Papers by Dr N Lakshmipathi Anantha
Advances in Modelling and Analysis B, 2018
Recommender systems is a big breakthrough for the field of e-commerce. Product recommendation is ... more Recommender systems is a big breakthrough for the field of e-commerce. Product recommendation is challenging task to e-commerce companies. Traditional Recommender Systems provided the solutions in recommending the products. This in turn help companies to generate good revenue. Now a day Deep Learning is using in every domain. Deep Learning techniques in the field of Recommender Systems can be directly applied. Deep Learning has ample number of algorithms. These algorithms can be used to give recommendations to users to purchase products. In this paper performance of Traditional Recommender Systems and Deep Learning-based Recommender Systems are compared.
International journal of advanced computer science and applications/International journal of advanced computer science & applications, 2024
Improved Emoji Identification for Sentence Usinglstm in RNN
Journal of Critical Reviews, 2020
The information available in the web is increasing daily. Searching for anything from web is very... more The information available in the web is increasing daily. Searching for anything from web is very difficult because of availability of huge data and the disadvantage with the searching is, it simply mines data based on the keyword given by the user. It doesn’t consider the context. The only solution for searching problem is Recommendation Systems. From the past 16 years Recommendation systems changed the way of searching the products. Products are like movies, videos, research articles, news, hotels, tours, music etc. Recommendation systems are being used in each field like Ecommerce or M-commerce, social networking, research articles, music and travel. Collaborative filtering, content based filtering, Hybrid approaches and popularity based approaches are used in Recommendation Systems. Collaborative filtering mines data based on the taste and interest of similar users. Collaborative filtering implemented on neighbourhood algorithms. Content based filtering mines data based on simil...
Indonesian Journal of Electrical Engineering and Computer Science, 2021
The concept of machine learning generate best results in health care data, it also reduce the wor... more The concept of machine learning generate best results in health care data, it also reduce the work load of health care industry. This algorithm potentially overcome the issues and find out the novel knowledge for development of medical date in health care industry. In this paper propose a new algorithm for finding the outliers using different datasets. Considering that medical data are analytic of mutually health problems and an activity. The proposed algorithm is working based on supervised and unsupervised learning. This algorithm detects the outliers in medical data. The effectiveness of local and global data factor for outlier detection for medical data in real time. Whatever, the model used in this scenario from their training and testing of medical data. The cleaning process based on the complete attributes of dataset of similarity operations. Experiments are conducted in built in various medical datasets. The statistical outcome describe that the machine learning based outlie...
Ingénierie des systèmes d'information, 2018
Recommender Systems are big breakthrough for the web enabled systems as Recommender Systems have ... more Recommender Systems are big breakthrough for the web enabled systems as Recommender Systems have the capability to analyze the behavior patterns of the user. And these systems are accomplishing the task of recommending the products the users are interested in. Existed models grabbing the insights of the users and items patterns will give satisfactory results to the users. This paper uses pretrained models to extract the knowledge from the data using the concept of transfer learning. Our models use the knowledge of pretrained models to extract patterns between users and items. To facilitate this objective, in this paper we presented our approach to generate recommendations in two phases. In the Classification phase, classification of product images and its experimental analysis following, the Ranking phase to rank the product images to the user and its experimental analysis are discussed. The result analysis discussed in this paper achieved promising results.
Ciphertext-Policy Attribute-Based Encryption for Access Control of Data in Cloud
International Journal of Software Engineering and Its Applications, 2016
International Journal of u- and e- Service, Science and Technology, 2016
The commitment of measurements to information mining can be followed back to the work by Bayes in... more The commitment of measurements to information mining can be followed back to the work by Bayes in 1763. The business organizations gather information and offer it to the Data Marts. The individuals who run little and medium association needs to set up information warehousing to touch base, best case scenario arrangement. Such datasets contain part of missing qualities, at some point the missing qualities range from 10% to 33%. A portion of the information might be fundamental; to recall such information is a troublesome undertaking and this kind of datasets won't yield better arrangement, to take care of this issue the Expectation Maximization (EM) calculation gauges missing qualities. Utilizing EM Algorithm the outcomes are supplanted in the missing positions of the specific information which serves to exact conclusion. In this paper, point estimators were connected, among which EM calculation gives best gauge. It is watched that the more straightforward models by and large yield the best results.
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Papers by Dr N Lakshmipathi Anantha