Title: Improving Neural Network-Based Multi-Label Classification with Pattern Loss Penalties
<br>Cover Date: 2024-01-01
<br>Cover Display Date: 2024
<br>DOI: 10.1109/ACCESS.2024.3386841
<br>Description: This research work introduces two novel loss functions, pattern-loss (POL) and label similarity-based instance modeling (LSIM), for improving the performance of multi-label classification using artificial neural network-based techniques. These loss functions incorporate additional optimization constraints based on the distribution of multi-label class patterns and the similarity of data instances. By integrating these patterns during the network training process, the trained model is tuned to align with the existing patterns in the training data. The proposed approach decomposes the loss function into two components: the cross entropy loss and the pattern loss derived from the distribution of class-label patterns. Experimental evaluations were conducted on eight standard datasets, comparing the proposed methods with three existing techniques.The results demonstrate the effectiveness of the proposed approach, with POL and LSIM consistently achieving superior accuracy performance compared to the benchmark methods.
<br>Citations: 3
<br>Aggregation Type: Journal
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<br><br><br>Title: Improving the Performance of CNN by Using Dominant Patterns of CNN for Hand Detection
<br>Cover Date: 2023-06-01
<br>Cover Display Date: June 2023
<br>DOI: 10.37936/ecti-cit.2023172.251265
<br>Description: Many applications have used hand gestures for software interaction, image-and video-based action analysis, and behavioral monitoring. Hand detection is an essential step in the pipeline of these applications, and Convolutional Neural Networks (CNN) has provided superior solutions. However, CNN has similar features between hand and non-hand images, called non-dominant features. These features affect miss-classifications and long-time computation. Therefore, this paper focuses on the selection of dominant CNN features for hand detection, and it is our proposed method (DP-CNN) that selects the dominant feature patterns (DP) from the trained CNN features and classifies them using the Extreme Learning Machine (ELM) method. Evaluation results show the proposed method (DP-CNN-ELM), which can increase the accuracy and the F1-score of CNN. In addition, the proposed method can reduce the time computation of CNN in training and testing.
<br>Citations: 2
<br>Aggregation Type: Journal
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<br><br><br>Title: Semi-Automated Mushroom Cultivation House using Internet of Things
<br>Cover Date: 2021-07-01
<br>Cover Display Date: July - December 2021
<br>DOI: 10.14456/mijet.2021.24
<br>Description: This research presents an application of the internet of things (IoT) technology. The technology is responsible for checking the temperature and humidity in a mushroom cultivation house and the operation of the IoT control box. It is a semi-automated system that does not rely on farmers' labor. The system can be checked and operated through an application that is installed on the farmer’s smartphone. In the case of offline operation, the system can be controlled manually by farmers. We designed a software and control system for the IoT control box with concern for the needs of farmers. Therefore, we can develop a suitable IoT control box that can be following farmers' needs. The farmer used the application for four months before their satisfaction was evaluated. The results showed that the semi-automated system obtained a high satisfaction rate towards system. However, when asked about “The value in using the internet of things technology to control the mushroom cultivation,” The satisfaction was on level 4 because of the high investment cost, including monthly internet cost. That cost might increase the overall production cost. If farmers want to reduce the monthly internet cost, the application architecture will cut the data transmission process via the cloud-connected to smartphones. The application is designed to be controlled through the IoT control box. The control system will be able to work automatically and manually.
<br>Citations: 6
<br>Aggregation Type: Journal
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<br><br><br>Title: Using User Generated Content in Mobile Application to Support Children with Special Needs
<br>Cover Date: 2018-12-20
<br>Cover Display Date: 20 December 2018
<br>DOI: 10.23919/INCIT.2018.8584883
<br>Description: Children with special needs are able to learn and live their life with normal people. They have certain symptoms that might affect their performance in daily life. Parents and teachers are an important part of the child's life. They have to find the way to get child's attention by creating content and media that suitable for them. This could be time consuming and costly. As mobile devices become more accessible to everyone. We propose an application that contains 2D animation of instructional media and interactive games. Moreover, based on the user generated content concept, we allow the parents and teachers to generate content with mobile devices' functionalities. The content is then fresh and more relevant to each particular student. We tested the application with 15 parents and teachers. After they tested the application with their children, the result from the questionnaire shows that 33 percents of users are very satisfied with the application while the remaining 67 percents satisfied with the application. The parents and teachers gave a high satisfaction towards its beneficial with an overall average at 4.6. They would like to use the application again.
<br>Citations: 0
<br>Aggregation Type: Conference Proceeding
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