检索结果-内蒙古大学图书馆

3rd IEEE Delhi Section Flagship Conference, DELCON 2024

作者： Raj, P. Michael Preetam Vittapu, Sravan K. Chinnasamy, P. Gupta, Manu Samatha, K. Sasivijaya, K. Mlr Institute of Technology Department of Computer Science and Engineering Hyderabad India Nalla Narasimha Reddy Education Society's Group of Institutions Department of Electronics and Communication Engineering Hyderabad India Kalasalingam Academy of Research and Education Dept. of Computer Science and Engineering Tamil Nadu India Sreenidhi Institute of Science and Technology Department of Electronics and Computer Engineering Hyderabad India

ISBN: (纸本)9798331518592

In earlier developments, CMOS technology played a great role in realizing majority identifiers. There are huge drawbacks that affect the circuit's performance. To overcome the drawbacks of CMOS-based majority Identifiers, novel memristor-based majority and minority Identifiers have been proposed in this work. The features offered by memristor have motivated us to use the component and achieve greatly improved results compared to CMOS-based majority identifier circuits. After observing the behavior of the basic memristor-based majority identifier circuit, we have decided to design and implement a memristor-based majority and minority identifier circuit which can be used in many automation systems to increase quality and productivity. © 2024 IEEE.

关键词： CMOS integrated circuits

来源：评论

学校读者我要写书评

暂无评论

A Study on Design, Development and Deployment of Web Crawler Algorithms and Their Metrics

A Study on Design, Development and Deployment of Web Crawler...

引用

2024 International Conference on Advances in Data engineering and Intelligent Computing Systems, ADICS 2024

作者： Arthy, J. Raja, K. Srm Institute Of Science And Technology Department Of Computer Science And Engineering Chennai Tamilnadu Ramapuram600089 India

ISBN: (纸本)9798350364828

Globally, the search engine is extremely important in reducing the difficulty of information exploration. An internet spider, bot, or program known as a web crawler is used by search engines to maintain an index and retrieve online sites in response to user queries. Web pages are really crawled in order to be explored, starting from the seed URL. In this study, the standard protocols of the literature survey were followed to describe the crawler mechanism also analyzed and compared several research publications to explain the crawler types, individual architectural types, policies, methodologies, metrics, techniques, and equipments for every category was covered. Additionally, the maximum crawler algorithms and the open-source crawler tools were listed and described. This paper gives insight into choosing the optimal crawler algorithm for search engine optimization and for various applications. © 2024 IEEE.

关键词： Web crawler

来源：评论

学校读者我要写书评

暂无评论

Exploiting machine learning and deep learning models for misbehavior detection in VANET

引用

International Journal of computers and Applications 2022年第11期44卷 1024-1038页

作者： Sultana, Rukhsar Grover, Jyoti Meghwal, Jitesh Tripathi, Meenakshi Department of Computer Science and Engineering Malaviya National Institute of Technology Jaipur Jaipur India

There is an unprecedented proliferation of vehicular traffic data as the number of interconnected vehicles has increased in Vehicular Ad Hoc Networks (VANET) scenario to realize the goals of Intelligent Transportation Systems (ITS). As a result, the quantity and variety of attacks using transmitted data have risen. However, these broadcasted messages can be used to launch many security attacks as these messages are not encrypted. With the increase in the number of vehicles, the risk of misbehavior also increases as more malicious vehicles broadcast inaccurate and faulty messages. Various trust-based and deterministic misbehavior detection techniques have been proposed to identify such misbehavior. Existing solutions only address specific types of network intrusions. However, because VANET situations may involve a wide spectrum of attacks, a more robust misbehavior detection mechanism is required. This article compares the performance of several machine learning and deep learning architectures using key evaluation metrics. Furthermore, we have demonstrated a comparison of performance metrics with existing learning model-based techniques in different network scenarios. © 2022 Informa UK Limited, trading as Taylor & Francis Group.

