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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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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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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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Generated Compressed Domain Images to the Rescue: Cross Distillation from Compressed Domain to Pixel Domain

Generated Compressed Domain Images to the Rescue: Cross Dist...

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IEEE International Symposium on Circuits and systems (ISCAS)

作者： Reyhan Kevser Keser Muhammet Sebul Beratoğlu Behçet Uğur Töreyin Signal Processing for Computational Intelligence Research Group (SP4CING) Informatics Institute İstanbul Technical University İstanbul Türkiye Dept. of Artificial Intelligence and Data Engineering Signal Processing for Computational Intelligence Research Group (SP4CING) İstanbul Technical University İstanbul Türkiye

ISBN: (数字)9798350330991

ISBN: (纸本)9798350331004

data are the essential component in the pipeline of training a model that determines the performance of the model. However, there may not be enough data that meet the requirements of some tasks. In this paper, we introduce a knowledge distillation-based approach that mitigates the disadvantages of data scarcity. Specifically, we propose a method that boosts the pixel domain performance of a model, by utilizing compressed domain knowledge via cross distillation between these two modalities. To evaluate our approach, we conduct experiments on two computer vision tasks which are object detection and recognition. Results indicate that compressed domain features can be utilized for a task in the pixel domain via our approach, where data are scarce or not completely available due to privacy or copyright issues.

关键词： Training data privacy Computer vision Image coding Computational modeling Pipelines Object detection

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Application for Airport Tenant Selection Using Weighted Product-based Decision Support system

Application for Airport Tenant Selection Using Weighted Prod...

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2023 International Conference on Electrical and Information Technology, IEIT 2023

作者： Sumari, Arwin Datumaya Wahyudi Saputra, Muhamad Andika Ayuningtyas, Astika Sudarmadi, Catur 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 Informatics Study Program Faculty of Industrial Technology Yogyakarta Indonesia Adisutjipto Institute of Aerospace Technology Aerospace Health Informatics Research Group Faculty of Industrial Technology Yogyakarta Indonesia

ISBN: (纸本)9798350327298

This research aims to develop an application for selecting the best new tenant at PT Angkasa Pura II Sultan Thaha Jambi Airport Branch by utilizing Weighted Product (WP) method as the decision support system. Our approach is descriptive research with data analysis technique using four prospective tenants coded A-1 to A-4. The data was collected through observation, interviews, documentation, and literature study. The steps for selecting the best new tenant using the WP method include determining the criteria, sorting the suitability of alternatives and criteria, determining the weight of each criterion and its normalization, calculating the score, S value as an alternative preference, and calculating the ranking, V value as the final alternative preference. Based on the final calculation results, the new tenant coded A-4 is the best alternative based on the data collected from the Financial Management (FHFM) Unit and the Commercial Unit, with a value of V-4 is 0.294. © 2023 IEEE.

关键词： Decision support systems

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HaLViT: Half of the Weights are Enough

HaLViT: Half of the Weights are Enough

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IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)

作者： Onur Can Koyun Behçet Uğur Töreyin Dept. of Artificial Intelligence and Data Engineering Informatics Institute Signal Processing for Computational Intelligence Research Group (SP4CING) İstanbul Technical University İstanbul Türkiye

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Deep learning architectures like Transformers and Convolutional Neural Networks (CNNs) have led to ground-breaking advances across numerous fields. However, their extensive need for parameters poses challenges for implementation in environments with limited resources. In our research, we propose a strategy that focuses on the utilization of the column and row spaces of weight matrices, significantly reducing the number of required model parameters without substantially affecting performance. This technique is applied to both Bottleneck and Attention layers, achieving a notable reduction in parameters with minimal impact on model efficacy. Our proposed model, HaLViT, exemplifies a parameter-efficient Vision Transformer. Through rigorous experiments on the ImageNet dataset and COCO dataset, HaLViT’s performance validates the effectiveness of our method, offering results comparable to those of conventional models.

关键词： Computer vision Conferences Computational modeling Deep architecture Transformers Pattern recognition Convolutional neural networks

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EDSep: An Effective Diffusion-Based Method for Speech Source Separation

EDSep: An Effective Diffusion-Based Method for Speech Source...

