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Akademický článek

Artificial intelligence assisted common maternal fetal planes prediction from ultrasound images based on information fusion of customized convolutional neural networks

Autor: Fatima Rauf, Muhammad Attique Khan, Hussain M. Albarakati, Kiran Jabeen, Shrooq Alsenan, Ameer Hamza, Sokea Teng, Yunyoung Nam

Publikováno v: Frontiers in Medicine, Vol 11 (2024)

Ultrasound imaging is frequently employed to aid with fetal development. It benefits from being real-time, inexpensive, non-intrusive, and simple. Artificial intelligence is becoming increasingly significant in medical imaging and can assist in resol

Externí odkaz: https://doaj.org/article/d15d59f9b3d24714a1ec1fd35458279f

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Akademický článek

Fruit and vegetable leaf disease recognition based on a novel custom convolutional neural network and shallow classifier

Autor: Syeda Aimal Fatima Naqvi, Muhammad Attique Khan, Ameer Hamza, Shrooq Alsenan, Meshal Alharbi, Sokea Teng, Yunyoung Nam

Publikováno v: Frontiers in Plant Science, Vol 15 (2024)

Fruits and vegetables are among the most nutrient-dense cash crops worldwide. Diagnosing diseases in fruits and vegetables is a key challenge in maintaining agricultural products. Due to the similarity in disease colour, texture, and shape, it is dif

Externí odkaz: https://doaj.org/article/b15be67828674d0ab5a4dbd2bf44f7f5

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Akademický článek

Analyzing Optimal Wearable Motion Sensor Placement for Accurate Classification of Fall Directions

Autor: Sokea Teng, Jung-Yeon Kim, Seob Jeon, Hyo-Wook Gil, Jiwon Lyu, Euy Hyun Chung, Kwang Seock Kim, Yunyoung Nam

Publikováno v: Sensors, Vol 24, Iss 19, p 6432 (2024)

Falls represent a significant risk factor, necessitating accurate classification methods. This study aims to identify the optimal placement of wearable sensors—specifically accelerometers, gyroscopes, and magnetometers—for effective fall-directio

Externí odkaz: https://doaj.org/article/6b038c2cd9e24e3d943a0adbd195cdcb

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