检索结果-内蒙古大学图书馆

7th International Symposium on Innovative Approaches in Smart Technologies, ISAS 2023

作者： Singh, Kamred Udham Kumar, Ankit Kumar, Gaurav Singh, Teekam Choudhury, Tanupriya Kotecha, Ketan Graphic Era Hill University School of Computing Dehradun India GLA University Department of Computer Engineering & Applications UP Mathura India Graphic Era Deemed to Be University Dehradun Department of Computer Science and Engineering India Symbiosis Institute of Technology CSE Dept. Lavale Campus Pune India Symbiosis Centre for Applied Artificial Intelligence Pune India

ISBN: (纸本)9798350383065

In this paper, a technique for the categorization of offline handwritten digits in the Devanagari script as well as the Roman script (English numbers) is presented. Finding out how to get the best possible recognition results while concurrently working with several scripts is the primary purpose of this study. The approach that has been described makes use of an easy profiling method for the extraction of features, together with an innovative implementation of Linear Discriminant Analysis (LDA), and a neural network architecture that has been adapted specifically for numeral classification. An exhaustive series of trials including 36,000 examples of handwritten numerals were carried out as part of the evaluation of the effectiveness of this approach. These samples were chosen at random from the CPAR datasets, with 22,000 being put to use in the construction of the training set and the remaining 14,000 being assigned to the role of the test set. In addition, the well-known MNIST database was used, which provided 60,000 samples for training purposes and 10,000 samples specifically for testing. It was determined via thorough testing that the LDA classifier produced variable results. These varying results are essential in emphasising the success of the method as well as possible areas for improvement for the method. According to the results, this strategy, which combines profile-based feature extraction with advanced classification algorithms, has the potential to lead to major breakthroughs in the area of handwritten number identification. © 2023 IEEE.

关键词： Extraction

来源：评论

学校读者我要写书评

暂无评论

DeepCare: Deep Learning-Based Smart Healthcare Framework using 5G Assisted Network Slicing

DeepCare: Deep Learning-Based Smart Healthcare Framework usi...

引用

2022 IEEE International Conference on Advanced Networks and Telecommunications Systems, ANTS 2022

作者： Basu, Deborsi Krishnakumar, Vikram Ghosh, Uttam Datta, Raja Indian Institute of Technology G. S. Sanyal School of Telecommunications Kharagpur India SASTRA Deemed Univ. Dept. of Electronics and Communication Engineering Tamil Nadu Thanjavur India School of Applied Computational Sciences MMC Dept. of Data Science and Computer Science United States Indian Institute of Technology Dept. of Electronics and Electrical Comm. Engg. Kharagpur India

ISBN: (纸本)9781665473408

5G and beyond communication networks require satisfying very low latency standards, high reliability, high-speed user connectivity, more security, improved capacity and better service demands. Augmenting such a wide range of KPIs (Key Performance Indicators) needs a smart, intelligent and programmable solution for TSPs (Telecommunication Service Providers). Resource availability and quality sustainability are challenging parameters in a heterogeneous 5G environment. Programmable Dynamic Network Slicing (PDNS) is a key technology enabling parameter that can allow multiple tenants to bring their versatile applications simultaneously over shared physical infrastructure. Latest emerging technologies like virtualized Software-Defined Networks (vSDN) and Artificial Intelligence (AI) play a pivotal supporting role in solving the above-mentioned constraints. Using the PDNS framework, we have proposed a novel slice backup algorithm leveraging Deep Learning (DL) neural network to orchestrate network latency and load efficiently. Our model has been trained using the available KPIs and incoming traffic is analyzed. The proposed solution performs stable load balancing between shared slices even if certain extreme conditions (slice unavailability) through intelligent resource allocation. The framework withstands service outage and always select the most suitable slice as a backup. Our results show latency-aware resource distribution for better network stability. © 2022 IEEE.

