检索结果-内蒙古大学图书馆

Generative Adversarial Networks for Motor Imagery Classification using Wavelet Packet Decomposition and Complex Morlet Transform

引用

Multimedia Tools and Applications 2025年 1-24页

作者： Shirodkar, Vaishali Edla, Damodar Reddy Kumari, Annu Department of Computer Science and Engineering National Institute of Technology Goa Kottamoll Plateau Goa Cuncolim403703 India Information Technology Department Goa College of Engineering Farmagudi Goa Ponda403401 India

The area of brain-computer interface research is widely spreading as it has a diverse array of potential applications. Motor imagery classification is a boon to several people with motor impairment. Low accuracy and datasets with few trial recordings present challenges for this classification. This research offers a novel approach to these problems in electroencephalography signal classification for efficient control of neuroprosthetic devices. By using wavelet packet decomposition the proposed method divides the EEG signal into eight frequency bands. Each of these bands were then evaluated by a one-dimensional convolution neural network to find the most discriminative band. Only EEG signals filtered with these bands were used to generate time-frequency images called scalograms using complex Morlet transform. These scalograms were then classified using a two-dimensional convolution neural network optimized for spatial-temporal feature extraction and classification. To mitigate data scarcity, generative adversarial networks were used for effective data augmentation. Experimental evaluation on a benchmark dataset demonstrated substantial improvement: the two-dimensional convolution neural network achieved 93.63% accuracy, surpassing the 85% achieved by the one-dimensional convolution neural network baseline. Precision, recall, and F1-score metrics validated robust performance across motor imagery tasks. Comparative analysis with the latest methods underscored the proposed model’s superiority in accuracy and reliability. Using wavelet transformations, convolution neural network architectures, and generative adversarial network-driven data augmentation presented a promising framework for advancing brain-computer interface applications, particularly in enhancing neuroprosthetic device control. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2025.

关键词： Wavelet decomposition

来源：评论

学校读者我要写书评

暂无评论

A Novel Framework for Gauging information Extracted from Smartphones Using Neutrosophic Logic

引用

Neutrosophic Sets and Systems 2025年 76卷 154-171页

作者： Abou Alzahab, R.M. Ismail, Amr Abd Elkhalik, S.H. Shams, M.Y. El-Bakry, H.M. Salama, A.A. Department of Mathematics and Computer Science Faculty of Science Port Said University Egypt Department of Cybersecurity College of Engineering and Information Technology Buraydah Private Colleges Buraydah51418 Saudi Arabia Department. of Communication and Computer Engineering Air Defense University Egypt Faculty of Artificial Intelligence Kafrelsheikh University Kafrelsheikh33516 Egypt Department of Information Systems. Faculty of Computer and Information Science Mansoura University Egypt

Smartphones contain a vast amount of information about their users, which can be used as evidence in criminal cases. However, the sheer volume of data can make it challenging for forensic investigators to identify and use the most relevant information. Neutrosophic logic is a generalization of fuzzy logic that allows for uncertainty and vagueness, making it a more potent tool for dealing with the ambiguity of real-world data. The proposed framework aims to identify and utilize the most relevant information for forensic investigators, making it easier to solve criminal cases using Neutrosophic logic. This novel approach provides a more powerful tool for dealing with the ambiguity of smartphone data, ultimately improving the accuracy and effectiveness of forensic investigations. Our research has utilized Neutrosophic logic to evaluate the degree of truth, falsity, or Indeterminacy of this information. Additionally, this study analyzes conversations between individuals using Excel’s fuzzy lookup add-in to determine the percentage of truth and false in each conversation. The results were compiled into a dataset and utilized a Neutrosophic Python code to evaluate the information. The results indicate the percentage of truth, false, and Indeterminacy in each conversation and which can be used to determine its admissibility as evidence and which not. © 2025, University of New Mexico. All rights reserved.

