检索结果-内蒙古大学图书馆

Research on Biomedical Engineering 2025年第2期41卷 1-19页

作者： A, Padmashree Sankar, P. Alkhayyat, Ahmad Muniyandy, Elangovan Department of Computer Science and Business Systems Bannari Amman Institute of Technology Tamil Nadu Sathyamangalam India Department of Computer Science and Engineering Saveetha Engineering College Tamil Nadu Chennai India Department of Computers Techniques Engineering College of Technical Engineering The Islamic University Najaf Iraq Department of Computers Techniques Engineering College of Technical Engineering The Islamic University of Al Diwaniyah Al Diwaniyah Iraq Department of Biosciences Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Tamil Nadu Chennai India Applied Science Research Center Applied Science Private University Amman Jordan

Purpose: The primary objective of this research is to develop a comprehensive framework for the analysis of brain tumor images, addressing the complexities of detection, segmentation, and classification. Given the intricate nature of brain anatomy and tumor variability, the study aims to enhance the accuracy and efficiency of brain tumor diagnosis using advanced imaging and processing techniques. Methods: The framework utilizes Brain Tumor Segmentation (BraTS) 2019 and 2020 datasets, applying an AMW-PM filter to enhance image quality. Tumor segmentation employs a slimmable transformer with hybrid axial attention for precision. Classification uses similarity navigated graph neural networks (SNGNNs), optimized with the crayfish algorithm (CFO) to minimize loss and enhance performance. Results: The framework’s effectiveness was validated using key performance metrics, showing significant improvements in tumor detection and classification. The combination of advanced segmentation techniques and the SNGNN classifier, optimized by the CFO, led to a fivefold increase in diagnostic accuracy. These improvements demonstrated a substantial enhancement in the model's ability to correctly identify tumor types and provide reliable outputs for clinical use. Conclusion: The proposed framework delivers a robust solution for brain tumor analysis, integrating cutting-edge techniques for image processing, segmentation, and classification. The results not only highlight improvements in diagnostic accuracy but also contribute to better clinical decision-making in managing brain tumors. This framework sets the foundation for future advancements in brain tumor diagnostics. © The Author(s), under exclusive licence to The Brazilian Society of Biomedical Engineering 2025.

关键词： Graph neural networks

来源：评论

学校读者我要写书评

暂无评论

Comparative Study of Optical Character Recognition Using Different Techniques on Scanned Handwritten Images 6th

Comparative Study of Optical Character Recognition Using Dif...

引用

6th International Conference on Microelectronics and Telecommunication Engineering, ICMETE 2022

作者： Singh, Anshika Garg, Sharvan Kumar Department of Computer Science Shaheed Rajguru College of Applied Sciences for Women University of Delhi Delhi India Department of Computer Science and Engineering Subharti Institute of Technology and Engineering Swami Vivekan and Subharti University Meerut India

ISBN: (纸本)9789811995118

Optical Character Recognition (OCR) is always a prominent area of research and has gained attention in academia and industries, and more advancements are being performed to improve accuracy and quality of text and images. OCR is a technique that converts the digital or optical image (which can be image of typed or printed document or scanned handwritten document or scene photo) into machine-encoded format. OCR takes an image containing text as input, scans the text character by character, analyzes it, and generates a text corresponding to the text in the image, thus reducing the storage size and making accessibility of data easy and faster. In this paper, we will be studying OCR using different techniques like Convolutional Neural Network, Recurrent Neural Network, pytesseract, Keras-OCR, EasyOCR and comparing their results on handwritten scanned images. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Recurrent neural networks

来源：评论

学校读者我要写书评

暂无评论

LDVNet: Lightweight and Detail-Aware Vision Network for Image Recognition Tasks in Resource-Constrained Environments 29

LDVNet: Lightweight and Detail-Aware Vision Network for Imag...

引用

29th IEEE International Conference on Parallel and Distributed Systems, ICPADS 2023

作者： Liu, Yuchen Liang, Hu Qiu, Ran Zhao, Shengrong Qilu University of Technology Shandong Academy of Sciences Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Jinan China Qilu University of Technology Shandong Academy of Sciences Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Jinan China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computer Networks Jinan China

ISBN: (纸本)9798350330717

In many underwater application scenarios, recognition tasks need to be executed promptly on computationally limited platforms. However, models designed for this field often exhibit spatial locality, and existing works lack the ability to capture crucial details in images. Therefore, a lightweight and detail-aware vision network (LDVNet) for resource-constrained environments is proposed to overcome the limitations of these approaches. Firstly, in order to enhance the accuracy of target image recognition, we introduce transformer modules to acquire global information, thus addressing the issue of spatial locality inherent in traditional convolutional neural networks (CNNs). Secondly, to maintain the network's lightweight nature, we integrate the transformer module with convolutional operations, thereby mitigating the substantial parameter and floating point operations (FLOPs) overhead. Thirdly, for the efficient extraction of crucial fine-grained details from feature maps, we have devised a channel and spatial attention module (C&SA). This module aids in recognizing intricate and fine-grained visual tasks and enhances image understanding. It is seamlessly integrated into LDVNet with nearly negligible parameter overhead. The experimental results demonstrate that LDVNet outperforms other lightweight networks and hybrid networks in different recognition tasks, while being suitable for resource-constrained environments. © 2023 IEEE.

