检索结果-内蒙古大学图书馆

7th International Conference on Sustainable information Engineering and Technology, SIET 2022

作者： Abdulmajeed, Hazem H. Alsawy, Assem Hefny, Hesham Ahmed Intelligent System Laboratory Faculty of Computer Science University of Brawijaya Malang Indonesia Faculty of Computer Science and Information Technology Ahram Canadian Unversity Giza Egypt

ISBN: (纸本)9781450397117

A neural network approach is presented to solve the delay constraint and loss data multicast routing problem, an NP-complete problem. The multicast tree is obtained in line with optimal (minimum) routing costs which are subjected to integrated delay and loss data constraints. The proposed routing algorithm is designed to find the optimal (shortest) path taking into account traffic conditions (the incoming traffic flow, routers occupancy, and link capacities), avoiding packet loss data to the input buffer overflow and combine them into the union. The experimental results showed that the proposed method using the discrete-Time equation has achieved a significant difference in iterations and execution time compared to other proposals such as Runge-Kutta, Euler equations, ant colony algorithm and Hierarchical Hopfield neural network which contributes to improving the performance of Internet of Things networks in terms of the durability of interconnection between devices. In accordance with the extent of the impact of quality of service restrictions on the network, and the maintenance of the cost-effectiveness of the route, the algorithm achieved a decrease in execution time ranging from 27% to 81%, in addition to a decrease in the number of iterations by 44% to 66%. © 2022 ACM.

关键词： Hopfield neural networks

来源：评论

学校读者我要写书评

暂无评论

A Hybrid Medical Images Watermarking Technique Using DWT and DCT 7th

A Hybrid Medical Images Watermarking Technique Using DWT and...

引用

7th International Conference on Artificial Intelligence in Renewable Energetic Systems, IC-AIRES 2023

作者： Hamami, Rania Zermi, Narima Boubchir, Larbi Khaldi, Amine LASA Laboratory Faculty of Technology Badji Mokhtar Annaba University P.O. BOX 12 Annaba23000 Algeria LIASD Laboratory Department of Computer Science University of Paris 8 Saint-Denis93526 France Computer Science Department Faculty of Sciences and Technology Artificial Intelligence and Information University of Kasdi Merbah Ouargla Algeria

ISBN: (纸本)9783031606311

Medical images, such as X-rays, MRI scans, and CT scans, play an important role in diagnosing and treating various diseases. However, the sensitive nature of these images requires protection against unauthorized use and distribution. This article presents a novel approach for embedding hospital logos, patient information, and hospital/doctor details into medical images using a combination of wavelet transform and discrete cosine transform, To ensure enhanced security and robustness, we hash the watermark data using the MD5 algorithm before the embedding. Experimental results demonstrate the exceptional performance of the proposed technique, with a peak signal-to-noise ratio (PSNR) exceeding 50 dB, and very good robustness against attacks. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Discrete cosine transforms

来源：评论

学校读者我要写书评

暂无评论

Optimal Method Combining Blockchain and Multi-Agent System to Ensure Data Integrity and Deduplication in the Cloud Environment

International Journal of Interactive Mobile Technologies

引用

International Journal of Interactive Mobile Technologies 2024年第10期18卷 90-105页

作者： El Ghazouani, Mohamed Ikidid, Abdelouafi Zaouiat, Charafeddine Ait Aziz, Layla Ichahane, Moulay Youssef Er-Rajy, Latifa ESIM Polydisciplinary Faculty of Sidi Bennour Chouaîb Doukkali University El Jadida Morocco Laboratory of System Analysis Information Processing and Industrial Management EST Salé Mohammed V University Salé Morocco Information Technology Laboratory National School of Applied Sciences Chouaîb Doukkali University El Jadida Morocco Computer Science Department Laboratory of Information Systems Engineering Cadi Ayyad University Marrakesh Morocco

