检索结果-内蒙古大学图书馆

Quantitative analysis of sign language translation using artificial neural network model

Procedia computer Science 2024年 245卷 998-1009页

作者： Fendy Wijaya Leonardo Dahendra Eko Setyo Purwanto Muhamad Keenan Ario Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia 11480 Mobile Application & Technology Program Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia 11480

Sign language is one of the technique to support communication with deaf and speech impaired people. Nowadays, human needs become more complex, so are the needs of people with those disabilities. Therefore, with the sophistication of modern technology in the field of computer science, it is necessary to have a sign language translation system which is capable to convert human gestures into words or spelling letters in a natural human language, especially Indonesian language and SIBI sign language. This research is conducted to analyze the capabilities of an Artificial Neural Network (ANN) system to translate SIBI sign language into Indonesian language. The analysis is performed quantitatively from the experiment results to gain a descriptive insights for the generated models. By creating two datasets containing alphabets and words in SIBI sign language, two models were generated to predict alphabets and words, respectively. The result shows that ANN model could effectively and efficiently perform a sign language translation with accuracy of alphabet prediction reached 96.15% and words prediction reached 99.45% in duration of prediction without exceeding 0.15 seconds. Regarding to the results, it can be concluded that ANN model quantitatively suitable to be implemented as an Indonesian sign language translator system.

关键词： sign language Artificial Neural Network SIBI prediction quantitative analysis

来源：评论

学校读者我要写书评

暂无评论

Analyzing the Efficiency of Key Management Techniques Used in Information Security Models for Networked applications 5th

Analyzing the Efficiency of Key Management Techniques Used i...

引用

5th International Conference on Data Science, Machine Learning and applications, ICDSMLA 2023

作者： Beemkumar, N. Singh, Vaishali Shree, Ritu Batra, Raman Karnataka Bangalore India Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh Lucknow India Department of Computer Science & Application Vivekananda Global University Jaipur India Department of Mechanical Engineering Noida Institute of Engineering and Technology Uttar Pradesh Greater Noida India

ISBN: (纸本)9789819780426

This study aims to investigate the performance of numerous key manipulation strategies utilized in modern statistics protection models. Especially, this painting examines the use of public key infrastructures (PKI), symmetric key infrastructures (SKI), and identity-based totally encryption (IBE) for securing application conversation in networked environments. By way of analyzing the strengths and weaknesses of each gadget, potential improvements are diagnosed. Moreover, a comparison among the unique fashions is done to pinpoint feasible weaknesses and endorse feasible mitigation strategies. The research results will enable the improvement of greater comfortable and green key management structures for networked programs, which can offer effective protection against malicious assaults. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Infrastructures Pinpoint Protection Symmetric Weaknesses

来源：评论

学校读者我要写书评

暂无评论

Domain Transfer Learning for Medical Image Segmentation Tasks 5th

Domain Transfer Learning for Medical Image Segmentation Task...

引用

5th International Conference on Data Science, Machine Learning and applications, ICDSMLA 2023

作者： Kharbas, Vikas Kumar Srivastava, Pulkit Bargavi, Manju Pandeya, Megha Department of Computer Science and Application Vivekananda Global University Jaipur India Department of Mechanical Engineering Noida Institute of Engineering and Technology Uttar Pradesh Greater Noida India Karnataka Bangalore India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh Lucknow India

ISBN: (纸本)9789819780426

Domain transfer learning is a powerful device for clinical image Segmentation duties. It involves transferring information from a source domain to a goal area so that related tasks on those domains can be advantageous from every difference. This era allows improving and robust clinical photo segmentation models by exploiting the prevailing area information and facts from a source area. This approach has been used to enhance the performance of sure segmentation responsibilities in medical imaging together with the segmentation of pathologies from imaging, segmentation of anatomic structures, and system getting to know-based total segmentation. The most critical domain version strategies encompass domain-adversarial neural networks, generative fashions, and meta-getting to know. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Adversarial machine learning

来源：评论

学校读者我要写书评

暂无评论

Hybrid Recommendation on Collaborative Filtering for Book Recommendation

Hybrid Recommendation on Collaborative Filtering for Book Re...

