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检索条件"任意字段=IEEE Conference on Local Computer Networks"

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Preserving edge with Transformer for deep image inpainting 3

Preserving edge with Transformer for deep image inpainting

引用

3rd ieee International conference on Information Technology, Big Data and Artificial Intelligence, ICIBA 2023

作者： Yan, Liang Li, Chengyang Wang, Chengduan Qufu Normal University School of Computer Rizhao China Peking University School of Computer Science Beijing China Weifang University School of Computer Engineering Weifang China

ISBN: (纸本)9781665490788

Most inpainting methods adopts convolutional neural networks (CNNs) due to its remarkable modeling capabilities. However, CNNs have certain limitations in understanding the global edge structure due to their inherent properties, such as local inductive priors. This has led to poor performance in reconstructing reasonable edge structures. Recently, deep generative methods have made considerable progress in image restoration by introducing edge priors. In contrast to CNNs, Transformers have demonstrated powerful capabilities in edge reconstruction. To this end, we propose a two-stage model PE-Net that combines a Transformer edge generator and a CNN image generator network. The edge generator is responsible for reconstructing the edges of the missing regions of the image, while the image generator uses the edges from the previous stage as auxiliary information to fill the missing regions. We conduct qualitative and quantitative experiments on the publicly available datasets CelebA and Places2, and the results demonstrate the superiority of the proposed method. © 2023 ieee.

关键词： Convolutional neural networks

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Pattern Recognition in Lung Disease Classification: Leveraging Convolutional Neural networks that Employs local Phase Quantization Features 15

Pattern Recognition in Lung Disease Classification: Leveragi...

引用

15th International conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Sree, M. Ramya Siddhartha, Mettu Reddy, Poli Vamsi Vardhan Singh, Tripty Nair, Rekha R. Amrita Vishwa Vidyapeetham Amrita School of Computing Dept. of Computer Science and Engineering Bengaluru India Alliance University Alliance School of Advanced Computing Dept. of Computer Science and Engineering Bangalore India

ISBN: (纸本)9798350370249

Lung diseases are wide group of diseases related to the respiratory system, and is ranging from acute infections to chronic disorders with multifactorial causes. Recognition of the lung disease is very crucial in the proper diagnosis, forecasting through prognosis, and remedies. Hence proposed lung disease classification with feature extraction technique. The proposed work carried out feature extraction with Gray-Level Co-occurrence Matrix (GLCM) and local Phase Quantization (LPQ) combined with Convolutional Neural Network(CNN). The goal is to create a model that reduces diagnostic errors, ensuring more precise early detection and classification of pulmonary disorders. This initiative facilitates the advancement of pulmonary medicine by promoting accurate radiologic imaging, while future directions emphasize integrating medical workflows to enable personalized and timely diagnosis and treatment. Additionally, the ResNet model demonstrates superior performance in hernia detection, achieving a test accuracy of 89.43% for GLCM based feature extraction and Pneumonia detection with 79.20% test accuracy for LPQ based feature extraction. © 2024 ieee.

关键词： CNNs Deep learning Early detection Feature extraction GLCM local Phase Quantization Lung disease diagnosis Medical imaging Texture analysis

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Comparative Analysis Between Feedforward Neural Network and CNN-LSTM Neural Network To Predict Household Electrical Energy Consumption

Comparative Analysis Between Feedforward Neural Network and ...

引用

2023 International conference on Electrical, computer, Communications and Mechatronics Engineering, ICECCME 2023

作者： Tufail, Shahid Tariq, Mohd Batool, Shanzeh Sarwat, Arif Florida International University Electrical and Computer Engineering Miami United States Vellore Institute of Technology School of Computing Science Bhopal India

ISBN: (纸本)9798350322972

This paper compares the accuracy of energy prediction using Feedforward Neural networks (FNN) with a hybrid Convolutional Neural Network- Long Short-Term Memory (CNN-LSTM) approach. The research builds two models, a FNN and a CNN-LSTM, and tests them on a large dataset of energy usage records. To extract local data and preserve long-range temporal relationships, the CNN-LSTM model draws on the best of convolutional and recurrent neural networks. Metrics like mean absolute error (MAE), root mean squared error (RMSE), and mean absolute percentage error (MAPE) are used to compare the two models' efficacy. The CNN-LSTM model shows superior accuracy and generalization capabilities over the FNN model when predicting energy usage. These results have the potential to improve energy management system accuracy and reliability, which in turn can increase building energy efficiency and sustainability. © 2023 ieee.

