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Graph Cuts and Deep Neural networks for Fire Detection 36

Graph Cuts and Deep Neural Networks for Fire Detection

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36th SIBGRAPI conference on Graphics, Patterns and Images (SIBGRAPI)

作者： Pereira, Davi Magalhaes Vieira, Marcelo Bernardes Villela, Saulo Moraes Univ Fed Juiz de Fora Dept Comp Sci Juiz De Fora Brazil

ISBN: (纸本)9798350338737;9798350338720

computer vision methods for fire detection have made significant advancements compared to traditional fire detection systems. The incorporation of fire segmentation masks enables precise analysis, offering valuable insights into the origin and spread of fires to prevent future incidents. This paper presents a novel approach that combines deep neural networks, graph cuts, and color thresholding to achieve fine-grained fire segmentation results. By incorporating graph cuts segmentation with global and local color information, our method enhances accuracy and detailed fire detection. As our results show, our method neatly improves recall, with a competitive precision, leading to an effective fire detection framework.

关键词： fire detection fire segmentation image classification graph cut deep learning color thresholding

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A Breast Image Classification Method Based on Attention Feature Fusion 1

A Breast Image Classification Method Based on Attention Feat...

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1st ieee International conference on Data Intelligence and Innovative Application, DIIA 2024

作者： Ke, Lin Wenying, Chen Zhisong, Qin Xiangfeng, Wen Yun, Liang Wenjun, Zhang School of Computer Science and Information Security Guilin University of Electronic Technology Guilin China Guilin Municipal Hospital of Traditional Chinese Medicine Medical Imaging Department Guilin China Information Guilin Municipal Hospital of Traditional Chinese Medicine Guilin China

ISBN: (纸本)9798331504434

As the number of breast MRI patients continues to rise, enhancing the accuracy and reliability of breast tumor dynamic contrast-enhanced magnetic resonance imaging (DCEMRI) benign-malignant cancer tasks is crucial. In this study, we introduce a novel local-global attention feature fusion network, building upon the framework of the local-global cross-attention fusion network model, and utilizing its foundation. This method alleviates the quadratic complexity of the self-attention mechanism. By focusing on different spatial and scale DCE-MRI information, important features from the outputs of SENet and Transformer networks are fully explored and utilized. Significant results are achieved on the BreastDM dataset, with the highest accuracy reaching 88.51% and an AUC value of 94.2%. This optimizes the breast tumor classification task. © 2024 ieee.

关键词： Dynamic contrast enhanced MRI

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Designing a Digital Repository for the Cultural and Natural Heritage of the Island of Zakynthos 9

Designing a Digital Repository for the Cultural and Natural ...

引用

9th South-East Europe Design Automation, computer Engineering, computer networks and Social Media conference, SEEDA-CECNSM 2024

作者： Palamas, Stergios Kabassi, Katerina Mylonas, Phivos Ionian University Department of Informatics Corfu Greece Ionian University Department of Environment Zakynthos Greece University of West Attica Department of Informatics and Computer Engineering Athens Greece

ISBN: (纸本)9798350342482

The present study discusses the design of a digital archive for the preservation and dissemination of the Cultural and Natural Heritage of Zakynthos Island. The proposed architecture suggests a Content Management System (CMS) to provide the core functionality, while a web application, interacting with the CMS through a RESTFul API, will provide an alternative front-end targeting mainly on-site end-users using mobile devices. Authors and contributors can submit multimedia content to the repository, which is reviewed and approved by system administrators before publishing, ensuring this way the information quality and *** system will also provide a RESTful API, allowing integration with 3rd party systems and web applications, thereby expanding the repository's reach and capabilities. Content delivery is personalized based on user profiles, location, and preferences, enhancing engagement and usability. By integrating these features, the repository effectively preserves and makes accessible the unique cultural and natural heritage of Zakynthos to both local and global audiences. © 2024 ieee.

关键词： Augmented Reality Biodiversity Content Management Systems Cultural Heritage Multimedia Digital Repositories Natural Heritage Web Applications

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Enhanced Capuchin Search Algorithm Using Cooperative Island Model with Application of Evolutionary Feedforward Neural networks

Enhanced Capuchin Search Algorithm Using Cooperative Island ...