Cloud computing (CC) refers to the transmission, storage, and processing of any type of information at a location that is not owned or controlled by the information owner. This information can be stored and accessed anytime, from anywhere, and using any device. However, several challenges threaten the deployment of cloud computing technology within organizations, including user privacy, data integrity, and managing the retention of the vast amount of data that will be stored and analyzed in this environment. In recent years, various data integrity checking systems and data storage mechanisms have been proposed to address these diverse challenges. Nevertheless, some protocols do not guarantee data confidentiality against auditors during the auditing process, while others do not provide batch auditing. Additionally, the majority of these protocols do not support the deduplication technique. Therefore, in this research work, the security features of blockchain technology and those of multi-agent systems (MAS) have prompted us to propose models that enable both data deduplication, saving storage space, and data integrity auditing, efficiently verifying the accuracy of data outsourced to CC environments. Implementation findings indicate that the suggested system is highly feasible and applicable in real-world scenarios. © 2024 by the authors of this article.

关键词： Multi agent systems

来源：评论

学校读者我要写书评

暂无评论

PNRITR:Research on Positive and Negative Entity Region Perception Methods for Image-Text Retrieval 36

PNRITR:Research on Positive and Negative Entity Region Perce...

引用

36th International Conference on Software Engineering and Knowledge Engineering, SEKE 2024

作者： Zhang, Zhiping Sun, Tao Zheng, Hongyan Liu, Hao Liu, Gengchen Yang, Zhi Wang, Xiaoyu Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Qilu University of Technology Shandong Academy of Sciences Jinan China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Qilu University of Technology Shandong Academy of Sciences Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China

ISBN: (纸本)1891706594

The aim of cross-modal image-text retrieval is to heighten comprehension and to create robust associations between visual and textual content. This process entails a mutual querying and synchronization across various data formats. While conventional techniques have seen enhancements via attention-based approaches, they frequently miss the dynamics between corresponding and non-corresponding entity areas within image-text pairs. These techniques, often fixated on aligning image entities with textual terms, can overlook the congruence of entity characteristics, leading to less accurate localization. This research introduces PNRITR, an innovative strategy for cross-modal retrieval that leverages label learning to discern attributes absent from images, broadening the scope beyond just positive entity areas. PNRITR has delivered notable advancements on the Flickr30K and MS-COCO datasets. © 2024 Knowledge Systems Institute Graduate School. All rights reserved.

关键词： Knowledge based systems

来源：评论

学校读者我要写书评

暂无评论

A New Transformer-Based Approach for Text Detection in Shaky and Non-shaky Day-Night Video 1

引用

7th Asian Conference on Pattern Recognition, ACPR 2023

作者： Halder, Arnab Shivakumara, Palaiahnakote Pal, Umapada Lu, Tong Blumenstein, Michael Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata India Faculty of Computer Science and Information Technology University of Malaya Kula Lumpur Malaysia Nanjing University Nanjing China University of Technology Sydney Sydney Australia

ISBN: (数字)9783031476372

ISBN: (纸本)9783031476365

Text detection in shaky and non-shaky videos is challenging because of variations caused by day and night videos. In addition, moving objects, vehicles, and humans in the video make the text detection problems more challenging in contrast to text detection in normal natural scene images. Motivated by the capacity of the transformer, we propose a new transformer-based approach for detecting text in both shaky and non-shaky day-night videos. To reduce the effect of object movement, poor quality, and other challenges mentioned above, the proposed work explores temporal frames for obtaining activation frames based on similarity and dissimilarity measures. For estimating similarity and dissimilarity, our method extracts luminance, contrast, and structural features. The activation frames are fed to the transformer which comprises an encoder, decoder, and feed-forward network for text detection in shaky and non-shaky day-night video. Since it is the first work, we create our own dataset for experimentation. To show the effectiveness of the proposed method, experiments are conducted on a standard dataset called the ICDAR-2015 video dataset. The results on our dataset and standard dataset show that the proposed model is superior to state-of-the-art methods in terms of recall, precision, and F-measure. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： Chemical activation

来源：评论

学校读者我要写书评

暂无评论

A Hybrid Genetic Algorithm for Hierarchical Routing in Wireless Sensor Networks 1

