检索结果-内蒙古大学图书馆

Point cloud completion via structured feature maps using a feedback network

Computational Visual Media 2023年第1期9卷 71-85页

作者： Zejia Su Haibin Huang Chongyang Ma Hui Huang Ruizhen Hu College of Computer Science&Software Engineering Shenzhen UniversityShenzhenChina Kuaishou Technology China

In this paper,we tackle the challenging problem of point cloud completion from the perspective of feature *** key observation is that to recover the underlying structures as well as surface details,given partial input,a fundamental component is a good feature representation that can capture both global structure and local geometric *** accordingly first propose FSNet,a feature structuring module that can adaptively aggregate point-wise features into a 2D structured feature map by learning multiple latent patterns from local *** then integrate FSNet into a coarse-to-fine pipeline for point cloud ***,a 2D convolutional neural network is adopted to decode feature maps from FSNet into a coarse and complete point ***,a point cloud upsampling network is used to generate a dense point cloud from the partial input and the coarse intermediate *** efficiently exploit local structures and enhance point distribution uniformity,we propose IFNet,a point upsampling module with a self-correction mechanism that can progressively refine details of the generated dense point *** have conducted qualitative and quantitative experiments on ShapeNet,MVP,and KITTI datasets,which demonstrate that our method outperforms stateof-the-art point cloud completion approaches.

关键词： 3D point clouds shape completion geometry processing deep learning

来源：评论

学校读者我要写书评

暂无评论

IoMT & ML Model to Detect Stress in Human 2

IoMT & ML Model to Detect Stress in Human

引用

2nd International Conference Computational and Characterization Techniques in engineering and sciences, IC3TES 2024

作者： Nadh Singh, Bhaludra R Ghanta, Sindhura Saishivani, Thokala Vaishnavi, Basingala Sai Sravya, Polishetty Shifa, Shaik Bhoj Reddy Engineering College for Women Department of Computer Science and Engineering Hyderabad India Bhoj Reddy Engineering College Department of Computer Science and Engineering Hyderabad India Bhoj Reddy Engineering College Department of Computer Science and Engineering Telangana Hyderabad India Hyderabad India

ISBN: (纸本)9798350364699

Today's fast-moving surroundings cause stress for heaps. Real-time stress administration may be troublesome, regardless of the allure of negative effects on tangible and insane fitness. ML, IoMT, and AI abilities change how we live and work and solve this question. "A Smart Biomedical Healthcare System to Detect Stress Utilizing IoT, Machine Learning and Artificial Intelligence,"intends to utilize physiological sensors like soul rate, hotness, dampness (sweat) sensors and first verbalizations to detect stress. The submitted approach can path and categories folk into "difficult"and "non-tense"circumstances. Our classification results manifest the advantage of our approach in certain-occasion stress identification. The main aim of the original search out supports a novel procedure of identifying early stress something did surroundings, knowledge atmospheres, and administrative settings to support laborers' and scholars' welfare and someday increase productivity. © 2024 IEEE.

关键词： mHealth

来源：评论

学校读者我要写书评

暂无评论

Privacy preserving using joint 2 K-means clustering and coati optimization algorithm for online social networks

引用

International Journal of Information Technology (Singapore) 2024年第4期16卷 2715-2724页

作者： Gowda, Nithish Ranjan Venkatesh Venugopal, K.R. Department of Computer Science and Engineering University Visvesvaraya College of Engineering Bangalore University Bengaluru India Department Computer Science and Engineering University Visvesvaraya College of Engineering Bangalore University Bengaluru India University Visvesvaraya College of Engineering Bangalore University Bangalore India

Social networks, that have grown so prevalent nowadays, enable users to exchange information and save a significant amount of personal data about them. Although the information that is stored can be useful for enhancing the quality of services provided to users, it also poses a risk to their privacy. This is because social networks contain private information about users. As a result, members of social networks seek to protect the confidentiality of their shared data. The most popular method for protecting confidentiality is anonymizing data, which involves modifying or eliminating some information while trying to preserve as much of the original data as possible. The high level of data loss, similarities attacks, and protection from attribute or link disclosure is problem with existing anonymity approaches. The study proposes a hybrid approach based on K-member k-means clustering and coati optimization algorithm (2KMCOA) as a successful solution for balanced clustering and anonymizing in social networks in order to get over these shortcomings. A K-member K-means clustering algorithm is used to divide the different users into C clusters and each cluster has at least K users, as part of the proposed anonymization procedure. Following clustering, an initial solution is generated that produces the modified data table which should satisfy objective functions and three constraints. The coati optimization algorithm (COA) is then utilized to optimize the primary clusters even more to anonymize the data as well as network graph. The efficiency of the proposed 2KMCOA is compared with other existing anonymity techniques K-means clustering with COA (KMCOA) and K- member K-means clustering without COA (2K) in terms of clustering error, balancing error, distortion rate, objective function, cost function and CPU running time. © The Author(s), under exclusive licence to Bharati Vidyapeeth's Institute of computer Applications and Management 2024.

