检索结果-内蒙古大学图书馆

Smart Generation Computing, Communication and Networking (SMART GENCON), International Conference on

作者： Aparna A. Junnarkar Sunil Kumar Jakhar Manish Nagpal Rakesh Kumar Yadav Vinod Moger M. Sindhu Department of Information Technology Vishwakarma Institute of Information Technology Pune INDIA Department of Mechanical Engineering Vivekananda Global University Jaipur India Centre of Interdisciplinary Research in Business and Technology Chitkara University Institute of Engineering and technology Chitkara University Punjab India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh India Department of Mechanical Engineering Faculty of Engineering and Technology JAIN (Deemed-to-be University) Ramnagar Karnataka India Department of Computer Science and Engineering Prince Shri Venkateshwara Padmavathy Engineering College Chennai

The text type is a vital undertaking within the realm of gadget-gaining knowledge of algorithms and can contain classifying text files into predefined classes. Text category is used in many herbal language processing obligations, sentiment evaluation, topic modeling, and record summarization. Design techniques for device mastering algorithms for textual content categories contain information preprocessing, feature engineering, model choice, and evaluation. Records preprocessing requires expertise in text information and includes the stairs of tokenizing the text records, removing stopwords, acting stemming and lemmatization, part-of-speech tagging, and vectorization. Feature engineering, which is a step that requires domain expertise, applies strategies consisting of feature selection, function extraction, and feature transformation. Version choice and optimization techniques, including hyperparameter tuning and version choice, are used to choose the most appropriate studying algorithm for the task. Lastly, assessment metrics evaluate the model's effectiveness, including accuracy, precision, remembering, and f-ratings. On average, these layout techniques are critical components while growing a successful text-type model.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Leveraging Reinforcement Learning to Improve Content Retrieval

Leveraging Reinforcement Learning to Improve Content Retriev...

引用

Smart Generation Computing, Communication and Networking (SMART GENCON), International Conference on

作者： G Indira Ganesh C. Shelke Bhuvnesh Sharma Vaishali Singh TR Mahesh B Reddy Department of Electrical and Electronics Engineering Prince Shri Venkateshwara Padmavathy Engineering College Chennai Department of Information Technology Vishwakarma Institute of Information Technology Pune India Department of Mechanical Engineering Vivekananda Global University Jaipur India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh India Computer Science Engineering Faculty of Engineering and Technology JAIN (Deemed-to-be University) Bangalore Karnataka India Centre of Interdisciplinary Research in Business and Technology Chitkara University Institute of Engineering and technology Chitkara University Punjab India

Reinforcement studying (RL) is an artificial intelligence (AI) paradigm that can be used to decorate content material retrieval systems, which frequently rent supervised gaining knowledge of strategies. It explains how traditional methods of content retrieval often struggle with providing accurate and relevant search results, and how reinforcement learning offers a potential solution by continuously learning and adapting to user behavior and preferences. The abstract also highlights the benefits and challenges of using this approach, as well as the potential impact it could have on the field of information retrieval. It could permit greater scalability and advanced user experience. The proposed method is predicted to surpass existing content material retrieval structures in terms of content material accuracy and efficiency.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Predicting the insulating paper state of the power transformer based on XGBoost/LightGBM models

引用

Scientific Reports 2025年第1期15卷 1-16页

作者： Ghoneim, Sherif S. M. Baz, Mohammed Alzaed, Ali Zewdie, Yohannes Tesfaye Department of Electrical Engineering College of Engineering Taif University Taif 21944 Saudi Arabia Department of Computer Engineering College of Computers and Information Technology Taif University Taif 21944 Saudi Arabia Architectural Engineering Program - Civil Engineering Department Faculty of Engineering Taif University Taif 21944 Saudi Arabia Department of Electrical and Computer Engineering Faculty of Technology Debre Markos University P. BOX 269 Debre Markos Ethiopia

