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  1. Article: Urban scan: A novel system to assess the urban landscapes in the regions deprived of street-view services.

    Puppala, Harish / Khatter, Kiran / Dwivedy, Maheshwar / Poonia, Ansh

    MethodsX

    2023  Volume 10, Page(s) 102155

    Abstract: Streetscape design can encourage social interaction and community building, creating a sense of place and improving the overall well-being of the resident community. Detailed investigation of streetscape quantitatively can identify the opportunities to ... ...

    Abstract Streetscape design can encourage social interaction and community building, creating a sense of place and improving the overall well-being of the resident community. Detailed investigation of streetscape quantitatively can identify the opportunities to reduce energy use, improve air quality, and enhance the natural environment. Data derived from street view services are typically used to analyze the streetscape. However, the availability of street view services is limited to selected regions, because of which conducting a study for an area deprived of street view services is a challenge. Building on this gap, this study proposes a new system introduced as Urban scan to overcome the limitation.•The proposed system can capture the streetscape in 360°.•Helps to analyze the streetscape composition with the least computational effort.•The accuracy of the classification is tested with different datasets and is noted to be above 96.02%.
    Language English
    Publishing date 2023-04-01
    Publishing country Netherlands
    Document type Journal Article
    ZDB-ID 2830212-6
    ISSN 2215-0161
    ISSN 2215-0161
    DOI 10.1016/j.mex.2023.102155
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  2. Book ; Online: Interval Valued Trapezoidal Neutrosophic Set for Prioritization of Non-functional Requirements

    Khatter, Kiran

    2019  

    Abstract: This paper discusses the trapezoidal fuzzy number(TrFN); Interval-valued intuitionistic fuzzy number(IVIFN); neutrosophic set and its operational laws; and, trapezoidal neutrosophic set(TrNS) and its operational laws. Based on the combination of IVIFN ... ...

    Abstract This paper discusses the trapezoidal fuzzy number(TrFN); Interval-valued intuitionistic fuzzy number(IVIFN); neutrosophic set and its operational laws; and, trapezoidal neutrosophic set(TrNS) and its operational laws. Based on the combination of IVIFN and TrNS, an Interval Valued Trapezoidal Neutrosophic Set (IVTrNS) is proposed followed by its operational laws. The paper also presents the score and accuracy functions for the proposed Interval Valued Trapezoidal Neutrosophic Number (IVTrNN). Then, an interval valued trapezoidal neutrosophic weighted arithmetic averaging (IVTrNWAA) operator is introduced to combine the trapezoidal information which is neutrosophic and in the unit interval of real numbers. Finally, a method is developed to handle the problems in the multi attribute decision making(MADM) environment using IVTrNWAA operator followed by a numerical example of NFRs prioritization to illustrate the relevance of the developed method.

    Comment: 21 pages, 2 figures, 5 tables
    Keywords Computer Science - Artificial Intelligence ; Computer Science - Neural and Evolutionary Computing ; Computer Science - Software Engineering
    Subject code 629
    Publishing date 2019-05-07
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  3. Article: Natural language processing: state of the art, current trends and challenges.

    Khurana, Diksha / Koli, Aditya / Khatter, Kiran / Singh, Sukhdev

    Multimedia tools and applications

    2022  , Page(s) 1–32

    Abstract: Natural language processing (NLP) has recently gained much attention for representing and analyzing human language computationally. It has spread its applications in various fields such as machine translation, email spam detection, information extraction, ...

    Abstract Natural language processing (NLP) has recently gained much attention for representing and analyzing human language computationally. It has spread its applications in various fields such as machine translation, email spam detection, information extraction, summarization, medical, and question answering etc. In this paper, we first distinguish four phases by discussing different levels of NLP and components of
    Language English
    Publishing date 2022-07-14
    Publishing country United States
    Document type Journal Article
    ZDB-ID 1479928-5
    ISSN 1573-7721 ; 1380-7501
    ISSN (online) 1573-7721
    ISSN 1380-7501
    DOI 10.1007/s11042-022-13428-4
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  4. Article: AI aiding in diagnosing, tracking recovery of COVID-19 using deep learning on Chest CT scans.

    Kuchana, Maheshwar / Srivastava, Amritesh / Das, Ronald / Mathew, Justin / Mishra, Atul / Khatter, Kiran

    Multimedia tools and applications

    2020  Volume 80, Issue 6, Page(s) 9161–9175

    Abstract: Coronavirus (COVID-19) has spread throughout the world, causing mayhem from January 2020 to this day. Owing to its rapidly spreading existence and high death count, the WHO has classified it as a pandemic. Biomedical engineers, virologists, ... ...

    Abstract Coronavirus (COVID-19) has spread throughout the world, causing mayhem from January 2020 to this day. Owing to its rapidly spreading existence and high death count, the WHO has classified it as a pandemic. Biomedical engineers, virologists, epidemiologists, and people from other medical fields are working to help contain this epidemic as soon as possible. The virus incubates for five days in the human body and then begins displaying symptoms, in some cases, as late as 27 days. In some instances, CT scan based diagnosis has been found to have better sensitivity than RT-PCR, which is currently the gold standard for COVID-19 diagnosis. Lung conditions relevant to COVID-19 in CT scans are ground-glass opacity (GGO), consolidation, and pleural effusion. In this paper, two segmentation tasks are performed to predict lung spaces (segregated from ribcage and flesh in Chest CT) and COVID-19 anomalies from chest CT scans. A 2D deep learning architecture with U-Net as its backbone is proposed to solve both the segmentation tasks. It is observed that change in hyperparameters such as number of filters in down and up sampling layers, addition of attention gates, addition of spatial pyramid pooling as basic block and maintaining the homogeneity of 32 filters after each down-sampling block resulted in a good performance. The proposed approach is assessed using publically available datasets from GitHub and Kaggle. Model performance is evaluated in terms of F1-Score, Mean intersection over union (Mean IoU). It is noted that the proposed approach results in 97.31% of F1-Score and 84.6% of Mean IoU. The experimental results illustrate that the proposed approach using U-Net architecture as backbone with the changes in hyperparameters shows better results in comparison to existing U-Net architecture and attention U-net architecture. The study also recommends how this methodology can be integrated into the workflow of healthcare systems to help control the spread of COVID-19.
    Keywords covid19
    Language English
    Publishing date 2020-11-08
    Publishing country United States
    Document type Journal Article
    ZDB-ID 1479928-5
    ISSN 1573-7721 ; 1380-7501
    ISSN (online) 1573-7721
    ISSN 1380-7501
    DOI 10.1007/s11042-020-10010-8
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  5. Book ; Online: Natural Language Processing

    Khurana, Diksha / Koli, Aditya / Khatter, Kiran / Singh, Sukhdev

    State of The Art, Current Trends and Challenges

    2017  

    Abstract: Natural language processing (NLP) has recently gained much attention for representing and analysing human language computationally. It has spread its applications in various fields such as machine translation, email spam detection, information extraction, ...

    Abstract Natural language processing (NLP) has recently gained much attention for representing and analysing human language computationally. It has spread its applications in various fields such as machine translation, email spam detection, information extraction, summarization, medical, and question answering etc. The paper distinguishes four phases by discussing different levels of NLP and components of Natural Language Generation (NLG) followed by presenting the history and evolution of NLP, state of the art presenting the various applications of NLP and current trends and challenges.

    Comment: 25 pages
    Keywords Computer Science - Computation and Language
    Publishing date 2017-08-17
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

    Full text online

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    Inter-library loan at ZB MED

    Your chosen title can be delivered directly to ZB MED Cologne location if you are registered as a user at ZB MED Cologne.

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