关键词： Deep learning

来源：评论

学校读者我要写书评

暂无评论

Reliable Data Transmission through Private CBRS Networks

arXiv

引用

arXiv 2023年

作者： Kuo, Hsun-Yu Liu, Szu-Yu Huang, Chin-Ya Chen, Yu-Chi Xie, Meng-Hua The Bachelor Degree Program of Applied Science and Technology National Taiwan University of Science and Technology Taiwan The Dept. of Electronic and Computer Engineering National Taiwan University of Science and Technology Taiwan

We consider the use of a domain proxy assisted private citizen broadband radio service (CBRS) network and propose a Maximum Transmission Continuity (MTC) scheme to transmit Internet of Things (IoT) data reliably. MTC dynamically allocates available CBRS channels to sustain the continuity of data transmission without violating the channel access requirements. MTC allocates the granted CBRS channels according to the priority of each user, the instant channel access status, interference among users, and the fairness. The simulation results demonstrate the improvement in managing reliable IoT data transmission in the private CBRS network. Copyright © 2023, The Authors. All rights reserved.

关键词： Internet of things

来源：评论

学校读者我要写书评

暂无评论

Amortized Variational Peak Fitting For Spectroscopic Data

Amortized Variational Peak Fitting For Spectroscopic Data

引用

IEEE Workshop on Machine Learning for Signal Processing

作者： David Frich Hansen Tommy Sonne Alstrøm Mikkel N. Schmidt Dept. of Applied Mathematics and Computer Science Technical University of Denmark Richard Petersens Plads 324 2800 Kgs. Lyngby Denmark

Spectroscopic analysis relies on identifying and understanding the spectral peaks that represent unique characteristics of an analyte. In high-speed real-time settings, current peak fitting techniques, particularly Bayesian methods involving MCMC or variational approximation, can be prohibitively expensive. We propose an unsupervised method using a convolutional neural network to estimate the number of peaks and their parameters along with posterior uncertainty, by amortizing variational inference in a classical parametric peak model. In a simulated data study, our method reliably determines the number of peaks, precisely estimates parameters similar to direct variational inference, and accurately captures uncertainty comparable to MCMC methods. Our novel, fast, and precise method for Bayesian spectral analysis opens new possibilities in real-time spectral data processing for high-speed monitoring and control.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Study on a Detection Method of Driving Scenes Using Driver’s Operation Data and In-Vehicle Camera Videos

Study on a Detection Method of Driving Scenes Using Driver’...

引用

On-Line Conference (ONCON), Industrial Electronics Society Annual

作者： Kohjiro Hashimoto Daichi Yanagihara Hiroshi Kuniyuki Kae Doki Yuki Funabora Shinji Doki Dept. Applied Information Engineering Suwa University of Science Chino City Japan Dept. Mechanical and Electrical Engineering Suwa University of Science Chino City Japan Dept. Electrical Engineering Aichi Institute of Technology Toyota City Japan Dept. Electrical Engineering and Computer Science Nagoya University Nagoya Japan

In this paper, a method to detect arbitrary driving scenes from actual driving behavior data is proposed. This is positioned as a necessary technology to evaluate a driver’s cognitive ability from actual driving behavior data. In order to evaluate a driver’s cognitive ability from actual driving behavior data, it is first necessary to be able to detect scenes in which cognitive ability can be evaluated. However, since there is no clear definition of the scenes in which cognitive ability can be evaluated. Therefore, this method detects arbitrary driving scenes defined by the *** method is a binary classifier generation method based on deep learning. However, a driving scene is considered to be composed of road information, appearing object information, and operation information, and each is expressed by four types of modalities: driving video, object label vector, grid feature, and operation data in this method. By adopting a deep learning model structure that incorporates the idea of stacking ensemble learning and training a binary classifier based on four types of modal information, a detector that detects the desired driving situation is generated. In this paper, the effectiveness of the proposed method through experiments is verified.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Enhancing Autism Disease Classification Using a Hybrid GA-KNN Approach for Feature Selection

Enhancing Autism Disease Classification Using a Hybrid GA-KN...

