A novel and proposed triad machine learning-based framework for the prognosis of Huntington’s disease

Deepak, Shashikant; Ojha, Ananta; Acharjya, Kalyan; Mishra, Reshmi; Gantra, Amit; Kalaiarasan, C.; Upadhyay, Ramakant; Walke, Suchita

doi:10.1007/s41870-023-01719-4

Cited by 8 publications

(1 citation statement)

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“…Numerous tourism-related sub-fields call for greater accuracy and precision; if they are attained, the industry could experience explosive growth. Machine learning [5] in the tourism sector reduces confusion and produces a more practical method of offering the sector's more advanced capabilities. 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.…”

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%