检索结果-内蒙古大学图书馆

2024 IEEE 14th Annual computing and Communication Workshop and Conference, CCWC 2024

作者： Chugh, Tushar Tyagi, Kanishka Srinivasan, Pranesh Challagundla, Jeshwanth Carnegie Mellon University School of Computer Science Pittsburgh United States The University of Texas Department of Electrical Engineering ArlingtonTX United States Google Mountain View United States

ISBN: (纸本)9798350360134

This paper introduces a novel method for enhancing robotic systems using Large Language Models (LLMs). We focus on leveraging LLMs to substantially improve robots' decision-making and interaction with their environment. Our proposed framework employs an agent-based approach, where robots utilize LLMs for sophisticated pattern recognition, environmental understanding, and autonomous decision-making. The main innovation of this research is the integration of LLMs into a robotic system, enabling robots to process large volumes of unstructured data, recognize complex patterns, and make informed decisions with increased precision and efficiency. This integration marks a significant leap in robotic cognitive abilities, surpassing the constraints of traditional programming. Our methodology transforms LLMs into dynamic elements within robotic systems, fostering enhanced and responsive interactions with the environment. Robots equipped with LLMs are thus capable of advanced autonomous operations, including navigating complex environments, solving intricate problems, and interacting more naturally with humans. The primary contribution of this work is the creation of an agent-based graph framework, designed to facilitate collaborative problem-solving in robotic systems. This framework consists of multiple agents working collaboratively, spanning from data ingestion to comprehensive world understanding and decision-making. These agents include modules responsible for various operational aspects, such as environmental analysis, data processing, and specialized LLMs for data interpretation and summarization. Positioning LLMs as proactive, inquisitive agents, our approach enables them to actively seek information and efficiently collaborate with other agents to complete tasks. The dynamic nature of this graph search and inter-agent communication model is a considerable innovation in robotics, offering a more integrated and effective approach for robots to tackle complex tasks, the

关键词： Decision making

来源：评论

学校读者我要写书评

暂无评论

Bacterial Classification in Microbiology using BiT

Bacterial Classification in Microbiology using BiT

引用

Communication, computing and Energy Efficient Technologies (I3CEET), International Conference on

作者： Shaik Shifa Tanveer Vishali Kumar Deva Hema Department of Computer Science and Engineering School of Computing SRM Institute of Science and Technology Chennai India

ISBN: (数字)9798331541583

ISBN: (纸本)9798331541590

Bacteria are microscopic living organisms which have only one cell in it and they are classified into many different types. Microbial classification holds a crucial role in shaping our understanding of the microscopic realm, impacting scientific exploration, medical practices, environmental sciences, and various industrial applications. In the medical field, microbial classification is indispensable for disease diagnosis, treatment, and prevention. In the industrial sector, microbial classification is fundamental for bioprocessing, biofuel production, and bioremediation. Understanding microbial characteristics enables industries to harness their capabilities for efficient production and environmental remediation. It plays very important role in both environment and human life however some other kinds of bacteria are considered as very harmful. Harmful bacteria can cause serious diseases and illness. So it’s very important to classify bacteria for proper diagnosis. Given the vast diversity of microorganisms and difference in appearance, manually classifying bacteria is a challenging task which takes lot of time. So,This paper aims to streamline this arduous task by employing deep learning techniques such as using ResNet50 architecture and using BiT. The integration of BiT and ResNet at different stages ensures efficient feature extraction and classification, leading to high accuracy and reliability in bacterial classification tasks.

关键词： Microscopy Prevention and mitigation Production computer architecture Bacteria Telecommunication computing Reliability Medical diagnostic imaging Tuning Residual neural networks

来源：评论

学校读者我要写书评

暂无评论

Wearable Sensor Configurations for Effective Tremor Assessment in Parkinson's Disease 46

Wearable Sensor Configurations for Effective Tremor Assessme...

