检索结果-内蒙古大学图书馆

4th International Informatics and software engineering Conference, IISEC 2023

作者： Parildar, Berke Sayin, Doruk Turkzeybek, Furkan Zeki Kucukoz, Onur Erten, Yusuf Murat Izmir University of Economics Computer Engineering Izmir Turkey Izmir University of Economics Software Engineering Izmir Turkey

ISBN: (纸本)9798350318036

The motivation of this study is to design a blockchain-based, decentralized, and trustworthy disaster management system. The proposed system provides an inventory of residents in the disaster areas such as earthquake zones. This information may be used by the relief agencies in case of natural disasters to track the people living in the area and initiate a rescue operation if needed. The system involves a mobile application for individual users to register and indicate their status, blockchain network to keep the data, and a desktop application to be used by disaster relief agencies to manage the whereabouts of the possible victims and organize their operations. We have built and tested a working system with Sepolia Blockchain (Ethereum Test Network) with test users, but a specific blockchain network designed for this purpose should be implemented for the project to be more feasible. © 2023 IEEE.

关键词： Blockchain

来源：评论

学校读者我要写书评

暂无评论

Waste Classification Using Random Forest Classifier with DenseNet201 Deep Features

Waste Classification Using Random Forest Classifier with Den...

引用

作者： Akyol, Kemal Karacı, Abdulkadir Faculty of Engineering and Architecture Computer Engineering Kastamonu University Kastamonu Turkey Faculty of Engineering Software Engineering Samsun University Samsun Turkey

The successful management of solid waste, which has become a major issue in urban life, reduces environmental pollution. To address this issue, numerous studies using various deep learning models have been conducted. This study focuses on traditional convolutional neural networks and transformer-based deep learning architectures and provides a detailed examination summary. In this context, many experimental studies dealing with the PoolFormer and ResNet transformers and the DenseNet201 and ResNet50 pre-trained CNN models were run on the TrashNet dataset containing waste images. The Random Forest (RF) classifier was trained on different deep features extracted from 80% of this dataset, and the remaining 20% was reserved as the test dataset. Test dataset was used to validate the performance of the RF. According to the results, RF with DenseNet201 deep features presents 96.4% classification accuracy. Furthermore, the RF-based hybrid model fed with the trained DenseNet201 outperformed other studies using the same dataset in the literature in terms of classification accuracy. The hybrid method employed used in this study may serve as a model for future research. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Deep learning

来源：评论

学校读者我要写书评

暂无评论

A New Deep Learning Model for Early Stage of Diabetes Disease Prediction

A New Deep Learning Model for Early Stage of Diabetes Diseas...

引用

2023 Innovations in Intelligent Systems and Applications Conference, ASYU 2023

作者： Ozdemir, Resul Ozturk, Celal Tasyurek, Murat Aslantas, Veysel Erciyes University Department of Computer Engineering Kayseri Turkey Erciyes University Department of Software Engineering Kayseri Turkey Kayseri University Department of Computer Engineering Kayseri Turkey

ISBN: (纸本)9798350306590

Diabetes mellitus is a chronic disease caused by several factors, including age, obesity, physical inactivity, inheritance, and unhealthy nutrition. Therefore, early stage detection of chronic diseases is of utmost importance for individuals. In this light, intelligent approaches are needed to predict the risk of diabetes mellitus. In this study, a new deep learning model was proposed to estimate the risk of diabetes disease. The performance of the new model was compared to the popular machine learning algorithms. In order to improve model performance, hyperparameter optimization was handled by the Grid Search algorithm. The proposed model achieved 98.71% accuracy, 98.74% precision, 98.71% recall, and 98.71% F1-score performance on the UCI diabetes data set. The proposed model outperformed compared machine learning models. © 2023 IEEE.

