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Sentinel-2 and MODIS data Fusion for Generation of Daily Cloud-Free Images at the Sentinel Resolution Level 7

Sentinel-2 and MODIS Data Fusion for Generation of Daily Clo...

7th International Conference on Computational Linguistics and Intelligent Systems. Volume III: Intelligent Systems Workshop, CoLInS 2023

作者： Ivanchuk, Natalya Kogut, Peter Martyniuk, Petro EOS Data Analytics Ukraine Desyatynny lane 5 Kyiv01001 Ukraine Department of Computer Sciences and Applied Mathematics National University of Water and Environmental Engineering Soborna str. 11 Rivne33028 Ukraine Department of Mathematical Analysis and Optimization Oles Honchar Dnipro National University Gagarin av. 72 Dnipro49010 Ukraine EOS Data Analytics Ukraine Gagarin av. 103a Dnipro49010 Ukraine Institute of Automation Cybernetics and Computer Engineering National University of Water and Environmental Engineering Soborna str. 11 Rivne33028 Ukraine

In this paperwe discuss a newvariational approach to the Date Fusion problem of multi-spectral satellite images from Sentinel-2 and MODIS that have been captureed at different resolution level and, arguably, on different days. The crucial assumption to our approach is that the MODIS image has to be cloud-free whereas the images from Sentinel-2 can be corrupted by clouds or noise. We formulate the data fusion problem as the two-level optimization problem. We discuss the well thoroughness and consistency of the proposed variational models. We also derive some optimality conditions and supply our approach by results of numerical simulations with the real satellite images. © 2023 Copyright for this paper by its authors.

关键词： Image reconstruction

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TurkishBERTweet: Fast and Reliable Large Language Model for Social Media Analysis

arXiv

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arXiv 2023年

作者： Najafi, Ali Varol, Onur Faculty of Engineering and Natural Sciences Sabanci University Turkey Center of Excellence in Data Analytics Sabanci University Turkey

Turkish is one of the most popular languages in the world. Wide us of this language on social media platforms such as Twitter, Instagram, or Tiktok and strategic position of the country in the world politics makes it appealing for the social network researchers and industry. To address this need, we introduce TurkishBERTweet, the first large scale pre-trained language model for Turkish social media built using almost 900 million tweets. The model shares the same architecture as base BERT model with smaller input length, making TurkishBERTweet lighter than BERTurk and can have significantly lower inference time. We trained our model using the same approach for RoBERTa model and evaluated on two text classification tasks: Sentiment Classification and Hate Speech Detection. We demonstrate that TurkishBERTweet outperforms the other available alternatives on generalizability and its lower inference time gives significant advantage to process large-scale datasets. We also compared our models with the commercial OpenAI solutions in terms of cost and performance to demonstrate TurkishBERTweet is scalable and cost-effective solution. As part of our research, we released TurkishBERTweet and fine-tuned LoRA adapters for the mentioned tasks under the MIT License to facilitate future research and applications on Turkish social media. Our TurkishBERTweet model is available at: https://***/ViralLab/TurkishBERTweet. Copyright © 2023, The Authors. All rights reserved.

关键词： Cost effectiveness

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A new algorithm for detection of nodes failures and enhancement of network coverage and energy usage in wireless sensor networks

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Materials Today: Proceedings 2023年 80卷 1717-1722页

作者： Satyanarayana, P. Mahalakshmi, T. Sivakami, R. Alahmari, Saad Ali Rajeyyagari, Sivaram Asadi, Srinivasulu ECE V. R. Siddhartha Engineering College Vijayawada India Department of ECE Prasad V Potluri Siddhartha Institute of Technology India Associate Professor CSE Sona College of Technology TN Salem 5 India Department of Computer Science Shaqra University Saudi Arabia Data Analytics Research Laboratory BlueCrest University College Monrovia Liberia

Present natural lives shows rising attention in various applications of the wireless sensor networks (WSNs). Significant applications are isolated and stiff regions where human involvement is hazardous or unfeasible. Some examples are space investigation, battlefield observation, costal, and edge security;nowadays, such networks are needful in several manufacturing and customer applications. A WSN is a device system, indicated as nodes;these nodes intellect the atmosphere and transfer the details collected from the ground through a wireless connection. Statistics are sent, probably via many hops, to a target that can use it nearby or is linked to various networks using the gateway. The sensor nodes might be stationary or changeable. Every node has clear data about the neighborhood or not. Communication over WSN over ecological hazards is a key issue. These constraints might impact the performance of the sensors/direction finding protocols and resource utilization;thus, they may show the way to the node crash situation, i.e., software/hardware breakdown, safety threats, extreme power utilization, etc. It is essential to examine the collision of breakdown over system performance. In the projected method, relay nodes are used as secure nodes for the positioned sensor nodes. The projected method is separated into two phases: the intra segregation stage and inter segregation stage. In the intra segregation stage, the unnecessary nodes reach nearer to the segregation border, and dispersion slowly enlarges the coverage. The projected model is examined through simulations and compares the results with existing methods. © 2021

