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Search Results (13)
  • Open Access


    CALTM: A Context-Aware Long-Term Time-Series Forecasting Model

    Canghong Jin1,*, Jiapeng Chen1, Shuyu Wu1, Hao Wu2, Shuoping Wang1, Jing Ying3

    CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.1, pp. 873-891, 2024, DOI:10.32604/cmes.2023.043230

    Abstract Time series data plays a crucial role in intelligent transportation systems. Traffic flow forecasting represents a precise estimation of future traffic flow within a specific region and time interval. Existing approaches, including sequence periodic, regression, and deep learning models, have shown promising results in short-term series forecasting. However, forecasting scenarios specifically focused on holiday traffic flow present unique challenges, such as distinct traffic patterns during vacations and the increased demand for long-term forecastings. Consequently, the effectiveness of existing methods diminishes in such scenarios. Therefore, we propose a novel long-term forecasting model based on scene matching and embedding fusion representation to… More >

  • Open Access


    Relevant Visual Semantic Context-Aware Attention-Based Dialog

    Eugene Tan Boon Hong1, Yung-Wey Chong1,*, Tat-Chee Wan1, Kok-Lim Alvin Yau2

    CMC-Computers, Materials & Continua, Vol.76, No.2, pp. 2337-2354, 2023, DOI:10.32604/cmc.2023.038695

    Abstract The existing dataset for visual dialog comprises multiple rounds of questions and a diverse range of image contents. However, it faces challenges in overcoming visual semantic limitations, particularly in obtaining sufficient context from visual and textual aspects of images. This paper proposes a new visual dialog dataset called Diverse History-Dialog (DS-Dialog) to address the visual semantic limitations faced by the existing dataset. DS-Dialog groups relevant histories based on their respective Microsoft Common Objects in Context (MSCOCO) image categories and consolidates them for each image. Specifically, each MSCOCO image category consists of top relevant histories extracted based on their semantic relationships… More >

  • Open Access


    Improving Recommendation for Effective Personalization in Context-Aware Data Using Novel Neural Network

    R. Sujatha1,*, T. Abirami2

    Computer Systems Science and Engineering, Vol.46, No.2, pp. 1775-1787, 2023, DOI:10.32604/csse.2023.031552

    Abstract The digital technologies that run based on users’ content provide a platform for users to help air their opinions on various aspects of a particular subject or product. The recommendation agents play a crucial role in personalizing the needs of individual users. Therefore, it is essential to improve the user experience. The recommender system focuses on recommending a set of items to a user to help the decision-making process and is prevalent across e-commerce and media websites. In Context-Aware Recommender Systems (CARS), several influential and contextual variables are identified to provide an effective recommendation. A substantial trade-off is applied in… More >

  • Open Access


    Context-Aware Practice Problem Recommendation Using Learners’ Skill Level Navigation Patterns

    P. N. Ramesh1,*, S. Kannimuthu2

    Intelligent Automation & Soft Computing, Vol.35, No.3, pp. 3845-3860, 2023, DOI:10.32604/iasc.2023.031329

    Abstract The use of programming online judges (POJs) has risen dramatically in recent years, owing to the fact that the auto-evaluation of codes during practice motivates students to learn programming. Since POJs have greater number of programming problems in their repository, learners experience information overload. Recommender systems are a common solution to information overload. Current recommender systems used in e-learning platforms are inadequate for POJ since recommendations should consider learners’ current context, like learning goals and current skill level (topic knowledge and difficulty level). To overcome the issue, we propose a context-aware practice problem recommender system based on learners’ skill level… More >

  • Open Access


    A Deep Learning Based Approach for Context-Aware Multi-Criteria Recommender Systems

    Son-Lam VU, Quang-Hung LE*

    Computer Systems Science and Engineering, Vol.44, No.1, pp. 471-483, 2023, DOI:10.32604/csse.2023.025897

    Abstract Recommender systems are similar to an information filtering system that helps identify items that best satisfy the users’ demands based on their preference profiles. Context-aware recommender systems (CARSs) and multi-criteria recommender systems (MCRSs) are extensions of traditional recommender systems. CARSs have integrated additional contextual information such as time, place, and so on for providing better recommendations. However, the majority of CARSs use ratings as a unique criterion for building communities. Meanwhile, MCRSs utilize user preferences in multiple criteria to better generate recommendations. Up to now, how to exploit context in MCRSs is still an open issue. This paper proposes a… More >

