检索结果-内蒙古大学图书馆

IEEE International conference on Data Mining Workshops (ICDM Workshops)

作者： Francesco Piccialli Fabio Giampaolo Vincenzo Schiano di Cola Federico Gatta Diletta Chiaro Edoardo Prezioso Stefano Izzo Salvatore Cuomo Department of Mathematics and Applications “R. Caccioppoli” University of Naples Federico II Napoli Italy

Thanks to the widespread use of mobile devices, analyses that in the past had to be carried out in specifically designated and equipped laboratories and which required long processing times, may now take place outdoor and in real time. In the marine science, for example, the development of a mobile and compact system for the on-site detection of heavy metals contamination in seawater would be helpful for scientists and society in at least two ways: i) reduction of time and costs associated with these experiments; ii) the implementation of a strategy for outdoor analysis, eventually embeddable in a lab-on-hardware system. This paper falls within the context of machine learning (ML) for utility pattern mining applied on interdisciplinary domains: starting from wellplates images, we provide a novel proof-of-concept (PoC) machine learning-based framework to assist scientists in their daily research on seawater samples, proposing a system which automatically recognise wells in a multiwell firstly and then predicts the degree of fluorescence in each of them, thus showing possible presence of heavy metals.

关键词： image recognition Pipelines neural networks Metals Feature extraction Prediction algorithms Real-time systems

来源：评论

学校读者我要写书评

暂无评论

End-to-End Hyperspectral image Classification Using Hybrid GCNN with SWIN Transformer

End-to-End Hyperspectral Image Classification Using Hybrid G...

引用

Ambient Intelligence, Knowledge Informatics and Industrial Electronics (AIKiiE), International conference on

作者： Rahul Ghotekar Kailash Shaw Minakhi Rout Department of Computer Science and Engineering KIIT University Odisha India Department of Artificial Intelligence and Machine Learning Symbiosis Institute of Technology Symbiosis International (Deemed University) Lavale Pune Maharashtra India

Hyperspectral image (HSI) classification holds immense significance in remote sensing applications like land cover mapping and environmental monitoring. This study introduces Graph Convolutional neural networks (GCNN) with the SWIN Transformer model (G_SwTr) to enhance HSI classification accuracy. GCNNs are utilized to incorporate spatial information through graph representations, capturing spectral-spatial dependencies. Graph convolutions, effectively harnesses spectral and spatial attributes, further the integration of the SWIN Transformer, recognized for its proficiency in capturing long-range dependencies via shifted windows and hierarchical processing, elevates feature extraction and contextual understanding. The Proposed G_SwTr model is evaluated across three HSI datasets (Pavia-University, Indian-Pines, and Salinas) and is compared against traditional Machine Learning and Deep Learning methods. The results achieved outperform state-of-art methods, with an outstanding Overall Accuracy (O.A) of 99.7, 99.9, and 99.5, an average accuracy (A.A) of 99.5, 99.6, and 98.2, and a kappa coefficient (K) of 99.5, 99.6, and 98.3 for all three datasets.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Editorial for Special Issue: Remote Sensing Based Building Extraction ii

引用

REMOTE SENSING 2023年第4期15卷 998-998页

作者： Tian, Jiaojiao Yan, Qin Awrangjeb, Mohammad Kallfelz (Sirmacek), Beril Demir, Nusret German Aerosp Ctr DLR Remote Sensing Technol Inst D-82234 Wessling Germany Chinese Acad Surveying & Mapping Beijing 100830 Peoples R China Griffith Univ Inst Integrated & Intelligent Syst Nathan Qld 4111 Australia Akdeniz Univ Dept Space Sci & Technol Remote Sensing Div TR-07058 Antalya Turkiye