关键词： Vehicular ad hoc networks

来源：评论

学校读者我要写书评

暂无评论

Face Recognition Application in Flutter 2

Face Recognition Application in Flutter

引用

2nd IEEE International Conference on Futuristic Technologies, INCOFT 2023

作者： Kulkarni, Vedant Kokaje, Suyog Yenkikar, Anuradha Patil, Dhanashree Shinde, Prajkta Pune Institute of Computer Technology Department of Information Technology Pune India Vishwakarma Institute of Information Technology Department of Artificial Intelligence Pune India Pimpri Chinchwad College of Engineering Department of Computer Engineering Pune India

ISBN: (纸本)9798350308846

This research paper explores the integration of Google's FaceNet model with the Flutter framework to create a highly accurate and efficient facial recognition system. The paper discusses the architecture of FaceNet, emphasizing its ability to extract facial features and perform accurate facial matching. The challenges and considerations involved in integrating a deep learning model into Flutter are addressed, focusing on optimization techniques and resource management. The proposed integration holds promise for secure authentication systems, personalized user experiences, and augmented reality applications. Performance evaluations and experiments validate the effectiveness of the integrated model, providing valuable insights for developers and researchers interested in leveraging facial recognition technology within the Flutter ecosystem. The integration of FaceNet with Flutter makes room for new possibilities in advanced mobile applications, bridging the gap between deep learning and cross-platform development. © 2023 IEEE.

关键词： Deep Learning Face Recognition FaceNet Flutter

来源：评论

学校读者我要写书评

暂无评论

Gastrointestinal Bleeding Classification in Capsule Endoscopy Images using Deep Learning 4

Gastrointestinal Bleeding Classification in Capsule Endoscop...

引用

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

作者： Asha Rani, K.P. Gowrishankar, S. Amrutesh, Aniverthy Dr. Ambedkar Institute of Technology Department of Computer Science and Engineering Karnataka Bengaluru560056 India

ISBN: (纸本)9798350393354

Gastrointestinal bleeding represents a critical health issue, underscoring the pressing need for precise and timely identification of bleeding or lesions within wireless capsule endoscopy (WCE) images to facilitate effective clinical intervention. This paper addresses this imperative by delving into an extensive exploration of various Convolutional Neural Network (CNN) architectures tailored for the classification of gastrointestinal bleeding in WCE *** comprehensively assess the efficacy of different CNN architectures in handling the nuances of WCE images, a diverse array of models are implemented. These include well-established architectures such as Vanilla CNN, Alexnet, Bottleneck, DenseNet-121, GoogLeNet, ResNet-50, VGG16, VGG19, Attention-based CNN, Capsule Networks (CapsNets). Each architecture undergoes meticulous scrutiny to discern its specific strengths and weaknesses in accurately classifying instances of gastrointestinal *** paper includes valuable insights and a benchmark for future advancements in the pursuit of accurate and efficient diagnosis through WCE images. Significantly, the outcomes of these assessments are provided, providing an in-depth analysis of how well the applied CNN architectures classified gastrointestinal bleeding. This comprehensive analysis contributes significantly to the understanding of the strengths and capabilities of each model, providing a foundation for further research and optimization in the field. © 2024 IEEE.

关键词： Image classification

来源：评论

学校读者我要写书评

暂无评论

Comparative Analysis of YOLO Models for Plant Disease Instance Segmentation

Comparative Analysis of YOLO Models for Plant Disease Instan...

