检索结果-内蒙古大学图书馆

Sustainable Communication Networks and Application (ICSCNA), International Conference on

作者： Dhananjay Bisen Nookala Venu Aditya Dubey Priyanka Garg Suresh Salendra T. Lakshmi Devi Center for Internet of Things Madhav Institute of Technology & Science Gwalior M.P India Data Science and Quantum Computing ABV Indian Institute of Information Technology and Management Gwalior M.P India Dept. of Compter Science and Engineering Balaji Institute of Technology & Science Warangal Telangana India Dept.of CSE-Data Science Siddhartha Institute of Engineering and Technology Hyderabad India

ISBN: (数字)9798331530013

ISBN: (纸本)9798331530020

The past few years have witnessed a remarkable change by the rapid expansion of the Internet of Things (IoT). The increasing spread of networked devices in various areas of the Internet of Things has resulted in a huge amount of machine data. This data contains rich information that can be harnessed across domains such as healthcare, agriculture, smart cities, and manufacturing. To effectively analyze and capitalize this information, the integration of machine learning (ML) methods is essential. Thus, well-known machine learning techniques including Logistic Regression, Decision Tree, KNN, SVM, Random Forest, and Naive Bayes are used in paper. The performance of the algorithms is analyzed using three diverse IoT datasets and compared to one another using pre-defined measures such as accuracy, precision, recall, F1-score and execution time. Furthermore, test findings shown that the random forest algorithm consistently outperformed other algorithms for a variety of datasets with up to 98% accuracy.

关键词： Support vector machines Logistic regression Accuracy Machine learning algorithms Nearest neighbor methods Real-time systems Bayes methods Internet of Things Decision trees Random forests

来源：评论

学校读者我要写书评

暂无评论

Causal Discovery Inspired Unsupervised Domain Adaptation for Emotion-Cause Pair Extraction

arXiv

引用

arXiv 2024年

作者： Hua, Yuncheng Huang, Yujin Huang, Shuo Feng, Tao Qu, Lizhen Bain, Chris Bassed, Richard Haffari, Gholamreza Department of Data Science & AI Monash University Australia Department of Human Centred Computing Monash University Australia Victorian Institute of Forensic Medicine Melbourne Australia

This paper tackles the task of emotion-cause pair extraction in the unsupervised domain adaptation setting. The problem is challenging as the distributions of the events causing emotions in target domains are dramatically different than those in source domains, despite the distributions of emotional expressions between domains are overlapped. Inspired by causal discovery, we propose a novel deep latent model in the variational autoencoder (VAE) framework, which not only captures the underlying latent structures of data but also utilizes the easily transferable knowledge of emotions as the bridge to link the distributions of events in different domains. To facilitate knowledge transfer across domains, we also propose a novel variational posterior regularization technique to disentangle the latent representations of emotions from those of events in order to mitigate the damage caused by the spurious correlations related to the events in source domains. Through extensive experiments, we demonstrate that our model outperforms the strongest baseline by approximately 11.05% on a Chinese benchmark and 2.45% on a English benchmark in terms of weighted-average F1 score. We have released our source code and the generated dataset publicly at: https://***/tk1363704/CAREL-VAE. © 2024, CC BY.

关键词： Domain Knowledge

来源：评论

学校读者我要写书评

暂无评论

A New Sentiment Analysis-based Approach for the Prediction of Users Beliefs

A New Sentiment Analysis-based Approach for the Prediction o...

