检索结果-内蒙古大学图书馆

10th International Conference on Advanced Computing and Communication Systems, ICACCS 2024

作者： Jenisha, J. Priyadharsini, C. Karunya Institute of Technology and Sciences Computer Science and Engineering Coimbatore641114 India

ISBN: (纸本)9798350384369

Image processing for the visually impaired is crucial. It empowers individual with visual impairments to perform daily tasks independently, contributing to improved quality of life, education, and employment opportunities. This paper tackles the challenge of image captioning with limited data by combining deep learning and supervised techniques like CNN and LSTM. the combination of Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) network involves training a computer to comprehend visual information in an image and generate descriptive captions. The proposed model employs a multi-layered architecture, combining the strengths of CNN s in feature extraction from images with the sequential learning capabilities of LSTMs to generate coherent and contextually relevant captions. The model utilizes the Google Text to Speech (gTTS) library for text-to-speech conversion. The findings suggest that among the tested models, the CNN- LSTM model achieved the highest predicted BLEU score of 95% for an image caption. Evaluation of the proposed model demonstrates its effectiveness in generating captions that align closely with human descriptions. © 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

Customer Departure Prognostication: Modern Data science Methods

Customer Departure Prognostication: Modern Data Science Meth...

引用

2024 International Conference on Signal Processing, Computation, Electronics, Power and Telecommunication, IConSCEPT 2024

作者： Deepika, I. Topno, Risabh Preethi, M. Karthik, N. National Institute of Technology Puducherry Department of Computer Science and Engineering Karaikal India

ISBN: (纸本)9798331540685

Churn prediction involves finding loss of customers over a service for a specific period of time. Prediction of churn rate gives a leeway for many companies. In telecommunication sector churn rate is important because it can be used to predict potentially customers who can retained. In this work we delve into machine learning approaches to predict churn rate. We have experimented with five different algorithms which are Logistic regression, KNN, SVM, Random Forest, XG Boost algorithm and lastly neural network model. Among the five ML approaches XG boost gives the highest accuracy of 82% while KNN give the least accuracy of 76%. To further improvise the performance, we have used a neural network model which gives a training accuracy of 92%. © 2024 IEEE.

关键词： Sales

来源：评论

学校读者我要写书评

暂无评论

Real-Time Application Layer Protocols to support lightweight mechanism in Internet of Things for e-Healthcare systems 5

Real-Time Application Layer Protocols to support lightweight...

引用

5th IEEE International Conference on Recent Trends in computer science and technology, ICRTCST 2024

作者： Kumar, Deobrata Kumar, Dilip National Institute of Technology Jamshedpur Computer Science & Engineering Department Jamshedpur India

ISBN: (纸本)9798350351378

The meteoric advancement of Internet of Things technology has facilitated the integration of various devices and systems into our daily lives. In particular, the field of e-healthcare has witnessed significant developments, leveraging IoT technology in e-healthcare systems enables continuous real-time data analysis, patient monitoring, and timely intervention, thereby increasing patient care and e-healthcare services. However, as IoT devices are resource-constrained in nature demand lightweight and efficient protocols to ensure real-time communication and seamless data exchange. This research paper focuses on exploring real-time application layer protocols to support lightweight mechanisms in IoT for e-healthcare systems. The paper begins by providing an overview of the IoT landscape in the e-healthcare domain, highlighting the key challenges faced when integrating IoT devices into healthcare environments. Next, the paper presents an in-depth analysis of various existing application layer protocols suitable for lightweight IoT deployments and identifies their limitations concerning real-time e-healthcare applications. Drawing from this analysis, one can propose and introduce novel lightweight mechanisms for efficient data aggregation and compression techniques to alleviate the burden on IoT devices optimize data transmission, reduce overhead, and prioritize critical health-related information without compromising the integrity of critical health data. © 2024 IEEE.

关键词： Internet of things

来源：评论

学校读者我要写书评

暂无评论

A Novel Framework for Iris Disease Detection Using MobileNetV2 and CNN with Streamlit Interface 10

A Novel Framework for Iris Disease Detection Using MobileNet...

引用

10th International Conference on Communication and Signal Processing, ICCSP 2024

作者： Abinay Shri, J. Denisha, M. Karunya Institute of Technology and Sciences Division of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798350353068

'Iris Disease Detection' project is a comprehensive approach to iris health assessment that combines deep learning techniques with image processing methodologies. The system consists of a Streamlit-based web application and a Mobile Net V2 and CNN Classifier, which use OpenCV and KMeans clustering for effective image analysis. The dataset, derived from medical imaging platforms, includes hundreds of high-resolution photos of the iris, annotated with detailed diagnostic information about a wide range of iris health problems. The project includes both normal and diseased irises, as well as those with cataracts, glaucoma, and iritis. The Streamlit web application provides a user-friendly interface for uploading iris images, which undergo preprocessing, grayscale conversion, and Gaussian blur before applying the Hough Circles method to detect the iris region. The system generates visualizations, including the original image, identified iris, and segmented representation achieved through KMeans clustering. The machine learning component enhances the accuracy of 95 percentage and reliability of iris detection, offering a holistic solution for iris disease detection. © 2024 IEEE.

