检索结果-内蒙古大学图书馆

3rd International Conference on Pervasive Computing and Social Networking, ICPCSN 2023

作者： Jebakumar Immanuel, D. Prakash, A. Mugundhan, M. Keerthivasan, U. Uma Makesan, A. Karpagam Institute of Technology Department of Artificial Intelligence and Data Science Coimbatore India Sns College of Engineering Department of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798350322842

There are several uses for information concealing today. The use of data hiding knowledge may be morally or immorally acceptable. data hiding methods, however, are difficult to classify into either the steganography or the watermarking categories because there is no clear distinction between these two concepts, and classification mostly depends on algorithmic application. Hence, regardless of how data concealing is classified, the most popular data hiding applications include copyright protection, covert communication, safe storage, and fingerprinting. Messages are altered by cryptography so they can't be understood. Secured data, varied size keys for data concealment, and quick and flexible ease of design are some of cryptography's main benefits. The biggest drawback of cryptography is that it is not suitable for the internet and is only compatible with devices like mobile phones, has complicated hardware, and uses unusual cypher patterns. Steganography is a centuries-old technique for obscuring data. In this essay, a survey of current audio steganography research is presented. © 2023 IEEE.

关键词： Mean square error

来源：评论

学校读者我要写书评

暂无评论

Spectral Normalized-Cut Graph Partitioning with Fairness Constraints 26

Spectral Normalized-Cut Graph Partitioning with Fairness Con...

引用

26th European Conference on Artificial Intelligence, ECAI 2023

作者： Li, Jia Wang, Yanhao Merchant, Arpit School of Data Science and Engineering East China Normal University Shanghai China Department of Computer Science University of Helsinki Helsinki Finland

ISBN: (纸本)9781643684369

Normalized-cut graph partitioning aims to divide the set of nodes in a graph into k disjoint clusters to minimize the fraction of the total edges between any cluster and all other clusters. In this paper, we consider a fair variant of the partitioning problem wherein nodes are characterized by a categorical sensitive attribute (e.g., gender or race) indicating membership to different demographic groups. Our goal is to ensure that each group is approximately proportionally represented in each cluster while minimizing the normalized cut value. To resolve this problem, we propose a two-phase spectral algorithm called FNM. In the first phase, we add an augmented Lagrangian term based on our fairness criteria to the objective function for obtaining a fairer spectral node embedding. Then, in the second phase, we design a rounding scheme to produce k clusters from the fair embedding that effectively trades off fairness and partition quality. Through comprehensive experiments on nine benchmark datasets, we demonstrate the superior performance of FNM compared with three baseline methods. © 2023 The Authors.

关键词： Embeddings

来源：评论

学校读者我要写书评

暂无评论

A Tree-Structure Enhanced Transformer for Cardinality Estimation

A Tree-Structure Enhanced Transformer for Cardinality Estima...

引用

2024 International Conference on Machine Learning and Intelligent Computing, MLIC 2024

作者： Hu, Mingjie Zhang, Qihang Ren, Jing Liu, Hengzhu Department of Intelligent Data Science National University of Defense Technology China College of Computer National University of Defense Technology China

Accurate cardinality estimation is crucial for query optimization by guiding plan selection. Traditional cardinality estimation approaches often fail to provide precise estimates, leading to suboptimal query plans. In recent years, learning-based methods have emerged as a promising alternative. For tree-based learning methods, a conventional way to simply encode nearby parent-child pairs and learn by iteratively training hampers the performance. But it leads to a significant loss of structural information and incurs substantial computational overhead by the iterative training process. In this paper, we proposed a tree-structure positional encoding scheme. It can not only extract effective features for each node, but also capture the inherent structural characteristics of the tree. Based on the tree-based feature, we designed a novel transformer-based cardinality estimation model, which enhances the parallelism of the model training process and reduces the overhead caused by iterative training. On real-world datasets, our method beats the current state-of-the-art techniques, QF, by 25% in terms of mean qerror. © 2024 M. Hu, Q. Zhang, J. Ren & H. Liu.

关键词： Cardinality estimation Positional Encoding Tree Transformer

来源：评论

学校读者我要写书评

暂无评论

Joint Communication and Synchronization Performance Optimization in Digital Twin Enabled Networks

Joint Communication and Synchronization Performance Optimiza...

