Matches in SemOpenAlex for { <https://semopenalex.org/work/W4366991076> ?p ?o ?g. }
- W4366991076 endingPage "5335" @default.
- W4366991076 startingPage "5335" @default.
- W4366991076 abstract "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)." @default.
- W4366991076 created "2023-04-27" @default.
- W4366991076 creator A5058596471 @default.
- W4366991076 creator A5081804645 @default.
- W4366991076 creator A5083828165 @default.
- W4366991076 date "2023-04-24" @default.
- W4366991076 modified "2023-10-14" @default.
- W4366991076 title "Computer Vision Techniques for Growth Prediction: A Prisma-Based Systematic Literature Review" @default.
- W4366991076 cites W1996760952 @default.
- W4366991076 cites W2003763337 @default.
- W4366991076 cites W2201742285 @default.
- W4366991076 cites W2327084999 @default.
- W4366991076 cites W2479748558 @default.
- W4366991076 cites W2536228793 @default.
- W4366991076 cites W2570728212 @default.
- W4366991076 cites W2593224872 @default.
- W4366991076 cites W2599374588 @default.
- W4366991076 cites W2754753428 @default.
- W4366991076 cites W2762935255 @default.
- W4366991076 cites W2766023553 @default.
- W4366991076 cites W2766110453 @default.
- W4366991076 cites W2766739066 @default.
- W4366991076 cites W2766956330 @default.
- W4366991076 cites W2768546357 @default.
- W4366991076 cites W2768701195 @default.
- W4366991076 cites W2774685105 @default.
- W4366991076 cites W2792523251 @default.
- W4366991076 cites W2793183233 @default.
- W4366991076 cites W2810069835 @default.
- W4366991076 cites W2888307014 @default.
- W4366991076 cites W2912554431 @default.
- W4366991076 cites W2919712930 @default.
- W4366991076 cites W2943695642 @default.
- W4366991076 cites W2944628717 @default.
- W4366991076 cites W2963073614 @default.
- W4366991076 cites W2970572493 @default.
- W4366991076 cites W2970636436 @default.
- W4366991076 cites W2977118703 @default.
- W4366991076 cites W2979413615 @default.
- W4366991076 cites W2979752408 @default.
- W4366991076 cites W2989466510 @default.
- W4366991076 cites W2998767467 @default.
- W4366991076 cites W3001104519 @default.
- W4366991076 cites W3035839299 @default.
- W4366991076 cites W3036293105 @default.
- W4366991076 cites W3039080781 @default.
- W4366991076 cites W3040592542 @default.
- W4366991076 cites W3046541806 @default.
- W4366991076 cites W3079760979 @default.
- W4366991076 cites W3082655606 @default.
- W4366991076 cites W3082841980 @default.
- W4366991076 cites W3087129589 @default.
- W4366991076 cites W3087478415 @default.
- W4366991076 cites W3090376474 @default.
- W4366991076 cites W3092044059 @default.
- W4366991076 cites W3097582741 @default.
- W4366991076 cites W3102506366 @default.
- W4366991076 cites W3108628588 @default.
- W4366991076 cites W3112374594 @default.
- W4366991076 cites W3118863954 @default.
- W4366991076 cites W3134231365 @default.
- W4366991076 cites W3146142859 @default.
- W4366991076 cites W3155764197 @default.
- W4366991076 cites W3164561746 @default.
- W4366991076 cites W3167823354 @default.
- W4366991076 cites W3172607018 @default.
- W4366991076 cites W3184216761 @default.
- W4366991076 cites W3194371359 @default.
- W4366991076 cites W3194480098 @default.
- W4366991076 cites W3200987578 @default.
- W4366991076 cites W3204748455 @default.
- W4366991076 cites W4200028113 @default.
- W4366991076 cites W4200223346 @default.
- W4366991076 cites W4200410702 @default.
- W4366991076 cites W4205398562 @default.
- W4366991076 cites W4206728824 @default.
- W4366991076 cites W4223526802 @default.
- W4366991076 cites W4224140539 @default.
- W4366991076 cites W4224308360 @default.
- W4366991076 cites W4225831710 @default.
- W4366991076 cites W4226314377 @default.
- W4366991076 cites W4229014397 @default.
- W4366991076 cites W4247331964 @default.
- W4366991076 cites W4289444084 @default.
- W4366991076 cites W4293828043 @default.
- W4366991076 cites W4295233585 @default.
- W4366991076 cites W4306954492 @default.
- W4366991076 cites W4310211342 @default.
- W4366991076 cites W4311731568 @default.
- W4366991076 cites W4313227060 @default.
- W4366991076 doi "https://doi.org/10.3390/app13095335" @default.
- W4366991076 hasPublicationYear "2023" @default.
- W4366991076 type Work @default.
- W4366991076 citedByCount "1" @default.
- W4366991076 countsByYear W43669910762023 @default.
- W4366991076 crossrefType "journal-article" @default.
- W4366991076 hasAuthorship W4366991076A5058596471 @default.
- W4366991076 hasAuthorship W4366991076A5081804645 @default.