关键词： Smartphones

来源：评论

学校读者我要写书评

暂无评论

Multi-Scale Time Series Segmentation Network Based on Eddy Current Testing for Detecting Surface Metal Defects

引用

IEEE/CAA Journal of Automatica Sinica 2025年第3期12卷 528-538页

作者： Xiaorui Li Xiaojuan Ban Haoran Qiao Zhaolin Yuan Hong-Ning Dai Chao Yao Yu Guo Mohammad S.Obaidat George Q.Huang the School of Intelligence Science and Technology University of Science and Technology Beijing the Beijing Advanced Innovation Center for Materials Genome Engineering the Key Laboratory of Intelligent Bionic Unmanned Systems and the Institute of Materials Intelligent Technology Liaoning Academy of Materials IEEE the Department of Computer Science Hong Kong Baptist University the School of Computer and Communication Engineering Key Laboratory of Advanced Materials and Devices for Post-Moore Chips Ministry of Education University of Science and Technology Beijing the Beijing Advanced Innovation Center for Materials Genome Engineering University of Science and Technology Beijing the School of Computer and Communication Engineering University of Science and Technology Beijing the King Abdullah Ⅱ School of Information Technology The University of Jordan the Department of Computational Intelligence the School of Computing SRM University the School of Engineering The Amity University The Hong Kong Polytechnic University

In high-risk industrial environments like nuclear power plants, precise defect identification and localization are essential for maintaining production stability and safety. However, the complexity of such a harsh environment leads to significant variations in the shape and size of the defects. To address this challenge, we propose the multivariate time series segmentation network(MSSN), which adopts a multiscale convolutional network with multi-stage and depth-separable convolutions for efficient feature extraction through variable-length templates. To tackle the classification difficulty caused by structural signal variance, MSSN employs logarithmic normalization to adjust instance distributions. Furthermore, it integrates classification with smoothing loss functions to accurately identify defect segments amid similar structural and defect signal subsequences. Our algorithm evaluated on both the Mackey-Glass dataset and industrial dataset achieves over 95% localization and demonstrates the capture capability on the synthetic dataset. In a nuclear plant's heat transfer tube dataset, it captures 90% of defect instances with75% middle localization F1 score.

关键词： Eddy current testing nondestructive testing semantic segmentation time series analysis

来源：评论

学校读者我要写书评

暂无评论

An enhanced framework for smart automated evaluations of answer scripts using NLP and deep learning methods

引用

Multimedia Tools and Applications 2025年第11期84卷 8491-8513页

作者： G, Mohanraj R.K, Nadesh M, Marimuthu V, Sathiyapriya School of Computer Science Engineering and Information Systems Vellore Institute of Technology Vellore632014 India School of Computer Science and Engineering Vellore Institute of Technology Chennai600127 India Knowledge Institute of Technology Salem637504 India

The manual process of evaluating answer scripts is strenuous. Evaluators use the answer key to assess the answers in the answer scripts. Advancements in technology and the introduction of new learning paradigms need automation of the evaluation process. This work aims to develop an enhanced novel hybrid framework that can evaluate answer scripts and automatically assign marks for different type of questions based on keywords, grammar, symbols, special keywords, and the given factors. First, the proposed system uses Optical Character Recognition (OCR) to convert image answer scripts into an editable text format. Second, the sentence transformers, the Natural Language Processing (NLP) technique flips the answer script and answers key texts into word embedding vectors. To find similarity measures, these vectors are matched using BERT encoding, spearmanś rank-order correlation, and fuzzy search. At last, the proposed model is trained using Deep Columnar Convolutional Neural Network (DCCNN) in the third step with MINST and Kaggle handwritten mathematical symbols and tested with the segmented mathematical equations to find the similarity. The performance of proposed model is measured using precision, recall, accuracy, and F1-score, and its gives highest accuracy of 93% and 95% when compared to the existing methodologies. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Optical character recognition

来源：评论

学校读者我要写书评

暂无评论

Review of Steganography and Cryptography: Tools for Covert Communications

引用

International Journal of Network Security 2025年第2期27卷 431-438页

作者： Fatima, Kanza Wu, Nan-I Chan, Chi-Shiang Hwang, Min-Shiang Department of Computer Science and Information Engineering Asia University Taichung41354 Taiwan Department of Information Management Lunghwa University of Science and Technology Taiwan Department of Artificial intelligence Asia University Taiwan

In today’s rapidly evolving digital media landscape, safeguarding content privacy and preventing unauthorized access to copyrighted material are major challenges. Cryptography plays a crucial role in modern digital media security, providing the essential tools to protect the confidentiality, reliability, and genuineness of digital media information. This paper examines cryptographic and steganographic techniques employed for securing digital media content. It provides an overview of key cryptographic principles, including symmetric and asymmetric encryption. It also provides different branches of steganography. This paper also discusses different evaluation matrices for cover object quality. Overall, this research offers a comprehensive exploration of steganography, highlighting its importance as a flexible communication method in the digital age and its future scope. © (2025), (International Journal of Network Security). All rights reserved.