引用

13th International Conference on information Systems and Advanced Technologies, ICISAT 2023

作者： Messiaid, Abd Essalam Bezziane, Mohamed Ben Brik, Bouziane Kettouche, Souhila Benguenane, Messaoud Siham, Hasan Sami, Lemtenneche LINATI Laboratory Department of Computer Science Faculty of New Technologies of Information and Communication University of Kasdi Merbah Ouargla Algeria Computer Science Department College of Computing and Information Sharjah University Sharjah United Arab Emirates Lab. of Computer Science Larbi Ben M’hidi University Oum Elbouaghi Algeria Faculty of Technology De Montfort University Gateway House LeicesterLE1 9BH United Kingdom

ISBN: (数字)9783031605949

ISBN: (纸本)9783031605932

Data transmission in wireless sensor networks (WSNs) is recognized as an interesting field of research due to the specificities of this type of network, where energy consumption is considered one of the major challenges of communication protocol options. Hierarchical data routing in Wireless Sensor Networks (WSNs) represents a distinct category of routing protocols. This category includes approaches that implement a reorganization of the physical network into a logical hierarchy system, aimed at optimizing energy consumption. In this paper, we propose a new scheme named Genetic-Firefly (GF) for the Hierarchical routing based on the hybridization of the genetic algorithm to select an alternative of cluster heads (CHs) with the firefly bio-inspired technique that allows forming clusters. The simulation of GF and the Low Energy Adaptive Clustering Hierarchy protocol (LEACH) has proved that our proposal has achieved good results in terms of the energy consumed, network lifetime, and received data. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Energy utilization

来源：评论

学校读者我要写书评

暂无评论

FERI: Feature Enhancement and Relational Interaction for Image-text Matching 30

FERI: Feature Enhancement and Relational Interaction for Ima...

引用

30th IEEE International Conference on Parallel and Distributed Systems, ICPADS 2024

作者： Zhang, Yu Zhang, Jianqiang Song, Gongpeng Lu, Qin Zhao, Shuo Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Jinan China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Jinan China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computer Networks Jinan China Shandong Branch of China Mobile Communication Group Design Institute Co. Jinan China

ISBN: (纸本)9798331515966

Image-text matching is an important problem at the intersection of computer vision and natural language processing. It aims to establish the semantic link between image and text to achieve high-quality semantic alignment between the two modalities. However, the existing methods have the problem that the meaning expressed in the image or the complex narrative in the text cannot be fully understood due to insufficient feature extraction. Moreover, due to the essential modal differences between images and texts, how to effectively and accurately align the semantic contents in images and texts has become the key of research. In order to solve the above problems, this paper proposes a method based on feature enhancement and relationship interaction. When processing images, the proposed method fuses labeled features, region features and location features to represent images. When processing text, a combination of Bi-GRU and self-attention mechanism is used to represent the text. In order to further align the semantic content in images and texts accurately, this paper improves two relational interaction mechanisms by identifying connection relationships and learning association relationships. Thus, the relation enhanced embedding is obtained. Finally, it calculated the similarity of the enhanced embedding to judge the matching degree of the image and text. Extensive experiments on the public datasets Flickr30K and MSCOCO demonstrate the effectiveness of our method. © 2024 IEEE.

关键词： cross-modal retrieval feature enhancement image-text matching relationship interaction

来源：评论

学校读者我要写书评

暂无评论

SMGC-SBERT: A Multi-Feature Fusion Chinese Short Text Similarity Computation Model Based on Optimised SBERT

SMGC-SBERT: A Multi-Feature Fusion Chinese Short Text Simila...

