检索结果-内蒙古大学图书馆

2nd International Conference on Augmented Intelligence and Sustainable Systems, ICAISS 2023

作者： Aswini, E. Vijayakumaran, C. School of Computing Srm Institute of Science and Technology Department of Computing Technologies Kattankulathur Chennai603203 India

ISBN: (纸本)9798350325799

Due to their great demand for both consumption and non-consumption applications, citrus plants are the most sought-after crop internationally. Therefore, producers take care to eradicate illnesses that affect citrus plants during their growing stage in order to maximize harvest. Even while they usually do this by using their own experience, this strategy is inappropriate when the orchard is huge and the plants show various signs of illness. As a result, deep learning and machine learning algorithms make it possible to develop an automated method for spotting diseases in citrus plants. This article investigated and examined various deep learning-based approaches for detecting citrus plant diseases in an effort to support the researchers in this direction. As a result, this research will aid in identifying current classification patterns for illnesses affecting citrus plants and paving the road for further study in this field. © 2023 IEEE.

关键词： Recurrent neural networks

来源：评论

学校读者我要写书评

暂无评论

Securing Neural Network-Based Personalized Medicine for Advanced Liver Cancer Detection in Healthcare

Securing Neural Network-Based Personalized Medicine for Adva...

引用

2024 IEEE International Conference on Information technology, Electronics and Intelligent Communication Systems, ICITEICS 2024

作者： Kasturi, Salvadi Singh, Jagendra Agarwal, Mohit Kumar, Ashwini Mishra, Komal Sharma, Yogita Cvr College of Engineering Department of Computer Science Engineering Vastunagar India School of Computer Science Engineering & Technology Bennett University Greater Noida India Compucom Institute of Technology & Management Department of Computer Science Engineering Jaipur India Chandigarh University Department of University Institute of Computing Chandigarh India School of Management and Commerce Manav Rachna University Faridabad India

ISBN: (纸本)9798350382693

This research is aimed at discussing the critical meeting point of healthcare and artificial intelligence, including the need to protect the required treatment solution. Investigation into the utility of various deep learning techniques such as VGG 16, VGG 19, Convolutional Neural Networks (CNN), and Recurrent Neural Network (RNN) for the prediction of liver cancer stages and their respective treatments has been completed. The generation of 2340 of medical images from multiple sources, including the internet and records of hospitals, as a dataset was performed in a secure cloud. The security measures have allowed only authorized personnel to access the data and ensured the privacy and secrecy of data. The created dataset was then separated into two parts: training, which consisted of training the model on 70% of images, and testing, which implied that 30% of images were used to test the generated model. For each of the run models, performance evaluation measurements such as accuracy, precision, recall, and F1 score were calculated. The results have shown the highest efficiency of VGG 19 model, which resulted in 98.44% of accuracy. For the generated model, a fictitious ROC curve and specificity rate were also provided to showcase the discrimination abilities of each of the submitted models. The results show that the utility of neural network-based personalized medicine is beneficial for the further progression of the early prediction stage of liver cancer and its treatment. The result showed that the proposed models has successfully contributed to the field of healthcare informatics and further opportunities for artificial intelligence application to increase patient outcomes in the form of personalized medicine that may be performed. © 2024 IEEE.

关键词： Convolutional neural networks

来源：评论

学校读者我要写书评

暂无评论

Enhancing Healthcare Record Privacy Through the Integration of Visual Cryptography and Diverse Image Encryption Techniques 9

Enhancing Healthcare Record Privacy Through the Integration ...

引用

9th International Conference on Communication and Electronics Systems, ICCES 2024

作者： Chinnasamy, P. Deepalakshmi, P. Sandeep, Dayina Ganesh, Addala Siva Krishna, Angalakurthi Jaya Priya, D. Divya School of Computing Kalasalingam Academy of Research and Education Department of Computer Science and Engineering Tamilnadu India Kalasalingam Academy of Research and Education Department of Computer Science and Engineering Krishnankoil Virudhunagar India Mlr Institute of Technology Department of Computer Science and Engineering Hyderabad India

