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A multiple sensitive attributes data publishing method with guaranteed information utility

CAAI Transactions on Intelligence Technology 2023年第2期8卷 288-296页

作者： Haibin Zhu Tong Yi Songtao Shang Minyong Shi Zhucheng Li Wenqian Shang Department of Computer Science and Mathematics Nipissing UniversityNorth BayOntarioCanada Key Lab of Education Blockchain and Intelligent Technology Ministry of EducationGuangxi Normal UniversityGuilinChina Guangxi Key Lab of Multi‐Source Information Mining and Security Guangxi Normal UniversityGuilinChina School of Computer and Communication Engineering Zhengzhou University of Light IndustryZhengzhouChina School of Computer Science Communication University of ChinaBeijingChina Business College Beijing Union UniversityBeijingChina

Data publishing methods can provide available information for analysis while preserving *** multiple sensitive attributes data publishing,which preserves the relationship between sensitive attributes,may keep many records from being grouped and bring in a high record suppression *** category of multiple sensitive attributes data publishing,which reduces the possibility of record suppression by breaking the relationship between sensitive attributes,cannot provide the sensitive attributes association for ***,the existing multiple sensitive attributes data publishing fails to fully account for the comprehensive information *** acquire a guaranteed information utility,this article defines comprehensive information loss that considers both the suppression of records and the relationship between sensitive attributes.A heuristic method is leveraged to discover the optimal anonymity scheme that has the lowest comprehensive information *** experimental results verify the practice of the proposed data publishing method with multiple sensitive *** proposed method can guarantee information utility when compared with previous ones.

关键词： data analysis data privacy security of data

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Advancements in Image Caption Bots: A Deep Learning Approach 6

Advancements in Image Caption Bots: A Deep Learning Approach

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6th IEEE International Conference on Emerging Smart Computing and Informatics, ESCI 2024

作者： Dabral, Adarsh Gusain, Raj Shekhar, Sushant Vidyarthi, Anurag Prakash, Rishi Gowri, R. Propagation Research Lab Department of Computer Science Engineering Dehradun India Propagation Research Lab Department of Electronics and Communication Engineering Dehradun India Kanpur India Graphic Era Hill University Department of Computer Science Engineering Dehradun India

ISBN: (纸本)9798350306613

In recent years, artificial intelligence (AI) has changed the area of computer vision, and image captioning is one of the many applications that have benefited from these advancements. An image caption bot is an intelligent system that generates a textual description of an image. This research paper will explore the development of image caption bots, the underlying technologies, and the various techniques used to build these systems. Image caption bots are developed with the help of deep learning models (DLM) such as convolutional neural networks (CNNs) the most popular nowadays and recurrent neural networks (RNN s) which is also popular in researchers. Featureextraction has been done from the images with CNN sand RNN s are used to generate the captions. These deep-learning models have significantly improved the accuracy and performance of image captioning systems. During training, the CNN extracts the relevant features from the images, and the RNN generates the corresponding captions. In order to decrease the discrepancy between generated and ground truth captions, the model is trained. The accuracy and efficiency of image caption procedures could be greatly increased by using the image caption bots developed in this research. These systems have several applications and can greatly aid visually impaired individuals in understanding images, assist search engines to better index and search images, and improve the accessibility of social media platforms for users with disabilities. Further research in this field could lead to more accurate and comprehensive Image captioning systems. © 2024 IEEE.

关键词： Caption-Bots CNN Deep Learning Image caption bot RNN

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RecStitchNet:Learning to stitch images with rectangular boundaries

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Computational Visual Media 2024年第4期10卷 687-703页

作者： Yun Zhang Yu-Kun Lai Lang Nie Fang-Lue Zhang Lin Xu Key Lab of Film and TV Media Technology of Zhejiang Province College of Media EngineeringCommunication University of ZhejiangHangzhou 310018China School of Computer Science and Informatics Cardiff UniversityCardiff CF244AGUK Institute of Information Science Beijing Jiaotong UniversityBeijing 100091China School of Engineering and Computer Science Victoria University of WellingtonWellington 6012New Zealand STEM University of South AustraliaAdelaide 5095Australia