Accurate building extraction from remotely sensed images is essential for topographic mapping, urban planning, disaster management, navigation, and many other applications [1]. The easily available very-high resolution 2D/3D dataset and the rapid development of image processing techniques, especially the convolutional neural networks (CNN) and deep learning techniques have further boosted the research on building-extraction-related topics. Especially in recent years, many research institutes and associations have provided open-source datasets and annotated training data to meet the demand for advanced artificial intelligence models, which brings new opportunities to develop advanced approaches for building extraction and monitoring. Hence, there are higher expectations of the efficiency, accuracy, and robustness of building extraction approaches. They should also meet the demand of processing large datasets at the city, national, and global levels. Moreover, challenges remain on transform learning and dealing with imperfect training data, as well as unexpected objects in urban scenes such as trees, clouds, and *** learning (DL) shows remarkable performance in extracting buildings from high-resolution remote sensing images. How to improve the performance of DL methods, especially the perception of spatial information, is worth further study. Paper [2] proposed a building extraction network (B-FGC-Net) with a feature highlighting, global awareness, and cross-level information fusion to achieve improved profitability of accurate extraction and information integration for both small- and large-scale buildings. Focusing on the promotion of the robustness of the interactive segmentation, Shu et al. [1] propose one Progress Guidance Representation Net (PGR-Net) to utilize the distance of newly added clicks to the boundary of the previous segmentation mask as an indication of the interactive segmentation progress, and this information is employed with the previous

关键词：

来源：评论

学校读者我要写书评

暂无评论

Self-Tuning PID Controller for an Arm-Angle Adjustable Quadrotor Using an Actor-Critic-Based neural Network

Self-Tuning PID Controller for an Arm-Angle Adjustable Quadr...

引用

Iranian conference on Intelligent Systems (ICIS)

作者： Arshia Rezaei Amin Talaeizadeh Aria Alasty Department of Mechanical Engineering Advanced Research Lab for Control and Agricultural Robotics (Sharif AgRoLab) Sharif University of Technology Tehran Iran

ISBN: (数字)9798331507565

ISBN: (纸本)9798331507572

The increasing prevalence of multirotors necessitates the development of new configurations for specific applications. Innovative morphing of multirotors has enhanced their stability, maneuverability, agility, and overall performance. The utilization of artificial Intelligence (AI) in robotics has enabled robots to exhibit autonomous behaviors in areas such as control, path planning, state estimation, system identification, and image processing. This paper introduces a novel approach to improving the control system of an adjustable arm-angle quadrotor through the integration of a self-tuning Proportional-Integral-Derivative (PID) controller, utilizing an Actor-Critic-based neural Network (NN). Arm-angle adjustable quadrotors, with their complex and variable dynamics, present challenges in achieving optimal control performance across different arm angles. Traditional PID controllers often struggle to adapt efficiently to these dynamics. To address this issue, the Actor-Critic framework from reinforcement learning is employed to develop a self-tuning mechanism for the PID controller. The Actor network learns the optimal control actions, while the Critic network evaluates the performance of the control system, enabling continuous improvement. Extensive Software-in-the-Loop (SIL) and Hardware-in-the-Loop (HIL) tests demonstrate the effectiveness and feasibility of the proposed approach in achieving robust and adaptive control for tilt-arm quadrotors under diverse operating conditions. The results highlight the superiority of the Actor-Critic-based self-tuning PID controller over conventional methods, showcasing its potential for real-world devlopment in autonomous aerial systems.

关键词： Uncertainty Optimal control Reinforcement learning artificial neural networks Autonomous aerial vehicles Real-time systems Adaptive control Robots Tuning Quadrotors

来源：评论

学校读者我要写书评

暂无评论

Inside the Spectrogram: Convolutional neural networks in Audio processing

Inside the Spectrogram: Convolutional Neural Networks in Aud...

引用

International conference on Sampling Theory and applications

作者： Monika Dorfler Roswitha Bammer Thomas Grill NuHAG Faculty of Mathematics Austrian Research Institute for Artificial Intelligence

ISBN: (纸本)9781538615669

Convolutional neural networks have established a new standard in many machine learning applications not only in image but also in audio processing. In this contribution we investigate the interplay between the primary representation mapping a raw audio signal to some kind of image (feature) and the convolutional layers of an ensuing neural network. We introduce a new notion of equivalence of feature-network pairs and show the relation of feature and networks for the example of mel-spectrogram input on the one hand and varying analysis windows on the other hand.

