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arXiv 2024年

作者： Sultan, Naveed Hajian, Amir Aramvith, Supavadee Department of Electrical Engineering Chulalongkorn University Bangkok Thailand Multimedia Data Analytics and Processing Research Unit Department of Electrical Engineering Chulalongkorn University Bangkok Thailand

In recent years, convolutional neural networks (CNNs) have achieved remarkable advancement in the field of remote sensing image super-resolution due to the complexity and variability of textures and structures in remote sensing images (RSIs), which often repeat in the same images but differ across others. Current deep learning-based super-resolution models focus less on high-frequency features, which leads to suboptimal performance in capturing contours, textures, and spatial information. State-of-the-art CNN-based methods now focus on the feature extraction of RSIs using attention mechanisms. However, these methods are still incapable of effectively identifying and utilizing key content attention signals in RSIs. To solve this problem, we proposed an advanced feature extraction module called Channel and Spatial Attention Feature Extraction (CSA-FE) for effectively extracting the features by using the channel and spatial attention incorporated with the standard vision transformer (ViT). The proposed method trained over the UCMerced dataset on scales 2, 3, and 4. The experimental results show that our proposed method helps the model focus on the specific channels and spatial locations containing high-frequency information so that the model can focus on relevant features and suppress irrelevant ones, which enhances the quality of super-resolved images. Our model achieved superior performance compared to various existing models. © 2024, CC BY-NC-ND.

关键词： Feature extraction

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Leveraging Machine Learning to Assess Factors Influencing Undernourishment in Nigeria 1st

Leveraging Machine Learning to Assess Factors Influencing Un...

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1st International Conference on Intelligent Systems, Blockchain, and Communication Technologies, ISBCom 2024

作者： Mobark, Nada Elfiky, Noha Ahmedin, Ahmed Ibrahim, Ameera Abdel-Aziz, M. Hamad, Safwat Faculty of Computers and Information South Valley University Qena83523 Egypt Business Analytics and Data Science Department School of Economics and Business Administration Saint Mary’s College of California MoragaCA94575 United States Department of Artificial Intelligence Engineering Faculty of Computer Science and Engineering King Salman International University South Sinai46511 Egypt

ISBN: (纸本)9783031823763

Notwithstanding economic progress, Nigeria continues to countenance significant food security issues. This study investigates the root causes of this problem and attempts to develop predictive models to address hunger in Nigeria. The research covering data from 2000 to 2022, sourced primarily from the World Bank and other institutions. The trend analysis, along with the linear model, indicates that investment in farming is crucial for reducing undernourishment. Additionally, there is a strong relationship between agricultural expenditure, exports, access to electricity, and unemployment in relation to undernourishment. In this paper, a linear model, correlation analysis, ARIMA, and trend analysis are utilized to demonstrate how undernourishment, representing hunger and food insecurity, is influenced by other factors. As a result, it is recommended to maintain the unemployment rate between 3.5% and 3.7%, while ensuring that exceeds 5% of government spending is recommended. In other words, to combat hunger in Nigeria, it is important to focus on investing in agriculture, creating jobs, and improving infrastructure. This aligns with our findings and with another research around the world. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： ARIMA Nigeria predictive model Undernourishment

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A cloud-based computational platform to manage risk and resilience of buildings and infrastructure systems 31st

A cloud-based computational platform to manage risk and resi...

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31st European Safety and Reliability Conference, ESREL 2021

作者： Hackl, Jürgen School of Engineering University of Liverpool United Kingdom Data Analytics Group University of Zurich Switzerland

ISBN: (纸本)9789811820168

The primary responsibility of asset managers is to ensure that their assets, such as buildings and infrastructure systems, provide adequate service needed. They have the continuous task of executing interventions to help prevent the loss of service and to restore service after it is lost, which can happen, for example, due to natural hazards such as floods, landslides, and earthquakes. In other words, they have the continuous task of making their assets resilient. To provide optimal mitigation measures, the risk and resilience of buildings and infrastructure systems have to be assessed. Therefore, different computational models from different disciplines have to be executed, and their results have to be brought together in order to make profound quantitative statements. Nonetheless, conducting such assessments can be a particularly challenging task due to numerous scenarios and chains of interrelated events that require considerations, the modelling of these events, the relationships among them, and the availability of support tools to run the models in an integrated way. Cloud-based simulations offer a solution to this problem, by providing almost unlimited storage and computational resources;furthermore, the cloud enables and facilitates collaborative approaches, and provide a Digital Twin of the assets for prediction and disaster management. This paper introduces a computational platform which enables cloud-based simulations to estimate risk and resilience of buildings and infrastructure systems. The setup of the computational platform follows the principles and ideas of systems engineering and allows to incorporate and link different events. The platform is centred on the integration of the spatial and temporal attributes of the events that need to be modelled to estimate the risk and resilience. Furthermore, the platform supports the inclusion of the uncertainty of these events and the propagation of these uncertainties throughout the risk and resilience modell

关键词： Simulation platform

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Modeling the Impact of Transportation Availability and Travel Distance on Healthcare Outcomes: A Bagged Random Forest Approach

Modeling the Impact of Transportation Availability and Trave...