Irregular boundaries in image stitching naturally occur due to freely moving *** deal with this problem,existing methods focus on optimizing mesh warping to make boundaries regular using the traditional explicit ***,previous methods always depend on hand-crafted features(e.g.,keypoints and line segments).Thus,failures often happen in overlapping regions without distinctive *** this paper,we address this problem by proposing RecStitchNet,a reasonable and effective network for image stitching with rectangular *** that both stitching and imposing rectangularity are non-trivial tasks in the learning-based framework,we propose a three-step progressive learning based strategy,which not only simplifies this task,but gradually achieves a good balance between stitching and imposing *** the first step,we perform initial stitching by a pre-trained state-of-the-art image stitching model,to produce initially warped stitching results without considering the boundary ***,we use a regression network with a comprehensive objective regarding mesh,perception,and shape to further encourage the stitched meshes to have rectangular boundaries with high content ***,we propose an unsupervised instance-wise optimization strategy to refine the stitched meshes iteratively,which can effectively improve the stitching results in terms of feature alignment,as well as boundary and structure *** to the lack of stitching datasets and the difficulty of label generation,we propose to generate a stitching dataset with rectangular stitched images as pseudo-ground-truth labels,and the performance upper bound induced from the it can be broken by our unsupervised *** and quantitative results and evaluations demonstrate the advantages of our method over the state-of-the-art.

关键词： image stitching boundaries convolutional neural network

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Resource Allocation in 6G Network for High-Speed Train Using D2D Outband communication 3rd

Resource Allocation in 6G Network for High-Speed Train Using...

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3rd International Conference on Machine Vision and Augmented Intelligence, MAI 2023

作者： Garg, Aakansha Arya, Rajeev Singh, Maheshwari Prasad Wireless Sensor Network Lab Department of Electronics and Communication Engineering Patna India Department of Computer Science and Engineering National Institute of Technology Patna India

ISBN: (纸本)9789819743582

The growth of 6G results in rising data transmission rate in static scenario. To achieve fast communication and computation with quality service in high-speed train is a challenge. This is due to fast handover and Doppler shift. To effectively communicate in high-speed train, the placement of a mobile relay (MR) with small server at the rooftop of train coach is proposed. The base station sends channel state information to mobile relay which stores it in the stack. The resource assignment has been conducted by an improved grey wolf optimization algorithm. The focus is at improving throughput with reduction in latency and energy consumption. The simulation outcomes manifests that the presented algorithm carry-out finer in comparison of the Hungarian algorithm. The accomplished results are refined almost three times in respect of energy consumption, latency, and throughput. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Resource allocation

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Learning Dual-Domain Calibration and Distance-Driven Correlation Filter:A Probabilistic Perspective for UAV Tracking

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computers, Materials & Continua 2023年第12期77卷 3741-3764页

作者： Taiyu Yan Yuxin Cao Guoxia Xu Xiaoran Zhao Hu Zhu Lizhen Deng Jiangsu Province Key Lab on Image Processing and Image Communication Nanjing University of Posts and TelecommunicationsNanjingChina School of Computer Science Qufu Normal UniversityQufuChina National Engineering Research Center of Communication and Network Technology Nanjing University of Posts and TelecommunicationsNanjingChina

Unmanned Aerial Vehicle(UAV)tracking has been possible because of the growth of intelligent information technology in smart cities,making it simple to gather data at any time by dynamically monitoring events,people,the environment,and other aspects in the *** traditional filter creates a model to address the boundary effect and time filter degradation issues in UAV tracking *** these methods ignore the loss of data integrity terms since they are overly dependent on numerous explicit previous regularization *** light of the aforementioned issues,this work suggests a dual-domain Jensen-Shannon divergence correlation filter(DJSCF)model address the probability-based distance measuring issue in the event of filter *** two-domain weighting matrix and JS divergence constraint are combined to lessen the impact of sample imbalance and *** new tracking models that are based on the perspectives of the actual probability filter distribution and observation probability filter distribution are proposed to translate the statistical distance in the online tracking model into response *** model is roughly transformed into a linear equality constraint issue in the iterative solution,which is then solved by the alternate direction multiplier method(ADMM).The usefulness and superiority of the suggested strategy have been shown by a vast number of experimental findings.