关键词： Anonymization Clusters COA Cost function K-anonymity K-means clustering L-diversity T-closeness

来源：评论

学校读者我要写书评

暂无评论

Features enhancement of micro-service based cab booking system using block-chain 4th

Features enhancement of micro-service based cab booking syst...

引用

4th International Conference on Computational Methods in science and Technology, ICCMST 2024

作者： Rohit Singh, Neelaksh Shukla, Riya Singh, Ashutosh Kumar Department of Computer Science and Engineering Institute of Engineering and Rural Technology Prayagraj India Department of Computer Science and Engineering United College of Engineering and Research Prayagraj India

ISBN: (纸本)9781032911571

Cab booking services help people order taxis. Existing cab booking services use client server-based architecture. The paper gives a study of the architecture and workings of the Uber cab booking website (Dissanayake, March 18, 2021). It has a micro-service based architecture (Li et al. 2021). Load balancing (Al-Jaroodi et al. 2012) and stream processing (Besnard et al. 2013) are used to handle a large number of requests. The workload is divided among servers using Dispatch Optimization (Abid et al. 2022). It ensures fast responses, easy connection minimal downtime, and efficient use of resources. However, the use of servers increases the cost of service. Rider and driver depend on the server to connect. They don’t know if the system is charging them fairly. It reduces transparency and trust. The paper proposes a blockchain-based cab booking application to solve some of the problems. Trust, transparency, and decentralization can be ensured using blockchain. Transparent and immutable ledgers of blockchain ensure trust and reduce reliance on intermediaries. Smart contracts govern ride management, optimizing efficiency. Blockchain capabilities are used to ensure fair rides, direct interaction of passengers with drivers, and automated payments. The proposed system aims at combining existing technology with blockchain solution to ensure security and transparency along with scalability and quick response time. © 2025 the Author(s).

关键词： Reservation systems

来源：评论

学校读者我要写书评

暂无评论

Enhancing aquaculture water quality forecasting using novel adaptive multi-channel spatial-temporal graph convolutional network

引用

International Journal of Agricultural and Biological engineering 2025年第1期18卷 279-291页

作者： Tianqi Xiang Xiangyun Guo Junjie Chi Juan Gao Luwei Zhang College of Computer Science Beijing Information Science&Technology UniversityBeijing 102206China College of Management Science and Engineering Beijing Information Science&Technology UniversityBeijing 102206China College of Engineering China Agricultural UniversityBeijing 100083China

In recent years,aquaculture has developed rapidly,especially in coastal and open ocean *** practice,water quality prediction is of critical ***,traditional water quality prediction models face limitations in handling complex spatiotemporal *** address this challenge,a prediction model was proposed for water quality,namely an adaptive multi-channel temporal graph convolutional network(AMTGCN).The AMTGCN integrates adaptive graph construction,multi-channel spatiotemporal graph convolutional network,and fusion layers,and can comprehensively capture the spatial relationships and spatiotemporal patterns in aquaculture water quality *** aquaculture water quality data and the metrics MAE,RMSE,MAPE,and R^(2) were collected to validate the *** results show that the AMTGCN presents an average improvement of 34.01%,34.59%,36.05%,and 17.71%compared to LSTM,respectively;an average improvement of 64.84%,56.78%,64.82%,and 153.16%compared to the STGCN,respectively;an average improvement of 55.25%,48.67%,57.01%,and 209.00%compared to GCN-LSTM,respectively;and an average improvement of 7.05%,5.66%,7.42%,and 2.47%compared to TCN,*** indicates that the AMTGCN,integrating the innovative structure of adaptive graph construction and multi-channel spatiotemporal graph convolutional network,could provide an efficient solution for water quality prediction in aquaculture.

