检索结果-内蒙古大学图书馆

Pose-graph optimization for efficient tie-point matching and 3D scene reconstruction from oblique UAV images

ISPRS Journal of Photogrammetry and Remote Sensing 2025年 225卷 461-491页

作者： Xu, Zhihua Niu, Yongze Jiang, Jincheng Qin, Rongjun Cui, Ximin College of Geoscience and Surveying Engineering China University of Mining and Technology-Beijing Beijing100083 China North Automatic Control Technology Institute Shanxi Taiyuan030006 China School of Intelligent Systems Engineering Sun Yat-sen University Guangdong Shenzhen518107 China Department of Civil Environmental and Geodetic Engineering The Ohio State University 2036 Neil Avenue ColumbusOH43210 United States Department of Electrical and Computer Engineering The Ohio State University Dreese Laboratories 205 2015 Neil Ave ColumbusOH43210 United States Translational Data Analytics Institute The Ohio State University 1760 Neil Ave ColumbusOH43210 United States

Oblique photogrammetry using unmanned aerial vehicles (UAV) is crucial to 3D scene reconstruction. Nevertheless, oblique images are generally acquired with large overlaps, and sometimes with multiple views. Although this increases the level of data completeness, it introduces additional and sometimes redundant computations in tie-point matching, creating overly dense camera connections in the pose graph for bundle adjustment (BA). This study optimizes the pose graph of oblique UAV images by removing redundant image connections to guide tie-point matching. Assuming a five-camera system for oblique image collection, a pose graph called a topologically connected camera network (TCN) was initially constructed using position and orientation system (POS) data to determine the spatial connectivity among oblique images. Second, five geometric meta-parameters of overlapping images were constructed, and their influence on tie-point matching was analyzed using a data-driven approach to generate a weighted pose graph. Third, the weighted pose graph was simplified to a degree-bounded skeletal camera network (D-SCN) using the proposed two-stage multi-objective graph optimization approach. Finally, the D-SCN was embedded into a structure from motion (SfM) pipeline to produce a novel D-SCN–SfM method to reduce the required computations for tie-point matching. The proposed D-SCN-SfM method was tested using data from three large sites, each containing over 5,000 images. In addition, the D-SCN-SfM method was compared with three state-of-the-art methods. The experimental results indicate that our method can significantly reduce the required computations for tie-point matching to 1/14–1/20 as compared to a method that uses only topological constraints, that is, TCN, saving 89–92% of the time expenditure. Furthermore, the accuracy and completeness of the 3D geometry produced by the proposed method were comparable to those produced by standard SfM methods. The source code of our approach

关键词： Drones

来源：评论

学校读者我要写书评

暂无评论

Detecting Cyber Bullying in the Social Media Using Deep Learning Algorithms

Detecting Cyber Bullying in the Social Media Using Deep Lear...

引用

15th International Conference on Soft Computing and Pattern Recognition, SoCPaR 2023 and 14th World Congress on Nature and Biologically Inspired Computing, NaBIC 2023

作者： Mridul, Aunik Hasan Ahsan, Nowreen Sultana, Zakia Asif, Asifuzzaman Computer Science and Engineering Daffodil International University Dhaka Bangladesh Department of Data Science Clarkson University New York United States Computer Science and Engineering Lovely Professional University Punjab India

ISBN: (纸本)9783031810824

Cyberbullying is a pressing issue in the digital age, especially in non-English-speaking communities. This study focuses on using deep learning techniques to detect cyberbullying in the Bangla language. The research employs a Bangla dataset that includes both cyberbullying and non-cyberbullying text, which undergoes preprocessing, tokenization, and sequence transformation for input into a LSTM recurrent neural network. The LSTM model incorporates word embedding and dropout techniques to enhance its performance. Evaluation metrics like accuracy and AUC-ROC score are employed to assess the model's effectiveness. The study investigates many methods, such as vernacular-specific preparation and data augmenting, that are specifically designed for the identification of cyberbullying in the Bangla language. The findings demonstrate deep learning's capacity to identify cyberbullying in Bangla, which makes a significant contribution to resolving this problem in non-English settings. The research reveals impressive accuracy rates, with LSTM achieving 99.99% accuracy, and a different approach, Convolutional Neural Network (CNN), yielding 99.022%. Ensemble models, including Gradient Boosting, XGBoost, and Random Forest, are also employed and optimized using hyperparameter tuning. Notably, LSTM stands out as the most reliable classifier for cyberbullying detection, as confirmed by the experimental findings and recent research. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Cyberbullying Deep Learning Dropout Embedding

来源：评论

学校读者我要写书评

暂无评论

A Design of Hybrid Model and Bayesian Neural Networks for Smart Grid Stability Prediction 1

A Design of Hybrid Model and Bayesian Neural Networks for Sm...

