检索结果-内蒙古大学图书馆

The 3rd International Conference on Frontiers in Computing and systems (COMSYS 2022)

作者： Bera, Asish Bhattacharjee, Debotosh Nasipuri, Mita Department of Computer Science and Information Systems BITS Rajasthan Pilani India Department of Computer Science and Engineering Jadavpur University WB Kolkata India

ISBN: (纸本)9789819926794

Fine-grained image classification (FGIC) is a challenging task due to small visual differences among inter-subcategories, but large intra-class variations. In this paper, we propose a fusion approach to address FGIC by combining global texture with local patch-based information. The first pipeline extracts deep features from various fixed-size non-overlapping patches and encodes features by sequential modeling using the long short-term memory (LSTM). Another path computes image-level textures at multiple scales using the local binary patterns (LBP). The advantages of both streams are integrated to represent an efficient feature vector for classification. The method is tested on six datasets (e.g., human faces, food-dishes, etc.) using four backbone CNNs. Our method has attained better classification accuracy over existing methods with notable margins. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Local binary pattern

来源：评论

学校读者我要写书评

暂无评论

Advanced Power and Voltage Regulation in a DC Microgrid with Solar Energy and Battery Storage 15

Advanced Power and Voltage Regulation in a DC Microgrid with...

引用

15th International Renewable Energy Congress, IREC 2025

作者： Elrashid, M. Rajab, Z. Elhashane, M. Younis, A. Gomez-Romero, J. Tnatin, Salah I.S Mohamed, F. Benghazi University Faculty if Engineering Electrical and Electronic Department Libya University of Granada Department of Computer Science and Artificial Intelligence Spain Omar Al-Mukhtar University Faculty of Engineering Electrical Department Libya Authority of Natural Science Research and Technology Tripoli Libya

ISBN: (纸本)9798331516772

This research investigates the modeling, design, and simulation of a microgrid with advanced energy management controller, developed using MATLAB/Simulink. This paper presents a comprehensive analysis, modeling, and simulation of a smart controller for a boost converter, using an artificial neural network (ANN) for maximum power point tracking (MPPT) to enhance power output. The study begins with a comparison between traditional MPPT techniques and the ANN-based approach. These MPPT strategies are compared in terms of their response to dynamic weather conditions (such as temperature and irradiance variations) and their ability to minimize oscillations around the maximum power point. The simulation results demonstrate that the ANN-based MPPT method outperforms the conventional P&O and IC methods, providing faster response times and higher output power. Additionally, the system's performance was evaluated under conditions of power shortage and during battery charging and discharging cycles, showing the effectiveness of the smart controller in maintaining system stability and optimizing energy management. © 2025 IEEE.

关键词： MATLAB

来源：评论

学校读者我要写书评

暂无评论

Machine Learning Based Diagnosis for Diabetic Retinopathy for SKPD-PSC

引用

Intelligent Automation & Soft Computing 2023年第5期36卷 1767-1782页

作者： M.P.Thiruvenkatasuresh Surbhi Bhatia Shakila Basheer Pankaj Dadheech Department of Computer Science and Engineering Erode Sengunthar Engineering CollegePerunduraiErode638057Tamil NaduIndia Department of Information Systems College of Computer Sciences and Information TechnologyKing Faisal UniversityAl HasaSaudi Arabia Department of Information Systems College of Computer and Information SciencePrincess Nourah bint Abdulrahman UniversityP.O.BOX 84428Riyadh11671Saudi Arabia Department of Computer Science&Engineering Swami Keshvanand Institute of TechnologyManagement&Gramothan(SKIT)Jaipur302017RajasthanIndia Corresponding

The study aimed to apply to Machine Learning(ML)researchers working in image processing and biomedical analysis who play an extensive role in compre-hending and performing on complex medical data,eventually improving patient *** a novel ML algorithm speciﬁc to Diabetic Retinopathy(DR)is a chal-lenge and need of the *** images include several challenges,including relevant feature selection,class variations,and robust classiﬁ*** the cur-rent research in DR has yielded favourable results,several research issues need to be *** is a requirement to look at novel pre-processing methods to discard irrelevant features,balance the obtained relevant features,and obtain a robust classi-ﬁ*** is performed using the Steerable Kernalized Partial Derivative and Platt Scale Classiﬁer(SKPD-PSC)*** novelty of this method relies on the appropriate non-linear classiﬁcation of exclusive image processing models in har-mony with the Platt Scale Classiﬁer(PSC)to improve the accuracy of DR ***,a Steerable Filter Kernel Pre-processing(SFKP)model is applied to the Retinal Images(RI)to remove irrelevant and redundant features and extract more meaningful pathological features through Directional Derivatives of Gaussians(DDG).Next,the Partial Derivative Image Localization(PDIL)model is applied to the extracted fea-tures to localize candidate features and suppress the background ***,a Platt Scale Classiﬁer(PSC)is applied to the localized features for robust classiﬁ*** the experiments,we used the publicly available DR detection database provided by Standard Diabetic Retinopathy(SDR),called DIARETDB0.A database of 130 image samples has been collected to train and test the ML-based classiﬁ*** results show that the proposed method that combines the image processing and ML models can attain good detection performance with a high DR detection accu-racy rate with minimum time and complexity compared to

