9 results on '"L. Minh Dang"'
Search Results
2. Deep Learning Enabled Disease Diagnosis for Secure Internet of Medical Things
- Author
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Sultan Ahmad, Shakir Khan, Mohamed Fahad AlAjmi, Ashit Kumar Dutta, L. Minh Dang, Gyanendra Prasad Joshi, and Hyeonjoon Moon
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Biomaterials ,Mechanics of Materials ,Modeling and Simulation ,Electrical and Electronic Engineering ,Computer Science Applications - Published
- 2022
3. Intelligent Satin Bowerbird Optimizer Based Compression Technique for Remote Sensing Images
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M. Saravanan, J. Jayanthi, U. Sakthi, R. Rajkumar, Gyanendra Prasad Joshi, L. Minh Dang, and Hyeonjoon Moon
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Biomaterials ,Mechanics of Materials ,Modeling and Simulation ,Electrical and Electronic Engineering ,Computer Science Applications - Published
- 2022
4. Facial Landmark Detection With Learnable Connectivity Graph Convolutional Network
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Le Quan Nguyen, Van Dung Pham, Yanfen Li, Hanxiang Wang, L. Minh Dang, Hyoung-Kyu Song, and Hyeonjoon Moon
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General Computer Science ,General Engineering ,General Materials Science ,Electrical and Electronic Engineering - Published
- 2022
5. Robust Korean License Plate Recognition Based on Deep Neural Networks
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Yanfen Li, Hyeonjoon Moon, L. Minh Dang, and Hanxiang Wang
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image preprocessing ,Computer science ,02 engineering and technology ,TP1-1185 ,Machine learning ,computer.software_genre ,Biochemistry ,Synthetic data ,Article ,Analytical Chemistry ,Republic of Korea ,0502 economics and business ,0202 electrical engineering, electronic engineering, information engineering ,Electrical and Electronic Engineering ,Instrumentation ,License ,050210 logistics & transportation ,business.industry ,Deep learning ,Chemical technology ,05 social sciences ,deep learning ,Atomic and Molecular Physics, and Optics ,Government (linguistics) ,Rapid rise ,Deep neural networks ,Korean license plate recognition ,020201 artificial intelligence & image processing ,Neural Networks, Computer ,Artificial intelligence ,business ,computer - Abstract
With the rapid rise of private vehicles around the world, License Plate Recognition (LPR) plays a vital role in supporting the government to manage vehicles effectively. However, an introduction of new types of license plate (LP) or slight changes in the LP format can break previous LPR systems, as they fail to recognize the LP. Moreover, the LPR system is extremely sensitive to the conditions of the surrounding environment. Thus, this paper introduces a novel deep learning-based Korean LPR system that can effectively deal with existing challenges. The main contributions of this study include (1) a robust LPR system with the integration of three pre-processing techniques (defogging, low-light enhancement, and super-resolution) that can effectively recognize the LP under various conditions, (2) the establishment of two original Korean LPR approaches for different scenarios, including whole license plate recognition (W-LPR) and single-character license plate recognition (SC-LPR), and (3) the introduction of two Korean LPR datasets (synthetic data and real data) involving a new type of LP introduced by the Korean government. Through several experiments, the proposed LPR framework achieved the highest recognition accuracy of 98.94%.
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- 2021
6. Vision-Based Defect Inspection and Condition Assessment for Sewer Pipes: A Comprehensive Survey
- Author
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Yanfen Li, Hanxiang Wang, L. Minh Dang, Hyoung-Kyu Song, and Hyeonjoon Moon
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Benchmarking ,Electrical and Electronic Engineering ,Biochemistry ,Instrumentation ,Algorithms ,Atomic and Molecular Physics, and Optics ,Analytical Chemistry - Abstract
Due to the advantages of economics, safety, and efficiency, vision-based analysis techniques have recently gained conspicuous advancements, enabling them to be extensively applied for autonomous constructions. Although numerous studies regarding the defect inspection and condition assessment in underground sewer pipelines have presently emerged, we still lack a thorough and comprehensive survey of the latest developments. This survey presents a systematical taxonomy of diverse sewer inspection algorithms, which are sorted into three categories that include defect classification, defect detection, and defect segmentation. After reviewing the related sewer defect inspection studies for the past 22 years, the main research trends are organized and discussed in detail according to the proposed technical taxonomy. In addition, different datasets and the evaluation metrics used in the cited literature are described and explained. Furthermore, the performances of the state-of-the-art methods are reported from the aspects of processing accuracy and speed.
