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2022 RIVF International Conference on Computing and Communication Technologies, RIVF 2022

作者： Nguyen, Thanh-Trung Nguyen, Tuan A. Nguyen, Hien Huynh, Qui Phu Research Group of Intelligent Data Processing System Ho Chi Minh City University of Foreign Languages - Information Technology Viet Nam

ISBN: (纸本)9781665461665

Nowadays, tracking the origin of a product as it goes from raw material to finished product is an essential need of consumers, which determines whether consumers choose to buy that product or not. At present, the most common method of product traceability is to scan the QR code stamp on the goods. These stamps, however, are still being counterfeited, resulting in inaccurate product information, not ensuring product integrity and transparency for consumers. Therefore, this research suggests a variety of blockchain-based solutions with different security levels to improve the security, integrity, and transparency of product data. In this paper, we propose to use blockchain technology 3.0 which has more advanced features than blockchain 1.0 and 2.0, including a transaction processing time of just roughly 6 seconds. © 2022 IEEE.

关键词： Blockchain

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Building an Electronic Medical Records system using Blockchain 3.0 technology and new data Mechanism

Building an Electronic Medical Records System using Blockcha...

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2022 RIVF International Conference on Computing and Communication Technologies, RIVF 2022

作者： Nguyen, Tuan A. Nguyen, Thanh-Trung Nguyen, Hien Huynh, Qui Phu Ho Chi Minh City University of Foreign Languages - Information Technology Research Group of Intelligent Data Processing System Viet Nam

ISBN: (纸本)9781665461665

Nowadays, access to electronic medical records is an essential requirement. However, in the current healthcare system, patient records are created by the healthcare provider and stored locally in the provider's data system. This is particularly detrimental when a service user needs to query their profile from different providers or providers desire to query a service user profile from other providers as needed. Hence, blockchain is effectively background technology to resolve this problem because of its existing features. Currently, there are many models proposed based on blockchain technology 2.0. However, these systems still take a long time for a transaction (from 6 to 10 minutes). Another obvious problem is that healthcare providers participating in the system must use the SQL database because the integrity of data is required. Meantime, the NoSQL database is primary care on the decentralized environments. Therefore, the problem of converting big data from SQL to NoSQL is also important in the healthcare system. In this article, we propose to realize an electronic medical records system by using a third-generation version of blockchain technology with many advantages. Especially the time for a transaction is only about 6 seconds based on the new data mechanism. Besides, there is a solution to convert big data from SQL to NoSQL safely and effectively. © 2022 IEEE.

关键词： Big data

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A Dynamic Transformer Network for Vehicle Detection

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IEEE Transactions on Consumer Electronics 2025年

作者： Tian, Chunwei Liu, Kai Zhang, Bob Huang, Zhixiang Lin, Chia-Wen Zhang, David Harbin Institute of Technology School of Computer Science and Technology Harbin15001 China Macao Special Administrative Region of China PAMI Research Group University of Macau 999078 China Anhui University Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Key Laboratory of Electromagnetic Environmental Sensing Hefei230601 China National Tsing Hua University Department of Electrical Engineering Institute of Communications Engineering Hsinchu Taiwan School of Data Science Shenzhen518172 China

Stable consumer electronic systems can assist traffic better. Good traffic consumer electronic systems require collaborative work between traffic algorithms and hardware. However, performance of popular traffic algorithms containing vehicle detection methods based on deep networks via learning data relation rather than learning differences in different lighting and occlusions is limited. In this paper, we present a dynamic Transformer network for vehicle detection (DTNet). DTNet utilizes a dynamic convolution to guide a deep network to dynamically generate weights to enhance adaptability of an obtained detector. Taking into relations of different information account, a mixed attention mechanism based channel attention and Transformer is exploited to strengthen relations of channels and pixels to extract more salient information for vehicle detection. To overcome the drawback of difference in an image account, a translation-variant convolution relies on spatial location information to refine obtained structural information for vehicle detection. Experimental results illustrate that our DTNet is competitive for vehicle detection. Code of the proposed DTNet can be obtained at https://***/hellloxiaotian/DTNet. © 2025 IEEE.

关键词： Dynamic convolution Hybrid Attention Mechanism Transformer Vehicle Detection

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Fuzzy Granule Density-Based Outlier Detection with Multi-Scale Granular Balls

arXiv

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arXiv 2025年

作者： Gao, Can Tan, Xiaofeng Zhou, Jie Ding, Weiping Pedrycz, Witold The College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China Guangdong Key Laboratory of Intelligent Information Processing Shenzhen518060 China The National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen518060 China The School of Artificial Intelligence and Computer Science Nantong University Nantong226019 China The Faculty of Data Science City University of Macau Macau999078 China The Department of Electrical and Computer Engineering University of Alberta Edmonton Canada Systems Research Institute Polish Academy of Sciences Warsaw Poland The Department of Electrical and Computer Engineering King Abdulaziz University Jeddah Saudi Arabia The Department of Computer Engineering Istinye University Istanbul Turkey