Power transformer plays a crucial role in the power networks. Most of the transformer malfunctions was due to failure in the insulating systems. The utilities keen on the contentious operation of the power network, so early detection of the transformer faults avoiding the undesirable outage of the transformer from the service. The insulating paper state is an indication to the transformer health and aging of it may lead to failure of the transformer, so some periodic and routine test must be performed on the insulting oil to get information about the insulating paper condition. The value of the degree of polymerization (DP) is a key of the insulating paper state. Various recommended tests such as the dissolved gases (DGA), breakdown voltage (BDV), oil interfacial tension (IF), oil acidity (ACI), moisture content (MC), oil color (OC), dielectric loss (Tan δ), and furans concentration specifically (2-furfuraldehyde (2-FAL)) were carried out to correlate between these variables and the DP and then to the insulating oil state. The collected data from these tests were used to supply XGBoost/LightGBM to build artificial intelligence model to predict the insulating paper state. The results indicated that the great ability of the proposed model to predict the insulating state with high accuracy. Of these various configurations for these two classification models, one achieved perfect prediction accuracy (100%) of the insulating paper state prediction accuracy. The other configurations showed accuracy values from 1.0 down to 0.955. © The Author(s) 2025.

关键词： Dissolved gas analysis Furnic compounds Insulating oil Insulating paper Power transformer XGBoost/LightGBM

来源：评论

学校读者我要写书评

暂无评论

Analyzing Adaptive End-to-End Congestion Control Strategies for Underwater High Speed Communication Networks

Analyzing Adaptive End-to-End Congestion Control Strategies ...

引用

Smart Generation Computing, Communication and Networking (SMART GENCON), International Conference on

作者： V Mythili Gitanjali R. Shinde Lakshya Swarup Dhananjay Yadav Deepak Kumar Sinha Rohit Kumar Department of Science and Humanities Prince Shri Venkateshwara Padmavathy Engineering College Chennai-127 Department of Computer Engineering Vishwakarma Institute of Information Technology Pune INDIA Centre of Interdisciplinary Research in Business and Technology Chitkara University Institute of Engineering and technology Chitkara University Punjab India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh India Department of Computer Science and Engineering Faculty of Engineering and Technology JAIN (Deemed-to-be University) Bangalore Karnataka India Department of Civil Engineering Vivekananda Global University Jaipur India

This paper investigates modern-day Adaptive stop-to-give up (AEE) congestion manage techniques for underwater high velocity verbal exchange networks. AEE techniques have the capability to provide a greater efficient and strong transmission for underwater acoustic channels. in this paper we examine the one-of-a-kind AEE congestion manage strategies, their overall performance blessings and limitations. We moreover talk the exchange-offs between a couples of criteria, including strength intake, through-placed and put off. A complete analysis of current strategies is made and a comprehensive summary of the relative performance of the techniques are furnished. finally, destiny studies instructions are mentioned, to bridge the gap between theoretical fashions and real-time structures.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Fault-tolerant Mobile Service Offloading in Mobile Edge Computing

引用

IEEE Transactions on Automation Science and engineering 2025年

作者： Long, Tingyan Xia, Yunni Zhou, Mengchu Li, Jianqi Ma, Yong Ai-Turki, Yusuf Guizhou University School of Computer Science and Technology Guiyang550025 China Chongqing University College of Computer Science Chongqing400030 China New Jersey Institute of Technology Department of Electrical and Computer Engineering NewarkNJ07102 United States Global energy interconnection research institute Co. Ltd Beijing102209 China Jiangxi Normal University School of Computer and Information Engineering Nanchang China King Abdulaziz University K. A. Care Energy Research and Innovation Center The Center of Research Excellence in Renewable Energy and Power Systems Department of Electrical and Computer Engineering Faculty of Engineering Jeddah21589 Saudi Arabia

Mobile edge computing (MEC) is an evolving paradigm for rendering services through network-accessible resources deployed over Internet of Things (IoT) nodes at the edge. Nevertheless, an MEC environment usually employs thousands of physical machines connected via hundreds of switches/routers that communicate and coordinate to deliver computing service. In such complicated systems, faults caused by software, human errors, and hardware are often unavoidable. The edge of network presents a dynamic environment with great quantities of terminals, high mobility of mobile devices, heterogeneous applications, and intermittent traffic. In such an environment, MEC can suffer from unbalanced resource provisioning and interruptions of faults occurring at different levels, which further causes task faults and affects service quality. To address this challenge, this work proposes a novel fault-tolerant offloading method for handling faults by leveraging a reinforcement-learning-based service offloading decision model. The model synthesizes a Dueling Deep Q Network (DQN)-based algorithm for deciding user offloading behaviors and an adaptive checkpointing method for improving task execution reliability. For the purpose of model validation and comparison, extensive simulations are conducted. Numerical results clearly demonstrate that the proposed method is highly effective and outperforms existing methods. © 2004-2012 IEEE.

