检索结果-内蒙古大学图书馆

7th International Conference on Information Technology, Information Systems and Electrical engineering, ICITISEE 2023

作者： Rauf, Naufal Haidar Santoso, Heru Agus Universitas Dian Nuswantoro Faculty of Computer Science Department of Informatics Engineering Semarang Indonesia

ISBN: (纸本)9798350382266

The growing elderly population and the increasing demand for nursing home care have led to a need to improve the quality of life for residents. A popular solution is developing a companion robot to assist residents with various tasks and provide human-like interaction. In this paper, we present the prototype of a companion robot currently website-based, equipped with a Dual Intent Entity Transformer to understand what the user wants. The results of the study show the prototype is capable of classifying user messages according to their appropriate intent with precision, recall, F-score, and accuracy on average 91 %, 91 %, 90%, and 87% respectively using privately owned dataset. Future works such as expanding the robot's insight and tuning the model would improve the service provided by the companion robot. © 2023 IEEE.

关键词： Human robot interaction

来源：评论

学校读者我要写书评

暂无评论

Poetry Form Identification - A Comparative Analysis of Embedding Models and Classifiers 2

Poetry Form Identification - A Comparative Analysis of Embed...

引用

2nd International Conference on Self Sustainable Artificial Intelligence Systems, ICSSAS 2024

作者： Rohan, Gamidi Manideep, Thotapalli Sri Surya Saragadam, Sneha Pati, Peeta Basa Sail, Smita Amrita School of Computing Department of Computer Science and Engineering Bengaluru India Amrita Vishwa Vidyapeetham Amrita School of Engineering Department of English Bengaluru India

ISBN: (纸本)9798350368413

In recent years, automated text classification has advanced significantly, yet poetry classification remains a relatively under-explored domain. By employing Machine Learning and pre-trained language models, the proposed paper aims to automate poetry classification and provide a comparative analysis for utilizing GPT2 (Generative Pre-Trained Transformer), BERT (Bidirectional Encoder Representations from Transformers), and RoBERTa (Robustly Optimized BERT Pre-Training Approach). We have employed tokenization, sequence padding, and fine-tuning on a diverse dataset of poetry forms, including Sonnet, Haiku, Limerick, Acrostic, Ballad, Epigram, Sestina, and Villanelle. The proposed model is trained and evaluated on a dedicated data set using the application of AdamW Optimizer, and strives for robust classification performance. This research study yields promising results, with the Support Vector Machine (SVM) classifier achieving the highest accuracy of 93.25 percent using the top 20 percent ranked features. This study contributes to the field of automated poetry analysis by integrating various embedding models and classifiers, presenting notable advancements in the categorization of poetry. © 2024 IEEE.

关键词： Support vector machines

来源：评论

学校读者我要写书评

暂无评论

Analyze the impact of feature selection techniques in the early prediction of CKD

引用

International Journal of Cognitive Computing in engineering 2024年第1期5卷 66-77页

作者： Hema, K. Meena, K. Pandian, Ramaraj Department of Computer Science and Engineering GITAM School of Technology GITAM University Bangalore India Netapp San JoseCA United States

Background: Chronic renal disease, often known as Chronic Kidney Disease (CKD), is an illness that causes a steady decline in kidney function. As per the World Health Organization survey, the incidence of CKD may increase from 10% to 13% by 2030. Because of the lack of symptoms in the initial phase, diagnosing CKD early on may be difficult. The key objective of this study is to develop a forecasting model for the early detection of chronic renal disease. Methods: In medical science, Machine Learning (ML) Techniques play a significant role in disease prediction despite numerous studies conducted to categorize CKD in patients using machine learning tools. Most researchers need to analyze the impact of feature selection techniques, yielding high-quality and reliable results. The efficiency of any Techniques/Algorithms depends on feature selection, feature extraction, and classifiers. In this work, the impact of feature selection is experimented with using the Exhaustive Feature Selection (EFS) method. For the early prediction of CKD, a comparative examination of machine learning classifiers, including Gradient Boost (GB), XGBoost, Decision Tree (DT), Random Forest (RF), and KNN (k-nearest neighbors), are utilized. Results: Two types of datasets, standard (New Model) & real-time data sets collected from the dialysis unit of a reputed hospital in Chennai, are used to carry out extensive experiment analysis. Various metrics, including Accuracy, Precision, Recall, and F1-score, are used to tabulate the results of experiments conducted to measure the performance of the proposed approach for various combinations of test and training data. Conclusion: CKD is an irreversible and silent disease;it might have a high impact on many people and begin to manifest themselves at an early age in life. This research paper analyses the effect of feature selection techniques on early CKD prediction. © 2023 The Authors

关键词： Forecasting

来源：评论

学校读者我要写书评

暂无评论

Integrated Coherent Photonic Crossbar Arrays for Efficient Optical Computing

Integrated Coherent Photonic Crossbar Arrays for Efficient O...

