OntoDSO: an ontological-based dolphin swarm optimization (DSO) approach to perform energy efficient routing in Wireless Sensor Networks (WSNs)

Boopathi, Mythili; Parikh, Swapnil; Awasthi, Aishwary; Malviya, Ashwini; Nachappa, M. N.; Mishra, Awakash; Shyam, Gopal Krishna; Narula, Gagandeep Singh

doi:10.1007/s41870-023-01698-6

Cited by 18 publications

(1 citation statement)

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“…Machine learning is the process by which computers pick up knowledge from many forms of data to produce accurate predictions in a variety of disciplines, such as demand forecasting, enhance accuracy, etc. A tourist recommendation system is thought to be a useful tool for fostering relationships and communication between travellers [6,7]. The recommender system compares the information provided by the tourist destinations or the information/reviews provided by the visitors, use specific Abstract The travel industry's contribution to sustainable development is noteworthy, since it offers travel experiences that are mutually beneficial to both local populations and tourists.…”

Section: Introductionmentioning

confidence: 99%

An intelligent information recommender system (IIRS) for next generation sustainable social tourism industry

Kumar,

Goyal,

Sharma

2024

Int. j. inf. tecnol.

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The travel industry's contribution to sustainable development is noteworthy, since it offers travel experiences that are mutually beneficial to both local populations and tourists. But in order to maximize multiple resources in social tourism, an intelligent information system (IIS) is necessary for giving valuable tourism recommendations. Such systems use cutting-edge technologies like artificial intelligence, machine learning, and data analytics to improve the social tourism sector's overall efficacy and efficiency. The proposed intelligent information recommender system (IIRS) combines a number of different components to handle a number of tourism-related issues, such as impact assessment, community involvement, travel planning, and destination selection with the purpose of promoting sustainable social tourism. Fuzzy C-means clustering (FCM) is used in the proposed system for extraction of features. After extraction of features, the model is trained and tested using ensemble machine learning classifiers such as decision tree (DT) and extreme gradient boosting (XGB). Lastly, the evaluation of the system can be done with distinguished parameters such as accuracy, precision, recall, and F1-score.

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Section: Introductionmentioning

confidence: 99%