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  1. Article: A unified approach of detecting misleading images via tracing its instances on web and analyzing its past context for the verification of multimedia content.

    Varshney, Deepika / Vishwakarma, Dinesh Kumar

    International journal of multimedia information retrieval

    2022  Volume 11, Issue 3, Page(s) 445–459

    Abstract: The verification of multimedia content over social media is one of the challenging and crucial issues in the current scenario and gaining prominence in an age where user-generated content and online social web-platforms are the leading sources in shaping ...

    Abstract The verification of multimedia content over social media is one of the challenging and crucial issues in the current scenario and gaining prominence in an age where user-generated content and online social web-platforms are the leading sources in shaping and propagating news stories. As these sources allow users to share their opinions without restriction, opportunistic users often post misleading/unreliable content on social media such as Twitter, Facebook, etc. At present, to lure users toward the news story, the text is often attached with some multimedia content (images/videos/audios). Verifying these contents to maintain the credibility and reliability of social media information is of paramount importance. Motivated by this, we proposed a generalized system that supports the automatic classification of images into credible or misleading. In this paper, we investigated machine learning-based as well as deep learning-based approaches utilized to verify misleading multimedia content, where the available image traces are used to identify the credibility of the content. The experiment is performed on the real-world dataset (Media-eval-2015 dataset) collected from Twitter. It also demonstrates the efficiency of our proposed approach and features using both Machine and Deep Learning Model (Bi-directional LSTM). The experiment result reveals that the Microsoft BING image search engine is quite effective in retrieving titles and performs better than our study's Google image search engine. It also shows that gathering clues from attached multimedia content (image) is more effective than detecting only posted content-based features.
    Language English
    Publishing date 2022-07-11
    Publishing country England
    Document type Journal Article
    ZDB-ID 2649485-1
    ISSN 2192-662X ; 2192-6611
    ISSN (online) 2192-662X
    ISSN 2192-6611
    DOI 10.1007/s13735-022-00235-8
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  2. Article: Framework for detection of probable clues to predict misleading information proliferated during COVID-19 outbreak.

    Varshney, Deepika / Vishwakarma, Dinesh Kumar

    Neural computing & applications

    2022  Volume 35, Issue 8, Page(s) 5999–6013

    Abstract: Spreading of misleading information on social web platforms has fuelled huge panic and confusion among the public regarding the Corona disease, the detection of which is of paramount importance. To identify the credibility of the posted claim, we have ... ...

    Abstract Spreading of misleading information on social web platforms has fuelled huge panic and confusion among the public regarding the Corona disease, the detection of which is of paramount importance. To identify the credibility of the posted claim, we have analyzed possible evidence from the news articles in the google search results. This paper proposes an intelligent and expert strategy to gather important clues from the top 10 google search results related to the claim. The N-gram, Levenshtein Distance, and Word-Similarity-based features are used to identify the clues from the news article that can automatically warn users against spreading false news if no significant supportive clues are identified concerning that claim. The complete process is done in four steps, wherein the first step we build a query from the posted claim received in the form of text or text additive images which further goes as an input to the search query phase, where the top 10 google results are processed. In the third step, the important clues are extracted from titles of the top 10 news articles. Lastly, useful pieces of evidence are extracted from the content of each news article. All the useful clues with respect to N-gram, Levenshtein Distance, and Word Similarity are finally fed into the machine learning model for classification and to evaluate its performances. It has been observed that our proposed intelligent strategy gives promising experimental results and is quite effective in predicting misleading information. The proposed work provides practical implications for the policymakers and health practitioners that could be useful in protecting the world from misleading information proliferation during this pandemic.
    Language English
    Publishing date 2022-11-13
    Publishing country England
    Document type Journal Article
    ZDB-ID 1480526-1
    ISSN 1433-3058 ; 0941-0643
    ISSN (online) 1433-3058
    ISSN 0941-0643
    DOI 10.1007/s00521-022-07938-3
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  3. Article: An automated multi-web platform voting framework to predict misleading information proliferated during COVID-19 outbreak using ensemble method.