  • Open Access


    A Smart Room to Promote Autonomy of Disabled People due to Stroke

    Moeiz Miraoui1,2,*

    Computer Systems Science and Engineering, Vol.44, No.1, pp. 677-692, 2023, DOI:10.32604/csse.2023.025799

    Abstract A cerebral vascular accident, known as common language stroke, is one of the main causes of mortality and remains the primary cause of acquired disabilities in adults. Those disabled people spend most of their time at home in their living rooms. In most cases, appliances of a living room (TV, light, cooler/heater, window blinds, etc.) are generally controlled by direct manipulation of a set of remote controls. Handling many remote controls can be disturbing and inappropriate for these people. In addition, in many cases these people could be alone at home and must open the door for visitors after their… More >

  • Open Access


    QL-CBR Hybrid Approach for Adapting Context-Aware Services

    Somia Belaidouni1,2, Moeiz Miraoui3,4,*, Chakib Tadj1

    Computer Systems Science and Engineering, Vol.43, No.3, pp. 1085-1098, 2022, DOI:10.32604/csse.2022.024056

    Abstract A context-aware service in a smart environment aims to supply services according to user situational information, which changes dynamically. Most existing context-aware systems provide context-aware services based on supervised algorithms. Reinforcement algorithms are another type of machine-learning algorithm that have been shown to be useful in dynamic environments through trial-and-error interactions. They also have the ability to build excellent self-adaptive systems. In this study, we aim to incorporate reinforcement algorithms (Q-learning) into a context-aware system to provide relevant services based on a user’s dynamic context. To accelerate the convergence of reinforcement learning (RL) algorithms and provide the correct services in… More >

  • Open Access


    Context-Aware Service Model of a Mobile Library Based on Internet of Things

    Wei Gao1, Haixu Xi1,2,*, Gyun Yeol Park3

    Intelligent Automation & Soft Computing, Vol.33, No.3, pp. 1893-1906, 2022, DOI:10.32604/iasc.2022.023207

    Abstract Appropriate technology needs to be applied in libraries to provide users with more humanized, intelligent, and convenient services to improve service quality. Using theories from library science, management, and modeling, this paper examines library personalized service in the intelligent Internet of Things (IoT) environment using a literature review, comparative analysis, and UML modeling to analyze the influencing factors of mobile library users’ acceptance of personalized recommendation services. Based on the situational awareness framework, the experimental results of the effect of these personalized service recommendations show that the load factor is greater than 0.6, which indicates that the dimensions of a… More >

  • Open Access


    Modelling and Verification of Context-Aware Intelligent Assistive Formalism

    Shahid Yousaf1,*, Hafiz Mahfooz Ul Haque2, Abbas Khalid1, Muhammad Adnan Hashmi3, Eraj Khan1

    CMC-Computers, Materials & Continua, Vol.71, No.2, pp. 3355-3373, 2022, DOI:10.32604/cmc.2022.023019

    Abstract Recent years have witnessed the expeditious evolution of intelligent smart devices and autonomous software technologies with the expanded domains of computing from workplaces to smart computing in everyday routine life activities. This trend has been rapidly advancing towards the new generation of systems where smart devices play vital roles in acting intelligently on behalf of the users. Context-awareness has emerged from the pervasive computing paradigm. Context-aware systems have the ability to acquire contextual information from the surrounding environment autonomously, perform reasoning on it, and then adapt their behaviors accordingly. With the proliferation of context-aware systems and smart sensors, real-time monitoring… More >

  • Open Access


    Exploiting Rich Event Representation to Improve Event Causality Recognition

    Gaigai Jin1, Junsheng Zhou1,*, Weiguang Qu1, Yunfei Long2, Yanhui Gu1

    Intelligent Automation & Soft Computing, Vol.30, No.1, pp. 161-173, 2021, DOI:10.32604/iasc.2021.017440

    Abstract Event causality identification is an essential task for information extraction that has attracted growing attention. Early researchers were accustomed to combining the convolutional neural network or recurrent neural network models with external causal knowledge, but these methods ignore the importance of rich semantic representation of the event. The event is more structured, so it has more abundant semantic representation. We argue that the elements of the event, the interaction of the two events, and the context between the two events can enrich the event’s semantic representation and help identify event causality. Therefore, the effective semantic representation of events in event… More >

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