关键词： Dual-domain weighting distance measure correlation filter ADMM

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LEO Satellite Oriented Wildfire Detection Model Using Deep Neural Networks: A Transfer Learning Based Approach 6

LEO Satellite Oriented Wildfire Detection Model Using Deep N...

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6th International Conference on Electrical Engineering and Information and communication Technology, ICEEICT 2024

作者： Gain, Mrityunjoy Raha, Avi Deb Biswas, Bristy Bairagi, Anupam Kumar Adhikary, Apurba Debnath, Rameswar Khulna University Computer Science and Engineering Discipline Khulna Bangladesh Ai Innovation Lab Khulna Bangladesh Noakhali Science and Technology University Department of Information and Communication Engineering Bangladesh

ISBN: (纸本)9798350385779

Wildfires are a recurring concern in several regions around the globe, often leading to catastrophic outcomes. The increasing accessibility of small satellites operating in Low Earth Orbit (LEO) has led to the potential for enhancing the speed of wildfire detection b y utilizing constellations of m any miniature satellites in the areas of interest. This research presents a system model for detecting and reporting wildfires to fire stations using LEO satellites. In this paper, we propose a transfer learning-based lightweight deep learning wildfire detection model at the base station to analyze pictures captured by LEO satellites and make predictions on the occurrence of wildfires. We use MobileNetV2 as our backbone model. MobileNetV2 is a lightweight model with low parameters. Lightweight models for image categorization offer faster inference, lower power consumption, efficient memory u sage, a nd ease of deployment, making them well-suited for real-time applications. However, a highly parameterized model can perform better than a poorly parameterized model. We apply transfer learning to ensure high performance in a low-parameterized model. The server must continuously check the images of all forest regions within a limited interval period to detect wildfires quickly. The lightweight model is very suitable for continuous image checking. Our experimental setup uses four baselines: VGG16, ResNet152, DenseNet161, and MobileNetv2. This study shows the efficacy of models in acquiring information from many sources within a given topic, as opposed to starting with no prior knowledge. We can also find from t he experimental results that our proposed low-parameterized transfer learning-based mobileNetV2 model achieves near 100 % accuracy, outperforming the other high-parameterized baselines. © 2024 IEEE.

关键词： Orbits

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A Multi-input Deep Neural Network Framework for Non-invasive Detection of Anemia using Finger Nail Images 10

A Multi-input Deep Neural Network Framework for Non-invasive...

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10th International Conference on Biosignals, Images and Instrumentation, ICBSII 2024

作者： Krithika, S. Reddy Poreddy, Ajay Kumar Balakrishna, Thunakala Kokil, Priyanka Amrita Vishwa Vidyapeetham Department of Computer Science and Engineering Chennai601103 India Lab Department of Electronics and Communication Engineering Chennai600 127 India

ISBN: (纸本)9798350350951

Anemia, characterized by a deficiency in red blood corpuscles or hemoglobin, poses a significant global health challenge, particularly affecting vulnerable populations. Traditional diagnostic methods often involve invasive procedures, posing challenges in resource-limited settings. This study aims to explore non-invasive anemia detection using fingernail images and convolutional neural networks (CNNs) as a promising alternative to conventional diagnostic approaches. The study utilizes a dataset of fingernail images collected from hospitals in Ghana, comprising both anemic and non-anemic cases. The dataset undergoes preprocessing, including selective enhancement of red components, conversion to the CIElab color space, and feature extraction. A multi-input Deep Neural Network (DNN) framework employing pre-trained CNNs is proposed for anemia classification. The pre-trained CNN architectures include EfficientNet B1, EfficientNet B4, and MobileNet V3. The framework's performance was assessed using two methodologies: The first involved random shuffling of the dataset, followed by division into training, testing, and validation sets, with evaluation metrics including Accuracy, Precision, F1 scores, and a Confusion Matrix. The second employed five-fold cross-validation, measured using accuracy. The evaluation of the proposed DNN framework using both of the methodologies indicates that EfficientNet B4 achieved the highest testing accuracy (97.87%), precision (97.88%), recall (97.87%), and F1 score (97.88%) and a cross-validation accuracy of 97.37% for the first and second methodologies respectively making it best fit for the proposed DNN framework. The findings demonstrate that the proposed framework yields promising results, especially under the second approach, and opens avenues for further exploration in transfer learning, fine-tuning of deep neural networks for multi-input feature integration, and cross-validation. © 2024 IEEE.