引用

1st IEEE International Conference on Smart and Sustainable Developments in Electrical engineering, SSDEE 2025

作者： Hole, Shreyas Rajendra Kolluru, Vinothkumar Salotagi, Shreekant Challagundla, Yagnesh Mungara, Sudeep Sriramkumar, R. Dayananda Sagar University AI & ML Bangalore India Stevens Institute of Technology Department of Data Science HobokenNJ07030 United States Dayananda Sagar University Computer Science & Engineering Bagalore India Herbert Wertheim College of Engineering University of Florida Department of Computer Science GainesvilleFL32611 United States Stevens Institute of Technology Masters in Information Systems HobokenNJ07030 United States

ISBN: (纸本)9798331542108

The latest evolution in power systems, is 'smart grids'that offers real time monitoring, control features as well as effective management of renewable energy sources. Nonetheless, with the increase in the system complexity, the chances for insta- bility also increase and thus it is crucial to forecast the stability accurately for the safety of the grid. Weather it is in terms of Gradient Boosting Tree Algorithm or any other traditional algorithm, these machine learning models are successful in achieving the objectives of the task but they lack a forthcoming metric to assist in high stake situations, uncertainty quantification. The paper is devoted to the analysis of the reliability of smart grid systems based on Bayesian Neural Networks (BNN) taking into account both the reliability of predictive modeling and forecasting uncertainty. The smart grid stability augmented dataset was used to benchmark the efficacy of using BNNs or BNNs combined hybrid models, one with XGBoost, for feature extraction and the other with LightGBM. The present study shows that the BNN used with the above mentioned smart grid system achieved an accuracy of 92% with precision of 0.89, recall of 0.91 and F1 of 0.90. These metrics are appending with the help of the additional features of BNN where they are capable of addressing the key questions around uncertainty and thus the metrics confirming the predictions are quite strong in terms of real-time grid stability evaluation. This article encapsulates a detailed study of BNN architecture, the role of uncertainty in machine learning models and the natural application of such models in the design of dependable and fault-tolerant smart grids. © 2025 IEEE.

关键词： Smart power grids

来源：评论

学校读者我要写书评

暂无评论

Integrating Instruction-based Learning with Bidirectional LSTM for Autonomous Vehicle Pedestrian Detection

Integrating Instruction-based Learning with Bidirectional LS...

引用

2025 International Conference on Multi-Agent Systems for Collaborative Intelligence, ICMSCI 2025

作者： Shoba, B. Raghunathan, T. Peter, S.Sam Karthick, K. Akshya, J. Sundarrajan, M. Choudhry, Mani Deepak Psn College of Engineering and Technology Department of Electronics and Communication Engineering Tirunelveli India Dr.NG.p.Institute of Technology Department of Artificial Intelligence and Data Science Coimbatore India Sri Krishna College of Technology Department of Computer Science and Engineering Coimbatore India Sona College of Technology Department of Information Technology Salem India Srm Institute of Science and Technology Department of Computational Intelligence Chennai India Srm Institute of Science and Technology Department of Networking and Communications Chennai India Srm Institute of Science and Technology Department of Computing Technologies Chennai India

