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  • Open Access


    Modified Computational Ranking Model for Cloud Trust Factor Using Fuzzy Logic

    Lei Shen*, Ting Huang, Nishui Cai, Hao Wu

    Intelligent Automation & Soft Computing, Vol.37, No.1, pp. 507-524, 2023, DOI:10.32604/iasc.2023.037640

    Abstract Through the use of the internet and cloud computing, users may access their data as well as the programmes they have installed. It is now more challenging than ever before to choose which cloud service providers to take advantage of. When it comes to the dependability of the cloud infrastructure service, those who supply cloud services, as well as those who seek cloud services, have an equal responsibility to exercise utmost care. Because of this, further caution is required to ensure that the appropriate values are reached in light of the ever-increasing need for correct decision-making. The purpose of this… More >

  • Open Access


    Weighted PageRank Algorithm Search Engine Ranking Model for Web Pages

    S. Samsudeen Shaffi1,*, I. Muthulakshmi2

    Intelligent Automation & Soft Computing, Vol.36, No.1, pp. 183-192, 2023, DOI:10.32604/iasc.2023.031494

    Abstract As data grows in size, search engines face new challenges in extracting more relevant content for users’ searches. As a result, a number of retrieval and ranking algorithms have been employed to ensure that the results are relevant to the user’s requirements. Unfortunately, most existing indexes and ranking algorithms crawl documents and web pages based on a limited set of criteria designed to meet user expectations, making it impossible to deliver exceptionally accurate results. As a result, this study investigates and analyses how search engines work, as well as the elements that contribute to higher ranks. This paper addresses the… More >

  • Open Access


    Modelling an Efficient Clinical Decision Support System for Heart Disease Prediction Using Learning and Optimization Approaches

    Sridharan Kannan*

    CMES-Computer Modeling in Engineering & Sciences, Vol.131, No.2, pp. 677-694, 2022, DOI:10.32604/cmes.2022.018580

    Abstract With the worldwide analysis, heart disease is considered a significant threat and extensively increases the mortality rate. Thus, the investigators mitigate to predict the occurrence of heart disease in an earlier stage using the design of a better Clinical Decision Support System (CDSS). Generally, CDSS is used to predict the individuals’ heart disease and periodically update the condition of the patients. This research proposes a novel heart disease prediction system with CDSS composed of a clustering model for noise removal to predict and eliminate outliers. Here, the Synthetic Over-sampling prediction model is integrated with the cluster concept to balance the… More >

  • Open Access


    An Enhanced Re-Ranking Model for Person Re-Identification

    Jayavarthini Chockalingam*, Malathy Chidambaranathan

    Intelligent Automation & Soft Computing, Vol.33, No.2, pp. 697-710, 2022, DOI:10.32604/iasc.2022.024142

    Abstract Presently, Person Re-IDentification (PRe-ID) acts as a vital part of real time video surveillance to ensure the rising need for public safety. Resolving the PRe-ID problem includes the process of matching observations of persons among distinct camera views. Earlier models consider PRe-ID as a unique object retrieval issue and determine the retrieval results mainly based on the unidirectional matching among the probe and gallery images. But the accurate matching might not be present in the top-k ranking results owing to the appearance modifications caused by the difference in illumination, pose, viewpoint, and occlusion. For addressing these issues, a new Hyper-parameter… More >

  • Open Access


    Rank-Order Correlation-Based Feature Vector Context Transformation for Learning to Rank for Information Retrieval

    Jen-Yuan Yeh

    Computer Systems Science and Engineering, Vol.33, No.1, pp. 41-52, 2018, DOI:10.32604/csse.2018.33.041

    Abstract As a crucial task in information retrieval, ranking defines the preferential order among the retrieved documents for a given query. Supervised learning has recently been dedicated to automatically learning ranking models by incorporating various models into one effective model. This paper proposes a novel supervised learning method, in which instances are represented as bags of contexts of features, instead of bags of features. The method applies rank-order correlations to measure the correlation relationships between features. The feature vectors of instances, i.e., the 1st-order raw feature vectors, are then mapped into the feature correlation space via projection to derive the context-level… More >

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