检索结果-内蒙古大学图书馆

9th International Conference on Information Management and Technology, ICIMTech 2024

作者： Prasetyo, Simeon Yuda School of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798350390025

The problem of pulmonary tuberculosis (PTB) worldwide is still considered to be a significant one, and it is therefore necessary to develop precise and effective diagnostic tools that will make it possible to carry out early interventions and treatment. In this research, the deep convolutional neural network (DCNN) architectures named VGG-16, VGG-19, ResNet-50, ResNet-101, and MobileNet were utilized for the detection of PTB from chest radiographs. By means of transfer learning and fine-tuning approaches it was possible to increase the diagnostic performance. Notably, VGG-16 consistently demonstrated exceptional performance, achieving a remarkable accuracy of 99.524% in both transfer learning and fine-tuning phases. Similarly, MobileNet exhibited strong performance, with an accuracy of 99.524% in transfer learning and 98.095% in fine-tuning. The results obtained show that VGG-16 and MobileNet are very good for the detection of PTB from chest X-ray images. Moreover, the fine-tuning showed the best performance of ResNet models, therefore, it confirmed the effectiveness of the iterative refinement process. In general, the results mainly highlight the significance of transfer learning and fine-tuning in the best model parameter adjustment, with VGG-16 being the most effective model. Possible future directions will be investigated through ensemble methods, domain-specific knowledge integration, advanced augmentation techniques, and multimodal data sources that will aim to improve PTB detection accuracy and deal with the changing healthcare challenges. © 2024 IEEE.

关键词： Deep neural networks

来源：评论

学校读者我要写书评

暂无评论

Movie Recommendation System: A Comparison of Content-Based and Collaborative Filtering 9th

Movie Recommendation System: A Comparison of Content-Based a...

引用

9th International Conference on computer science and Computational Intelligence, ICCSCI 2024

作者： Kurniawan, Hans Hendersen Lukman, William Susanto Fredyan, Renaldy Ibrahim, Muhammad Amien Computer Science Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia

Watching movies has been our source of entertainment for decades. In this modern world, watching movies has never been more convenient with the existence of video streaming services. This convenience also comes with new problems because there are a lot of movies and series on the platform that exploring it on our own will take time and effort. To solve that problem, companies use recommendation systems to improve user experience. In the field of recommendation systems, there are two famous approaches, content-based filtering, and collaborative filtering. This research aims to compare both methods and find the best possible method to use in a video streaming service platform. We found that the difference between the two is located on the data that they need for the model to work properly. Content-based recommendation systems have higher accuracy when using a limited amount of data while collaborative filtering needs more data about user's behavior. Other than that, both methods give different recommendations because content-based only gives recommendations that are like the content that the user likes while collaborative filtering recommends contents that other users like. © 2024 The Authors.

关键词： Collaborative filtering

来源：评论

学校读者我要写书评

暂无评论

Optimizing the Use of Technology in Efforts to Improve Public Health, Especially with Appropriate Food Calorie Intake 9th

Optimizing the Use of Technology in Efforts to Improve Publi...

引用

9th International Conference on computer science and Computational Intelligence, ICCSCI 2024

作者： Lo, Yossy Frenlie Fernandi, Fery Saputri, Hanis Amalia Sari, Azani Cempaka Computer Science Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia

NutriFitPal is a web-based online application developed to help someone monitor and control their calorie intake and maintain a healthy lifestyle. This web application was built using React TypeScript for frontend development and Firebase for backend operations. The main features contained in this web application are user authentication, real-time data storage, an extensive food database, and personalized calorie recommendations based on individual user data. In testing its reliability and functionality, this web application carries out black-box testing and System Usability Scale (SUS). Future improvements will focus on incorporating user feedback and expanding the food database. © 2024 The Authors.

关键词： Food ingredients

来源：评论

学校读者我要写书评

暂无评论

Comparative Analysis of Encoder-Based Pretrained Models: Investigating the Performance of BERT Variants in Indonesian Question-Answering

Comparative Analysis of Encoder-Based Pretrained Models: Inv...