引用

International Conference on Information and Communication Systems (ICICS)

作者： Maria Yousef Laith Al Shehab Doaa Abdel Ghani Hadeel Alazzam Mohammad Ghatasheh Department of Computer Science Middel East University (MEU) Amman Jordan Department of Computer Science Jordan University of Science and Technology Irbid Jordan Department of Intelligence Systems Al-Balqa Applied University Al-Salt Jordan Dept. of Computer Information Systems Middel East University (MEU) Amman Jordan

ISBN: (数字)9798331540760

ISBN: (纸本)9798331540777

These days, autism spectrum disorder (ASD) is considered to be one of the most common health problems. A person with ASD is said to have a neurodegenerative disorder that affects their ability to engage and communicate. Using screening tests to identify autism symptoms is highly costly and time-consuming. The development of machine learning (ML) and artificial intelligence (AI) has made it possible to predict autism symptoms early on. Early detection of autism symptoms is now feasible due to advancements in AI and ML. The main contribution of this study is to address the high dimensionality problem by proposing a new hybrid feature selection approach using the genetic algorithm (GA) with K-nearest neighbor (KNN). This hybrid approach employs the GA-KNN to identify the ideal feature subset that enhances the performance of autism classification. The model uses three classification algorithms: Support Vector Machine (SVM), Random Forest (RF), and Convolution Neural Network (CNN) to compare their impact on classification performance and accuracy based on the proposed approach based on three ASD datasets for adults, adolescents, and children. The experimental results show that: 1) the hybrid GA-KNN approach used in the feature selection step can reduce the total number of features from 21 to 9 and increase the classifiers accuracy by 9% to 17%; 2) out of all the tested classifiers, the CNN classifier performed the best in classification, yielding the highest accuracy of 97.5%.

关键词： Support vector machines Autism Accuracy Nearest neighbor methods Feature extraction Classification algorithms Random forests

来源：评论

学校读者我要写书评

暂无评论

Model and Procedure for Assessing the Qualification of a Software Developer

Model and Procedure for Assessing the Qualification of a Sof...

引用

Annual Siberian Russian Workshop on Electron Devices and Materials (EDM)

作者： Erchimen I. Gavriliev Tatiana V. Avdeenko dept. of Theoretical and Applied Computer Science Novosibirsk State Technical University Novosibirsk Russia

ISBN: (数字)9781665498043

ISBN: (纸本)9781665498050

Developers’ qualifications and their professional development are the important aspects of IT project’s success. Software developers are interested in advancing their careers and improving their skills in order to maintain their level of professional value both for organizations and for the labor market. However, the evaluation of developers' qualifications is frequently based on the subjective opinion of managers, which can negatively affect the further professional development of employees. Quantitative metrics become particularly relevant in the assessment, because they can reduce the level of subjectivity. Therefore, the objective of this work is to develop data model for assessing the qualifications of software developers. The proposed model is based on data from version control system, knowledge management system and task management system. In order to collect information from these systems, as well as to carry out calculations, a decision support system based on the *** software platform was developed. The designed model and system were implemented in a software development company. For the assessment of relations between selected metrics and developers’ qualifications a multiple regression analysis was carried, where the employee's qualification rating assessed by the manager was the dependent variable. As a result, 2 models were prepared to assess the responsibility and the level of technical skills. Together, these models will provide a more comprehensive picture of developer’s qualification.

关键词： Measurement Process control Metadata Software Knowledge management Regression analysis Planning

来源：评论

学校读者我要写书评

暂无评论

Deep Reinforcement Learning Optimized Intelligent Resource Allocation in Active RIS-Integrated TN-NTN Networks

Deep Reinforcement Learning Optimized Intelligent Resource A...