关键词： Diabetic retinopathy retinal images machine learning image localization Platt Scale classiﬁer accuracy

来源：评论

学校读者我要写书评

暂无评论

WSports-50: An Image Dataset for Women’s Sport Action Classification 4th

WSports-50: An Image Dataset for Women’s Sport Action Class...

引用

4th International Conference on Frontiers in Computing and systems, COMSYS 2023

作者： Ray, Palash Bera, Asish Krejcar, Ondrej Bhattacharjee, Debotosh Nasipuri, Mita Department of Computer Science and Engineering Haldia Institute of Technology West Bengal Haldia India Department of Computer Science and Information Systems BITS Pilani Rajasthan Pilani India Center for Basic and Applied Science Faculty of Informatics and Management University of Hradec Kralove Rokitanskeho 62 Hradec Kralove500 03 Czech Republic Department of Computer Science and Engineering Jadavpur University West Bengal Kolkata India

ISBN: (纸本)9789819726134

Sport action recognition is an interesting area in computer vision. Categorization of sport actions, representing difficult and complex body postures, is regarded as a fine-grained visual classification problem. The Convolutional Neural Networks (CNNs) have attained enhanced performance over conventional feature descriptors in recognizing various sport activities. In general, though decent improvement has been gained using deep learning for sport action recognition, however, recognition of women’s sport activities is not widely explored. Even, no benchmark dataset depicting women’s sport action with sufficient variations is available yet for study. Hence, fine-grained image classification of diverse sport categories involving female/women athletics requires immediate research attention. To overcome this limitation, this paper proposes an image dataset comprising worldwide popular 50 sport categories of women players only. A simple deep learning model is proposed that extracts the high-level deep features using a backbone CNN. Then, these features are pooled from a collection of regular regions representing local discriminative information. The spatial pyramid pooling is applied for mining semantic information and enhancing feature aggregation for classification. The proposed method has achieved satisfactory performance on the Women Sports dataset using four standard backbone CNNs. Moreover, our method has achieved better accuracy on the Yoga-82 pose recognition dataset with a significant margin, e.g., 11.6% gain using ResNet-50 base CNN. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Classification (of information)

来源：评论

学校读者我要写书评

暂无评论

Maximum Channel Coding Rate of Finite Block Length MIMO Faster-Than-Nyquist Signaling

Maximum Channel Coding Rate of Finite Block Length MIMO Fast...

引用

2025 IEEE Wireless Communications and Networking Conference, WCNC 2025

作者： Zhang, Zichao Yuksel, Melda Yanikomeroglu, Halim Ng, Benjamin K. Lam, Chan-Tong Carleton University Department of Systems and Computer Engineering OttawaON Canada Middle East Technical University Department of Electrical and Electronics Engineering Ankara Turkey Faculty of Applied Sciences Macao Polytechnic University China

ISBN: (纸本)9798350368369

The pursuit of higher data rates and efficient spectrum utilization in modern communication technologies necessitates novel solutions. In order to provide insights into improving spectral efficiency and reducing latency, this study investigates the maximum channel coding rate (MCCR) of finite block length (FBL) multiple-input multiple-output faster-than-Nyquist (FTN) channels. By optimizing power allocation, we derive the system's MCCR expression. Simulation results are compared with the existing literature to reveal the benefits of FTN in FBL transmission. © 2025 IEEE.

关键词： Signaling

来源：评论

学校读者我要写书评

暂无评论

Enhancing Event Extraction and Impact Analysis with Knowledge Graph Augmented Transformer 21

Enhancing Event Extraction and Impact Analysis with Knowledg...

