检索结果-内蒙古大学图书馆

2023 International Workshop on Artificial Intelligence and Image Processing, IWAIIP 2023

作者： Nafi'an, Muh. Hafidh Ilmi Bachtiar, Fitra Abdurrachman Setiawan, Budi Darma Brawijaya University Computer Science Faculty Informatics Engineering Department Malang Indonesia

ISBN: (纸本)9798350382914

Facial emotion recognition is one of the artificial intelligence implementations used to recognize emotions based on data learned by computers. Unlike humans, who can recognize a person's emotions directly, computers need several trainings procedures conducted by humans to be able to recognize a person's emotions. Previous research has proposed various methods with deep learning and traditional machine learning approaches to classify emotions based on faces. Some studies obtained relatively high accuracy, but on evaluation by cross-validation, the results were much lower than the accuracy obtained. Therefore, this study proposes an approach using a modified MobileNetV2 deep learning architecture in the residual layer by adding a Convolutional Block Attention Module (CBAM) to improve accuracy and data generalization. This experiment uses the Karolinska Directional Emotional Faces (KDEF) public benchmark dataset. Preprocessing is done with image augmentation to enrich the training data by resizing, center cropping, random horizontal flipping, random affine, and random rotation. The proposed method is evaluated by 5-fold cross-validation and compared with MobileNetV2 without modification and state-of-the-art methods. Experimental results show that the proposed model outperform all comparative methods by achieving a 5 -fold cross-validation accuracy of 94.387%. © 2023 IEEE.

关键词： Emotion Recognition

来源：评论

学校读者我要写书评

暂无评论

Revolutionizing Agricultural Education with Virtual Reality and Gamification: A Novel Approach for Enhancing Knowledge Transfer and Skill Acquisition 1

引用

3rd International Conference on Novel and Intelligent Digital Systems, NiDS 2023

作者： Strousopoulos, Panagiotis Troussas, Christos Papakostas, Christos Krouska, Akrivi Sgouropoulou, Cleo Department of Informatics and Computer Engineering University of West Attica Egaleo12243 Greece

ISBN: (数字)9783031441462

ISBN: (纸本)9783031441455

Agricultural education holds significant importance as it provides comprehensive training in the principles and practices of agriculture, including apiculture, which plays a crucial role in sustainable practices and the cultivation of skilled farmers and apiarists. However, traditional approaches to agricultural education face inherent limitations when it comes to engaging students effectively and facilitating optimal knowledge transfer and skill acquisition. Recognizing this compelling need for innovative methodologies, this research paper introduces a novel approach to apiculture education that harnesses the power of virtual reality (VR) and gamification principles in a captivating application named Beekeeper World. Beekeeper World immerses users in an interactive and engaging VR environment where they assume the role of beekeepers responsible for safeguarding bees from threats, symbolized by spiders. By leveraging digital technologies and gamification techniques, this application aims to cultivate a new generation of passionate and skilled apiarists. This study explores the implications of Beekeeper World for apiculture education and the broader agricultural sector, underscoring the importance of innovative teaching methods in preparing future professionals for the dynamic realm of apiculture and ensuring the long-term sustainability of global food systems. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Virtual reality

来源：评论

学校读者我要写书评

暂无评论

Eye Disease Classification Based on Fundus Images Using Convolutional Neural Network 25

Eye Disease Classification Based on Fundus Images Using Conv...

引用

25th International Electronics Symposium, IES 2023

作者： Denandra, Rafi Fariza, Arna Prayogi, Yanuar Risah Politeknik Elektronika Negeri Surabaya Department of Informatics and Computer Engineering Surabaya Indonesia