引用

2024 Conference on Lasers and Electro-Optics, CLEO 2024

作者： Kari, Sadra Rahimi Hastings, Allison Nobile, Nicholas A. Pantin, Dominique Shah, Vivswan Youngblood, Nathan Department of Electrical and Computer Engineering Swanson School of Engineering University of Pittsburgh PittsburghPA15213 United States

ISBN: (纸本)9781957171395

We present a scalable approach to optical computing using coherent crossbar arrays for processing temporally multiplexed signals. Our design enables scalable matrix-matrix operations, and correlation detection, enabling efficient on-chip optical computing for diverse AI applications. © Optica Publishing Group 2024 © 2024 The Author(s)

关键词： Photonic integrated circuits

来源：评论

学校读者我要写书评

暂无评论

Tackling Class Imbalance and Client Heterogeneity for Split Federated Learning in Wireless Networks

引用

IEEE Transactions on Wireless Communications 2025年第6期24卷 4920-4936页

作者： Xie, Chunfeng Chen, Zhixiong Yi, Wenqiang Shin, Hyundong Nallanathan, Arumugam Queen Mary University of London School of Electronic Engineering and Computer Science LondonE1 4NS United Kingdom University of Essex School of Computer Science and Electronic Engineering ColchesterCO4 3SQ United Kingdom Kyung Hee University Department of Electronics and Information Convergence Engineering Yongin-si17104 Korea Republic of Kyung Hee University Department of Electronic Engineering Yongin-si17104 Korea Republic of

As the complexity of deep neural networks escalates, traditional federated learning (FL) frameworks increasingly struggle since the training overhead of the full model is costly for resource-limited clients. In addition, the class imbalance among local datasets and client heterogeneity may lead to significant deterioration in learning performance. To address these challenges, we first propose a novel wireless split federated learning (SFL) framework to enhance learning efficiency and performance in resource-constrained networks, which adaptively splits the global model between the clients and server to alleviate the computation burden for clients. Then, we theoretically analyze how the client sampling and wireless network parameters impact on the convergence bound. Based on the analysis, we identify the extent of class imbalance that significantly impacts learning performance. Inspired by this, we formulate an optimization problem to strike a balance between latency and performance by jointly optimizing the client selection, model splitting, and bandwidth allocation policies. To solve this problem, we introduce a latency and class imbalance-aware double greedy algorithm to obtain client scheduling policy. Additionally, bisection-enabled optimal bandwidth allocation and model splitting algorithms are developed to adaptively determine bandwidth allocation and model splitting policies, respectively. Extensive experimental results demonstrate that our approach significantly reduces latency and enhances learning performance. © 2002-2012 IEEE.

关键词： Federated learning

来源：评论

学校读者我要写书评

暂无评论

Enhancing Panoramic Competency Through Link Prediction in Question Knowledge Graphs using a Language Representation Model 7

Enhancing Panoramic Competency Through Link Prediction in Qu...

引用

7th IEEE International Conference on computer and Communication engineering Technology, CCET 2024

作者： Okuhara, Fumika Egami, Shusaku Sei, Yuichi Tahara, Yasuyuki Ohsuga, Akihiko Graduate School of Informatics and Engineering The University of Electro-Communications Department of Informatics Tokyo Japan Artificial Intelligence Research Center Tokyo Japan

ISBN: (纸本)9798350355673

Recently, panoramic knowledge has been required. On the other hand, multiple choice questions are suitable for efficient self-learning. Therefore, the purpose of this study is to create multiple choice questions that can reinforce learners' panoramic knowledge. Specifically, we proposed a method for automatically generating multiple choice questions that use Linked Data to present relevant information to give respondents an overall picture of relevant knowledge. There is some research on the methods that generated questions by extracting small subgraphs from the knowledge graphs consisting of entities(words) and relations(links) between the entities and hiding target words (correct answer words). In this study, our goal is to enhance the panoramic of the subgraphs of a specified size by using the link prediction method to complement edges and represent relationships not present in the knowledge graph when generating questions targeted at specific fields. The method of complementing edges involves first inputting two words as subject and object in Knowledge Graph to calculate the cosine similarity using a pretrained language model based on Wikipedia and Wikidata, then predicting the links as a predicate that should be complemented, and finally generating subgraphs by using the Graph Database added the complemented edges. For this study, we generated questions in the field of history, and since history requires temporal and spatial panoramic knowledge, words related to these aspects were focused on and complemented the relationships between them. As a result, 2,746 relationships were complemented by the proposed method in the subgraphs, and the subgraphs contained more words to learn (words found in textbooks that need to be learned) in a specific field compared to those generated using existing methods. © 2024 IEEE.

