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Pedestrian wind flow prediction using spatial-frequency generative adversarial network

Building Simulation 2024年第2期17卷 319-334页

作者： Pengyue Wang Maozu Guo Yingeng Cao Shimeng Hao Xiaoping Zhou Lingling Zhao School of Architecture and Urban Planning Beijing University of Civil Engineering and ArchitectureBeijing100044China Beijing Key Laboratory of Intelligent Processing for Building Big Data Beijing100044China School of Electrical and Information Engineering Beijing University of Civil Engineering and ArchitectureBeijing100044China School of Computer Science and Technology Harbin Institute of TechnologyHarbin150001China

Pedestrian wind flow is a critical factor in designing livable residential environments under growing complex urban *** pedestrian wind flow during the early design stages is essential but currently suffers from inefficiencies in numerical *** learning,particularly generative adversarial networks(GAN),has been increasingly adopted as an alternative method to provide efficient prediction of pedestrian wind ***,existing GAN-based wind flow prediction schemes have limitations due to the lack of considering the spatial and frequency characteristics of wind flow *** study proposes a novel approach termed SFGAN,which embeds spatial and frequency characteristics to enhance pedestrian wind flow *** the spatial domain,Gaussian blur is employed to decompose wind flow into components containing wind speed and distinguished flow edges,which are used as the embedded spatial *** information of wind flow is obtained through discrete wavelet transformation and used as the embedded frequency *** spatial and frequency characteristics of wind flow are jointly utilized to enforce consistency between the predicted wind flow and ground truth during the training phase,thereby leading to enhanced *** results demonstrate that SFGAN clearly improves wind flow prediction,reducing Wind_MAE,Wind_RMSE and the Fréchet Inception Distance(FID)score by 5.35%,6.52%and 12.30%,compared to the previous best method,*** also analyze the effectiveness of incorporating the spatial and frequency characteristics of wind flow in predicting pedestrian wind *** reduces errors in predicting wind flow at large error intervals and performs well in wake regions and regions surrounding *** enhanced predictions provide a better understanding of performance variability,bringing insights at the early design stage to improve pedestrian wind *** proposed spatial-frequen

关键词： pedestrian wind flow prediction generative adversarial network Gaussian kernel wavelet transform objective function

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Supply Chain Management Using Internet of Things and Blockchain for Smart Meat Packaging 5

Supply Chain Management Using Internet of Things and Blockch...

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5th International Conference on data Intelligence and Cognitive Informatics, ICDICI 2024

作者： Kour, Kanwal Preet Singh, S. Kanwal Deep Bali, Malvinder Singh Ghantasala, G. S. Pradeep Rajesh Sharma, R. Ananthanagu, U. Manipal University School of Computer Science and Engineering Jaipur India Federation University Department of Computer Science Australia Manipal University Department of Data Science and Engineering Rajasthan Jaipur India Alliance College of Engineering and Design Alliance University Department of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350389609

In recent times, blockchain has evolved the security of traditional supply chain systems. Different issues of supply chain management like flexibility and reliability can be easily addressed using blockchain. Meat Packing Industry involves the complete process of processing, packaging and supplying the meat and its products throughout the world for human consumption. The animals used for the meat supply chain include pigs, sheep, cattle etc. Due to the enormous increase in the number of meat-eaters worldwide, its packing and timely supply are of utmost importance. The meat packing industry has revolutionized a lot by using the latest supply chain methods based on new technologies like blockchain. These include adulteration, long-term delays leading to spoilage of the product, mixing of other similar products which are lower in price etc. All these issues can be easily handled by using blockchain and IoT for meat supply chain management. Using these technologies, the challenges ranging from timely transportation of good quality meat to the best quality meat delivered to the customer can be easily addressed. In this paper, an effective blockchain and IoT-based model has been proposed which will counter the above challenges and improve the reliability and security factor. © 2024 IEEE.