关键词： Energy efficiency

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LCN 2024 Best Paper Awards for Prior ieee LCN conferences

LCN 2024 Best Paper Awards for Prior IEEE LCN Conferences

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conference on local computer networks (LCN)

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Node centrality algorithm based on Fluid Bernoulli Static Pressure 9

Node centrality algorithm based on Fluid Bernoulli Static Pr...

引用

9th International conference on computer and Communications, ICCC 2023

作者： Borong, Zhang Wu, Muqing Zhao, Min Beijing University of Posts and Telecommunications School of Information and Communication Engineering Beijing Laboratory of Advanced Information Networks Beijing China

ISBN: (纸本)9798350317251

Node importance measurement is of great significance for studying the robustness and vulnerability of power communication network. The structure of large-scale real power communication networks tends to change over time and has limitations in obtaining global network information for assessing the importance of nodes. A node importance assessment algorithm Similar Fluid Bernoulli Static Pressure (SBSP) is proposed by quantifying the current intensity generated by the external sources of the local network topology. Aiming at the neighborhood connection information in two hops of the central node and the characteristics of the neighboring nodes, the algorithm combines the generalized resistance distance and Fluid Bernoulli equation to characterize the structure importance of the central node in the network. Based on six classical ieee power grid topologies, the network is gradually attacked in order of node importance. By comparing the experimental results of the maximum connectivity subgraph, network efficiency and node importance differentiation, it is proved that the proposed algorithm is superior to the four traditional algorithms and three abstract algorithms based on physical model proposed in recent years. © 2023 ieee.

关键词： Network security

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Efficient CNN-Transformer Aggregation Network for Remote Sensing Image Change Detection

Efficient CNN-Transformer Aggregation Network for Remote Sen...

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2024 ieee International conference on Systems, Man, and Cybernetics, SMC 2024

作者： Kong, Bingjie Meng, Qinglong Hao, Fengqi Bai, Jinqiang Jinan China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computer Networks Jinan China

ISBN: (纸本)9781665410205

Change detection is a crucial task in the field of remote sensing, focusing on identifying areas where significant changes occur between two remote sensing images captured at different time points. However, existing change detection methods based on CNNs or Transformers face two main challenges. Firstly, CNNs often lack the ability to effectively model long-range dependencies, resulting in diminished recognition performance for targets sharing the same semantics but having different features. Secondly, Transformers leverage self-attention mechanisms to capture long-range dependencies adeptly but struggle to capture local details and multi-scale features effectively. In response to these challenges, we propose a CNN and Transformer Aggregation Network (CTA-Net) for change detection in remote sensing images. Specifically, we devise two encoders based on Transformers and CNNs, respectively, facilitating the generation of complementary global and local features during the encoding phase. In the decoding phase, we design a pyramid-structured complementary decoder to aggregate multi-level complementary features from the CNN and Transformer branches. Furthermore, we propose a novel skip connection strategy to exploit the relationship between decoded features and multi-level complementary features, thereby enhancing the model's multi-scale invariance. Extensive experiments conducted on multiple benchmark datasets validate the effectiveness of our proposed CTA-Net model. © 2024 ieee.

关键词： Decoding

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GATBind: Accurate protein-RNA binding sites prediction via graph attention networks with pre-trained language model

GATBind: Accurate protein-RNA binding sites prediction via g...

引用

2024 ieee International conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Shen, Siyuan Wu, Shangqian Chen, Jie Huang, Zhijian Deng, Lei Central South University School of Computer Science and Engineering Changsha China Xinjiang University School of Software Urumqi China

ISBN: (纸本)9798350386226

Protein-RNA interactions play a pivotal role in various biological processes, making them essential for discovering novel therapeutic targets. Understanding these interactions is crucial for identifying potential drug targets and designing effective therapeutics. However, traditional experimental approaches are costly and time-consuming, highlighting the need for accurate and efficient computational methods to predict RNA-binding sites. In this study, we propose a novel method called GATBind, designed to predict RNA-binding sites on proteins. GATBind introduces a local environment-aware module that captures the local structural information surrounding target residues. Additionally, we incorporate sequence and structural features, including PSSM, HMM, DSSP, and sequence embeddings generated by the pre-trained protein language model ESMFold-2. These features are integrated and processed through a Graph Attention Network (GAT) module to predict RNA-binding sites accurately. Experimental results demonstrate that GATBind outperforms existing state-of-the-art methods, and case studies further validate its effectiveness in predicting RNA-binding sites. © 2024 ieee.

关键词： Binding sites

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Deep Feature Adaptive Fusion Algorithm for Breast Cancer Histopathology Image Classification

Deep Feature Adaptive Fusion Algorithm for Breast Cancer His...