关键词： Deep learning

来源：评论

学校读者我要写书评

暂无评论

Lithium battery SOH and RUL prediction based on time-frequency multi-feature extraction and fusion networks 5

Lithium battery SOH and RUL prediction based on time-frequen...

引用

5th International Conference on computer Vision, Image and Deep Learning, CVIDL 2024

作者： Wang, Xiaohua Dai, Ke Hu, Min Ni, Nanbing Hefei University of Technology School of Computer Science and Information Engineering Hefei China

ISBN: (纸本)9798350373820

Accurate prediction of battery state of health (SOH) and remaining useful life (RUL) is crucial for reducing the risk of lithium battery failure and intelligent management of energy storage power plants. Currently, most of the existing research methods only take the features between charge/discharge cycles as model inputs, ignoring the interconnectivity of feature data within the battery cycle. A model based on time-frequency multi-feature extraction and fusion (TFMEFNet) is proposed to improve the accuracy of lithium-ion battery SOH and RUL prediction. The time-frequency multi-feature extraction module is used to extract features of time and frequency domains respectively from the data within the discharge cycle, and then the global, time-domain and frequency-domain features are fused by the feature fusion module. Long-term dependencies in continuous data then captured by a TCN-GRU (Temporal Convolutional Network-Gated Recurrent Unit) module. A Temporal Attention module is used to better represent the decaying trend of the capacity series, weighted by attention, resulting in more accurate predictions. Extensive experiments are conducted on NASA datasets and compared with existing methods. The experimental results show that the proposed model can predict the SOH and RUL of Li-ion batteries more accurately. The root mean square error (RMSE) on the NASA dataset is less than 0.786%. © 2024 IEEE.

关键词： Feature extraction

来源：评论

学校读者我要写书评

暂无评论

Comprehensive Data Analysis of Bacillus Thuringiensis Cry Gene 10

Comprehensive Data Analysis of Bacillus Thuringiensis Cry Ge...

引用

10th International Conference on Advanced Computing and Communication Systems, ICACCS 2024

作者： Evangeline, M. Sharon Thankappan, Sugitha Raimond, Kumudha Karunya Institute of Technology and Sciences Computer Science and Engineering Coimbatore641114 India

ISBN: (纸本)9798350384369

Crop pest damage accrues to fifty per cent loss in yield which leads to severe monetary losses and an overall rise in farming costs. Understanding the complex processes by which the host plants acquire resistance to insect pests is crucial for effective management of pests. Genetically modified plants to express the insecticidal incorporated proteins such as Bt insecticidal protein causes genetic drift and a threat to biodiversity. With an emphasis on genomics, this project gives a detailed examination of cry gene extracted from bacterium Bacillus thuringiensis. The three pivotal aspects incorporated in the investigation are K-Mer analysis, base composition and sequence homology prediction. Methodologically, these studies are performed with the help of the Biopython bioinformatics module, to provide a relevant insight. The project additionally makes use of visualization tools like Matplotlib and Seaborn. The goal of the comprehensive investigation is to understand the properties of cry gene, possibly revealing the novel insights that will drive the advancement in the field of computer science. In conclusion, a thorough examination of the cry gene from Bacillus thuringiensis provides rich sequence patterns, a complex base composition, and evolutionary insights that pave the way for future developments in genomics and pest control. © 2024 IEEE.

关键词： Bacteriology

来源：评论

学校读者我要写书评

暂无评论

A QoS-aware routing approach for Internet of Things-enabled wireless sensor networks in smart cities

引用

Multimedia Tools and Applications 2024年第17期84卷 1-27页

作者： Karunkuzhali, D. Meenakshi, B. Lingam, Keerthi Department of Information Technology Panimalar Engineering College Chennai India Department of Electrical and Electronics Engineering Sri Sairam Engineering College Chennai India Department of Computer Science and Engineering Gitam University Visakhapatnam India

In the Internet of Things (IoT), optimizing machine performance through data analysis and improved connectivity is pivotal. Addressing the growing need for environmentally friendly IoT solutions, we focus on "green IoT." Quality in smart cities heavily relies on efficient data gathering and communication. In this study, we propose a novel software architecture tailored for data collection and communication in IoT-enabled smart applications, emphasizing sustainability from the outset. Specifically, for smart cities utilizing IoT-enabled wireless sensor networks, we introduce a Quality of Service (QoS)-aware routing strategy. In the data collection phase, we employ Chaotic Bird Swarm Optimization (CBSO) for creating IoT sensor clusters, accompanied by Improved Differential Search (IDS) to assess the reliability of individual sensor nodes, ultimately designating the most trusted node as the Cluster Head (CH). In the data transport phase, we implement lightweight signcryption to enhance the security of IoT sensor data. Furthermore, we employ an Optimal Data Routing (ODM) approach to compute the most efficient data transmission paths between source and destination nodes within the IoT network. Finally, the performance of our QoS-aware routing strategy is evaluated through network simulations using NS2 and compared against existing approaches, offering valuable insights into its effectiveness and suitability for smart city IoT applications. The main findings of the study indicate that the proposed QoS-aware routing strategy (QOS-AWARE ROUTING) consistently outperforms existing routing algorithms in terms of energy efficiency, network longevity, and latency, both in scenarios with varying sensor node density and over multiple cycles. Additionally, it demonstrates significant advantages, such as up to 90.8% less energy consumption, 66.8% longer network lifespan, and 80.1% reduced latency when compared to the benchmarked methods. © The Author(s), under exclusive licence to S