关键词： Supply chain management

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Boosting Transferability and Discriminability for Time Series Domain Adaptation 38

Boosting Transferability and Discriminability for Time Serie...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Liu, Mingyang Chen, Xinyang Shu, Yang Li, Xiucheng Guan, Weili Nie, Liqiang School of Computer Science and Technology Harbin Institute of Technology Shenzhen China School of Data Science and Engineering East China Normal University China School of Electronics and Information Engineering Harbin Institute of Technology Shenzhen China

Unsupervised domain adaptation excels in transferring knowledge from a labeled source domain to an unlabeled target domain, playing a critical role in time series applications. Existing time series domain adaptation methods either ignore frequency features or treat temporal and frequency features equally, which makes it challenging to fully exploit the advantages of both types of features. In this paper, we delve into transferability and discriminability, two crucial properties in transferable representation learning. It's insightful to note that frequency features are more discriminative within a specific domain, while temporal features show better transferability across domains. Based on the findings, we propose Adversarial CO-learning Networks (ACON), to enhance transferable representation learning through a collaborative learning manner in three aspects: (1) Considering the multi-periodicity in time series, multi-period frequency feature learning is proposed to enhance the discriminability of frequency features;(2) Temporal-frequency domain mutual learning is proposed to enhance the discriminability of temporal features in the source domain and improve the transferability of frequency features in the target domain;(3) Domain adversarial learning is conducted in the correlation subspaces of temporal-frequency features instead of original feature spaces to further enhance the transferability of both features. Extensive experiments conducted on a wide range of time series datasets and five common applications demonstrate the state-of-the-art performance of ACON. Code is available at https://***/mingyangliu1024/ACON. © 2024 Neural information processing systems foundation. All rights reserved.

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Pancreatic Cancer Prediction Machine Learning using Multi Fusion Model

Pancreatic Cancer Prediction Machine Learning using Multi Fu...

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2024 International Conference on Distributed Systems, computer Networks and Cybersecurity, ICDSCNC 2024

作者： Sathiyapriya, V. Devi, K. Nethra Deepa, S. Marimuthu, M. Manikandan, P. Prabu, S. Knowledge Institute of Technology Department of Computer Science and Engineering Kakapalayam India Gnanamani College of Technology Department of Artificial Intelligence and Data Science Namakkal India Vellore Institute of Technology School of Computer Science and Engineering Chennai India Srm Institute of Science and Technology School of Computing Tiruchirappalli India

ISBN: (纸本)9798350375442

Pancreatic cancer poses a significant challenge in early detection and treatment due to its malignant nature within the digestive tract. Recent studies, such as those conducted by the Pancreatic Cancer Action Network, predict that pancreatic cancer will rank as the world's second leading cause of cancer-related deaths in 2020. Various methods have been explored in the literature to segment pancreatic tumors and detect lesions from CT images, crucial for assessing severity. One such method is the Min-Max segmentation algorithm, which effectively removes the jaundiced sclera region from affected eyes by analysing color gradients, offering more feasible and discriminative solutions. Additionally, the utilization of Whale Optimized feature-based selection aids in extracting optimal features for optimization purposes. Through an analysis of different classification algorithms and the implementation of a suggested technique for achieving optimum solutions and maximum accuracy, the proposed work is structured into two phases. Phase I involves pre-processing and artifact removal, followed by segmentation, while Phase II focuses on feature extraction, feature selection, and classification. The initial step of pre-processing the image facilitates pixel location using intensity measures, crucial for accurate tumor segmentation and minimizing false detection. Ultimately, the proposed approach aims to design and develop an optimal feature selection strategy for classifying pancreatic cancer tumors in PET/CT images, resulting in increased classification efficiency 92% and improved cancer tumor detection rates with compare other existing approaches. © 2024 IEEE.

关键词： computerized tomography

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Partial Compilation of SAT Using Selective Backbones 26

Partial Compilation of SAT Using Selective Backbones

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26th European Conference on Artificial Intelligence, ECAI 2023

作者： Balogh, Andrea Escamocher, Guillaume O'Sullivan, Barry Insight Sfi Research Centre for Data Analytics School of Computer Science & It University College Cork Ireland Confirm Centre for Smart Manufacturing School of Computer Science & It University College Cork Ireland

ISBN: (纸本)9781643684369

Our goal in this paper is to significantly decrease the compiled size of a given Boolean instance with a large representation, while preserving as much information about the instance as possible. We achieve this by assigning values to a subset of the variables in such a way that the resulting instance has a much smaller representation than the original one, and its number of solutions is almost as high as the starting one. We call the set of variable instantiations that we make the selective backbone of the solutions that we keep. Large selective backbones allow for smaller representations, but also eliminate more solutions. We compare different methods of computing the selective backbone that offer the best compromise. © 2023 The Authors.