Outlier detection refers to the identification of anomalous samples that deviate significantly from the distribution of normal data and has been extensively studied and used in a variety of practical tasks. However, most unsupervised outlier detection methods are carefully designed to detect specified outliers, while real-world data may be entangled with different types of outliers. In this study, we propose a fuzzy rough sets-based multi-scale outlier detection method to identify various types of outliers. Specifically, a novel fuzzy rough sets-based method that integrates relative fuzzy granule density is first introduced to improve the capability of detecting local outliers. Then, a multi-scale view generation method based on granular-ball computing is proposed to collaboratively identify group outliers at different levels of granularity. Moreover, reliable outliers and inliers determined by the three-way decision are used to train a weighted support vector machine to further improve the performance of outlier detection. The proposed method innovatively transforms unsupervised outlier detection into a semi-supervised classification problem and for the first time explores the fuzzy rough sets-based outlier detection from the perspective of multi-scale granular balls, allowing for high adaptability to different types of outliers. Extensive experiments carried out on both artificial and UCI datasets demonstrate that the proposed outlier detection method significantly outperforms the state-of-the-art methods, improving the results by at least 8.48% in terms of the Area Under the ROC Curve (AUROC) index. The source codes are released at https://***/Xiaofeng-Tan/MGBOD. © 2025, CC BY.

关键词： Granulation

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Building 3 levels of security for traceability using Blockchain 3.0 Generation

Building 3 levels of security for traceability using Blockch...

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IEEE International Conference on research, Innovation and Vision for the Future

作者： Thanh-Trung Nguyen Tuan A. Nguyen Hien Nguyen Qui Phu Huynh Research Group of Intelligent Data Processing System Ho Chi Minh City University of Foreign Languages - Information Technology Vietnam

ISBN: (纸本)9781665461672

关键词： Buildings QR codes Raw materials Communications technology Blockchains Security

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Building an Electronic Medical Records system using Blockchain 3.0 technology and new data Mechanism

Building an Electronic Medical Records System using Blockcha...

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IEEE International Conference on research, Innovation and Vision for the Future

作者： Tuan A. Nguyen Thanh-Trung Nguyen Hien Nguyen Qui Phu Huynh Research Group of Intelligent Data Processing System Ho Chi Minh City University of Foreign Languages - Information Technology Vietnam

ISBN: (纸本)9781665461672

关键词： databases NoSQL databases Ecosystems Medical services Big data data systems Blockchains

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Automatic extraction of lightweight and efficient neural network architecture of heavy convolutional architectures to predict microsatellite instability from hematoxylin and eosin histology in gastric cancer

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Neural Computing and Applications 2024年第25期36卷 15295-15321页

作者： Rostami, Habib Ashkpour, Maryam Behzadi-Khormouji, Hamed Mokhtari, Maral Khayati, Armin Keshavarz, Ahmad Talatian Azad, Saeed Tabesh, Yahya Computer Engineering Department Faculty of Intelligent Systems Engineering and Data Science Persian Gulf University Bushehr75168 Iran Artificial Intelligence and Intelligent Healthcare Lab Artificial Intelligence and Data Mining Research Group Faculty of Intelligent Systems Engineering and Data Science ICT Research Institute Persian Gulf University Bushehr75168 Iran Faculty of New Science and Technologies University of Tehran Tehran Iran Department of Computer Science Imec-IDLab University of Antwerp Sint-Pietersvliet 7 Antwerp2000 Belgium Shahid Faghihi Hospital Shiraz University of Medical Sciences Shiraz Iran Department of Computer Science Engineering and IT School of Electrical and Computer Engineering Shiraz University Shiraz Iran Electrical Engineering Department Faculty of Intelligent Systems Engineering and Data Science Persian Gulf University Bushehr75168 Iran IoT and Signal Processing Research Group Faculty of Intelligent Systems Engineering and Data Science ICT Research Institute Persian Gulf University Bushehr75168 Iran Department of Mathematical Sciences Sharif University of Technology Tehran Iran

Cancers have emerged as a significant concern due to their impact on public health and society. The examination and interpretation of tissue sections stained with Hematoxylin and Eosin (H&E) play a crucial role in disease assessment, particularly in cases like gastric cancer. Microsatellite instability (MSI) is suggested to contribute to the carcinogenesis of specific gastrointestinal tumors. However, due to the nonspecific morphology observed in H&E-stained tissue sections, MSI determination often requires costly evaluations through various molecular studies and immunohistochemistry methods in specialized molecular pathology laboratories. Despite the high cost, international guidelines recommend MSI testing for gastrointestinal cancers. Thus, there is a pressing need for a new diagnostic modality with lower costs and widespread applicability for MSI detection. This study aims to detect MSI directly from H&E histology slides in gastric cancer, providing a cost-effective alternative. The performance of well-known deep convolutional neural networks (DCNNs) and a proposed architecture are compared. Medical image datasets are typically smaller than benchmark datasets like ImageNet, necessitating the use of off-the-shelf DCNN architectures developed for large datasets through techniques such as transfer learning. Designing an architecture proportional to a custom dataset can be tedious and may not yield desirable results. In this work, we propose an automatic method to extract a lightweight and efficient architecture from a given heavy architecture (e.g., well-known off-the-shelf DCNNs) proportional to a specific dataset. To predict MSI instability, we extracted the MicroNet architecture from the Xception network using the proposed method and compared its performance with other well-known architectures. The models were trained using tiles extracted from whole-slide images, and two evaluation strategies, tile-based and whole-slide image (WSI)-based, were employed and comp