ISBN: (纸本)9798350377972

The methodology of this research focuses on analyzing real-world breaches, such as the Star Health Insurance leak, and demonstrating how multi-layered encryption techniques could have mitigated the impact. By splitting sensitive healthcare information into multiple shares using visual cryptography, unauthorized access is prevented unless all shares are combined, adding an additional layer of security. Furthermore, image-based encryption adds another line of defense, ensuring that even if hackers gain access to data, it remains unusable without the correct decryption methods. Through the implementation of these techniques, we demonstrate a significant enhancement in the security of healthcare records. By combining diverse encryption methods, our approach not only strengthens the protection of personal health information but also prevents unauthorized data dissemination, as seen in the Star Health breach. The results indicate that our model is resilient to various cyber-attacks, reducing the likelihood of large-scale data leaks and improving the integrity of healthcare systems. © 2024 IEEE.

关键词： Electronic health record

来源：评论

学校读者我要写书评

暂无评论

Intelligence COVID-19 Monitoring Framework Based on Deep Learning and Smart Wearable IoT Sensors

引用

computers, Materials & Continua 2023年第10期77卷 583-599页

作者： Fadhil Mukhlif Norafida Ithnin Roobaea Alroobaea Sultan Algarni Wael Y.Alghamdi Ibrahim Hashem Information Assurance and Security Research Group(IASRG) Faculty of ComputingUniversiti Teknologi Malaysia(UTM)Johor BahruJohor81310Malaysia Department of Computer Science College of Computers and Information TechnologyTaif UniversityTaif21944Saudi Arabia Department of Information Systems Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia College of Computing and Informatics Department of Computer ScienceUniversity of SharjahSharjah27272United Arab Emirates

The World Health Organization(WHO)refers to the 2019 new coronavirus epidemic as COVID-19,and it has caused an unprecedented global crisis for several *** every country around the globe is now very concerned about the effects of the COVID-19 outbreaks,which were previously only experienced by Chinese *** of these nations are now under a partial or complete state of lockdown due to the lack of resources needed to combat the COVID-19 epidemic and the concern about overstretched healthcare *** time the pandemic surprises them by providing new values for various parameters,all the connected research groups strive to understand the behavior of the pandemic to determine when it will *** prediction models in this research were created using deep neural networks and Decision Trees(DT).DT employs the support vector machine method,which predicts the transition from an initial dataset to actual figures using a function trained on a *** short-term memory networks(LSTMs)are a special sort of recurrent neural network(RNN)that can pick up on long-term *** an added bonus,it is helpful when the neural network can both recall current events and recall past events,resulting in an accurate prediction for *** provided a solid foundation for intelligent healthcare by devising an intelligence COVID-19 monitoring *** developed a data analysis methodology,including data preparation and dataset *** examine two popular algorithms,LSTM and Decision tree on the official ***,we have analysed the effectiveness of deep learning and machine learning methods to predict the scale of the *** issues and challenges are discussed for future *** is expected that the results these methods provide for the Health Scenario would be reliable and credible.

关键词： Healthcare framework AI COVID-19 machine&deep learning LSTM RNN decision tree

来源：评论

学校读者我要写书评

暂无评论

OSAP‐Loss:Efficient optimization of average precision via involving samples after positive ones towards remote sensing image retrieval

引用

CAAI Transactions on Intelligence technology 2023年第4期8卷 1191-1212页

作者： Xin Yuan Xin Xu Xiao Wang Kai Zhang Liang Liao Zheng Wang Chia‐Wen Lin School of Computer Science and Technology Wuhan University of Science and TechnologyWuhanChina Hubei Province Key Laboratory of Intelligent Information Processing and Real‐time Industrial System Wuhan University of Science and TechnologyWuhanChina Nanyang Technological University SingaporeSingapore School of Computer Science Wuhan UniversityWuhanChina Department of Electrical Engineering Industrial Technology Research InstituteNational Tsing Hua UniversityHsinchuChina

In existing remote sensing image retrieval(RSIR)datasets,the number of images among different classes varies dramatically,which leads to a severe class imbalance *** studies propose to train the model with the ranking‐based metric(e.g.,average precision[AP]),because AP is robust to class ***,current AP‐based methods overlook an important issue:only optimising samples ranking before each positive sample,which is limited by the definition of AP and is prone to local *** achieve global optimisation of AP,a novel method,namely Optimising Samples after positive ones&AP loss(OSAP‐Loss)is proposed in this ***,a novel superior ranking function is designed to make the AP loss differentiable while providing a tighter upper ***,a novel loss called Optimising Samples after Positive ones(OSP)loss is proposed to involve all positive and negative samples ranking after each positive one and to provide a more flexible optimisation strategy for each ***,a graphics processing unit memory‐free mechanism is developed to thoroughly address the non‐decomposability of AP *** experimental results on RSIR as well as conventional image retrieval datasets show the superiority and competitive performance of OSAP‐Loss compared to the state‐of‐the‐art.