    Varshney, Deepika / Vishwakarma, Dinesh Kumar

    Data & knowledge engineering

    2022  Volume 143, Page(s) 102103

    Abstract: The spreading of misleading information on social web platforms has fuelled massive panic and confusion among the public regarding the Corona disease, the detection of which is of paramount importance. Previous studies mainly relied on a specific web ... ...

    Abstract The spreading of misleading information on social web platforms has fuelled massive panic and confusion among the public regarding the Corona disease, the detection of which is of paramount importance. Previous studies mainly relied on a specific web platform to collect crucial evidence to detect fake content. The analysis identifies that retrieving clues from two or more different sources/web platforms gives more reliable prediction and confidence concerning a specific claim. This study proposed a novel multi-web platform voting framework that incorporates 4 sets of novel features: content, linguistic, similarity, and sentiments. The features have been gathered from each web-platforms to validate the news. To validate the fact/claim, a unique source platform is designed to collect relevant clues/headlines from two web platforms (YouTube, Google) based on specific queries and extracted features concerning each clue/headline. The proposed idea is to incorporate a unique platform to assist researchers in gathering relevant and vital evidence from diverse web platforms. After evaluation and validation, it has been identified that the built model is quite intelligent, gives promising results, and effectively predicts misleading information. The model correctly detected about 98% of the COVID misinformation on the constraint Covid-19 fake news dataset. Furthermore, it is observed that it is efficient to gather clues from multiple web platforms for more reliable predictions to validate the news. The suggested work depicts numerous practical applications for health policy-makers and practitioners that could be useful in safeguarding and implicating awareness among society from misleading information dissemination during this pandemic.
    Language English
    Publishing date 2022-11-11
    Publishing country Netherlands
    Document type Journal Article
    ZDB-ID 1466273-5
    ISSN 0169-023X
    ISSN 0169-023X
    DOI 10.1016/j.datak.2022.102103
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  4. Article ; Online: A unified approach for detection of Clickbait videos on YouTube using cognitive evidences.

    Varshney, Deepika / Vishwakarma, Dinesh Kumar

    Applied intelligence (Dordrecht, Netherlands)

    2021  Volume 51, Issue 7, Page(s) 4214–4235

    Abstract: Clickbait is one of the form of false content, purposely designed to attract the user's attention and make them curious to follow the link and read, view, or listen to the attached content. The teaser aim behind this is to exploit the curiosity gap by ... ...

    Abstract Clickbait is one of the form of false content, purposely designed to attract the user's attention and make them curious to follow the link and read, view, or listen to the attached content. The teaser aim behind this is to exploit the curiosity gap by giving information within the short statement. Still, the given statement is not sufficient enough to satisfy the curiosity without clicking through the linked content and lure the user to get into the respective page via playing with human psychology and degrades the user experience. To counter this problem, we develop a Clickbait Video Detector (CVD) scheme. The scheme leverages to learn three sets of latent features based on User Profiling, Video-Content, and Human Consensus, these are further used to retrieve cognitive evidence for the detection of clickbait videos on YouTube. The first step is to extract audio from the videos, which is further transformed to textual data, and later on, it is utilized for the extraction of video content-based features. Secondly, the comments are analyzed, and features are extracted based on human responses/reactions over the posted content. Lastly, user profile based features are extracted. Finally, all these features are fed into the classifier. The proposed method is tested on the publicly available fake video corpus [FVC], [FVC-2018] dataset, and a self-generated misleading video dataset [MVD]. The achieved result is compared with other state-of-the-art methods and demonstrates superior performance.
    Language English
    Publishing date 2021-01-02
    Publishing country Netherlands
    Document type Journal Article
    ZDB-ID 1479519-X
    ISSN 1573-7497 ; 0924-669X
    ISSN (online) 1573-7497
    ISSN 0924-669X
    DOI 10.1007/s10489-020-02057-9
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  5. Book ; Online: Multimodal Sentiment Analysis

    Aggarwal, Aditi / Varshney, Deepika / Patel, Saurabh

    Perceived vs Induced Sentiments

    2023  

    Abstract: Social media has created a global network where people can easily access and exchange vast information. This information gives rise to a variety of opinions, reflecting both positive and negative viewpoints. GIFs stand out as a multimedia format offering ...

