RETRACTED ARTICLE: Two phase cluster validation approach towards measuring cluster quality in unstructured and structured numerical datasets

Kumar, Sushant; Ahmed, Syed Thouheed; Vigneshwaran, P.; Sandeep, H.; Singh, Harinder

doi:10.1007/s12652-020-02487-w

Cited by 23 publications

(8 citation statements)

References 22 publications

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“…Unicast authentication proves the message is sent to only one node. Multicast authentication proves the message is sent to many nodes [18]. Broadcast authentication proves that the message is sent to all nodes in the network.…”

Section: Authentication Techniques In Vanetsmentioning

confidence: 99%

VANET’s Security, Privacy and Authenticity: A Study

Parveen¹,

Ahmed²,

Gulmeher³

et al. 2021

Proceedings of the Fist International Conference on Advanced Scientific Innovation in Science, Engineering and Technology, ICA

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Vehicular Adhoc Network's (VANET) has gained popularity and focus of research in recent days, due to their distinctive characteristics like frequently changing topology with predictable mobility. Much attention of Industry and academia both are attracted towards VANETs. Security of data is a crucial aspect of safety related applications of vehicular networks due to their distributed nature and mobility of vehicular ad hoc networks VANETs a critical challenge arises such as collisions of uncoordinated data transmissions and unstable topologies. This paper presents the basic vehicular ad hoc network's architecture then shows research issues. This paper provides a survey of research perspective of main aspects of VANETs such as general authentication and security techniques.

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Section: Authentication Techniques In Vanetsmentioning

confidence: 99%

VANET’s Security, Privacy and Authenticity: A Study

Parveen¹,

Ahmed²,

Gulmeher³

et al. 2021

Proceedings of the Fist International Conference on Advanced Scientific Innovation in Science, Engineering and Technology, ICA

Self Cite

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“…These MooM datasets include a signal processing unit for a standardized approach and can be used for intimated processing in the proposed study, with a supported algorithm from ( 10 ). The method of detecting and validating speech signals is also proposed in this article, based on the influence of telemedicine approaches with a numerical clustering validation by ( 11 ) and ( 12 ).…”

Section: Literature Surveymentioning

confidence: 99%

Neural Network Based Mental Depression Identification and Sentiments Classification Technique From Speech Signals: A COVID-19 Focused Pandemic Study

Ahmed

Singh

Basha

et al. 2021

Front. Public Health

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COVID-19 (SARS-CoV-2) was declared as a global pandemic by the World Health Organization (WHO) in February 2020. This led to previously unforeseen measures that aimed to curb its spread, such as the lockdown of cities, districts, and international travel. Various researchers and institutions have focused on multidimensional opportunities and solutions in encountering the COVID-19 pandemic. This study focuses on mental health and sentiment validations caused by the global lockdowns across the countries, resulting in a mental disability among individuals. This paper discusses a technique for identifying the mental state of an individual by sentiment analysis of feelings such as anxiety, depression, and loneliness caused by isolation and pauses to the normal chains of operations in daily life. The research uses a Neural Network (NN) to resolve and extract patterns and validate threshold trained datasets for decision making. This technique was used to validate 2,173 global speech samples, and the resulting accuracy of mental state and sentiments are identified with 93.5% accuracy in classifying the behavioral patterns of patients suffering from COVID-19 and pandemic-influenced depression.

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“…A literature survey is brief summary of previous research work on a particular topic [6] [8] [9]. The work should enumerate, summarize and objectively evaluate the previous research.…”

Section: Literature Surveymentioning

confidence: 99%

An Improved Approach of Unstructured Text Document Classification Using Predetermined Text Model and Probability Technique

Ahmed¹,

Flora²,

Hemanth³

et al. 2021

Proceedings of the Fist International Conference on Advanced Scientific Innovation in Science, Engineering and Technology, ICA

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Document classification is the task to split the document set into distinct highly relative classes or groups based on nature of the document contents.Here, an improved approach of document classification called keywordbased document classification (KBDC) is introduced. It focuses on splitting the unstructured text document set into K number of dissimilar classes based on K predetermined keywords text models by improved probability technique. This new system comprises of the following stages. Namely, pre-processing, classification and classifier stage respectively. Initial, the proposed system (KBDC) recognizes all the immaterial existing contents in the input text document through constructed Predetermined Irrelevant Text Pattern Model (PITPM). Next, it divides the pre-processed document set into 'K' different groups or classes by K number of Predetermined Keyword Text Pattern Models (PKTPM) through probability technique, where K denotes the number of groups or classes or models. Finally, the KBDC system classifies the trial test text document without any class label that belongs to either of the existing group based on the K different class models (PKTPs). Experimentation results show that the KBDC is appropriate to split and identifies the unstructured text document set into K distinct extremely comparative classes.

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RETRACTED ARTICLE: Two phase cluster validation approach towards measuring cluster quality in unstructured and structured numerical datasets

Cited by 23 publications

References 22 publications

VANET’s Security, Privacy and Authenticity: A Study

VANET’s Security, Privacy and Authenticity: A Study

Neural Network Based Mental Depression Identification and Sentiments Classification Technique From Speech Signals: A COVID-19 Focused Pandemic Study

An Improved Approach of Unstructured Text Document Classification Using Predetermined Text Model and Probability Technique

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