ISBN: (纸本)9798350314731

The eye is a vital organ in the human body. Through the eyes, humans can absorb over 80% of visual information used to carry out various daily activities. However, in Indonesia, there are many cases of vision impairments that, if not properly addressed, can lead to blindness. Examples of such impairments include cataracts, glaucoma, and diabetic retinopathy. Currently, there are many research studies on eye diseases that are aided by technologies like Artificial Intelligence (AI). One of the AI technologies that is commonly developed is Convolutional Neural Network (CNN). CNN is a deep learning framework that excels in solving image or picture classification problems because its architecture applies convolution layers, which can break down images to extract features and easily reduce the high dimensionality without losing the image's information itself. In this study, the author used various CNN models to find the most suitable model for classifying eye diseases, including EfficientNet, ResNet, Inception V3, and many others. Through various model training experiments, the accuracy for the ResNet50 model was found to be 95% with a loss of 17%, followed by Xception with an accuracy of 95% and a loss of 21%, and EfficientNetV2B2 with an accuracy of 92% and a loss of 25%. © 2023 IEEE.

关键词： Convolutional neural networks

来源：评论

学校读者我要写书评

暂无评论

Fine-Tuning Deep Learning Model for Detecting Indonesian Traffic Accident Tweet 6

Fine-Tuning Deep Learning Model for Detecting Indonesian Tra...

引用

6th International Conference on computer and informatics engineering, IC2IE 2023

作者： Adi, Rizky Fukumoto, Fumiyo Irnawan, Bassamtiano Renaufalgi Politeknik Negeri Jakarta Department of Computer and Informatics Engineering Depok Indonesia University of Yamanashi Department of Computer Science and Engineering Yamanashi Japan

ISBN: (纸本)9798350345162

Twitter is one of Indonesia's most used social media, with total visits reaching 192 million visits per month, making it the fifth most-visited website in Indonesia. Twitter users exchange information with each other daily. One of them is a traffic accident event. With over 100,000 cases every year from 2017-2021, it is not usual to see a tweet that contains traffic accident information. By implementing text classification, traffic accidents can be detected through tweets and used to send alert messages to emergency responders to respond as quickly as possible. Previous research used a machine learning approach to solve this text classification problem. The study resulted in the best accuracy of 85% from the SVM model, which leaves room for improvement using state-of-the-art deep learning models. This research proposes a transfer learning method using two BERT-based and two RoBERTa-based pre-trained models, namely IndoBERT, IndoBERTweet, Indonesian RoBERTa Base Wiki, and Indonesian RoBERTa Base OSCAR to create the best model to classify tweets that contain traffic accident information. We used two kinds of fine-tuning architecture: the vanilla version fine-tuning architecture and the modified version, which combines the pre-trained model with CNN. This study used 1,002 manually labeled tweets, where 360 were labeled as traffic accident tweets. The experiment found that transfer learning from a pre-trained model Indonesian RoBERTa OSCAR provided the best result with an f1-score of 88.73 % and an accuracy of 92%, which means a 7% accuracy improvement over the previous research's best model. © 2023 IEEE.

关键词： BERT RoBERTa Text Classification Traffic Accident Transfer Learning Tweet

来源：评论

学校读者我要写书评

暂无评论

Tackling an Unbalanced Dataset for Classifying Indonesian E-Commerce Reviews Using Multi Word Embedding Model 8

Tackling an Unbalanced Dataset for Classifying Indonesian E-...

引用

8th International Conference on informatics and Computing, ICIC 2023

作者： Adi, Rizky Irnawan, Bassamtiano Renaufalgi Li, Jiyi Politeknik Negeri Jakarta Department of Computer and Informatics Engineering Depok Indonesia University of Yamanashi Department of Computer Science and Engineering Kofu Japan

ISBN: (纸本)9798350342604

Product reviews are an integral part of ecommerce. By Q2 2020 alone, the average of e-commerce visitors in Indonesia almost reached 400 million. This vast amount of traffic resulting a massive amount of product review information. Product reviews in Indonesia are usually paired with a rating system from one to five based on user satisfaction with the product. Mostly, the number of reviews in each rating is unbalanced. Ratings 5, 4, and 1 are usually more frequent than ratings 2 and 3. This imbalanced data makes classification challenging to categorize the review rating, leading to inaccurate predictions for the rating with fewer data than the others. Previous research used various approaches for classifying review ratings, such as machine learning and fine-tuning a state-of-the-art deep learning model. In this research, we propose a new approach that leverages multiple-word embedding combined with CNN to deal with the highly unbalanced dataset problem. By leveraging multiple-word embedding techniques, the model can extract more features from the review text. We also proposed an extended pipeline to enhance the model performance. The proposed model and pipeline perform better than the fine-tuned Indonesian BERT-based and RoBERTa-based pre-trained models baseline with 0.93 accuracy and 0.82 F1-Macro scores. © 2023 IEEE.

