检索结果-内蒙古大学图书馆

A Nature-Inspired AI Framework for Accurate Glaucoma Diagnosis

Computer Modeling in Engineering & sciences 2025年第4期143卷 539-567页

作者： Jahanzaib Latif Ahsan Wajahat Alishba Tahir Anas Bilal Mohammed Zakariah Abeer Alnuaim School of Software Northwestern Polytechnical UniversityXi’an710072China Shifa College of Medicine Shifa Tameer-e-millat UniversityIslamabad44000Pakistan College of Information Science and Technology Hainan Normal UniversityHaikou571158China Department of Computer Science and Engineering College of Applied Studies and Community ServiceKing Saud UniversityRiyadh11495Saudi Arabia

Glaucoma,a leading cause of blindness,demands early detection for effective *** AI-based diagnostic systems are gaining traction,their performance is often limited by challenges such as varying image backgrounds,pixel intensity inconsistencies,and object size *** address these limitations,we introduce an innovative,nature-inspired machine learning framework combining feature excitation-based dense segmentation networks(FEDS-Net)and an enhanced gray wolf optimization-supported support vectormachine(IGWO-SVM).This dual-stage approach begins with FEDS-Net,which utilizes a fuzzy integral(FI)technique to accurately segment the optic cup(OC)and optic disk(OD)from retinal images,even in the presence of uncertainty and *** the second stage,the IGWO-SVM model optimizes the SVM classification process,leveraging a gray wolf-inspired optimization strategy to fine-tune the kernel function for superior *** testing on three benchmark glaucoma image databases DRIONS-DB,Drishti-GS,and Rim-One-r3 demonstrates the efficacy of our method,achieving classification accuracies of 97.65%,94.88%,and 93.2%,*** results surpass existing state-of-the-art techniques,offering a promising solution for reliable and early glaucoma detection.

关键词： Glaucoma fundus images machine learning nature-inspired algorithm segmentation classification

来源：评论

学校读者我要写书评

暂无评论

Image Guidance Encoder-Decoder Model in Image Captioning and Its Application

IAENG International Journal of Computer Science

引用

IAENG International Journal of Computer science 2024年第9期51卷 1385-1392页

作者： Yang, Zhen Zhou, Ziwei Wang, Chaoyang Xu, Liang School of Applied Technology University of Science and Technology Liaoning Anshan China School of Computer and Software Engineering University of Science and Technology Liaoning Anshan China School of Computer and Software Engineering University of Science and Technology Liaoning Anshan China

This paper introduces a new network model - the Image Guidance Encoder-Decoder Model (IG-ED), designed to enhance the efficiency of image captioning and improve predictive accuracy. IG-ED, a fusion of the convolutional network VGGNet-16 and the long short-term memory network (LSTM), is designed based on the encoder-decoder structure. The image captioning performance sees significant enhancements when leveraging the IG-ED network model. The network training process unfolds in a series of steps. Initially, the input image undergoes convolution via the VGGNet-16 network, producing a 512-dimensional vector. Concurrently, each word in the image's caption is encoded to generate a corresponding 512-dimensional vector consistent with the image feature dimension. These two vectors form the input for the decoding process. Subsequently, the vectors are fed into the redesigned fusion LSTM (F-LSTM) network at different time steps to gradually train the parameters of the IG-ED framework. The training process is completed by utilizing a loss function for determining convergence. Evaluation of the IG-ED model's performance is conducted using CIDEr and seven other evaluation metrics on the MSCOCO 2014 dataset. The results exhibit substantial improvements over the "Adaptive Attention Mode" network and "Neural Talk" network. Additionally, the parameter count of the IG-ED architecture is significantly reduced compared to the "Adaptive Attention Mode" network, leading to decreased computational resource requirements and enabling edge computing on the neural network. © (2024), (International Association of Engineers). All Rights Reserved.

关键词： Long short-term memory

来源：评论

学校读者我要写书评

暂无评论

Underwater Object Detection from Sonar Images Using Transfer Learning 21

Underwater Object Detection from Sonar Images Using Transfer...

引用

21st International Bhurban Conference on applied sciences and technology, IBCAST 2024

作者： Mehmood, Saba Irfan, Muhammad Hamid, Umar Ali, Shahid CESAT-Islamabad Center of Excellence in Science and Applied Technologies Pakistan School of Software Northwestern Polytechnical University Xian China COMSATS Institute of Information Technology CIIT-Islamabad Pakistan

ISBN: (纸本)9798331516680

Underwater systems are always prone to complex environmental conditions and various interferences that is the reason why Sonar images are filled up with contamination, noise, and blurriness. Collection of real-world underwater images data is very costly with respect to human resources, time, equipment and logistics. Due to these challenges, there are very few publicly available datasets of underwater sonar images and only few datasets are available for underwater images which are scarce because of lack of resources. Precise and fast detection of objects in underwater sonar images is very less explored and still an open challenge for underwater systems. This paper evaluates performance of state-of-the-art object detection deep learning network YOLO (You Only Look Once) for underwater sonar images through transfer learning on publicly available benchmark Marine Debris dataset and custom-tailored dataset captured in real sea environment. Experimental results shows that YOLOv8n was able to achieve high mAP score of 95.3% on Marine Debris dataset with inference speed of 1.3ms. © 2024 IEEE.