关键词： Network architecture

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Multi-scale residual convolutional encoder decoder with bidirectional long short-term memory for single channel speech enhancement 28

Multi-scale residual convolutional encoder decoder with bidi...

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28th European Signal processing Conference, EUSIPCO 2020

作者： Xian, Yang Sun, Yang Wang, Wenwu Naqvi, Syed Mohsen Intelligent Sensing and Communications Research Group Newcastle University United Kingdom Big Data Institute University of Oxford United Kingdom Centre for Vision Speech and Signal Processing University of Surrey United Kingdom

ISBN: (纸本)9789082797053

The existing convolutional neural network (CNN) based methods still have limitations in model accuracy, latency and computational cost for single channel speech enhancement. In order to address these limitations, we propose a multi-scale convolutional bidirectional long short-term memory (BLSTM) recurrent neural network, which is named as McbNet, a deep learning framework for end-to-end single channel speech enhancement. The proposed McbNet enlarges the receptive fields in two aspects. Firstly, every convolutional layer employs filters with varied dimensions to capture local and global information. Secondly, the BLSTM is applied to evaluate the interdependency of past, current and future temporal frames. The experimental results confirm the proposed McbNet offers consistent improvement over the state-of-the-art methods and public datasets. © 2021 European Signal processing Conference, EUSIPCO. All rights reserved.

关键词： Long short-term memory

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intelligent Aerospace Terminologies Recognition based on Spectrogram using Machine Learning

Intelligent Aerospace Terminologies Recognition based on Spe...

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2023 International Conference on Electrical and Information Technology, IEIT 2023

作者： Sumari, Arwin Datumaya Wahyudi Sitorus, Zest Amborgang Abdie, Syafrudin Adisutjipto Institute of Aerospace Technology Aerospace Intelligent System and Data Engineering Research Group Yogyakarta Indonesia Politeknik Negeri Malang Cognitive Artificial Intelligence Research Group Malang Indonesia Adisutjipto Institute of Aerospace Technology Faculty of Industrial Technology Department of Electrical Engineering Yogyakarta Indonesia Adisutjipto Institute of Aerospace Technology Faculty of Industrial Technology Aerospace Avionics System Research Group Department of Electrical Engineering Yogyakarta Indonesia

ISBN: (纸本)9798350327298

A speech recognition system is an application to recognize spoken words through audio inputs. On the other hand, spectrograms are visual representations of audio signals and effectively represent words numerically. In this research, we developed a spectrogram-based speech recognition system using machine learning's K-Nearest Neighbors (KNN) method to recognize aerospace terminologies, some uncommon for ordinary people. We used 300 audio signal data consisting of two categories, namely 15 spectrogram data for aerospace terminologies and 15 spectrogram data for non-aerospace ones, each taken ten times. By conducting a 70:30 training and testing scheme, strengthened by k-fold cross-validation, the optimum K for KNN is 13. With such value of K, our developed intelligent system for aerospace terminologies recognition based on spectrogram can achieve an accuracy of 75.56% with a precision of 75.56%, recall of 75.56%, and F-1 score of 75.56%. With that accuracy, our developed intelligent system is considered as a good system. © 2023 IEEE.

关键词： Terminology

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Wavelet-based image watermarking using the genetic algorithm 8th

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8th International Conference on Knowledge-Based intelligent Information and Engineering systems, KES 2004

作者： Kumsawat, Prayoth Attkitmongcol, Kitti Srikaew, Arthit Sujitjorn, Sarawut Signal and Image Processing Research Group School of Electrical Engineering Institute of Engineering Suranaree University of Technology 111 University Avenue Muang District Nakhon Ratchasrima Thailand Intelligent System Research Group School of Electrical Engineering Institute of Engineering Suranaree University of Technology 111 University Avenue Muang District Nakhon Ratchasrima Thailand

ISBN: (纸本)9783540232056

Image watermarking provides copyright protection of digital image by hiding appropriate information in the original image in such a way that it does not cause degradation of the perceptual image quality and cannot be removed. The watermarking methods for transform domains are usually achieved by using the discrete cosine transform or the discrete wavelet transform. In this paper, we develop a technique for optimizing the image watermarking using the genetic algorithm applied to the wavelet transform domain to improve the quality of the watermarked image and the robustness of the watermark. We then compare our experimental results with the results of previous works. © Springer-Verlag Berlin Heidelberg 2004.

关键词： Image watermarking

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