检索结果-内蒙古大学图书馆

1st IEEE International Conference on Advances in Computing, Communication and Networking, ICAC2N 2024

作者： Mall, Shachi Singh, Jitendra Nath Malik, Anshika Mahur, Lakshay Mundher Adnan, Myasar Galgotias University School of Computer Science Engineering Greater Noida India Galgotias University School of Computer Science U.P Gr. Noida India Raj Kumar Goel Institute of Technology Department of Computer Science U.P Ghaziabad India The Islamic University Department of Computer Science Najaf Iraq

ISBN: (纸本)9798350356816

heart disease is a leading global cause of death, and predicting it is complex, requiring advanced expertise beyond doctors' ease. The medical environment remains "Information rich"and "Information poor". There is a lot of medical information available on the internet. However, there are no good analytical tools for discovering relationships and patterns in the data. Automated diagnostic systems offer greater efficiency and cost-effectiveness. This web application uses Cleveland Medical Research Foundation and Kaggle heart disease data to predict the likelihood of disease occurrence. The goal is to apply knowledge mining techniques to the dataset to extract hidden patterns important to heart disease and predict whether a patient has heart disease where heart disease is dedicated to something. Modern technology struggles to process and analyze the massive data volumes required for accurate heart disease prediction, highlighting the need for advanced tools to handle such complex healthcare challenges. Our goal is to find suitable machine learning methods with high performance and accuracy in predicting heart disease. data mining enables the discovery of hidden patterns and relationships in large datasets by combining statistical analysis, machine learning, and advanced database techniques for insightful and efficient data interpretation. © 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

Exploring Recurrent Neural Network Models for Depression Detection Through Facial Expressions: A Systematic Literature Review 6

Exploring Recurrent Neural Network Models for Depression Det...

引用

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

作者： Rumahorbo, Brilyan Nathanael Pardamean, Bens Elwirehardja, Gregorius Natanael Bina Nusantara University Binus Graduate Program - Master of Computer Science Computer Science Department Jakarta11480 Indonesia Bina Nusantara University Bioinformatics and Data Science Research Center Jakarta11480 Indonesia

ISBN: (纸本)9798350345162

Major Depressive Disorder (MDD) is a prevalent mental disorder, affecting a significant number of individuals, with estimates reaching 300 million cases worldwide. Currently, the diagnosis of this condition relies heavily on subjective assessments based on the experience of medical professionals. Therefore, researchers have turned to deep learning models to explore the detection of depression. The objective of this review is to gather information on detecting depression based on facial expressions in videos using deep learning techniques. Overall, this research found that RNN models achieved 7.22 MAE for AVEC2014. LSTM models produced 4.83 MAE for DAIC-WOZ, while GRU models achieved an accuracy of 89.77% for DAIC-WOZ. Features like Facial Action Units (FAU), eye gaze, and landmarks show great potential and need to be further analyzed to improve results. Analysis can include applying feature engineering techniques. Aggregation methods, such as mean calculation, are recommended as effective approaches for data processing. This Systematic Literature Review found that facial expressions do have relevant patterns related to MDD. © 2023 IEEE.

关键词： deep learning facial expression GRU LSTM major depressive disorder

来源：评论

学校读者我要写书评

暂无评论

Task Offloading and Resource Management for IIoT with Satellite-Terrestrial Integrated Computing Power Network Based on D3QN

引用

IEEE Internet of Things Journal 2025年第12期12卷 21770-21783页

作者： Li, Meng Li, Meihui Wang, Kan Yu, F. Richard Wang, Zhuwei Si, Pengbo Beijing University of Technology School of Information Science and Technology Beijing100124 China Xi'an University of Technology School of Computer Science and Engineering Xi'an710048 China Carleton University Department of Systems and Computer Engineering OttawaONK1S 5B6 Canada

The management of computing resources through the computing power network (CPN) has gradually become a focal point of research. With the development of the 6th generation (6G) mobile networks, some promising technologies such as satellite-terrestrial integrated network (STIN) and smart endogenous network driven by artificial intelligence (AI) are increasingly being applied in Industrial Internet of Things (IIoT). However, several issues in current studies are worthy of attention: 1) the large number of devices powered by battery in IIoT, 2) the complex communication environments, 3) the finite computing resources for task data processing. To cope with these challenges, a satellite-terrestrial integrated computing power network (STICPN) framework is introduced in this article. Within this framework, a task offloading link selection scheme is proposed, which minimizes the delay and the consumption of energy. The task offloading optimization problem is modeled as a Markov Decision Process (MDP). Meanwhile, deep reinforcement learning (DRL) algorithm is employed to adapt to the dynamic states of environment. Specifically, a Dueling Double Deep Q Network (D3QN) is used to make optimal decisions and delay as well as energy consumption can be reduced significantly. Moreover, the D3QN-based scheme extends the usage time of IIoT devices. The simulation results indicate that the proposed scheme outperforms the comparison schemes significantly. © 2025 IEEE.

