检索结果-内蒙古大学图书馆

Advances in Computing, Communication and Applied informatics (ACCAI), International Conference on

作者： Anil Pandurang Shinde Ramandeep Singh Renuka Shantappa Anami Noor Mohammed R. Selvameena Ashwini Jotiram Patil Department of Computer Technology Bharati Vidyapeeth's Jawaharlal Nehru Institute of Technology (Poly) Pune Maharashtra Department of Computer Science Guru Nanak Khalsa College Daroli Kalan Jalandhar Jalandhar Punjab Department of Computer Technology Bharati Vidyapeeth's Jawaharlal Nehru Institute of Technology (Poly) Pune Department of Computer Science Engineering Graphic Era Deemed to be University Dehradun Uttrakhand Department of Computer Science and Engineering Dr.M.G.R Educational and Research Institute Chennai TamilNadu India

An persistent eye infection called glaucoma causes irreversible vision loss. A crucial problem in the clinical field is glaucoma identification. There have been very few studies designed to detect glaucoma in its early stages. However, using current techniques to carry out glaucoma infection detection was ineffective. Moreover, the conventional glaucoma infection diagnosis method required additional time. Glaucoma identification in various fundus images is a challenging task that necessitates mastery and extensive training. Glaucoma is an ocular neuropathy characterized by mild retinal ganglion cell degeneration. There are several different types of glaucoma, each having a unique a etiology and set of pathogenic factors, but they all have alterations in the structure and function of the optic nerve that are essentially universal. Convolution organisations, a type of deep learning computation, have swiftly emerged as a method for making decisions while analyzing clinical images. A chronic eye condition called glaucoma causes irreversible vision loss. In this section, we discuss two state-of-the-art glaucoma detection methods in the light of deep learning. The glaucoma dataset is used in this deep learning computation proposed for glaucoma photo analysis. This finding contrasts with convolutional brain network grouping techniques in the context of deep learning. Each rating indicates that the newly proposed worldview is more favorable than the one currently in use.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Grid Search Hyperparameters Tuning with Supervised Machine Learning for Awngi Language Named Entity Recognition

Grid Search Hyperparameters Tuning with Supervised Machine L...

引用

International Conference on Computational and Characterization Techniques in engineering & Sciences (CCTES)

作者： Amogne Andualem Ayalew Gizachew Bitew Ayodeji Olalekan Salau Melaku Lake Tegegne Shashi Kant Gupta Mohammad Israr Tesfa Tegegne Tsehay Admassu Assegie Department of Information Technology School of Computing and Informatics Mizan-Tepi University Tepi Ethiopia Computer Science Department School of Computing Mizan Tepi University Mizan-Tepi Ethiopia Department of Electrical/Electronics and Computer Engineering Afe Babalola University Ado-Ekiti Nigeria Department of Information Technology College of Engineering and Technology Injibara University Injibara Ethiopia Department of Computer Science and Engineering Eudoxia Research University USA Hotoro GRA Maryam Abacha American University of Nigeria Kano State Federal Republic of Nigeria Faculty of Computing Bahir Dar Institute of Technology Bahir Dar University Bahir Dara Ethiopia Department of Computer Science College of Engineering and Technology Injibara University Injibara Ethiopia

ISBN: (数字)9798350364699

ISBN: (纸本)9798350364705

Named entity recognition has emerged as a critical step in recognizing, classifying, and extracting the most significant information from unstructured text without human intervention. It is used in information retrieval, conversation systems, machine translation, data mining, and information extraction. However, the Awngi language lacks a NER system due to inadequate datasets. This study combines feature extraction techniques with supervised machine learning algorithms, including support vector machines, Naïve Bayes, and conditional random fields with a grid search algorithm parameter optimization. These supervised machine-learning models often have various hyperparameters that must be tuned to optimize the model's performance by automatically selecting the parameter. Furthermore, the dataset used for training and testing consists of 20,193 annotated Awngi tokens, collected from Amhara Media Corporation, Awngi Telegram, and Facebook pages. The proposed SVM with grid search algorithm approach provided a promising result in ANER with an accuracy of 92% compared with Naïve Bayes, conditional random field, and decision-tree supervised machine learning models. Finally, the experiment results show that the proposed parameter tuning-based model can detect and categorize Awingi-named entities and outperforms the performance accuracy without parameter adjustment.

关键词： Support vector machines Training Accuracy Machine learning algorithms Named entity recognition Feature extraction Conditional random fields Bayes methods Optimization Tuning

来源：评论

学校读者我要写书评

暂无评论

Safety Guaranteed Power-Delivered-to-Load Maximization for Magnetic Wireless Power Transfer

Safety Guaranteed Power-Delivered-to-Load Maximization for M...