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IISE Annual Conference and Expo 2023

作者： Kasasbeh, Anemone Yildirim, Mehmet Booth, Amy Khan, Naseem Sayama, Hiroki Department of Systems Science and Industrial Engineering State University of New York at Binghamton BinghamtonNY13902 United States Advanced Data Analytics United Health Services Hospitals Inc. VestalNY13850 United States

ISBN: (纸本)9781713877851

The lack of access to healthcare can lead to poor health outcomes. Factors that prevent patients from getting to their medical care appointments can be associated with an increased risk of adverse health conditions. These factors, such as limited access to transportation and long travel distance, may lead to higher no-show rates and gaps in care. No-shows and gaps in care are claimed to be associated with more emergency department (ED) visits as well as more hospitalizations. In this study, we investigated the impact of travel distance and transportation availability on attending a medical care appointment. data used in this research were extracted from a community hospital in Broome County, New York. The acquired data spans over the time between January 2021 and July 2022. The response variable in this study is the patient-associated risk score. A bagged random forest meta-ensemble predictive model was developed using a k-fold cross-validation training approach. The model adequacy was assessed using performance measures, including accuracy, sensitivity, and ROC. Results showed that travel distance between a patient’s residential address and their healthcare provider negatively impacted their health outcomes. This finding can help design solutions and interventions such as transportation funding programs and/or expanding bus routes to provide broader transportation coverage. © IISE and Expo *** rights reserved.

关键词： Health risks

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Review of Mathematical Optimization in Federated Learning

arXiv

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arXiv 2024年

作者： Yang, Shusen Zhao, Fangyuan Zhou, Zihao Shi, Liang Ren, Xuebin Xu, Zongben School of Mathematics and Statistics Xi’an Jiaotong University China National Engineering Laboratory for Big Data Analytics Xi’an Jiaotong University China

Federated Learning (FL) has been becoming a popular interdisciplinary research area in both applied mathematics and information sciences. Mathematically, FL aims to collaboratively optimize aggregate objective functions over distributed datasets while satisfying a variety of privacy and system constraints. Different from conventional distributed optimization methods, FL needs to address several specific issues (e.g., non-i.i.d. data distributions and differential private noises), which pose a set of new challenges in the problem formulation, algorithm design, and convergence analysis. In this paper, we will systematically review existing FL optimization research including their assumptions, formulations, methods, and theoretical results. Potential future directions are also discussed. © 2024, CC BY.

关键词： Federated learning

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An Advanced Features Extraction Module for Remote Sensing Image Super-Resolution

An Advanced Features Extraction Module for Remote Sensing Im...

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International Conference on Electrical engineering/Electronics, Computer, Telecommunications and Information Technology, ECTI-CON

作者： Naveed Sultan Amir Hajian Supavadee Aramvith Department of Electrical Engineering Chulalongkorn University Bangkok Thailand Department of Electrical Engineering Multimedia Data Analytics and Processing Research Unit Chulalongkorn University Bangkok Thailand

ISBN: (数字)9798350381559

ISBN: (纸本)9798350381566

关键词： Computational modeling Superresolution Feature extraction Transformers Decoding Telecommunications Spatial resolution

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BERD+: A Generic Sequential Recommendation Framework by Eliminating Unreliable data with Item- and Attribute-level Signals

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ACM Transactions on Information Systems 2023年第2期42卷 1-33页

作者： Sun, Yatong Yang, Xiaochun Sun, Zhu Wang, Bin School of Computer Science and Engineering Northeastern University School of Computing Macquarie University China Australia Institute of High Performance Computing Centre for Frontier AI Research A*STAR Singapore School of Computer Science and Engineering Northeastern University China National Frontiers Science Center for Industrial Intelligence and Systems Optimization China Key Laboratory of Data Analytics and Optimization for Smart Industry [Northeastern University Ministry of Education China

A preliminary work of our study was published at IJCAI’21 [47], which is substantially extended in the following aspects: (1) In Section 1, we analyze the necessity of introducing item attributes for detecting unreliable instances, together with the problems and challenges that attributes may bring in. (2) In Section 2, we add discussions about the limitations of existing attribute-aware recommender systems (Section 2.2) and denoising methods (Section 2.3) in the context of detecting unreliable instances. (3) In Section 4.2, we further conduct an in-depth analysis at the attribute level to demonstrate the capability of attributes for rectifying instance loss and uncertainty, as well as the disturbance caused by attributes. (4) We generalize BERD to a generic framework BERD+ in Section 5.1, equipped with novel modules, i.e., HU-GCN (Section 5.2) and EPE (Section 5.4), which properly incorporate item attributes while reducing their disturbance for rectifying instance uncer tainty (Section 5.5) and loss (Section 5.6). The generic BERD+ can be flexibly plugged into existing SRSs for performance enhanced recommendation via eliminating unreliable data. (5) In Section 6.2, we apply our BERD+ framework to seven state-of-the-art SRSs on five real-world datasets to illustrate its superiority. (6) To avoid unfair comparison caused by item attributes, we build and compare with the baseline that combines the original BERD and an advanced attribute-aware recommender system, KSR [19]. (7) For more comprehensive comparison, in Section 6.2.2, we compare BRED+ with two state-of-the-art denoising approaches;in Section 6.2.3, to examine the efficacy of HU-GCN and EPE, we compare HU-GCN with various attribute embedding techniques, i.e., variants of graph neural networks, and compare EPE with different attribute fusing methods, i.e., adding, concatenation, and weighted sum. (8) In Section 6.2.4, a detailed ablation study is conducted to verify the effectiveness of each module of BERD+. (

