检索结果-内蒙古大学图书馆

Deep neural networks and weighted word embeddings for sentiment analysis of drug product reviews

Procedia computer science 2023年 216卷 664-671页

作者： Derwin Suhartono Kartika Purwandari Nicholaus Hendrik Jeremy Samuel Philip Panji Arisaputra Ivan Halim Parmonangan Computer Science Department School of Computer Science Bina Nusantara University Jakarta 11480 Indonesia Computer Science Department BINUS Graduate Program Master of Computer Science Bina Nusantara University Jakarta 11480 Indonesia

One of the major tasks of natural language processing is sentiment analysis. The web is a source of unstructured and rich informa-tion with thousands of opinions and reviews. Individuals, businesses, and governments can all benefit from recognizing sentiment. As part of this study, we propose a deep learning-based approach for sentiment analysis on drug product review data obtained from the UCI machine learning repository. As an alternative to deep learning models, this architecture integrates glove word embedding with convolutional neural networks (CNN). Word2vec and GloVe word embedding schemes have been evaluated empirically for their predictive performance in CNN architectures. Based on a comparison of the deep learning architecture with RoBERTa, itcan be seen that BERT architecture outperforms both of them in training and validation. However, CNN models using Glove word embedding provided superior results in testing.

关键词： transformers machine learning sentiment analysis weighted word embeddings

来源：评论

学校读者我要写书评

暂无评论

Data Analytics and Visualization in Bimanual Rehabilitation Monitoring Systems: A User-Centered Design Approach to Healthcare Professionals Decision Support

Data Analytics and Visualization in Bimanual Rehabilitation ...

引用

2024 International Conference on Electrical Engineering and Informatics, ICELTICs 2024

作者： Wisedsri, Parkpoom Anutariya, Chutiporn Sujarae, Aekavute Vachalathiti, Roongtiwa Bovonsunthonchai, Sunee School of Engineering and Technology Asian Institute of Technology Computer Science Program Pathum Thani Thailand Punsarn Asia Co. Ltd Thailand Mahidol University Faculty of Physical Therapy Nakhon Pathom Thailand

ISBN: (纸本)9798350366822

This study presents the design of a user-centered data visualization dashboard for stroke rehabilitation, which integrates the principles of visualization data analytics techniques in the field of healthcare. Addressing the complexities of visualizing for robotic bimanual rehabilitation, the design introduces novel methods to enhance data visualization, broadening monitoring capabilities, and improving the clarity of torque data. These enhancements lead to a clearer understanding of patient robot interactions. A prototype has been developed to visualize transparent torque data from a bimanual master slave robotic system, enabling healthcare professionals to monitor patient progress effectively. The dashboard features intuitive visual representations of master, slave, and assistive torques, simulating elbow joint movements through line and sunburst charts. Feedback from rehabilitation therapists during the design process ensures that the visualization approach is aligned with clinical needs. The findings suggest that the system can assist physiotherapists in devising personalized treatment strategies by providing actionable insights from the recorded data. However, additional research is needed to integrate real clinical data into the system for further enhancement and validation. This innovation has the potential to significantly improve stroke rehabilitation effectiveness and patient outcomes through a bimanual rehabilitation visualization and decision support system. © 2024 IEEE.

关键词： Clinical research

来源：评论

学校读者我要写书评

暂无评论

Implementation of Dynamic Image for Facial Expression Recognition on Indonesian Facial Expression Dataset

Implementation of Dynamic Image for Facial Expression Recogn...

