Results 21 to 30 of about 17,494 (194)

Incorporating a Novel Dual Transfer Learning Approach for Medical Images

Sensors, 2023
Recently, transfer learning approaches appeared to reduce the need for many classified medical images. However, these approaches still contain some limitations due to the mismatch of the domain between the source domain and the target domain.
Abdulrahman Abbas Mukhlif, Belal Al-Khateeb, Mazin Abed Mohammed   +2 more
doaj   +1 more source

Face Swapping Detection Based on Multi-Channel Attention Mechanism [PDF]

Jisuanji gongcheng, 2022
In recent years, the face swapping technology based on deep learning has developed rapidly, but the images of face swapping automatically generated by DeepFake may endanger people's privacy and security.To detection the DeepFake images, a deep learning ...
WU Qian, JIA Shijie
doaj   +1 more source

Co-training for Demographic Classification Using Deep Learning from Label Proportions

, 2017
Deep learning algorithms have recently produced state-of-the-art accuracy in many classification tasks, but this success is typically dependent on access to many annotated training examples.
Ardehaly, Ehsan Mohammady, Culotta, Aron
core   +1 more source

A Comparative Analysis of Convolutional Neural Network (CNN): MobileNetV2 and Xception for Butterfly Species Classification

Jurnal Masyarakat Informatika
This study aims to compare the effectiveness and efficiency of two convolutional neural network architectures, MobileNetV2 and Xception, for automated butterfly species classification.
Mehta Pradnyatama, Christy Atika Sari, Eko Hari Rachmawanto, Hussain Md Mehedul Islam   +3 more
doaj   +1 more source

The Bangkok Urbanscapes Dataset for Semantic Urban Scene Understanding Using Enhanced Encoder-Decoder With Atrous Depthwise Separable A1 Convolutional Neural Networks

IEEE Access, 2022
Semantic segmentation is one of the computer vision tasks which is widely researched at present. It plays an essential role to adapt and apply for real-world use-cases, including the application with autonomous driving systems.
Kitsaphon Thitisiriwech, Teerapong Panboonyuen, Pittipol Kantavat, Yuji Iwahori, Boonserm Kijsirikul   +4 more
doaj   +1 more source

Analysis of Deep Learning Implementation Using Xception for Rice Leaf Disease Classification

Journal of Applied Informatics and Computing
Identifying rice leaf diseases plays a crucial role in maintaining agricultural productivity and preventing massive losses. In recent years, deep learning models have shown very promising performance in plant disease classification tasks.
Niken Puspitaningrum, Majid Rahardi
doaj   +1 more source

Benchmark Analysis of Representative Deep Neural Network Architectures

, 2018
This work presents an in-depth analysis of the majority of the deep neural networks (DNNs) proposed in the state of the art for image recognition. For each DNN multiple performance indices are observed, such as recognition accuracy, model complexity ...
Bianco, Simone, Cadene, Remi, Celona, Luigi, Napoletano, Paolo   +3 more
core   +1 more source

Exploration of Data Augmentation in Xception for Waste Classification

Sistemasi: Jurnal Sistem Informasi
The increasing volume of waste worldwide has led to significant challenges related to pollution, waste management, and recycling. These issues require innovative solutions to enhance the waste management ecosystem, such as the implementation of Smart ...
Ariza Ikhlas, Syafri Arlis
doaj   +1 more source

BlockCrime: Blockchain and Deep Learning-Based Collaborative Intelligence Framework to Detect Malicious Activities for Public Safety

Mathematics, 2022
Detecting malicious activity in advance has become increasingly important for public safety, economic stability, and national security. However, the disparity in living standards incites the minds of certain undesirable members of society to commit ...
Dev Patel, Harshil Sanghvi, Nilesh Kumar Jadav, Rajesh Gupta, Sudeep Tanwar, Bogdan Cristian Florea, Dragos Daniel Taralunga, Ahmed Altameem, Torki Altameem, Ravi Sharma   +9 more
doaj   +1 more source

Underwater Fish Detection with Weak Multi-Domain Supervision

, 2019
Given a sufficiently large training dataset, it is relatively easy to train a modern convolution neural network (CNN) as a required image classifier. However, for the task of fish classification and/or fish detection, if a CNN was trained to detect or ...
Bradley, Michael, Konovalov, Dmitry A., Marini, Simone, Saleh, Alzayat, Sankupellay, Mangalam, Sheaves, Marcus   +5 more
core   +1 more source