• Journal of Information Processing Systems
• /
• v.16 no.4
• /
• pp.809-819
• /
• 2020

Gender prediction accuracy increases as convolutional neural network (CNN) architecture evolves. This paper compares voting and ensemble schemes to utilize the already trained five CNN models to further improve gender prediction accuracy. The majority voting usually requires odd-numbered models while the proposed softmax-based voting can utilize any number of models to improve accuracy. The ensemble of CNN models combined with one more fully-connected layer requires further tuning or training of the models combined. With experiments, it is observed that the voting or ensemble of CNN models leads to further improvement of gender prediction accuracy and that especially softmax-based voters always show better gender prediction accuracy than majority voters. Also, compared with softmax-based voters, ensemble models show a slightly better or similar accuracy with added training of the combined CNN models. Softmax-based voting can be a fast and efficient way to get better accuracy without further training since the selection of the top accuracy models among available CNN pre-trained models usually leads to similar accuracy to that of the corresponding ensemble models.

• Journal of the Korea Society of Computer and Information
• /
• v.27 no.3
• /
• pp.33-43
• /
• 2022

In this paper, we propose an ensemble model facilitated by multi-channel palm images with attention U-Net models and pretrained convolutional neural networks (CNNs) for establishing a contactless palm-based user identification system using conventional inexpensive camera sensors. Attention U-Net models are used to extract the areas of interest including hands (i.e., with fingers), palms (i.e., without fingers) and palm lines, which are combined to generate three channels being ped into the ensemble classifier. Then, the proposed palm information-based user identification system predicts the class using the classifier ensemble with three outperforming pre-trained CNN models. The proposed model demonstrates that the proposed model could achieve the classification accuracy, precision, recall, F1-score of 98.60%, 98.61%, 98.61%, 98.61% respectively, which indicate that the proposed model is effective even though we are using very cheap and inexpensive image sensors. We believe that in this COVID-19 pandemic circumstances, the proposed palm-based contactless user identification system can be an alternative, with high safety and reliability, compared with currently overwhelming contact-based systems.

• International Journal of Computer Science & Network Security
• /
• v.24 no.4
• /
• pp.211-221
• /
• 2024

Pakistan is a top producer and exporter of high-quality rice, but traditional methods are still being used for detecting rice diseases. This research project developed an automated rice blast disease diagnosis technique based on deep learning, image processing, and transfer learning with pre-trained models such as Inception V3, VGG16, VGG19, and ResNet50. The modified connection skipping ResNet 50 had the highest accuracy of 99.16%, while the other models achieved 98.16%, 98.47%, and 98.56%, respectively. In addition, CNN and an ensemble model K-nearest neighbor were explored for disease prediction, and the study demonstrated superior performance and disease prediction using recommended web-app approaches.

• Proceedings of the Korean Society of Computer Information Conference
• /
• 2023.01a
• /
• pp.35-38
• /
• 2023

포트홀은 주행하는 자동차와 접촉이 이뤄지면 차체나 운전자에게 충격을 주고 제어를 잃게 하여 도로 위 안전을 위협할 수 있다. 포트홀의 검출을 위한 국내 동향으로는 진동을 이용한 방식과 신고시스템 이용한 방식과 영상 인식을 기반한 방식이 있다. 이 중 영상 인식 기반 방식은 보급이 쉽고 비용이 저렴하나, 컴퓨터 비전 알고리즘은 영상의 품질에 따라 정확도가 달라지는 문제가 있었다. 이를 보완하기 위해 영상 인식 기반의 딥러닝 모델을 사용한다. 따라서, 본 논문에서는 사전 학습된 딥러닝 모델의 정확도 향상을 위한 Feature Level Ensemble 기법을 제안한다. 제안된 기법은 사전 학습된 CNN 모델 중 Test 데이터의 정확도 기준 Top-3 모델을 선정하여 각 딥러닝 모델의 Feature Map을 Concatenate하고 이를 Fully-Connected(FC) Layer로 입력하여 구현한다. Feature Level Ensemble 기법이 적용된 딥러닝 모델은 평균 대비 3.76%의 정확도 향상을 보였으며, Top-1 모델인 ShuffleNet보다 0.94%의 정확도 향상을 보였다. 결론적으로 본 논문에서 제안된 기법은 사전 학습된 모델들을 이용하여 각 모델의 다양한 특징을 통해 기존 모델 대비 정확도의 향상을 이룰 수 있었다.

• The Transactions of the Korea Information Processing Society
• /
• v.13 no.2
• /
• pp.41-47
• /
• 2024

When there were disparities in performance between models trained in the time and frequency domains, even after conducting an ensemble, we observed that the performance of the ensemble was compromised due to imbalances in the individual model performances. Therefore, this paper proposes a leakage detection technique to enhance the accuracy of pipeline leakage detection through a step-wise learning approach that extracts features from both the time and frequency domains and integrates them. This method involves a two-step learning process. In the Stage 1, independent model training is conducted in the time and frequency domains to effectively extract crucial features from the provided data in each domain. In Stage 2, the pre-trained models were utilized by removing their respective classifiers. Subsequently, the features from both domains were fused, and a new classifier was added for retraining. The proposed transfer learning-based feature fusion technique in this paper performs model training by integrating features extracted from the time and frequency domains. This integration exploits the complementary nature of features from both domains, allowing the model to leverage diverse information. As a result, it achieved a high accuracy of 99.88%, demonstrating outstanding performance in pipeline leakage detection.