引用

IEEE International Conference on Artificial Intelligence in Engineering and technology (IICAIET)

作者： Irene Anindaputri Iswanto Andry Chowanda Haryono Soeparno Widodo Budiharto Computer Science Department BINUS Graduate Program - Doctor of Computer Science Bina Nusantara University Jakarta Indonesia Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia

ISBN: (数字)9798350389692

ISBN: (纸本)9798350389708

In recent times, there has been considerable attention directed towards Facial Expression Recognition (FER) due to its extensive utility across diverse domains. However, the universality of facial expressions has been challenged by studies suggesting that cultural backgrounds significantly influence the perception and recognition of emotions. This paper addresses the need for culturally specific datasets in FER tasks, particularly in underrepresented regions like Indonesia. The study introduces dynamic images as an alternative input representation for facial expression recognition tasks, aiming to assess their efficacy using the Indonesian Mixed Emotions Dataset (IMED). Through experimentation using EfficientNet model, the performance of dynamic images is compared with static image and video inputs. Results indicate that dynamic images exhibit promising performance, with an accuracy of 94.28%. These results outperform static image datasets and nearly match the performance of video-based models, which achieved an accuracy of 97.93 %, despite using fewer data. Nonetheless, challenges such as data imbalance and the quality of generated dynamic images persist, suggesting avenues for further research and model refinement. This study provides valuable insights into methodological advancements in FER, particularly in limited dataset conditions, laying the groundwork for future developments in dynamic image-based facial expression recognition algorithms.

关键词： Emotion recognition Image recognition Accuracy Face recognition Heuristic algorithms Data models Cultural differences Artificial intelligence

来源：评论

学校读者我要写书评

暂无评论

Artificial Intelligence-Powered Intent Classification for Indonesian E-Wallet Customer Service Chatbots

Artificial Intelligence-Powered Intent Classification for In...

引用

Intelligent Cybernetics technology & Applications (ICICyTA), International Conference on

作者： Christopher Owen Derwin Suhartono Computer Science Department BINUS Graduate Program - Master of Computer Science Bina Nusantara University Jakarta Indonesia Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia

ISBN: (数字)9798331506490

ISBN: (纸本)9798331506506

The rapid expansion of e-wallet services in Indonesia has significantly heightened the need for efficient customer service solutions, making chatbots an essential tool for user support. However, many providers continue to rely on rule-based chatbots, resulting in rigid interactions that struggle to meet diverse user needs or handle the linguistic complexities of the Indonesian language. This study addresses these limitations by developing an AI-powered intent classification model specifically tailored for Indonesian e-wallet customer service, aimed at delivering a more accurate, adaptive, and user-centered experience. A custom dataset was created from user comments on social media associated with Indonesia's top e-wallet providers, followed by data pre-processing and clustering using the BERTopic model. To improve interpretability, OpenAI's GPT-4 was employed for label refinement, resulting in enhanced clarity. Various models were tested, including IndoBERT, RoBERTa, and Convolutional Neural Network (CNN) architectures in both 2D and 3D configurations. The highest-performing model combined IndoBERT embeddings with a 3D CNN classifier, achieving an accuracy of 84.30%, precision of 84.33%, recall of 84.30%, and an F1-score of 84.24%. This study contributes a unique Indonesian-specific dataset and demonstrates the potential of AI to transform customer service interactions in Indonesia's e-wallet sector, offering a clear advancement over traditional rule-based approaches.

关键词： Adaptation models Solid modeling Three-dimensional displays Accuracy Online banking Intent recognition Customer services Transforms Chatbots Convolutional neural networks

来源：评论

学校读者我要写书评

暂无评论

Analyzing the Influence of Driving Experience on Difference Reverse Parking Behaviors Through Eye-Tracking Data Analysis

Analyzing the Influence of Driving Experience on Difference ...

引用

2024 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2024

作者： Wang, Qirun Huang, Xuan Wu, Bo Graduate School of Bionics Computer and Media Sciences Tokyo University of Technology Hachioji Tokyo192-0982 Japan Waseda University Faculty of Human Sciences Saitama Tokorozawa359-1192 Japan School of Computer Science Tokyo University of Technology Hachioji Tokyo192-0982 Japan

ISBN: (纸本)9781665410205

In daily driving, reverse parking into a garage often leads to collision accidents. However, current studies mostly focus on analyzing drivers' eye movements while using a certain specific parking style, lacking comparative research on different parking behaviors. In this study, 200 experiments were conducted with 20 participants of varying driving experience to collect their eye movement data during different types of reverse parking into the garage. Based on the collected eye-tracking data, we try to analyze how driving experience impacts reverse parking behaviors. The findings shown that reveal significant differences in gaze behavior and fixation positions between novice and experienced drivers. Novice drivers exhibit more erratic gaze patterns, focusing more on the right door mirror and frequently shifting their gaze between areas of interest. To be specific, in situation A (entering the garage from the right side), novice drivers feel insecure due to their inability to visually assess road conditions directly, prompting them to rely more on the right door mirror. On the other hand, in situation B (entering the garage from the left side), reliance on interior mirrors is reduced for both novice and experienced drivers. Insights from these findings could enhancing overall driving safety and efficiency. © 2024 IEEE.