关键词： Energy utilization

来源：评论

学校读者我要写书评

暂无评论

Harnessing the Power of Vocal Signals in COVID-19 Detection Utilizing Machine Learning

Harnessing the Power of Vocal Signals in COVID-19 Detection ...

引用

2024 IEEE International Conference on Big data, Bigdata 2024

作者： Mann, Aleesa Jadhav, Ajinkya P. Matovu, Richard Gupta, Vibhuti Meharry Medical College School of Applied Computational Sciences Department of Computer Science and Data Science NashvilleTN United States Gannon University Department of Computing and Information Science EriePA United States

ISBN: (纸本)9798350362480

The global COVID-19 pandemic has strained health-care systems and highlighted the need for accessible and efficient diagnostic methods. Traditional diagnostic tools, such as nasal swabs and biosensors, while accurate, pose significant logistical challenges and high costs, limiting their scalability. This paper explores an alternative, non-invasive approach to COVID-19 detection using machine learning algorithms to analyze vocal patterns, particularly cough and breathing sounds. Leveraging a publicly available dataset, we developed machine learning models capable of classifying audio samples as COVID-19 positive or negative. Our models achieve an AUC of up to 85% and an F1-score of 81%, demonstrating the potential of machine learning in enabling rapid, cost-effective COVID-19 diagnosis. These findings suggest that audio-based diagnostics could be a practical and scalable solution, particularly in resource-limited settings where traditional methods are less feasible. © 2024 IEEE.

关键词： COVID-19

来源：评论

学校读者我要写书评

暂无评论

Towards Privacy in Decentralized IoT:A Blockchain-Based Dual Response DP Mechanism

引用

Big data Mining and Analytics 2024年第3期7卷 699-717页

作者： Kai Zhang Pei-Wei Tsai Jiao Tian Wenyu Zhao Xinyi Cai Longxiang Gao Jinjun Chen Department of Computing Technologies Swinburne University of TechnologyMelbourne 3122Australia School of Computer Science and Technology Xinjiang UniversityUrumchi 830000China Shandong Computer Science Center(National Supercomputer Center in Jinan) Qilu University of Technology(Shandong Academy of Sciences)Jinan 250000China

Differential Privacy(DP)stands as a secure and efficient mechanism for privacy preservation,offering enhanced data utility without compromising computational *** adaptability is evidenced by its integration into blockchain-based Internet of Things(IoT)contexts,including smart wearables,smart homes,***,a notable vulnerability surfaces in decentralized environments where existing DP mechanisms falter in withstanding collusion *** vulnerability stems from the absence of an efficient strategy to synchronize the privacy budget consumption and historical query information among all network *** can exploit this weakness,collaborating to inject a substantial volume of queries simultaneously into disparate blockchain nodes to extract more precise *** address this issue,we propose a novel dual response DP mechanism to preserve privacy in blockchain-based IoT *** encompasses both direct and indirect response strategies,enabling an adaptive response to external queries,aiming to provide better data utility while preserving ***,this mechanism can synchronize historical query information and privacy budget consumption within the blockchain network to prevent privacy *** employ Relative Error(RE),Mean Square Error(MSE),and privacy budget consumption as evaluation metrics to measure the performance of the proposed *** outcomes substantiate that the proposed mechanism can adapt to blockchain networks well,affirming its capacity for privacy and great utility.

关键词： differential privacy dual response strategies collusion attack blockchain

来源：评论

学校读者我要写书评

暂无评论

A Survey of Integrating Generative Artificial Intelligence and 6G Mobile Services: Architectures, Solutions, Technologies and Outlooks

引用

IEEE Transactions on Cognitive Communications and Networking 2025年第3期11卷 1334-1356页