关键词： Forecasting

来源：评论

学校读者我要写书评

暂无评论

PKEST: Public-Key Encryption With Similarity Test for Medical Consortia Cloud Computing

引用

IEEE Transactions on Cloud Computing 2025年第2期13卷 680-693页

作者： Chen, Junsong Zeng, Shengke Han, Song Yin, Jin Chen, Peng Xihua University School of Computer and Software Engineering Chengdu610039 China Hangzhou City University School of Computer and Computing Science Hangzhou310015 China Sichuan University West China Biomedical Big Data Center West China Hospital Chengdu610041 China

Cloud computing eliminates the limitations of local hardware architecture while also enabling rapid data sharing between healthcare institutions. Encryption of electronic medical records (EMRs) before uploading to cloud servers is necessary for privacy. However, encryption brings challenges for computation. Public Key Encryption with Equality Test (PKEET) allows cloud servers to test the underlying message equality without decryption. Therefore, it can be used to classify the encrypted EMRs corresponding to different medical symptoms. However, traditional PKEETs have limitations in testing the similarity between the ciphertexts. Undoubtedly, it can not handle EMR classification with similar medical symptoms efficiently. In this work, we propose a lightweight public key encryption with similarity test (PKEST) for the EMR classification shared in medical consortia. Our scheme can resist offline message recovery attacks, which may be launched by the insider manager, and the traditional paring computation is not necessary. Our experiment simulation shows that the similarity error between ciphertext and plaintext is tiny when the parameters are set properly. Compared to previous works, our scheme not only achieves the classification of similar encrypted EMRs but is also more efficient than traditional PKEETs since our construction does not need paring computation anymore. © 2013 IEEE.

关键词： Ciphertext

来源：评论

学校读者我要写书评

暂无评论

Fast identification of road traffic safety: WOMDI-Apriori algorithm

Advances in Transportation Studies

引用

Advances in Transportation Studies 2023年第Special issue期2卷 159-170页

作者： Yang, L. Chu, Y.X. School of Electronic Information and Intelligent Manufacturing Sias University Zhengzhou451150 China School of Computer and Software Engineering Sias University Zhengzhou451150 China

Fast identification of road traffic safety is crucial for improving traffic safety levels. In order to improve the accuracy of road traffic safety identification and reduce the identification time, a fast identification method of road traffic safety based on WOMDI-Apriori algorithm is proposed. Based on the WOMDI-Apriori algorithm, the method searches for frequent itemsets, derives and optimizes association rules to obtain road traffic data mining results. The combination rules between road traffic data are determined, and the road traffic safety risks are classified into levels using the triangular distribution method. Based on the risk level classification results, a road traffic safety fast identification model is built. The road traffic data is inputted into the identification model to obtain relevant identification results. The experimental results show that the accuracy of road traffic safety identification using this method can reach up to 99.8%, with a maximum identification time of 6.2min, the identification recall rate varies from 96% to 100%. © 2023, Aracne Editrice. All rights reserved.

关键词： Data mining

来源：评论

学校读者我要写书评

暂无评论

Diagnosis of lung cancer based on CT scans using Vision Transformers 14

Diagnosis of lung cancer based on CT scans using Vision Tran...

引用

14th International Conference on Electrical and Electronics engineering, ELECO 2023

作者： Gulsoy, Tolgahan Kablan, Elif Baykal Karadeniz Technical University Department of Computer Engineering Turkey Karadeniz Technical University Department of Software Engineering Turkey

ISBN: (纸本)9798350360493

Lung cancer remains a major global health problem that requires early and accurate diagnosis to improve patient outcomes. Assessment by specialists is time-consuming, tedious and leads to diagnostic inconsistencies. This has led to the development of computer-aided diagnosis systems for lung cancer diagnosis. Traditionally, proposed systems have used a transfer learning approach with CNN-based models. It has also been observed that cross-validation is usually not applied in these studies. On the other hand, cross-validation contributes to more reliable results by enabling the developed models to generalise to various image samples. In recent years, Vision Transformer (ViT) models have shown remarkable success in various computer vision tasks, including image classification. In this paper, we present our pioneering approach to lung cancer classification using various Vision Transformer-based models. However, we recognised the potential of these models to capture fine-grained patterns in medical images. To ensure the robustness and reliability of our models, we also introduced 5-fold cross-validation as a key component of our methodology. The proposed system yielded the highest results with 99.69% accuracy, 99.62% precision and 98.80% recall. This research represents an important step in utilising the latest computer vision techniques to improve lung cancer diagnosis, ultimately contributing to better patient care and outcomes. © 2023 IEEE.