引用

46th Annual International Conference of the IEEE engineering in Medicine and Biology Society, EMBC 2024

作者： Reardon, Shay Shuqair, Mustafa Jimenez-Shahed, Joohi Ghoraani, Behnaz Florida Atlantic University Department of Electrical Engineering & Computer Science Boca RatonFL United States Icahn School of Medicine at Mount Sinai New YorkNY United States

ISBN: (纸本)9798350371499

Smartwatches play an increasingly vital role in health monitoring, offering practical solutions for patients to manage their conditions in daily life. This is particularly significant for Parkinson's Disease (PD) patients, as tremors, a notable symptom, significantly impact their quality of life. Our research addresses unanswered questions about the optimal configurations for these devices, considering factors such as the placement of the smartwatch, the type of sensor used (accelerometer vs. gyroscope), and the sampling frequency. Employing a gradient tree-boosting approach, we analyze data from the wrist and ankle sensors of 24 PD subjects engaged in daily living activities. The findings highlight a cost-effective, practical, and resource-friendly configuration: utilizing accelerometer wrist data sampled at 32 Hz demonstrates a robust correlation between the estimations of total tremors (0.82) and rest tremors (0.84) compared to their clinical ground-truth values. This underlines the significant potential of employing smartwatches in a natural environment for clinicians to monitor their patients efficiently, minimizing the necessity for costly, inconvenient in-clinic assessments. © 2024 IEEE.

关键词： Accelerometers

来源：评论

学校读者我要写书评

暂无评论

Optimizing Crisis Management with Advanced UAV Imaging Technology

Optimizing Crisis Management with Advanced UAV Imaging Techn...

引用

Sustainable Expert Systems (ICSES), International Conference on

作者： Jambula Snehith Reddy Sri Chaithanya Mathi Polu Sai Nadh Reddy Vedha Pranava Mateti Sudha Yadav Department of Electrical and Electronics Engineering Amrita School of Engineering Bengaluru India Department of Computer Science and Engineering Amrita School of Engineering Bengaluru India

ISBN: (数字)9798331540364

ISBN: (纸本)9798331540371

The UAV systems that are fitted with current technology have helped in boosting the crisis management activities such as disaster relief operations, search and rescues, and policing. This paper integrates the developmental function of advanced detective UAVs into episodes of crises. These UAVs are capable of quickly and effectively assessing the damage, find people and observe post-disaster occurrences through with the help of complicated image analysis. It incorporates cameras with HD resolutions, thermal imagery systems, and numerous ML algorithms that allow entities and people to be identified besides detecting heat through barriers and thus increase the operational effectiveness during response operations. Indeed, the social implication as noticed with detective UAVs is highly useful in that it provides support services in disaster, bringing relief directly to the affected groups and guaranteeing the security of the rescue squads. Therefore, these UAVs enhance crisis consequence management, reducing death rates, the number of casualties and the extent of property damage. They do also help in safety and security to the public through assisting the police in surveillance and the fight against crime. Analyzing UAV's physical and virtual units, the challenges describe the UAV's use cases in different kinds of crises, such as natural ones, industrial failures, and criminal events. It also deals with issues such as restrictive policies, data security, and the requirement of professional personnel. From case studies and more personal accounts, detective UAVs' potential of crisis management is revolutionary: loss of life is avoided, damage to property is limited, and order is maintained. If further developed and properly integrated into the emergency management systems, UAVs will be the part of the equipment of the future for enhancing the efficiency of response to critical situations.

关键词： Heating systems Crisis management Image resolution Image analysis Law enforcement Disasters Surveillance Autonomous aerial vehicles Personnel Expert systems

来源：评论

学校读者我要写书评

暂无评论

Hyperspectral Image Denoising Via Nonlocal Rank Residual Modeling 48

Hyperspectral Image Denoising Via Nonlocal Rank Residual Mod...