引用

2024 International Conference on Artificial Intelligence, Blockchain, Cloud Computing, and data Analytics, ICoABCD 2024

作者： Islamey, Javier Jonathan, Vannes Nurzaki, Muhammad Lucky, Henry School of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798350368451

Question-Answering (QA) is an NLP task designed to answer questions automatically. In Indonesian QA systems, various methods, such as rule-based, semantic-based, and machine-learning approaches with deep learning, have been implemented. The unique linguistic features of Bahasa Indonesia may pose significant challenges for some of these methods. Additionally, research on generative QA systems using pre-trained models in Bahasa Indonesia is underexplored compared to English. To address these issues, this paper aims to develop a deep learning-based question-answering system for Bahasa Indonesia using a sequence-to-sequence approach with pre-trained Bidirectional Encoder Representations from Transformers (BERT) models. The proposed solution implements IndoBERT, IndoRoBERTa, and mBERT in an encoder-decoder architecture with each BERT model as both the encoder and the decoder. The models employ different weight configurations as part of an ablation experiment. The models are then evaluated on the SQuAD-ID dataset. Results have shown that IndoBERT2BERT with pre-trained weights in both encoder and decoder have consistently outperformed every model on BLEU, ROUGE-N, BERTScore, EM, and F1. This proves IndoBERT2BERT's capability to generate texts that are lexically and semantically similar to the target answer while capturing relevant information. Findings also show that IndoBERT2BERT is particularly capable of handling longer answers, showcasing its ability to work with complex passages. © 2024 IEEE.

关键词： Question answering

来源：评论

学校读者我要写书评

暂无评论

Advancements in Remote Sensing Image Dehazing: Introducing URA-Net with Multi-Scale Dense Feature Fusion Clusters and Gated Jump Connection

引用

computer Modeling in Engineering & sciences 2024年第9期140卷 2397-2424页

作者： Hongchi Liu Xing Deng Haijian Shao School of Computer Jiangsu University of Science and TechnologyZhenjiang212003China Department of Electrical and Computer Engineering University of NevadaLas Vegas89119USA

The degradation of optical remote sensing images due to atmospheric haze poses a significant obstacle,profoundly impeding their effective utilization across various *** methodologies have emerged as pivotal components of image preprocessing,fostering an improvement in the quality of remote sensing *** enhancement renders remote sensing data more indispensable,thereby enhancing the accuracy of target *** defogging techniques based on simplistic atmospheric degradation models have proven inadequate for mitigating non-uniform haze within remotely sensed *** response to this challenge,a novel UNet Residual Attention Network(URA-Net)is *** paradigmatic approach materializes as an end-to-end convolutional neural network distinguished by its utilization of multi-scale dense feature fusion clusters and gated jump *** essence of our methodology lies in local feature fusion within dense residual clusters,enabling the extraction of pertinent features from both preceding and current local data,depending on contextual *** intelligently orchestrated gated structures facilitate the propagation of these features to the decoder,resulting in superior outcomes in haze *** validation through a plethora of experiments substantiates the efficacy of URA-Net,demonstrating its superior performance compared to existing methods when applied to established datasets for remote sensing image *** the RICE-1 dataset,URA-Net achieves a Peak Signal-to-Noise Ratio(PSNR)of 29.07 dB,surpassing the Dark Channel Prior(DCP)by 11.17 dB,the All-in-One Network for Dehazing(AOD)by 7.82 dB,the Optimal Transmission Map and Adaptive Atmospheric Light For Dehazing(OTM-AAL)by 5.37 dB,the Unsupervised Single Image Dehazing(USID)by 8.0 dB,and the Superpixel-based Remote Sensing Image Dehazing(SRD)by 8.5 *** noteworthy,on the SateHaze1k dataset,URA-Net attains preeminence in overall performance,yieldi

关键词： Remote sensing image image dehazing deep learning feature fusion

来源：评论

学校读者我要写书评

暂无评论

Enhancing Progressive Diagnosis Prediction in Healthcare with Continuous Normalizing Flows 24

Enhancing Progressive Diagnosis Prediction in Healthcare wit...

