In recent years, social media has been increasing widely and obviously as a media for users expressing their emotions and feelings through thousands of posts and comments related to tourism companies. As a consequence, it became difficult for tourists to read all the comments to determine whether these opinions are positive or negative to assess the success of a tourism company. In this paper, a modest model is proposed to assess e-tourism companies using Iraqi dialect reviews collected from Facebook. The reviews are analyzed using text mining techniques for sentiment classification. The generated sentiment words are classified into positive, negative and neutral comments by utilizing Rough Set Theory, Naïve Bayes and K-Nearest Neighbor methods. After experimental results, it was determined that out of 71 tested Iraqi tourism companies, 28% from these companies have very good assessment, 26% from these companies have good assessment, 31% from these companies have medium assessment, 4% from these companies have acceptance assessment and 11% from these companies have bad assessment. These results helped the companies to improve their work and programs responding sufficiently and quickly to customer demands.
The implementation of an intelligent system for network control and monitoring that is built on an Internet of Things (IoT) is a focus of this line of research, with the end objective of improving the level of precision inside the network and its applications. You did indeed read it correctly; the system that is being referred to here is a deep neural network. The manner that it is constructed makes it possible for the layer that cannot be seen to contain more data. The application of element-modified deep learning and network buffer capacity control helps to improve the overall service quality that is provided by each sensor node. One method that can be applied to the process of instructing a machine to pay more attention includes deep learning in its various incarnations. The team was able to do calculations with a precision of 96.68 percent and the quickest execution time, thanks to the usage of wireless sensors. Using a sensor-based technique that has a brief implementation period, this piece has a degree of accuracy of 97.69 % when it comes to detecting and classifying proxies, and it does so using a method that is very efficient. On the other hand, our research represents a significant leap forward in comparison to earlier studies due to the fact that we were able to accurately identify and categorize a wide variety of invasions and real-time proxies.
Mobile Robot is an extremely essential technology in the industrial world. Optimal path planning is essential for the navigation of mobile robots. The firefly algorithm is a very promising tool of Swarm Intelligence, which is used in various optimization areas. This study used the firefly algorithm to solve the mobile robot path-planning problem and achieve optimal trajectory planning. The objective of the proposed method is to find the free-collision-free points in the mobile robot environment and then generate the optimal path based on the firefly algorithm. It uses the A∗ algorithm to find the shortest path. The essential function of use the firefly algorithm is applied to specify the optimal control points for the corresponding shortest smooth trajectory of the mobile robot. Cubic Polynomial equation is applied to generate a smooth path from the initial point to the goal point during a specified period. The results of computer simulation demonstrate the efficiency of the firefly algorithm in generating optimal trajectory of mobile robot in a variable degree of mobile robot environment complexity.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.