引用

International Conference on Control, Decision and Information Technologies (CoDIT)

作者： Sadia Abdulhalim Rakif Pathan Belkacem Chikhaoui Department of Data Science and Artificial Intelligence College of Computing and Information Technology University of Doha for Science and Technology Doha Qatar Department of Science and Technology Institute of Applied Artificial Intelligence TELUQ University Montreal Canada

ISBN: (数字)9798350373974

ISBN: (纸本)9798350373981

This paper proposes a new approach based on sentiment analysis for predicting the relevance of religious beliefs in contemporary relationships within the context of online dating. The motivation for this research lies in understanding how shared values, common missions, and rituals in various religions might influence decision-making in relationships, the study aims to predict users’ religious beliefs based on OkCupid profiles. Our approach starts first by 1) extracting relevant features using sentiment analysis from text data, and 2) developing machine learning models based on state-of-the-art algorithms such as random forests, logistic regression, and decision trees. The results obtained demonstrated the effectiveness of our proposed approach in accurately predicting religious beliefs and its superiority compared to the state-of-the-art methods. Through this investigation, the paper seeks insights into the interplay between religious beliefs and romantic choices.

关键词： Sentiment analysis Logistic regression Machine learning algorithms Shape Predictive models Feature extraction Prediction algorithms Decision trees Information technology Random forests

来源：评论

学校读者我要写书评

暂无评论

A Simple Unified Approach to Testing High-Dimensional Conditional Independences for Categorical and Ordinal data

arXiv

引用

arXiv 2022年

作者： Ankan, Ankur Textor, Johannes Data Science Institute for Computing and Information Sciences Radboud University Nijmegen Netherlands

Conditional independence (CI) tests underlie many approaches to model testing and structure learning in causal inference. Most existing CI tests for categorical and ordinal data stratify the sample by the conditioning variables, perform simple independence tests in each stratum, and combine the results. Unfortunately, the statistical power of this approach degrades rapidly as the number of conditioning variables increases. Here we propose a simple unified CI test for ordinal and categorical data that maintains reasonable calibration and power in high dimensions. We show that our test outperforms existing baselines in model testing and structure learning for dense directed graphical models while being comparable for sparse models. Our approach could be attractive for causal model testing because it is easy to implement, can be used with non-parametric or parametric probability models, has the symmetry property, and has reasonable computational requirements. © 2022, CC BY.

关键词： Learning systems

来源：评论

学校读者我要写书评

暂无评论

Implementing Multi-factor Authentication in Cloud Services for Enhanced Security

Implementing Multi-factor Authentication in Cloud Services f...

引用

2024 IEEE International Conference on Recent Advances in science and Engineering Technology, ICRASET 2024

作者： Rekha, K. Sashi Sivagami, V.M. Usharani, R. Amutha, T. Pushparani, S. Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Department of Computer Science and Engineering Tamil Nadu Chennai India Sri Venkateswara College of Engineering Department of Information Technology Tamil Nadu Chennai India Srm Institute of Science and Technology School of Computing Faculty of Engineering and Technology Department of Computational Intelligence Kattankulathur Chennai India Care College of Engineering Department of Artificial Intelligence & Data Science Tamil Nadu Trichy India Meenakshi College of Engineering Department of Computer Science and Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350388602

The cloud services which are now the most common data transmission and endanger organizations' confidential information, it's more and more visible that security of any data should be a main priority for companies. MFA, which consists of adopting multi factor approach to authentication and thereby provides multiple types of verification thus ensures effective security, is leveraged to ensure resource access. This paper proposes a full-fledged way of implementing MFA for cloud services in order to strengthen the security given and manage the constatoing risks. The proposed methodology consists of main stages like requirements determining, technology reviewing, risks assessment, technology design, integration with cloud services, users training, pilot deployment and testing, implementation and configuration, monitoring and maintenance as well as constant improvement. Through a systemic approach, the organizations can be equipped with the capability to personalize the MFA solutions according to their security goals, to bring the cloud environments on board, as well as to ensure that the organizations comply with the security standards as set by various governing bodies. This research adds a grain of salt to the development of cloud security good practices through offering a disciplined approach to MFA implementation, which allows partners to harden their cloud assets and protect against growing cyber threats. © 2024 IEEE.