关键词： information hiding

来源：评论

学校读者我要写书评

暂无评论

Domain Adaptation for Vehicle Detection Under Adverse Weather

IEEE Open Journal of Intelligent Transportation Systems

引用

IEEE Open Journal of Intelligent Transportation Systems 2025年 6卷 568-578页

作者： Lin, Huei-Yung Huang, Yi-Chao Lai, Jing-Xian You, Ting-Ting National Taipei University of Technology Department of Computer Science and Information Engineering Taipei106 Taiwan National Chung Cheng University Department of Electrical Engineering Chiayi621 Taiwan

The images captured under varying illumination or adverse weather conditions exhibit distinct distributions in the high-dimensional feature space, hindering the performance of object detection networks. To address this issue, we propose a domain adaptation method based on adversarial learning. This approach ensures that extracted features have a similar distribution, even when input images originate from different data acquisition domains. Due to the lack of driving images recorded under a variety of weather conditions in existing datasets, we incorporate a semi-supervised learning framework to enhance detection performance by training with unlabeled images. Experimental results on public and our latest datasets demonstrate that the proposed adversarial learning technique surpasses recent traffic scene object detection networks across various driving scenarios. © 2020 IEEE.

关键词： Adversarial machine learning

来源：评论

学校读者我要写书评

暂无评论

M-Bi-GRU-CNN: a hybrid deep learning model with optimized feature selection for enhanced crop yield prediction

引用

Multimedia Tools and Applications 2025年 1-25页

作者： J, Madhuri M, Indiramma N, Nagarathna Department of Computer Science and Engineering Bangalore Institute of Technology Karnataka Bengaluru560004 India Department of Computer Science and Engineering BMS College of Engineering Karnataka Bengaluru560019 India

Weather variability significantly impacts crop yield, posing challenges for large-scale agricultural operations. This study introduces a deep learning-based approach to enhance crop yield prediction accuracy. A Multi-Head Attention-based Bi-Directional Gated Recurrent Unit (M-Bi-GRU) integrated with a Convolutional Neural Network (CNN) is proposed. The publicly available Crop Recommendation dataset is pre-processed using data cleaning and Min–Max normalization to eliminate inconsistencies. Then, the Adaptive Reptile Search Optimization (ARSO) algorithm is used to identify the most relevant features for the prediction task. The M-Bi-GRU-CNN model uses these selected features to predict crop yields. The implementation is carried out in Python, and the performance is measured using a variety of metrics. Experimental results demonstrate that the proposed model outperforms existing methods, achieving a Mean Absolute Error (MAE) of 0.45, Mean Absolute Percentage Error (MAPE) of 0.07, and Root Mean Square Error (RMSE) of 0.28. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2025.

关键词： Convolutional neural networks

来源：评论

学校读者我要写书评

暂无评论

Neuraltalk+: neural image captioning with visual assistance capabilities

引用

Multimedia Tools and Applications 2025年第10期84卷 6843-6871页

作者： Sharma, Himanshu Padha, Devanand Department of Computer Science and Information Technology Central University of Jammu Kashmir Jammu181124 India

Image captioning is a technique that generates concise and meaningful descriptions of the visual contents present in an image. Image captioning frameworks generally employ an encoder-decoder-based pipeline to generate image descriptions. Multimodal meaning space, visual and semantic fusion, and influential recurrent decoding are some of the highlights of these frameworks. However, the lack of cutting-edge implementation schemes, such as ensemble feature extraction, context-aware fusion, and real-time captioning, limit their integration in the vision assistance domain. In this research work, we introduce Neuraltalk+, which comprises various structural and functional enhancements, and feature-based extensions, making it lightweight, robust, effective, and automated. Neuraltalk+ uses ensemble feature extraction to extract visual and spatial image features for efficient image comprehension. We then map these feature vectors with multimodal semantic knowledge using dual context-aware feature fusion followed by self-attention-assisted decoding. Lastly, we introduce two new features: real-time captioning and visual similarity comparison, which allow vision assistance and sight comprehension capabilities. Experimental analysis on the Flickr 8K and Flickr 30K datasets demonstrates that our model trains faster and generates improved quantitative (BLEU(72.08), METEOR(33.65), and CIDEr(143.5)) and qualitative results. Neuraltalk+ also demonstrates high performance in real-time captioning for both familiar and unfamiliar contexts. We also offer potential suggestions for extending our work in the future. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Decoding