关键词： Detail-aware Image recognition Lightweight

来源：评论

学校读者我要写书评

暂无评论

computer Vision for Agricultural Automation - Algorithmic Solutions for Crop Yield Predictions

Computer Vision for Agricultural Automation - Algorithmic So...

引用

2024 IEEE International Conference on Recent Advances in science and Engineering technology, ICRASET 2024

作者： Ramachandran, A. Ganesh Saravanan, S.K. Bhanumathi, M. Sangeetha, M. Fernandez, F. Mary Harin Saveetha Institute of Medical and Technical Sciences Saveetha University Saveetha School of Engineering Department of Computer Science Chennai India Saveetha School of Engineering Simats Department of Computers Science and Engineering Chennai India Department of Computer Science and Engineering Chennai India University of Technology and Applied Sciences Computer Engineering Department Nizwa Oman Saveetha Institute of Medical and Technical Sciences Saveetha University Saveetha School of Engineering Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350388602

computer vision is defined in this study and one of the applications demonstrated is crop yield prediction in agricultural automation. Here we have emphasized on its use for computer vision for the agricultural domain. Using multispectral images that can be obtained from drones satellites and pairs with ground truths of average crop yield, weather conditions and parts of the soil treated with practices performed by an agronomist the proffered methodology would take advantage of the uniqueness of the concept to predict crop production. Further, elaborate machine learning methods such as line regression, decision trees, Random Forest, Support Vector Machine and CNN models are applied in order that the ultimate selection of the model would be contingent on the accuracy in relation to a crop yield. The DL based approaches as seen majority of this work and particularly the CNN exhibited improved results over regression models since there are more of classification than regression;In addition DL models are more appropriate for spatial relations and variations characteristic of crop data. Besides that, data fusion methods which map the accuracy of the images, weather and fields data develops a more accurate forecast as proven by the above crop modeling that involves multidimensional data. While transferring on fresh test data, it also demonstrates that the trained models have the capability to use the collected data in order to not only provide intelligent advices to the farmers on resource goodwill and crop productivity that will pave way for better results. Most importantly therefore for the hear of it the research show that with computer visions and machine learning the agricultural process can be reinvented this is how food security can be improved as the environment evolves. © 2024 IEEE.

关键词： Agricultural automation computer vision Crop yield prediction Deep learning Machine learning Multispectral imagery

来源：评论

学校读者我要写书评

暂无评论

Throughput Prediction Using LS-SVM For 4g-LTE Network and Beyond

Throughput Prediction Using LS-SVM For 4g-LTE Network and Be...

引用

Telecommunications and Intelligent Systems (ICTIS), International Conference on

作者： Mostefa Hamdani Ahmed Sahnoune Mokhtar Merzougui Abdelmalek Douara Fouad Khatemi Department of Mathematics and Computer Science Laboratory of Electronics Computer Science and Applied Mathematics Faculty of Science and Technology University El Wancharissi Department of Science and Technology Laboratory of Electronics Computer Science and Applied Mathematics Faculty of Science and Technology University El Wancharissi

ISBN: (数字)9798331527396

ISBN: (纸本)9798331527402

The integration of Artificial Intelligence(AI) with Long-Term Evolution (LTE) networks offers substantial potential for improving communication infrastructure. By harnessing AI algorithms, it is possible to dynamically optimize network parameters, proactively predict and mitigate equipment failures, and enhance Quality of Service (QoS) by prioritizing critical services through real-time analysis of key performance indicators. This study focuses on predicting network throughput using Least Square Support Vector Machine (LS-SVM). The predictive models developed can trigger adaptive network reconfigurations, ensuring that the network responds promptly to changing conditions. Simulation results reveal that the integration of LS-SVM significantly improves prediction accuracy, enabling more effective implementation of self-organizing network strategies in real-time.

