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Idea plagiarism detection with recurrent neural networks and vector space model

International Journal of Intelligent Computing and Cybernetics 2021年第3期14卷 321-332页

作者： azra nazir Roohie Naaz Mir Shaima Qureshi Department of Computer Science and Engineering National Institute of Technology SrinagarSrinagarIndia

Purpose-Natural languages have a fundamental quality of suppleness that makes it possible to present a single idea in plenty of different *** feature is often exploited in the academic world,leading to the theft of work referred to as *** approaches have been put forward to detect such cases based on various text features and grammatical structures of ***,there is a huge scope of improvement for detecting intelligent ***/methodology/approach-To realize this,the paper introduces a hybrid model to detect intelligent plagiarism by breaking the entire process into three stages:(1)clustering,(2)vector formulation in each cluster based on semantic roles,normalization and similarity index calculation and(3)Summary generation using *** effective weighing scheme has been introduced to select terms used to build vectors based on K-means,which is calculated on the synonym set for the said *** the value calculated in the last stage lies above a predefined threshold,only then the next semantic argument is *** the similarity score for two documents is beyond the threshold,a short summary for plagiarized documents is ***-Experimental results show that this method is able to detect connotation and concealment used in idea plagiarism besides detecting literal ***/value-The proposed model can help academics stay updated by providing summaries of relevant *** would eliminate the practice of plagiarism infesting the academic community at an unprecedented *** model will also accelerate the process of reviewing academic documents,aiding in the speedy publishing of research articles.

关键词： Natural language processing Vector space model Recurrent neural networks Plagiarism detection

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Exploring compression and parallelization techniques for distribution of deep neural networks over Edge-Fog continuum-a review

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International Journal of Intelligent Computing and Cybernetics 2020年第3期13卷 331-364页

作者： azra nazir Roohie Naaz Mir Shaima Qureshi Department of Computer Science and Engineering National Institute of Technology SrinagarSrinagarIndia

Purpose-The trend of“Deep Learning for Internet of Things(IoT)”has gained fresh momentum with enormous upcoming applications employing these models as their processing engine and Cloud as their resource *** this picture leads to underutilization of ever-increasing device pool of IoT that has already passed 15 billion mark in ***,it is high time to explore a different approach to tackle this issue,keeping in view the characteristics and needs of the two *** at the Edge can boost applications with realtime deadlines while complementing ***/methodology/approach-This review paper contributes towards three cardinal directions of research in the field of DL for *** first section covers the categories of IoT devices and how Fog can aid in overcoming the underutilization of millions of devices,forming the realm of the things for *** second direction handles the issue of immense computational requirements of DL models by uncovering specific compression *** appropriate combination of these techniques,including regularization,quantization,and pruning,can aid in building an effective compression pipeline for establishing DL models for IoT *** third direction incorporates both these views and introduces a novel approach of parallelization for setting up a distributed systems view of DL for ***-DL models are growing deeper with every passing ***-coordinated distributed execution of such models using Fog displays a promising future for the IoT application *** is realized that a vertically partitioned compressed deep model can handle the trade-off between size,accuracy,communication overhead,bandwidth utilization,and latency but at the expense of an additionally considerable memory *** reduce the memory budget,we propose to exploit Hashed Nets as potentially favorable candidates for distributed ***,the critical point between accuracy and size for such models needs further i

关键词： Distributed deep neural networks Fog Internet of things Compression Parallelization Paper type Research paper

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