检索结果-内蒙古大学图书馆

2022 IEEE Delhi Section Conference, DELCON 2022

作者： Maniktalia, Riddhi Tanwar, Satwik Billa, Ritvik Deepa, K. School of Computer Science and Engineering Vellore Institute of Technology Vellore India School of Computer Science and Engineering Vellore Institute of Technology Department of Software Systems Vellore India

ISBN: (纸本)9781665458832

During recent years, we have seen many technological advancements which help to take better care of patient's health and assure them fast and safe recovery. The most basic item necessary is competent patient care in hospitals, as well as proper management of fluid and electrolytes. In hospital almost all patients, mainly ICU patients, have to regulate the volume of fluids and electrolyte into the bloodstream 247 which is done using drip. These drips need regular monitoring or changing to maintain constant flow of fluids or to prevent any infection to patients. Though this system is simple, overcrowded hospitals and the risk of deficiency of nurses can put patients at health risk which can deteriorate patients' health more aggressively or in worse case it can lead to death. Almost in all hospitals, nurses or the hospital staff are responsible for monitoring the drip level. But unfortunately, because of their hectic schedule, the observer may forget to change the bottle at the appropriate time. During the pandemic, hospitals were overrun with patients, and nurses were unable to do manual regular checks on the drip conditions and drip level of every patient, even after working extra shifts. Many patients even died due to not being able to get proper care from nurses. The next step in providing more effective and easy healthcare is to automate such vital procedures. To overcome this critical situation, we are proposing an IoT (Internet of Things) based Drip Monitor System using Arduino UNO which eases the process of measuring and solves the issues of bubble formation in drips. © 2022 IEEE.

关键词： Health risks

来源：评论

学校读者我要写书评

暂无评论

A CTR Prediction Model Based on Attention Mechanism and Logarithmic Transformation 2

A CTR Prediction Model Based on Attention Mechanism and Loga...

引用

2nd International Conference on Digital Signal and computer Communications, DSCC 2022

作者： Meng, Lu Shi, Tian-Wei Chang, Guang-Ming Qiang, Jiao-Feng Cui, Wen-Hua School of Computer Science and Software Engineering University of Science and Technology Liaoning Liaoning Anshan114051 China

ISBN: (纸本)9781510656734

To address the problems of inadequate feature interaction and lack of targeting in feature combination in the click-through rate prediction model. We propose a click-through prediction model called SELFM. It based on attention mechanism and logarithmic transformation structure. The model first incorporates the attention mechanism in the feature embedding stage to distinguish the importance of different features and avoids the effects of invalid features. Then the field-aware factorization machine is used to learn low-order feature interactions. The logarithmic transformation structure is used to convert the power of each feature in the feature combination into the coefficients to be learned and combined with the hidden layer for higher-order nonlinear feature interactions. The final output layer is processed with the Sigmoid function to get the click-through rate prediction results. The experimental results show that the AUC and Logloss of this paper's model are better than the existing click-through rate prediction models, which effectively improves the prediction accuracy and enhances the ability of the recommendation system to process data. © 2022 SPIE.

关键词： Forecasting

来源：评论

学校读者我要写书评

暂无评论

A Short Review of Truck and Drone Collaborative Delivery Problem 10

A Short Review of Truck and Drone Collaborative Delivery Pro...

引用

10th IEEE International Conference on Cyber Security and Cloud Computing and 9th IEEE International Conference on Edge Computing and Scalable Cloud, CSCloud-EdgeCom 2023

作者： Zhu, Yena Liu, Min Jin, Dongyao Hunan University of Science and Technology School of Computer Science and Engineering Xiangtan China Hunan Key Laboratory for Service computing and Novel Software Technology Xiangtan China

ISBN: (纸本)9798350312461

With the emergence of efficient logistics models and the development of drone technology, the last-mile delivery problem has become a focus of attention for scholars in various fields. The advantages of drones, such as low cost, energy saving, and independence from road conditions, have made them increasingly popular in the delivery industry. However, drones have shortcomings such as short endurance, small payload capacity, and susceptibility to interference. Therefore, the cooperative delivery model of vehicles and drones has important theoretical research significance and practical application value. Existing literature reviews focus on static truck-drone delivery problems (TDDPs) and methods such as exact algorithms, heuristics, and metaheuristics. However, real-life truck-drone delivery problems are easily affected by dynamic environmental changes. Accordingly, this article introduces some new dynamic variants of TDDPs proposed in recent years, which consider variable drone flight endurance, urban crowded road conditions, stochastic customer demands, and the influence of weather changes. In recent years, some new methods for solving TDDPs have been introduced, such as the increasingly popular machine learning and reinforcement learning methods. Finally, the possible future research directions and development trends of TDDPs are presented. © 2023 IEEE.