引用

2024 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2024

作者： Zhao, Shuo Gu, Qiliang Zhang, Jianqiang Song, Gongpeng Lu, Qin Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Jinan China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China Shandong Branch of China Mobile Communication Group Design Institute Co. Jinan China

ISBN: (纸本)9781665410205

Chinese short text similarity computation stands as a pivotal task within natural language processing, garnering significant attention. However, existing models grapple with limitations in handling intricate semantic relationships, such as the challenge of discerning subtle semantic nuances in text, inadequacies in effectively integrating diverse levels of semantic information, and the struggle to capture polysemous meanings accurately. In addressing these issues, this paper introduces an innovative Chinese short text similarity computation model, SMGC-SBERT. This model addresses shortcomings of the existing models by employing a multi-module fusion strategy, thereby enabling a more precise measurement of semantic similarity between texts. Primarily, the model incorporates SAT embedding to acquire phrase-level semantic information and leverages the MS-BERT model to encode text, improving the model's comprehension of textual polysemy and obtaining richer semantic representation. Then, the fusion of module features, including multi-branch convolutional networks and mix pooling, enables the extraction of textual features from varied levels, bolstering the model's representational capacity. Additionally, to further reduce overfitting risks while improving accuracy and other performance, a multi-layer feature adjustment network is utilized for short text similarity calculation. The final resultant experimental findings showcase the superiority of the SMGC-SBERT model over other neural network models, demonstrating significant advancements across both Chinese-SNLI and CCKS2018_Task3 Chinese short text datasets. © 2024 IEEE.

关键词： Semantics

来源：评论

学校读者我要写书评

暂无评论

RDMMLND: A New Robust Deep Model for Multiple License Plate Number Detection in Video 3rd

RDMMLND: A New Robust Deep Model for Multiple License Plate ...

引用

3rd International Conference on Pattern Recognition and Artificial Intelligence, ICPRAI 2022

作者： Kumar, Amish Shivakumara, Palaiahnakote Pal, Umapada Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata India Faculty of Computer Science and Information Technology University of Malaya Kuala Lumpur Malaysia

ISBN: (纸本)9783031090363

Accurate multiple license plate detection without affecting speed, occlusion, low contrast and resolution, uneven illumination effect and poor quality is an open challenge. This study presents a new Robust Deep Model for Multiple License Plate Number Detection (RDMMLND). To cope with the above-mentioned challenges, the proposed work explores YOLOv5 for detecting vehicles irrespective of type to reduce background complexity in the images. For detected vehicle regions, we propose a new combination of Wavelet Decomposition and Phase Congruency Model (WD-PCM), which enhances the license plate number region such that the license plate number detection step fixes correct bounding boxes for each vehicle of the input images. The proposed model is tested on our own dataset containing video images and standard dataset of license plate number detection to show that the proposed model is useful and effective for multiple license plate number detection. Furthermore, the proposed method is tested on natural scene text datasets to show that the proposed method can be extended to address the challenges of natural scene text detection. © 2022, Springer Nature Switzerland AG.

关键词： Wavelet decomposition

来源：评论

学校读者我要写书评

暂无评论

Removing Scattered Light in Biomedical Images via an Unsupervised Deep Neural Network

Removing Scattered Light in Biomedical Images via an Unsuper...

引用

2023 IEEE EMBS Special Topic Conference on Data science and Engineering in Healthcare, Medicine and Biology, IEEECONF 2023

作者： Gong, Yuanhao Xu, Meng Li, Yawei Magno, Michele Shenzhen University Faculty of Electronics and Information Engineering China Queen Mary University of London School of Computer Science United Kingdom Eth Zurich Computer Vision Lab Switzerland Eth Zurich Center for Project-based Learning Switzerland

ISBN: (纸本)9798350383386

Scattered light is unavoidable during biomedical imaging, leading to downgraded images. Removing such scattered light, however, is challenging, because the imaging objects are complex and the light path is complicated. In this paper, we propose to use deep learning methods to computationally remove such scattered light. Inspired by the dehazing methods for natural images, we first developed a novel mathematical model for biomedical images. Then, we developed a deep neural network method to solve this model. Our network simultaneously estimates the clear image and the scattered light. Several experiments are conducted to confirm the network's effectiveness and efficiency. © 2023 IEEE.

关键词： Deep 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：