引用

33rd Companion of the ACM World Wide Web Conference, WWW 2023

作者： Tan, Yanchao Zhang, Hengyu Zhou, Zihao Ma, Guofang Wang, Fan Liu, Weiming Liao, Xinting Hertzberg, Vicki S. Yang, Carl College of Computer and Data Science Fuzhou University China College of Computer Science Zhejiang University China School of Computer Science Zhejiang Gongshang University China Nell Hodgson Woodruff School of Nursing Emory University United States Department of Computer Science Emory University United States

ISBN: (纸本)9798400701726

Progressive diagnosis prediction in healthcare is a promising yet challenging task. Existing studies usually assume a pre-defined prior for generating patient distributions (e.g., Gaussian). However, the inferred approximate posterior can deviate from the real-world distribution, which further affects the modeling of continuous disease progression over time. To alleviate such inference bias, we propose an enhanced progressive diagnostic prediction model (i.e., ProCNF), which integrates continuous normalizing flows (CNF) and neural ordinary differential equations (ODEs) to achieve more accurate approximations of patient health trajectories while capturing the continuity underlying disease progression. We first learn patient embeddings with CNF to construct a complex posterior approximation of patient distributions. Then, we devise a CNF-enhanced neural ODE module for progressive diagnostic prediction, which aims to improve the modeling of disease progression for individual patients. Extensive experiments on two real-world longitudinal EHR datasets show significant performance gains brought by our method over state-of-the-art competitors. © 2024 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Diagnosis

来源：评论

学校读者我要写书评

暂无评论

Predictive Modelling on Caloric Expenditure: Exploring Machine Learning Techniques for Enhanced Analysis

Predictive Modelling on Caloric Expenditure: Exploring Machi...

引用

2024 International Conference on Control and Automation, Electronics, Robotics, Internet of Things, and Artificial Intelligence, CERIA 2024

作者： Justin, Javier Tan, Kezia Revalina Edbert, Ivan Sebastian Suhartono, Derwin School of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798331511166

Predicting caloric expenditure accurately remains a significant challenges due to individual variability, which frequently results in mistakes in established approaches such as metabolic computation and self-reported data. This research tackles this issue by examining the efficacy of several machine learning methods for forecasting calorie expenditure. The study compares a variety of models, including Linear Regression, eXtreme Gradient Boosting (XGB), Gradient Boosting Regressor (GBR), Ada Regressor, ExtraTrees Regressor, Random Forest, and Stacking Regressor. The XGBoost model has outstanding performance characteristics, including a mean absolute error (MAE) of 8.494312, mean square error (MSE) of 159.6425, root mean square error (RMSE) of 12.6350, and a high R-squared (R2) score of 0.9603. These results demonstrate that machine learning algorithms can produce more precise forecasts of calorie expenditure than traditional methods. This study improves knowledge and implementation of predictive models in the realm of health by employing sophisticated modeling methodologies. © 2024 IEEE.

关键词： Adversarial machine learning

来源：评论

学校读者我要写书评

暂无评论

A Systematic Literature Review: Object Detection in Semi-Autonomous Car 6

A Systematic Literature Review: Object Detection in Semi-Aut...

引用

6th International Conference on computer and Informatics Engineering, IC2IE 2023

作者： Sunjaya, Eric Kurniadi Daniel, Christian Kurniawan, Afdhal Anggreainy, Maria Susan School of Computer Science Bina Nusantara University Computer Science Department Jakarta Indonesia Bina Nusantara University Binus Graduate Program Doctor of Computer Science Computer Science Department Jakarta Indonesia

ISBN: (纸本)9798350345162

Traffic accidents are a serious problem in developing countries. These accidents can be caused by poor infrastructure and the arrogance of drivers. In addition, semi-autonomous systems are automotive technologies that have been developed in recent years and are starting to be implemented in existing vehicles. This literature review paper will mainly discuss how object detection works in semi-autonomous systems, how semi-autonomous systems operate, and whether semi-autonomous cars can reduce the number of traffic accidents. Its main objective is to build driver confidence in semi-autonomous systems by assisting in driving and supporting the development of them. In the process of writing this paper, the method we use follows the guidelines stated in the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Protocols (PRISMA-P). © 2023 IEEE.