关键词： Convolution

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EfficientRec an unlimited user-item scale recommendation system based on clustering and users interaction embedding profile

arXiv

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arXiv 2024年

作者： Quan, Vu Hong Ngan, Le Hoang Duc, Le Minh Linh, Nguyen Tran Ngoc Quynh-Le, Hoang Data Analytics Center Viettel Telecom Viettel Group Viet Nam University of Engineering and Technology Vietnam National University Viet Nam

Recommendation systems are highly interested in technology companies nowadays. The businesses are constantly growing users and products, causing the number of users and items to continuously increase over time, to very large numbers. Traditional recommendation algorithms with complexity dependent on the number of users and items make them difficult to adapt to the industrial environment. In this paper, we introduce a new method applying graph neural networks with a contrastive learning framework in extracting user preferences. We incorporate a soft clustering architecture that significantly reduces the computational cost of the inference process. Experiments show that the model is able to learn user preferences with low computational cost in both training and prediction phases. At the same time, the model gives a very good accuracy. We call this architecture EfficientRec3 with the implication of model compactness and the ability to scale to unlimited users and products. Copyright © 2024, The Authors. All rights reserved.

关键词： Recommender systems

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Environmental Sound Classification with Tiny Transformers in Noisy Edge Environments 7

Environmental Sound Classification with Tiny Transformers in...

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7th IEEE World Forum on Internet of Things, WF-IoT 2021

作者： Wyatt, Steven Elliott, David Aravamudan, Akshay Otero, Carlos E. Otero, Luis D. Anagnostopoulos, Georgios C. Smith, Anthony O. Peter, Adrian M. Jones, Wesley Leung, Steven Lam, Eric Center for Advanced Data Analytics and Systems Department of Computer Engineering and Sciences United States Florida Institute of Technology Center for Advanced Data Analytics and Systems Department of Computer Engineering and Sciences MelbourneFL32901 United States

ISBN: (纸本)9781665444316

The unprecedented growth of edge sensor infrastructure is driving the demand function for in situ analytics, i.e. automated decision support at the point of data collection. In the present work, we detail our state-of-the-art Environmental Sound Classification (ESC) framework that is capable of near real-time acoustic categorization directly at the edge. Existing ESC algorithms primarily train and test on pristine datasets that fail in real-world deployments due their inability to handle real-world noisy environments. Methods to denoise the sounds are often computationally expensive for edge devices and do not guarantee performance improvements. To this end, we investigate a way to make existing ESC models robust and make them work in operational resource-constrained settings. Our framework employs a noisy classification model consisting of a tiny BERT-based Transformer (less than 20,000 parameters) and considers hardening of this model through the use of transmission channel noise augmentation. We detail real-world results through its deployment on a Raspberry Pi Zero and demonstrate its classification performance. © 2021 IEEE.

关键词： Machine learning

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Improving Medical Predictions with Label Noise Tolerant Classification 4

Improving Medical Predictions with Label Noise Tolerant Clas...

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4th International Conference on Advances in Computing, Communication Control and Networking, ICAC3N 2022

作者： Vernekar, Shivam Ayyar, Arun Rajagopalan, Arun Kumar, Bijendra Mishra, Vivek Kumar Evive Software Analytics Pvt. Ltd. Bengaluru India Santa Fe Research Pvt Ltd Chennai India Evive Software Analytics Pvt. Ltd. Data Science Bengaluru India Galgotia College of Engineering and Technology Electrical and Electronics Engineering Greater Noida India

ISBN: (纸本)9781665474368

Supervised machine learning algorithms depend crucially on availability of a large pool of correctly labelled instances. However many real world datasets may contain noisy features and label values. This is even more problematic for medical datasets since noise can be asymmetric (class dependent) due to imprecise class definitions or subjective human biases during data collection. There is easy availability of a large pool of unlabelled instances which can't be directly used for training, for example raw data from medical devices like fitness trackers, data from health surveys etc. Labelling them (with respect to certain task) with the help of human experts might be a time consuming and a costly process. Besides, learning algorithms trained on noisy datasets can reduce the quality of future inferences making them unsuitable for business use-cases. In this paper we propose a semi-supervised learning based approach where we combine information embedded in unlabelled and incorrectly labelled instances with a very small set of correctly labelled instances. To the best of our knowledge this is the first work based on semi-supervised learning for reducing the noise in generated predictions using imperfect datasets. It is demonstrated through extensive experimentation using a medical diagnostic diabetes dataset, that by using only a fraction of correctly labelled instances, we are able to generate high quality predictions on unseen data. © 2022 IEEE.

关键词： Forecasting

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