引用

IEEE Annual Joint Conference: INFOCOM, IEEE computer and Communications Societies

作者： Wangqiu Zhou Xinyu Wang Hao Zhou Shenyao Jiang Zhi Liu Yusheng Ji LINKE Lab School of Computer Science and Technology University of Science and Technology of China CAS Key Laboratory of Wireless-Optical Communications University of Science and Technology of China Deqing Alpha Innovation Institute Huzhou China Department of Computer and Network Engineering The University of Electro-Communications Tokyo Japan Information Systems Architecture Science Research Division National Institute of Informatics Tokyo Japan

ISBN: (数字)9798350383508

ISBN: (纸本)9798350383515

Electromagnetic radiation (EMR) safety has always been a critical reason for hindering the development of magneticenabled wireless power transfer technology. People focus on the actual received energy at charging devices while paying attention to their health. Thus, we study this significant problem in this paper, and propose a universal safety guaranteed power-delivered-to-load (PDL) maximization scheme (called SafeGuard). Technically, we first utilize the off-the-shelf electromagnetic simulator to perform the EMR distribution analysis to ensure the universality of the method. Then, we innovatively introduce the concept of multiple importance sampling for achieving efficient EMR safety constraint extraction. Finally, we treat the proposed optimization problem as an optimal boundary point search problem from the perspective of space geometry, and devise a brand-new grid-based multi-constraint parallel processing algorithm to efficiently solve it. We implement a system prototype for SafeGuard, and conduct extensive experiments to evaluate it. The results indicate that our SafeGuard can obviously improve the achieved PDL by up to 1.75× compared with the state-of-the-art baseline while guaranteeing EMR safety. Furthermore, SafeGuard can accelerate the solution process by 29.12× compared with the traditional numerical method to satisfy the fast optimization requirement of wireless charging systems.

关键词： Performance evaluation Monte Carlo methods System performance Inductive charging Prototypes Wireless power transfer Parallel processing

来源：评论

学校读者我要写书评

暂无评论

Extracting Behavioral Characteristics of College Students Using Data Mining on Big Data

Extracting Behavioral Characteristics of College Students Us...

引用

Artificial Intelligence and Knowledge Discovery in Concurrent engineering (ICECONF), International Conference on

作者： R. Meena T. Kavitha Arun Kumar S Daya Mary Mathew R. Anusuya G. Karthik Department of Computer Science Engineering Prathyusha Engineering College Chennai Department of Information Technology VeltechMultitech Dr. Rangarajan Dr. Sakunthala Engineering College Chennai Department of Electrical and Electronics Engineering Sona College of Technology Salem Department of Computer Science Engineering Loyola ICAM college of Engineering and Technology Chennai Department of Computer Science and Engineering Modern Institute of Technology and Research Centre Alwar Rajasthan Department of Information Technology Karpagam College of Engineering Coimbatore

When it comes to quickly and accurately understanding behavior trends among undergraduates, teachers may find that sophisticated analytics become useful. There has been a shift in the management of student affairs from anecdotal, qualitative information to data-driven, quantitative analysis, thanks in large part to the emergence and advancement of big data techniques that enable educators to analyze the behavior patterns of learners in a timely and effective manner, particularly to find the groups of learners that need to be concentrated on in a timely fashion. Using the clustering technique of data mining, the authors of this research suggest a Behavioral Characteristics Extraction Model (BCEM) for studying student networking habits on campus. According to the findings, there are 400 pupils who make heavy use of the Internet and are spread over four distinct categories. There is an impact on these students' academic performance and outcomes. Data mining of student network behavior was conducted for this study, which may be utilized as a real-world example of data mining in student affairs management and so offers solid empirical backing for the advancement of that field.

关键词： Statistical analysis Big Data Solids Market research Knowledge discovery Data models Behavioral sciences

来源：评论

学校读者我要写书评

暂无评论

Enhancing Security with Machine Learning-based Finger-Vein Biometric Authentication System

Enhancing Security with Machine Learning-based Finger-Vein B...

引用

Mobile Computing and Sustainable informatics (ICMCSI), International Conference on

作者： G Prakash Babu Upendra Singh Aswal M. Sindhu Vijay Jagdish Upadhye Natrayan L Harshal Patil Department of Computer Science and Engineering Acharya Institute of Technology Bangalore Karnataka India Department of Computer Science & Engineering Graphic Era Deemed to be University Dehradun Uttarakhand India Department of Computer Science and Business Systems R.M.K. Engineering College Thiruvallur Tamil Nadu India Department of Microbiology Research and Development Cell Parul Institute of Applied Sciences Parul University Waghodia Gujarat India Department of Mechanical Engineering Saveetha School of Engineering SIMATS Chennai Tamil Nadu India Computer Science and Engineering Symbiosis Institute of Technology Symbiosis International (Deemed University) Pune Maharashtra India