关键词： Underwater imaging

来源：评论

学校读者我要写书评

暂无评论

Audio-Text Multimodal Speech Recognition via Dual-Tower Architecture for Mandarin Air Traffic Control Communications

引用

Computers, Materials & Continua 2024年第3期78卷 3215-3245页

作者： Shuting Ge Jin Ren Yihua Shi Yujun Zhang Shunzhi Yang Jinfeng Yang School of Computer Science and Software Engineering University of Science and Technology LiaoningAnshan114051China Institute of Applied Artificial Intelligence of the Guangdong-Hong Kong-Macao Greater Bay Area Shenzhen Polytechnic UniversityShenzhen518055China Shenzhen Institutes of Advanced Technology Chinese Academy of SciencesShenzhen518055China Industrial Training Centre Shenzhen Polytechnic UniversityShenzhen518055China

In air traffic control communications (ATCC), misunderstandings between pilots and controllers could result in fatal aviation accidents. Fortunately, advanced automatic speech recognition technology has emerged as a promising means of preventing miscommunications and enhancing aviation safety. However, most existing speech recognition methods merely incorporate external language models on the decoder side, leading to insufficient semantic alignment between speech and text modalities during the encoding phase. Furthermore, it is challenging to model acoustic context dependencies over long distances due to the longer speech sequences than text, especially for the extended ATCC data. To address these issues, we propose a speech-text multimodal dual-tower architecture for speech recognition. It employs cross-modal interactions to achieve close semantic alignment during the encoding stage and strengthen its capabilities in modeling auditory long-distance context dependencies. In addition, a two-stage training strategy is elaborately devised to derive semantics-aware acoustic representations effectively. The first stage focuses on pre-training the speech-text multimodal encoding module to enhance inter-modal semantic alignment and aural long-distance context dependencies. The second stage fine-tunes the entire network to bridge the input modality variation gap between the training and inference phases and boost generalization performance. Extensive experiments demonstrate the effectiveness of the proposed speech-text multimodal speech recognition method on the ATCC and AISHELL-1 datasets. It reduces the character error rate to 6.54% and 8.73%, respectively, and exhibits substantial performance gains of 28.76% and 23.82% compared with the best baseline model. The case studies indicate that the obtained semantics-aware acoustic representations aid in accurately recognizing terms with similar pronunciations but distinctive semantics. The research provides a novel modeling pa

关键词： Speech-text multimodal automatic speech recognition semantic alignment air traffic control communications dual-tower architecture

来源：评论

学校读者我要写书评

暂无评论

3D Gaze Estimation via Binocular Disparity and Feature Refinement 10

3D Gaze Estimation via Binocular Disparity and Feature Refin...

引用

10th International Conference on Big Data Computing and Communications, BIGCOM 2024

作者： Wang, Chengxin Chen, Qiuxia Tian, Ying Yang, Jinfeng School of Computer Science and Software Engineering University of Science and Technology Liaoning Anshan China Institute of Applied Artificial Intelligence of the Guangdong-Hong Kong-Macao Greater Bay Area Shenzhen Polytechnic Shenzhen China

ISBN: (纸本)9798331509538

The eyes are the windows to the soul and are crucial for studying human behavior. Therefore, gaze estimation has attracted much attention in the field of computer vision. In recent years, convolutional neural networks have driven significant progress in gaze estimation research. Despite the remarkable achievements of gaze technology based on full-face images, the inability to obtain full-face images in specific scenarios makes research based on eye images particularly important. This paper proposes a three-dimensional gaze estimation model that takes binocular images as input, using VGG16 as the backbone network for feature extraction. To enhance the feature extraction capability, we have embedded channel attention and spatial attention mechanisms. Meanwhile, to improve the stability of gaze estimation, we classify real gaze angles. First, we perform a preliminary screening of gaze direction through a classification task, and then perform linear regression with the true angle. Finally, we optimize gaze features using binocular vision differences to finetune the gaze estimator. Experiments have shown that our method performs well on three public datasets. © 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

Sentiment Analysis of Low-Resource Language Literature Using Data Processing and Deep Learning