关键词： Embeddings

来源：评论

学校读者我要写书评

暂无评论

Mental Confusion Prediction in E-Learning Contexts with EEG and Machine Learning 3rd

Mental Confusion Prediction in E-Learning Contexts with EE...

引用

3rd International Conference on Novel and Intelligent Digital Systems, NiDS 2023

作者： Trigka, Maria Dritsas, Elias Mylonas, Phivos Department of Informatics and Computer Engineering University of West Attica Athens Greece Department of Electrical and Computer Engineering University of Patras Patras Greece

ISBN: (纸本)9783031440960

In cognitive science, the term confusion is used to capture the decline in learners’ cognitive ability, which affects their ability to think, solve a problem, learn and understand. Unlike classroom education, in online e-learning contexts (e.g., in Massive Open Online Courses (MOOC)), confusion hinders the smooth evolution of the learning process from the learners’ side, as the educator can’t immediately interact with the students to restore cognitive equilibrium. This paper presents a Machine Learning (ML) based approach by comparing several classifiers that were trained and tested exploiting Electroencephalogram (EEG) data (namely, band power, attention and mediation features) acquired by the MindSet device to efficiently distinguish "Confused" from "Not-Confused" subjects. In particular, J48 was the dominant model reaching an optimal performance with accuracy, precision and recall equal to 99.9% and an Area Under the Curve (AUC) of 100%. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Electroencephalography

来源：评论

学校读者我要写书评

暂无评论

High performance of Dengue shock syndrome detection using extreme gradient boosting with ANOVA feature selection

Journal of Biotech Research

引用

Journal of Biotech Research 2024年 16卷 22-31页

作者： Muflikhah, Lailil Iskandar, Agustin Yudistira, Novanto Nadlori, Isbat Uzzin Dewanto, Bambang Nur Faculty of Computer Science Brawijaya University Malang India Faculty of Medicine Brawijaya University Malang India Department of Informatics Engineering and Computer Politeknik Elektronika Negeri Surabaya India Faculty of Technology Information Merdeka University Malang India

Dengue shock syndrome (DSS) is an infectious disease that affects millions of people every year all over the world. Early detection of DSS is essential for providing effective therapy and promoting patient recovery. In this work, we proposed a method to enhance DSS detection by combining the Extreme Gradient Boosting (XGBoost) algorithm with variance (ANOVA) feature selection analysis. We used a clinical dataset that contained important information gleaned from people with dengue virus infection. The dataset used for the research was collected from patients at Syaiful Anwar Hospital and consisted of 501 instances. Of these, 401 cases were related to DSS, while the other instances were unrelated to this specific medical condition. An analysis of variance (ANOVA) evaluated the most significant factors that distinguished persons with DSS from those with other dengue diseases. After that, the XGBoost model gave the characteristics of the selected features. For evaluation, we split the data into 80% and 20% for training and testing, respectively. The experimental result showed that the created model had a high degree of performance evaluation in detecting DSS. The highest performance achieved an accuracy of 0.839, precision of 0.875, recall of 0.92, and f1-score of 0.897. Most importantly, the model could detect DSS early on, enabling more proactive therapy and faster responses for individuals at risk of developing the condition. © (2024), (Bio Tech System). All Rights Reserved.

