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Menampilkan 1–3 dari 3 artikel
Enhanced Vision Transformer and Transfer Learning Approach to Improve Rice Disease Recognition
Rachman, Rahadian Kristiyanto
; Setiadi, De Rosal Ignatius Moses
; Susanto, Ajib
; Nugroho, Kristiawan
; Islam, Hussain Md Mehedul
Journal of Computing Theories and Applications
Vol 1
, No 4
(2024)
In the evolving landscape of agricultural technology, recognizing rice diseases through computational models is a critical challenge, predominantly addressed through Convolutional Neural Networks (CNN). However, the localized feature extraction of CNNs often falls short in complex scenarios, necessitating a shift towards models capable of global contextual understanding. Enter the Vision Transformer (ViT), a paradigm-shifting deep learning model that leverages a self-attention mechanism to trans...
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27 Sitasi
Exploring DQN-Based Reinforcement Learning in Autonomous Highway Navigation Performance Under High-Traffic Conditions
Nugroho, Sandy
; Setiadi, De Rosal Ignatius Moses
; Islam, Hussain Md Mehedul
Journal of Computing Theories and Applications
Vol 1
, No 3
(2024)
Driving in a straight line is one of the fundamental tasks for autonomous vehicles, but it can become complex and challenging, especially when dealing with high-speed highways and dense traffic conditions. This research aims to explore the Deep-Q Networking (DQN) model, which is one of the reinforcement learning (RL) methods, in a highway environment. DQN was chosen due to its proficiency in handling complex data through integrated neural network approximations, making it capable of addressing h...
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5 Sitasi
Butterflies Recognition using Enhanced Transfer Learning and Data Augmentation
Adityawan, Harish Trio
; Farroq, Omar
; Santosa, Stefanus
; Islam, Hussain Md Mehedul
; Sarker, Md Kamruzzaman
; Setiadi, De Rosal Ignatius Moses
Journal of Computing Theories and Applications
Vol 1
, No 2
(2023)
Butterflies’ recognition serves a crucial role as an environmental indicator and a key factor in plant pollination. The automation of this recognition process, facilitated by Convolutional Neural Networks (CNNs), can expedite this task. Several pre-trained CNN models, such as VGG, ResNet, and Inception, have been widely used for this purpose. However, the scope of previous research has been somewhat constrained, focusing only on a maximum of 15 classes. This study proposes to modify the CNN Ince...
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14 Sitasi