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Proceedings ArticleDOI

Learning Automata Based Sentiment Analysis for recommender system on cloud

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TLDR
Experiments indicate that by using LA, the proposed Learning Automata-Based Sentiment Analysis System (LASA) can improve the performance of the proposed system, and, thus, help a user to find a specific location according to the need.
Abstract
The development of personalized recommendation systems has been an interesting research topic after the rapid evolution in social networking sites. In this paper, we propose a recommendation system using Learning automata (LA) and sentiment analysis. LA is used to optimize the recommendation score produced by the proposed system using sentiment analysis. The proposed Learning Automata-Based Sentiment Analysis System (LASA) recommends the places nearby the current location of the users by analyzing the feedback from the places and thus calculating the score based on it. Experiments performed by us indicate that by using LA, we can improve the performance of the proposed system, and, thus, help a user to find a specific location according to the need.

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Journal ArticleDOI

Cloud Computing Applications for Smart Grid: A Survey

TL;DR: This survey presents a synthesized overview of the current state of research on smart grid development, and identifies the current research problems in the areas of cloud-based energy management, information management, and security in smart grid.
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A cloud computing framework on demand side management game in smart energy hubs

TL;DR: This paper modifies the classic Energy Hub model to present an upgraded model in the smart environment entitling “Smart Energy Hub”, and uses game theory to model the demand side management among the smart energy hubs.
Journal ArticleDOI

Cloud services recommendation

TL;DR: The results of the present review revealed that previous studies contributed scalability and accuracy to the recommender system, but the contribution of the trust and security improvement has not been considerable well.
Patent

Relativistic sentiment analyzer

TL;DR: Sentiment analyzer systems may include feedback analytics servers configured to receive and analyze feedback data from various client devices Feedback data may be received and analyzed to determine feedback context and sentiment scores in some embodiments, natural language processing neural networks may be used to determine sentiment scores for the feedback data as discussed by the authors.
Journal ArticleDOI

QoS-Guaranteed Bandwidth Shifting and Redistribution in Mobile Cloud Environment

TL;DR: This paper identifies, formulate, and addresses the problem of QoS-guaranteed bandwidth shifting and redistribution among the interfacing gateways for maximizing their utility, and formulate bandwidth redistribution as a utility maximization problem, and solve it using a modified descending bid auction.
References
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Book

Learning Algorithms Theory and Applications

TL;DR: Theory and Applications: Two-Person Zero-Sum Sequential, Stochastic Games with Imperfect and Incomplete Information - General Case, and The LAR?P - Algorithm and Statement of Results.
Book

Learning Automata: Theory and Applications

TL;DR: The connection between two level adaptive control and bilinear programming problem and two level hierarchical system of learning automata using a projectional stochastic approximation algorithm is studied.
Journal ArticleDOI

Lacas: learning automata-based congestion avoidance scheme for healthcare wireless sensor networks

TL;DR: The proposed algorithm, named as learning automata-based congestion avoidance algorithm in sensor networks (LACAS), can counter the congestion problem in healthcare WSNs effectively and improves its performance significantly as time progresses.
BookDOI

Networks of Learning Automata

TL;DR: Reading is a hobby to open the knowledge windows and by this way, concomitant with the technology development, many companies serve the e-book or book in soft file.
Proceedings ArticleDOI

A Scalable, Accurate Hybrid Recommender System

TL;DR: A unique cascading hybrid recommendation approach by combining the rating, feature, and demographic information about items is proposed that outperforms the state of the art recommender system algorithms, eliminates recorded problems with recommender systems.
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