引用

Computers, Materials & Continua 2024年第4期79卷 713-733页

作者： Aizaz Ali Maqbool Khan Khalil Khan Rehan Ullah Khan Abdulrahman Aloraini Department of IT and Computer Science Pak-Austria Fachhochschule:Institute of Applied Sciences and TechnologyHaripur22620Pakistan Software Competence Center Hagenberg Softwarepark 32aHagenberg4232Austria Department of Computer Science School of Engineering and Digital SciencesNazarbayev UniversityAstana010000Kazakhstan Department of Information Technology College of ComputerQassim UniversityP.O.Box 1162BuraydahSaudi Arabia

Sentiment analysis, a crucial task in discerning emotional tones within the text, plays a pivotal role in understandingpublic opinion and user sentiment across diverse *** numerous scholars conduct sentiment analysisin widely spoken languages such as English, Chinese, Arabic, Roman Arabic, and more, we come to grapplingwith resource-poor languages like Urdu literature which becomes a challenge. Urdu is a uniquely crafted language,characterized by a script that amalgamates elements from diverse languages, including Arabic, Parsi, Pashtu,Turkish, Punjabi, Saraiki, and more. As Urdu literature, characterized by distinct character sets and linguisticfeatures, presents an additional hurdle due to the lack of accessible datasets, rendering sentiment analysis aformidable undertaking. The limited availability of resources has fueled increased interest among researchers,prompting a deeper exploration into Urdu sentiment analysis. This research is dedicated to Urdu languagesentiment analysis, employing sophisticated deep learning models on an extensive dataset categorized into fivelabels: Positive, Negative, Neutral, Mixed, and Ambiguous. The primary objective is to discern sentiments andemotions within the Urdu language, despite the absence of well-curated datasets. To tackle this challenge, theinitial step involves the creation of a comprehensive Urdu dataset by aggregating data from various sources such asnewspapers, articles, and socialmedia comments. Subsequent to this data collection, a thorough process of cleaningand preprocessing is implemented to ensure the quality of the data. The study leverages two well-known deeplearningmodels, namely Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN), for bothtraining and evaluating sentiment analysis performance. Additionally, the study explores hyperparameter tuning tooptimize the models’ efficacy. Evaluation metrics such as precision, recall, and the F1-score are employed to assessthe effectiv

关键词： Urdu sentiment analysis convolutional neural networks recurrent neural network deep learning natural language processing neural networks

来源：评论

学校读者我要写书评

暂无评论

Heterologous Image Matching Based on Salience Region Using Q-test and Kernel Density Estimation 4

Heterologous Image Matching Based on Salience Region Using Q...

引用

4th International Conference on Computer Vision and Pattern Analysis, ICCPA 2024

作者： Zhai, Jinlong Yan, Xiaohu Xu, Yang School of Computer Science and Software Engineering University of Science and Technology Liaoning Anshan114051 China Institute of Applied Artificial Intelligence of the Guangdong-Hong Kong-MacaoGreater Bay Area Shenzhen Polytechnic University Shenzhen518055 China

ISBN: (纸本)9781510682528

To address the matching problem caused by the salience differences in spatial features, spectrum and contrast between heterologous images, a heterologous image matching method based on salience region using Q-test and kernel density estimation is proposed in this paper. Firstly, the center pixel point of each sub-region in the image is detected for salience difference with other points in its neighborhood by using the Q-test method. The detected points are defined as Q-points. Secondly, the point with the largest kernel density in all Q-points is calculated by using kernel density estimation function and defined as the M-point. Then, the region size is defined according to the coordinates of M-point, and the salience region is detected. Finally, to comprehensively evaluate the performance of the proposed method, five common types of heterologous images are selected as data sources, including point cloud depth maps, infrared images, electronic navigational maps, synthetic aperture radar images, and night-time light images. Based on the salience regions, comparative experiments are carried out using histogram of absolute phase consistency gradients (HAPCG) and histogram of the orientation of the weighted phase descriptor (HOWP). The results show that the matching performance of the proposed method is better than that of the original algorithm without salience region, especially on the type of electronic navigational map. © 2024 SPIE.

关键词： Radar imaging

来源：评论

学校读者我要写书评

暂无评论

Moreau Envelope for Nonconvex Bi-Level Optimization: A Single-Loop and Hessian-Free Solution Strategy 41

Moreau Envelope for Nonconvex Bi-Level Optimization: A Singl...