引用

IEEE Conference on Wireless Communications and Networking

作者： Muhammad Ahmed Mohsin Hassan Rizwan Muhammad Jazib Muhammad Iqbal Muhammad Bilal Tabinda Ashraf Muhammad Farhan Khan Jen-Yi Pan Dept. of Electrical Engineering Stanford University USA Dept. of Electrical Engineering University of California Riverside USA Dept. of Electrical Engineering Pakistan Institute of Engineering and Applied Sciences Pakistan Dept. of Communication Engineering National Chung Cheng University Taiwan Dept. of Electrical Engineering University of California Irvine USA School of Computer Science and Information Technology University College Cork Ireland

ISBN: (数字)9798350368369

ISBN: (纸本)9798350368376

This work explores the deployment of active reconfigurable intelligent surfaces (A-RIS) in integrated terrestrial and non-terrestrial networks (TN-NTN) while utilizing coordinated multipoint non-orthogonal multiple access (CoMP-NOMA). Our system model incorporates a UAV-assisted RIS in coordination with a terrestrial RIS which aims for signal enhancement. We aim to maximize the sum rate for all users in the network using a custom hybrid proximal policy optimization (H-PPO) algorithm by optimizing the UAV trajectory, base station (BS) power allocation factors, active RIS amplification factor, and phase shift matrix. We integrate edge users into NOMA pairs to achieve diversity gain, further enhancing the overall experience for edge users. Exhaustive comparisons are made with passive RIS-assisted networks to demonstrate the superior efficacy of active RIS in terms of energy efficiency, outage probability, and network sum rate.

关键词： NOMA System performance Reconfigurable intelligent surfaces Probability Energy efficiency Trajectory Power system reliability Resource management Optimization Terahertz communications

来源：评论

学校读者我要写书评

暂无评论

Deep ViT Features as Dense Visual Descriptors

arXiv

引用

arXiv 2021年

作者： Amir, Shir Gandelsman, Yossi Bagon, Shai Dekel, Tali Dept. of Computer Science and Applied Math The Weizmann Inst. of Science United States

We study the use of deep features extracted from a pre-trained Vision Transformer (ViT) as dense visual descriptors. We observe and empirically demonstrate that such features, when extracted from a self-supervised ViT model (DINO-ViT), exhibit several striking properties, including: (i) the features encode powerful, well-localized semantic information, at high spatial granularity, such as object parts;(ii) the encoded semantic information is shared across related, yet different object categories, and (iii) positional bias changes gradually throughout the layers. These properties allow us to design simple methods for a variety of applications, including co-segmentation, part co-segmentation and semantic correspondences. To distill the power of ViT features from convoluted design choices, we restrict ourselves to lightweight zero-shot methodologies (e.g., binning and clustering) applied directly to the features. Since our methods require no additional training nor data, they are readily applicable across a variety of domains. We show by extensive qualitative and quantitative evaluation that our simple methodologies achieve competitive results with recent state-of-the-art supervised methods, and outperform previous unsupervised methods by a large margin. © 2021, CC BY.

关键词： Zero-shot learning

来源：评论

学校读者我要写书评

暂无评论

Tangispeak: A Digital Game Based Language Learning System by Using Augmented Reality Technology and Tangible Objects

Tangispeak: A Digital Game Based Language Learning System by...

引用

IEEE Global Conference on Consumer Electronics (GCCE)

作者： Catherine Akoth Ongoro Yong-Yi Fanjiang Chi-Huang Hung Bing-Jun Lin Mu-En Lee Graduate Institute of Applied Science and Engineering Fu Jen Catholic University New Taipei City Taiwan Dept. of Computer Science and Information Engineering Graduate Institute of Applied Science and Engineering Fu Jen Catholic University New Taipei City Taiwan Dept. of Information Technology Graduate Institute of Applied Science and Engineering Fu Jen Catholic University Lee-Ming Institute of Technology New Taipei City Taiwan Dept. of Computer Science and Information Engineering Fu Jen Catholic University New Taipei City Taiwan

In this study, Tangispeak is introduced as a language learning system for preschoolers, incorporating Digital Game-Based learning (DGBL), Augmented Reality (AR), and tangible objects. It aims to enhance English language skills through interactive and fun experiences. Tangispeak utilizes a micro-controller, gameboard, Unity 3D, and Vuforia database platform, offering scrambling and speaking steps for word formation and pronunciation. The system provides rewards, hints, and timing to engage learners while fostering cognitive and innovative skills.

关键词：

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：