关键词： computerized tomography

来源：评论

学校读者我要写书评

暂无评论

Parkinson’s Disease Detection from Resting State EEG Using Multi-head Graph Structure Learning with Gradient Weighted Graph Attention Explanations 7th

Parkinson’s Disease Detection from Resting State EEG Using...

引用

7th International Workshop on Machine Learning in Clinical Neuroimaging, MLCN 2024, Held in Conjunction with 27th International Conference on Medical Image Computing and computer-Assisted Intervention, MICCAI 2024

作者： Neves, Christopher Zeng, Yong Xiao, Yiming Department of Computer Science and Software Engineering Concordia University MontrealQC Canada Concordia Institute for Information Systems Engineering Concordia University MontrealQC Canada

ISBN: (纸本)9783031787607

Parkinson’s disease (PD) is a debilitating neurodegenerative disease that has severe impacts on an individual’s quality of life. Compared with structural and functional MRI-based biomarkers for the disease, electroencephalography (EEG) can provide more accessible alternatives for clinical insights. While deep learning (DL) techniques have provided excellent outcomes, many techniques fail to model spatial information and dynamic brain connectivity, and face challenges in robust feature learning, limited data sizes, and poor explainability. To address these issues, we proposed a novel graph neural network (GNN) technique for explainable PD detection using resting state EEG. Specifically, we employ structured global convolutions with contrastive learning to better model complex features with limited data, a novel multi-head graph structure learner to capture the non-Euclidean structure of EEG data, and a head-wise gradient-weighted graph attention explainer to offer neural connectivity insights. We developed and evaluated our method using the UC San Diego Parkinson’s disease EEG dataset, and achieved 69.40% detection accuracy in subject-wise leave-one-out cross-validation while generating intuitive explanations for the learnt graph topology. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Contrastive Learning

来源：评论

学校读者我要写书评

暂无评论

Decentralized Game-based Auction Algorithm for Scheduling Robotic Chargers to Service EVs with Uncertain Demands

IEEE Transactions on Intelligent Vehicles

引用

IEEE Transactions on Intelligent Vehicles 2024年 1-14页

作者： Fang, Qiuyang Zhang, Jianlei Wang, Chen Xie, Guangming Zhang, Chunyan Department of Automation College of Computer and Control Engineering Nankai University Tianjin China National Engineering Research Center of Software Engineering Peking University Beijing China State Key Laboratory for Turbulence and Complex Systems Intelligent Biomimetic Design Lab College of Engineering Peking University Beijing China

In this study, we introduce a novel auction-based algorithm modeled as a decentralized coalition formation game, designed for the complex requirements of large-scale multi-robot task allocation under uncertain demand. This context is particularly illustrative in scenarios where robots are tasked to charge electric vehicles. The algorithm begins by partitioning a composite task sequence into distinct subsets based on spatial similarity principles. Subsequently, we employ a coalition formation game paradigm to coordinate the assembly of robots into cooperative coalitions focused on these distinct subsets. To mitigate the impact of unpredictable task demands on allocations, our approach utilizes the conditional value-at-risk to assess the risk associated with task execution, along with computing the potential revenue of the coalition with an emphasis on risk-related outcomes. Additionally, integrating consensus auctions into the coalition formation framework allows our approach to accommodate assignments for individual robot-task pairings, thus preserving the stability of individual robotic decision autonomy within the coalition structure and assignment distribution. Simulative analyses on a prototypical parking facility layout confirm that our algorithm achieves Nash equilibrium within the coalition structure in polynomial time and demonstrates significant scalability. Compared to competing algorithms, our proposal exhibits superior performance in resilience, task execution efficiency, and reduced overall task completion times. The results demonstrate that our approach is an effective strategy for solving the scheduling challenges encountered by multi-robot systems operating in complex environments. IEEE