关键词： Garages (parking)

来源：评论

学校读者我要写书评

暂无评论

Synonym Replacement Augmentation for Handling Data Imbalance in Personality Classification

Synonym Replacement Augmentation for Handling Data Imbalance...

引用

International Conference on Information technology, Information Systems and Electrical Engineering (ICITISEE)

作者： Rilo Chandra Pradana Derwin Suhartono Computer Science Department BINUS Graduate Program Master of Computer Science Bina Nusantara University Jakarta Indonesia Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia

ISBN: (数字)9798350368970

ISBN: (纸本)9798350368987

The Myers-Briggs Type Indicator (MBTI) classification is a widely utilized instrument for personality assessment. However, it frequently encounters challenges due to imbalanced data distributions across personality dimensions. It is paramount to address this issue to enhance the accuracy and reliability of personality predictions. Most current research in this field is focused on data balancing techniques, such as oversampling and undersampling, which have been demonstrated to enhance model performance. Nevertheless, there is a dearth of research exploring text augmentation methods, particularly synonym replacement, for this purpose. This study examines the efficacy of synonym replacement as a data augmentation technique for MBTI classification. Experiments are conducted with varying levels of synonym replacement (10%, 30%, 50%, 70%, and 90%) to assess its impact on model accuracy and F1 scores across the four MBTI dimensions. Our findings indicate that low levels of synonym replacement, particularly at 10% to 50%, can enhance the performance of the model in predicting MBTI dimensions. On the other hand, a higher number of replaced words in the synonym replacement augmentation can harm the model's performance. These observations suggest that synonym replacement can effectively address data imbalance in MBTI classification, although its application must be tailored to specific personality dimensions.

关键词： Training Electrical engineering Accuracy Instruments Predictive models Data augmentation Data models Reliability Information technology Information systems

来源：评论

学校读者我要写书评

暂无评论

High-Level Feature Fusion Network for Session-Based Social Recommendation 48

High-Level Feature Fusion Network for Session-Based Social R...

引用

48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023

作者： Wang, Liuyin Li, Mingchao Zheng, Hai-Tao Tsinghua University Department of Computer Science and Technology Beijing China Pengcheng Laboratory Shenzhen China Tsinghua University Shenzhen International Graduate School China

ISBN: (纸本)9781728163277

The session-based social recommendation task aims to leverage knowledge from social networks to predict user actions based on their sessions. Most previous studies pay more attention to complex transitions to get item embedding in various ways and neglect the importance of users' role in social network. Therefore, we design a High-level Feature Fusion Network to address these issues. Firstly, to better leverage the knowledge from social networks, we use a heterogeneous graph neural network to enhance the user/item representation. Secondly, a user-based graph attention network is adapted to learn the user's deep interest evolution process. Next, user and session features are transferred to our feature fusion module to generate fusion features, and the original and fusion features are combined to make recommendations. Extensive experiments on three public datasets show that the proposed model outperforms existing state-of-the-art models. © 2023 IEEE.

关键词： Graph attention networks High-level feature fusion Session-based social recommendation

来源：评论

学校读者我要写书评

暂无评论

Identification of Clustering Brisk Keypoint Feature in Grocery Product Using the Elbow Method

Identification of Clustering Brisk Keypoint Feature in Groce...