Biometric authentication systems have gained popularity as the necessity for secure individual identification has expanded. Iris, hand geometry, fingerprints, retina, vein patterns on the fingers and palms, and voice patterns are all common biometric features. Biometric recognition systems based on finger-veins are becoming more common due to their numerous advantages, including low danger of duplication or fraud, minimum invasiveness, and excellent identification accuracy. Motivated by these advantages, our research looks into the subject of finger-vein authentication. Similar to identification, exploration of hidden patterns within data is essential for effective finger-vein recognition. The study employs two feature extraction approaches, the Local Binary Pattern (LBP) and the Gabor filter, as well as two machine learning models, the Adaptive Neuro-Fuzzy Inference System (ANFIS) and the Support Vector Machine (SVM). Using finger-vein images from the SDUMLA HMT database, this study evaluates four distinct feature extraction and ML model combinations. The simulation results highlight the excellent accuracy of 97.72%, False Acceptance Rate (FAR) of 3.1%, and False Rejection Rate (FRR) of 1.41% attained by LBP with ANFIS. In addition to performance measurements, the study examines the amount of time required for each task.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Transfer Learning for Keypoint Detection in Low-Resolution Thermal TUG Test Images

arXiv

引用

arXiv 2025年

作者： Chen, Wei-Lun Hsieh, Chia-Yeh Kao, Yu-Hsiang Liu, Kai-Chun Peng, Sheng-Yu Tsao, Yu Research Center for Information Technology Innovation Academic Sinica Taiwan Graduate Institute of Electrical Engineering National Taiwan University Taiwan Bachelor’s Program in Medical Informatics and Innovative Applications Fu Jen Catholic University Taiwan Department of Electrical Engineering National Taiwan University Taiwan College of Information and Computer Sciences University of Massachusetts AmherstMA01003 United States Department of Electrical Engineering National Taiwan University of Science of Technology Taiwan

This study presents a novel approach to human keypoint detection in low-resolution thermal images using transfer learning techniques. We introduce the first application of the Timed Up and Go (TUG) test in thermal image computer vision, establishing a new paradigm for mobility assessment. Our method leverages a MobileNetV3-Small encoder and a ViTPose decoder, trained using a composite loss function that balances latent representation alignment and heatmap accuracy. The model was evaluated using the Object Keypoint Similarity (OKS) metric from the COCO Keypoint Detection Challenge. The proposed model achieves better performance with AP, AP50, and AP75 scores of 0.861, 0.942, and 0.887 respectively, outperforming traditional supervised learning approaches like Mask R-CNN and ViTPose-Base. Moreover, our model demonstrates superior computational efficiency in terms of parameter count and FLOPS. This research lays a solid foundation for future clinical applications of thermal imaging in mobility assessment and rehabilitation monitoring. Copyright © 2025, The Authors. All rights reserved.

关键词： Thermography (imaging)

来源：评论

学校读者我要写书评

暂无评论

ECG-Image-Kit: A Synthetic Image Generation Toolbox to Facilitate Deep Learning-Based Electrocardiogram Digitization

arXiv

引用

arXiv 2023年

作者： Shivashankara, Kshama Kodthalu Deepanshi Shervedani, Afagh Mehri Clifford, Gari D. Reyna, Matthew A. Sameni, Reza The School of Electrical and Computer Engineering Georgia Institute of Technology AtlantaGA United States The Electrical and Computer Engineering Department University of Illinois Chicago ChicagoIL United States The Department of Biomedical Informatics Emory University AtlantaGA United States The Department of Biomedical Engineering Georgia Institute of Technology GA United States

Cardiovascular diseases (CVDs) are a leading cause of mortality worldwide, and the electrocardiogram (ECG) plays a crucial role in diagnosing CVDs. For decades, ECGs have been recorded in printed formats, and their digitization offers significant potential for training machine learning (ML) models in algorithmic ECG diagnosis. However, the valuable information in physical ECG archives risks deterioration, leading to a considerable loss in CVD research, particularly in low and low-middle-income countries where paper-based ECGs are common. Furthermore, merely scanning printed ECGs is inadequate, as most ML models require ECG time-series data. Therefore, digitizing and converting paper ECG archives into time-series data is essential. In this context, deep learning models for image processing are promising. Nonetheless, the scarcity of clinical ECG archives with reference time-series data presents a challenge. To overcome this, data augmentation techniques using digital twins offer a potential solution. We introduce ECG-Image-Kit, an open-source toolbox for generating synthetic ECG images with realistic artifacts from time-series data, and showcase its application in developing algorithms for ECG image digitization. Synthetic data is generated by producing distortionless ECG images on a standard ECG paper background. Subsequently, various distortions, including handwritten text artifacts, wrinkles, creases, and perspective transformations, are applied to these ECG images. The artifacts and text are synthetically generated, excluding personally identifiable information. The toolbox is used for data augmentation in the 2024 PhysioNet Challenge on Digitization and Classification of ECG Images. As a case study, we employed ECG-Image-Kit to create an ECG image dataset of 21,801 records from the PhysioNet QT database [1, 2]. A denoising convolutional neural network (DnCNN)-based model was developed and trained on this synthetic dataset and used to convert the synthetically ge

