检索结果-内蒙古大学图书馆

2024 International Conference on Communication, computer sciences and engineering, IC3SE 2024

作者： Dasari, Kavitha Yadav, Suman Avdhesh Kansal, Lavish Adilakshmi, J. Kaliyaperumal, Gopal Albawi, Ali Institute Of Aeronautical Engineering Dundigal Hyderabad India Amity University Department Of Computer Science & Engineering Greater Noida India Lovely Professional University Phagwara India Mlr Institute Of Technology Department Of Information Technology Telangana Hyderabad India New Horizon College Of Engineering Department Of Mechanical Engineering Bangalore India College Of Medical Technology The Islamic University Radiology Techniques Department Najaf Iraq

ISBN: (纸本)9798350366846

This research investigates the integration of hyperspectral imaging (HSI) and advanced convolutional neural networks (CNNs) for the early detection of crop diseases, a critical aspect in the realm of precision agriculture. Hyperspectral imaging, known for capturing a wide spectrum of light per pixel, provides detailed information about crops that is not visible to the naked eye. The study focuses on developing a novel framework wherein these detailed spectral signatures are utilized as inputs for deep learning models, specifically tailored CNNs. The proposed CNN architecture is designed to efficiently process the high-dimensional HSI data, extracting salient features relevant to various crop diseases. This integration facilitates the early identification of subtle physiological changes in plants that precede visible symptoms, thereby enabling timely interventions. The methodology involves collecting a comprehensive hyperspectral dataset from different crop types under varied conditions. This data is preprocessed to mitigate issues like atmospheric interference and sensor noise. The CNN models are trained and validated on this dataset, with a focus on generalizability across different crop species and disease types. The paper also addresses challenges such as the high computational cost and the need for large labeled datasets in HSI-CNN fusion. The results demonstrate a significant improvement in early disease detection accuracy compared to traditional methods, underscoring the potential of this approach in transforming precision agriculture practices. This study opens new pathways for sustainable farming by enabling proactive disease management, ultimately contributing to increased crop yield and reduced pesticide usage. © 2024 IEEE.

关键词： Crops

来源：评论

学校读者我要写书评

暂无评论

Personalised Travel Ratings using AI and Enhancing User Experience Through Multi-Dimensional Analytics 4

Personalised Travel Ratings using AI and Enhancing User Expe...

引用

4th International Conference on Sentiment Analysis and Deep Learning, ICSADL 2025

作者： Poornima, S. Krishnaveni, A. Francis Raj, J. Relin Divya, R. Rajkumar, G. Vinoth Sakthidevi, I. Sri Ramakrishna College of Arts and Science for Women Department of Computer Science Coimbatore India Government College of Engineering Department of Mechanical Engineering Tirunelveli India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Electronics and Communication Engineering Chennai India Adhiyamaan College of Engineering Department of Information Technology Hosur India J. P. College of Engineering Department of Electronics and Communication Engineering Tenkasi India

ISBN: (纸本)9798331523923

The rapid advancements in Artificial Intelligence (AI) have enabled the development of intelligent systems for personalized travel recommendations. This study proposes a novel framework, Generative Adversarial Networks with Real-Time Analytics for Travel Experience (GAN-RATE), designed to enhance user satisfaction by integrating synthetic data generation and real-time feedback mechanisms. The GAN component addresses the challenge of sparsely rated travel attributes by generating high-quality synthetic data, while the Real- Time Analytics module evaluates user interactions and feedback to refine the recommendation process dynamically. The proposed framework considers multiple dimensions, including comfort, cleanliness, timeliness, entertainment amenities, and customer feedback, ensuring a holistic approach to travel rating prediction. Simulation analysis is conducted to compare the performance of GAN-RATE with existing algorithms. Metrics such as accuracy, mean absolute error (MAE), precision, recall, and computational efficiency are employed to evaluate system performance. Results indicate that GAN-RATE outperforms baseline models in prediction accuracy and adaptability to sparse datasets while demonstrating superior real-time response capabilities. The findings highlight the potential of GAN-RATE in improving user experience through personalized, data-driven recommendations, setting a new benchmark for AI-based travel recommendation systems. © 2025 IEEE.

