检索结果-内蒙古大学图书馆

2nd Edition of IEEE Delhi Section Owned Conference, DELCON 2023

作者： Chirag, N. Kshitij, K. Gupta, Gaurav Sir M Visvesvaraya Institute of Technology Dept. of Information Science Engineering Bangalore India Shoolini University Yogananda School of Artificial Intelligence Computer and Data Sciences H.P Solan India

ISBN: (纸本)9798350322064

The amount of data loss on corporate servers in cloud environments has increased significantly. In the cloud, there are many security compromises and account hijackings, resulting in severe vulnerabilities for the service. Cloud computing is one of the most popular on-demand services. Over the past decade, cloud computing has been incorporated into various industries, including health care and the military. Security has, however, been a major concern with cloud computing. There have been many approaches and security implications implemented. However, when it comes to the security and privacy of data, there are loopholes. By decentralizing the cloud, which is currently a centralized entity, the authors attempt to improve cloud security. It is possible to solve these problems by deploying a decentralized cloud. Client software encrypts, fragments data, and then a client engine distributes the data among a number of hosting nodes (servers) spread throughout the world. As a result, the user has complete ownership of his data. In their study, the authors found that decentralizing cloud solved most of the problems of centralized cloud, since it increases the security of data by fragmenting and encrypting fragments. © 2023 IEEE.

关键词： Cloud security

来源：评论

学校读者我要写书评

暂无评论

HealthLink: A Cutting-Edge System for Secure Healthcare Data with IoT, Blockchain, and IPFS

HealthLink: A Cutting-Edge System for Secure Healthcare Data...

引用

Blockchain and Distributed Systems Security (ICBDS), IEEE International Conference on

作者： Kallur V Vijayakumar Chethan A. S. Muneshwara M S Swetha M S Anand R Shivakumara T Dept. of Mathematics BMSIT&M India Dept. of Computer Science & Engineering BMSIT&M India Dept. of Information Science & Engineering BMSIT&M India Dept. of MCA BMSIT&M India

ISBN: (数字)9798350354348

ISBN: (纸本)9798350354355

In today's rapidly evolving healthcare landscape, the integration of cutting-edge technologies such as IoT and wearable devices has led to significant advancements, particularly in the realm of remote patient monitoring. Despite these advancements, the traditional client/server architecture prevalent in current implementations presents substantial challenges related to security and privacy, leaving healthcare systems vulnerable to various attacks. Consequently, stringent regulatory and security measures are imperative to safeguard health data. To address these challenges and meet regulatory requirements, transitioning to a distributed architecture is essential. Blockchain technology, renowned for its distributed nature and robust security features, offers a promising solution to mitigate the security risks inherent in IoT-based systems. Motivated by these considerations, this study introduces HealthLink—a secure healthcare framework that seamlessly integrates IoT and Blockchain *** is designed to facilitate remote patient monitoring, particularly for chronic diseases that require continuous oversight. The framework prioritizes security, scalability, and processing efficiency. Security measures include the use of re-encryption proxies combined with Blockchain for hash data storage, while access control is managed through smart contracts. To enhance Blockchain scalability, an off-chain IPFS-based database is employed for data storage, and the Ethereum Blockchain-based proof of authority is leveraged to expedite data storage processes. As a practical demonstration, we apply the HealthLink system to diabetes management, presenting execution results through system interfaces. Experimental findings highlight substantial enhancements in the security of healthcare systems compared to conventional methodologies, underscoring the potential of HealthLink to revolutionize remote patient monitoring and data security in healthcare.

关键词： Patient monitoring Privacy Scalability Smart contracts Memory Medical services Blockchains Security Wearable devices Diseases

来源：评论

学校读者我要写书评

暂无评论

Discriminative Anchor Learning for Efficient Multi-view Clustering

arXiv

引用

arXiv 2024年

作者： Qin, Yalan Pu, Nan Wu, Hanzhou Sebe, Nicu The School of Communication and Information Engineering Shanghai University Shanghai200444 China The Dept. of Information Engineering and Computer Science University of Trento Trento38100 Italy

