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  • Open Access

    ARTICLE

    Identification of Type of a Fault in Distribution System Using Shallow Neural Network with Distributed Generation

    Saurabh Awasthi*, Gagan Singh, Nafees Ahamad

    Energy Engineering, Vol.120, No.4, pp. 811-829, 2023, DOI:10.32604/ee.2023.026863

    Abstract A distributed generation system (DG) has several benefits over a traditional centralized power system. However, the protection area in the case of the distributed generator requires special attention as it encounters stability loss, failure re-closure, fluctuations in voltage, etc. And thereby, it demands immediate attention in identifying the location & type of a fault without delay especially when occurred in a small, distributed generation system, as it would adversely affect the overall system and its operation. In the past, several methods were proposed for classification and localisation of a fault in a distributed generation system. Many of those methods were… More >

  • Open Access

    ARTICLE

    An Ophthalmic Evaluation of Central Serous Chorioretinopathy

    L. K. Shoba1,*, P. Mohan Kumar2

    Computer Systems Science and Engineering, Vol.44, No.1, pp. 613-628, 2023, DOI:10.32604/csse.2023.024449

    Abstract Nowadays in the medical field, imaging techniques such as Optical Coherence Tomography (OCT) are mainly used to identify retinal diseases. In this paper, the Central Serous Chorio Retinopathy (CSCR) image is analyzed for various stages and then compares the difference between CSCR before as well as after treatment using different application methods. The first approach, which was focused on image quality, improves medical image accuracy. An enhancement algorithm was implemented to improve the OCT image contrast and denoise purpose called Boosted Anisotropic Diffusion with an Unsharp Masking Filter (BADWUMF). The classifier used here is to figure out whether the OCT… More >

  • Open Access

    ARTICLE

    Shallow Neural Network and Ontology-Based Novel Semantic Document Indexing for Information Retrieval

    Anil Sharma1,*, Suresh Kumar2

    Intelligent Automation & Soft Computing, Vol.34, No.3, pp. 1989-2005, 2022, DOI:10.32604/iasc.2022.026095

    Abstract Information Retrieval (IR) systems are developed to fetch the most relevant content matching the user’s information needs from a pool of information. A user expects to get IR results based on the conceptual contents of the query rather than keywords. But traditional IR approaches index documents based on the terms that they contain and ignore semantic descriptions of document contents. This results in a vocabulary gap when queries and documents use different terms to describe the same concept. As a solution to this problem and to improve the performance of IR systems, we have designed a Shallow Neural Network and… More >

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