关键词： neural network audio signals image feature Audio Spectrogram equivalence imagery (Psychotherapy) images Hand

来源：评论

学校读者我要写书评

暂无评论

Production of MusicXML from Locally Inclined Sheet-music Photo image by Using Measure-based Multi-modal Deep-learning-driven Assembly Method

引用

Transactions of the Japanese Society for artificial Intelligence 2023年第3期38卷

作者： Shishido, Tomoyuki Fati, Fehmiju Tokushige, Daisuke Ono, Yasuhiro Kumazawa, Itsuo Biomedical AI Research Unit Tokyo Institute of Technology Shishido&Associates Japan SK Intellectual Property Law Firm Japan Enspirea LLC Japan Laboratory for Future Interdisciplinary Research of Science and Technology Tokyo Institute of Technology Japan

Deep learning has been applied to optical music sheet recognition (OMR). However, OMR processing from various sheet-music images still lacks precision to be widely applicable. We propose a measure-based multimodal deep-learning-driven assembly (MMdA) method enabling end-to-end OMR processing from various images including inclined photo images. Using this method, measures are extracted using a deep-learning model, aligned, and resized to be used for inference of given musical-symbol components by using multiple deep-learning models in sequence or in parallel. The use of each standardized measure enables efficient training of the deep-learning models and accurate adjustment of five staff lines in each measure, which enables locally inclined sheet-music images to be precisely positioned. Thus, a score can be reproduced from the inclined image with the proposed MMdA method while current OMR applications cannot. Multiple musical-symbol-component deep-learning feature-category models with a small number of feature types can represent a diverse set of notes and other musical symbols including chords. The proposed MMdA method provides a solution to end-to-end OMR processing and enhances the utility of OMR of mobile phone-based sheet-music photo images. © 2023, Japanese Society for artificial Intelligence. All rights reserved.

关键词： Music

来源：评论

学校读者我要写书评

暂无评论

Automatic person identification system using handwritten signatures

Automatic person identification system using handwritten sig...

引用

International conference on Computer and Communication Engineering, ICCCE

作者： A.A.M. Abushariah T.S. Gunawan J. Chebil M.A.M. Abushariah ECE Department Faculty of Engineering International Islamic University Malaysia Kuala Lumpur Malaysia Department of Computer Information Systems King Abdullah II School for Information Technology University of Jordan Amman Jordan

This paper reports the design, implementation, and evaluation of a research work for developing an automatic person identification system using hand signatures biometric. The developed automatic person identification system mainly used toolboxes provided by MATLAB environment.. In order to train and test the developed automatic person identification system, an in-house hand signatures database is created, which contains hand signatures of 100 persons (50 males and 50 females) each of which is repeated 30 times. Therefore, a total of 3000 hand signatures are collected. The collected hand signatures have gone through pre-processing steps such as producing a digitized version of the signatures using a scanner, converting input images type to a standard binary images type, cropping, normalizing images size, and reshaping in order to produce a ready-to-use hand signatures database for training and testing the automatic person identification system. Global features such as signature height, image area, pure width, and pure height are then selected to be used in the system, which reflect information about the structure of the hand signature image. For features training and classification, the Multi-Layer Perceptron (MLP) architecture of artificial neural Network (ANN) is used. This paper also investigates the effect of the persons' gender on the overall performance of the system. For performance optimization, the effect of modifying values of basic parameters in ANN such as the number of hidden neurons and the number of epochs are investigated in this work. The handwritten signature data collected from male persons outperformed those collected from the female persons, whereby the system obtained average recognition rates of 76.20% and74.20% for male and female persons, respectively. Overall, the handwritten signatures based system obtained an average recognition rate of 75.20% for all persons.

关键词： Feature extraction artificial neural networks Testing Training Accuracy Databases Computer architecture

来源：评论

学校读者我要写书评

暂无评论

Deep-learning microscopy image reconstruction with quality control reveals second-scale rearrangements in RNA polymerase ii clusters

引用

PNAS NEXUS 2022年第3期1卷 pgac065页

作者： Hajiabadi, Hamideh Mamontova, Irina Prizak, Roshan Pancholi, Agnieszka Koziolek, Anne Hilbert, Lennart Helmholtz Informat & Data Sci Sch Hlth HIDSS4Hlth D-76131 Karlsruhe Germany Karlsruhe Inst Technol Inst Biol & Chem Syst Dept Biol Informat Proc D-76344 Eggenstein Leopoldshafen Germany Karlsruhe Inst Technol KASTEL Inst Informat Secur & Dependabil D-76131 Karlsruhe Germany Karlsruhe Inst Technol Zool Inst Dept Syst Biol & Bioinformat D-76131 Karlsruhe Germany

