Outlier detection in neutrosophic sets by using rough entropy based weighted density method

Sangeetha, T.; Amalanathan, Geetha Mary

doi:10.1049/trit.2019.0093

Cited by 17 publications

(8 citation statements)

References 40 publications

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“…is tabular comparison reveals the effectiveness of the presented method evaluating its credibility for sentence recognition and semantic model construction. is method utilizes structural modeling in order to create a link between natural language processing and the object-oriented knowledge system [56][57][58][59][60].…”

Section: Validation Of the Object-oriented Knowledge-basedmentioning

confidence: 99%

Research on the Natural Language Recognition Method Based on Cluster Analysis Using Neural Network

Liu

Sharma

et al. 2021

Mathematical Problems in Engineering

119

108

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Withthe technological advent, the clustering phenomenon is recently being used in various domains and in natural language recognition. This article contributes to the clustering phenomenon of natural language and fulfills the requirements for the dynamic update of the knowledge system. This article proposes a method of dynamic knowledge extraction based on sentence clustering recognition using a neural network-based framework. The conversion process from natural language papers to object-oriented knowledge system is studied considering the related problems of sentence vectorization. This article studies the attributes of sentence vectorization using various basic definitions, judgment theorem, and postprocessing elements. The sentence clustering recognition method of the network uses the concept of prereliability as a measure of the credibility of sentence recognition results. An ART2 neural network simulation program is written using MATLAB, and the effect of the neural network on sentence recognition is utilized for the corresponding analysis. A postreliability evaluation indexing is done for the credibility of the model construction, and the implementation steps for the conjunctive rule sentence pattern are specifically introduced. A new method of structural modeling is utilized to generate the structured derivation relationship, thus completing the natural language knowledge extraction process of the object-oriented knowledge system. An application example with mechanical CAD is used in this work to demonstrate the specific implementation of the example, which confirms the effectiveness of the proposed method.

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Section: Validation Of the Object-oriented Knowledge-basedmentioning

confidence: 99%

Research on the Natural Language Recognition Method Based on Cluster Analysis Using Neural Network

Liu

Sharma

et al. 2021

Mathematical Problems in Engineering

119

108

View full text Add to dashboard Cite

show abstract

“…ese are classified as different scenarios so that the lapse of the architecture in the aspect of security or, in other words, the weak points and vulnerabilities could be identified. To further address the security vulnerabilities within the architecture and to pave the way for future scope of this work entropy-based algorithms [24], machine-learning approaches and genetic algorithms [25] could be considered within the architecture.…”

Section: Evolving Architecture: In the Perspective Of Security Behavior And Lapsesmentioning

confidence: 99%

Software-Defined Networking: An Evolving Network Architecture—Programmability and Security Perspective

Kaliyamurthy

Taterh

Shanmugasundaram

et al. 2021

Security and Communication Networks

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Software-defined networking is an evolving network architecture beheading the traditional network architecture focusing its disadvantages in a limited perspective. A couple of decades before, programming and networking were viewed as different domains which today with the lights of SDN bridging themselves together. This is to overcome the existing challenges faced by the networking domain and an attempt to propose cost-efficient effective and feasible solutions. Changes to the existing network architecture are inevitable considering the volume of connected devices and the data being held together. SDN introduces a decoupled architecture and brings customization within the network making it easy to configure, manage, and troubleshoot. This paper focuses on the evolving network architecture, the software-defined networking. Unlike a generic view on the evolving network, which makes work as a review, this work addresses various perspectives of the architecture leaving it an intermediate work in between the review of the literature and implementation, contributing towards factors like the design, programmability, security, security behaviors, and security lapses. This paper also analyses various weak points of the architecture and evolves the attack vectors in each plane leaving a conclusion to further progress towards identifying the impacts of the attacks and proposing mitigation strategies.

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“…Currently, object detection achieved a lot of progress due to the use of GA, PSO, ANN, and DCNNs [13][14][15][16][17][18]. Modern object detectors founded on these networks-such as Faster R-CNN [19], SSD [20], and YOLOv3 [21]-are now robust enough to be deployed in customer products (e.g., Google Photos and Pinterest Visual Search), and some are fast enough to be run on mobile devices such as MobileNet.…”

Section: Introductionmentioning

confidence: 99%

Computer Vision-Based Wildfire Smoke Detection Using UAVs

Rahman

Khan

Algarni

et al. 2021

Mathematical Problems in Engineering

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This paper presents a new methodology based on texture and color for the detection and monitoring of different sources of forest fire smoke using unmanned aerial vehicles (UAVs). A novel dataset has been gathered comprised of thin smoke and dense smoke generated from the dry leaves on the floor of the forest, which is a source of igniting forest fires. A classification task has been done by training a feature extractor to check the feasibility of the proposed dataset. A meta-architecture is trained above the feature extractor to check the dataset viability for smoke detection and tracking. Results have been obtained by implementing the proposed methodology on forest fire smoke images, smoke videos taken on a stand by the camera, and real-time UAV footages. A microaverage F1-score of 0.865 has been achieved with different test videos. An F1-score of 0.870 has been achieved on real UAV footage of wildfire smoke. The structural similarity index has been used to show some of the difficulties encountered in smoke detection, along with examples.

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Outlier detection in neutrosophic sets by using rough entropy based weighted density method

Cited by 17 publications

References 40 publications

Research on the Natural Language Recognition Method Based on Cluster Analysis Using Neural Network

Research on the Natural Language Recognition Method Based on Cluster Analysis Using Neural Network

Software-Defined Networking: An Evolving Network Architecture—Programmability and Security Perspective

Computer Vision-Based Wildfire Smoke Detection Using UAVs

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