ISBN: (纸本)9798331509828

Pedestrian detection is a crucial aspect of autonomous vehicle (A V) safety, as it enables accurate identification and tracking of individuals in an A V environment. Effective pedestrian detection supports essential A V functions like braking, steering, and speed control to prevent accidents in urban settings. Traditional computer vision methods, such as Support Vector Machines and Random Forests, rely on pre-designed features like the Histogram of Oriented Gradients. While the existing techniques offer some performance, it face significant challenges in dynamic environments, especially under varying lighting conditions, occlusions, and diverse pedestrian poses. Traditional models also require extensive manual feature engineering and tuning, which limits scalability and adaptability. Recent advances in deep learning, particularly with Convolutional Neural Networks (CNNs), have allowed for automatic feature learning, yielding practical improvements over classical methods. However, CNNs can struggle in low light and when facing adversarial attacks, as they mainly capture spatial features. To address these limitations, this research proposes a new methodology combining CNNs for spatial feature extraction with Bidirectional Long Short-Term Memory (BiLSTM) networks to capture temporal dependencies. This hybrid approach enhances detection capabilities by learning both static and dynamic patterns. Our proposed CNN-BiLSTM model achieves a 92 % detection accuracy and an F1 score of 88.5% across diverse conditions, outperforming existing models by up to 15%. By integrating temporal features, the model reduces missed detections and false positives by up to 20%, providing a more reliable and adaptable solution for pedestrian detection in autonomous vehicles. © 2025 IEEE.

关键词： Convolutional neural networks

来源：评论

学校读者我要写书评

暂无评论

Enhancing Breast Cancer Diagnosis with Vision Transformers: A Novel Approach using MaskGuided Attention for Ultrasound Image Classification

Enhancing Breast Cancer Diagnosis with Vision Transformers: ...

引用

Pervasive Computational Technologies (ICPCT), International Conference on

作者： Deena Sivakumar Anamika Kumari Divya Dharshini K Samiksha S Hemachandran M Department of Computer Science and Engineering Sri Ramachandra Institute of Higher Education and Research Chennai Department of Computer Science (AI & Data Analytics) Sri Ramachandra Institute of Higher Education and Research Chennai

ISBN: (数字)9798331508685

ISBN: (纸本)9798331519476

Deep learning models, notably convolutional neural networks (CNNs), demonstrate great promise in medical image processing. Nonetheless, CNNs frequently encounter challenges in capture holistic context and long- range dependencies in high-resolution images, which are crucial for accurate medical diagnostics. A new state-of-the-art technique called Vision Transformers (ViTs) uses self-attention mechanisms for gathering global correlations between image patches, thereby addressing some of the inherent limitations of CNNs. This study investigates the application of ViTs for diagnosing breast cancer from ultrasound images of tumor. The findings suggest that ViTs not only achieve competitive accuracy in identifying breast cancer lesions but also offer superior interpretability through attention mechanism, which can aid clinicians in understanding model decisions. This paper highlights the potential of ViTs as a powerful tool in medical imaging, particularly for breast cancer diagnosis, and outlines future research directions for enhancing clinical applicability.

关键词： Deep learning computer vision Accuracy Attention mechanisms Ultrasonic imaging Transformers Breast cancer Convolutional neural networks Medical diagnosis Biomedical imaging

来源：评论

学校读者我要写书评

暂无评论

Proposed Hybrid AI-Driven Framework for Stock Market Prediction

Proposed Hybrid AI-Driven Framework for Stock Market Predict...

引用

Advancements in Electrical, Electronics, Communication, Computing and Automation (ICAECA), International Conference on

作者： Rajini S Shreya Vijay Mayuri G Moses John Wesley Jyothi Lakshmi N Department of Computer Science Engineering Kumaraguru College of Technology Coimbatore India Department of Artificial Intelligence and Data Science Kumaraguru College of Technology Coimbatore India

ISBN: (数字)9798331525439

ISBN: (纸本)9798331525446

The Stock Market has developed as a revolutionary force, transforming global economies through democratizing wealth creation. With the use of Artificial Intelligence and Machine Learning, investors can navigate complicated financial terrain. While improving predictive accuracy, the underlying complexities and varied market opinions can render decision-making effective. This study emphasizes trend analysis and prediction of future values, enabling users to make smart investment decisions with little effort. Using a Hybrid Machine Learning Model, the method brings together several top-performing algorithms to improve predictive power and trend observation. The most important components of the project are Market data Retrieval, Time Series Forecasting, Sentiment Analysis and data Visualization, all of which assist in creating thorough reports that aid strategic investment decisions.