关键词： Sensor nodes

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An Interactive Attention Mechanism Fusion Network for Aspect-Based Multimodal Sentiment Analysis

An Interactive Attention Mechanism Fusion Network for Aspect...

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2023 International Conference on Machine Learning and Cybernetics, ICMLC 2023

作者： Wang, Jinghong Gao, Yuan Li, Haokang College of Computer and Cyber Security Hebei Normal University Shijiazhuang050024 China Hebei Normal University Hebei Key Laboratory of Network and Information Security Shijiazhuang050024 China Hebei Normal University Hebei Provincial Engineering Research Center for Supply Chain Big Data Analytics & Data Security Shijiazhuang050024 China Hebei Polytechnic Institute College of Artificial Intelligence and Big Data Shijiazhuang050000 China

ISBN: (纸本)9798350303780

The goal of aspect-based multimodal sentiment analysis (ABMSA) is to classify the sentiment associated with aspect words in a given context. Most current ABMSA models focus only on general inter-modal information interactions without considering both intra-modal and inter-modal information interactions and ignoring image noise. To address these issues, this paper proposes an Interactive Attention Mechanism Fusion Network (IAMFN) model. The model first designs an image-text fusion module based on the attention, which applies the attention mechanism to a recurrent neural network to fuse text and images while filtering the noise in the images, and finally adds the fused information to the aspect information step by step to obtain the dynamic inter-modal representation. In addition, this paper proposes an aspect-text fusion module based on the attention, which learns the intra-modal contextual representation by calculating the weights of each aspect word in the context. Finally, this paper stitches the information obtained from the two modules and feeds it into the fully connected softmax layers to predict sentiment polarity. We have conducted extensive experiments on two benchmark datasets and the experimental results show that our model achieves state-of-the-art performance. © 2023 IEEE.

关键词： Sentiment analysis

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Assessing the Impact of Environment and Electrode Configuration on P300 Speller Performance and EEG Signal Quality

Assessing the Impact of Environment and Electrode Configurat...

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Annual International Conference of the IEEE engineering in Medicine and Biology Society (EMBC)

作者： Sandra-Carina Noble Tomás Ward John V. Ringwood Department of Electronic Engineering Maynooth University Maynooth Ireland Insight SFI Research Centre for Data Analytics Dublin City University Dublin Ireland

ISBN: (数字)9798350371499

ISBN: (纸本)9798350371505

Recent years have seen extensive use of brain-computer interfaces (BCIs) using electroencephalography (EEG). A critical element in BCI research is electrode selection, which influences performance, experiment duration, resource utilization, and consequently, cost. Electrode choice is partly dictated by the study location, as environmental electrical noise can impact EEG signal quality. This study evaluates the performance of a P300 speller and EEG signal quality using 4-, 6-, 8-, and 16-electrode configurations in two different office environments. Ten healthy adults participated in a single session, using a P300 speller to spell three words with each electrode set. Participants were split between two locations, with five individuals in each. Significant performance disparities were observed between the locations. Notably, within each location, the performance differences among 4-, 6-, and 8-electrode sets were minimal; only the 16-electrode set outperformed the others in both settings. The location associated with poorer performances also exhibited lower P300 amplitudes and higher levels of mains electricity noise.

关键词： Electrodes Costs Electricity Noise Electroencephalography Brain-computer interfaces Artificial light Resource management engineering in medicine and biology

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Steel Defect Detection Algorithm Based on Multiobjective ENAS Sharing Strategy 24

Steel Defect Detection Algorithm Based on Multiobjective ENA...