关键词： Generative adversarial networks

来源：评论

学校读者我要写书评

暂无评论

Exponentially Fast Collaborative Broadcasting in 3D Space

Exponentially Fast Collaborative Broadcasting in 3D Space

引用

information and Network Technologies (ICINT), International Conference on

作者： Yotam Ashkenazi Shlomi Dolev Sergey Frenkel Manish Kumar Sweta Kumari Ram Prasadh Narayanan Archit Somani Department of Computer Science Ben-Gurion University of the Negev Beersheba Israel The Institute of Informatics Problems Russian Academy of Science Russia Department of Computer Science New Jersey Institute of Technology New Jersey United States Department of Computer Science and Engineering Shiv Nadar University Delhi India Institute for Information and Communication Systems Science Walton Institute Ireland

ISBN: (数字)9798331536756

ISBN: (纸本)9798331536763

Broadcasting stands as a vital function within wireless sensor networks (WSNs), where nodes disseminate messages throughout the entire network, radiating in all possible directions. Our focus lies on the issue of three-dimensional broadcasting, involving n sensors/robots positioned uniformly across a three-dimensional space. We propose an exponentially fast hybrid collaborative broadcasting algorithm in three-dimensional space "growing ball" through successive propagation steps. We study the effect of various parameters such as the density of the growing ball (ρ), distance (d), the energy emitted by node (E emit ), and ϵ used by the collaborative broadcast. Our approach is based on the idea of dividing the 3D space into two halves that reduce mathematical intricacy. The number of nodes N k in the k th step of the growing ball is the factor of (1+ϵ) k implies the exponential fast broadcast.

关键词： Wireless sensor networks Three-dimensional displays Collaboration Broadcasting

来源：评论

学校读者我要写书评

暂无评论

Framework for detecting the patients affected by COVID-19 at early stages using Internet of Things along with Machine Learning approaches with improved Accuracy 12

Framework for detecting the patients affected by COVID-19 at...

引用

12th International Conference on computer Communication and informatics, ICCCI 2022

作者： Devi, T. Priya, J. Sathya Deepa, N. Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Chennai India Velammal Engineering College Department of Information Technology Chennai India

ISBN: (纸本)9781665480352

COVID-19 has been affecting the entire world from the year 2019 and in order to handle this pandemic situation, it is necessary to follow the measures till valid medicine has been found. The proposed approach helps in detecting as well as monitoring the COVID-19 on real time basis. Data is collected with the help of IoT devices for detecting this disease at the early stage. The components of the system are, (i) Collection of symptom data, (ii) Center of Isolation, (iii) Machine Learning approaches for analysis, (iv) Healthcare analysts and (v) Cloud. The three algorithms in machine learning used for detection of the virus are Decision tree, Support vector machine and Neural Network. These three algorithms are tested with the real time dataset and it is observed that all these algorithms have accuracy greater than 91%. Identifying the disease accurately with the three machine learning algorithms, effective results are produced. The response of treatment for every person who gets affected by the virus is then documented. © 2022 IEEE.

关键词： COVID-19

来源：评论

学校读者我要写书评

暂无评论

A Deep Learning Model For Intelligent Energy Management 4

A Deep Learning Model For Intelligent Energy Management

引用

4th International Conference on Advances in Computing, Communication Control and Networking, ICAC3N 2022

作者： Krishnasamy, Lalitha Somasundaram, K. Quadir, Munleef Dhanaraj, Rajesh Kumar Roopa, C. Kowshika, P. Kongu Engineering College Department of It India Saveetha Institute of Medical and Technical Sciences Institute of Information Technology Saveetha School of Engineering Tamilnadu Chennai India Jazan University College of Computer Science and Information Technology Department of Computer Science Jazan Saudi Arabia Galgotias University School of Computing Science and Engineering India Kongu Engineering College Department of CSE India