Multi-view clustering aims to study the complementary information across views and discover the underlying structure. For solving the relatively high computational cost for the existing approaches, works based on anchor have been presented recently. Even with acceptable clustering performance, these methods tend to map the original representation from multiple views into a fixed shared graph based on the original dataset. However, most studies ignore the discriminative property of the learned anchors, which ruin the representation capability of the built model. Moreover, the complementary information among anchors across views is neglected to be ensured by simply learning the shared anchor graph without considering the quality of view-specific anchors. In this paper, we propose discriminative anchor learning for multi-view clustering (DALMC) for handling the above issues. We learn discriminative view-specific feature representations according to the original dataset and build anchors from different views based on these representations, which increase the quality of the shared anchor graph. The discriminative feature learning and consensus anchor graph construction are integrated into a unified framework to improve each other for realizing the refinement. The optimal anchors from multiple views and the consensus anchor graph are learned with the orthogonal constraints. We give an iterative algorithm to deal with the formulated problem. Extensive experiments on different datasets show the effectiveness and efficiency of our method compared with other methods. Copyright © 2024, The Authors. All rights reserved.

关键词： Contrastive Learning

来源：评论

学校读者我要写书评

暂无评论

A Heart Disease Prediction Model using Merged XGBoost-SVM Classifier and Particle Swarm Optimization

A Heart Disease Prediction Model using Merged XGBoost-SVM Cl...

引用

Mobile Computing and Sustainable Informatics (ICMCSI), International Conference on

作者： Annwesha Banerjee Majumder Somsubhra Gupta Sourav Majumder Dharmpal Singh Dept. of Information Technology JIS College of Engineering Kalyani India Dept. of Comouter Science Swami Vivekananda University Kolkata India Capgemini Kolkata India Dept. of Computer Science and Engineering JIS College of Engineering Kalyani India

This study aims to develop an advanced heart disease prediction model using machine learning techniques to address the global health challenge of early identification and accurate diagnosis, especially considering the projected deficits in healthcare by 2030. The proposed model integrates two powerful algorithms, XGBoost and Support Vector Machine Classifier, leveraging their complementary strengths to achieve effective classification. To enhance the model's performance, feature selection has been conducted using the LASSO method, which identifies the most relevant features, while outlier removal based on the Z score ensures data quality. StandardScaler has been utilized to standardize the data, mitigating any biases caused by differing scales. Furthermore, Particle Swarm Optimization has been employed to optimize the model's performance by fine-tuning its parameters. The reported accuracy of 91.8% underscores the model's potential to accurately predict heart disease, offering valuable support in prevention efforts. These findings highlight the significance of technological advancements in healthcare, particularly in addressing the growing demand for accurate and timely diagnoses.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Twitter Data Analysis using Natural Language Processing and Recurrent Neural Network 15

Twitter Data Analysis using Natural Language Processing and ...

引用

15th International Conference on Advances in Computing, Control, and Telecommunication Technologies, ACT 2024

作者： Deshmukh, Sachin Ali, M.S. Bhagat, Amol P. Kakde, Vikram Dept. of Computer Science and Engineering Prof Ram Meghe College of Engg. & Mgmt. Amravati Badnera India Dept. of Information Technology Prof Ram Meghe College of Engg. & Mgmt. Amravati Badnera India Department of Electronics and Telecommunication Engineering Prof Ram Meghe College of Engg. & Mgmt. Amravati Badnera India

ISBN: (纸本)9798331300579

The essence of this work presented is the conception and construction of a unique model. This model harnesses the power of Natural Language Processing (NLP) and Recurrent Neural Networks (RNN) to decipher the sentiments expressed across various social media platforms. The ultimate goal is to illuminate the emotional landscape of the digital world. In the work presented in this paper the twitter social media posts/messages are retrieved having a particular keyword. Many posts are made using identical message formats which users are unaware about. For effective retrieving of the post identifying the relevant information is required. Many social media post have data which are irrelevant to analyze. For effective analysis removing this irrelevant data is necessary. Once the posts are processed, tokens and keywords are identified. Keywords are important aspects which are used further to identify the sentiment. The processed data is then stored in database and split into two category for training and testing purpose. The polarity and subjectivity of the data is then calculated for analyzing the sentiments using NLP and RNN. The proposed NLP-RNN approach is evaluated using accuracy, loss, and evaluation time required. © Grenze Scientific Society, 2024.