Fluorescence microscopy, a central tool of biological research, is subject to inherent trade-offs in experiment design. For instance, image acquisition speed can only be increased in exchange for a lowered signal quality, or for an increased rate of photo-damage to the specimen. Computational denoising can recover some loss of signal, extending the trade-off margin for high-speed imaging. Recently proposed denoising on the basis of neural networks shows exceptional performance but raises concerns of errors typical of neural networks. Here, we present a work-flow that supports an empirically optimized reduction of exposure times, as well as per-image quality control to exclude images with reconstruction errors. We implement this work-flow on the basis of the denoising tool Noise2Void and assess the molecular state and 3D shape of RNA polymerase ii (Pol ii) clusters in live zebrafish embryos. image acquisition speed could be tripled, achieving 2-s time resolution and 350-nm lateral image resolution. The obtained data reveal stereotyped events of approximately 10 s duration: initially, the molecular mark for recruited Pol ii increases, then the mark for active Pol ii increases, and finally Pol ii clusters take on a stretched and unfolded shape. An independent analysis based on fixed sample images reproduces this sequence of events, and suggests that they are related to the transient association of genes with Pol ii clusters. Our work-flow consists of procedures that can be implemented on commercial fluorescence microscopes without any hardware or software modification, and should, therefore, be transferable to many other applications.

关键词： image processing reliable deep learning fluorescence microscopy gene regulation transcription

来源：评论

学校读者我要写书评

暂无评论

image and Video Captioning Using Deep Learning and Natural Language processing

Image and Video Captioning Using Deep Learning and Natural L...

引用

International conference on Computing Communication Control and Automation (ICCUBEA)

作者： Manoj Naidu Athrva Kulkarni Sahil Kadam Siddhesh Joshi Nilesh P. Sable Anuradha Yenkikar Department of CSE – Artificial Intelligence Vishwakarma Institute of Information Technology Pune India

ISBN: (数字)9798350391770

ISBN: (纸本)9798350391787

Deep learning models have been a huge success in image recognition which hence can be used for the purpose of text generation. In the field of imaging science, captioning images and videos is regarded as an intellectually difficult job. Visual Geometry Group (VGG); is a standard deep Convolutional neural Network (CNN) architecture with multiple layers, specifically focusing on the integration of CNN for image feature extraction. Exploring this underlying method, the use of another model is essential for caption generation. Here the Recurrent neural Network (RNN) comes in use for caption generation from the extracted features. Models named Long Short-Term Memory (LSTM) based on RNN and Bidirectional encoder representation transformer (BERT) based on Transformers have been prominent in ensuring accurate results. The Flicker8k dataset is used which provides a variety of information useful for model training. By testing validation data along with evaluation metrics, we analyze the effectiveness of different models to create consistent and descriptive headlines. Extending our inquiry to encompass title generation using transformer models, while also exploring learning techniques for real-time title generation and delivery using the Open-CV library available in Python to get the output from the camera and display it on screen. The result shows that the LSTM is the best model for captioning, with an accuracy of 65.07% at the epochs of 300 and the BERT model has an accuracy of 31% at the epochs of 2. The findings of this study not only contribute to advancing subtitle enhancement methodologies but also broaden the potential applications of deep learning techniques in this domain.

关键词： Deep learning Recurrent neural networks Accuracy Computational modeling Bidirectional control Transformers Feature extraction Encoding Data models Long short term memory

来源：评论

学校读者我要写书评

暂无评论

Learning compact representations of neural networks using DiscriminAtive Masking (DAM) 21

Learning compact representations of neural networks using Di...

引用

Proceedings of the 35th International conference on neural Information processing Systems

作者： Jie Bu Arka Daw M. Maruf Anuj Karpatne Virginia Tech

ISBN: (纸本)9781713845393

A central goal in deep learning is to learn compact representations of features at every layer of a neural network, which is useful for both unsupervised representation learning and structured network pruning. While there is a growing body of work in structured pruning, current state-of-the-art methods suffer from two key limitations: (i) instability during training, and (ii) need for an additional step of fine-tuning, which is resource-intensive. At the core of these limitations is the lack of a systematic approach that jointly prunes and refines weights during training in a single stage, and does not require any fine-tuning upon convergence to achieve state-of-the-art performance. We present a novel single-stage structured pruning method termed DiscriminAtive Masking (DAM). The key intuition behind DAM is to discriminatively prefer some of the neurons to be refined during the training process, while gradually masking out other neurons. We show that our proposed DAM approach has remarkably good performance over a diverse range of applications in representation learning and structured pruning, including dimensionality reduction, recommendation system, graph representation learning, and structured pruning for image classification. We also theoretically show that the learning objective of DAM is directly related to minimizing the L0 norm of the masking layer.

关键词：

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：