引用

2024 IEEE Global Communications Conference, GLOBECOM 2024

作者： Yu, Hanzhi Liu, Yuchen Chen, Mingzhe University of Miami Department of Electrical and Computer Engineering Frost Institute for Data Science and Computing Coral GablesFL33146 United States North Carolina State University Department of Computer Science RaleighNC27695 United States

ISBN: (纸本)9798350351255

In this paper, we investigate an accurate synchronization between a physical network and its digital network twin (DNT) that is a virtual representation of the physical network. The considered network includes a physical network where a base station (BS) serves a set of users, and a DNT that evolves with the status of both DNT and the physical network. The BS must use its limited spectrum resources to serve the users, as well as transmit the physical network information to the cloud server for DNT synchronization. Since the DNT can predict the physical network status, the BS may not need to transmit physical network information to the server at each time slot thus saving spectrum resources to serve users. However, if the BS does not transmit physical information to the DNT over a long period of time, the DNT may not be able to represent the physical network accurately. To this end, the BS must determine whether to send physical network information to the server to update DNT and the spectrum resources used for physical network information transmission and serving users. We formulate this resources allocation problem as an optimization problem aiming to maximize the sum of data rates of all users, while minimizing the gap between the states of the physical network and the DNT. The formulated problem is challenging to solve by conventional optimization methods, since the BS may not be able to know the future status of the DNT. To solve this problem, we design a gate recurrent unit (GRU) and soft action-critic (SAC) based algorithm. The GRU enables the DNT to predict its future states by using historical state data, and updating the DNT when the BS does not transmit physical network information. The SAC based algorithm enables the BS to learn the relationship between the physical network information transmission and the future status estimation accuracy of the DNT thus determining whether to transmit physical network information to the cloud server, ensuring an accurac

关键词： Resource allocation

来源：评论

学校读者我要写书评

暂无评论

ResPho(SC)Net: A Zero-Shot Learning Framework for Norwegian Handwritten Word Image Recognition 11th

ResPho(SC)Net: A Zero-Shot Learning Framework for Norwegian...

引用

11th Iberian Conference on Pattern Recognition and Image Analysis, IbPRIA 2023

作者： Gurav, Aniket Jensen, Joakim Krishnan, Narayanan C. Chanda, Sukalpa Department of Computer Science and Communication Østfold University College Halden1757 Norway Department of Data Science Indian Institute of Technology Palakkad Kerala India

ISBN: (纸本)9783031366154

Recent advances in deep Convolutional Neural Networks (CNNs) have established them as a premier technique for a wide range of classification tasks, including object recognition, object detection, image segmentation, face recognition, and medical image analysis. However, a significant drawback of utilizing CNNs is the requirement for a large amount of annotated data, which may not be feasible in the context of historical document analysis. In light of this, we present a novel CNN-based architecture ResPho(SC)Net, to recognize handwritten word images in a zero-shot learning framework. Our method proposes a modified version of the Phosc(Net) architecture with a much lesser number of trainable parameters. Experiments were conducted on word images from two languages (Norwegian and English) and encouraging results were obtained. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Optical character recognition

来源：评论

学校读者我要写书评

暂无评论

Improved optimizer with deep learning model for emotion detection and classification

引用

Mathematical Biosciences and Engineering 2024年第7期21卷 6631-6657页

作者： Joseph, Willson C. Kathrine, G. Jaspher Willsie Shanmuganathan, Vimal Sumathi, S. Pelusi, Danilo Valencia, Xiomara Patricia Blanco Verdú, Elena Department of Computer Science and Engineering Karunya Institute of Technology and Sciences Coimbatore India Department of Computer Science and Engineering Adi Shankara Institute of Engineering and Technology Kerala India Department of Artificial Intelligence and Data Science Sri Eshwar College of Engineering Coimbatore India Sri Eshwar College of Engineering Coimbatore India Communication Sciences University of Teramo Coste Sant’agostino Campus Teramo Italy Universidad Internacional de La Rioja La Rioja Logroño Spain