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2023 International conference on Intelligent Computing, Communication, Networking and Services, ICCNS 2023

作者： Thaher, Thaer Awad, Mohammed Sheta, Alaa Aldasht, Mohammed Arab American University Department of Computer Systems Engineering Jenin Palestine Southern Connecticut State University Computer Science Department CT06515 United States Palestine Polytechnic University Department of Computer Engineering Hebron Palestine

ISBN: (纸本)9798350339291

This paper introduces an enhanced version of the Capuchin Search Algorithm (CapSA) called ECapSA. CapSA draws inspiration from the collective intelligence of Capuchin monkeys and has shown success in solving real-world problems. However, it may encounter challenges handling complex optimization tasks, such as premature convergence or being trapped in local optima. ECapSA employs a local escaping mechanism operating the abandonment limit concept to exploit potential solutions and introduce diversification trends. Additionally, the ECapSA algorithm is improved by integrating the principles of the cooperative island model, resulting in the iECapSA. This modification enables better management of population diversity and a more optimal balance between exploration and exploitation. The efficiency of iECapSA is validated through a series of experiments, including the ieee-CEC2014 benchmark functions and training the feedforward neural network (FNN) on seven biomedical datasets. The performance of iECapSA is compared to other metaheuristic techniques, namely differential evolution (DE), sine cosine algorithm (SCA), and whale optimization algorithm (WOA). The results of the comparative study demonstrate that iECapSA is a strong contender and surpasses other training algorithms in most datasets, particularly in terms of its ability to avoid local optima and its improved convergence speed. © 2023 ieee.

关键词： Feedforward neural networks

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The Impact of TV White Space (TVWS) and 5G networks on Bimodal Learning for Master's Programs in Engineering Science at the Higher Institute of Technology, University of Bangui

The Impact of TV White Space (TVWS) and 5G Networks on Bimod...

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2025 ieee International conference on Computational, Communication and Information Technology, ICCCIT 2025

作者： Ndassimba, Edgard Zoh-Ponguele, Nathan Kossingou, Ghislain Mervyl Saint-Juste Faculty of Science University of Bangui Department of Computer Engineering Bangui Central African Republic

ISBN: (数字)9798331512965

ISBN: (纸本)9798331512965

In this study, we presente digital solution for training master's students in engineering sciences, using a bimodal learning approach at the Higher Institute of Technology of Bangui's University. This proposal addresses the lack of master's-level programs in DevOps and Network Telecommunications, as well as the insufficient expertise of local educators in Information and Communication Technology (ICT). It also aims to strengthen the instruction of science, technology, engineering, and mathematics (STEM). The solution is based on remote learning platforms hosted in the public cloud, engaging students and teachers in a bimodal teaching model that delivers simultaneous instruction to both inperson and remote students via a high-speed TV White Space (TVWS) and 5G network. The expected impact is the training of competent engineers capable of successfully carrying out projects in the domain of information and communication technology, thereby contributing to the development of the Central African Republic. © 2025 ieee.

关键词： Students

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Research on Genetic Algorithm of Network Search under computer Artificial Intelligence Big Data Technology 4

Research on Genetic Algorithm of Network Search under Comput...

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4th Asia-Pacific conference on Communications Technology and computer Science, ACCTCS 2024

作者： Li, Yuan Yu, Xin Modern Finance Industry School Shandong Institute of Commerce and Technology Shandong Jinan China

ISBN: (纸本)9798350359985

In this paper, an improved genetic simulated annealing algorithm (SAGA) for wireless sensor networks is studied and designed. The aim of this project is to better study the optimal transmission path in wireless sensor networks (WSN s) based on inter-node communication and reduce node energy consumption. Firstly, the model is built based on the optimal control index. Then the fitness function, cross operation and variation operation in the evolutionary process are studied. The routing calculation and genetic evolution calculation are carried out at the same time until the approximate optimal path is found. This method can solve the traditional local optimization problem well. Then the Metropolis criterion is given to make the jump variable in the simulated annealing algorithm have some regularity. Simulation results show that the proposed algorithm can effectively solve the problem of wireless sensor path optimization. Compared with the existing methods, the proposed method not only saves a lot of energy, but also saves a lot of calculation time. © 2024 ieee.

关键词： Optimization algorithms

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Dam Displacement Prediction Method Combining Image Sequence Input with Residual networks 3

Dam Displacement Prediction Method Combining Image Sequence ...

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3rd International conference on Artificial Intelligence and computer Information Technology, AICIT 2024

作者： Zhao, Han Zheng, Dongjian College of Water Conservancy and Hydropower Engineering Hohai University Nanjing China State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering Hohai University Nanjing China

ISBN: (纸本)9798350365443

In this paper, a new dam displacement prediction method is proposed to solve the problems of traditional recurrent neural networks that require a large amount of data for training to avoid overfitting, poor generalization ability, and difficulty in capturing distant local information in the sequence. In this method, the time series is transformed into image series under fixed coordinates, and the residual network is used to predict, and the predicted images are restored to the specific values under the original coordinates. Compared with traditional deep learning models such as LSTM, GRU and RNN, the proposed method has significant advantages in prediction accuracy. The experimental results show that the proposed method has significant advantages in the face of limited training data, noise interference, and dealing with distant dependencies. The proposed method provides a new and effective solution for dam displacement prediction. © 2024 ieee.