引用

48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023

作者： Zha, Zhiyuan Wen, Bihan Yuan, Xin Zhou, Jiantao Zhu, Ce Nanyang Technological University School of Electrical & Electronic Engineering 639798 Singapore Westlake University School of Engineering Hangzhou310024 China University of Macau Department of Computer and Information Science 999078 China University of Electronic Science and Technology of China School of Information and Communication Engineering Chengdu611731 China

ISBN: (纸本)9781728163277

Nonlocal low-rank (LR) tensor modeling has shown great potential in hyperspectral image (HSI) denoising, which first uses the nonlocal self-similarity (NSS) prior to search for many similar full-band patches to form three-dimensional nonlocal full-band groups (tensors), and then usually enforces an LR penalty on each nonlocal full-band group. However, in most existing methods, the LR tensor is only approximated directly from the degraded nonlocal full-band tensor, which is subject to certain issues (e.g., in heavy noise environments) in obtaining a suboptimal tensor approximation, and thus leading to unsatisfactory denoising results. In this paper, we propose a novel nonlocal rank residual (NRR) approach for highly effective HSI denoising, which progressively approximates the underlying L-R tensor via minimizing the rank residual. Towards this end, we first obtain a good estimate of the original nonlocal full-band group by using the NSS prior, and then the rank residual between the de-graded nonlocal full-band group with the corresponding estimated nonlocal full-band group is minimized to achieve a more accurate LR tensor. Moreover, the global spectral LR prior is employed to reduce the spectral redundancy of HSI in the proposed denoising framework. Finally, we develop a simple yet effective alternating minimization algorithm to jointly refine global spectral information and nonlocal full-band groups. Experimental results clearly show that the proposed NRR algorithm outperforms many state-of-the-art HSI denoising methods. The source code of the proposed NRR algorithm for HSI denoising is available at: https://***/zhazhiyuan/NRR_HSI_Denoising_***. © 2023 IEEE.

关键词： Image denoising

来源：评论

学校读者我要写书评

暂无评论

MatrikalinDiabetes: User-Centered Design of a mHealth App for Gestational Diabetes Mellitus Management and Education Among Bangladeshi Women 26th

MatrikalinDiabetes: User-Centered Design of a mHealth App ...

引用

26th International Conference on Human-computer Interaction, HCII 2024

作者： Ratul, Mohammad Arshad Hossain Yanoor Bristy, Tunisha Sayeed, Noorjahan Islam, Ashraful Chaudhry, Beenish Moalla Center for Computational and Data Sciences Independent University Bangladesh Dhaka Bangladesh Department of Computer Science and Engineering Independent University Bangladesh Dhaka Bangladesh School of Computing and Informatics University of Louisiana at Lafayette Louisiana United States

ISBN: (纸本)9783031768088

This study employs the User-Centered Design (UCD) methodology to develop a mobile health (mHealth) application (app) specifically tailored for Bangladeshi women with Gestational Diabetes Mellitus (GDM). GDM affects approximately 10% of pregnant women globally and around 35% within Bangladesh, as reported by the World Health Organization (WHO). Despite its high prevalence, there is a significant lack of awareness and education about GDM in Bangladesh, compounded by language barriers that make existing digital solutions less accessible. The MatrikalinDiabetes mHealth app aims to overcome these barriers by providing comprehensive management and educational resources for GDM in the Bangla language, addressing both pre-pregnancy and post-pregnancy needs. To ensure the app meets the actual needs of potential users, personas were created based on a literature review, and a survey was conducted. This process informed the development of both a low-fidelity, paper-based prototype and a high-fidelity digital prototype. Key features of the MatrikalinDiabetes app include tracking of food and water intake, physical activity monitoring, reminders, GDM education, chat forums for community support, blood glucose level (BGL) monitoring, an SOS button for emergency contacts, and quizzes for user engagement. Preliminary feedback indicated that 53.8% of participants favored the integrated features and expressed willingness to use and recommend the app. The survey of the high-fidelity prototype yielded positive responses, with 57.9% of participants strongly agreeing and 42.1% agreeing that the features were beneficial. The user-friendly design of the high-fidelity prototype ensures intuitive interaction, aiding in health management, enhancing GDM understanding, and promoting a healthier lifestyle among pregnant women in Bangladesh. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Obstetrics