关键词： Cloud security

来源：评论

学校读者我要写书评

暂无评论

SleepCVT: Combination of Convolution Neural Network and Vision Transformer for Automatic Sleep Scoring

SleepCVT: Combination of Convolution Neural Network and Visi...

引用

International Joint Conference on Neural Networks (IJCNN)

作者： Chih-En Kuo Hao-Hsiang Wang Tsung-Hua Lu Institute of Data Science and Information Computing National Chung Hsing University Taichung Taiwan Department of Psychiatry National Cheng Kung University Hospital Tainan Taiwan

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

Sleep scoring is a subjective and time-consuming job. In this paper, SleepCVT that combination of convolution neural network and vision transformer for automatic sleep scoring was proposed. All-night sleep physiological signals from 994 patients with sleep disorders were used. First, the all-night polysomnography signals including electroencephalogram, electrooculogram, and electromyogram were segmented into 60-sec segments with 30-sec overlapping. Subsequently, each segment was transformed into spectrograms by continuous wavelet transform and a simple merged processing was applied to generate the merged spectrograms. Next, the merged spectrogram images were utilized as an input of our proposed SleepCVT with horizontal attention. The experimental results showed that the accuracy, kappa coefficient, and F1 score of the proposed method were 82.1%, 0.754, and 79.65%, respectively. The results proved that the proposed SleepCVT had highly accuracy for patients with sleep disorder.

关键词： Image segmentation Computer vision Accuracy Continuous wavelet transforms Sleep Convolution Neural networks

来源：评论

学校读者我要写书评

暂无评论

Bayesian Over-the-Air FedAvg via Channel Driven Stochastic Gradient Langevin Dynamics

Bayesian Over-the-Air FedAvg via Channel Driven Stochastic G...

引用

IEEE Conference on Global Communications (GLOBECOM)

作者： Boning Zhang Dongzhu Liu Osvaldo Simeone Guangxu Zhu School of Computing Science University of Glasgow Department of Engineering King's College London Shenzhen Research Institute of Big Data

The recent development of scalable Bayesian inference methods has renewed interest in the adoption of Bayesian learning as an alternative to conventional frequentist learning that offers improved model calibration via uncertainty quantification. Recently, federated averaging Langevin dynamics (FALD) was introduced as a variant of federated averaging that can efficiently implement distributed Bayesian learning in the presence of noiseless communications. In this paper, we propose wireless FALD (WFALD), a novel protocol that realizes FALD in wireless systems by integrating over-the-air computation and channel-driven sampling for Monte Carlo updates. Unlike prior work on wireless Bayesian learning, WFALD enables (i) multiple local updates between communication rounds; and (ii) stochastic gradients computed by mini-batch. A convergence analysis is presented in terms of the 2- Wasserstein distance between the samples produced by WFALD and the targeted global posterior distribution. Analysis and experiments show that, when the signal-to-noise ratio is sufficiently large, channel noise can be fully repurposed for Monte Carlo sampling, thus entailing no loss in performance.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Human-Computer Interface Challenges in Mobile data Mining

Human-Computer Interface Challenges in Mobile Data Mining

引用

2023 International Conference on Sustainable computing and Smart Systems, ICSCSS 2023

作者： Padmanaban, K. Sathiya, A. Jeevitha, K. Rosaline, R. Anto Arockia Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering Andhra Pradesh Vijayawada India Sri Sairam Institute of Technology Department of Artificial Intelligence and Data Science Tamil Nadu Chennai India R.M.K. Engineering College Department of Electronics and Communication Engineering Tamil Nadu Chennai India Srm Institute of Science and Technology Department of Computing Technologies Tamil Nadu Chennai India

ISBN: (纸本)9798350333602

The continuous advancement of computer software, hardware, and artificial intelligence, has allowed for tremendous progress in the development of human-machine communication systems in recent years. The development of such methods provides more accurate results than humans. The core of the human-computer interface is the Natural Language Understanding (NLU) component. The success of Human-Computer Interaction (HCI) and the time it takes to accomplish tasks are both directly affected by the results of this module's semantic comprehension. In this research study, a neural network and HCI-based engine is developed for understanding and interacting with natural languages. data from the appropriate literature is used to comprehend the necessary understanding of HCI. Next, the interactive engine is used to develop a model of HCI based on natural language comprehension using a neural network, and also the model's underlying neural network reasoning structure is assessed. © 2023 IEEE.