    Abstract Social media has created a global network where people can easily access and exchange vast information. This information gives rise to a variety of opinions, reflecting both positive and negative viewpoints. GIFs stand out as a multimedia format offering a visually engaging way for users to communicate. In this research, we propose a multimodal framework that integrates visual and textual features to predict the GIF sentiment. It also incorporates attributes including face emotion detection and OCR generated captions to capture the semantic aspects of the GIF. The developed classifier achieves an accuracy of 82.7% on Twitter GIFs, which is an improvement over state-of-the-art models. Moreover, we have based our research on the ReactionGIF dataset, analysing the variance in sentiment perceived by the author and sentiment induced in the reader
    Keywords Computer Science - Computer Vision and Pattern Recognition ; Computer Science - Machine Learning ; Computer Science - Social and Information Networks
    Subject code 306
    Publishing date 2023-12-12
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  6. Article: Toll-like receptor 2 (-196 to -174) del

    Varshney, Deepika / Singh, Shoor Vir / Mohanty, Keshar Kunja / Kumar, Santosh / Varshney, Nitin / Sinha, Ekata / Barik, Sushanta Kumar

    Frontiers in microbiology

    2024  Volume 14, Page(s) 1305974

    Abstract: Objectives: The objective of this study is to analyze the association between : Methods: The present case-control study included 101 pulmonary TB patients, 104 multidrug-resistant TB patients, 48 extremely drug-resistant TB patients, and 130 healthy ... ...

    Abstract Objectives: The objective of this study is to analyze the association between
    Methods: The present case-control study included 101 pulmonary TB patients, 104 multidrug-resistant TB patients, 48 extremely drug-resistant TB patients, and 130 healthy and unrelated controls residing in the same locality. The genotyping method for
    Results: The frequency of heterozygous (
    Conclusion: The findings suggested that in the present population, the heterozygous (
    Language English
    Publishing date 2024-01-24
    Publishing country Switzerland
    Document type Journal Article
    ZDB-ID 2587354-4
    ISSN 1664-302X
    ISSN 1664-302X
    DOI 10.3389/fmicb.2023.1305974
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  7. Book ; Online: An Automated Multi-Web Platform Voting Framework to Predict Misleading Information Proliferated during COVID-19 Outbreak using Ensemble Method

    Varshney, Deepika / Vishwakarma, Dinesh Kumar

    2021  

    Abstract: Spreading of misleading information on social web platforms has fuelled huge panic and confusion among the public regarding the Corona disease, the detection of which is of paramount importance. To address this issue, in this paper, we have developed an ... ...

    Abstract Spreading of misleading information on social web platforms has fuelled huge panic and confusion among the public regarding the Corona disease, the detection of which is of paramount importance. To address this issue, in this paper, we have developed an automated system that can collect and validate the fact from multi web-platform to decide the credibility of the content. To identify the credibility of the posted claim, probable instances/clues(titles) of news information are first gathered from various web platforms. Later, the crucial set of features is retrieved that further feeds into the ensemble-based machine learning model to classify the news as misleading or real. The four sets of features based on the content, linguistics/semantic cues, similarity, and sentiments gathered from web-platforms and voting are applied to validate the news. Finally, the combined voting decides the support given to a specific claim. In addition to the validation part, a unique source platform is designed for collecting data/facts from three web platforms (Twitter, Facebook, Google) based on certain queries/words. This unique platform can also help researchers build datasets and gather useful/efficient clues from various web platforms. It has been observed that our proposed intelligent strategy gives promising results and quite effective in predicting misleading information. The proposed work provides practical implications for the policy makers and health practitioners that could be useful in protecting the world from misleading information proliferation during this pandemic.