引用

2024 ieee International conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Liu, Wei Yu, Wenming Wuxi University School of Internet of Things Engineering Wuxi China Nanjing University of Information Science & Technology School of Automation Nanjing China

ISBN: (纸本)9798350386226

computer-aided automatic classification of tissue pathological images is crucial for the early diagnosis of breast cancer. When dealing with multiple features, a Convolutional Neural Network (CNN) typically prioritizes obtaining global semantic features, while overlooking local details, which results in insufficient feature information. In this paper, we propose a dual-channel deep feature adaptive fusion algorithm called DEADF-Net. DEADF-Net first uses the EfficientNetV2 architecture network with an efficient channel-attention ECA to obtain global semantic features, and then utilizes the MobileNetV2 architecture network to obtain local detail features. Then, combine the two normalized depth features into fused features. Finally, the fused features are inputted into the fully connected layers for classification of benign and malignant tumors. In addition, the method uses transfer learning to optimize the depth features, thus improving the performance and robustness of the model. The performance of DEADF-Net was evaluated using two commonly used benchmark datasets for breast cancer histopathological image classification, namely BreakHis and BHI. The results showed that the accuracy on BreakHis is 40 ×: 95.8%, 100 ×: 96.1%, 200 ×: 97.4%, 400 ×: 96.6%, and BHI is 96.3%, which is superior to most mainstream single feature classification models. © 2024 ieee.

关键词： Convolutional neural networks

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Network Forensics using Deep Q-networks and Explainable Artificial Intelligence 8

Network Forensics using Deep Q-Networks and Explainable Arti...

引用

8th ieee International conference on Computing, Communication, Control and Automation, ICCUBEA 2024

作者： Solanki, Meghana Chaudhari, Sangita D. Y. Patil Deemed to be University RAIT Computer Engineering Department Nerul India

ISBN: (纸本)9798350391770

The increasing complexity and frequency of Distributed Denial-of-Service (DDoS) attacks demand sophisticated and effective network forensics methodologies. Utilizing Deep Q networks (DQN) and Explainable Artificial Intelligence (XAI) techniques such as local Interpretable Model-Agnostic Explanations (LIME) and DeepSHAP (SHapley Additive exPlanations), our approach initiates with the collection of extensive data from various network endpoints. Employing k-Means Clustering for data preprocessing enhances dataset quality, a critical factor for accurate pattern detection and reducing redundancy. Transforming data into multiple representations extracts essential features, exposing hidden events that signal DDoS attacks. Our core approach involves network forensics analysis using a sophisticated DQN Classifier capable of identifying and analyzing intricate DDoS attack patterns, significantly reducing false positives and adapting to new attack vectors. Integration of LIME and DeepSHAP for XAI enhances interpretability, facilitating comprehension of the model's decisions and fostering trust. By employing this methodology, organizations can substantially improve the accuracy and effectiveness of their network forensics efforts, maintain compliance with relevant regulations, and adapt to the ever-changing DDoS attack patterns that characterize the dynamic cybersecurity landscape. © 2024 ieee.

关键词： K-means clustering

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Heterogeneous Wireless Network Evaluation using VIKOR Approach 1

Heterogeneous Wireless Network Evaluation using VIKOR Approa...

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1st International conference on Cognitive Computing and Engineering Education, ICCCEE 2023

作者： Kumar, Rakesh Varshney, Neeraj GLA University Department of Computer Engineering and Applicationss Mathura India

ISBN: (纸本)9798350332803

Wireless networks offer flexible and diverse wireless connectivity by integrating cellular networks, wireless LANs, and temporary networks with the Internet. A diversified network connects computers and other devices to different operating systems and/or protocols. For instance, a complex network would connect local Microsoft Windows and Linux-based personal computers to Apple Macintosh computers. As wireless communication networks continue to evolve, they are becoming more complex and integrated, combining different wireless and wired networks into what is known as multivariate networks, where each network has its unique protocols and characteristics. While identical sensor networks have te same nodes, multifaceted sensor networks have multiple types of nodes. Device-To-device and multifaceted networks are essential to the success of 5G networks. Many networks have fixed location-based stations or small clls, each with a different range and type of connection with the mobile network operator. All computers have an identical or similar network. In this study, we evaluated five networks, named Network 1, 2, 3, 4, and 5 (Mbps) allowed Bandwidth (Mbps), and Utilization (%). After the evaluation, we found that the VIKOR method is the most ideal solution for short-distance and negative-best solutions, and the solution with the longest distance from the solution determines, but the comparison of these distances does not consider the importance. As a result, Network 1 ranked first, whereas Network 3 ranked the lowest. © 2023 ieee.

关键词： Heterogeneous networks

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