关键词： Tweets

来源：评论

学校读者我要写书评

暂无评论

Amazing Power of DINOv2 for Automatic Diagnosis of 12-Lead ECG

Amazing Power of DINOv2 for Automatic Diagnosis of 12-Lead E...

引用

2023 International Conference on Computational science and Computational Intelligence, CSCI 2023

作者： Singh, Kushal Pal Chandra, B. Kalra, Prem Kumar Narang, Rajiv Ansk School of Information Technology Indian Institute of Technology New Delhi Delhi India Indian Institute of Technology Dept. of Computer Science and Engineering New Delhi Delhi India All India Institute of Medical Sciences Dept. of Cardiology New Delhi India

ISBN: (纸本)9798350361513

The Electrocardiogram (ECG) is a reliable and fast diagnostic test for detecting several heart anomalies to enable timely intervention. However, accurate interpretation is crucial and requires skilled professionals. Automatic diagnosis of heart disease from ECG can solve the problem. The proposed method in this paper uses a Visual transformer, DINOv2, for ECG diagnosis. DINOv2 is trained on many real-world object images. The model has been finetuned with ECG images. This finetuned model is used for extracting relevant features required by the classification method for anomaly detection. The main aim of this paper is to classify the widely known CODE-15 dataset with increased accuracy. This dataset is 15% of data from a larger CODE dataset. This 12-lead ECG dataset has samples for six heart conditions: 1dAVb, RBBB, LBBB, SB, AF, and ST. The model was finetuned for 10 epochs and achieved a test accuracy of 96.30%. This accuracy has been achieved with a short duration (2 seconds) of ECG data only. Ribeiro et al. (2020) have tried to classify the entire CODE data and have been able to achieve an average precision of 92.37%. In this paper, we have achieved an increase in accuracy to the tune of 3.3% with a smaller subset dataset. © 2023 IEEE.

关键词： Electrocardiograms

来源：评论

学校读者我要写书评

暂无评论

Portable VR-based Concussion Diagnostics of Mild Traumatic Brain Injury 3

Portable VR-based Concussion Diagnostics of Mild Traumatic B...

引用

3rd IEEE Eurasia Conference on IOT, Communication and engineering, ECICE 2021

作者： Wu, Hsiaokuang Chang, Tsai-Ting Yeh, Shih-Ching Wang, Junlong Poole, James M Shao, Charles National Central University Dept. of Computer Science and Information Engineering Taiwan

ISBN: (纸本)9781665445160

People in the United States have high incidence of mild traumatic brain injury (mTBI), especially the symptoms of concussion. However, there are questions about the traditional measurements of concussion. It is difficult to detect, and its key information may be easily ignored or become too *** study measures the eye-tracking data by using VR and VR environments. We use the one-way univariate analyses of variance to examine the differences in performance on different ages, gender, and TBI severity between participants with concussion and controls. We not only check the effects of saccades, fixations, and reaction time but also make sure that three data can represent a biomarker for differentiating patients with a concussion or *** addition, the analysis technology of electroencephalography (EEG) is used in this project, and the visual method in the VR environment is applied to generate non-invasive, visual stimulation to the experimenter to collect the response of the human brain. This technology is called Visual Evoked Potential (VEP) to analyze the brain waves of ordinary people generated by the stimulus, compare, and even combine artificial intelligence methods and eye movement values to detect concussion symptoms of mTBI. We hoped that the fusion technology of machine learning can combine the above two different analysis results to achieve the effect of optimizing the accuracy and convenience of concussion detection. © 2021 IEEE.

关键词： Eye movements

来源：评论

学校读者我要写书评

暂无评论

Detect Bangladeshi Mango Leaf Diseases Using Lightweight Convolutional Neural Network

Detect Bangladeshi Mango Leaf Diseases Using Lightweight Con...

