检索结果-内蒙古大学图书馆

A Novel Hybrid Approach-Based on Heartbeat and Accelerometer Signals for Enhanced Security in WBSNs

IEEE Sensors Journal 2025年第6期25卷 10295-10308页

作者： Yan, Guixiang Zhang, Guanghe Liu, Fanghua Williams Samuel, Oluwarotimi Aborokbah, Majed Guo, Jiquan Zhu, Liqing Wei, Sikang Jiangxi Normal University School of Computer and Information Engineering Nanchang330022 China University of Derby School of Computing and Data Science Research Centre DerbyDE22 3AW United Kingdom INTI International University Faculty of Data Science and Information Technology Nilai71800 Malaysia University of Tabuk Faculty of Computers and Information Technology Tabuk71491 Saudi Arabia Jiangxi Normal University School of Digital Industry Shangrao334000 China

In wireless body sensor networks (WBSNs), ensuring secure and efficient key distribution is critical, particularly given the limited computational and energy resources of the sensors. Existing methods often struggle to balance security with these resource constraints, especially in environments involving physiological data such as acceleration (ACC) and electrocardiogram (ECG) signals. For the first time, this study proposes a novel hybrid approach that integrates fuzzy commitment with ACC signal noise and ECG features for efficient key generation and distribution in WBSNs. By employing a low-pass filter to process ACC signal noise, we generated highly random binary sequences (BSs), leveraging the inherent randomness of the signal for secure key generation. Concurrently, an optimized coding scheme was built for ECG feature construction to ensure secure key distribution between devices. Extensive experiments, including entropy analysis and National Institute of Standards and technology (NIST) statistical tests, confirm the robustness and security of our method. The proposed scheme achieves a false acceptance rate (FAR) of 5.23%, demonstrating superior performance across multiple databases in comparison to benchmark approaches. This novel dual-key generation strategy that combines the unpredictability of ACC noise with the individual-specific traits of ECG signals can significantly enhance the security, applicability, and versatility of WBSNs. © 2001-2012 IEEE.

关键词： Acceptance tests

来源：评论

学校读者我要写书评

暂无评论

Improving Robustness of CLIP by Adversarial Training Enhanced by Brain Activity

Improving Robustness of CLIP by Adversarial Training Enhance...

引用

2025 International Workshop on Advanced Imaging technology, IWAIT 2025

作者： Nakajima, Tasuku Maeda, Keisuke Togo, Ren Ogawa, Takahiro Haseyama, Miki Graduate School of Information Science and Technology Hokkaido University Japan Data-Driven Interdisciplinary Research Emergence Department Hokkaido University Japan Faculty of Information Science and Technology Hokkaido University Japan

ISBN: (数字)9781510688131

ISBN: (纸本)9781510688124

Contrastive Language-Image Pre-training (CLIP) is vulnerable to adversarial attacks which cause misclassification by subtle modifications undetectable to the human *** adversarial training strengthens CLIP models against such attacks, it often degrades their accuracy on clean *** tackle this challenge, we propose a novel defense strategy that leverages human brain activity *** proposed method combines features of brain activity with those of adversarial examples, which enhances the robustness of CLIP while maintaining high accuracy on clean *** results demonstrate the effectiveness of our method for the accurate retrieval of clean and adversarial *** results highlight the potential ability of brain data to overcome the existing challenges of adversarial defense in foundation models. © 2024 SPIE.

关键词： Brain Functional magnetic resonance imaging Adversarial training Education and training data modeling Neuroimaging Defense and security Feature extraction Semantics Image processing

来源：评论

学校读者我要写书评

暂无评论

Enhanced Framework for Generating Counterfactual Images with Sophisticated Caption and Inversion-free Image Editing

Enhanced Framework for Generating Counterfactual Images with...