来源：评论

学校读者我要写书评

暂无评论

Automatic Detection of COVID-19 Using Ensemble Transfer Learning Based on Lung CT Scans 1

引用

2nd International Conference on Pan-African Intelligence and Smart Systems, PAAISS 2022

作者： Pillay, Ricardo Viriri, Serestina Heymann, Reolyn School of Mathematics Statistics and Computer Science University of KwaZulu-Natal Durban South Africa Department of Electrical and Electronic Engineering Science University of Johannesburg Johannesburg South Africa

ISBN: (数字)9783031252716

ISBN: (纸本)9783031252709

In order to curb the rapid spread of COVID-19, early and accurate detection is required. computer Tomography (CT) scans of the lungs can be utilized for accurate COVID-19 detection because these medical images highlight COVID-19 infection with high sensitivity. Transfer learning was implemented on six state-of-the-art Convolutional Neural Networks (CNNs). From these six CNNs, the three with the highest accuracies (based on empirical experiments) were selected and used as base learners to produce hard voting and soft voting ensemble classifiers. These three CNNs were identified as Vgg16, EfficientNetB0 and EfficientNetB5. This study concludes that the soft voting ensemble classifier, with base learners Vgg16 and EfficientNetB5, outperformed all other ensemble classifiers with different base learners and individual models that were investigated. The proposed classifier achieved a new state-of-the-art accuracy on the SARS-CoV-2 dataset. The accuracy obtained from this framework was 98.13%, the recall was 98.94%, the precision was 97.40%, the specificity was 97.30% and the F1 score was 98.16%. © 2023, ICST Institute for computer sciences, Social Informatics and Telecommunications engineering.

关键词： COVID-19

来源：评论

学校读者我要写书评

暂无评论

Advancing Brain Image Segmentation: A Comprehensive Exploration of Enhanced VGG16 Architecture for Precise Neuroanatomical Mapping 2

Advancing Brain Image Segmentation: A Comprehensive Explorat...

引用

2nd International Conference on computing and Data Analytics, ICCDA 2024

作者： Sheeja Kumari, V. Wilfred Blessing, N.R. Palarimath, Suresh Gunasekaran, Hemalatha Poorana Senthilkumar, S. Sutherlin Subitha, G. Saveetha School of Engineering Simats University Department of Computational Intelligence Chennai600 025 India College of Computing and Information Sciences University of Technology and Applied Sciences Ibri Oman College of Computing and Information Sciences University of Technology and Applied Sciences Salalah Oman Dr. N. G. P Arts and Science College Department of Computer Applications Tamil Nadu Coimbatore India Stella Mary's College of Engineering Department of Computer Science and Engineering Tamil Nadu Kanyakumari India

ISBN: (纸本)9798331542573

This study introduces a novel approach to brain image segmentation by enhancing the VGG16 architecture. Neuroanatomical mapping is a critical aspect of understanding brain structures, and our research focuses on refining the VGG16 model to achieve more accurate segmentation results. We address the intricacies of brain image analysis, optimizing the architecture for the nuanced structures inherent in neuroimaging data. Our enhanced VGG16 model is thoroughly evaluated across diverse datasets, demonstrating superior performance compared to state-of-the-art segmentation methods. The framework proves robust and efficient in accurately delineating complex brain regions. We also delve into the interpretability of the segmentation results, shedding light on the learned features that contribute to the model's efficacy. The significance of our work extends beyond improved segmentation, offering valuable insights into the interpretability of deep learning models in neuroimaging. This research contributes to the broader field of biomedical image analysis and holds promise for applications in neuroscience and medical diagnostics. The proposed enhanced VGG16 framework emerges as a potent tool for advancing our understanding of brain structures and enhancing the capabilities of neuroimaging technologies. © 2024 IEEE.

关键词： Brain mapping

来源：评论

学校读者我要写书评

暂无评论

Traffic Prediction Analysis (TPA) Using Machine Learning Methodologies 6

Traffic Prediction Analysis (TPA) Using Machine Learning Met...