关键词： Deep neural networks

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Development of a CNN Model for Efficient Leaf Disease Detection

Development of a CNN Model for Efficient Leaf Disease Detect...

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2024 OPJU International Technology Conference on Smart Computing for Innovation and Advancement in Industry 4.0, OTCON 2024

作者： Dagar, Ishant Sharma, Harsh Gusain, Raj Vidyarthi, Anurag Prakash, Rishi Gowri, R. Propagation Research Lab Department of Electronics & Communication Engineering Dehradun India Graphic Era Hill University Department of Computer Science Engineering Dehradun India

ISBN: (纸本)9798350373783

In order to mitigate financial losses for producers and safeguard crops against diseases, the proposed solution presents a CNN model that enables early detection of leaf issues. In the agriculturally dominant nation of India, foliage maladies annually reduce agricultural output by 35%. The process of manually identifying diseases in foliage is time-consuming due to the lack of apparatus and expertise in laboratories required to identify early indicators of such conditions. The following are examples of such diseases: late blight, early blight, black root, bacterial mark, infected leaf, healthy leaf, and so forth. The importance of developing self-governing algorithms to diagnose diseases in leaves through the analysis of visual symptoms has been underscored by recent developments in deep learning and computer vision models. The main aim of the proposed model is to address the challenge of leaf disease diagnosis through the implementation of a straightforward method that minimizes the demand for computational resources. We implemented the multi-layered architecture of convolutional neural networks (CNN) in order to identify and eradicate leaf diseases. A model for image classification is trained utilizing our proposed approach, and it attains an accuracy of 91.5%, surpassing the performance of existing methodologies. © 2024 IEEE.

关键词： Multilayer neural networks

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DP-GenFL: a local differentially private federated learning system through generative data

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science China(Information sciences) 2023年第8期66卷 275-276页

作者： Jun LI Kang WEI Chuan MA Feng SHU School of Electronic and Optical Engineering Nanjing University of Science and Technology Zhejiang Lab Key Laboratory of Computer Network and Information Integration (Southeast University) Ministry of Education School of Information and Communication Engineering Hainan University

With the rapid development of the Internet of Things(Io T),the amount of data from intelligent devices is propagating at unprecedented scales. Meanwhile, machine learning(ML),which relies heavily on such data, is revolutionizing many aspects of our lives [1]. However, conventional centralized ML offers little scalability for efficiently processing this huge amount of data.

关键词： Learning systems

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A Many-Objective Evolutionary Algorithm with Spatial Division and Angle Culling Strategy

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Chinese Journal of Electronics 2021年第3期30卷 437-443页

作者： WANG Hongbo YANG Fan TIAN Kena TU Xuyan School of Computer and Communication Engineering Beijing Key Lab.of Knowledge Engineering for Materials ScienceUniversity of Science and Technology Beijing

In a specific project, how to find a reasonable balance between a plurality of objectives and their optimal solutions has always been an important aspect for researchers. As a trade off between fast convergence and a rich diversity, a Many-objective evolutionary algorithm based on a spatial division and angle-culling strategy(Ma OEA-SDAC) is proposed. In the reorganization stage, a restricted matching selection can enhance the reproductivity. In the environment selection stage, a space division and angle-based elimination strategy can effectively improve the convergence and diversity imbalance of its solution set. Through detailed experiments and a comparative analysis of the proposed Ma OEA-SDAC with five other state-of-the-art algorithms on classical benchmark problems, the effectiveness of Ma OEA-SDAC in solving high-dimensional optimization problems has been verified.

关键词： reasonable balance many-objective evolutionary algorithm convergence environment selection stage angle culling strategy elimination strategy spatial division rich diversity Pareto optimisation angle-culling strategy diversity imbalance restricted matching selection specific project evolutionary computation fast convergence reorganization stage MaOEA-SDAC space division optimal solutions

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