作者： Liu, Yi-Jing Du, Hongyang Xu, Xinyi Zhang, Ruichen Feng, Gang Cao, Bin Niyato, Dusit Kim, Dong In Jamalipour, Abbas Letaief, Khaled B. Tafazolli, Rahim University of Electronic Science and Technology of China National Key Lab on Wireless Communications Chengdu China University of Hong Kong Department of Electrical and Electronic Engineering Pok Fu Lam Hong Kong Nanyang Technological University College of Computing and Data Science Singapore Singapore Beijing University of Posts and Telecommunications State Key Laboratory of Networking and Switching Technology Beijing China Sungkyunkwan University Department of Electrical and Computer Engineering Suwon Korea Republic of University of Sydney School of Electrical and Computer Engineering Sydney Australia Hong Kong University of Science and Technology Department of Electronic and Computer Engineering Hong Kong Hong Kong University of Surrey Institute for Communication Systems School of Computer Science and Electronic Engineering United Kingdom

Generative artificial intelligence (GenAI) is rapidly driving a new phase of artificial intelligence revolution, marked by various applications such as ChatGPT, Sora and DeepSeek. With powerful capabilities in content generation, personalization and user interaction, GenAI can drive significant enhancements in various mobile services to satisfy individual user preferences. Meanwhile, mobile services offer substantial support for accessibility, functionality and intelligence of GenAI applications. However, current research on integrating GenAI and mobile services remains nascent, lacking necessary theoretical and technical support. In this survey, we break down a new vision to guide research on integrating GenAI and mobile services, aiming to improve 6G intelligence levels and enable 6G mobile networks to support the integration of GenAI and mobile services effectively. We begin by examining the necessity of integrating GenAI with 6G mobile services and exploring potential learning architectures of GenAI models within mobile networks. Next, we analyze potential solutions from two perspectives: how 6G mobile networks empower GenAI and how GenAI enables mobile services. We subsequently explore a promising use case, along with essential features and techniques, to facilitate the integration of GenAI and mobile services. Finally, we discuss future research directions, aiming at realizing intelligent 6G networks. © 2015 IEEE.

关键词： Generative adversarial networks

来源：评论

学校读者我要写书评

暂无评论

Optimized Consensus for Blockchain in Internet of Things Networks via Reinforcement Learning

引用

Tsinghua science and Technology 2023年第6期28卷 1009-1022页

作者： Yifei Zou Zongjing Jin Yanwei Zheng Dongxiao Yu Tian Lan Institute of Intelligent Computing School of Computer Science and TechnologyShandong UniversityQingdao 266237China Department of Electrical and Computer Engineering George Washington UniversityWashingtonDC 20052USA

Most blockchain systems currently adopt resource-consuming protocols to achieve consensus between miners;for example,the Proof-of-Work(PoW)and Practical Byzantine Fault Tolerant(PBFT)schemes,which have a high consumption of computing/communication resources and usually require reliable communications with bounded ***,these protocols may be unsuitable for Internet of Things(IoT)networks because the IoT devices are usually lightweight,battery-operated,and deployed in an unreliable wireless ***,this paper studies an efficient consensus protocol for blockchain in IoT networks via reinforcement ***,the consensus protocol in this work is designed on the basis of the Proof-of-Communication(PoC)scheme directly in a single-hop wireless network with unreliable communications.A distributed MultiAgent Reinforcement Learning(MARL)algorithm is proposed to improve the efficiency and fairness of consensus for miners in the blockchain *** this algorithm,each agent uses a matrix to depict the efficiency and fairness of the recent consensus and tunes its actions and rewards carefully in an actor-critic framework to seek effective *** results from the simulation show that the fairness of consensus in the proposed algorithm is guaranteed,and the efficiency nearly reaches a centralized optimal solution.

关键词： consensus in blockchain Proof-of-Communication(PoC) MultiAgent Reinforcement Learning(MARL) Internet of Things(IoT)networks

来源：评论

学校读者我要写书评

暂无评论

Lighter Student Engagement Recognition in a Classroom Environment Using Skeletal Keypoints

IAENG International Journal of Computer Science

引用

IAENG International Journal of computer science 2025年第6期52卷 1997-2014页

作者： Tarigan, Gabriel Asael Elwirehardja, Gregorius Natanael Nugroho, Kuncahyo Setyo Pardamean, Bens Computer Science Department Binus Graduate Program – Master of Computer Science Program Bina Nusantara University Jakarta11480 Indonesia Computer Science Department School of Computer Science Bina Nusantara University Jakarta1140 Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta11480 Indonesia BINUS Graduate Program – Master of Computer Science Program Bina Nusantara University Jakarta11480 Indonesia