关键词： Mobile applications

来源：评论

学校读者我要写书评

暂无评论

Ameliorating Semantic Search Through Advanced AI Techniques

Ameliorating Semantic Search Through Advanced AI Techniques

引用

Smart Generation Computing, Communication and Networking (SMART GENCON), International Conference on

作者： Ravikumar V Dharmesh Dhabliya Mala Mathur Sidhant Das Ritesh Kumar Srikrishna B Rao Department of Computer Science and Engineering Prince Shri Venkateshwara Padmavathy Engineering College Chennai Department of Information Technology Vishwakarma Institute of Information Technology Pune Maharashtra India Department of Civil Engineering Vivekananda Global University Jaipur India Centre of Interdisciplinary Research in Business and Technology Chitkara University Institute of Engineering and technology Chitkara University Punjab India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh India Department of Mechanical Engineering Faculty of Engineering and Technology JAIN (Deemed-to-be University) Ramnagar Karnataka India

Recent synthetic intelligence (AI) advances have enabled some thrilling semantic search applications. In particular, the development of advanced AI strategies, which include natural language processing (NLP) and deep learning, has enabled the creation of search engines like Google and Yahoo that leverage semantic data to enhance the accuracy of the quest outcomes. No longer simplest does this enable customers to find applicable statistics speedy. However, it additionally facilitates ameliorating any semantic mistakes resulting from typos, incorrect syntax, or wrong interpretations of the question. In this paper, we present a survey of new advances in semantic seek powered by AI and techniques for query expansion, generating question-precise training statistics, and semi-supervised gaining knowledge. We additionally talk about the results for the future of semantic search and gift some case research on the successful implementation of semantic seek with AI structures. Our assessment concludes with a dialogue of the open challenges and potential research avenues.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Enabling Autonomous Deep Learning Strategies for Real-Time Data Analysis in Machine Learning

Enabling Autonomous Deep Learning Strategies for Real-Time D...

引用

Smart Generation Computing, Communication and Networking (SMART GENCON), International Conference on

作者： Girija Shankar Sahoo Ramesh Sekaran G. Renuka Dharmesh Dhabliya Pramod Kumar Faujdar Deepak Minhas Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh India Department of Computer Science and Engineering Faculty of Engineering and Technology JAIN (Deemed-to-be University) Ramnagar Karnataka India Department of Electronics and Communication Engineering Prince Shri Venkateshwara Padmavathy Engineering College Chennai Department of Information Technology Vishwakarma Institute of Information Technology Pune Maharashtra India Department of Mechanical Engineering Vivekananda Global University Jaipur India Centre of Interdisciplinary Research in Business and Technology Chitkara University Institute of Engineering and technology Chitkara University Punjab India

This studies focuses on leveraging self-sustaining deep getting to know techniques for real-time records analysis in device getting to know. We propose a machine this is enabled by means of artificial intelligence to streamline the technique of extracting significant insights from sensor-based records. Our approach considers two middle components: interpreting data with recurrent neural networks and deploying a mastering agent to routinely whole the statistics analysis process. The getting to know agent's training is based on supervised mastering, enabling it to discover and traverse styles inside the information. The recurrent neural community (RNN) offers the functionality to seize temporal styles within the information, with its capacity to make predictions based on previous input. As a result, our system can produce fairly correct insights with minimal human intervention. This makes it suitable for time-sensitive information analysis situations in the industrial area in which decisions have to be taken in actual-time.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Long Short Term Memory Networks for Stroke Activity Recognition base on Smartphone 5

Long Short Term Memory Networks for Stroke Activity Recognit...

引用

5th IEEE International Conference on information technology, information Systems and Electrical engineering, ICITISEE 2021

作者： Siagian, Pandapotan Fernando, Erick Del Institute Of Technology Faculty Of Informatics And Electrical Engineering Computer Engineering Study Program North Sumatera 22381 Indonesia Bina Nusantara University School Of Information Systems Information Systems Department Jakarta11480 Indonesia

ISBN: (纸本)9781665401968

Aims this study to analyze rehabilitation exercises using sensor data embedded in smartphones is widely used to recognize human activities regularly to gain a better understanding of human behavior. However, it is rarely used to measure accelerators of patient movement in hospital wards. This paper proposes an optimized neural network-based feature extraction approach based on the Long Short Term Memory technique to recognize human activity using data from a tri-axial accelerometer. The activity to be measured is the Range of Motion for patient rehabilitation, and rehabilitation exercises are one of the most important steps for recovery. The results of the softmax regression trained on the dataset set have 1, 076, 105 records and 12 class attributes (class distribution). The experimental results on the SAR dataset show that our Long Short Term Memory-based approach is practical and successful, with an accuracy of 89.74%. © 2021 IEEE.

