检索结果-内蒙古大学图书馆

4th IEEE International Conference on Data engineering and Communication Systems, ICDECS 2024

作者： Prema Kumar, M. Srinivas Goud, D. Sankar, S. Sharath Kumar, S. Department of Electronics and Communication Engineering Andhra Pradesh Bhimavaram India Vignana Bharathi Institute of Technology Department of Computer Science and Engineering Andhra Pradesh India Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Department of Computer Science and Engineering Chennai India Rmd Engineering College Kavaraipettai Department of Computer Science and Engineering Tiruvallur India

ISBN: (纸本)9798350393354

Macular edema, diabetic retinopathy, and other apparent microvascular abnormalities in the human retina are common consequences of diabetes. The primary cause of blindness in the past ten years, diabetic retinopathy (DR), is the subject of this study. Preserving blood glucose control and preventing certain diabetic reactions require immediate or early therapy. Due of its intricacy and depth, DR is difficult to diagnose by laborious manual diagnostic procedures. This paper uses a novel methodology. Instead of manually creating features, we used the Long Short-Term Memory (LSTM) model to extract features for the Region-based Convolutional Neural Network (R-CNN) system. It is an R-CNN-LSTM model with two parts. The proposed R-CNN-LSTM system adds certain ground truth labels to training samples, which enables it to first identify the most informative patches and images before learning basic to complicated retinal properties. Using two independent public datasets, the suggested method investigates the retina vasculature and hard-exudate symptoms of diabetic retinopathy. The recommended illumination normalisation methodology improved the deep learning model and excelled other cutting-edge techniques in terms of accuracy, recall, and precision, according to extensive experimental results. © 2024 IEEE.

关键词： Long short-term memory

来源：评论

学校读者我要写书评

暂无评论

Silver nano-particles' size prediction using deep learning model with green method

引用

Materials Today: Proceedings 2022年 69卷 1193-1199页

作者： Mathumathi, M. Vetriselvi, T. Matheswaran, P. Nandhini, R. Selvi, A. Department of Computer Science and Engineering K.Ramakrishnan College of Technology Trichy India Department of Computer Science and Engineering K.Ramakrishnan College of Engineering Trichy India Department of Computer Science and Engineering M.Kumarasamy College of Engineering Karur India

When it comes to casting technologies and other production processes such as welding, DL models minimise the need for costly experimental inquiry. One of the most important criteria in nanotechnology is the measurement of particle size. They've received a lot of interest for their chemical, physical, and clinical uses because of their outstanding qualities. By adopting a green chemical reduction process, the nano-silver crystals are generated in the biopolymer interface without any agglomeration. Nano-metal production relies on precise control of particle size, and this approach provides that. For the prediction size of Ag-NPs via the green approach, a hybrid ideal of Long-Short Term Memory (LSTM) and Convolutional Neural Network (CNN) and model is presented in this research. Temperature, starch stabiliser, and AgNO3 concentration are among the experimental variables fed into the CNN-LSTM model. The size of a nano particle is the output of the CNN-LSTM model. Making three subsets of the experimental data for training, validation, and testing was one of the initial steps in creating an adapting model. The silver nanoparticles were identified using X-ray diffraction and transmission electron microscopy. Ag-NPs' crystallographic airplanes were found to be face-centered cubic, according to XRD analysis (fcc).When comparing with other existing classifiers, DNN achieved high performance on precision, i.e. 89.87%, and this is due to the presence of simple structure. The CNN and auto encoder achieved nearly 82% and 80% of accuracy, where LSTM achieved 77.25% of accuracy and proposed CNN-LSTM achieved 83.50% of recall. © 2022

关键词： Long short-term memory

来源：评论

学校读者我要写书评

暂无评论

Stratification of Depressed and Non-Depressed Texts from Social Media using LSTM and its Variants 2

Stratification of Depressed and Non-Depressed Texts from Soc...