引用

2024 IEEE International Conference on computer Vision and Machine Intelligence, CVMI 2024

作者： Singh, Agamjot Yadav, Aryan Verma, Anshul Rana, Prashant Singh Thapar Institute of Engineering and Technology Department of Computer Science and Engineering Patiala India Institute of Science Banaras Hindu University Department of Science Varanasi India

ISBN: (纸本)9798350376876

Accurate detection and segmentation of plant diseases are essential for sustaining agricultural productivity and global food security. This report conducts a comparative analysis of three state-of-the-art YOLO models-YOLOv5, YOLOv7, and YOLOv8-emphasizing their efficiency in instance segmentation of plant diseases. The research employs a comprehensive dataset featuring various crops like rice, sugarcane, wheat, bell pepper, potato, and tomato, each suffering from different diseases. The methodology includes fine-tuning YOLO models with pretrained weights and optimizing them with the curated *** assessment of performance is based on crucial metrics such as recall, mean average precision (MAP) and precision. YOLOv8 exhibits superior performance, with over 9 0 % average precision across all disease categories, significantly outperforming YOLOv5 and YOLOv7. This report offers detailed insights into the architectural features, training procedures, and evaluation metrics of each model. It also addresses the implications of the findings for practical agricultural applications, highlighting the role of advanced deep learning techniques in improving crop protection and management strategies. Despite the positive results, the report acknowledges limitations like dataset dependency and challenges in real-world deployment. © 2024 IEEE.

关键词： Agriculture Comparative study Deep learning Plant disease instance segmentation YOLOv5 YOLOv7 YOLOv8

来源：评论

学校读者我要写书评

暂无评论

Bridging Language Barriers: An Open Source Typing Tool with a Vision for Indian Language Integration

Bridging Language Barriers: An Open Source Typing Tool with ...

引用

2024 IEEE International Conference on Intelligent Computing and Emerging Communication Technologies, ICEC 2024

作者： Swain, Satyaprakash Kumar Jayasingh, Suvendra Mishra, Raj Kumar Kabat, Manas Ranjan Swain, Chinmaya Kumar Institute of Management of Information Technology Cuttack Bput Department of Computer Science and Engineering Odisha Cuttack India

ISBN: (纸本)9798331508432

In an increasingly interconnected world, language diversity should be celebrated rather than hindered. This paper delves into the significance of open-source technology as a means of breaking down language barriers and promoting linguistic inclusivity. Focusing on the development of an open-source language typing tool, this research addresses a critical need in digital communication. While several commercial software solutions exist for language input, their proprietary nature limits accessibility and customization. In contrast, the proposed tool, crafted in Python, embraces the open-source ethos, fostering collaboration and allowing for tailored solutions to accommodate the unique linguistic nuances of Indian Languages. The core aim of this study is twofold. First, it seeks to establish a robust and user-friendly open-source input tool for Odia and Hindi with both Rule Based and free typing. Second, it lays the foundation for broader Indian language integration, recognizing the rich tapestry of languages spoken throughout the country. © 2024 IEEE.

关键词： Python

来源：评论

学校读者我要写书评

暂无评论

Real-time Obesity and Heart Disease Monitoring with IoT and Deep Learning

Real-time Obesity and Heart Disease Monitoring with IoT and ...

引用

2024 International Conference on Smart Devices, ICSD 2024

作者： Singh, Jagendra Govindaraju, P. Kumar, Vinish Yamsani, Nagendar Umamaheswari, J. Munagala, Madhu Bennett University School of Computer Science Engineering & Technology Greater Noida India K S R Institute for Engineering and Technology Department of Electronics and Communication Engineering Tiruchengode India Raj Kumar Goel Institute of Technology Department of Computer Science Engineering Ghaziabad India SR University School of Computer Science and Artificial Intelligence Warangal India Brainware University Department of Computer Science Kolkata India Koneru Lakshmaiah Education Foundation Department of of English Vaddeswaram India