ISBN: (纸本)9781665474368

Green energy management is a cost-effective method of reducing energy use, but current research overlooks the edge intelligence potential of managed IoT. As a result, it focuses on today's smart grid, residential, and commercial demands to develop a deep learning-based paradigm for successful energy management. In our system, we want to forecast or analyze household power usage. The dataset on home electricity usage can be found in the dataset repository. The pre-processing phase is then implemented to avoid inaccurate predictions. We use a range of pre-processing approaches to deal with various types of power data, then create an effective decision-making system for short-term forecasting on reliable resource-constrained devices. The system employs deep learning techniques such as Long Short Term Memory (LSTM) and Gate Recurrent Unit to analyze energy use (GRU). © 2022 IEEE.

关键词： Energy management

来源：评论

学校读者我要写书评

暂无评论

Improving Land Cover Segmentation Using Multispectral Dataset 8

Improving Land Cover Segmentation Using Multispectral Datase...

引用

8th International Conference on informatics and Computing, ICIC 2023

作者： Herlawati, Herlawati Handayanto, Rahmadya Trias Heryadi, Yaya Abdurachman, Edi Soeparno, Haryono Irwansyah, Edy Miranda, Eka Informatics Universitas Bhayangkara Jakarta Raya Jakarta Indonesia Universitas Islam 45 Computer Engineering Bekasi Indonesia BINUS Graduate Program - Doctor of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia Trisakti Institute of Transportation and Logistic Jakarta13410 Indonesia Bina Nusantara University School of Computer Science Computer Science Department Jakarta11480 Indonesia Bina Nusantara University School of Information Systems Information Systems Department Jakarta11480 Indonesia

ISBN: (纸本)9798350342604

computer vision has been used in many areas such as medical, transportation, military, geography, etc. The fast development of sensor devices inside camera and satellite provides not only red-greed-blue (RGB) images but also multispectral dataset with some channels including RGB, infrared, short-wave, and thermal wave. Most of the dataset is panchromatic (black and white) and RGB, for example Google Map and other satellite-based map applications. This study examines the effects of multispectral dataset for semantic segmentation of land cover. The comparison between RGB with band 2 to band 7 of Landsat 8 Satellite shows an improvement of accuracy from 90.283 to 94.473 for U-Net and from 91.76 to 95.183 for DeepLabV3+. In addition, this research also compares two well-known semantic segmentation methods, namely U-Net and DeepLabV3+, that shown that DeepLabV3+ outperformed U-Net regarding to speed and accuracy. Testing was conducted in the Karawang Regency area, West Java, Indonesia. © 2023 IEEE.