    Comment: 22 pages, 06 figures
    Keywords Computer Science - Information Retrieval
    Subject code 302
    Publishing date 2021-09-19
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  8. Book ; Online: A Unified Approach of Detecting Misleading Images via Tracing its Instances on Web and Analysing its Past Context for the Verification of Content

    Varshney, Deepika / Vishwakarma, Dinesh Kumar

    2021  

    Abstract: The verification of multimedia content over social media is one of the challenging and crucial issues in the current scenario and gaining prominence in an age where user-generated content and online social web platforms are the leading sources in shaping ...

    Abstract The verification of multimedia content over social media is one of the challenging and crucial issues in the current scenario and gaining prominence in an age where user-generated content and online social web platforms are the leading sources in shaping and propagating news stories. As these sources allow users to share their opinions without restriction, opportunistic users often post misleading/ unreliable content on social media such as Twitter, Facebook, etc. At present, to lure users towards the news story, the text is often attached with some multimedia content (images/videos/audios). Verifying these contents to maintain the credibility and reliability of social media information is of paramount importance. Motivated by this, we proposed a generalized system that supports the automatic classification of images into credible or misleading. In this paper, we investigated machine learning-based as well as deep learning-based approaches utilized to verify misleading multimedia content, where the available image traces are used to identify the credibility of the content. The experiment is performed on the real-world dataset (Media-eval-2015 dataset) collected from Twitter. It also demonstrates the efficiency of our proposed approach and features using both Machine and Deep Learning Model (Bi-directional LSTM). The experiment result reveals that the Microsoft bings image search engine is quite effective in retrieving titles and performs better than our study's Google image search engine. It also shows that gathering clues from attached multimedia content (image) is more effective than detecting only posted content-based features.

    Comment: 22 pages, 8 figures
    Keywords Computer Science - Social and Information Networks
    Subject code 070
    Publishing date 2021-09-20
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  9. Article ; Online: Systematic review and meta-analysis of human Toll-like receptors genetic polymorphisms for susceptibility to tuberculosis infection.

    Varshney, Deepika / Singh, Shoorvir / Sinha, Ekata / Mohanty, Keshar Kunja / Kumar, Santosh / Kumar Barik, Sushanta / Patil, Shripad A / Katara, Pradhumn

    Cytokine

    2022  Volume 152, Page(s) 155791

    Abstract: Epidemiological data from the world health organization (WHO) show that Globally an estimated 10 million (range, 8.9-11.0 million) people around the world were infected with TB in 2019. M.tuberculosis (M.tb) is the major cause of tuberculosis. Infection ... ...