关键词： Accuracy Navigation Time series analysis data visualization Machine learning data retrieval Complexity theory Stock markets Forecasting Investment

来源：评论

学校读者我要写书评

暂无评论

Mapping the Landscape of Personalization: A Comprehensive Review of Prediction and Trends in Recommendation Systems

引用

Wiley Interdisciplinary Reviews: data Mining and Knowledge Discovery 2025年第2期15卷

作者： Sachdeva, Tamanna Goyal, Lalit Mohan Mittal, Mamta Department of Computer Engineering J.C. Bose University of Science and Technology YMCA Faridabad India Department of Data Analytics Delhi Skill and Entrepreneurship University New Delhi India

Recommendation systems (RSs) have become indispensable features in nearly all web applications. Sifting through data and alleviating information overload, these systems offer more streamlined and personalized recommendations. E-commerce giants such as Amazon, Netflix, and YouTube are offering recommendations to users based on their interests, past experiences, demographic information, etc. hence, increasing the user's engagement on these applications. This study offers a comprehensive review of recommendation systems, covering their types, fundamental techniques, and emerging trends, with a focus on the predictive models and algorithms that power personalization. This study shows how in comparison to traditional collaborative and content-based recommendation systems-building techniques, the novel approaches of deep learning, graph-based techniques, meta-learning, few-shot learning, exploration, and federated learning offer promising prospects to improve recommendation systems' scalability, privacy-preserving abilities, and accuracy. These advanced methods deliver more diverse, context-aware, and personalized recommendations by leveraging large-scale data and complex predictive algorithms. Furthermore, this paper depicts forthcoming trajectories in the field of recommendation systems, including the adoption of graph-based approaches, federated learning, and the exploration of ethical considerations. By mapping the current landscape of prediction-driven personalization and identifying emerging trends, this review serves as a valuable resource for scholars and practitioners seeking to deepen their understanding of the field and drive innovation in recommendation systems. Readers can expect to gain insights into both foundational and cutting-edge techniques and how these can shape the future of personalized recommendations. © 2025 Wiley Periodicals LLC.

关键词： Federated learning

来源：评论

学校读者我要写书评

暂无评论

Involution-based efficient autoencoder for denoising histopathological images with enhanced hybrid feature extraction

引用

computers in Biology and Medicine 2025年 192卷 110174-110174页

作者： Islam, Md. Farhadul Reza, Md. Tanzim Manab, Meem Arafat Zabeen, Sarah Islam, Md. Fahim-Ul Shahriar, Md. Fahim Kaykobad, Mohammad Husna, Md. Golam Zel Asmaul Noor, Jannatun Research Group Department of Computer Science and Engineering United International University Dhaka Bangladesh Department of Computer Science and Engineering School of Data and Sciences BRAC University Dhaka Bangladesh Department of Mathematics and Natural Sciences School of Data and Sciences BRAC University Dhaka Bangladesh School of Law and Government Dublin City University Dublin Ireland National Institute of Cancer Research and Hospital Dhaka Bangladesh

Noise in histopathology images from hardware limitations, preparation artifacts, and environmental factors complicates disease analysis and increases risks. With growing workloads and the complexity of histopathology images, developing efficient and precise histopathology image analysis methods is essential. However, many denoising models struggle to extract spatial features and are computationally expensive, primarily due to the limited capacity of convolutions to capture visual patterns across spatial locations, and tend to occupy the largest share of computational costs. In histopathology, many spatial features, such as anomalies or microorganisms, located sparsely across an image, are crucial for the final diagnosis, and many denoising processes often either blur them or introduce artifacts. In this study, we propose a lightweight autoencoder (43.11 kilobytes) for denoising histopathology images by fusing a single involution layer within a small convolution model, resulting in better denoising performance in a hybrid model, which has both channel-specific and location-specific feature extraction capabilities. Building upon the idea of a shallow autoencoder, our model results in much lower memory and compute overhead requirements, while also not avoiding the generation of artifacts. On Malaria Blood Smear and CRC datasets, SSIM Loss and Peak-Signal-to-Noise-Ratio were used for performance evaluation, with lower SSIM Loss (0.058 and 0.34) in denoising images with an added Gaussian noise of 0.3. Our proposed autoencoder, with low weight parameters of 11,037 and 81,630,000 floating point operations (FLOPs), is over 20 times less computationally expensive than Xception, the second-best performing model, establishing ours as the most efficient denoising autoencoder for histopathology images. © 2025 Elsevier Ltd

关键词： Risk assessment

来源：评论

学校读者我要写书评

暂无评论

Optimizing Logistics in E-Commerce Using Deep Random Forest for Enhanced User Satisfaction 3

Optimizing Logistics in E-Commerce Using Deep Random Forest ...