引用

21st International System-on-Chip Design Conference, ISOCC 2024

作者： Songklang, Kallaya Srisomboon, Kanabadee Lee, Wilaiporn Prayote, Akara King Mongkut's University of Technology North Bangkok Faculty of Applied Science Department of Computer and Information Science Bangkok10800 Thailand King Mongkut's University of Technology North Bangkok Faculty of Engineering Department of Electrical and Computer Engineering Bangkok10800 Thailand

ISBN: (纸本)9798350377088

Understanding the impact of events reported in news articles plays pivotal roles in various tasks. In this study, we propose a framework for extracting event components from the news and analyzing their multifaceted impact. The framework employs transformers techniques for event extraction and leverages a biography knowledge base to assess the potential impact of events across social, political, economic, and culture (SPEC) dimensions. Our framework achieves an F1-score of 78.12% in event identification as well as significant accuracy score in evaluating the impact levels prediction. By integrating results from event extraction and impact analysis, the level of multifaceted influence of the event along with the event information are effectively visualized. This comprehensive approach enhances understanding of multi-perspective interpretation and supports insights for various applications. © 2024 IEEE.

关键词： Distribution transformers

来源：评论

学校读者我要写书评

暂无评论

Cloud-Driven Wearables using Naive Bayes Algorithm and Advanced Sensors in Smart Fabrics 3

Cloud-Driven Wearables using Naive Bayes Algorithm and Advan...

引用

3rd International Conference for Innovation in Technology, INOCON 2024

作者： Praveena, M.D. Anto Gupta, Nukala Sujata Marin, V. Bini Jeslin, J Gnana Meenakshi, R. Muthulekshmi, M. Sathyabama Institute of Science and Technology Department of Computer Science and Engineering Tamil Nadu Chennai India Chaitanya Bharathi Institute of Technology Department of Computer Engineering and Technology Telangana Hyderabad India Srm Institute of Science and Technology Faculty of Management Studies Tamil Nadu Chennai India Rmk College of Engineering and Technology Department of Computer Science and Engineering Tamil Nadu Chennai India Chennai Institute of Technology Department of Computer Science and Engineering Tamil Nadu Chennai India Saveetha Institute of Medical and Technical Sciences Saveetha University Department of Biomedical Engineering Saveetha School of Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350381931

In the realm of fashion technology, the introduction of smart textiles and wearable sensors may be attributed to the fast progression of technology and the rising need for customized, interactive, and intelligent apparel. This study investigates the interface of electronics and textiles to develop cutting-edge, multifunctional, and flexible apparel. This research digs into the fascinating world of smart textiles. It investigates the many different kinds of sensors, such as temperature sensors, heart rate monitors, motion detectors, and chemical sensors that may be easily incorporated into the fabric. These sensors make a wide variety of applications possible, such as improved user experiences, monitoring of health conditions, and data collecting on the surrounding environment. In addition, to assure the practicability and usage of smart textiles, it addresses the problems and possibilities connected with various power sources, connection choices, and data processing units. The purpose of this study is to contribute to the continuous development of intelligent clothing, which has the potential to revolutionize the fashion industry by producing clothes that not only represent an individual's sense of style but also give useful insights, functionality, and connectedness to the digital world. © 2024 IEEE.

关键词： Smart textiles

来源：评论

学校读者我要写书评

暂无评论

COVID-19 Prediction Model Empowered with Fused Computational Intelligence Technique

引用

IEIE Transactions on Smart Processing and Computing 2025年第1期14卷 109-117页

作者： Khan, Muhammad Adnan Naseer, Iftikhar Ali, Muhammad Nadeem Kim, Byung-Seo Department of Software Faculty of Artificial Intelligence & Software Gachon University Seongnam13120 Korea Republic of Faculty of Computer Science & Information Technology The Superior University Lahore54600 Pakistan Department of Software and Communications Engineering Hongik University Sejong Campus Korea Republic of

The novel Coronavirus (COVID-19) spread rapidly around the world and caused overwhelming effects on the health and economy of the world. It first appeared in Wuhan city of China and was declared a pandemic by the World Health Organization (WHO). Many researchers, as well as experts in clinical and artificial intelligence experts, are working together to control the rapid spread of COVID-19 with early detection. This study focused on intelligent prediction for coronavirus using computational intelligence approaches (IPC-FCIA) like convolutional neural networks, support vector machines, and fuzzy logic techniques. The proposed IPC-FCIA model is based on two sections namely the training section and the validation section. Features fusion and decision-level fusion are used in this study to enhance the performance of the recommended IPC-FCIA model. The proposed model predicts the early detection of COVID-19 in two types COVID-negative and COVID-positive. The benchmark results of the model show an accuracy of 97.66 % on decision-level fusion in the detection of COVID-19. The proposed model can be helpful for medical experts as well as COVID-19-affected patients. © 2025 Institute of Electronics Engineers of Korea. All rights reserved.