引用

6th International Conference on Contemporary computing and Informatics, IC3I 2023

作者： Solanki, Mohit Rawat, Vandana Pandey, Monika Singh, Neelam Aswal, Shobha Graphic Era Deemed to Be University Department of Computer Application Dehradun India Graphic Era Deemed to Be University Department of Computer Science and Engineering Dehradun India School of Computing Graphic Era University Dehradun India

ISBN: (纸本)9798350304480

A traffic prediction system might help drivers in making better travel choices. Minimizing carbon emissions, reducing traffic congestion, and improving the effectiveness of traffic management to deliver such traffic flow data is the main aim of traffic flow prediction. Prediction of traffic is essential in a system of intelligent transportation. Effective traffic forecasting can help with route design, vehicle dispatching, and reducing traffic congestion. Due to its incredible capacity to predict traffic patterns, a branch of machine learning which is also called 'deep learning,'has recently attracted a lot of attention. In this study, a comparative evaluation has been done to predict traffic flow based on deep learning. To prepare the stacked auto encoder model, basic traffic flow features are included. The goal of this study is to conduct a thorough screening for deep earning -based traffic forecast algorithms from multiple angles. This study recommends a deep learning method to predict urban traffic that combines data from twitter messages with traffic and weather data. The predictive model uses a deep Bi-directional long- short-term memory (LSTM) Stacked Auto Encoder (SAE) architecture for multi-step traffic flow prediction that was trained from tweets, traffic, and weather datasets. To support study in this area, it also gathers and arranges frequently available public datasets. © 2023 IEEE.

关键词： Long-short Tem Memory (LSTM Stacked Auto Encoder (SAE etc

来源：评论

学校读者我要写书评

暂无评论

Statistical Data Mining with Slime Mould Optimization for Intelligent Rainfall Classification

引用

computer Systems science & engineering 2023年第10期47卷 919-935页

作者： Ramya Nemani G.Jose Moses Fayadh Alenezi K.Vijaya Kumar Seifedine Kadry Jungeun Kim Keejun Han Department of Mathematics Vignan’s Institute of Information TechnologyVisakhapatnam530049India Department of Computer Science and Engineering University Institute of Engineering and Technology(UIET)Guru Nanak UniversityHyderabadIndia Department of Electrical Engineering College of EngineeringJouf UniversitySaudi Arabia Department of Computer Science and Engineering GITAM School of TechnologyVishakhapatnam CampusGITAM(Deemed to be a University)VishakhapatnamIndia Department of Applied Data Science Noroff University CollegeKristiansandNorway Artificial Intelligence Research Center(AIRC) College of Engineering and Information TechnologyAjman UniversityAjmanUnited Arab Emirates Department of Electrical and Computer Engineering Lebanese American UniversityByblosLebanon Department of Software Kongju National UniversityCheonan31080Korea Division of Computer Engineering Hansung UniversitySeoul02876Korea

Statistics are most crucial than ever due to the accessibility of huge counts of data from several domains such as finance,medicine,science,engineering,and so *** data mining(SDM)is an interdisciplinary domain that examines huge existing databases to discover patterns and connections from the *** varies in classical statistics on the size of datasets and on the detail that the data could not primarily be gathered based on some experimental strategy but conversely for other ***,this paper introduces an effective statistical Data Mining for Intelligent Rainfall Prediction using Slime Mould Optimization with Deep Learning(SDMIRPSMODL)*** the presented SDMIRP-SMODL model,the feature subset selection process is performed by the SMO algorithm,which in turn minimizes the computation *** rainfall *** neural network with long short-term memory(CNN-LSTM)technique is *** last,this study involves the pelican optimization algorithm(POA)as a hyperparameter *** experimental evaluation of the SDMIRP-SMODL approach is tested utilizing a rainfall dataset comprising 23682 samples in the negative class and 1865 samples in the positive *** comparative outcomes reported the supremacy of the SDMIRP-SMODL model compared to existing techniques.

关键词： Statistical data mining predictive models deep learning rainfall prediction parameter tuning

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：