检索结果-内蒙古大学图书馆

9th International Conference on Business and Industrial Research, ICBIR 2024

作者： Laohakijvitoon, Patpon Nilwong, Sivapong Thai-Nichi Institute of Technology Master of Engineering Program in Engineering Technology Bangkok Thailand Thai-Nichi Institute of Technology Department of Computer Engineering and Artificial Intelligence Bangkok Thailand

ISBN: (纸本)9798350383010

Dispensing generic drugs instead of original drugs from pharmacies may cause adverse drug events to patients. It is difficult to separate between generic drugs and originals due to their similarities, except for imprints on the tablets. In this paper, a vision-based medicine tablets classification system using Convolutional Neural Networks (CNN) with a transfer learning approach is proposed. The proposed classification system aims to helps its user distinguish between the original drugs and generic drugs based on imprints on the medicine tablets. Multiple CNN models are created and tested as the classifier for the drug classification system. Three well-known CNN models are chosen as base models for creating CNNs with the transfer learning, including VGG16, Inception-V3, and ResNet50-V2. The developed CNN models are tested with images of medicine tablet taken from smartphones. Results from the experiments are evaluated through accuracies, precisions, recalls, and F-1 scores. Experimental results show satisfying performances of the proposed system in classifying original drugs and generic drugs using visual information. © 2024 IEEE.

关键词： convolutional neural network deep learning image classification Medicine tablet transfer learning

来源：评论

学校读者我要写书评

暂无评论

Camera-Only Perception System for Traffic Jam Assistance

Camera-Only Perception System for Traffic Jam Assistance

引用

2023 International Automatic Control Conference, CACS 2023

作者： Huang, Xiao-Wei Chen, Xiu-Zhi Chen, Yen-Lin National Taipei University of Technology Master Program in Artificial Intelligence Technology Taipei Taiwan National Taipei University of Technology Department of Computer Science and Information Engineering Taipei Taiwan

ISBN: (纸本)9798350306354

Traditional perception systems for TJA (Traffic Jam Assistance) are mostly implemented by fusing images with radar or lidar. As computer vision techniques become more powerful, cameras can almost replace the need for radar and lidar in perception tasks, which reduces the hardware cost of the system. In this research, we propose a camera-only perception system for TJA, which is able to provide the information of the vehicles ahead and the drivable area. The proposed system has been evaluated through real-world scenario sequences, and proved that it achieves high robustness, which is highly possible to be adopted for TJA development. © 2023 IEEE.

关键词： Cameras

来源：评论

学校读者我要写书评

暂无评论

Robust Object Detection Model for UAV Application

Robust Object Detection Model for UAV Application

引用

2023 International Automatic Control Conference, CACS 2023

作者： Ku, Chun Chen, Xiu-Zhi Chen, Yen-Lin National Taipei University of Technology Master Program in Artificial Intelligence Technology Taipei Taiwan National Taipei University of Technology Department of Computer Science and Information Engineering Taipei Taiwan

ISBN: (纸本)9798350306354

Not only common issues on object detection task need to be deal with, for Unmanned Aerial Vehicle (UAV) applications, small object is one of the critical problems that needs to be solved. YOLOv7 is a powerful network architecture that provides high efficiency and accuracy object detection results. This paper adopts YOLOv7 as an object detection model for two different kinds of targets, one is vehicle, and the other is ocean flotsam. By training the model with open datasets and fine-tuning the model with self-collected datasets, we prove through sequences collected from real-world scenarios that YOLOv7 is able to provide robust and accurate object detection results, including vehicles and ocean flotsam, with real-time efficiency. Based on such experimental result, we confirmed that YOLOv7 can be the baseline for object detection model development. © 2023 IEEE.

关键词： Object detection

来源：评论

学校读者我要写书评

暂无评论

A Hybrid Deep Learning and Natural Language Processing Model for Plant Ubiquitination Sites Prediction 3rd

A Hybrid Deep Learning and Natural Language Processing Model...