关键词： Smartphones

来源：评论

学校读者我要写书评

暂无评论

Systematic Advancements in IoT: Integrating Edge Computing for Enhanced Architectures in Next-Generation Devices 1st

Systematic Advancements in IoT: Integrating Edge Computing f...

引用

1st International Conference on Advanced Network Technologies and Computational Intelligence, ICANTCI 2024

作者： Rajawat, Anand Singh Goyal, S.B. Islam, Sardar M. N. Malik, Varun School of Computer Sciences and Engineering Sandeep University Nashik India Faculty of Information Technology City University Petaling Jaya46100 Malaysia Institute for Sustainable Industries and Liveable Cities Victoria University Melbourne Australia Chitkara University Institute of Engineering and Technology Chitkara University Punjab Rajpura India

ISBN: (纸本)9783031860683

This paper introduces a methodical strategy for creating and executing IoT designs that are enhanced by edge computing. The goal is to meet the changing requirements of future devices. With the rise of the Internet of Things (IoT), there is a significant increase in the amount of data being generated due to the large number of interconnected devices. Conventional cloud-based processing models frequently encounter difficulties related to latency, bandwidth, and privacy problems. In order to address these difficulties, we provide a pioneering framework that incorporates edge computing into Internet of Things (IoT) systems. Our methodology disperses data processing, relocating computing in proximity to the data origin at the periphery of the network. By implementing this, the latency is reduced, the bandwidth utilization is minimized, and the data privacy is enhanced. In this document, we delineate the fundamental concepts that govern the design of this architecture, with a specific emphasis on modularity, scalability, and security. The architecture is intentionally built to be flexible, capable of accommodating a diverse array of IoT devices and applications, spanning from smart home systems to industrial automation. We showcase a preliminary version of our framework, illustrating its efficacy in several settings. Performance assessments demonstrate substantial enhancements in reaction time and bandwidth economy when compared to conventional cloud-based models. Our architecture additionally integrates sophisticated security protocols to protect against potential risks in IoT contexts. We analyze the consequences of our methodology on the future of IoT, emphasizing how architectures improved by edge computing can enable innovative opportunities for intelligent, effective, and protected IoT systems. This study establishes the foundation for future research and development in this potential subject, providing a detailed plan for the creation of the next generation of IoT

关键词： Cloud computing architecture

来源：评论

学校读者我要写书评

暂无评论

CaPAN: Class-aware Prototypical Adversarial Networks for Unsupervised Domain Adaptation

CaPAN: Class-aware Prototypical Adversarial Networks for Uns...

引用

IEEE International Conference on Multimedia and Expo (ICME)

作者： Zhenyu Yu Pei Wang Guangzhou University Guangzhou China CISiP Faculty of Computer Science and Information Technology Universiti Malaya Kuala Lumpur Malaysia National Pilot School of Software Yunnan University Kunming China School of Information Science and Engineering Yunnan University Kunming China

ISBN: (数字)9798350390155

ISBN: (纸本)9798350390162

Adversarial domain adaptation has achieved impressive performances for unsupervised domain adaptation (UDA). However, existing adversarial UDA methods often rely on multiple domain discriminators to capture diverse patterns, which limit their scalability and resulting in dispersed features. To address these issues, we propose a novel method called Class-aware Prototypical Adversarial Network (CaPAN), which efficiently extracts transferable and discriminative features. Specifically, our class-aware adversarial learning employs a single multi-class discriminator to capture various patterns, aligning class-level features. Furthermore, to enhance the discriminative ability of our model, we introduce a prototypical domain discriminator to enhance the discriminatively of the learned features by aligning target sample towards prototypes (centers of each class), resulting in a more compact feature space. Extensive experiments validate the effectiveness of CaPAN, which can also be integrated as a regularization technique for existing methods to further improve their performance. Code is available at https://***/YuZhenyuLindy/CaPAN.

关键词： Codes Scalability Prototypes Feature extraction Adversarial machine learning Periodic structures

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：