引用

International Conference on Electrical, computer and Communication engineering (ECCE)

作者： Nosin Ibna Mahbub Feroza Naznin Md Imran Hasan Syed Mahfuzur Rahman Shifat Md. Alamgir Hossain Md Zahidul Islam Dept. of Information and Communication Technology Islamic University Kushtia Bangladesh Dept. of Computer Science and Engineering Green University Dhaka Bangladesh Dept. of Computer Science and Engineering Islamic University Kushtia Bangladesh Dept. of Computer Science and Engineering University of Global Village Barishal Bangladesh

This research concentrates on the diagnosis of common mango leaf diseases in Bangladesh using image processing via deep learning. Mango production could be raised by at least 28% globally if the crop could be safeguarded from a variety of diseases. However, without the assistance of an expert, it is challenging for the farmer to detect the disease at the appropriate time. Few studies have been conducted to identify the mango leaf disease present in Bangladesh. So far, no study has been done to identify the seven distinct mango leaf diseases reported in Bangladesh. We proposed a lightweight convolutional neural network (LCNN) in this paper to accurately classify seven distinct mango leaf diseases as well as normal mango leaf. To assess the proposed LCNN model, performance is compared to several pre-trained models such as VGG16, Resnet50, Resnet101, and Xception, and it is found that LCNN achieves the highest testing accuracy (98%).

关键词： Deep learning Computational modeling Image processing Crops Production Convolutional neural networks Diseases

来源：评论

学校读者我要写书评

暂无评论

BB-PATCH: BLACKBOX ADVERSARIAL PATCH-ATTACK USING ZEROTH-ORDER OPTIMIZATION

arXiv

引用

arXiv 2024年

作者： Kumar, Satyadwyoom Gupta, Saurabh Buduru, Arun Balaji Dept. of Electronics and Communication Engineering Netaji Subhas Institute of Technology Delhi India Dept. of Computer Science Engineering Indraprastha Institute of Information Technology Delhi India

Deep Learning has become popular due to its vast applications in almost all domains. However, models trained using deep learning are prone to failure for adversarial samples and carry a considerable risk in sensitive applications. Most of these adversarial attack strategies assume that the adversary has access to the training data, the model parameters, and the input during deployment, hence, focus on perturbing the pixel level information present in the input image. Adversarial Patches were introduced to the community which helped in bringing out the vulnerability of deep learning models in a much more pragmatic manner but here the attacker has a white-box access to the model parameters. Recently, there has been an attempt to develop these adversarial attacks using black-box techniques. However, certain assumptions such as availability large training data is not valid for a real-life scenarios. In a real-life scenario, the attacker can only assume the type of model architecture used from a select list of state-of-the-art architectures while having access to only a subset of input dataset. Hence, we propose an black-box adversarial attack strategy that produces adversarial patches which can be applied anywhere in the input image to perform an adversarial attack. © 2024, CC BY.

关键词： Deep learning

来源：评论

学校读者我要写书评

暂无评论

Remaining Useful Life Prediction of Multi-Component System Clustering with Time-Varying Kernel Density

Remaining Useful Life Prediction of Multi-Component System C...

引用

2023 China Automation Congress, CAC 2023

作者： Li, Ke Fan, Xingle Shi, Hui Zhang, Xiaohong Sun, Chaoli Wang, Jianan Taiyuan University of Science and Technology Shanxi Key Laboratory of Advanced Control and Equipment Intelligence Dept. of Electronic and Information Engineering Taiyuan China Taiyuan University of Science and Technology Shanxi Key Laboratory of Advanced Control and Equipment Intelligence Dept. of Economics and Management Taiyuan China Taiyuan University of Science and Technology Shanxi Key Laboratory of Advanced Control and Equipment Intelligence Dept. of Computer Science and Technology Taiyuan China

ISBN: (纸本)9798350303759

Aiming at the uncertainty of the degradation procedure of a complex multi-component system in a time-varying environment and the influence of the random dependence between different components, a remaining useful life prediction model for time-varying adaptive kernel density estimation based on the dependence between multiple components is established. First, the random dependence between multi-component systems is clustered. Considering that different components in the same cluster are affected by the random dependence, the components between different clusters do not affect each other. Judge the dependence characteristics between components, and use the dependence characteristics of different components to conduct clustering degradation modelling. Secondly, the parameters of the system change with time. Considering that monitoring data near the current time has a greater impact on the remaining useful life prediction compared with historical data, data in the vicinity of the current time is accorded a more significant weight, and a time-varying kernel density estimation prediction method is designed, in which the nearest-neighbour adaptive window-width selection method is used. Finally, give an example to demonstrate the effectiveness by the proposed method. © 2023 IEEE.

关键词： Forecasting

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：