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- 2022
7. Multiple Object Tracking in Deep Learning Approaches: A Survey
- Author
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Yesul Park, Dongil Han, Hyeonjoon Moon, L. Minh Dang, and Su-Jin Lee
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Information retrieval ,TK7800-8360 ,Computer Networks and Communications ,Computer science ,business.industry ,Deep learning ,association ,occlusion ,Object (computer science) ,Motion (physics) ,Categorization ,Hardware and Architecture ,Control and Systems Engineering ,Video tracking ,Signal Processing ,Trajectory ,Benchmark (computing) ,ID switch ,Artificial intelligence ,Electronics ,Electrical and Electronic Engineering ,Set (psychology) ,business ,multiple object tracking ,appearance - Abstract
Object tracking is a fundamental computer vision problem that refers to a set of methods proposed to precisely track the motion trajectory of an object in a video. Multiple Object Tracking (MOT) is a subclass of object tracking that has received growing interest due to its academic and commercial potential. Although numerous methods have been introduced to cope with this problem, many challenges remain to be solved, such as severe object occlusion and abrupt appearance changes. This paper focuses on giving a thorough review of the evolution of MOT in recent decades, investigating the recent advances in MOT, and showing some potential directions for future work. The primary contributions include: (1) a detailed description of the MOT’s main problems and solutions, (2) a categorization of the previous MOT algorithms into 12 approaches and discussion of the main procedures for each category, (3) a review of the benchmark datasets and standard evaluation methods for evaluating the MOT, (4) a discussion of various MOT challenges and solutions by analyzing the related references, and (5) a summary of the latest MOT technologies and recent MOT trends using the mentioned MOT categories.
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- 2021
8. A Survey on Internet of Things and Cloud Computing for Healthcare
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Dongil Han, Kyungbok Min, L. Minh Dang, Md. Jalil Piran, and Hyeonjoon Moon
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IoT ,Computer Networks and Communications ,Computer science ,Big data ,networking ,Wearable computer ,lcsh:TK7800-8360 ,Cloud computing ,security ,privacy ,Patient safety ,Health care ,Electrical and Electronic Engineering ,Sustainable development ,business.industry ,communication ,Public sector ,cloud computing ,lcsh:Electronics ,healthcare ,Computer security model ,Data science ,Hardware and Architecture ,Control and Systems Engineering ,Data exchange ,Signal Processing ,fog computing ,business ,Internet of Things - Abstract
The fast development of the Internet of Things (IoT) technology in recent years has supported connections of numerous smart things along with sensors and established seamless data exchange between them, so it leads to a stringy requirement for data analysis and data storage platform such as cloud computing and fog computing. Healthcare is one of the application domains in IoT that draws enormous interest from industry, the research community, and the public sector. The development of IoT and cloud computing is improving patient safety, staff satisfaction, and operational efficiency in the medical industry. This survey is conducted to analyze the latest IoT components, applications, and market trends of IoT in healthcare, as well as study current development in IoT and cloud computing-based healthcare applications since 2015. We also consider how promising technologies such as cloud computing, ambient assisted living, big data, and wearables are being applied in the healthcare industry and discover various IoT, e-health regulations and policies worldwide to determine how they assist the sustainable development of IoT and cloud computing in the healthcare industry. Moreover, an in-depth review of IoT privacy and security issues, including potential threats, attack types, and security setups from a healthcare viewpoint is conducted. Finally, this paper analyzes previous well-known security models to deal with security risks and provides trends, highlighted opportunities, and challenges for the IoT-based healthcare future development.
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- 2019
9. UAV based wilt detection system via convolutional neural networks
- Author
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Irfan Mehmood, Hyeonjoon Moon, Seungmin Rho, Arun Kumar Sangaiah, Im Suhyeon, Syed Ibrahim Hassan, Sanghyun Seo, and L. Minh Dang
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General Computer Science ,Disease detection ,business.industry ,Computer science ,020209 energy ,food and beverages ,020206 networking & telecommunications ,02 engineering and technology ,Agricultural engineering ,Food safety ,Convolutional neural network ,Coverage ratio ,Agriculture ,0202 electrical engineering, electronic engineering, information engineering ,Classification methods ,Electrical and Electronic Engineering ,Cluster analysis ,business - Abstract
The significant role of plants can be observed through the dependency of animals and humans on them. Oxygen, materials, food and the beauty of the world are contributed by plants. Climate change, the decrease in pollinators, and plant diseases are causing a significant decline in both quality and coverage ratio of the plants and crops on a global scale. In developed countries, above 80 percent of rural production is produced by sharecropping. However, due to widespread diseases in plants, yields are reported to have declined by more than a half. These diseases are identified and diagnosed by the agricultural and forestry department. Manual inspection on a large area of fields requires a huge amount of time and effort, thereby reduces the effectiveness significantly. To counter this problem, we propose an automatic disease detection and classification method in radish fields by using a camera attached to an unmanned aerial vehicle (UAV) to capture high quality images from the fields and analyze them by extracting both color and texture features, then we used K-means clustering to filter radish regions and feeds them into a fine-tuned GoogleNet to detect Fusarium wilt of radish efficiently at early stage and allow the authorities to take timely action which ensures the food safety for current and future generations.
- Published
- 2020
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