Facial emotion recognition (FER) is largely utilized to analyze human emotion in order to address the needs of many real-time applications such as computer-human interfaces, emotion detection, forensics, biometrics, and human-robot collaboration. Nonetheless, existing methods are mostly unable to offer correct predictions with a minimum error rate. In this paper, an innovative facial emotion recognition framework, termed extended walrus-based deep learning with Botox feature selection network (EWDL-BFSN), was designed to accurately detect facial emotions. The main goals of the EWDL-BFSN are to identify facial emotions automatically and effectively by choosing the optimal features and adjusting the hyperparameters of the classifier. The gradient wavelet anisotropic filter (GWAF) can be used for image pre-processing in the EWDL-BFSN model. Additionally, SqueezeNet is used to extract significant features. The improved Botox optimization algorithm (IBoA) is then used to choose the best features. Lastly, FER and classification are accomplished through the use of an enhanced optimization-based kernel residual 50 (EK-ResNet50) network. Meanwhile, a nature-inspired metaheuristic, walrus optimization algorithm (WOA) is utilized to pick the hyperparameters of EK-ResNet50 network model. The EWDL-BFSN model was trained and tested with publicly available CK+ and FER-2013 datasets. The Python platform was applied for implementation, and various performance metrics such as accuracy, sensitivity, specificity, and F1-score were analyzed with state-of-the-art methods. The proposed EWDL-BFSN model acquired an overall accuracy of 99.37 and 99.25% for both CK+ and FER-2013 datasets and proved its superiority in predicting facial emotions over state-of-the-art methods. ©2024 the Author(s), licensee AIMS Press.

关键词： Emotion Recognition

来源：评论

学校读者我要写书评

暂无评论

Confidentiality. Integrity, and Availability (CIA) Approach for Private dataset Management System Design 9

Confidentiality. Integrity, and Availability (CIA) Approach ...

引用

9th International Conference on Information Technology and Digital Applications, ICITDA 2024

作者： Isnan, Mahmud Elwirehardja, Gregorius Natanael Ariansyah, Dedy Rahutomo, Reza Pardamean, Bens School of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta11480 Indonesia School of Information Systems Bina Nusantara University Information Systems Department Jakarta11480 Indonesia Binus Graduate Program Bina Nusantara University Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798331510732

dataset management systems are essential for assisting research and development (R&D) organizations in applying data governance protocols, especially in managing the utilization of datasets. In R&D, datasets are vital sources for producing analysis, machine learning prediction, and supporting decision-making. As such, it is important to deploy dataset management systems with considerations regarding the aspects of Confidentiality, Integrity, and Availability (CIA) which is a design principle that emphasized information protection and collaboration facilitation among dataset users. The study proposed an analysis and a design for a dataset management system that takes CIA aspects into account. In the future, the proposed design will be utilized as a benchmark for building a dataset management system. In this study, the Waterfall method was adopted. A use case diagram, an ERD (Entity Relationship Diagram), and an activity diagram were constructed in the design stage. In the project implementation stage, the Laravel framework including Laravel Jetstream was considered to be used. © 2024 IEEE.

关键词： data integrity

来源：评论

学校读者我要写书评

暂无评论

Remote Sensing data-Based Satellite Image Analysis in Water Quality Detection for Public Health data Modelling

引用

Remote Sensing in Earth Systems sciences 2024年第4期7卷 532-541页

作者： Balakrishnan, S. Preetam Raj, P Michael Somasekar, J. Kumar, Kambala Vijaya Amutha, S. Sangeetha, A. Department of Computer Science and Engineering Aarupadai Veedu Institute of Technology (AVIT) Vinayaka Mission’s Research Foundation (Deemed to be University) Chennai India Department of Computer Science and Engineering MLR Institute of Technology Telangana Dundigal Hyderabad 500043 India Department of Computer Science and Engineering Faculty of Engineering and Technology JAIN (Deemed-to-be-University) Karnataka Bengaluru 562112 India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram AP India Department of Computer Science and Engineering School of Computing Vel Tech Rangarajan Dr. Sagunthala R & amp D Institute of Science and Technology Tamilnadu Chennai India Department of Artificial Intelligence and Data Science Kongunadu College of Engineering and Technology TamilNadu Tholurpatti India