引用

3rd IEEE International Conference on Automation and Computation, AUTOCOM 2025

作者： Nisha, G.Anne Sreetha, P. Seetha, A. Manickam, K. Jagannathan, Sharath Kumar Sekar, S.D. Kyoto Computer Gakuin Department of Informatics Kyoto Japan Kpr Institute of Engineering and Technology Department of Biomedical Engineering Tamil Nadu India S. A. Engineering College Department of Information Technology Thiruverkadu Chennai India Sona College of Technology Department of Mathematics Tamil Nadu India Data Science Institute Saint Peter's University NJ United States R.M.K.Engineering College Department of Mechanical Engineering TamilNadu Chennai India

ISBN: (纸本)9798331542375

Operational efficiency is one of the most important factors affecting customer satisfaction directly in e-commerce. Still, constant addressing of timely delivery, path simplification, and reduction of delays remains challenging problems. Deep Random Forest (DRF) is a new approach presented in this work to examine and forecast significant logistics metrics including delivery times and satisfaction scores. Development of this approach targeted to solve these issues. The model makes use of e-commerce past performance including order volumes, customer comments, and delivery times. Findings: The suggested approach achieves 10% increase in user satisfaction, 12% cost reduction, and 15% faster delivery times than baseline methods. This is accomplished by means of DRF, applied to examine trends and carry out predictive modifications. Results and Methodology: These results outperform those of the benchmarks established by Gradient Boosting and XGBoost, which displayed respective rises in satisfaction of 9% and 8%. Apart from improving the quality of service, the use of DRF not only strengthened user loyalty but also contributed to improve the transportation efficiency. This approach exposes the transforming opportunities of artificial intelligence in logistics management for e-commerce systems. This approach guarantees operations with a customer-centric concentration benefiting from measurable advantages. © 2025 IEEE.

关键词： Marketplaces

来源：评论

学校读者我要写书评

暂无评论

Transfer Learning and Domain Adaptation in Hyperspectral Image Processing: an Overview

Transfer Learning and Domain Adaptation in Hyperspectral Ima...

引用

Machine Learning and Autonomous Systems (ICMLAS), International Conference on

作者： Komal Pokale Shiv Nath Chaudhri Department of Artificial Intelligence and Data Science Faculty of Engineering and Technology DMIHER (DU) Maharashtra India Department of Computer Science and Design Faculty of Engineering and Technology DMIHER (DU) Maharashtra India

ISBN: (数字)9798331505745

ISBN: (纸本)9798331505752

Fine-tuning a pre-trained model is a type of transfer learning: a pre-trained model that works on a large dataset is transferred onto a particular task or a smaller dataset, such as a hyperspectral image. The efficacy of such a technique is most visible in hyperspectral imaging since the data is usually too high-dimensional with a very scarce number of available annotated datasets. By using the pre-trained model, fine-tuning enables spatial and spectral features to be captured with lower computational overheads than fully training models. Domain adaptation approaches, including feature alignment and adversarial training, are also adapted to overcome limitations caused by the domain shift phenomenon due to the variability of the sensor, environmental changes, and temporal changes. Hyperspectral imaging is applied in diverse fields, including agriculture, environmental monitoring, mineral exploration, defense, medical diagnostics, and food quality assessment. Such applications are unique in the sense that HSI can integrate spatial and spectral information to achieve detailed material analysis. Advanced learning techniques will enhance the effectiveness of HSI models in real-world scenarios.

关键词： Training Deep learning Adaptation models Computational modeling Transfer learning data models Agriculture Medical diagnosis Environmental monitoring Hyperspectral imaging

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：