引用

2025 International Workshop on Advanced Imaging technology, IWAIT 2025

作者： Li, Xiang Togo, Ren Maeda, Keisuke Ogawa, Takahiro Haseyama, Miki Graduate School of Information Science and Technology Hokkaido University Japan Faculty of Information Science and Technology Hokkaido University Japan Data-Driven Interdisciplinary Research Emergence Department Hokkaido University Japan

ISBN: (数字)9781510688131

ISBN: (纸本)9781510688124

The training process of classification models is commonly based on real-world data and models may learn certain spurious relationships, leading to an over-reliance on features not directly relevant to the *** this paper, we propose a novel framework for generating counterfactual *** framework enables us to confirm whether classification models are sensitive to changes in the features under *** have introduced the latest caption and image generator, which enables better counterfactual image generation as well as more efficient *** results show that the counterfactual images generated by our method have superior feature perturbation capabilities, which allows us to assess the robustness of the classification model more *** main improvements our framework offers over existing methods are the generation of higher-quality counterfactual images and the reduction of the computational cost of this process. © 2024 SPIE.

关键词： Image enhancement

来源：评论

学校读者我要写书评

暂无评论

Latency- and Privacy-Aware Convolutional Neural Network Distributed Inference for Reliable Artificial Intelligence Systems

IEEE Transactions on Artificial Intelligence

引用

IEEE Transactions on Artificial Intelligence 2025年第2期6卷 365-377页

作者： Hu, Yuhao Xu, Xiaolong Qi, Lianyong Zhou, Xiaokang Xia, Xiaoyu Nanjing University of Information Science and Technology School of Software Nanjing210044 China College of computer science and technology Qingdao266850 China Shiga University Faculty of Data Science Hikone522-8522 Japan RMIT University School of Computing Technologies MelbourneVIC3000 Australia

Reliable artificial intelligence (AI) systems not only propose a challenge on providing intelligent services with high quality for customers but also require customers' privacy to be protected as much as possible during process of the services. Given the ultrahigh computing load brought by deep-learning-based intelligent services to edge devices and the ultralong distance between edge and cloud, low-latency requirement of intelligent services is hard to meet in single edge computing or cloud computing. Edge-cloud collaborative inference of deep neural networks (DNNs) is considered a feasible solution to the problem. However, former work has not reduced the inference latency to the greatest extent and has not considered privacy protection in distributed systems. To solve the problem, we first establish a novel queue mechanism. Then, the convolution layer split decisions are made based on deep reinforcement learning (DRL) to realize the parallel inference of convolutional neural networks (CNNs) for inference latency reduction. Next, for each CNN, the partition decision is made based on brute force algorithm to further reduce inference latency and protect customers' privacy. Finally, simulation results show that our method performs better than the existing other methods. © 2020 IEEE.

关键词： Cloud computing

来源：评论

学校读者我要写书评

暂无评论

Rice Variety Identification Based on Transfer Learning Architecture Using DENS-INCEP

引用

IEEE Access 2025年 13卷 78007-78020页

作者： Agustiono, Wahyudi Permana, Kurniawan Eka Chan, Caroline Dewi, Deshinta Arrova Husain, Moch. Miftachur Rifqi Al University of Trunojoyo Madura East Java Bangkalan69162 Indonesia University of Newcastle CallaghanNSW2308 Australia INTI International University Faculty of Data Science and Information Technology Nilai71800 Malaysia

Rice is a vital staple food for billions of people worldwide, especially in Asia, Africa, and Latin America, where it plays a key role in daily caloric intake and nutrition. With numerous varieties differing in size, shape, colour, texture, and nutritional content, accurate rice variety identification is critical for optimizing production and ensuring food quality. Environmental factors such as soil type and climate further influence these variations, making precise identification essential for improving productivity and reducing waste. However, traditional manual methods of identification, relying on visual characteristics, are prone to human error, resulting in variety mixing, reduced quality, and higher costs. This study addresses these challenges by employing the DENS-INCEP model, a transfer learning approach that integrates DenseNet-201 with the Inception module. DenseNet-201 serves as the backbone for feature extraction, while the Inception module enhances the model’s ability to capture multi-scale shape-related features, significantly improving classification accuracy. The model achieved remarkable performance, with an average accuracy of 99.94% across multiple rice varieties. By implementing the DENS-INCEP model, this study contributes to Sustainable Development Goal (SDG) 2 by improving food security through enhanced rice production and supply chain stability. Additionally, it supports SDG 9 by fostering innovation and advancing sustainable agricultural technologies. Furthermore, by reducing errors, waste, and inefficiencies in production and distribution, the model aligns with SDG 12, which emphasizes sustainable consumption and production. Overall, the DENS-INCEP model offers a robust and efficient solution to rice variety identification, addressing global food security challenges while promoting sustainability. © 2013 IEEE.