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Privacy Preservation Fully Homomorphic Encryption for Cloud-assisted Biometric Identification with Multi-key

Privacy Preservation Fully Homomorphic Encryption for Cloud-...

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2024 IEEE Global Communications Conference, GLOBECOM 2024

作者： Peng, Shenghui Jia, Peiheng Xiong, Jinbo Zhu, Liehuang Liu, Ximeng Beijing Institute of Technology School of Cyberspace Science and Technology Beijing China Fujian Normal University College of Computer and Cyber Security Fuzhou China Fuzhou University College of Computer and Data Science Fuzhou China

ISBN: (纸本)9798350351255

Biometrics is a technology that utilizes individual physiological or behavioral characteristics for identity authentication and identification, ensuring secure and accurate identity verification by comparing biometric information with samples in a database. However, traditional biometric technology raises concerns about privacy leakage, which has led to the emergence of privacy-preserving biometric technology. This study introduces a cloud-assisted privacy-preserving biometric authentication system based on multi-key full homomorphic encryption. Our scheme employs a TFHE encryption scheme to encrypt user biometrics and utilizes a cloud server to assist in computation. We design secure computing protocols based on multi-key TFHE and develop a privacy-preserving biometric system based on a single cloud server. Privacy analysis and performance evaluation demonstrate that the proposed solution is efficient and feasible. Our experimental findings indicate that the proposed scheme exhibits a superior accuracy rate to current schemes. Compared to the best solution, our method has increased the iris recognition accuracy by approximately 68.42%. Although the experimental results demonstrate high accuracy and feasibility, implementing TFHE in biometric models could be improved by its execution time, presenting challenges for broad adoption. Nevertheless, through optimization and technological advancements, this protocol is anticipated to elevate privacy protection standards in practical biometric recognition systems moving forward. © 2024 IEEE.

关键词： Differential privacy

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SMR-Net: Semantic-Guided Mutually Reinforcing Network for Cross-Modal Image Fusion and Salient Object Detection 39

SMR-Net: Semantic-Guided Mutually Reinforcing Network for Cr...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Xiao, Guobao Liu, Xinyu Lin, Zebin Ming, Rui School of Computer Science and Technology Tongji University China College of Computer and Data Science Fuzhou University China School of Computer and Data Science Minjiang University China

ISBN: (纸本)157735897X

This paper introduces a lightweight Semantic-guided Mutually Reinforcing network (SMR-Net) for the tasks of cross-modal image fusion and salient object detection (SOD). The core concept of SMR-Net is to leverage semantics for directing the mutual reinforcing between image fusion and SOD. Specifically, a Progressive Cross-modal Interaction (PCI) image fusion subnetwork is designed to exploit local interactions via convolution operations and extend to global interactions utilizing spatial and channel attention mechanisms. Subsequently, a cross-modal Bit-Plane Slicing-based SOD subnetwork (BPS) is developed by incorporating the fused image as a third modality. This component employs bit-plane slicing and the deformable convolution technique to effectively extract irregular semantic information embedded in fusion features. The refined semantic information then guides the feature extraction process of the source modalities in a reweighted fashion. By cascading these two subnetworks, BPS leverages final semantic results to direct PCI towards focusing more on semantic information. Ultimately, through this semantic-guided mutual enhancement process, SMR-Net excels in both producing high-quality fused images and achieving effective salient object detection. Our extensive experiments on image fusion and SOD tasks convincingly demonstrate the superiority of our network over existing state-of-the-art alternatives without introducing noticeable computational costs. Compared to nearest competitors, our method demonstrates a stronger generalization ability with 26% fewer parameters. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Object detection

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3D Based CNN for Improved Segmentation from the Hyperspectral Images 1

3D Based CNN for Improved Segmentation from the Hyperspectra...