关键词： Reinforcement learning

来源：评论

学校读者我要写书评

暂无评论

Graph-in-Graph Convolutional Network for Hyperspectral Image Classification

引用

IEEE Transactions on Neural Networks and Learning Systems 2024年第1期35卷 1157-1171页

作者： Jia, Sen Jiang, Shuguo Zhang, Shuyu Xu, Meng Jia, Xiuping College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China Key Laboratory for Geo-Environmental Monitoring of Coastal Zone Shenzhen University Ministry of Natural Resources Shenzhen518060 China School of Engineering and Information Technology University of New South Wales CanberraACT 2612 Australia

With the development of hyperspectral sensors, accessible hyperspectral images (HSIs) are increasing, and pixel-oriented classification has attracted much attention. Recently, graph convolutional networks (GCNs) have been proposed to process graph-structured data in non-Euclidean domains and have been employed in HSI classification. But most methods based on GCN are hard to sufficiently exploit information of ground objects due to feature aggregation. To solve this issue, in this article, we proposed a graph-in-graph (GiG) model and a related GiG convolutional network (GiGCN) for HSI classification from a superpixel viewpoint. The GiG representation covers information inside and outside superpixels, respectively, corresponding to the local and global characteristics of ground objects. Concretely, after segmenting HSI into disjoint superpixels, each one is converted to an internal graph. Meanwhile, an external graph is constructed according to the spatial adjacent relationships among superpixels. Significantly, each node in the external graph embeds a corresponding internal graph, forming the so-called GiG structure. Then, GiGCN composed of internal and External graph convolution (EGC) is designed to extract hierarchical features and integrate them into multiple scales, improving the discriminability of GiGCN. Ensemble learning is incorporated to further boost the robustness of GiGCN. It is worth noting that we are the first to propose the GiG framework from the superpixel point and the GiGCN scheme for HSI classification. Experiment results on four benchmark datasets demonstrate that our proposed method is effective and feasible for HSI classification with limited labeled samples. For study replication, the code developed for this study is available at https://***/ShuGuoJ/***. © 2012 IEEE.

关键词： Convolution

来源：评论

学校读者我要写书评

暂无评论

Research on global register allocation for code containing array-unit dual-usage register names

Research on global register allocation for code containing a...

引用

作者： Hu, Yonghua Zhang, Xin Wang, Shuying Liang, Wei Li, Kuan-Ching School of Computer Science and Engineering Hunan University of Science and Technology Xiangtan China Hunan Key Laboratory for Service Computing and Novel Software Technology Xiangtan China

Array-unit dual-usage register is a kind of register resource that can be read or written as a whole or individually. It is mainly configured in processors with SIMD processing units and provides register-level speed data transfer between the scalar and vector processing units. To improve the efficiency of algorithms by using an array-unit dual-usage register, we investigate in this article the problem of adapting register allocation to code containing array-unit dual-usage register names. We propose a corresponding global register allocation method by combining the allocation of regular registers with array-unit dual-usage register, ensuring that the names of array-unit dual-usage register can be used in the input code of register allocation. Moreover, we present the processing framework of this method and the specific algorithms of some related vital aspects and demonstrate the working principles of the algorithms by an example. Experimental studies were conducted on a platform based on the FT-M7002 DSP core, and showing that our register allocation method can effectively handle codes containing array-unit dual-usage register names and support relevant application algorithms to improve their data transfer scheme. For some typical algorithms with input matrix, substantial performance improvements of twofold or higher are achieved. © 2022 John Wiley & Sons, Ltd.

关键词： Data transfer

来源：评论

学校读者我要写书评

暂无评论

Development of a Soft Hand and Wrist Rehabilitation Robot Utilizing Lattice Structure Actuators

Development of a Soft Hand and Wrist Rehabilitation Robot Ut...