引用

41st International Conference on Machine Learning, ICML 2024

作者： Liu, Risheng Liu, Zhu Yao, Wei Zeng, Shangzhi Zhang, Jin School of Software Technology Dalian University of Technology Dalian China Guangzhou China National Center for Applied Mathematics Shenzhen Southern University of Science and Technology Shenzhen China Department of Mathematics Southern University of Science and Technology Shenzhen China Department of Mathematics and Statistics University of Victoria VictoriaBC Canada

This work focuses on addressing two major challenges in the context of large-scale nonconvex Bi-Level Optimization (BLO) problems, which are increasingly applied in machine learning due to their ability to model nested structures. These challenges involve ensuring computational efficiency and providing theoretical guarantees. While recent advances in scalable BLO algorithms have primarily relied on lower-level convexity simplification, our work specifically tackles large-scale BLO problems involving nonconvexity in both the upper and lower levels. We simultaneously address computational and theoretical challenges by introducing an innovative single-loop gradient-based algorithm, utilizing the Moreau envelope-based reformulation, and providing non-asymptotic convergence analysis for general nonconvex BLO problems. Notably, our algorithm relies solely on first-order gradient information, enhancing its practicality and efficiency, especially for large-scale BLO learning tasks. We validate our approach's effectiveness through experiments on various synthetic problems, two typical hyper-parameter learning tasks, and a real-world neural architecture search application, collectively demonstrating its superior performance. Copyright 2024 by the author(s)

关键词： Optimization algorithms

来源：评论

学校读者我要写书评

暂无评论

Reseed Skipping of Linear Congruential Generator for Multi-level Image Steganography Security of Financial Data

引用

SN Computer science 2023年第2期4卷 124页

作者： Danso, Juliana Mantebea Missah, Yaw Marfo Gyamfi, Enoch Opanin Dankwa, Stephen Kwabena, Sarpong Department of Computer Science Kwame Nkrumah University of Science and Technology (KNUST) Kumasi Ghana School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China School of Information and Software Engineering University of Electronic Science and Technology of China Chengdu China Department of Cyber Security and Computer Engineering Technology School of Computing and Information Sciences C.K. Tedam University of Technology and Applied Sciences Upper East Region Navrongo Ghana School of Automation Engineering University of Electronic Science and Technology of China Chengdu China

This paper proposed a multi-level image steganography model that integrates with image and plaintext encryption schemes. The model starts by partitioning the cover-image pixel positions into two regions of respective purposes, one is reserved to hold the data, and through the implementation of the dynamic difference expansion principle, the other sloughs some parts to the reserved region in responsive to the size of the data to embed. Continuously reseeding and initializing the parameter values of a Linear Congruential Generator (LCG), generate a set of random pixel positions as members of the reserved region. Before data embedding, the region reserved to hold the data as well as the data bits to embed, are then encrypted using AES with respective keys. Encryption keys are randomly generated using a Cryptographically Secure Pseudo-Random Number Generator (CSPRNG), the Fortuna algorithm. Multiplex security nature of using a randomization generator to reserve image region for encryption and encrypting plaintext data before performing steganography still ensures stability in computational complexities better than a logarithmic O(n log n). A visual inspection by evaluators who made 3000 choices to correctly differentiate stego images from its cover-images shows that only 47 choices were correct, 1884 choices were wrong, while 1069 choices were uncertain. An experiment shows an average Embedding Rates (ERs) of 0.00109 bpp and an average PSNR of 72. This concludes, as our integrated steganography and image encryption which implements the dynamic difference expansion principle, outputs encrypted stego mages that can deceive the naked human eyes into identifying them as rather cover-images, its embedding rate is faster (i.e. ER < 0.1), suitably good, and ensures better imperceptibility of cover-image alterations. When comparing these experimental results with existing methods, our proposed model shows significant competitiveness. We even show real-life scenarios on how this

关键词： AES encryption scheme Dynamic difference expansion principle Linear congruential generator Pseudo-random number generator Reserving room before encryption Steganography

来源：评论

学校读者我要写书评

暂无评论

Light field depth estimation:A comprehensive survey from principles to future

引用

High-Confidence Computing 2024年第1期4卷 92-107页

作者： Tun Wang Hao Sheng Rongshan Chen Da Yang Zhenglong Cui Sizhe Wang Ruixuan Cong Mingyuan Zhao State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and EngineeringBeihang UniversityBeijing 100191China Zhongfa Aviation Institute Beihang UniversityHangzhou 311115China Faculty of Applied Sciences Macao Polytechnic UniversityMacao 999078China State Key Laboratory of Software Development Environment School of Computer Science and EngineeringBeihang UniversityBeijing 100191China

Light Field(LF)depth estimation is an important research direction in the area of computer vision and computational photography,which aims to infer the depth information of different objects in threedimensional scenes by capturing LF *** this new era of significance,this article introduces a survey of the key concepts,methods,novel applications,and future trends in this *** summarize the LF depth estimation methods,which are usually based on the interaction of radiance from rays in all directions of the LF data,such as epipolar-plane,multi-view geometry,focal stack,and deep *** analyze the many challenges facing each of these approaches,including complex algorithms,large amounts of computation,and speed *** addition,this survey summarizes most of the currently available methods,conducts some comparative experiments,discusses the results,and investigates the novel directions in LF depth estimation.

关键词： Light field Depth estimation Deep learning Sub-aperture image Epipolar-plane image

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：