Student retention is crucial for educational institutions, influencing reputation, finances, and ranking metrics. Engagement, reflecting a student’s connection, interest, and effort, plays a vital role in learning, fostering critical thinking, and supporting retention. Recent advancements use students’ poses to predict engagement, providing valuable insights without disrupting the teaching-learning dynamic. The prevailing research trend leans toward employing multi-modal approaches, such as a combination of pose detection with object detection. However, current methods use out-of-date object detection methods and manual dataset creation, which is cumbersome, requiring thousands of manually annotated data. This problem is then addressed by proposing a novel method of student engagement detection, using a combination of You Only Look Once Version 8 Mini (YOLOv8m) and MediaPipe as state-of-the-art alternatives to improve both object detection and human pose estimation. The (YOLOv8m + MediaPipe) method surpasses the baseline (YOLOv4 + OpenPose) with higher accuracy (0.70 vs. 0.41) and lower cross-entropy loss (0.40 vs. 0.60) on the test set, confirmed by a statistically significant paired t-test. It also exhibits a remarkable speed advantage, around 16 times faster than the baseline in pose detection data collection rates. Despite not being designed for it, the proposed method achieves multiple keypoint detection, matching the baseline’s amount. © (2025), (International Association of Engineers). All rights reserved.

关键词： Object detection

来源：评论

学校读者我要写书评

暂无评论

DisP+V: A Unified Framework for Disentangling Prototype and Variation From Single Sample per Person

引用

IEEE Transactions on Neural Networks and Learning Systems 2023年第2期34卷 867-881页

作者： Pang, Meng Wang, Binghui Ye, Mang Cheung, Yiu-Ming Chen, Yiran Wen, Bihan Nanyang Technological University School of Electrical and Electronic Engineering 639798 Singapore Illinois Institute of Technology Department of Computer Science ChicagoIL60616 United States Wuhan University School of Computer Science Wuhan430072 China Hong Kong Baptist University Department of Computer Science Kowloon Hong Kong Duke University Department of Electrical and Computer Engineering DurhamNC27708 United States

Single sample per person face recognition (SSPP FR) is one of the most challenging problems in FR due to the extreme lack of enrolment data. To date, the most popular SSPP FR methods are the generic learning methods, which recognize query face images based on the so-called prototype plus variation (i.e., P+V) model. However, the classic P+V model suffers from two major limitations: 1) it linearly combines the prototype and variation images in the observational pixel-spatial space and cannot generalize to multiple nonlinear variations, e.g., poses, which are common in face images and 2) it would be severely impaired once the enrolment face images are contaminated by nuisance variations. To address the two limitations, it is desirable to disentangle the prototype and variation in a latent feature space and to manipulate the images in a semantic manner. To this end, we propose a novel disentangled prototype plus variation model, dubbed DisP+V, which consists of an encoder-decoder generator and two discriminators. The generator and discriminators play two adversarial games such that the generator nonlinearly encodes the images into a latent semantic space, where the more discriminative prototype feature and the less discriminative variation feature are disentangled. Meanwhile, the prototype and variation features can guide the generator to generate an identity-preserved prototype and the corresponding variation, respectively. Experiments on various real-world face datasets demonstrate the superiority of our DisP+V model over the classic P+V model for SSPP FR. Furthermore, DisP+V demonstrates its unique characteristics in both prototype recovery and face editing/interpolation. © 2020 IEEE.

关键词： Face recognition

来源：评论

学校读者我要写书评

暂无评论

Biomedical data Management and Analytics in IOMT 8th

Biomedical Data Management and Analytics in IOMT

引用

8th International Conference on data Management, Analytics and Innovation, ICDMAI 2024

作者： Niha, K. Surendiran, B. Amutha, S. School of Computer Science and Engineering VIT Vellore Campus Tamil Nadu Vellore India Department of Computer Science and Engineering National Institute of Technology Puducherry India School of Computer Science and Engineering VIT Chennai Campus Chennai India

ISBN: (纸本)9789819732449

The IoMT is a device that works with different healthcare systems to help develop networking technologies. It reduces the unnecessary burden of visiting the hospital. Instead, it enables the transfer of medical related data with physicians through a secured network. Smart devices, such as wearable and medical monitors are used for healthcare use in the IoMT network. Telehealth and other services are adapted in homes and hospitals in other real-time locations. Approximately in the globe, 80% of healthcare have implemented IoT (Internet of Things) technologies and 20% are expected to do the same in the future. This paper focuses on four topics: (1) recent advances on Internet of medical things, (2) highlights the emerging data fusion process for data preparation, (3) discusses the life cycle of sensor devices and their protocols, and (4) processing data with different techniques and theories. In a nutshell, this paper gives a comprehensive overview of IoMT research advancement, application, data management, and analytics techniques along with the current research challenges. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Electronic health record

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：