关键词： computer vision image retrieval metric learning

来源：评论

学校读者我要写书评

暂无评论

An EnKF-LSTM Assimilation Algorithm for Crop Growth Model

IEEE Transactions on AgriFood Electronics

引用

IEEE Transactions on AgriFood Electronics 2024年第2期2卷 372-380页

作者： Zhou, Siqi Wang, Ling Liu, Jie Tang, Jinshan Harbin Institution of Technology Department of Computer Science and Technology Harbin150001 China Harbin Institute of Technology Department of Computer Science and Technology Harbin150001 China National Key Laboratory of Smart Farming Technology and System Heilongjiang Harbin150000 China George Mason University Department of Health Administration and Policy College of Public Health FairfaxVA22033 United States

Accurate and timely prediction of crop growth is of great significance to ensure crop yields, and researchers have developed several crop models for the prediction of crop growth. However, there are large differences between the simulation results obtained by the crop models and the actual results;thus, in this article, we proposed to combine the simulation results with the collected crop data for data assimilation so that the accuracy of prediction will be improved. In this article, an EnKF-LSTM data assimilation method for various crops is proposed by combining an ensemble Kalman filter and long short-term memory (LSTM) neural network, which effectively avoids the overfitting problem of the existing data assimilation methods and eliminates the uncertainty of the measured data. The verification of the proposed EnKF-LSTM method and the comparison of the proposed method with other data assimilation methods were performed using datasets collected by sensor equipment deployed on a farm. © 2024 IEEE.

关键词： Crops

来源：评论

学校读者我要写书评

暂无评论

Explorative Deployment of Fine-Tuned Large Language Model for On-Site computerized Numeric Control Machine Operator Assistance

Explorative Deployment of Fine-Tuned Large Language Model fo...

引用

2024 IEEE Silchar Subsection Annual Conference, SILCON 2024

作者： Kanimozhi, S. Sriker, Y.S. Vasan, Vinod School of Computer Science and Engineering Vellore Institute of Technology Chennai India Vellore Institute of Technology School of Mechanical Department Chennai India

ISBN: (纸本)9798331540821

In a dynamic workshop environment focused on optimal machine utilization and minimal downtime, it is imperative that a greater emphasis is laid on the tools required to localize faults in essential equipment on the shop floor. One of the major detractors of productivity is the requirement of repeatedly cross-referencing equipment user manuals, consulting consumer portals, and acquiring trained personnel for assistance. This presents an opportunity to blend the intelligence of AI with an emphasis on simplicity of use in a machine shop. CNCGPT is a fine-tuned LLM (Large Language Model) that aims to address machine specific queries that conventional LLM models currently deployed en masse may not be able to ascertain. This model has been tailored to serve as a companion for operators on the shop floor, providing consistent, high-quality responses without tiring. This study aims to present an overview of the model's architecture and an account of a real-time operational assessment conducted during the deployment of the model for assistance with the operation of a specific CNC machine. © 2024 IEEE.

关键词： computerized Numeric Control (CNC) Generative Pre-trained Transformer (GPT) Large Language Model (LLM) Model Fine Tuning Operational Companion User Assistance

来源：评论

学校读者我要写书评

暂无评论

Underwater Image Enhancement and Restoration Using Cycle GAN 1

引用

6th International Conference on Innovative computing and Communication, ICICC 2023

作者： Spandana, Chereddy Srisurya, Ippatapu Venkata Priyadharshini, A.R. Krithika, S. Nandhini, S. Aasha Kumar, R. Prasanna Mohan, G. Bharathi Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Chennai India Department of Networking and Communications School of Computing SRM Institute of Science and Technology Kattangulathur India