关键词： Fertilizers

来源：评论

学校读者我要写书评

暂无评论

Phasmatodea Population Evolution Algorithm Based on Spiral Mechanism and Its Application to data Clustering

引用

Computers, Materials & Continua 2025年第4期83卷 475-496页

作者： Jeng-Shyang Pan Mengfei Zhang Shu-Chuan Chu Xingsi Xue Václav Snášel College of Computer Science and Engineering Shandong University of Science and TechnologyQingdao266590China School of Artificial Intelligence Nanjing University of Information Science and TechnologyNanjing210044China Department of Information Management Chaoyang University of TechnologyTaichung41349TaiwanChina Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian University of TechnologyFuzhou350118China Faculty of Electrical Engineering and Computer Science VŠB-Technical University of OstravaOstrava70833Czech Republic

data clustering is an essential technique for analyzing complex datasets and continues to be a central research topic in data *** clustering algorithms,such as K-means,are widely used due to their simplicity and *** paper proposes a novel Spiral Mechanism-Optimized Phasmatodea Population Evolution Algorithm(SPPE)to improve clustering *** SPPE algorithm introduces several enhancements to the standard Phasmatodea Population Evolution(PPE)***,a Variable Neighborhood Search(VNS)factor is incorporated to strengthen the local search capability and foster population ***,a position update model,incorporating a spiral mechanism,is designed to improve the algorithm’s global exploration and convergence ***,a dynamic balancing factor,guided by fitness values,adjusts the search process to balance exploration and exploitation *** performance of SPPE is first validated on CEC2013 benchmark functions,where it demonstrates excellent convergence speed and superior optimization results compared to several state-of-the-art metaheuristic *** further verify its practical applicability,SPPE is combined with the K-means algorithm for data clustering and tested on seven *** results show that SPPE-K-means improves clustering accuracy,reduces dependency on initialization,and outperforms other clustering *** study highlights SPPE’s robustness and efficiency in solving both optimization and clustering challenges,making it a promising tool for complex data analysis tasks.

关键词： Phasmatodea population evolution algorithm data clustering meta-heuristic algorithm

来源：评论

学校读者我要写书评

暂无评论

Cloud Cost Prediction with LSTM on Parametric data 16th

Cloud Cost Prediction with LSTM on Parametric Data

引用

16th International Conference on Genetic and Evolutionary Computing, ICGEC 2024

作者： San, Khin Pyae Phyo Thein, Nwe Nwe Myint Data Distribution Management Lab University of Information Technology Parami Road Hlaing Campus Yangon Myanmar Faculty of Computer Science Data Distribution Management Lab University of Information Technology Parami Road Hlaing Campus Yangon Myanmar

ISBN: (纸本)9789819615308

In the distributed computing paradigm, cloud computing provides a wide range of software, platforms and computing infrastructure. It has ability sharable elastic resource pools in dynamic. In this way, cloud computing offers many benefits. It reduces the cost of deployment in applications. It makes easy to increase the capacity of local infrastructures. And it can reduce power consumption by sharing resources. There are challenges over the benefits. The virtual instances needed to be shared equally distributed across different availability zones per demand. That can be categorized into operational and business challenges. In this paper, we implement the cloud prediction system using historical billing data and study the measurement of the model accuracy using Mean Absolute Error and Root Mean Squared Error. The trained model evaluates by the predicted and targeted value graph in this study. By leveraging historical financial data, this method aims to aid organizations in better foreseeing and managing AWS expenditures, leading to improved cost control and financial planning for cloud infrastructure. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Cloud platforms

来源：评论

学校读者我要写书评

暂无评论

Evaluating Pipeline Architecture with Apache Kafka and Apache Flink: data-Driven Architecture 16th

Evaluating Pipeline Architecture with Apache Kafka and Apach...

