457

    Abstract The developed system for eye and face detection using Convolutional Neural Networks (CNN) models, followed by eye classification and voice-based assistance, has shown promising potential in enhancing accessibility for individuals with visual impairments. The modular approach implemented in this research allows for a seamless flow of information and assistance between the different components of the system. This research significantly contributes to the field of accessibility technology by integrating computer vision, natural language processing, and voice technologies. By leveraging these advancements, the developed system offers a practical and efficient solution for assisting blind individuals. The modular design ensures flexibility, scalability, and… More >

  • Open Access


    A Novel High-Efficiency Transaction Verification Scheme for Blockchain Systems

    Jingyu Zhang1,2, Pian Zhou1, Jin Wang1, Osama Alfarraj3, Saurabh Singh4, Min Zhu5,*

    CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.2, pp. 1613-1633, 2024, DOI:10.32604/cmes.2023.044418

    Abstract Blockchain can realize the reliable storage of a large amount of data that is chronologically related and verifiable within the system. This technology has been widely used and has developed rapidly in big data systems across various fields. An increasing number of users are participating in application systems that use blockchain as their underlying architecture. As the number of transactions and the capital involved in blockchain grow, ensuring information security becomes imperative. Addressing the verification of transactional information security and privacy has emerged as a critical challenge. Blockchain-based verification methods can effectively eliminate the need for centralized third-party organizations. However,… More >

  • Open Access


    Use of Geographic Information System and Digital Elevation Model to Analyze the Hydro-Morphometric Characteristics of the Tshopo River Sub-Catchments, Democratic Republic of Congo

    Utilisation du système d’information géographique et modèle numérique de terrain dans l’analyse des caractéristiques hydro-morphométriques des sous-bassins versants de la rivière Tshopo, République démocratique du Congo

    Faidance Mashauri1,2,*, Mokili Mbuluyo1,3, Nsalambi Nkongolo2,4

    Revue Internationale de Géomatique, Vol.32, pp. 99-122, 2023, DOI:10.32604/rig.2023.044899

    Abstract The analysis and quantification of hydro-morphometric characteristics are essential for better management of water resources and more effective planning of hydroelectric projects in the Tshopo basin. Unfortunately, few studies have been carried out to assess these characteristics at the scale of this basin. Our methodological approach consists of using Geographic Information System (GIS) software analysis tools applied to the Digital Elevation Model (DEM) derived from the Advanced Land Observing Satellite (ALOS) World 3D-30m image. This enabled us to automatically extract the hydrographic network and generate the Tshopo sub-watersheds. The results of this analysis show that the Tshopo catchment area is… More >

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