关键词： Support vector machines Correlation coefficient Accuracy Quality of service Predictive models Throughput Real-time systems Telecommunications Analysis of variance Testing

来源：评论

学校读者我要写书评

暂无评论

FreqRISE: Explaining time series using frequency masking 6

FreqRISE: Explaining time series using frequency masking

引用

6th Northern Lights Deep Learning Conference, NLDL 2025

作者： Brüsch, Thea Wickstrøm, Kristoffer Knutsen Schmidt, Mikkel N. Alstrøm, Tommy Sonne Jenssen, Robert Department of Applied Mathematics and Computer Science Technical University of Denmark Denmark Department of Physics and Technology UiT The Arctic University of Norway Norway Norwegian Computing Center Oslo Norway

Time series data is fundamentally important for many critical domains such as healthcare, finance, and climate, where explainable models are necessary for safe automated decision-making. To develop explainable artificial intelligence in these domains therefore implies explaining salient information in the time series. Current methods for obtaining saliency maps assumes localized information in the raw input space. In this paper, we argue that the salient information of a number of time series is more likely to be localized in the frequency domain. We propose FreqRISE, which uses masking-based methods to produce explanations in the frequency and time-frequency domain, and outperforms strong baselines across a number of tasks. The source code is available here: https://***/theabrusch/FreqRISE. © NLDL *** rights reserved.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Adaptive Weighted Low-Rank Sparse Representation for Multi-View Clustering

引用

IEEE Access 2023年 11卷 60681-60692页

作者： Khan, Mohammad Ahmar Khan, Ghufran Ahmad Khan, Jalaluddin Anwar, Taushif Ashraf, Zubair Atoum, Ibrahim A. Ahmad, Naved Shahid, Mohammad Ishrat, Mohammad Alghamdi, Abdulrahman Abdullah Dhofar University College of Commerce and Business Administration Salalah211 Oman Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering Guntur522502 India Southwest Jiaotong University School of Computing and Artificial Intelligence Chengdu611756 China University of Electronic Science and Technology of China School of Computer Science and Engineering Chengdu611731 China GLA University Department of Computer Engineering and Applications Uttar Pradesh Mathura281406 India AlMaarefa University College of Applied Sciences Department of Computer Science and Information Systems Riyadh13731 Saudi Arabia National Taiwan University of Science and Technology Department of Computer Science and Information Engineering Taipei10607 Taiwan Shaqra University College of Computing and Information Technology Shaqra11961 Saudi Arabia

Ongoing researches on multiple view data are showing competitive behavior in the machine learning field. Multi-view clustering has gained widespread acceptance for managing multi-view data and improves clustering efficiency. Large dimensionality in data from various views has recently drawn a lot of interest from researchers. How to efficiently learns the appropriate lower dimensional subspace which can manage the valuable information from the diverse views is challenging and considerable issue. To concentrate on the mentioned issue, we asserted a novel clustering approach for multiple view data through low-rank representation. We consider the importance of each view by assigning the weight control factor. We combine consensus representation with the degree of disagreement among lower rank matrices. The single objective function unifies all factors. Furthermore, we give the efficient solution to update the variable and to optimized the objective function through the Augmented Lagrange's Multiplier strategy. Real-world datasets are utilized in this study to exemplify the efficiency of the introduced technique, and it is contemplated to preceding algorithms to demonstrate its superiority. © 2013 IEEE.

关键词： Data mining

来源：评论

学校读者我要写书评

暂无评论

TweetACE: A Fine-grained Classification of Disaster Tweets using Transformer Model

TweetACE: A Fine-grained Classification of Disaster Tweets u...

引用

2023 IEEE applied Imagery Pattern Recognition Workshop, AIPR 2023

作者： Adesokan, Ademola Madria, Sanjay Nguyen, Long Missouri University of Science and Technology Computer Science Rolla United States Meharry Medical College School of Applied Computational Sciences Nashville United States

ISBN: (纸本)9798350359527

Disaster management teams play a crucial role in responding to catastrophic events with speed and efficiency. However, when faced with large data of disaster-related information, manual systems can struggle to classify the information accurately, especially when they are unavailable. This challenge highlights the need for integrating social media and implementing machine learning models to address the issue. However, the development of such models is dependent on the availability of adequately annotated data, which presents a significant obstacle in the field of crisis management. To address this challenge, our study focuses on the need for disaster event classification through social media. Thus, we present a new technique called TweetACE, a fine-grained disaster tweet annotation technique that utilizes tweets from two disaster events. Our dual annotation approach assigns two labels to each tweet, reducing ambiguity and improving model training efficiency. The high-quality data annotation achieved through our rigorous post-Annotation processes is evident in the refined labels and impressive Krippendorff's alpha of 0.87 for event types and 0.84 for sub-event types, which indicate the reliability of annotation agreement between annotators. Furthermore, the preprocessing of textual data eliminates extraneous elements that contain noise, which can hinder the accuracy of classification models. This step enables us to refine the dataset and spotlight meaningful tweet content, ultimately boosting model accuracy. Our benchmark model, which uses the Bidirectional Encoder Representations from Transformers (BERT) model, achieved 66% and 57% accuracy for the event and sub-event categories, respectively, highlighting potential avenues for enhancement. In conclusion, our work presents a promising solution for disaster event classification through social media that could significantly enhance the effectiveness of disaster management teams. By providing a well-Annotated dataset fo