引用

3rd International Conference on Advances in Information and Communication technology, ICTA 2024

作者： Tran, Thi-Xuan Nguyen, Thi-Tuyen Le, Nguyen Quoc Khanh Nguyen, Van-Nui University of Economics and Business Administration Thai Nguyen Viet Nam University of Information and Communication Technology Thai Nguyen Viet Nam Professional Master Program in Artificial Intelligence in Medicine Taipei Medical University Taipei Taiwan Research Center for Artificial Intelligence in Medicine Taipei Medical University Taipei Taiwan

ISBN: (纸本)9783031809422

Protein ubiquitination is a critical post-translational modification involved in numerous biological processes, playing a vital role in regulating both physiological and pathological mechanisms. Despite existing tools for predicting ubiquitination sites, variations in ubiquitination patterns among species remain inadequately understood. This study presents LBiPlantUbi, a novel hybrid deep learning model that incorporates Long Short-Term Memory (LSTM) and Bidirectional LSTM (Bi-LSTM) networks combined with natural language processing (NLP) techniques for predicting ubiquitination sites in plants. Our model uniquely learns features directly from raw protein sequences using word embedding and integrates feature extraction and prediction within a single architecture. LBiPlantUbi achieved superior performance with an accuracy of 84%, sensitivity of 88.9%, F1-score of 0.844, MCC of 0.675, and AUC of 0.902 on independent testing sets, outperforming existing methods. This advanced model opens new avenues for studying ubiquitination in plants and other species. The implementation is publicly accessible on GitHub at https://***/nuinvtnu/LBiPlantUbi. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Deep learning

来源：评论

学校读者我要写书评

暂无评论

Improved Linear B-Cell Epitope Prediction Using CNN and BiLSTM 3rd

Improved Linear B-Cell Epitope Prediction Using CNN and BiLS...

引用

3rd International Conference on Advances in Information and Communication technology, ICTA 2024

作者： Nguyen, Thi-Tuyen Nguyen, Van-Nui Tran, Thi-Xuan Le, Nguyen Quoc Khanh University of Information and Communication Technology Thai Nguyen Viet Nam University of Economics and Business Administration Thai Nguyen Viet Nam Professional Master Program in Artificial Intelligence in Medicine College of Medicine Taipei Medical University Taipei110 Taiwan AIBioMed Research Group Taipei Medical University Taipei110 Taiwan

ISBN: (纸本)9783031809422

Proteins contain epitopes that serve as their antigenic determinants, with antigenicity referring to an epitope's ability to react with an antibody. These antigenic determinants exist as either continuous or discontinuous epitopes and are pivotal in integrative biology. The presence of B-cell epitopes has been extensively studied due to their potential in synthetic vaccine design. Thus, identifying antigenic sites on proteins is critical for developing synthetic peptide vaccines, immunodiagnostic tests, and antibody production. Recent years have seen the development of several computational methods, particularly machine learning-based approaches, to predict B-cell epitopes. However, these methods often fall short in reliably predicting linear B-cell epitopes, presenting an ongoing challenge. In this study, we propose and develop a novel method that integrates multiple deep learning models, aiming to harness the strengths of each model type while mitigating their individual weaknesses. During the model construction process, we addressed data imbalance and fine-tuned the model parameters to achieve optimal performance for our dataset. Our model demonstrated promising results, achieving an ACC of 0.846, AUC of 0.808, BAC of 0.716, MCC of 0.568 and PR-AUC of 0.705 on an independent dataset. This represents a significant performance improvement over previous algorithms. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Epitopes

来源：评论

学校读者我要写书评

暂无评论

Vision-Based Drug Classification System Using Transfer Learning Approach

Vision-Based Drug Classification System Using Transfer Learn...

引用

International Conference on Business and Industrial Research (ICBIR)

作者： Patpon Laohakijvitoon Sivapong Nilwong Master of Engineering Program in Engineering Technology Thai-Nichi Institute of Technology Bangkok Thailand Department of Computer Engineering and Artificial Intelligence Thai-Nichi Institute of Technology Bangkok Thailand

ISBN: (数字)9798350383027

ISBN: (纸本)9798350383034

关键词： Drugs Visualization Image processing Transfer learning Data preprocessing Medical services Mobile applications Convolutional neural networks Smart phones Biomedical imaging

来源：评论

学校读者我要写书评

暂无评论

Unitig-centered pan-genome machine learning approach for predicting antibiotic resistance and discovering novel resistance genes in bacterial strains

引用

Computational and Structural Biotechnology Journal 2024年 23卷 1864-1876页

作者： Do, Duyen Thi Yang, Ming-Ren Vo, Tran Nam Son Le, Nguyen Quoc Khanh Wu, Yu-Wei Graduate Institute of Biomedical Informatics College of Medical Science and Technology Taipei Medical University Taipei Taiwan Department of Electrical Engineering National Taiwan University of Science and Technology Taipei Taiwan Department of Business Administration College of Management Lunghwa University of Science and Technology Taoyuan City Taiwan Professional Master Program in Artificial Intelligence in Medicine College of Medicine Taipei Medical University Taipei Taiwan Clinical Big Data Research Center Taipei Medical University Hospital Taipei Taiwan TMU Research Center for Digestive Medicine Taipei Medical University Taipei Taiwan