关键词： Patient treatment

来源：评论

学校读者我要写书评

暂无评论

Machine Learning Prediction Based UI for Aircraft Cockpit 14

Machine Learning Prediction Based UI for Aircraft Cockpit

引用

14th International Conference on Electrical and Electronics engineering, ELECO 2023

作者： Topal, Bilge Çarkacioǧlu, Levent Töreyin, Behçet Uǧur Istanbul Technical University Faculty of Computer and Informatics Engineering Dept. of Computer Engineering Istanbul Turkey Aselsan Inc. Ankara Turkey Istanbul Technical University Informatics Institute Istanbul Turkey

ISBN: (纸本)9798350360493

Pilot vehicle interfaces on aircraft, similar to the automobile digital displays found in automobiles, are used to present the current status of various systems (e.g., navigation, engine information). Aircraft interfaces exhibit deterministic behavior due to safety concerns, ensuring that user input yields predictable outputs. Consequently, integrating text-based pattern recognition or artificial neural network based solutions to aircraft systems are challenging because of their deterministic nature. In this paper, an approach complementary to the existing user interface models is proposed. This approach is based on learning recurring user interactions during flight. It utilizes text-based pattern recognition and prediction models based on user interaction logs and sensor readings. Test results on sample data suggest that the proposed approach yields faster and context-aware user interaction model that decreases the time needed to perform tasks in the aircraft cockpit, where timing is crucial. © 2023 IEEE.

关键词： User interfaces

来源：评论

学校读者我要写书评

暂无评论

Enabling LoRa energy awareness: A Multihop One-Hop Routing Protocol for Prolonging Network Lifetime 19

Enabling LoRa energy awareness: A Multihop One-Hop Routing P...

引用

19th Annual International Conference on Distributed Computing in Smart Systems and the Internet of Things, DCOSS-IoT 2023

作者： Katsidimas, Ioannis Manolopoulos, Alexandros-Ioannis Nikoletseas, Sotiris University of Patras Department of Computer Engineering and Informatics Patras Greece University of Athens Department of Physics National and Kapodistrian Athens Greece University of Patras and Computer Technology Institute and Press Diophantus Department of Computer Engineering and Informatics Patras Greece

ISBN: (纸本)9798350346497

IoT applications often rely on battery-powered nodes that may be challenging to recharge, making energy efficiency a crucial concern. In this context, routing protocols play a vital role as they dictate the paths data packets take between nodes, directly influencing the energy consumption of IoT networks. Energy-aware routing protocols have been proposed to address this issue, with the aim of optimizing energy consumption by selecting paths that minimize the energy required for data transmission and reception. These protocols employ various strategies, such as distance-based, traffic-based, and hybrid schemes, to achieve energy efficiency in IoT networks. LoRaWAnprotocol has been adopted for various IoT applications with requirements in low-power and extended coverage. Over the recent years attempts have been made to replace the typical star topology employed by LoRa with multihop solutions, in an effort to further increase its capabilities. In this paper, we present a multihop algorithm that is based on an energy-balanced routing scheme adopted by LoRa nodes to increase energy efficiency and network lifetime. Multiple one-hop routes are configured for critical nodes and a different route is selected in each transmission. Additionally, critical nodes adjust their Spreading Factor and Transmission Power according to the distance of intermediate nodes. Simulation testing results verify the improved performance of our algorithm over the direct link approach. © 2023 IEEE.

关键词： Energy efficiency

来源：评论

学校读者我要写书评

暂无评论

Semi-Automated Video Archiving Using Object Detection, Tracking and Ontological Annotation 4

Semi-Automated Video Archiving Using Object Detection, Track...

引用

4th International Conference on Artificial Intelligence, Robotics and Control, AIRC 2023

作者： Ayman, Shehab Eldeen Hussein, Walid Karam, Omar H. The British University in Egypt Faculty of Informatics and Computer Science Department of Software Engineering Cairo Egypt The British University in Egypt Faculty of Informatics and Computer Science Department of Computer Science Cairo Egypt

ISBN: (纸本)9798350348248

In light of this unmistakable exponential data expansion, visual media archiving must be rethought. Human generated meta-data might not be sufficient for efficient data retrieval. Object detection and object recognition enable the generation of computer-generated meta data that improve the storage and retrieval of data. We present a semi-automated approach for preserving video footage that combines object identification and ontologies. This significantly cuts down on search time, data scrubbing by providing only the relevant information making for more efficient queries. © 2023 IEEE.

关键词： computer vision

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：