关键词： neural network object detection semi-autonomous car semi-autonomous system

来源：评论

学校读者我要写书评

暂无评论

Analyzing Microservices and Monolithic Systems: Key Factors in Architecture, Development, and Operations 6

Analyzing Microservices and Monolithic Systems: Key Factors ...

引用

6th International Conference on computer and Informatics Engineering, IC2IE 2023

作者： Christian, Juan Steven Kurniawan, Afdhal Anggreainy, Maria Susan School of Computer Science Bina Nusantara University Computer Science Department Jakarta Indonesia Bina Nusantara University Binus Graduate Program Doctor of Computer Science Computer Science Department Jakarta Indonesia

ISBN: (纸本)9798350345162

Microservices and monolithic systems are two prevalent architectural approaches in software development. This study provides a complete review and analysis of the key components involved in design, development, and operation in software development. A systematic review of the literature was conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. This review examined various sources debating monolithic systems vs microservices, highlighting their benefits, drawbacks, and implementation in enterprises. The paper addresses important research questions, aiming to further analyze this architectural approach. Findings show that microservices offer benefits such as scalability, flexibility, and independent deployment, while monolithic systems provide simplicity and ease of development. However, challenges related to network communication, data consistency, and operational complexity were also found with microservices. This research focuses on discussing the trade-offs and factors to consider when deciding between monolithic systems and microservices, which provides in-depth information for practitioners in decision-making for software development. This research aims to help readers understand the effects of using monolithic or microservice-based systems in software development. © 2023 IEEE.

关键词： benefits drawbacks implementation Microservices Monolithic Systems PRISMA

来源：评论

学校读者我要写书评

暂无评论

Learning to Communicate Among Agents for Large-Scale Dynamic Path Planning With Genetic programming Hyperheuristic

IEEE Transactions on Artificial Intelligence

引用

IEEE Transactions on Artificial Intelligence 2025年第5期6卷 1269-1283页

作者： Liao, Xiao-Cheng Hu, Xiao-Min Chen, Xiang-Ling Mei, Yi Jia, Ya-Hui Chen, Wei-Neng Victoria University of Wellington Centre for Data Science and Artificial Intelligence School of Engineering and Computer Science Wellington6140 New Zealand Guangdong University of Technology School of Computer Science and Technology Guangzhou510006 China Hanyang University Department of Electrical and Electronic Engineering Ansan15588 Korea Republic of South China University of Technology School of Future Technology Guangzhou510006 China Pazhou Lab Guangzhou510005 China South China University of Technology School of Computer Science and Engineering State Key Laboratory of Subtropical Building and Urban Science Guangzhou510006 China

Genetic programming hyperheuristic (GPHH) has recently become a promising methodology for large-scale dynamic path planning (LDPP) since it can produce reusable heuristics rather than disposable solutions. However, in this methodology, the extracted local and decentralized heuristic for agents that lack a global systemic view sometimes may be problematic. Therefore, a new challenge is to strike a balance between conciseness to improve generalization ability and incorporation of more global information to obtain better performance. In this work, we target the LDPP problem and propose a communication learning mechanism (ComLGP) for GPHH to address the above difficulties. In ComLGP, a communication function is introduced to serve as a communication protocol and exist in the form of an extra terminal in GPHH. Compared to the classic terminals which are fixed in genetic programing, this communication function undergoes optimization along with the evolutionary process of GPHH. In this way, the communication function can be learned which enables agents to communicate without a predefined communication protocol. Then, a caching and lazy updating mechanism for ComLGP is presented to accelerate the calculation of communication content. Last, we verified our method on 22 scenarios including two real world road networks. The experimental results demonstrate that the proposed ComLGP can successfully learn to communicate. Although in the absence of any manually designed communication features, ComLGP is capable of achieving performance competitive to the state-of-the-art method that employs a predefined communication protocol and outperforms the remaining compared methods in most scenarios. © 2020 IEEE.

关键词： Genetic programming

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：