引用

2nd International Conference on Machine Learning and Data engineering, ICMLDE 2023

作者： Kumar, Keerthan G.T. Anoop, R. Koolagudi, Shashidhar G. Rao, Trupthi Kodipalli, Ashwini Department of Information Science and Engineering Siddaganga Institute of Technology Tumkur India Department of Artificial Intelligence and Data Science Global academy of Technology Bengaluru India Department of Computer Science and Engineering National Institute of Technology Karnataka Surathkal India

This work examines the performance of various LSTM (long short-term memory) variants on social media text data. This study evaluates the performance of LSTM models with different architectures, namely, classic LSTM, Bidirectional LSTM, Stacked LSTM, gated recurrent unit (GRU), and bidirectional GRU, on a social network dataset comprising texts extracted from multiple social media platforms. We aim to identify the most effective LSTM variant of the five considered LSTM models for text analysis through a comparative study of the models' precision, recall, F1-score, and accuracy. The research findings show that the Classic LSTM and the GRU model perform better than the other models in accuracy. In contrast, the bidirectional models (Bidirectional LSTM and Bidirectional GRU) provide better precision scores than their respective primitive models. This research has significant implications for developing more efficient models for natural language processing applications. It offers beneficial insights into the implications involving the scrutiny of depression on social media platforms through text data analysis. © 2024 Elsevier B.V.. All rights reserved.

关键词： Long short-term memory

来源：评论

学校读者我要写书评

暂无评论

Dueling Double DQN with Attention for Optimized Offloading in Wireless-Powered Edge-Enabled Mobile Computing Networks

Dueling Double DQN with Attention for Optimized Offloading i...

引用

2024 IEEE International Conference on Big Data, BigData 2024

作者： Tripathi, Shivani Kumar, Praveen Chaitanya, Mailram Sai Teja, Nagireddy Sai Tarun Misra, Rajiv Singh, T.N. Indian Institute of Technology Patna Department of Computer Science and Engineering Patna India B.P Mandal College of Engineering Madhepura Department of Computer Science and Engineering Bihar Madhepura India Indian Institute of Technology Patna Department of Civil and Environmental Engineering Patna India

ISBN: (纸本)9798350362480

This research work introduces a new strategy to enhance computation offloading in wireless-powered Edge-Enabled Mobile Computing (EEMC) networks by utilizing Dueling Double Deep Q-Networks with Attention (Dueling DDQN-A). EEMC facilitates the offloading of computational tasks from mobile devices to local edge servers, leading to decreased latency and improved energy efficiency. However, the dynamic and stochastic nature of wireless environments presents significant challenges for real-time task offloading decisions. To address this, we enhance the Dueling DDQN framework by integrating an attention mechanism to prioritize the most relevant features in the state space. The proposed Dueling DDQN-A model enhances decision-making by separating state value estimation from action advantage estimation. Additionally, the model uses attention to prioritize key state features, leading to improved adaptability in dynamic wireless channel and network environments. We frame the offloading decision problem as a Markov Decision Process (MDP) and apply deep reinforcement learning to optimize both the computation rate and energy efficiency. We compare and evaluate the model through comprehensive simulations using the latest techniques like Simple DQN, Double DQN, and Dueling DDQN. Our results demonstrate that Dueling DDQN-A achieves a 59.12% improvement in average computation rate and a 16.19% reduction in energy consumption over the baseline models. Additionally, it significantly outperforms the baselines in terms of training loss and convergence speed. These findings suggest that Dueling DDQN-A offers a robust and highly efficient solution for real-time computation offloading in wireless-powered EEMC networks, making it a strong candidate for deployment in real-world scenarios. © 2024 IEEE.

关键词： Stochastic systems

来源：评论

学校读者我要写书评

暂无评论

Blockchain based EHR system architecture and the need of blockchain inhealthcare

引用

Materials Today: Proceedings 2023年 80卷 2064-2070页

作者： Singh Chouhan, Arun Sanaullah Qaseem, Mohammad Mohammed Abdul Basheer, Qazi Asma Mehdia, Ms. Department of Computer Science School of Engineering Malla Reddy University India Nawab Shah Alam Khan College of Engineering and Technology India CSE Department Nawab Shah Alam Khan College of Engineering and Technology India

Purchases are concurrently safe and secure as well as credible because of making use of cryptographic guidelines. Recently, blockchain technology has become extremely cool and trendy and infiltrated various domain names, usually as a result of the appeal of cryptocurrencies. One field where blockchain technology possesses significant ability is medical care, because of the requirement for an extra patient-centric method to medical care systems and to link inconsonant devices and also boost the reliability of digital medical care files (EHRs). In this organized review, an analysis of cutting edge blockchain research study in the field of health care is carried out. It's own' prospective in the multi-stakeholder operated market like health and wellness has been responsible for several expenditures, researches, and executions. Electronic Health Records (EHR) devices typically made use of for the swap of wellness details amongst healthcare stakeholders have been criticised for centralising energy, failures and attack-points along with exchange records protectors. EHRs have battled when faced with multi-stakeholder as well as unit criteria while adhering to safety and security, privacy, reliable as well as other regulative constraints. Blockchain is promising among others to attend to the many EHR challenges, largely trustless and also protected swap of health-relevant information among stakeholders. This paper provides the demand for blockchain in medical care and also blockchain-based EHR device architecture. © 2021