关键词： Knowledge graph

来源：评论

学校读者我要写书评

暂无评论

Predicting the Power Spectrum of Amplified OFDM Signals Using Higher-Order Intercept Points

引用

Chinese Journal of Electronics 2022年第2期31卷 213-219页

作者： YAN Siyuan YANG Xianzhen WANG Xiaoru LI Fu Department of Electrical and Computer Engineering Portland State University School of Computer Science and Technology Beijing University of Posts and Telecommunications

Orthogonal frequency-division multiplexing(OFDM) has been developed into a popular modulation scheme for wireless communication systems, used in applications such as LTE and 5 G. In wireless communication systems, nonlinearity caused by radio frequency(RF)amplifiers will generate distortions to both passband and adjacent channels such that the transmission quality is degraded. The study of this article aims to predict the power spectrum for OFDM based signals at the output of an RF amplifier due to the nonlinearity. In this article,based on Taylor polynomial coefficients, a power spectrum expression for amplified OFDM signals in terms of intercept points(up to nth-order) is derived. This model is useful to RF engineers in choosing and testing RF amplifiers with appropriate specifications, such as intercept points and gain, to meet the requirements of wireless standards. Measurements are carried out to confirm the results of the proposed model.

关键词： adjacent channels modulation scheme radio frequency amplifiers power spectrum expression Taylor polynomial coefficients RF amplifier higher-order intercept points passband channels amplified OFDM signals OFDM modulation wireless communication systems radiofrequency amplifiers wireless standards radio networks polynomials orthogonal frequency-division multiplexing OFDM based signals

来源：评论

学校读者我要写书评

暂无评论

Deep Spectral Learning Features in an Adaptive Dense Net Convolution Neural Network for Macular Degeneration

Deep Spectral Learning Features in an Adaptive Dense Net Con...

引用

2024 IEEE International Conference on Electronic Systems and Intelligent Computing, ICESIC 2024

作者： Mahalakshmi, D. John Justin Thangaraj, S. Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798331522988

Age-related macular degeneration (AMD) and diabetic retinopathy (DR) are vision-impairing ocular disorders that impact the elderly. The macula is a special area of the retina at the center of vision. In serious situations, emergency care is needed and the treatment regimen will vary with a case-to-case basis. Deep convolutional neural networks are proposed here to automate the fast classification of dry and wet Age-related Macular Degeneration (AMD). We leverage convolutional neural networks (CNN) spectrum learning capabilities, to introduce a novel approach to detect age-related macular degeneration (ARMD). The treatment should be guided by the precise differentiation between dry and moist types. to present an ADCNN-invasive spectral learning feature. Deep neural networks performance results reveal that dry vision loss can be identified with high precision, but not wet visual impairment. Afterward, by means of a dense net layer features are selected and tune before being operated into a deep neural network. The objective of this research is to define the type of transfer learning used for assessing at-risk individuals, specifically with structural analysis from deep convolutional neural networks. The model represent an authentication of 99.56%, a uniqueness and sensitivity of 98.51% in the AMD database indicating that deep learning is a well-suited approach for intelligent health monitoring. © 2024 IEEE.

关键词： Convolutional neural networks

来源：评论

学校读者我要写书评

暂无评论

EnCR-XGBoost: Ensemble Convolutional Recurrent Based XGBoost for Autism Detection Using MRI 9

EnCR-XGBoost: Ensemble Convolutional Recurrent Based XGBoost...

引用

9th International Conference on Communication and Electronics Systems, ICCES 2024

作者： Balasubramani, Jagadesh Surendran, R. Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350377972

The Autism detection is crucial for enhancing the social behavior and interaction of the people because the autism is a complex neurodevelopmental disorder. The deep learning based autism detection is employed for obtaining the accurate detection of disease. Still, inaccurate detection, computation complexity and over-fitting issues limit the performance. Thus, deep learning based autism detection is introduced in this research for solving the issues faced by the existing models. The input obtained from the MRI image is pre-processed using the median filter to enhance the quality of the image. Then, the appropriate features are extracted using proposed Ensemble Convolutional Recurrent (EnCR) model designed with the ensemble of convolutional and recurrent model for extracting both spatial and temporal features for enhancing the detection accuracy. Finally, XGBoost method is utilized for performing the autism detection task using the EnCR based features. The designed EnCR+XGBoost model is evaluated based on Accuracy, Precision, Recall, and F-score and obtained the values of 98.1500,98.4320,98.0320, and 98.2316 respectively. © 2024 IEEE.

关键词： Median filters

来源：评论

学校读者我要写书评

暂无评论

Comparative Analysis of Machine Learning Models for Diabetes Prediction 13

Comparative Analysis of Machine Learning Models for Diabetes...

引用

13th International Conference on System Modeling and Advancement in Research Trends, SMART 2024

作者： Shukla, Anshita Kumar, Rahul Chole, Vikrant Moolchandani, Jhankar Sahu, Santosh Kumar, Ashok Amity School of Engineering and Technology Amity University Department of Computer Science and Engineering Madhya Pradesh Gwalior India

ISBN: (纸本)9798350380583

This paper aims at comparing performance of various machine learning models for the task of predicting diabetes using one of the public datasets. The research assesses the big data analysis in the determination of the possibilities of risk of diabetes and the preferred models of Logistic Regression and KNearest Neighbors (KNN). Performance measures including accuracy, precision and recall were used in assessing the performance of the two models. The study do reveals the high level of potential of both the models yet reveals that Logistic Regression just edges a bit ahead of KNN in terms of accuracy and precision. These results show the potential of using machine learning in hospital diagnosis and specifically in diagnosis of Chronic diseases such as diabetics. © 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

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：