    Abstract Epidemiological data from the world health organization (WHO) show that Globally an estimated 10 million (range, 8.9-11.0 million) people around the world were infected with TB in 2019. M.tuberculosis (M.tb) is the major cause of tuberculosis. Infection with M.tb has varied host immune responses because of the host genetic factor and its response to the infection. Genetic polymorphism in TLRs imparts susceptibility or resistance to the host against several diseases. In the present study, a systematic review and meta-analysis were performed to describe the relationship among various TLRs and SNPs involved in M.tb infection and their association with susceptibility to pulmonary tuberculosis in various populations of the world. PubMed and Scihub databases from 2008 to 2019 were searched and 58 articles were shortlisted for the present study to explore the association between TLRs gene polymorphisms and susceptibility to tuberculosis infection. The combined analysis showed that the polymorphisms TLR1 (rs5743618), TLR1 (rs4833095), TLR2 (-196 to -174) del, TLR2 (rs3804099), TLR4 (rs4986790), TLR4 (rs4986791), TLR4 (rs7873784), TLR6 (rs5743810), TLR8 (rs3764880), TLR9 (rs5743836), TLR9 (rs352139) were significantly associated with TB disease in certain ethnic population. In our meta-analysis study, we have also found variations between studies in some polymorphism, for example. The TLR1 (rs 5743618), TLR2 (rs5743708), TLR4 Asp299Gly, TLR4 Thr399Ile, TLR4 (rs7873784), TLR6 (rs5743810), TLR9 (rs5743836) was associated with the protection against TB. Meta-analysis was performed between polymorphisms and pulmonary tuberculosis to define increase or decrease in susceptibility to tuberculosis in various populations, which indicated that a relationship exists between SNPs/host genetic factors and susceptibility or resistance in patients suffering from pulmonary tuberculosis our finding concludes that this gene polymorphism may be associated with susceptibility to TB. The present study adds value to the various researches and studies going on various populations of the world in better understanding the role of TLR polymorphism in TB.
    MeSH term(s) Genetic Predisposition to Disease ; Humans ; Latent Tuberculosis ; Mycobacterium tuberculosis ; Polymorphism, Single Nucleotide/genetics ; Toll-Like Receptor 1/genetics ; Toll-Like Receptor 2/genetics ; Toll-Like Receptor 4/genetics ; Toll-Like Receptor 6/genetics ; Toll-Like Receptor 9 ; Toll-Like Receptors/genetics ; Tuberculosis/genetics ; Tuberculosis, Pulmonary/genetics
    Chemical Substances Toll-Like Receptor 1 ; Toll-Like Receptor 2 ; Toll-Like Receptor 4 ; Toll-Like Receptor 6 ; Toll-Like Receptor 9 ; Toll-Like Receptors
    Language English
    Publishing date 2022-02-11
    Publishing country England
    Document type Journal Article ; Meta-Analysis ; Research Support, Non-U.S. Gov't ; Review ; Systematic Review
    ZDB-ID 1018055-2
    ISSN 1096-0023 ; 1043-4666
    ISSN (online) 1096-0023
    ISSN 1043-4666
    DOI 10.1016/j.cyto.2021.155791
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  10. Article: SWATH-MS analysis of plasma proteins among Indian HIV-1 infected patients.

    Barik, Sushanta Kumar / Tripathy, Srikanth Prasad / Bisht, Deepa / Singh, Praveen / Chakraborty, Rahul / Patil, Shripad A / Singh, Tej Pal / Varshney, Deepika / Jena, Srikanta / Mohanty, Keshar Kunja

    Bioinformation

    2023  Volume 19, Issue 4, Page(s) 392–398

    Abstract: The identification and characterization of plasma proteins in drug resistant and drug sensitive in HIV-1 infected/AIDS patients were carried out using the SWATH-MS protocol. In total, 204 proteins were identified and quantified, 57 proteins were ... ...

    Abstract The identification and characterization of plasma proteins in drug resistant and drug sensitive in HIV-1 infected/AIDS patients were carried out using the SWATH-MS protocol. In total, 204 proteins were identified and quantified, 57 proteins were differentially expressed, out of which 25 proteins were down regulated and 32 proteins were up regulated in drug resistant patients. Six proteins such as complement C4-A, immunoglobulin heavy variable 1-2, carboxylic ester hydrolase, fibulin-1, immunoglobulin lambda constant7, secreted phosphoprotein 24 were differentially expressed in individuals with drug resistant HIV as compared to individuals with drug sensitive HIV. Gene ontology of 57 differentially expressed proteins was analysed and documented.
    Language English
    Publishing date 2023-04-30
    Publishing country Singapore
    Document type Journal Article
    ZDB-ID 2203786-X
    ISSN 0973-2063
    ISSN 0973-2063
    DOI 10.6026/97320630019392
    Database MEDical Literature Analysis and Retrieval System OnLINE

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