引用

2024 International Convention on Rehabilitation engineering and Assistive Technology and World Rehabilitation Robot Convention, i-CREATE and WRRC 2024

作者： Lai, Jianwei Song, Aiguo Zhang, Wenbin Lu, Ye Wang, Jiajin Li, Ye School of Instrument Science and Engineering Southeast University State Key Laboratory of Digital Medical Engineering Jiangsu Key Laboratory of Remote Measurement and Control Nanjing210096 China College of Computer Science and Software Engineering Hohai University Nanjing210024 China

ISBN: (纸本)9798350355123

Soft robots offer promising solutions for rehabilitation due to their inherent attributes such as suppleness, lightweight construction, and safety features. However, prevalent soft actuators face limitations in output force and bending angle, impeding their effective application in rehabilitation. This paper proposes a novel design approach employing a two-layer lattice structure actuator, comprising flexion, guiding, and extension layers. This innovative design facilitates supple and efficient bi-directional rehabilitation training for fingers and wrists. Experimental results demonstrate the efficacy of the proposed dual-layer lattice structure actuator. At an air pressure of 150 kPa, it achieves an end force of 14.88 N, marking a remarkable 906% increase compared to its single-layer counterpart. Additionally, the dual-layer structure exhibits a minimum bending radius of 2.77 cm under 60 kPa, representing a 38.5% reduction relative to the single-layer actuator. Leveraging these actuators, a hand and wrist rehabilitation robot was developed and subjected to a clinical trial involving two healthy subjects. The trial outcomes reveal significant improvements in finger grasping ability and wrist range of motion with the assistance of the rehabilitation robot. These results underscore the practical usability and effectiveness of the soft hand and wrist rehabilitation robot, thereby holding promise for enhancing patient rehabilitation outcomes. © 2024 IEEE.

关键词： Patient rehabilitation

来源：评论

学校读者我要写书评

暂无评论

Heart failure prediction: a comparative analysis of machine learning algorithms

Heart failure prediction: a comparative analysis of machine ...

引用

2024 International Conference on Optics, Electronics, and Communication engineering, OECE 2024

作者： Dahri, Fida Husain Laghari, Asif Ali Sajnani, Dileep Kumar Shazia, Asima Kumar, Teerath School of Computer Science and Engineering Southeast University Jiangsu Nanjing211100 China Software College Shenyang Normal University Liaoning Shenyang110034 China National University of Computer and Emerging Sciences Islamabad Pakistan

ISBN: (纸本)9781510685437

In recent, machine learning techniques have been employed to predict various diseases such as lung, blood, liver, heart, etc. As the heart is considered one of the human body’s significant organs, this research proposes a comparative analysis of heart disease failure using various machine learning algorithms. Since the ECG and EEG are primary sources for analyzing heart performance, the lipid profile also provides information to determine good and bad cholesterol levels. Although this research proposes a comparative analysis of Heart Failure (HF) and other Cardiovascular diseases (CVDs), it assists in finding a better way to predict the root cause of HF and CVDs. The machine learning models are employed and trained, such as the Support Vector Classifier (SVC), Random Forest Classifier (RF), Logistic Regression (LR), Decision Tree Classifier (DT), and K-Nearest Neighbors Classifier (KNN) on the real-time dataset from Kaggle to predict and classify heart disease patients. The experimental set-up outcomes show that the Logistic Regression (LR) classifier has proven the best accuracy (88.00%) among all other machine learning classifiers. Our research results significantly contribute to predicting (HF) and nurturing advancements in AI-powered tools for improved heart failure (HF) prediction and patient care. © 2024 SPIE.

关键词： Logistic regression

来源：评论

学校读者我要写书评

暂无评论

Blockchain-Based Cognitive Computing Model for Data Security on a Cloud Platform

引用

computers, Materials & Continua 2023年第12期77卷 3305-3323页

作者： Xiangmin Guo Guangjun Liang Jiayin Liu Xianyi Chen Department of Computer Information and Cyber Security Jiangsu Police InstituteNanjing210031China Jiangsu Electronic Data Forensics and Analysis Engineering Research Center Jiangsu Police InstituteNanjing210031China School of Computer and Software Nanjing University of Information Science and TechnologyNanjing210044China

