Web User Profile Generation and Discovery Analysis using LSTM Architecture

Sudhakar, K; Smail, Boussaadi; Reddy, Tatireddy Subba; S, Shitharth; Tripathi, Diwakar Ramanuj; Fahlevi, Mochammad

doi:10.1109/ictacs56270.2022.9988505

Cited by 2 publications

(1 citation statement)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The LSTM algorithm was first introduced in 1995 and had a long history as a machine learning algorithm. However, it still provides highly accurate region detection and stock price prediction results [87][88][89]. LSTMs [31] are trained as outputs by combining several interacting gates.…”

Section: Rq4: Which Mathematical Models Can Be Used To Explain the Ma...mentioning

confidence: 99%

Computer Vision Techniques for Growth Prediction: A Prisma-Based Systematic Literature Review

2023

View full text Add to dashboard Cite

Growth prediction technology is not only a practical application but also a crucial approach that strengthens the safety of image processing techniques. By supplementing the growth images obtained from the original images, especially in insufficient data sets, we can increase the robustness of machine learning. Therefore, predicting the growth of living organisms is an important technology that increases the safety of existing applications that target living organisms and can extend to areas not yet realized. This paper is a systematic literature review (SLR) investigating biological growth prediction based on the PRISMA 2020 guidelines. We systematically survey existing studies from 2017 to 2022 to provide other researchers with current trends. We searched four digital libraries—IEEE Xplore, ACM Digital Library, Science Direct, and Web of Science—and finally analyzed 47 articles. We summarize the methods used, year, features, accuracy, and dataset of each paper. In particular, we explained LSTM, GAN, and STN, the most frequently used methods among the 20 papers related to machine learning (40% of all papers).

show abstract