关键词： Blockchain

来源：评论

学校读者我要写书评

暂无评论

Prediction of quality in production using optimized Hyper-parameter tuning based deep learning model

引用

Materials Today: Proceedings 2022年 69卷 703-709页

作者： Rajendra Kannammal, G. Sivamalar, P. Santhi, P. Vetriselvi, T. Kalpana, V. Nithya, T.M. Department of Computer Science and Engineering K Ramakrishnan College of Technology Tamilnadu Trichy India Department of Computer Science and Engineering M Kumarasamy College of Engineering Tamilnadu Karur India

Large volumes of manufacturing data may now be collected because to the growing popularity of smart Industry 4.0. Product quality may be predicted from manufacturing data acquired during production using machine learning approaches such as classification. A supply chain can benefit from eliminating uncertainty by precise forecasting at any point in the process. As a result, knowing the quality of a product batch early on can save money on recalls, packaging, and shipping. Classification methods have been extensively studied for forecasting the quality of certain manufacturing processes, but the overall obedience of production batches has not been carefully studied. Classification methods based on deep learning (Convolutional Neural Network) and optimal hyper-parameter tuning are the focus of this article, which aims to evaluate the suggested appliance production process. Existing approaches for classifying unit batches are compared to the proposed classification model in terms of several quality parameters for compliance. As a result, a model for predicting compliance quality may be built using the new method. Features and dataset knowledge are also critical in training classification models, according to this study. © 2022

关键词： Supply chains

来源：评论

学校读者我要写书评

暂无评论

Deep Learning Feature Extraction Architectures for Real-Time Face Detection

引用

SN computer science 2023年第5期4卷 645页

作者： Ravi Teja, B. Mythili, D. Duvva, Laxmiprasanna Bethu, Srikanth Garapati, Yugandhar Department of Computer Science and Engineering GITAM School of Technology GITAM Deemed-to-be University Telangana Hyderabad 502329 India Department of Computer Science and Engineering Vasavi College of Engineering Telangana Hyderabad 500089 India Department of Computer Science and Engineering CVR College of Engineering Telangana Hyderabad 501510 India

A Video Surveillance system can be used for a variety of purposes, including protection, secure data, crowd flux analytics and congestion analysis, individual recognition, anomalous activity detection, and so on. Video Surveillance systems play the key role in the human detection using the face features extraction. It helps in many applications like terrorists attack, thief identifying by detecting the face of the person but mostly failed in real-time aspect. In this context, we propose a method that significantly aids in the extraction and learning of features. To reduce the face recognition error, we use a bounding box regression model. To train the features, we utilized a CNN-based feature learning model with log-likelihood ratio calculations between inter- and intra-features. To increase the quality of video frames, we used a histogram redistribution image enhancement technique. Finally, a Background Subtracted Faster RCNN for video-based face recognition (BSF-RCNN-VFR) is used to discriminate the groups of detected faces. A comprehensive experiment is carried out on their datasets to demonstrate that the proposed solution performs better, and we compared the existing models with proposed models. We achieved 94.2 accuracy percentage. In this paper, the CNN models like AlexNet, ResNet and datasets like UADFV, Celeb-DF, FF++, DFDC, etc., accuracies are compared. © 2023, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： CNN Deep Learning Feature extraction

来源：评论

学校读者我要写书评

暂无评论

Applying sentiment analysis in social web for smart decision support marketing

引用

Journal of Ambient Intelligence and Humanized Computing 2024年第3期15卷 1927-1936页

作者： Wu, Shih-Jung Chiang, Rui-Dong Chang, Han-Chi Department of Innovative Information and Technology Tamkang University New Taipei Taiwan Department of Computer Science and Information Engineering Tamkang University New Taipei Taiwan

Because of the rapid development of communication and service in Taiwan, competition among telecommunication companies has become ever fiercer. Differences in marketing strategy usually become the key factor in keeping existing customers while attracting new ones. Although electronic word-of-mouth (e-WOM) is one of the most important pieces of information to a consumer making a purchase decision, very few articles on opinion mining have discussed and compared the relationship between multifaceted word-of-mouth (WOM) and marketing strategy. In this paper, we use our Chinese opinion-mining system (Wu et al. in J Supercomput 73:2987–3001, 2017) not only to retrieve articles related to 4G and conduct reputation analysis but also to discuss the relation between WOM and marketing strategy. The results show that (1) e-WOM can immediately and directly reflect the results of marketing strategy, and (2) although users are primarily concerned with aspects of price, online speed, and signal quality, for most Taiwanese customers, price is the key in choosing a telecommunication company. Moreover, although this paper used 4G-related articles from June 2014 to June 2015 for analysis, the results are consistent with the Taiwanese telecommunication companies’ current marketing strategy of attracting customers through low pricing. © Springer-Verlag GmbH Germany, part of Springer Nature 2018.