ISBN: (数字)9789819930104

ISBN: (纸本)9789819930098

Underwater image augmentation is a technique for recovering low-resolution underwater photographs to produce equivalently high-resolution images. Deep learning-based techniques for enhancing photos commonly use paired data to train the model. Another crucial issue is how to effectively keep the fine details in the improved image. In order to address these problems, providing a revolutionary unpaired underwater picture-enhancing technique that fixes the underwater photos using a cycle generative adversarial network. At last test results on two datasets of unpaired underwater image datasets showed the recommended model’s utility by surpassing cutting-edge image enhancement methods. The CycleGAN generator includes a content loss regularizer that retains the important information of one low-resolution image in the corresponding clear image that is generated. The results are given based on mean square error and mean absolute error after the findings have been optimized using the ADAM optimizer. This architecture has a 4 × 4 kernel and a 2 × 2 stride, using Adam as the optimizer and a learning rate of 0.002. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Image enhancement

来源：评论

学校读者我要写书评

暂无评论

NfvInsight:A Framework for Automatically Deploying and Benchmarking VNF Chains

引用

Journal of computer science & technology 2022年第3期37卷 680-698页

作者： Tian-Ni Xu Hai-Feng Sun Di Zhang Xiao-Ming Zhou Xiu-Feng Sui Sa Wang Qun Huang Yun-Gang Bao State Key Laboratory of Computer Architecture Institute of Computing TechnologyChinese Academy of SciencesBeijing 100190China University of Chinese Academy of Sciences Beijing 100049China School of Information and Electronics Beijing Institute of TechnologyBeijing 100081China Peng Cheng Laboratory Shenzhen 518055China Department of Computer Science and Technology Peking UniversityBeijing 100871China

With the advent of virtualization techniques and software-defined networking(SDN),network function virtualization(NFV)shifts network functions(NFs)from hardware implementations to software appliances,between which exists a performance *** to narrow the gap is an essential issue of current NFV ***,the cumbersomeness of deployment,the water pipe effect of virtual network function(VNF)chains,and the complexity of the system software stack together make it tough to figure out the cause of low performance in the NFV *** pinpoint the NFV system performance,we propose NfvInsight,a framework for automatic deployment and benchmarking VNF *** framework tackles the challenges in NFV performance *** framework components include chain graph generation,automatic deployment,and fine granularity *** design and implementation of each component have their *** the best of our knowledge,we make the first attempt to collect rules forming a knowledge base for generating reasonable chain *** deploys the generated chain graphs automatically,which frees the network operators from executing at least 391 lines of bash commands for a single *** diagnose the performance bottleneck,NfvInsight collects metrics from multiple layers of the software ***,we collect the network stack latency distribution ingeniously,introducing only less than 2.2%*** showcase the convenience and usability of NfvInsight in finding bottlenecks for both VNF chains and the underlying *** our framework,we find several design flaws of the network stack,which are unsuitable for packet forwarding inside one single server under the NFV *** optimization for these flaws gains at most 3x performance improvement.

关键词： network function virtualization(NFV) service chain performance bottleneck network stack latency

来源：评论

学校读者我要写书评

暂无评论

Live Audio Recognition and Text Conversion System 2

Live Audio Recognition and Text Conversion System

引用

2nd International Conference on Computational Intelligence, Communication technology and Networking, CICTN 2025

作者： Sharma, Pratyaksha Gupta, Sumita Bhavya, Boddu Asmitha Sharma, Khushi Dhir, Saru Gambhir, Sapna Department of Computer Science & Engineering Amity School of Engineering & Technology Amity University UP India Department of Information Technology Amity School of Engineering & Technology Amity University UP India Dept. of Computer Science College of Engineering and Computing George Mason University United States

ISBN: (纸本)9798331530389

With the advancement of technologies, different methods are currently being used for converting spoken language into text. These systems offer a hands-free alternative to traditional input methods, especially for individuals with physical disabilities or those requiring efficient documentation of spoken content. The goal of this study is to review recent advancements in this field and highlight gaps in current research, with a focus on accuracy, language adaptability, ease of use, and the integration of various technologies. We also suggest future directions for enhancing and developing this technology further. © 2025 IEEE.

关键词： Hidden Markov models

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：