引用

16th International Conference on Genetic and Evolutionary Computing, ICGEC 2024

作者： Kyaw, Chan Min Thein, Nwe Nwe Myint Data Distribution Management Lab University of Information Technology Parami Road Hlaing Campus Yangon Myanmar Faculty of Computer Science Data Distribution Management Lab University of Information Technology Parami Road Hlaing Campus Yangon Myanmar

ISBN: (纸本)9789819615308

Nowadays, the ability to process, generate and collect various data in real-time has become more relevant for faster reactions to important events in technical enterprises. The numerous distributed stream processing engines is developed to provide the advance large streaming applications. Apache Kafka and Flink also become more popular in the processing and analyzing of large amount of data. Apache Flink is best suited for data analytics, event-driven and data pipeline applications. Applying Apache Kafka to Flink provides low idle time and low latency, and the most demanding stream processing applications in the world. This paper investigates the effective pipeline architecture for developing data driven analytics. Confirming the experimental results, the system proves to optimize the maximum latency, average latency, and idle time of the pipeline architecture. Additionally, further experiments were performed by implementing different levels of parallelism while streaming data, allowing for a comprehensive analysis of maximum latency and average latency of the pipeline. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： data streams

来源：评论

学校读者我要写书评

暂无评论

An optimized ensemble model based on meta-heuristic algorithms for effective detection and classification of breast tumors

引用

Neural Computing and Applications 2025年第6期37卷 4881-4894页

作者： Saber, Abeer Elbedwehy, Samar Awad, Wael A. Hassan, Esraa Information Technology Department Faculty of Computers and Artificial Intelligence Damietta University Damietta34517 Egypt Department of Data Science Faculty of Artificial Intelligence Kafrelsheikh University Kafrelsheikh33511 Egypt Department of Computer Science Faculty of Computers and Artificial Intelligence Damietta University Damietta34517 Egypt Faculty of Artificial Intelligence Kafrelsheikh University Kafrelsheikh33511 Egypt

One of the most common cancers among women worldwide is breast cancer (BC), and early diagnosis can save lives. Early detection of BC increases the likelihood of a successful outcome by enabling treatment to start sooner. Even in areas without access to a specialist physician, machine learning (ML) aids in early BC detection. The medical imaging community is becoming more interested in using ML, and deep learning (DL) to increase the accuracy of cancer screening. Many disease-related data are sparse. However, for DL models to perform well, a large amount of data is required. Because of this, the DL models that are currently in use on medical images are not as effective as they could be. Convolutional neural network (CNN) models have recently gained popularity in the medical industry, and they perform admirably in terms of high performance and robustness at image classification. The proposed method classifies data using ensemble pre-trained models such as the dense convolutional network (DenseNet)-121 and EfficientNet-B5 feature extractor networks, as well as the support vector machine for classification. Using a modified meta-heuristic optimizer, the selected pre-trained CNN hyperparameters were optimized to improve the performance. The experimental results for the presented model on the INbreast dataset show that the EfficientNet-B5 model is effective for BC classification, with overall accuracy, sensitivity, specificity, precision, and area under the ROC curve (AUC) values of 99.9%, 99.9%, 99.8%, 99.1%, 1.0, respectively. © The Author(s) 2024.

关键词： Adversarial machine learning

来源：评论

学校读者我要写书评

暂无评论

Using Hash and Deep Learning for Content-Based Image Retrieval 6th

Using Hash and Deep Learning for Content-Based Image Retriev...

引用

6th International Conference on Computational Intelligence in Pattern Recognition, CIPR 2024

作者： Hung, Bui Thanh Thuy, Tran Xuan Tam, Le Duy Data Science Laboratory Faculty of Information Technology Industrial University of Ho Chi Minh City Ho Chi Minh Viet Nam

ISBN: (纸本)9789819780891

Given the exponential growth of global data, research attention has increasingly focused on content-based image retrieval. While deep learning has optimized computer vision tasks, existing image search tools continue to face challenges in processing speed. If these issues are resolved, image search has the potential to fully replace traditional search methods. The drawback of prior methods lies in lengthy feature vectors, resulting in time-consuming searches and inconvenience. To mitigate these issues, we employ feature extraction through convolutional neural networks and subsequently apply binary hashing. Experimental results on the fashion-product-images dataset show that using a convolutional neural network combined with binary hashing in image search achieves a mean average precision (MAP 20) of 0.99 and significantly improves the image retrieval time. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Convolutional neural networks

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：