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2nd International Conference on Frontiers of Intelligent Manufacturing and Automation, CFIMA 2024

作者： Zhao, Tianchen Dong, Zhiming Wang, Xianpeng National Frontiers Science Center for Industrial Intelligence and Systems Optimization Northeastern University Shenyang China Ministry of Education Shenyang China Liaoning Engineering Laboratory of Data Analytics and Optimization for Smart Industry Northeastern University Shenyang China Liaoning Key Laboratory of Manufacturing System and Logistics Optimization Northeastern University Shenyang China

ISBN: (纸本)9798400710681

When employing the network architecture search approach for designing a steel surface defect detector, there are issues with conflicting evaluation metrics and limited computational resources. To address this challenge, we propose a multiobjective evolutionary sharing strategy-based network architecture search method (MESNAS). MESNAS defines network architecture search as a bi-level multiobjective optimization problem combined with a weight sharing acceleration strategy designed to quickly and simultaneously optimize the accuracy and complexity of classification models. A refined search space suitable for different image classification and detection tasks is designed. Using the MOEA/D algorithm, which incorporates a variable-length encoding scheme, the search space is co-utilized and explored through targeted improvements to the crossover and mutation operators in the offspring generation process. The optimal network architecture obtained from the search is tested on the concrete surface defect classification task, and the steel continuous casting slab surface defect detection task. The experimental results show that the proposed algorithm has good model performance and portability in image classification as well as defect detection tasks with different data complexities and different categories. © 2024 Copyright held by the owner/author(s).

关键词： Continuous casting

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Video Summarisation with Incident and Context Information using Generative AI

Video Summarisation with Incident and Context Information us...

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Annual Conference of Industrial Electronics Society

作者： Ulindu De Silva Leon Fernando Kalinga Bandara Rashmika Nawaratne Department of Electronic and Telecommunication Engineering University of Moratuwa Sri Lanka Research Centre for Data Analytics and Cognition La Trobe University Victoria Australia

ISBN: (数字)9781665464543

ISBN: (纸本)9781665464550

The proliferation of video content production has led to vast amounts of data, posing substantial challenges in terms of analysis efficiency and resource utilization. Addressing this issue calls for the development of robust video analysis tools. This paper proposes a novel approach leveraging Generative Artificial Intelligence (GenAI) to facilitate streamlined video analysis. Our tool aims to deliver tailored textual summaries of user-defined queries, offering a focused insight amidst extensive video datasets. Unlike conventional frameworks that offer generic summaries or limited action recognition, our method harnesses the power of GenAI to distil relevant information, enhancing analysis precision and efficiency. Employing YOLO-V8 for object detection and Gemini for comprehensive video and text analysis, our solution achieves heightened contextual accuracy. By combining YOLO with Gemini, our approach furnishes textual summaries extracted from extensive CCTV footage, enabling users to swiftly navigate and verify pertinent events without the need for exhaustive manual review. The quantitative evaluation revealed a similarity of 72.8%, while the qualitative assessment rated an accuracy of 85%, demonstrating the capability of the proposed method.

关键词： YOLO Accuracy Text analysis Generative AI Reviews Navigation Pipelines Production Streaming media Resource management

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Advancing Air Mobility: Few-Shot Learning in Airspace Research and Development

Advancing Air Mobility: Few-Shot Learning in Airspace Resear...

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AIAA Science and Technology Forum and Exposition, AIAA SciTech Forum 2025

作者： Samani, Saeideh Beecham, Nena Bonner, Joshua Phojanamongkolkij, Nipa NASA Langley Research Center Research Aerospace Engineer Crew Systems and Aviation Operations Branch HamptonVA23681 United States NASA Langley Research Center Department of Data Science George Washington University HamptonVA23681 United States NASA Langley Research Center Information Data Analytics Services Branch HamptonVA23681 United States NASA Langley Research Center Systems Engineering and Engineering Methods Branch HamptonVA23681 United States

ISBN: (数字)9781624107238

ISBN: (纸本)9781624107238

The advancement of Air Mobility, particularly in the context of Advanced Air Mobility (AAM) and Urban Air Mobility (UAM), represents a transformative shift in aviation's role in modern society. A comprehensive understanding of requirement consistency is paramount for fostering interoperability, standardization, and cost-effectiveness within airspace systems. This paper introduces a novel approach utilizing a pretrained Sentence Transformers model and few-shot learning to address this crucial aspect task of flagging potentially inconsistent requirements. Few-shot learning supports the development of this future through ensuring the accuracy and consistency of identified requirements with little human oversight. This approach offers a promising solution to the challenges of requirement consistency identification in airspace systems. By harnessing the power of advanced NLP techniques with fine-tuned models, stakeholders can enhance efficiency, accuracy, and scalability;ultimately fostering improved interoperability, standardization, and cost-effectiveness in airspace management. © 2025, American Institute of Aeronautics and Astronautics Inc, AIAA. All rights reserved.