关键词： water quality prediction aquaculture spatial-temporal graph convolutional network multi-channel adaptive graph construction

来源：评论

学校读者我要写书评

暂无评论

Double integral-enhanced Zeroing neural network with linear noise rejection for time-varying matrix inverse

引用

CAAI Transactions on Intelligence Technology 2024年第1期9卷 197-210页

作者： Bolin Liao Luyang Han Xinwei Cao Shuai Li Jianfeng Li College of Computer Science and Engineering Jishou UniversityJishouChina School of Management Shanghai UniversityShanghaiChina School of Engineering Swansea UniversitySwanseaUK

In engineering fields,time-varying matrix inversion(TVMI)issue is often *** neural network(ZNN)has been extensively employed to resolve the TVMI ***,the original ZNN(OZNN)and the integral-enhanced ZNN(IEZNN)usually fail to deal with the TVMI problem under unbounded noises,such as linear ***,a neural network model that can handle the TVMI under linear noise interference is urgently *** paper develops a double integral-enhanced ZNN(DIEZNN)model based on a novel integral-type design formula with inherent linear-noise ***,its convergence and robustness are verified by deriva-tion *** comparison and verification,the OZNN and the IEZNN models are adopted to resolve the TVMI under multiple identical noise *** experi-ments proved that the DIEZNN model has excellent advantages in solving TVMI problems under linear *** general,the DIEZNN model is an innovative work and is proposed for the first ***,the errors of DIEZNN are always less than 1�10−3 under linear noises,whereas the error norms of OZNN and IEZNN models are not convergent to *** addition,these models are applied to the control of the controllable permanent magnet synchronous motor chaotic system to indicate the superiority of the DIEZNN.

关键词： neural network real-time systems

来源：评论

学校读者我要写书评

暂无评论

Manifold grasshopper optimization based extremely disruptive vision transformer model for automatic heart disease detection in raw ECG signals

引用

Multimedia Tools and Applications 2024年 1-31页

作者： Golande, Avinash L. Pavankumar, T. Department of Computer Engineering JSPM’s Rajarshi Shahu College of Engineering Pune India Department of Computer Science & amp Engineering Koneru Lakshmaiah Education Foundation Andhra Pradesh Vijayawada520002 India

Automated detection of cardiovascular diseases based on heartbeats is a difficult and demanding task in signal processing because the routine analysis of the patient’s cardiac arrhythmia is crucial to reducing the mortality rate. Detecting and preventing these deaths requires long-term monitoring and manual examination of electrocardiogram (ECG) signals, which takes a lot of time. This article uses an optimized Vision Transformer technique to effectively detect heart disease. The four key processes are pre-processing input data, feature extraction from pre-processed data, and optimal feature selection and classification to detect heart disease. In the pre-processing phase, single-channel adaptive blind source separation is used for artifact removal and empirical mode decomposition for noise reduction of the ECG signal. After pre-processing, the ECG signal is fed into the Enhanced Pan-Tompkins algorithm (EPTA) and the Hybrid Gabor-Walsh-Hadamard transform (HGWHT) for feature extraction. The extracted feature is selected using a Manifold Grasshopper Optimization algorithm (MGOA). Finally, an Optimized Vision Transformer (OVT) detects heart disease. The experiment is carried out on PTB diagnostic ECG and PTB-XL database, a publicly accessible research datasets. The experiment obtained the following values: accuracy 99.9%, sensitivity 98%, F1 score 99.9%, specificity 90%, processing time 13.254 s, AUC 99.9% and MCC 91% using PTB diagnostic ECG. On the other hand, the proposed method has obtained an accuracy of 99.57%, f1-score of 99.17% and AUC of 99% using PTB-XL dataset. Thus, the overall findings prove that the proposed method outperforms the existing methodology. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Blind source separation

来源：评论

学校读者我要写书评

暂无评论

NVS-GAN: Benefit of generative adversarial network on novel view synthesis

引用

International Journal of Intelligent Networks 2024年第1期5卷 184-195页

作者： Shrisha, H.S. Anupama, V. Department of Computer Science & Engineering St Joseph Engineering College Mangaluru575028 India Visvesvaraya Technological University Belagavi590018 India

The methodology to generate new views for an object from provided input object view is called Novel View Synthesis (NVS). Humans imagine novel views through prior knowledge gathered through their lifetime. NVS-GAN predicts the novel views through computation. Literature survey reveals that there are limited NVS models with low Trainable Parameter Count (TPC) and low model size. Also, a study on the effect of different loss functions on NVS models was lacking. Lowering the TPC indicates less computational steps for the model to predict the output, therefore desirable. Combined with a low model size, the proposed model will become more suitable for deployment in diverse devices having limited resources for computation. Application of right combination of loss functions yield better accuracy. To address these research gaps, NVS-GAN is proposed. NVS-GAN is a Generative Adversarial Network (GAN) approach which yields NVS-Generator which performs NVS. NVS-Generator incorporates identity skip connections, bilinear sampling module, Depthwise Separable Convolution (DSC) as design features and results in low TPC, model size. In addition to discriminator loss, NVS-GAN is trained with different combinations of loss functions i.e. Mean Absolute Error (MAE) loss, Structural Similarity Index Measure (SSIM) loss, Huber loss on chair and car objects of ShapeNet dataset. The performance of NVS-Generator on test set measured in terms of MAE and SSIM is tabulated and analysed. The performance is compared with existing NVS models. The proposed NVS-GAN experiment recorded reduction in NVS-Generator TPC in 37 %–54.6 % range and reduction in model size between 37.2 % and 47.6 % range. NVS-Generator reduced MAE upto 55 % and improved SSIM upto 4 % than existing models. Summarily, NVS-GAN increased model performance and made the model "lightweight". © 2024 The Authors