引用

Next Generation Communication & Information Processing (INCIP), International Conference on

作者： Keshav Gupta Sudeshna Chakraborty Kalyani Singh Dewan Imdadul Islam Himanshu Agrawal Shams Alam School of Computer Application and Technology Galgotias University Greater Noida India School of Computer Science and Engineering Galgotias University Greater Noida India

ISBN: (数字)9798331528140

ISBN: (纸本)9798331528157

As the volume of available books grows exponentially, selecting the right book has become increasingly challenging for users. To address this, a hybrid recommendation system that incorporates both collaborative filtering (CF) and popularity-based filtering is proposed. The system aims to provide personalized recommendations by analyzing user preferences, while also leveraging the collective preferences of the broader user base. Collaborative filtering uses a user-item interaction matrix and applies cosine similarity to identify patterns in user ratings, predicting books that align with individual preferences. Popularity-based filtering complements this by considering books that are highly rated and frequently accessed by many users, ensuring that popular and widely appreciated books are recommended alongside personalized suggestions. Byintegrating both approaches, the system achieves a balance between personalization and popularity, enhancing the overall user experience. Experimental results indicate that this hybrid model not only improves recommendation accuracy but also provides users with diverse options that reflect both their interests and the wider reading trends. This makes the system particularly effective for recommending both niche and popular books.

关键词： Filtering Collaborative filtering Soft sensors Collaboration Machine learning Information processing Market research Feature extraction User experience Recommender systems

来源：评论

学校读者我要写书评

暂无评论

Intelligent Personality Assessment and Verification from Handwriting using Machine Learning

引用

International Journal of Information technology (Singapore) 2024年第7期16卷 4541-4546页

作者： Mishra, Pradeep Kumar Mishra, Gouri Sankar Abidi, Ali Imam Kumar, Rajiv Sahoo, Ashok Kumar Maini, Tarun Department of Computer Science and Application SSET Sharda University Department of Computer Science and Engineering SSET Sharda University NVIDIA AI Learning & amp Research Center Department of Applied Computational Science and Engineering G L Bajaj Institute of Technology and Management Greater Noida Graphic Era Hill University School of Computer Science and Engineering Galgotias University

It is possible to tell a lot about a person just by looking at their handwriting. The way someone writes might tell you a lot about who, they are as a person. There has been a lot of study on the possibility of deducing aspects of a person’s character from their handwriting. In this piece, we’ll look at the different personality qualities and how they might be utilized to make an evaluation. The report includes a dataset showing a correlation between several traits of personality. The Big Five Personality Traits are mapped using handwritten personality data. It was suggested that a comparison table should be used to spot distinctive character quirks. The article includes a review of previous studies and an assessment of the current status of the topic. Handwriting embodies have both creativity and expressiveness. © Bharati Vidyapeeth's Institute of computer applications and Management 2024.

关键词： Agreeableness Conscientiousness Extraversion Neuroticism Openness

来源：评论

学校读者我要写书评

暂无评论

A fine-tuned vision transformer-based on limited dataset for facial expression recognition

引用

Procedia computer Science 2024年 245卷 574-582页

作者： Rio Febrian Ronald Richie Huang Nicholas Setiono Dimas Ramdhan Andry Chowanda Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia 11480 Game Application & Technology Program Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia 11480

With the increase of interest in Facial Expression Recognition (FER) in the past few decades. Several challenges surfaced with the invention of many different FER models which are often based on Convolution Neural Network (CNN) architectures. Recently, an attention-based transformer model has been presented to address FER. One of the major issues with Transformers is the need for a large data quantity for training. Therefore, in this paper, we propose to learn how to fine-tune a vision transformer-based (ViT) model using a limited dataset. We will be using the JAFFE Dataset, which consists of only 213 images containing seven different emotions. The proposed method is evaluated using several fine-tuning methods, such as adding dropout, data augmentation, and layer freezing. We compared the models implemented with 5% dropout regularization, augmented dataset (up to 5000 images), and freezing the initial model's layers, fine-tuning around a fourth of the last layers. The best model was achieved by fine-tuning ViT L-16 with 96.06% accuracy, trained with 5% dropout in the augmented dataset, and freezing the initial 21st layers. We also compared our model to the other previous work model and the results showed that our model reached the state-of-the art for the JAFFE dataset.

关键词： Facial Expression Recognition Vision Transformer Attention Deep Learning

来源：评论

学校读者我要写书评

暂无评论

The future of virtual reality: Prospect and problems

引用

Procedia computer Science 2024年 245卷 355-364页

作者： Andrew Alfonso Lie Owen Tamashi Buntoro Eko Setyo Purwanto Muhamad Keenan Ario Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia 11480 Mobile Application & Technology Program Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia 11480

Virtual Reality (VR) is a technology that allows users to interact with a simulated environment created by virtual reality capabilities through the intermediary of computers, simulator tools, and others, providing new experiences and can feel experiences that are not possible in the real world. This research aims to identify and analyze what challenges may occur such as technical, social, and ethical barriers in the development and application of VR technology. Using a mixed methods approach, we collected quantitative data from Kaggle's dataset and conducted in-depth interviews with active VR users. Our findings show that although VR has significant potential in various fields, such as education, healthcare, and entertainment, challenges such as motion sickness, high costs, and limited and engaging content hinder the widespread adoption of VR technology. The study also highlights the importance of improving technologies that need to be implemented such as visual capabilities, motion responsiveness, and audio quality to enhance the user experience when using VR. By overcoming these barriers, VR can maximize its positive impact and become a revolutionary tool in various sectors.

