Web Recommendation by Exploiting User Profile and Collaborative Filtering

Tiwari, Raj Gaurang; Khullar, Vikas; Garg, Kamal Deep

doi:10.1007/978-981-16-8248-3_27

Cited by 3 publications

(2 citation statements)

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“…The majority of modern deep learning applications depend on transfer learning, especially in the field of computer vision [18], [19]. By using a model that was developed for one task and then applied to another, this technique is known as "transfer learning".…”

Section: Deep Learning In Medical Imagingmentioning

confidence: 99%

Skin cancer diagnosis using the deep learning advancements: a technical review

Pandey,

Shankhdhar

2024

Bulletin EEI

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It is vital in today's technologically advanced society to combat skin cancer using machines rather than human intervention. Any time the look of the skin changes abnormally, there is a danger that the person might be at risk for skin cancer. Dermatology expertise and computer vision methods must be merged to diagnose melanoma more effectively. Because of this, it is necessary to learn about numerous detection methods to help doctors discover skin cancer at an early stage. This research paper provides a comprehensive technical review of the advancements in using deep learning techniques for the diagnosis of skin cancer. Since skin cancer is so prevalent, early identification is essential for better treatment results. Among the medical uses where deep learning, a kind of machine learning, has shown promise is in the identification of skin cancer. This research investigates the most cutting-edge skin cancer diagnostic deep-learning approaches, datasets, and assessment metrics currently in use. This study discusses the benefits and drawbacks of using deep learning for skin cancer detection. Challenges include ethical and privacy considerations about patient data, the incorporation of models into clinical procedures, and problems with dataset bias and generalisation.

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Section: Deep Learning In Medical Imagingmentioning

confidence: 99%

Skin cancer diagnosis using the deep learning advancements: a technical review

Pandey,

Shankhdhar

2024

Bulletin EEI

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“…There are two general methods to construct users' profile information. One is the preference model, and the other involves users' data history [26,27]. Technically, the profile information can be converted to a discrete score or number rating.…”

Section: Content-based Filteringmentioning

confidence: 99%

Developing an Intelligent Recommendation System for Non-Information and Communications Technology Major University Students

Kim,

Lim

2023

Applied Sciences

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Various services and applications based on information and communications technology (ICT) are converging with cultural aspects of historical implementations. At the same time, developing a convergence course for non-ICT majors is becoming increasingly popular in universities. In this paper, we develop an AI application course for non-ICT major university students toward convergence with recommendation systems and Silk Road studies. Based on our five-year research on the martial arts, dance, and play of seven Silk Road countries, we have created and categorized an accessible database for 177 items in those countries. For our convergence course, we measure the similarity between the items for summary and perform collaborative filtering based on alternating least squares (ALS) matrix factorization so that our prototyped intelligent recommendation engine can predict the items in which a user might be interested. The course is designed to teach non-ICT major university students not only historical aspects of the Silk Road but also implementation aspects of recommendation systems with web services.

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