关键词： Engines

来源：评论

学校读者我要写书评

暂无评论

Mobile-enabled Prosthetic System with Machine Learning Support

Mobile-enabled Prosthetic System with Machine Learning Suppo...

引用

2024 IEEE International Conference on E-Health Networking, Application and Services, HealthCom 2024

作者： Williams, Christian Lee, Andrew Tolah, Ahmed Einafshar, Mohammadjavad Massaad, Elie Kiapour, Ali Yu, Chen-Hsiang School of Computing and Data Science Wentworth Institute of Technology BostonMA United States Department of Biomedical Engineering Wentworth Institute of Technology BostonMA United States Department of Materials and Production Aalborg University Aalborg Denmark Massachusetts General Hospital Harvard Medical School BostonMA United States College of Engineering Northeastern University BostonMA United States

ISBN: (纸本)9798350350548

Machine learning (ML) and its applications have expanded over the past few years. ML inevitably makes its way to the world of prosthetics and amputees. Smart prosthetics have been studied and growing recently. Through the collection of data from these devices, results can help the user in numerous ways. On the other hand, mobile devices and applications are widely used. However, how to combine mobile applications and ML to enhance the prosthetic device was less addressed. In this research, we studied ML with an IoT-based prosthetic device paired with a mobile application. The hypothesis was that the ML methods could help evaluate the user’s status. The study results showed that some evaluated ML methods were able to see through the average temperature, humidity and contraction percentage of the people who wear a designed prosthetic device. The results also indicated that the users could tell if the contractions reached a concerning level. © 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

Multi-Task Learning with LLMs for Implicit Sentiment Analysis: data-level and Task-level Automatic Weight Learning

arXiv

引用

arXiv 2024年

作者： Lai, Wenna Xie, Haoran Xu, Guandong Li, Qing The Department of Computing Hong Kong Polytechnic University Hong Kong The School of Data Science Lingnan University Hong Kong The School of Computer Science The Data Science Institute University of Technology Sydney SydneyNSW2007 Australia The Education University of Hong Kong Hong Kong

Implicit sentiment analysis (ISA) presents significant challenges due to the absence of salient cue words. Previous methods have struggled with insufficient data and limited reasoning capabilities to infer underlying opinions. Integrating multi-task learning (MTL) with large language models (LLMs) offers the potential to enable models of varying sizes to reliably perceive and recognize genuine opinions in ISA. However, existing MTL approaches are constrained by two sources of uncertainty: data-level uncertainty, arising from hallucination problems in LLM-generated contextual information, and task-level uncertainty, stemming from the varying capacities of models to process contextual information. To handle these uncertainties, we introduce MT-ISA, a novel MTL framework that enhances ISA by leveraging the generation and reasoning capabilities of LLMs through automatic MTL. Specifically, MT-ISA constructs auxiliary tasks using generative LLMs to supplement sentiment elements and incorporates automatic MTL to fully exploit auxiliary data. We introduce data-level and task-level automatic weight learning (AWL), which dynamically identifies relationships and prioritizes more reliable data and critical tasks, enabling models of varying sizes to adaptively learn fine-grained weights based on their reasoning capabilities. We investigate three strategies for data-level AWL, while also introducing homoscedastic uncertainty for task-level AWL. Extensive experiments reveal that models of varying sizes achieve an optimal balance between primary prediction and auxiliary tasks in MT-ISA. This underscores the effectiveness and adaptability of our approach. © 2024, CC BY-NC-SA.

关键词： Multi-task 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：