ISBN: (纸本)9798350394412

The accelerating spread of junk food consumption is progressively putting the younger generations at risk of obesity. The concept of health prioritization is deeply rooted in the development of nurturing and general welfare in society. The job has further become more complex by the glaring statistics of people across the globe who are fighting obesity. The World Health Organization (WHO) puts a staggering estimate that approximately 1 billion people across the globe suffer from obesity-related issues, a statistic heavy in terms of impact primarily because obesity greatly affects one's health but also holds fatal risks. Diagnosing the early stages of obesity can just be simple and prevent one from being obese;however, several obstacles stand in the way of its early diagnosis. Symptoms or signs that one is in the trajectory of suffering from obesity generally go unnoticed by the individual, and the availability of specialists to diagnose such risks persistently on time is not always possible. In this regard, the current paper shall showcase an application hitherto unknown using cloud-based diagnosis, in collaboration with machine learning through Secure Access Internet of Things to predict obesity-related risks without having to depend on glaring physical parameter indicators - in this regard, redefining how such information could be sourced even without the need of regularly visiting health professional people. To ensure the best possible health predictability from this machine learning, we gathered and plotted the datasets consisting of obese and overweight people. Other than machine learning, we use a Secure Access IoT sensor network. This allows for real-time monitoring and diagnosis, making early detection achievable. Consequently, this system paves the way for mitigating and substantially reducing the complications associated with obesity. © 2024 IEEE.

关键词： Nutrition

来源：评论

学校读者我要写书评

暂无评论

Indian Sign Language Classification using Deep Convolutional Neural Network with Transfer Learning 2

Indian Sign Language Classification using Deep Convolutional...

引用

2nd International Conference on Self Sustainable Artificial Intelligence Systems, ICSSAS 2024

作者： Mehta, Parjanya Vrishin, K.P. Prajapati, Purvam Chavda, Siddharth Goel, Parth Devang Patel Institute of Advance Research and Technology Computer Science and Engineering Department Changa Anand India

ISBN: (纸本)9798350368413

The Indian Sign Language (ISL) classification model uses Convolutional Neural Networks (CNN) and analysis with Transfer Learning technology to understand ISL gestures by deaf people. This process entails categorizing hand signals into thirty-five groups, which comprise digits 1-9 (9) and alphabets A-Z (26). The developed model is a great support for people who want to know what the deaf say. The ultimate aim of this project is to categorize ISL Gestures into 35 distinct categories. These divisions are made using Convolutional Neural Networks (CNN). To achieve higher accuracy in classification, various transfer learning models such as ResNet50, Xception, DenseNet121, EfficientNetB5 and VGG19 were used in this study. Consequently, ResNet50 achieves an accuracy of 99.96%, Xception at 27.15%, DenseNet121 at 77.94%, and EfficientNetB5 at 50.93%. © 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 Medical Imaging Resolution: Exploring SRGAN for High-Quality Medical Image Reconstruction 2

Enhancing Medical Imaging Resolution: Exploring SRGAN for Hi...

引用

2nd IEEE International Conference for Women in Innovation, technology and Entrepreneurship, ICWITE 2024

作者： Varshitha, S. Lavanya, N. Shirisha, M. Manmatti, Shilpitha Asha Rani, K.P. Gowrishankar, S. Dr. Ambedkar Institute of Technology Department of Computer Science and Engineering Karnataka Bengaluru560056 India

ISBN: (纸本)9798350383287

In healthcare diagnostics, medical imaging is essential for accurate diagnosis and treatment planning. A major challenge is the limited resolution of some medical images, which can obscure crucial details and affect diagnostic accuracy, especially in telemedicine. This study examines the use of Super-Resolution Generative Adversarial Networks (SRGANs) to improve the resolution of medical images. SRGANs, known for enhancing conventional images, are adapted for medical imaging using a deep convolutional neural network (CNN) generator and discriminator, with a perceptual loss function guiding the reconstruction process. The research involves training the network on a varied dataset of low-resolution medical images, including MRI, CT scans, and ultrasound, aiming to capture intrinsic features of medical imagery. The performance of the model is assessed using a mix of quantitative and qualitative metrics. © 2024 IEEE.

关键词： Medical imaging

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：