来源：评论

学校读者我要写书评

暂无评论

AR-Light: Enabling Fast and Lightweight Multi-user Augmented Reality via Semantic Segmentation and Collaborative View Synchronization

引用

IEEE Transactions on computers 2025年第6期74卷 2073-2086页

作者： Wen, Yu Shah, Aamir Bader Cao, Ruizhi Zhang, Chen Chen, Jiefu Wu, Xuqing Xie, Chenhao Fu, Xin University of Houston Department of Electrical and Computer Engineering HoustonTX77204 United States Beihang University School of Computer Science and Engineering Beijing100191 China University of Houston Department of Information Science Technology TX77204 United States

Multi-user Augmented Reality (MuAR) allows multiple users to interact with shared virtual objects, facilitated by exchanging environment information. Current MuAR systems rely on 3D point clouds for real-world analysis, view synchronization, object rendering, and movement tracking. However, the complexity of 3D point clouds leads to significant processing delays, with approximately 80% of overhead in commercial frameworks. This hampers usability and degrades user experience. Our analysis reveals that maintaining the facing side of the real-world scene in a stable environment provides sufficient information for virtual object placement and rendering. To address this, we introduce a lightweight quadtree structure, representing 2D scenes through semantic segmentation and geometry, as an alternative to 3D point clouds. Additionally, we propose a novel correction method to handle potential shifts in virtual object placement during view synchronization among users. Combining all designs, we implement a fast and lightweight MuAR framework named AR-Light and test our framework on commercial AR devices. The evaluation results on real-world applications demonstrate that AR-Light can achieve high performance in various real-world scenes while maintaining a comparable virtual object placement accuracy. © 1968-2012 IEEE.

关键词： Semantic Segmentation

来源：评论

学校读者我要写书评

暂无评论

CCGS-based discriminatory recognition of skeleton-based actions

引用

Multimedia Tools and Applications 2025年第6期84卷 3161-3174页

作者： Sameem, M Shujah Islam Department of Computer Science College of Computer Sciences & Information Technology King Faisal University Al Ahsa Saudi Arabia

As life forms feature joints, dynamic skeletons have been studied extensively as a potential tool for action detection. As a result of their focus on modelling skeletons, older methods were unable to adequately convey the whole range of motion at more erratic skeletal joints. Despite introducing spatial methods for unstructured data, detecting many simultaneous motions remains challenging. As part of this study, now article proposes a CCGS for skeleton-based action recognition by depicting skeletons organically on joints and therefore attempting to capture the spatiotemporal variation in the data. Let here propose a motion regression to learn the shape statistically from numerous data points since the representation of the joints is crucial to motion convolution. To achieve this sparsity in the underlying CCGS for efficient representation, now give spatial modelling of skeletons and formulate an optimisation problem with a slip-critical joint. Each joint has a strong or weak connection to the other joints in the same frame and to key joints in the action frames that have been processed. The coordinates of the skeleton sequence and the positions of the joints are then sent to the motion-caused graph, which has been tuned, so that features based on Chebyshev, spatial, and angular approximations of the motion may be learned. Also, the article examines the motion features approximation's representation of variation. The results of the experiments show that the recommended joints regression works and the proposed CCGS achieves state-of-the-art performance on the most popular datasets. The present research suggests a CCGS-based method for skeleton-based action recognition that captures spatiotemporal variations by intuitively depicting skeletons on joints. The experimental results suggest that the proposed method obtains state-of-the-art performance, with accuracy enhancements of up to 2% over existing methods on prominent datasets. © The Author(s), under exclusive licenc

关键词： Chebyshev approximation

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：