关键词： COVID-19

来源：评论

学校读者我要写书评

暂无评论

AI-Driven Digital Mirror Technology for Securing IoT-Enabled Smart Infrastructures 2

AI-Driven Digital Mirror Technology for Securing IoT-Enabled...

引用

2nd IEEE International Conference on Integrated Intelligence and Communication systems, ICIICS 2024

作者： Chaudhary, Jyoti Jain, Upma Sudarshan, N. Pai, Vasudeva Sutaria, Kamal Natrayan, L. Department of Computer Engineering The Technological Institute of Textile and Sciences Bhiwani India Department of Computer Science & Engineering Graphic Era Deemed to be University Dehradun India Department of Mechanical Engineering The National Institute of Engineering Mysuru India Department of Information Science and Engineering NMAM Institute of Technology NITTE Deemed to be University Karkala India Faculty of Engineering and Technology Parul institute of Engineering and Technology Vadodara India Department of Mechanical Engineering Saveetha School of Engineering Chennai India

ISBN: (纸本)9798331504960

The expansion of the Internet of Things (IoT) is driven by the proliferation of interconnected wireless gadgets, made possible by ever-improving computer and Internet infrastructure. The IoT is an extensive system of interconnected sensors and gateways. In the course of their constant environmental interaction, IoT devices produce a wealth of data resources, including images, audio, video, digital signals, and more. Connectivity to the Internet allows all Internet of Things (IoT) equipment and apps to exchange data and resources more effectively. The proposed AI-Driven Digital Mirror Technology effectively enhances the security of IoT-enabled smart infrastructures by integrating real-time monitoring, anomaly detection, and adaptive response mechanisms. By employing advanced AI algorithms such as Graph Neural Networks (GNNs) and Reinforcement Learning (RL), the technology mirrors IoT environments digitally, enabling dynamic modeling and threat assessment. The system demonstrated a significant improvement in threat detection accuracy, achieving a precision rate of 95.6% and a recall rate of 93.2% during simulations. Additionally, the adaptive response mechanism reduced the average response time to security breaches by 40%, ensuring minimal disruption to infrastructure operations. Comparative analysis with traditional security solutions highlighted the system's superiority in mitigating complex, multi-layered attacks, and its scalability makes it suitable for various smart infrastructure applications, including smart cities, industrial automation, and intelligent transportation systems. The results underscore the potential of AI-Driven Digital Mirror Technology in securing IoT ecosystems against evolving cyber threats while maintaining operational efficiency and resilience. © 2024 IEEE.

关键词： Gateways (computer networks)

来源：评论

学校读者我要写书评

暂无评论

A Deep Learning Approach to Recognizing New Thai Currency Using Transfer Learning for Visually Impaired Individuals 17

A Deep Learning Approach to Recognizing New Thai Currency Us...

引用

17th International Conference on Signal Processing and Communication systems, ICSPCS 2024

作者： Ahmad, Irfan Sharif, Abubakar Azmat, Aneeka Jamil, Muhammad Usman Maynooth University Maynooth International Engineering College Faculty of Science and Engineering Ireland Maynooth University Maynooth International Engineering College Faculty of Science and Engineering Fuzhou China University of Glasgow James Watt School of Engineering United Kingdom National Tsinghua University College of Electrical Engineering and Computer Science Institute of Information Systems and Application Hsinchu Taiwan Chuzhou University School of Mechanical and Electrical Engineering Chuzhou China

ISBN: (纸本)9798350389630

Assistive mobile applications play a pivotal role for visually impaired individuals worldwide. These applications often face challenges in currency recognition due to varying perspectives, inconsistent illumination, and background clutter. This issue is especially pressing in developing countries like Thailand, where there is a notable gap in robust currency recognition systems, particularly for the new Thai currency notes. This study employs a deep learning approach using a convolutional neural network (CNN) to automate the recognition of the new Thai currency notes. Using transfer learning, we fine-tuned the CNN using the Xception model, renowned for its depth-wise separable convolution. The network trained on a meticulously curated dataset comprising 3600 images (without data augmentation) of five different denominations of the new Thai currency (20, 50, 100, 500, and 1000 baht) notes, captured under various conditions. The resulting model achieved an average training accuracy of 99.5% and a validation accuracy of 99.8%. Given its robustness and high accuracy, the model can be integrated into an Android application. Such an application would offer a user-friendly and reliable tool for visually impaired individuals to effortlessly identify the new Thai currency notes in their everyday transactions. © 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

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：