关键词： Landsat

来源：评论

学校读者我要写书评

暂无评论

Soft,full Wheatstone bridge 3D pressure sensors for cardiovascular monitoring

引用

npj Flexible Electronics 2024年第1期8卷 910-917页

作者： Yoonseok Park Haiwen Luan Kyeongha Kwon Ted S.Chung Seyong Oh Jae-Young Yoo Gooyoon Chung Junha Kim Suhyeon Kim Sung Soo Kwak Junhwan Choi Hoang-Phuong Phan Seonggwang Yoo Hyoyoung Jeong Jaeho Shin Sang Min Won Hong-Joon Yoon Yei Hwan Jung John A.Rogers Department of Advanced Materials Engineering for Information and Electronics Integrated Education Institute for Frontier Science&Technology(BK21 Four)Kyung Hee UniversityYongin-siGyeonggi-do 17104Republic of Korea Department of Mechanical and Aerospace Engineering University of California San DiegoLa JollaCA 92093USA School of Electrical Engineering Korea Advanced Institute of Science and TechnologyDaejeonRepublic of Korea Department of Mechanical Engineering Columbia UniversityNew YorkNYUSA Division of Electrical Engineering Hanyang University ERICAAnsan 15588Republic of Korea Department of Semiconductor Convergence Engineering Sungkyunkwan UniversitySuwonRepublic of Korea Biomedical Research Division Korea Institute of Science and TechnologySeoul 02792Republic of Korea Department of Chemical Engineering Dankook UniversityYonginGyeonggi-do 16890Republic of Korea School of Mechanical and Manufacturing Engineering Tyree Institute of Health EngineeringUniversity of New South WalesSydneyNSW 2052Australia Querrey Simpson Institute for Bioelectronics Northwestern University EvanstonEvanstonIL 60208USA Department of Electrical and Computer Engineering University of California DavisDavisCA 95616USA Department of Electrical and Computer Engineering Sungkyunkwan University2066Seobu-roJangan-guSuwon-siGyeonggi-do 16419Republic of Korea Department of Electronic Engineering Gachon UniversitySeongnamsiGyeonggi-do 13120Republic of Korea Department of Electronic Engineering Hanyang UniversitySeoulRepublic of Korea Department of Materials Science and Engineering Biomedical EngineeringNeurological SurgeryChemistryMechanical EngineeringElectrical Engineering and Computer Science Simpson Querrey Institute and Feinberg Medical SchoolCenter for Bio-Integrated ElectronicsNorthwestern University EvanstonEvanstonIL 60208USA

Variations in parameters associated with the ambient environment can introduce noise in soft,body-worn *** example,many piezoresistive pressure sensors exhibit a high degree of sensitivity to fluctuations in temperature,thereby requiring active compensation *** research presented here addresses this challenge with a multilayered 3D microsystem design that integrates four piezoresistive sensors in a full-Wheatstone bridge *** optimized layout of the sensors relative to the neutral mechanical plane leads to both an insensitivity to temperature and an increased sensitivity to pressure,relative to previously reported devices that rely on similar operating *** this 3D pressure sensor into a soft,flexible electronics platform yields a system capable of real-time,wireless measurements from the surface of the *** above the radial and carotid arteries yields high-quality waveforms associated with pulsatile blood flow,with quantitative correlations to blood *** results establish the materials and engineering aspects of a technology with broad potential in remote health monitoring.

关键词： Wheat bridge thereby

来源：评论

学校读者我要写书评

暂无评论

DynoGraph: Dynamic Graph Construction for Nonlinear Dimensionality Reduction 24

DynoGraph: Dynamic Graph Construction for Nonlinear Dimensio...

引用

24th IEEE International Conference on Data Mining, ICDM 2024

作者： Qian, Li Plant, Claudia Qin, Yalan Qian, Jing Bohm, Christian Institute of Informatics Ludwig Maximilian University of Munich Munich Germany University of Vienna Faculty of Computer Science Vienna Austria ds:Univie University of Vienna Faculty of Computer Science Vienna Austria School of Communication and Information Engineering Shanghai University Shanghai China China Railway Hi-Tech Industry Corporation Limited Carbon Neutral R&D Center Beijing China

ISBN: (纸本)9798331506681

Most well-known graph-based dimensionality re-duction algorithms, such as t-SNE and UMAP, use a two-step approach: first to construct a graph out of the high-dimensional data and then to embed the graph into the low-dimensional space. The main challenges of these algorithms include how to construct a good graph and how to maintain the similarity structure of the high-dimensional data in the low-dimensional space. This study proposes DynoGraph, a novel algorithm called Dynamic Graph Construction for Nonlinear Dimensionality Reduction, to address these two challenges. First, we develop an adaptive neighborhood graph construction method that accurately captures the intrinsic geometry of the high-dimensional data. Second, for the first time, we introduce a dynamic graph modification process during dimensionality reduction, ensuring that the data structure in the low-dimensional space faithfully reflects the high-dimensional data. For vertex pairs that are connected by edges in high-dimensional space exhibit far apart in low-dimensional space, additional edges are inserted to strengthen the connection between them. Conversely, for vertex pairs that are not connected in high-dimensional space exhibit close together in the low-dimensional space, edges are deleted to reduce the connection between them. These adjustments help to update their positions in subsequent embeddings, aligning them toward the high-dimensional data. Extensive experiments have demonstrated the superiority of DynoGraph against various comparative algorithms in tasks such as visualization, classification and clustering. © 2024 IEEE.