关键词： Polynomial approximation

来源：评论

学校读者我要写书评

暂无评论

Intelligent Agents in Educational Institutions: NEdBOT - NLP-based Chatbot for Administrative Support Using DialogFlow

Intelligent Agents in Educational Institutions: NEdBOT - NLP...

引用

2022 IEEE International Conference on Agents, ICA 2022

作者： Ali, Muhammad Shahroze Azam, Farooque Safdar, Aon Anwar, Muhammad Waseem Department of Computer & Software Engineering Islamabad Pakistan

ISBN: (数字)9781665469364

ISBN: (纸本)9781665469364

Artificial intelligence (AI)-based chatbot systems have seen increased adaption in the educational domain in recent years owing to increased sophistication in the AI domain. However, most of the communication between students and educational institutions is still performed physically and causes major administrative overhead, especially during the time of admission. Contemporary pattern-matching-based and generative-based chatbots underperform to queries outside a limited scope, grammatically and structurally ambiguous inputs, outliers to pre-defined rule-set, and longer response times for a huge knowledge base. We proposed a NEdBOT-An NLP-based Educational Bot, developed by Natural Language Processing models integrated within the DialogFlow platform utilizing a Retrieval-based approach. We evaluate the developed chatbot on a custom dataset generated for the admissions use case of a prominent university. We used an objective evaluation criterion with real-world users to achieve an intent classification accuracy of 76.8% at an average mean response time of 216.43ms per query and a user-friendliness score of 72% on the System Usability Scale (SUS). The results demonstrate the proposed approach's ability to create robust, reliable, responsive, and user-friendly web-based smart chatbots that are highly scalable with the capability to handle wider scopes and vague inputs with ease. © 2022 IEEE.

关键词： Natural language processing systems

来源：评论

学校读者我要写书评

暂无评论

Emotional Synchronization for Audio-Driven Talking-Head Generation 24

Emotional Synchronization for Audio-Driven Talking-Head Gene...

引用

24th IEEE International Conference on Data Mining, ICDM 2024

作者： Zhang, Zhao Luo, Yan Zuo, Zhichao Hong, Richang Yang, Yi Wang, Meng School of Computer Science and Information Engineering University of Technology Hefei China Yunnan Key Laboratory of Software Engineering Yunan China School of Computer Science and Technology Zhejiang University Hangzhou China

ISBN: (纸本)9798331506681

Audio-driven talking-head synthesis has become a significant focus in the field of virtual human applications. However, existing methodologies face challenges in effectively synchronizing audio and video, especially in maintaining emotional consistency. Additionally, there is a notable inefficiency in leveraging emotional prompts to guide expression generation. To address these limitations, this paper introduces an Emotion Synchronized audio-driven Talking-head synthesis (EST) approach. The EST approach aims to enhance the emotion-agnostic talking-head models by enabling emotion control, and it incorporates a diffusion module to learn diverse latent rep-resentations. Furthermore, EST utilizes null-text embedding to align the latent code with emotional prompts. Additionally, a novel Sync Attention Block (SAB) is developed to broaden the spatial perceptual field, thus preventing the loss of critical information. Extensive experiments demonstrate the effectiveness of the EST method, showcasing state-of-the-art performance across widely-adopted datasets. Moreover, the EST approach exhibits exceptional generalization capabilities, even in scenarios where emotional training videos are unavailable. © 2024 IEEE.

关键词： Synchronization

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：