关键词： Prediction models

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Proposing a novel approach based on GAN neural networks to address data poisoning attacks in federated learning 29

Proposing a novel approach based on GAN neural networks to a...

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29th International computer conference, computer Society of Iran, CSICC 2025

作者： Rahmat, Mohammad Ghabel Zarafshan, Faraneh Department of Computer Engineering Karaj Branch Islamic Azad University Karaj Iran

ISBN: (纸本)9798331523114

Data poisoning attacks are one of the serious threats in federated learning, which can disrupt the performance of the central model by injecting poisoned data into local nodes. These attacks are particularly significant in cloud environments, where distributed and decentralized data are often managed, due to the increasing need for data security. Identifying and isolating poisoned data from clean data in local nodes is a fundamental step toward enhancing the security and stability of these systems. In this paper, a method based on Generative Adversarial networks (GANs) is proposed, which can act as an effective tool for identifying poisoned samples. In the proposed method, the GAN discriminator’s task shifts from solving a binary problem, as is standard in GANs, to identifying the true label of data in a multi-class problem. During the GAN training phase, the semantic information of the data, including the true labels, is incorporated into the discriminator. To evaluate the proposed method, experiments were conducted on the standard MNIST dataset. Compared to standard GANs, the proposed method showed better performance for classes where label-flipping data poisoning was applied. These results demonstrate the effectiveness and applicability of the proposed method in various federated learning environments. The presented method can be used as a solution to enhance the security of data and models in federated learning and to identify the true labels of data in cases where there is no prior information about the occurrence of an attack. © 2025 ieee.

关键词： Federated learning

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Generative AI for Context-Aware 3D Object Creation Using Vision-Language Models in Augmented Reality 7

Generative AI for Context-Aware 3D Object Creation Using Vis...

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7th ieee International conference on Artificial Intelligence and eXtended and Virtual Reality, AIxVR 2025

作者： Behravan, Majid Matković, Krešimir Gračanin, Denis Virginia Tech Department of Computer Science VA United States VRVis Research Vienna Austria

ISBN: (纸本)9798331521578

We present a novel Artificial Intelligence (AI) system that functions as a designer assistant in augmented reality (AR) environments. Leveraging Vision Language Models (VLMs) like LLaVA and advanced text-to-3D generative models, users can capture images of their surroundings with an Augmented Reality (AR) headset. The system analyzes these images to recommend contextually relevant objects that enhance both functionality and visual appeal. The recommended objects are generated as 3D models and seamlessly integrated into the AR environment for interactive use. Our system utilizes open-source AI models running on local systems to enhance data security and reduce operational costs. Key features include context-aware object suggestions, optimal placement guidance, aesthetic matching, and an intuitive user interface for real-time interaction. Evaluations using the COCO 2017 dataset and real-world AR testing demonstrated high accuracy in object detection and contextual fit rating of 4.1 out of 5. By addressing the challenge of providing context-aware object recommendations in AR, our system expands the capabilities of AI applications in this domain. It enables users to create personalized digital spaces efficiently, leveraging AI for contextually relevant suggestions. © 2025 ieee.

关键词： Generative adversarial networks

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Image deblurring algorithm based on improved generative adversarial networks 7

Image deblurring algorithm based on improved generative adve...

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7th ieee Advanced Information Technology, Electronic and Automation Control conference, IAEAC 2024

作者： Shi, Jianguo Yin, Jing'Ping Huang, Yule Anhui Polytechnic University School of Computer and Information Anhui 241000 China

ISBN: (纸本)9798350339161

The image deblurring algorithm based on improved generative adversarial network is an image restoration algorithm that uses generative adversarial network. In this algorithm, the generator adopts an improved residual block method, which effectively solved the problem of gradient vanishing and reduced network parameters by combining dilated convolution and residual blocks. At the same time, the loss function of the generator has been improved, increasing the generator's attention to important information and structures in the image. By performing deconvolution pixel transformation on the output of the generator, the deblurred image is obtained. The experimental results show that the algorithm can effectively remove image blur and improve the quality of repaired images. © 2024 ieee.

关键词： Generative adversarial networks

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