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2025 IEEE International Conference on Acoustics, Speech, and Signal processing, ICASSP 2025

作者： Dong, Jinwei Wang, Xinsheng Mao, Qirong School of Computer Science and Communication Engineering Jiangsu University China Jiangsu Engineering Research Center of Big Data Ubiquitous Perception and Intelligent Agriculture Applications China Provincial Key Laboratory of Computational Intelligence and New Technologies in Low-Altitude Digital Agriculture Zhenjiang China Audio Speech and Language Processing Group School of Computer Science Northwestern Polytechnical University Xi'an China

ISBN: (纸本)9798350368741

Generative models have attracted considerable attention for speech separation tasks, and among these, diffusion-based methods are being explored. Despite the notable success of diffusion techniques in generation tasks, their adaptation to speech separation has encountered challenges, notably slow convergence and suboptimal separation outcomes. To address these issues and enhance the efficacy of diffusion-based speech separation, we introduce EDSep, a novel single-channel method grounded in score matching via stochastic differential equation (SDE). This method enhances generative modeling for speech source separation by optimizing training and sampling efficiency. Specifically, a novel denoiser function is proposed to approximate data distributions, which obtains ideal denoiser outputs. Additionally, a stochastic sampler is carefully designed to resolve the reverse SDE during the sampling process, gradually separating speech from mixtures. Extensive experiments on databases such as WSJ0-2mix, LRS2-2mix, and VoxCeleb2-2mix demonstrate our proposed method's superior performance over existing diffusion and discriminative models, validating its efficacy. © 2025 IEEE.

关键词： diffusion score matching speech separation stochastic differential equation

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HSBNN: A High-Scalable Bayesian Neural Networks Accelerator Based on Field Programmable Gate Arrays (FPGA)

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Cognitive Computation 2025年第3期17卷 1-14页

作者： Liu, Yinghao Zhang, Hao Sun, Zhe Duan, Feng Ma, Yuan Lu, Wenyi Caiafa, Cesar F. Solé-Casals, Jordi Graduate School of Arts and Sciences The University of Tokyo Meguro City153-8902 Japan Gusu Laboratory of Materials Suzhou215000 China Faculty of Health Data Science Juntendo University Chiba279-0013 Japan College of Artificial Intelligence Nankai University Tianjin300350 China Institute of Information Engineering Chinese Academy of Sciences Beijing100093 China Instituto Argentino de Radioastronomía CONICET CCT La Plata/CIC-PBA/UNLP V. Elisa Argentina Data and Signal Processing Research Group University of Vic - Central University of Catalonia Catalonia Vic Spain

Traditional artificial neural networks have inherent overfitting problems and tend to produce overly confident predictions due to their reliance on point estimation methods. In contrast, Bayesian theory offers a probabilistic framework that replaces point estimation with probability distributions, effectively addressing issues of overconfidence. The brain is also believed working under the Bayesian rules, the neural networks of which evaluate the precision of prior knowledge and incoming evidence, achieving the balance of weight updating to the most reliable information sources [1]. By integrating Bayesian principles with artificial neural networks, the bio-inspired Bayesian Neural Networks (BNNs) can generate predictions accompanied by confidence evaluations, enhancing their practical applicability. To further improve the computational efficiency of BNNs and enable scalable deployment on edge devices, we propose a High-Scalable Bayesian Neural Network (HSBNN) accelerator based on field-programmable gate arrays (FPGAs) with multiple optimizations. A resource-saving Gaussian random number generator (RS-GRNG) optimized for FPGAs shows high efficiency, which seamlessly extends to support parallel sampling of weight distributions, enabling reliable confidence probability evaluations. Furthermore, the parameterization of BNN architectures with configuration files and employment of a layer-by-layer computing mode ensure that different BNNs can be accelerated without reprogramming the FPGA, offering excellent scalability. The entire system, implemented with the OpenCL heterogeneous computing library, leverages parallel processing units and pipeline channels to achieve high acceleration performance and efficient data transfer. The experiment results demonstrate that the system achieves a data processing throughput of 1.002 milliseconds per image, exceeding CPU performance by 1000-fold and GPU performance by nearly 500-fold. © The Author(s), under exclusive licence to Spring

关键词： Gaussian distribution

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Event-triggered H∞ Consensus of Leader-follower Multi-agent systems 43

Event-triggered H∞ Consensus of Leader-follower Multi-agent...

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43rd Chinese Control Conference, CCC 2024

作者： Gong, Cheng Ai, Chenguang Zuo, Xuemin Ma, Zhuang Dai, W. Hebei Jianzhi Casting Group Ltd Pangzhuangzi Yutian County Tangshan064100 China College Tangshan University Key Lab of Intelligent Data Information Processing and Control of Hebei Province Key Lab of Intelligent Motion Control System of Tangshan City Intelligence and Information Engineering Tangshan063000 China North China University of Science and Technology College of Electrical Engineering Tangshan063210 China Kernel University FullertonCA92832 United States

ISBN: (纸本)9789887581581

This paper discusses the H∞ consensus problem of leader-follower multi-agent systems. The controller for each agent is crafted to utilize comprehensive information from all connected agents, while an innovative event-triggered strategy is incorporated to minimize communication costs. Based on the Lyapunov function theory, an H∞ controller is proposed that can ensure both system stability and a prescribed disturbance attenuation level γ. Finally, a numerical example validated the effectiveness of the proposed method. © 2024 Technical Committee on Control Theory, Chinese Association of Automation.

关键词： Multi agent systems

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