关键词： Generative adversarial networks

来源：评论

学校读者我要写书评

暂无评论

Conditional Generative Adversarial Network Approach for Autism Prediction

引用

computer Systems science & engineering 2023年第1期44卷 741-755页

作者： K.Chola Raja S.Kannimuthu Department of Computer Science and Engineering Sri Eshwar College of EngineeringCoimbatore641202TamilnaduIndia Department of Information Technology Karpagam College of EngineeringCoimbatore641032TamilnaduIndia

Autism Spectrum Disorder(ASD)requires a precise diagnosis in order to be managed and ***-invasive neuroimaging methods are disease markers that can be used to help diagnose *** majority of available techniques in the literature use functional magnetic resonance imaging(fMRI)to detect ASD with a small dataset,resulting in high accuracy but low *** supervised machine learning classification algorithms such as support vector machines function well with unstructured and semi structured data such as text,images,and videos,but their performance and robustness are restricted by the size of the accompanying training *** learning on the other hand creates an artificial neural network that can learn and make intelligent judgments on its own by layering *** takes use of plentiful low-cost computing and many approaches are focused with very big datasets that are concerned with creating far larger and more sophisticated neural *** modelling,also known as Generative Adversarial Networks(GANs),is an unsupervised deep learning task that entails automatically discovering and learning regularities or patterns in input data in order for the model to generate or output new examples that could have been drawn from the original *** are an exciting and rapidly changingfield that delivers on the promise of generative models in terms of their ability to generate realistic examples across a range of problem domains,most notably in image-to-image translation tasks and hasn't been explored much for Autism spectrum disorder prediction in the *** this paper,we present a novel conditional generative adversarial network,or cGAN for short,which is a form of GAN that uses a generator model to conditionally generate *** terms of prediction and accuracy,they outperform the standard *** pro-posed model is 74%more accurate than the traditional methods and takes only around 10 min for training even with a huge dat

关键词： Autism classification attributes imaging adversarial fMRI functional graph neural networks

来源：评论

学校读者我要写书评

暂无评论

MediServe:An IoT-Enhanced Deep Learning Framework for Personalized Medication Management for Elderly Care

引用

computers, Materials & Continua 2025年第4期83卷 935-976页

作者： Smita Kapse Ganesh Yenurkar Vincent Omollo Nyangaresi Gunjan Balpande Shravani Kale Manthan Jadhav Sahil Lawankar Vikrant Jaunjale Department of Computer Technology Yeshwantrao Chavan College of EngineeringWanadongariNagpur441110MaharashtraIndia Computer Science&Engineering Jaramogi Oginga Odinga University of Science&TechnologyBondo40601Kenya Department of Applied Electronics Saveetha School of EngineeringSIMATSChennai602105TamilnaduIndia

In today’s fast-paced world,many elderly individuals struggle to adhere to their medication schedules,especially those with memory-related conditions like Alzheimer’s disease,leading to serious health risks,hospital-izations,and increased healthcare *** reminder systems often fail due to a lack of personalization and real-time *** address this critical challenge,we introduce MediServe,an advanced IoT-enabled medication management system that seamlessly integrates deep learning techniques to provide a personalized,secure,and adaptive *** features a smart medication box equipped with biometric authentication,such as fingerprint recognition,ensuring authorized access to prescribed medication while preventing misuse.A user-friendly mobile application complements the system,offering real-time notifications,adherence tracking,and emergency alerts for caregivers and healthcare *** system employs predictive deep learning models,achieving an impressive classification accuracy of 98%,to analyze user behavior,detect anomalies in medication adherence,and optimize scheduling based on an individual’s habits and health ***,MediServe enhances accessibility by employing natural language processing(NLP)models for voice-activated interactions and text-to-speech capabilities,making it especially beneficial for visually impaired users and those with cognitive ***-based data analytics and wireless connectivity facilitate remote monitoring,ensuring that caregivers receive instant alerts in case of missed doses or medication ***,machine learning-based clustering and anomaly detection refine medication reminders by adapting to users’changing health *** combining IoT,deep learning,and advanced security protocols,MediServe delivers a comprehensive,intelligent,and inclusive solution for medication *** innovative approach not only improves the quality of life for elderly

关键词： MediServe medication health risks smart medication box

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：