In current genomic research, the widely used methods for predicting antimicrobial resistance (AMR) often rely on prior knowledge of known AMR genes or reference genomes. However, these methods have limitations, potentially resulting in imprecise predictions owing to incomplete coverage of AMR mechanisms and genetic variations. To overcome these limitations, we propose a pan-genome-based machine learning approach to advance our understanding of AMR gene repertoires and uncover possible feature sets for precise AMR classification. By building compacted de Brujin graphs (cDBGs) from thousands of genomes and collecting the presence/absence patterns of unique sequences (unitigs) for Pseudomonas aeruginosa, we determined that using machine learning models on unitig-centered pan-genomes showed significant promise for accurately predicting the antibiotic resistance or susceptibility of microbial strains. Applying a feature-selection-based machine learning algorithm led to satisfactory predictive performance for the training dataset (with an area under the receiver operating characteristic curve (AUC) of > 0.929) and an independent validation dataset (AUC, approximately 0.77). Furthermore, the selected unitigs revealed previously unidentified resistance genes, allowing for the expansion of the resistance gene repertoire to those that have not previously been described in the literature on antibiotic resistance. These results demonstrate that our proposed unitig-based pan-genome feature set was effective in constructing machine learning predictors that could accurately identify AMR pathogens. Gene sets extracted using this approach may offer valuable insights into expanding known AMR genes and forming new hypotheses to uncover the underlying mechanisms of bacterial AMR. © 2024 The Authors

关键词： Genes

来源：评论

学校读者我要写书评

暂无评论

Robust Object Detection Model for UAV Application

Robust Object Detection Model for UAV Application

引用

International Automatic Control Conference

作者： Chun Ku Xiu-Zhi Chen Yen-Lin Chen Master Program in Artificial Intelligence Technology National Taipei University of Technology Taipei Taiwan Department of Computer Science and Information Engineering National Taipei University of Technology Taipei Taiwan

关键词：

来源：评论

学校读者我要写书评

暂无评论

Camera-Only Perception System for Traffic Jam Assistance

Camera-Only Perception System for Traffic Jam Assistance

引用

International Automatic Control Conference

作者： Xiao-Wei Huang Xiu-Zhi Chen Yen-Lin Chen Master Program in Artificial Intelligence Technology National Taipei University of Technology Taipei Taiwan Department of Computer Science and Information Engineering National Taipei University of Technology Taipei Taiwan

关键词：

来源：评论

学校读者我要写书评

暂无评论

Integrating CNN and Bi-LSTM for protein succinylation sites prediction based on Natural Language Processing technique

引用

Computers in Biology and Medicine 2025年 186卷 109664-109664页

作者： Tran, Thi-Xuan Khanh Le, Nguyen Quoc Nguyen, Van-Nui Thai Nguyen University of Economics and Business Administration Thai Nguyen City Viet Nam In-Service Master Program in Artificial Intelligence in Medicine Taipei Medical University Taiwan AIBioMed Research Group Taipei Medical University Taiwan Thai Nguyen University of Information and Communication Technology Thai Nguyen City Viet Nam

Protein succinylation, a post-translational modification wherein a succinyl group (-CO-CH₂-CH₂-CO-) attaches to lysine residues, plays a critical regulatory role in cellular processes. Dysregulated succinylation has been implicated in the onset and progression of various diseases, including liver, cardiac, pulmonary, and neurological disorders. However, identifying succinylation sites through experimental methods is often labor-intensive, costly, and technically challenging. To address this, we introduce an approach called CbiLSuccSite, that integrates Convolutional Neural Networks (CNN) with Bidirectional Long Short-Term Memory (Bi-LSTM) networks for the accurate prediction of protein succinylation sites. Our approach employs a word embedding layer to encode protein sequences, enabling the automatic learning of intricate patterns and dependencies without manual feature extraction. In 10-fold cross-validation, CBiLSuccSite achieved superior predictive performance, with an Area Under the Curve (AUC) of 0.826 and a Matthews Correlation Coefficient (MCC) of 0.502. Independent testing further validated its robustness, yielding an AUC of 0.818 and an MCC of 0.53. The integration of CNN and Bi-LSTM leverages the strengths of both architectures, establishing CBiLSuccSite as an effective tool for protein language processing and succinylation site prediction. Our model and code are publicly accessible at: https://***/nuinvtnu/CBiLSuccSite. © 2025

关键词： Convolutional neural networks

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：