关键词： Electrocardiograms

来源：评论

学校读者我要写书评

暂无评论

Effective solutions to prevent digital fraud by introducing electronic signature of PDF files

Effective solutions to prevent digital fraud by introducing ...

引用

International Conference on Control Systems and computer Science (CSCS)

作者： Ionuţ Petrişor Anghel Dragoş Rădulescu Ion Alexandru Marinescu Computer Science and Engineering Department Polytechnic University of Bucharest Bucharest Romania Digital Transformation And Governance National Institute for Research & Development in Informatics Bucharest Romania

With the exponential growth of digital transactions, the creation and verification of electronic signatures contributes decisively to the creation of a safer environment for business development and innovation, based on security factors such as confidentiality, integrity, authenticity or non-repudiation. Implementing electronic signatures in PDF files can improve the prevention of digital fraud by providing an additional level of security and authenticity. For this reason, in addition to choosing a reliable electronic signature provider, it is necessary to establish clear signing procedures in order to minimize the risk of fraud. For this purpose, a software solution intended mainly for the academic environment is presented, which facilitates the electronic signature with legal value of PDF documents using a digital certificate. This application offers the possibility of simultaneously signing of multiples files in a folder, thus streamlining the process of digital signature and avoiding the use of third-party applications.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Reducing the Number of Qubits from n2 to nlog2(n) to Solve the Traveling Salesman Problem with Quantum computers: A Proposal for Demonstrating Quantum Supremacy in the NISQ Era

arXiv

引用

arXiv 2024年

作者： Ramezani, Mehdi Salami, Sadegh Shokhmkar, Mehdi Moradi, Morteza Bahrampour, Alireza Department of Physics Sharif University of Technology Tehran14588 Iran Centre for Quantum Engineering and Photonics Technology Sharif University of Technology Tehran14588 Iran Department of Computer Engineering Sharif University of Technology Tehran14588 Iran Institute of Informatics Faculty of Mathematics Informatics and Mechanics University of Warsaw Banacha 2 Warsaw02-097 Poland

In our pursuit of quantum supremacy during the NISQ era, this research introduces a novel approach rooted in the Quantum Approximate Optimization Algorithm (QAOA) framework to address the Traveling Salesman Problem (TSP). By strategically reducing the requisite qubit count from n2 to n log2 (n), our QAOA-based algorithm not only contributes to the ongoing discourse on qubit efficiency but also demonstrates improved performance based on established metrics, underscoring its potential for achieving NISQ-era supremacy in solving real-world optimization challenges. © 2024, CC BY-NC-ND.

关键词： Traveling salesman problem

来源：评论

学校读者我要写书评

暂无评论

Analysis Role of ML and Big Data Play in Driving Digital Marketing's Paradigm Shift

Analysis Role of ML and Big Data Play in Driving Digital Mar...

引用

2023 IEEE World Conference on Applied Intelligence and Computing, AIC 2023

作者： Ravisankar, A. Ponniah, M. Tripathi, Vikas Kulshreshtha, Kushagra Srivastava, Arun Pratap Singh, Vikram Erode Sengunthar Engineering College Department of Management Studies Erode India G.T.N Arts College Pg and Research Department of Commerce Dindigul India Graphic Era Deemed University Department of Computer Science and Engineering Uttarakhand Dehradun India Institute of Business Management Gla University Mathura India Lloyd Institute of Engineering and Technology Department of CSE Greater Noida India Quantum University Uttarakhand Roorkee India

ISBN: (纸本)9798350310061

Marketing strategies are being revolutionized by the development of user data and the expanding usability of Machine Learning (ML) as well as Big Data approaches. The wide variety of options that ML and Big Data applications provide in building and sustaining a competitive corporate edge are not fully understood by researchers and marketers. Based on a thorough analysis of academic and commercial literature, we offer a classification of ML and Big Data use cases in marketing in this article. In order to effectively employ ML and Big Data in marketing, we have discovered 11 recurrent use cases that are grouped into 4 homogenous families. These families are: fundamentals of the consumer, the consumer experience, decision-making, and financial impact. We go over the taxonomy's repeating patterns and offer a conceptual framework for understanding and extending it, emphasizing the practical ramifications for marketers and academics. © 2023 IEEE.

关键词： Big data

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：