关键词： Virtual Reality immersive experience technical barriers social impact ethical considerations

来源：评论

学校读者我要写书评

暂无评论

Trend analysis in competitive programming using topic modeling

引用

Procedia computer Science 2024年 245卷 591-600页

作者： Albert Yulius Ramahalim Christoffer Edbert Karuniawan Eko Setyo Purwanto Muhamad Keenan Ario Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia 11480 Mobile Application & Technology Program Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia 11480

Competitive programming (CP) is a mind sports activity where people solve problems using command-line computer programs to provide correct output for the given test cases. Competitors need to practice problem-solving and mathematics as well as study algorithms and data structures to perform well in CP. This study aims to provide an original way to perform a trend analysis in CP, distinguishing topics frequently used in CP contests. To fulfill our goal, we create topic models based on previous topic modeling works to do natural language processing tasks using Latent Dirichlet Allocation (LDA) and Biterm Topic Model (BTM). For our dataset, we constructed a corpus from Codeforces blog posts, a popular website for competitive programmers, by extracting its content and user comments. Our results indicate that BTM is powerful enough to do trend analysis in CP. The trend analysis recognized that dynamic programming and complexity analysis have been the most prominent topics for the last ten years. Data structures and string algorithms are runners-up that may have potential trends in the future. This study opens up further research on other methods to perform trend analysis using better topic models and corpora.

关键词： competitive programming topic modeling trend analysis

来源：评论

学校读者我要写书评

暂无评论

Text2NKG: Fine-Grained N-ary Relation Extraction for N-ary relational Knowledge Graph Construction 38

Text2NKG: Fine-Grained N-ary Relation Extraction for N-ary r...

引用

38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Luo, Haoran Haihong, E. Yang, Yuhao Yao, Tianyu Guo, Yikai Tang, Zichen Zhang, Wentai Peng, Shiyao Wan, Kaiyang Song, Meina Lin, Wei Zhu, Yifan Tuan, Luu Anh School of Computer Science Beijing University of Posts and Telecommunications China School of Automation Science and Electrical Engineering Beihang University China Beijing Institute of Computer Technology and Application China Inspur Group Co. Ltd. China College of Computing and Data Science Nanyang Technological University Singapore

Beyond traditional binary relational facts, n-ary relational knowledge graphs (NKGs) are comprised of n-ary relational facts containing more than two entities, which are closer to real-world facts with broader applications. However, the construction of NKGs remains at a coarse-grained level, which is always in a single schema, ignoring the order and variable arity of entities. To address these restrictions, we propose Text2NKG, a novel fine-grained n-ary relation extraction framework for n-ary relational knowledge graph construction. We introduce a span-tuple classification approach with hetero-ordered merging and output merging to accomplish fine-grained n-ary relation extraction in different arity. Furthermore, Text2NKG supports four typical NKG schemas: hyper-relational schema, event-based schema, role-based schema, and hypergraph-based schema, with high flexibility and practicality. The experimental results demonstrate that Text2NKG achieves state-of-the-art performance in F1 scores on the fine-grained n-ary relation extraction benchmark. Our code and datasets are publicly available. © 2024 Neural information processing systems foundation. All rights reserved.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Mental illness detection using sentiment analysis in social media

引用

Procedia computer Science 2024年 245卷 971-978页

作者： Kasimirus Derryl Odja Jasson Widiarta Eko Setyo Purwanto Muhamad Keenan Ario Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia 11480 Mobile Application & Technology Program Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia 11480

This research tries to detect mental illness using sentiment analysis on Reddit data, as well as comparing the performance of the k-Nearest Neighbors (k-NN), Random Forest, and Neural Network models. Using text post data from the pre-pandemic and post-pandemic periods, we concluded that the Random Forest model had the highest overall performance with an F1 Score, accuracy, recall and precision of 80.6%, making it quite effective in detecting depression. Even though the Neural Network model shows slightly lower accuracy, namely 79%, in fact this model has the lowest error rate, namely 0.06496. The k-NN model showed the lowest accuracy and higher error rate. These findings highlight the potential of sentiment analysis and machine learning in identifying mental health issues on social media and suggest that better models can improve early detection and intervention efforts.

关键词： Sentiment Analysis Mental Illness k-NN Random Forest Neural Network

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：