关键词： Air mobility

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DRA-Net: densely residual attention based low-light image enhancement 14

DRA-Net: densely residual attention based low-light image en...

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14th International Conference on Graphics and Image Processing, ICGIP 2022

作者： Ul Rehman, Sami Hawbani, Ammar Wang, Xingfu Hamza, Muhammad Zhao, Liang H. Alsamhi, Saeed Al-Qatf, Majjed Computer Science and Technology University of Science and Technology of China JinZhai Anhui Hefei230026 China Shenyang Aerospace University China Insight Center for Data Analytics University of Galway Galway Ireland Faculty of Engineering Ibb University Ibb70270 Yemen

ISBN: (纸本)9781510666313

The visual quality of nighttime photographs diminishes greatly due to low contrast and high noise. We need a robust image enhancement methodology to improve such low-light images close to standard daylight images. Due to deteriorated conditions of uneven light and noise, this image enhancement problem becomes ill-posed. Our paper has proposed a Densely Residual Attention Network (DRANet), an end-to-end attention base densely residual network. The architecture of DRANet consists of sub-modules convolution block (CB) and densely residual feature - convolutional block attention module (DRF-CBAM). DFR-CBAM also has sub-modules, deep residual feature block (DRFB), and convolutional block attention module (CBAM). Using the most recent results from attention and deep residual-based convolution networks in number of computer vision problems, we have used DRFB to enhance the features in-depth by using its dense and residual skip connections. Similarly, features in both spatial and channel axis have been extracted by using a lightweight CBAM attention module. Contrast, luminosity, and noise of the enhanced images have been balanced by additionally implementing a color balancing function at the end of the proposed network. Furthermore, we have used a combination of LLab, LSSIM and LMAE loss functions to make the proposed network stable and recover both contextual and local details while training. MAE, PSNR, SSIM, MS-SSIM, FSIM, Cosine Similarity, and deltaE2000 have been used as referenced while NIQE as nonreferenced based image quality assessment (IQA) metric. Experiment results showed that our proposed methodology is very effective with higher referenced and lower non-referenced image IQA metrics values. Furthermore, the effectiveness of our method has also demonstrated by the visual and perceptual quality of enhanced images. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： Image enhancement

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Churn Prediction via Multimodal Fusion Learning: Integrating Customer Financial Literacy, Voice, and Behavioral data

Churn Prediction via Multimodal Fusion Learning: Integrating...

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International Conference on Behavior, Economic and Social Computing (BESC)

作者： David Hason Rudd Huan Huo Md Rafiqul Islam Guandong Xu Faculty of Engineering and IT The University of Technology Sydney Sydney Australia Information Systems (Data Analytics) Australian Institute of Higher Education (AIH) Sydney Australia Faculty of Engineering and IT Advanced Analytics Institute (AAi) Sydney Australia

In today’s competitive landscape, businesses grapple with customer retention. Churn prediction models, although beneficial, often lack accuracy due to the reliance on a single data source. The intricate nature of human behavior and high-dimensional customer data further complicate these efforts. To address these concerns, this paper proposes a multimodal fusion learning model for identifying customer churn risk levels in financial service providers. Our multimodal approach integrates customer sentiments, financial literacy (FL) level, and financial behavioral data, enabling more accurate and bias-free churn prediction models. The proposed FL model utilizes a SMOGNCOREG supervised model to gauge customer FL levels from their financial data. The baseline churn model applies an ensemble artificial neural network and oversampling techniques to predict churn propensity in high-dimensional financial data. We also incorporate a speech emotion recognition model employing a pre-trained CNN-VGG16 to recognize customer emotions based on pitch, energy, and tone. To integrate these diverse features while retaining unique insights, we introduced late and hybrid fusion techniques that complementary boost coordinated multimodal co-learning. Robust metrics were utilized to evaluate the proposed multimodal fusion model and hence the approach’s validity, including mean average precision and macro-averaged F1 score. Our novel approach demonstrates a marked improvement in churn prediction, achieving a test accuracy of 91.2%, a Mean Average Precision (MAP) score of 66, and a Macro-Averaged F1 score of 54 through the proposed hybrid fusion learning technique compared with late fusion and baseline models. Furthermore, the analysis demonstrates a positive correlation between negative emotions, low FL scores, and high-risk customers.

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