关键词： Internet of things

来源：评论

学校读者我要写书评

暂无评论

Neural network-supported study on erosive wear performance analysis of Y2O3/WC-10Co4Cr HVOF coating

引用

Journal of King Saud University - engineering sciences 2024年第8期36卷 662-676页

作者： Singh, Jashanpreet Singh, Simranjit Department of Engineering Punjab State Aeronautical Engineering College Punjab Patiala147001 India School of Computer Science Engineering and Technology Bennett University Uttar Pradesh Greater Noida201310 India

In this work, a study was carried out by modifying the conventional Tungsten Carbide Cobalt Chrome (WC–10Co4Cr) powder with a small addition of yttrium-oxide (Y2O3). Reinforcement was done by adding yttria (Y2O3) ceramics in WC–10Co4Cr powder by using a jar ball mill process. The surface microstructure, chemical composition, and phase compositions of coating powder and coatings were examined by using scanning electron microscopy, energy dispersive spectroscopy, and X-ray diffractometry. Silt erosion was evaluated through a pot tester by preparing equi- and multi-sized slurries at different velocities, impact angles, concentrations, and rates. Results show that the WC–10Co4Cr powder coating reinforced by Y2O3 ceramics possesses low porosity, providing higher erosive performance as compared to conventional WC–10Co4Cr coating. The present study reveals that the deposition of conventional WC–10Co4Cr coating helps improve the wear resistance of AISI 316L stainless steel (UNS S31600) by 9.98% for the variation in rotational speed. However, the erosive wear performance of conventional WC–10Co4Cr coating was improved by 45.9% by blending it with the Y2O3 ceramics. © 2021 The Authors

关键词： Wear of materials

来源：评论

学校读者我要写书评

暂无评论

Anomaly Network Traffic Detection of Wireless Network System 5

Anomaly Network Traffic Detection of Wireless Network System

引用

5th International Conference on Electronics and Sustainable Communication Systems, ICESC 2024

作者： Deepika, D. Pogiri, Deepika Pandravisham, Lokesh Raj Prudvi, Yashwanth Kumar Ramannagari, Sathvik Reddy B v Raju Institute of Technology Computer Science and Engineering Narsapur India

ISBN: (纸本)9798350379945

Network traffic anomaly detection is a current topic in network security. Based on unsupervised learning, this paper constructs a Model for Detecting Irregularities in Network Data to solve the problems of high dimensions of abnormal traffic. Among the prevalent threats to network security, anomalies stand out as a significant menace, capable of causing system mal-functions and impeding proper network functionality. Detecting these anomalies is imperative for ensuring the uninterrupted operation of networks. While DL and ML algorithms have showcased their potential in detecting network anomalies, their efficacy remains relatively uncertain. This research study performs a detailed analysis of the algorithms (Isolation Forest and Local Outlier Factor) using the datasets to assess the ability to identify the network anomalies. This study proposes a thorough analysis of various applications of deep learning and machine learning, with the goal of enhancing network security. © 2024 IEEE.

关键词： Anomalies Comprehensive analysis Efficacy Fortifying network security Isolation Forestand Local Outlier Factor Network traffic anomaly detection System malfunctions Unsupervised learning

来源：评论

学校读者我要写书评

暂无评论

Intelligent Learning Models for Cancer Diagnosis and Treatment: A Comprehensive Analysis 7

Intelligent Learning Models for Cancer Diagnosis and Treatme...

引用

7th International Conference on Contemporary Computing and Informatics, IC3I 2024

作者： Hussain, Shahadat Ahmad, Shahnawaz Wasid, Mohammed Sneh Chawla, Saanvi Sangwan, Aakash Bennett University School of Computer Science Engineering and Technology Greater Noida India

ISBN: (纸本)9798350350067

Cancer poses a significant global health and economic challenge, with $20 million new cases and 9.7 million deaths reported in 2022 itself. Early detection of cancer is crucial for improved survival rates, reduced mortality, making it imperative to develop advanced diagnostic tools. Intelligent Learning Models (ILMs) utilizing Deep Learning (DL) and Machine Learning (ML) by leveraging large datasets and complex pattern recognition are posing as the potential diagnostics solution for cancers. This paper reviews the current state of ILMs, evaluates existing models and case study for diagnosis prognosis and treatment of cancers. © 2024 IEEE.

关键词： Contrastive Learning

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：