关键词： Dimensionality reduction

来源：评论

学校读者我要写书评

暂无评论

Online E-learning Mobile Application for Self-learning

Online E-learning Mobile Application for Self-learning

引用

2022 International Conference on Data science, Agents and Artificial Intelligence, ICDSAAI 2022

作者： Deepa, N. Priya, J. Sathya Devi, T. Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Department of Computer Science and Engineering Chennai India Velammal Engineering College Department of Information Technology Chennai India

ISBN: (纸本)9798350333848

This research work is focused on the accessible Mobile user application developments to facilitate student and faculty communication through native android applications. Covid'19 and this pandemic brings E-learning systems as majority education levels. Mobile technology has efficient learning systems in many countries like the United States where the students use google paths such as the classroom to extend learning effectively. Limitations in existing apps are that students are not appreciated and monitored for self learning, schedule sharing is only at the end of the course and also knowing the mapping concepts of teaching pedagogy is also less approachable. To overcome these problems mobile technology support is proposed in this work with these three modules such as i) Authentication self learning and performance (ASLP) - Authentication for right users along with improvement of monitoring self learning to analyze performance ii) Syllable Schedule (SS) - prior scheduling on syllable and organization of time table matching based on outcome iii) Authorized facilitator (AF) - Set permission based on designation such that facilitator communicates based on needs. To achieve the above highlighted terms Google API is applied by peer reviews and interactions enabled such that efficient mobile applications development is proved. Also the abstraction hierarchy is improved when setting the interaction module implies less complexity, less storage. Thus the scope of research work is to use classroom overall performance, interaction, development process to upgrade the results of students. Education level is also enhanced through online E-learning mobile technology (OELMT) that has native applications to develop students' knowledge in a better way. © 2022 IEEE.

关键词： E-learning

来源：评论

学校读者我要写书评

暂无评论

Cloud Computing’s Effect on Video Games Streaming 2

Cloud Computing’s Effect on Video Games Streaming

引用

2nd International Conference on Automation, Computing and Renewable Systems, ICACRS 2023

作者： Komathi, A. Lenin, J. Asha, S. Suresh, A. Suguna, M. Srinivasan, C. Department of Computer Science Nadar Saraswathi College of Arts and Science College Tamilnadu Theni India Department of ComputerScience and Engineering Alliance College of Engineering and Design Alliance University Karnataka Bengaluru India Department of Information Technology S.A. Engineering College Tamil Nadu Chennai India Department of Computer Science and Engineering Siddharth Institute of Engineering & Technology Andhra Pradesh Puttur India Tamil Nadu Salem India Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Tamil Nadu Chennai India

ISBN: (纸本)9798350340235

The disruptive power of cloud computing is altering the gaming industry. Real-time gaming is now feasible because to advancements in streaming algorithms, further blurring the lines between the digital and physical worlds. Understanding the potential of Cloud computing to change the gaming industry is the emphasis which investigates the complicated relationship between numerous technologies. By using cutting-edge streaming protocols and techniques like Adaptive Bitrate Streaming (ABS), buffering, caching, and compression, users may have uninterrupted, high-quality gaming experiences. The study's findings herald a new era of interactive entertainment and shed light on the transformative possibilities of these technologies. Asynchronous Bit-Rate (ABR) encoding, buffering, caching algorithms, video compression standards, and streaming protocols are just few of the numerous digital components that work together to provide uninterrupted data transmission. The rapid progress of ABR algorithms has made it possible to dynamically alter video quality to maximize the user experience across a broad variety of network scenarios. Buffering and caching algorithms play crucial roles by proactively managing data flow and storage to avoid disruptions in the streaming experience. © 2023 IEEE.

关键词： Autism buffering algorithms caching methods Cloud Computing Impact Interactive Entertainment Standards for video compression Streaming Algorithms streaming protocols Video Games

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：