引用

IEEE International Conference on Industry 4.0, Artificial Intelligence, and Communications technology (IAICT)

作者： Rissa Rahmania Maria Susan Anggreainy Iman Herwidiana Kartowisastro Widodo Budiharto Computer Science Department BINUS Graduate Program Doctor of Computer Science Bina Nusantara University Jakarta Indonesia Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia

ISBN: (数字)9798350353464

ISBN: (纸本)9798350353471

In smart retail, the problem in grocery product image classification is fine-grained image representation. Product variants are characterized by differences in color, text, and size. However, while the process of image classification involves quite complex stages, there is still a misclassification of grocery products. This is because there are many areas of the image being calculated, which makes the models not optimally calculate the most important area. This paper aims to identify a number of patch image formations as an important area of the product by involving Binary Robust Invariant Scalable Keypoints (BRISK) as a feature descriptor, K-Means Clustering, and the elbow method. First, we experiment with the BRISK keypoint feature for grocery products with different categories of brands. Second, the keypoint features are clustered using K-Means clustering. Lastly, the number of clusters is analyzed using the elbow method to find the optimal number. The result shows that the number of clusters from 8 products has variations of 5, 5, and 6 clusters. Finally, each cluster represents an important area in forming patches to support fine-grained grocery product image classification.

关键词： Image color analysis Image representation Communications technology Pattern recognition Fourth Industrial Revolution Elbow Artificial intelligence

来源：评论

学校读者我要写书评

暂无评论

Learning Based Opportunistic Data Transmission for Cognitive IoT 17

Learning Based Opportunistic Data Transmission for Cognitive...

引用

17th IEEE International Conference on Social Computing and Networking, SocialCom 2024

作者： Li, Li Zhong, Xiaoxiong Shenzhen Institute of Information Technology School of Computer Science Shenzhen518172 China Tsinghua Shenzhen International Graduate School Shenzhen518055 China Guilin University of Electronic Technology Guangxi Key Laboratory of Trusted Software Guilin541004 China

ISBN: (纸本)9798331521097

As the rapid development of communication technology, more and more smart devices will be connected in cognitive radio Internet of Things (CIoT) using an opportunistic manner. However, the dynamicity and heterogeneity of spectrum will make it more difficult in data transmission strategy design for unreliable IoT. Opportunistic routing exploits the broadcast property of wireless medium to improve network performance in an opportunistic communication manner. Inspired by this, in this paper, we propose a Nash-learning and Transfer Learning based Opportunistic Data Transmission scheme, NTLODT, for differentiated services CIoT, which exploits Nash-learning for candidate set optimization and transfer learning for data transmission decision from a trust mechanism perspective. In addition, we prove that the convergence and complexity of the proposed algorithm. The simulation results demonstrate that the effectiveness of NTLODT in terms of average energy cost per bit, average delay, expected cost of routing and throughput. © 2024 IEEE.

关键词： Contrastive Learning

来源：评论

学校读者我要写书评

暂无评论

Dealing with Imbalanced Classes in Bot-IoT Dataset

arXiv

引用

arXiv 2024年

作者： Atuhurra, Jesse Kasahara, Shoji Sasabe, Masahiro Zhang, YuanYu Hara, Takanori Program of Information Science and Engineering Graduate School of Science and Technology Nara Institute of Science and Technology Japan Division of Information Science Xidian University China

With the rapidly spreading usage of internet of things (IoT) devices, a network intrusion detection system (NIDS) has an important role to detect and protect various types of attacks in the IoT network. To evaluate the robustness of the NIDS in the IoT network, the existing work proposed a realistic botnet dataset in the IoT network (Bot-IoT dataset) and applied it to machine learning based anomaly detection. This dataset contains imbalanced normal and attack packets because the number of normal packets is much smaller than that of attack ones. The nature of imbalanced data may make it difficult to identify the minority class correctly. In this thesis, to address the class imbalance problem in the Bot-IoT dataset, we propose a binary classification method with synthetic minority over-sampling techniques (SMOTE). The proposed classifier aims at detecting the attack packets and overcoming the class imbalance problem with the help of SMOTE algorithm. Through numerical results, we demonstrate the fundamental characteristics of the proposed classifier and the impact of imbalanced data on the classifiers' performance. Copyright © 2024, The Authors. All rights reserved.

关键词： Internet of things

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：