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การวิเคราะห์การมีส่วนร่วมของนักเรียนในห้องเรียนออนไลน์ โดยใช้ Convolutional Neural Networks (CNN)

วารสารงานวิจัยและพัฒนาเชิงประยุกต์ โดยสมาคม ECTI, 2023
การระบาดของเชื้อไวรัสโคโรนา (COVID-19) ส่งผลกระทบในภาคการศึกษา เช่น การเรียนจาก ห้องเรียนปกติสู่ห้องเรียนออนไลน์ ทำให้การติดตามการมีส่วนร่วมในห้องเรียนออนไลน์เป็นไปด้วยความ ยากลำบาก นอกจากจะส่งผลต่อประสิทธิภาพของผู้เรียนแล้ว กรณีที่ร้ายแรงที่สุดที่อาจจะเกิดขึ้นคือการ หลุดจากการศึกษาของผู้เรียน เพื่อให้ผู้สอนได้ทราบถึงการมีส่วนร่วมของผู้เรียนและสามารถปรั
openaire   +1 more source

Empirical Remarks on the Translational Equivariance of Convolutional Layers

Applied Sciences, 2020
In general, convolutional neural networks (CNNs) maintain some level of translational invariance. However, the convolutional layer itself is translational-equivariant. The pooling layers provide some level of invariance. In object recognition, invariance
Kyung Joo Cheoi, Hyeonyeong Choi, Jaepil Ko   +2 more
doaj   +1 more source

Creating Deep Convolutional Neural Networks for Image Classification

The Programming Historian, 2023
This lesson provides a beginner-friendly introduction to convolutional neural networks (CNNs) for image classification. The tutorial provides a conceptual understanding of how neural networks work by using Google’s Teachable Machine to train a model on ...
Nabeel Siddiqui
doaj   +1 more source

Fine Tuning Hyperparameters of Deep Learning Models Using Metaheuristic Accelerated Particle Swarm Optimization Algorithm

IEEE Access
In recent years, Convolutional Neural Networks (CNNs) have emerged as powerful tools for solving complex real-world problems, particularly in the domain of image processing.
Abdel-Hamid M. Emara, Ghada Atteia, Jawad Hasan Alkhateeb   +2 more
doaj   +1 more source

YOLO Network with a Circular Bounding Box to Classify the Flowering Degree of Chrysanthemum

AgriEngineering, 2023
Detecting objects in digital images is challenging in computer vision, traditionally requiring manual threshold selection. However, object detection has improved significantly with convolutional neural networks (CNNs), and other advanced algorithms, like
Hee-Mun Park, Jin-Hyun Park
doaj   +1 more source

Improved Handwritten Digit Recognition Using Convolutional Neural Networks (CNN) [PDF]

Sensors, 2020
Traditional systems of handwriting recognition have relied on handcrafted features and a large amount of prior knowledge. Training an Optical character recognition (OCR) system based on these prerequisites is a challenging task. Research in the handwriting recognition field is focused around deep learning techniques and has achieved breakthrough ...
Savita Ahlawat, Amit Choudhary, Anand Nayyar, Saurabh Singh, Byungun Yoon   +4 more
openaire   +3 more sources

Convolutional Neural Networks in the Inspection of Serrasalmids (Characiformes) Fingerlings

Animals
Aquaculture produces more than 122 million tons of fish globally. Among the several economically important species are the Serrasalmidae, which are valued for their nutritional and sensory characteristics.
Marília Parreira Fernandes, Adriano Carvalho Costa, Heyde Francielle do Carmo França, Alene Santos Souza, Pedro Henrique de Oliveira Viadanna, Lessandro do Carmo Lima, Liege Dauny Horn, Matheus Barp Pierozan, Isabel Rodrigues de Rezende, Rafaella Machado dos S. de Medeiros, Bruno Moraes Braganholo, Lucas Oliveira Pereira da Silva, Jean Marc Nacife, Kátia Aparecida de Pinho Costa, Marco Antônio Pereira da Silva, Rodrigo Fortunato de Oliveira   +15 more
doaj   +1 more source

The effect of image resolution on convolutional neural networks in breast ultrasound

Heliyon, 2023
Purpose: The objective of this research was to investigate the efficacy of various parameter combinations of Convolutional Neural Networks (CNNs) models, namely MobileNet and DenseNet121, and different input image resolutions (REZs) ranging from 64×64 to
Shuzhen Tang, Chen Jing, Yitao Jiang, Keen Yang, Zhibin Huang, Huaiyu Wu, Chen Cui, Siyuan Shi, Xiuqin Ye, Hongtian Tian, Di Song, Jinfeng Xu, Fajin Dong   +12 more
doaj   +1 more source

Humans can decipher adversarial images

Nature Communications, 2019
Convolutional Neural Networks (CNNs) have reached human-level benchmarks in classifying images, but they can be “fooled” by adversarial examples that elicit bizarre misclassifications from machines.
Zhenglong Zhou, Chaz Firestone
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Deep Learning–Assisted Differentiation of Four Peripheral Neuropathies Using Corneal Confocal Microscopy

Annals of Clinical and Translational Neurology, EarlyView.
ABSTRACT Objective Peripheral neuropathies contribute to patient disability but may be diagnosed late or missed altogether due to late referral, limitation of current diagnostic methods and lack of specialized testing facilities. To address this clinical gap, we developed NeuropathAI, an interpretable deep learning–based multiclass classification ...
Chaima Ben Rabah, Ioannis N. Petropoulos, Mark Stettner, Maryam Ferdousi, Uazman Alam, Nathan Efron, Ahmed Serag, Rayaz A. Malik   +7 more
wiley   +1 more source