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Perspicacious Apprehension of HDTbNB Algorithm Opposed to Security Contravention

Intelligent Automation & Soft Computing 2023年第2期35卷 2431-2447页

作者： Shyla vishal bhatnagar NSUT East Campus(Formerly Ambedkar Institute of Advanced Communication Technologies and Research) Guru Gobind Singh Indraprastha UniversityNew Delhi110031India NSUT East Campus(Formerly Ambedkar Institute of Advanced Communication Technologies and Research) New Delhi110031India

The exponential pace of the spread of the digital world has served as one of the assisting forces to generate an enormous amount of informationﬂow-ing over the *** data will always remain under the threat of technolo-gical suffering where intruders and hackers consistently try to breach the security systems by gaining personal information *** this paper,the authors pro-posed the HDTbNB(Hybrid Decision Tree-based Naïve Bayes)algorithm toﬁnd the essential features without data scaling to maximize the model’s performance by reducing the false alarm rate and training period to reduce zero frequency with enhanced accuracy of IDS(Intrusion Detection System)and to further analyze the performance execution of distinct machine learning algorithms as Naïve Bayes,Decision Tree,K-Nearest Neighbors and Logistic Regression over KDD 99 *** performance of algorithm is evaluated by making a comparative analysis of computed parameters as accuracy,macro average,and weighted ***ﬁndings were concluded as a percentage increase in accuracy,precision,sensitiv-ity,speciﬁcity,and a decrease in misclassiﬁcation as 9.3%,6.4%,12.5%,5.2%and 81%.

关键词： Naïve bayes decision tree k-nearest neighbors logistic regression neighbors classiﬁer

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Intelligent Automation & Soft Computing 2023年第4期36卷 205-221页

作者： Navdeep Bohra vishal bhatnagar Amit Choudhary Savita Ahlawat Dinesh Sheoran Ashish Kumari USICT GGSIPUNew Delhi110058India Maharaja Surajmal Institute of Technology New Delhi110058India NSUT East Campus(Formerly AIACTR)New Delhi110031India Maharaja Surajmal Institute New Delhi110058India

The traditional method of doing business has been disrupted by socialmedia. In order to develop the enterprise, it is essential to forecast the level ofinteraction that a new post would receive from social media users. It is possiblefor the user’s interest in any one social media post to be impacted by external factors or to dwindle as a result of changes in his behaviour. The popularity detectionstrategies that are user-based or population-based are unable to keep up with theseshifts, which leads to inaccurate forecasts. This work makes a prediction abouthow popular the post will be and addresses any anomalies caused by factors outside of the study. A novel improved PARAFAC (A-PARAFAC) method that istensor factorization-based has been presented in order to cope with the user criteria that will be used in the future to rate any project. We consolidated the information on the historically popular content, and we accelerated the computation bychoosing the top contents that were most like each other. The tensor is factorisedwith the application of the Adam optimization. It has been modified such that thebias is now included in the gradient function of A-PARAFAC, and the value ofthe bias is updated after each iteration. The prediction accuracy is improved by32.25% with this strategy compared to other state of the art methods.

关键词： Tensor decomposition popularity prediction group level popularity graphical clustering PARAFAC

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