关键词： Sentiment analysis

来源：评论

学校读者我要写书评

暂无评论

Comparitive Analysis on Neural Networks based on their performance in Pneumonia Detection 13

Comparitive Analysis on Neural Networks based on their perfo...

引用

13th International Conference on Cloud Computing, Data science and engineering, Confluence 2023

作者： Puri, Naman Munjal, Geetika Amity School of Engineering and Technology Department of Computer Science Engineering Noida India

ISBN: (纸本)9781665462631

Pneumonia has been a concerning issue worldwide. This infectious disease has a higher mortality rate than Covid-19. More than two million individuals lost their lives in 2019 out of which almost 600,000 were infants less than 5 years of age. Globally, identification of the disease is done manually by radiologists, but this method is highly unreliable as its accuracy is not sufficiently good. With the evolution of computational resources, especially the computing power of GPUs, it has become possible to train very deep CNNs. This study involves a comparative analysis of neural networks for pneumonia recognition. The goal is to do binary image classification for pneumonia recognition on each of the three models, namely, a Sequential model using TensorFlow (built from scratch), ResNet50 and InceptionV3 and comparing their efficiency, to discover which model suits best for smaller datasets and which suits best for larger datasets. Dataset consists of 5856 anterior and posterior Chest X-Ray images labeled as either Normal or Pneumonic. © 2023 IEEE.

关键词： Deep learning

来源：评论

学校读者我要写书评

暂无评论

AI-Driven Learning Management Systems:Modern Developments, Challenges and Future Trends during theAge of ChatGPT

引用

computers, Materials & Continua 2024年第8期80卷 3289-3314页

作者： Sameer Qazi Muhammad Bilal Kadri Muhammad Naveed Bilal AKhawaja Sohaib Zia Khan Muhammad Mansoor Alam Mazliham Mohd Su’ud Department of Computer Science Faculty of EngineeringScience and TechnologyIQRA UniversityKarachi75500Pakistan College of Computer and Information Sciences Prince Sultan UniversityRiyadh11586Saudi Arabia Faculty of Engineering Department of Electrical EngineeringIslamic University of MadinahP.O.BOX 170Madinah41411Saudi Arabia Faculty of Engineering Department of Mechanical EngineeringIslamic University of MadinahP.O.BOX 170Madinah41411Saudi Arabia Faculty of Computing Riphah International UniversityIslamabad46000Pakistan Faculty of Computing and Informatics(FCI)Multimedia University Cyberjaya63100Malaysia Faculty of Engineering and Information Technology School of Computer ScienceUniversity of Technology SydneySydneyNSW 2007Australia

COVID-19 pandemic restrictions limited all social activities to curtail the spread of the *** foremost and most prime sector among those affected were schools,colleges,and *** education system of entire nations had shifted to online education during this *** shortcomings of Learning Management Systems(LMSs)were detected to support education in an online mode that spawned the research in Artificial Intelligence(AI)based tools that are being developed by the research community to improve the effectiveness of *** paper presents a detailed survey of the different enhancements to LMSs,which are led by key advances in the area of AI to enhance the real-time and non-real-time user *** AI-based enhancements proposed to the LMSs start from the Application layer and Presentation layer in the form of flipped classroom models for the efficient learning environment and appropriately designed UI/UX for efficient utilization of LMS utilities and resources,including AI-based *** layer enhancements are also required,such as AI-based online proctoring and user authentication using *** extend to the Transport layer to support real-time and rate adaptive encrypted video transmission for user security/privacy and satisfactory working of *** also needs the support of the Networking layer for IP-based geolocation features,the Virtual Private Network(VPN)feature,and the support of Software-Defined Networks(SDN)for optimum Quality of Service(QoS).Finally,in addition to these,non-real-time user experience is enhanced by other AI-based enhancements such as Plagiarism detection algorithms and Data Analytics.

关键词： Learning management systems chatbots ChatGPT online education Internet of Things(IoT) artificial intelligence(AI) convolutional neural networks natural language processing

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：