关键词： Disasters

来源：评论

学校读者我要写书评

暂无评论

GAF-GAN: Gated Attention Feature Fusion Image Inpainting Network Based on Generative Adversarial Network 29

GAF-GAN: Gated Attention Feature Fusion Image Inpainting Net...

引用

29th IEEE International Conference on Parallel and Distributed Systems, ICPADS 2023

作者： Qiu, Ran Liang, Hu Liu, Yuchen Zhao, Shengrong Qilu University of Technology Shandong Academy of Sciences Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Jinan China Qilu University of Technology Shandong Academy of Sciences Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Jinan China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computer Networks Jinan China

ISBN: (纸本)9798350330717

Image inpainting, which aims to reconstruct reasonably clear and realistic images from known pixel information, is one of the core problems in computer vision. However, due to the complexity and variability of the underwater environment, the inability to extract valid pixel points and insufficient correlation between feature information in existing image inpainting techniques lead to blurring in the generated images. Therefore, a novel gated attention feature fusion image inpainting network based on generative adversarial networks (GAF-GAN) is proposed. The accuracy of feature similarity matching depends heavily on the validity of the information contained in the features. On the one hand, gating values are dynamically generated by gated convolution to reduce the interference of invalid information. On the other hand, semantic information at distant locations in an image is accurately acquired by the attention mechanism. For these reasons, we designed an improved gated attention mechanism. Gated attention mechanism make the network focus on effective information such as high-frequency texture and color fidelity of restored images. In addition, the dense feature fusion module is added to expand the overall receptive field of the network to fully learn the image features. Experimental results show that the proposed method can effectively repair defective images with complex texture structures and improve the reality and integrity of image details and structures. © 2023 IEEE.

关键词： Dense feature fusion module Gated attention mechanism Image inpainting Underwater environment

来源：评论

学校读者我要写书评

暂无评论

Deep learning and ensemble methods for anomaly detection in ICS security

引用

International Journal of Information technology (Singapore) 2025年第3期17卷 1761-1775页

作者： Hossain, Md. Alamgir Hasan, Tahmid Karovic, jr, Vincent Abdeljaber, Hikmat A. M. Haque, Md. Alimul Ahmad, Sultan Zafar, Aasim Nazeer, Jabeen Mishra, B.K. Department of Computer Science and Engineering State University of Bangladesh South Purbachal Kanchan Dhaka 1461 Bangladesh Department of Computer Science and Engineering Prime University Dhaka Bangladesh Department of Information Systems Faculty of Management Comenius Bratislava Odbojárov 10 Bratislava 25 82005 Slovakia Department of Computer Science Faculty of Information Technology Applied Science Private University Amman 11931 Jordan MEU Research Unit Middle East University Amman 11831 Lebanon Department of Computer Science Veer Kunwar Singh University Bihar Ara India Department of Computer Science College of Computer Engineering and Sciences Prince Sattam Bin Abdulaziz University Alkharj 11942 Saudi Arabia University Center for Research and Development (UCRD) Department of Computer Science and Engineering Chandigarh University Gharuan Punjab Mohali 140413 India Department of Computer Science Aligarh Muslim University U.P Aligarh 202002 India P.G. Department of Physics Veer Kunwar Singh University Bihar Ara India

This research addresses the escalating threats to industrial control systems by introducing a novel approach that combines deep learning for feature selection with a robust ensemble-based classification technique to enhance anomaly detection. Our method utilizes a tailored autoencoder architecture to efficiently select features, followed by a Random Forest classifier to ensure reliable and generalizable detection of anomalies. Evaluated on the HAI Security Dataset, the proposed approach demonstrates exceptional performance, with significant improvements in detecting anomalous activities in ICS. The results highlight the method’s potential to enhance ICS security by providing a scalable and adaptable framework that evolves with emerging threats. This study not only advances cybersecurity methodologies for industrial systems but also lays the groundwork for future research, emphasizing the need for innovative and effective security solutions in the face of evolving cyber challenges. © Bharati Vidyapeeth's Institute of computer Applications and Management 2024.

关键词： Anomaly detection in ICS Cyber-resilience in critical infrastructure Deep learning for feature selection Industrial control systems security Random forest classifier for ICS

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：