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Extracting Features to Classify Students Based on their Academic Performance
Mr. P. Subba Rao
Pages: 1-8 | First Published: 05 Jun 2022
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Abstract
In today's educational climate, developing tools to support students and learning in a traditional or online context is a crucial responsibility. The first stages in employing machine learning techniques to enable such technology centered on forecasting a student's success in terms of marks earned. The disadvantage of these methods is that they are not as effective at predicting low-achieving students. The goal of our efforts is twofold. To begin, we investigate whether badly performing students may be more accurately predicted by recasting the task as a binary classification problem. Second, in order to learn more about the reasons that contribute to bad performance, we created a set of human-interpretable attributes that quantify these aspects. We conduct a study based on these characteristics to identify distinct student groups of interest while also determining their value.
Keywords: Binary classification, Performance, Human Interpretable Attributes

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Instagram Filtering Hashtags using the hits Algorithm and Crowd Tagging
Dr.Vignesh Janarthanan
Pages: 15-19 | First Published: 05 Jun 2022
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Abstract
Instagram is a great place to look for descriptive tags for photographs and other types of information. Inaccordance with the learning by example paradigm, the tags–image pairs can be utilised to train automated image annotation (AIA) systems. In earlier research, we found that, on average. Approximately 22% of Instagram hashtags are related to the image's visual content,accompany, in the sense that they are descriptive hashtags, whereas there are many irrelevant hashtags, in the sense that they are not descriptive hashtags.Stop using hashtags on completely different photographs merely to get more clicks and likes.Enhancement of searchability We provide a revolutionary methodology in this study that is based on the collective intelligence principles that aid in the discovery of those hashtags. We demonstrate this in particular that the use of a modified version of the widely used hyper link induced topic search. In the context of crowd tagging, the (HITS) algorithm provides an effective and consistent method for locating pairs of Instagram photographs and hashtags, resulting in representative and noise-free results. Content-based image retrieval training sets We used crowdsourcing as a proof of concept platform Figure-eight to enable for the collection of collective intelligence in the form of tag selection.For Instagram hashtags, this is known as (crowdtagging). Figure-crowdtagging eight's data is utilised to create bipartite networks in which the first kind of node relates to the annotators and the second type of node corresponds to the annotations input the hashtags they've chosen. The HITS algorithm is used to rank the annotators in the first place,in terms of their efficiency in the crowdtagging activity, and then to find the appropriate hashtags for each situation image.
Keywords: Bipartite graphs, collective intelligence, crowdtagging, FolkRank, hyperlinkinduced topic search (HITS) algorithm, image retrieval, image tagging, Instagram hashtags.

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25. Umamaheswari, M., & Rengarajan, N. (2020). Intelligent exhaustion rate and stability control on underwater wsn with fuzzy based clustering for efficient cost management strategies. Information Systems and e-Business Management, 18(3), 283-294.
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Analysing Reviewer’s Credibility and Sentiment to Build A Profile Model for Product Recommendation of User
Dr. Vignesh Janarthanan
Pages: 20-26 | First Published: 05 Jun 2022
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Abstract
Understanding a particular customers product needs, likes, and dislikes and to make an automation based on it is a very convolute job. This project augments heuristic-driven user interest profiling with reviewer credibility analysis and fine-grained feature sentiment analysis to conceive a vigorous recommendation methodology. The proposed credibility, interest and sentiment enhanced recommendation (CISER) model has five modules: candidate, feature extraction, reviewer credibility analysis, user interest mining, candidate feature sentiment assignment, and recommendation module. Review corpus is given as an input. The first module uses context and sentiment confidence to procure useful, crucial features.To detect the untrustworthy reviews and reviewers, reviewer credibility analysis proffers an approach to weigh reviews according to the parameters of credibility. The user interest mining module, uses fairness of review writing as heuristics for interest-pattern mining. The candidate feature sentiment assignment module comparesexisting features in review based on their fast Text sentiment polarity. The final module uses credible sentiment scoring for purchase recommendations. The proposed recommendation modelutilizes not only numeric reviewsbut also uses sentiment expressions connected with components, customer preference profiles, and reviewer credibility for quantitative analysis of various alternative products. The mean average precision (MAP@1) for CISER is 93%, and MAP@3 is 49%, which is better than current state-of-the-art systems.
Keywords: Recommendation system, sentiment analysis, user credibility, user interest.

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