One of the most important aspects of environmental sustainability is water quality monitoring. The ecology is impacted by poor water quality in addition to aquatic life. With the volume of data being collected from satellite systems, the issue of effectively and automatically extracting water bodies is growing more pressing. One of the most important things to do for managing and safeguarding water resources is to conduct extensive monitoring of water quality parameters, or WQPs. It is still difficult to monitor optically inactive WQPs in inland waters, such as total nitrogen (TN), ammoniacal nitrogen (AN), and total phosphorus (TP). Water quality (WQ) classification and prediction can be greatly enhanced with the use of artificial intelligence (AI). This research proposes novel method in healthcare detection by water quality analysis based on classification utilising ML model. Input is collected as healthcare dataset based on water quality as well as processed for noise removal and normalisation. The processed data features are extracted and classified utilising reinforcement Gaussian stacked layer network with variational fuzzy Markov basis neural network. In order to suggest best course of action for the water bodies, it is imperative that any study look into the spatiotemporal changes of the dominant water quality parameters (WQPs). WQP concentrations have typically been determined by extensive fieldwork. These findings could help identify and regulate non-point source pollution, offer a large-scale spatial picture of the water quality, and pinpoint vulnerable locations and times of water pollution. Experimental analysis is carried out for various water sample dataset in terms of detection accuracy, mean precision, recall, F-1 score and AUC. The proposed technique obtained 97% of detection accuracy, 95% of recall, 91% of F-1 score, 96% of AUC, and 94% of MAP. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024.

关键词： Classification Fuzzy Markov basis Healthcare detection Machine learning model Water quality analysis

来源：评论

学校读者我要写书评

暂无评论

Masking of Colposcopy Images- A New Approach

Masking of Colposcopy Images- A New Approach

引用

2024 International Conference on Advances in Modern Age Technologies for Health and Engineering science, AMATHE 2024

作者： Chatterjee, Priyadarshini Das, Surajit Siddiqui, Shadab B V Raju Institute Of Technology Department Of Computer Science And Engineering Medak Telengana Narsapur India B V Raju Institute Of Technology Department Of Artificial Intelligence And Data Science Medak Telengana Narsapur India Koneru Lakshmaiah Education Foundation Department Of Computer Science And Engineering Hyderabad India

ISBN: (纸本)9798350371567

Masking, in the context of image processing and computer vision, refers to the process of isolating specific regions or objects of interest within an image while suppressing or removing irrelevant information. It involves the creation of a mask, which is essentially a binary image that defines which pixels in the original image belong to the region of interest and which do not. The mask typically consists of black and white pixels, where white pixels represent the areas to be preserved or highlighted, and black pixels represent the areas to be ignored or masked out. This paper presents a novel approach aimed at enhancing the accuracy of masking algorithms for colposcopy images, a crucial component in the diagnosis of cervical cancer among women. While previous research has demonstrated promising outcomes in this domain, our study introduces a novel masking algorithm tailored specifically to improve segmentation accuracy. The colposcopy dataset utilized in this research is sourced from the International Agency for Research on Cancer in France. © 2024 IEEE.

关键词： Diseases

来源：评论

学校读者我要写书评

暂无评论

Enhancing Residential Real Estate Search with Classification Strategies Using Diffusion and CLIP

Enhancing Residential Real Estate Search with Classification...

引用

2024 IEEE International Conference on Consumer Electronics, ICCE 2024

作者： Deng, Haojin Zhang, Wandong Yang, Yimin Nejad, Eman Western University Department of Electrical and Computer Engineering London Canada Wahi Realty Inc. Data Science Toronto Canada

ISBN: (纸本)9798350324136

The significance of the real estate search engine in the economy necessitates the development of a reliable room image luxury level annotation method that addresses current limitations, including the inability to assess room quality, underutilization of deep network capacities, and the need for more annotated house images. This paper proposes a novel real estate image annotation model, leveraging the diffusion model and contrastive language-image pre-Training (CLIP) network, through a multi-stage algorithm. First, the diffusion network is employed as a data augmentation technique to generate additional real estate images for network training. Then, a CLIP model is utilized to categorize images into the kitchen, bathroom, dining room, living room, and foyer. Finally, five CLIP models assess the condition of each room, categorizing it as contemporary and standard. Experimental results on a newly collected real estate image dataset demonstrate that the proposed approach surpasses existing house image classification algorithms. © 2024 IEEE.

关键词： contrastive language-image pre-Training image classification luxury level annotation Real-estate image

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：