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1st International Conference on computer science and Emerging Technologies, CSET 2023

作者： Narmadha, T. Arockia Raj, A. Saravanan, P. Kumar, A. Suresh Sekaran, Ramesh Gowri Shankar, J. School of Computer Science and Engineering Jain Deemed to Be University Bengaluru India Madanapalle Institute of Technology and Science Department of Computer Science and Engineering - Data Science Madanapalle India School of Computing Science and Engineering Galgotias University Delhi NCR India

ISBN: (纸本)9798350341737

In this paper, we develop a 3D based CNN for Improved Segmentation of Paddy Fields from the HSI. Within the scope of this research, we will investigate a unique deep learning model, specifically 3D-CNN. In order to evaluate the validity of the model, it is applied to three separate datasets, each of which is then subjected to one of four distinct dimensionality reduction strategies. The purpose of this is to determine which of these four models is the most effective at reducing the number of dimensions. According to the findings, the 3D-CNN model is superior to both the AlexNet and CNN models in terms of the accuracy of its segmentations. In addition to this, the findings demonstrate that the 3D-CNN models perform better than the other approaches in terms of segmentation accuracy with a smaller number of datasets. © 2023 IEEE.

关键词： Image enhancement

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XGCN:a library for large-scale graph neural network recommendations

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Frontiers of computer science 2024年第3期18卷 247-249页

作者： Xiran SONG Hong HUANG Jianxun LIAN Hai JIN National Engineering Research Center for Big Data Technology and System Services Computing Technology and System LabCluster and Grid Computing LabSchool of Computer Science and TechnologyHuazhong University of Science and TechnologyWuhan 430074China Microsoft Research Asia Beijing 100080China

1 Introduction Graph Neural Networks(GNNs)have gained widespread adoption in recommendation systems,and nowadays there is a pressing need to effectively manage large-scale graph data[1].When it comes to large graphs,GNNs may encounter the scalability issue stemming from their multi-layer messagepassing ***,scaling GNNs has emerged as a crucial research area in recent years,with numerous scaling strategies being proposed.

关键词： scaling gained operations

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Cancer Diseases Diagnosis Using Deep Transfer Learning Architectures 1st

Cancer Diseases Diagnosis Using Deep Transfer Learning Archi...

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1st International Conference on Machine Intelligence and Emerging Technologies, MIET 2022

作者： Promy, Tania Ferdousey Joya, Nadia Islam Turna, Tasfia Haque Sukhi, Zinia Nawrin Ashraf, Faisal Bin Uddin, Jia Department of Computer Science and Engineering School of Data Science Brac University Dhaka Bangladesh AI and Big Data Department Endicott College Woosong University Daejeon Korea Republic of

ISBN: (纸本)9783031346187

Cancer is a lethal disease among the diseases in the world. It is clinically known as ‘Malignant Neoplasm’ which is a vast group of diseases that encompasses unmonitored cell expansion. It can begin anywhere in the body such as the breast, skin, liver, lungs, brain, and so on. As reported by the National Institutes of Health (NIH), the projected growth of new cancer cases is forecast at 29.5 million and cancer-related deaths at 16.4 million through 2040. There are many medical procedures to identify the cancer cell, such as mammography, MRI, CT scan, which are common methods for cancer diagnosis. The methods used above have been found to be ineffective and necessitate the development of new and smarter cancer diagnostic technologies. Persuaded by the phenomena of medical image classification using deep learning, our recommended initiative targets to analyze the performance of different deep transfer learning models for cancer cell diagnosis. In this paper, we have used VGG16, Inception V3 and MobileNet V2 deep architectures to diagnosis the breast cancer (KAU-BCMD dataset), lung cancer (IQ-OTH/NCCD dataset) and skin cancerHam10000). Experimental results demonstrate that VGG16 architecture shows comparatively higher accuracy by exhibiting 98.5% of accuracy for breast cancer, 99.90% for lung cancer and 93% for skin cancer dataset. © 2023, ICST Institute for computer sciences, Social Informatics and Telecommunications Engineering.

关键词： Diseases

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