Cloud storage is widely used by large companies to store vast amounts of data and files,offering flexibility,financial savings,and ***,information shoplifting poses significant threats,potentially leading to poor performance and privacy ***-based cognitive computing can help protect and maintain information security and privacy in cloud platforms,ensuring businesses can focus on business *** ensure data security in cloud platforms,this research proposed a blockchain-based Hybridized Data Driven Cognitive Computing(HD2C)***,the proposed HD2C framework addresses breaches of the privacy information of mixed participants of the Internet of Things(IoT)in the ***2C is developed by combining Federated Learning(FL)with a Blockchain consensus algorithm to connect smart contracts with Proof of ***“Data Island”problem can be solved by FL’s emphasis on privacy and lightning-fast processing,while Blockchain provides a decentralized incentive structure that is impervious to *** with Blockchain allows quick consensus through smart member selection and *** HD2C paradigm significantly improves the computational processing efficiency of intelligent *** analysis results derived from IIoT datasets confirm HD2C *** compared to other consensus algorithms,the Blockchain PoA’s foundational cost is *** accuracy and memory utilization evaluation results predict the total benefits of the *** comparison to the values 0.004 and 0.04,the value of 0.4 achieves good *** to the experiment results,the number of transactions per second has minimal impact on memory *** findings of this study resulted in the development of a brand-new IIoT framework based on blockchain technology.

关键词： Blockchain Internet of Things(IoT) blockchain based cognitive computing Hybridized Data Driven Cognitive Computing(HD2C) Federated Learning(FL) Proof of Authority(PoA)

来源：评论

学校读者我要写书评

暂无评论

Emotionally Challenging Games Can Satisfy Older Adults' Psychological Needs: From Empirical Study to Design Guidelines 25

Emotionally Challenging Games Can Satisfy Older Adults' Psyc...

引用

2025 CHI Conference on Human Factors in Computing Systems, CHI 2025

作者： Zhou, Min Peng, Xiaolan Liu, Binjie Denisova, Alena Barathi, Soumya C. Li, Zhuying Xie, Xurong Huang, Jin Tian, Feng Institute of Software Chinese Academy of Sciences Beijing China Department of Computer Science University of York York United Kingdom School of Computer Science and Informatics Cardiff University Cardiff United Kingdom School of Computer Science and Engineering Southeast University Nanjing China

ISBN: (纸本)9798400713941

Older adults often struggle to meet their psychological needs due to retirement and living alone. Recent studies suggest that games featuring emotional challenge (EC) can help fulfill basic psychological needs such as autonomy, competence, and relatedness by facilitating emotional exploration. However, it remains unclear whether older adults can benefit from EC games, whether they find this genre enjoyable, and how these games should be designed to better meet their needs. This work explores older adults' experiences and perceptions of playing EC games through two studies. The first study involved playing Detroit: Become Human, revealing that older adults derived multifaceted psychological experiences from playing the game. The second study involved a custom-designed game scenario tailored to older adults, demonstrating that meaningful choices significantly influenced autonomy need satisfaction. Based on these findings, we offer five design guidelines for developing EC games that satisfy psychological needs of older adults. © 2025 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Interactive computer graphics

来源：评论

学校读者我要写书评

暂无评论

Robust Imagined Speech Production Using AI-Generated Content Network for Patients with Language Impairments

引用

IEEE Transactions on Consumer Electronics 2024年第1期71卷 1402-1411页

作者： Xu, Xu Fu, Chong Dalian University of Technology School of Software Dalian116621 China Northeastern University School of Computer Science and Engineering Shenyang110004 China Northeastern University Key Laboratory of Intelligent Computing in Medical Image Ministry of Education Shenyang110819 China Ministry of Education Engineering Research Center of Security Technology of Complex Network System Shenyang110004 China

Imagined speech production is critical for brain-computer interface systems. It is able to provide the communication ability for patients with language impairments. Nowadays, many studies have developed algorithms for this purpose. However, they often struggle with an issue that is the noise in physiological signals from various devices. This makes current methods have low robustness when performing speech generation. To this end, we propose a framework to robustly producing imagined speech for patients with language impairments. It is based on artificial intelligent-generated content (AIGC). In specific, this work develops a diffusion model to remove noise from brain signals, which are then converted and normalized into log-mel spectrograms. Next, the product feeds into a transformer-based model to extract local and global features. Finally, these enhanced features are inputted into full connected layers to generate the imagined speech. The effectiveness of our model is validated using the high-quality, i.e., single-word-production-Dutch-iBIDS dataset. It achieves Pearson correlation scores above 0.8 and standard deviations below 0.2 across different volunteers. Experimental results well demonstrate that our model is robust and has advantages over peer methods. © 2024 IEEE.

关键词： Deep 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：