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Fluorescence Image Denoising Based on Self-supervised Deep Learning 7

Fluorescence Image Denoising Based on Self-supervised Deep L...

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7th international conference on Intelligent Computing and Signal processing, ICSP 2022

作者： Huang, Haojian Liu, Yixuan Li, Yang College of Computer Science and Technology Harbin Engineering University Harbin Heilongjiang150001 China College of Transportation Engineering Tongji University Shanghai201804 China College of Electrical Information Wuhan Institute of Technology Wuhan430200 China

ISBN: (纸本)9781665478571

Fluorescence microscopy imaging technology is a crucial imaging technology widely used in biomedical fields such as brain science. However, its images often have random noise without a fixed pattern, causing a series of problems such as image degradation, image segmentation, and classification misalignment. In order to obtain higher signal-to-noise ratio imaging, the input of higher doses of photons increases the cost and causes adverse effects such as photobleaching, phototoxicity, or tissue heating. therefore, researchers have studied many traditional denoising methods and deep learning-based denoising methods. Traditional denoising methods have the advantage of fast and straightforward processing and tend to perform well when the noise level of the image is low, or the noise distribution is known. However, its effectiveness and generalization are minimal when the noise level is high and randomly distributed without a fixed modality. On the contrary, image denoising methods based on self-supervised deep learning can extract high-dimensional features of images, have better effectiveness and robustness in practical denoising applications. Morever, these methods reduce the need for manual annotation of large-scale data, making timely research on cutting- edge pathology possible when there are insufficient labeled data. In this paper, to tackle the denoising problem of fluorescence microscopy images, two classic traditional denoising methods, Wiener filtering, and wavelet transformation, and methods based on deep learning, DeepCAD, are used for comparative experiments. the experimental results show that the deep learning method has good effectiveness and generalization in dealing with high-level random noise, and it performs better than traditional denoising methods. © 2022 IEEE.

关键词： Wavelet transforms

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Performance Driven Experimental Analysis of Apache Spark Configuration Parameters 7

Performance Driven Experimental Analysis of Apache Spark Con...

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7th IEEE international conference on Computational Systems and information technology for Sustainable Solutions, CSITSS 2023

作者： Akshatha, G.C. Sanjay, H.A. Kumar, Ashish Ravi Bharadwaj, Avani Krishanamurthy, G.N. Bangalore India Visvesvaraya Technological University Belagavi590018 India Ramaiah Institute of Technology Department of Information Science and Engineering Bangalore India Carelon Global Solutions Banaglore India Ig Group Bangalore India B N M Institute of Technology Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350343144

Big Data refers to huge volume of data in the range of peta bytes or higher. In order to store and process such large volume of data there is a need for distributed processing platforms as conventional stand-alone platforms cannot store or process bigdata. Distributed bigdata analytics frameworks helps in storage and processing of bigdata. the most popularly used open source bigdata analytics framework provided by Apache foundation includes Hadoop and Spark. Apache Spark is widely considered for processing bigdata because of its in-memory computation capability. the major challenges involved in big data analytics are managing the huge volume and variety of data originating from different sources in different formats. Apache Spark has around 180 configurable parameters and all of these parameters are set to some default values. It is very difficult for the normal user to identify the optimal values for these configuration parameters. And also, not all parameters may have impact on the application performance. therefore, the proposed work aims at conducting an experimental research for identifying the subset of key apache spark configuration parameters that impacts on the execution time of an application and its optimal values to maximize performance by analyzing the effect of various parameters and its values. In the proposed work the Execution Time of the spark job/application is considered as the performance indicator. the result section of the proposed work presents the various key parameters and its optimal values. © 2023 IEEE.

关键词： Big data

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Data processing Methods for Financing Terrorism: the Role of Microsoft Power BI in Money Laundering Detection 15

Data Processing Methods for Financing Terrorism: The Role of...

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15th international conference on Ambient Systems, Networks and Technologies Networks, ANT 2024 / the 7th international conference on Emerging Data and Industry 4.0, EDI40 2024

作者： Kulbayeva, A.K. Rakhmetulayeva, S.B. Bolshibayeva, A.K. Yasar, Ansar-Ul-Haque Information Systems Department International Information Technology University Almaty050040 Kazakhstan Transportation Research Institute Hasselt University Hasselt3500 Belgium

this article explores the imperative landscape of money laundering detection, emphasizing the role of advanced financial monitoring and the integration of Microsoft Power BI for in-depth data analysis. Addressing challenges in fraud detection, the study employs synthetic datasets from Kaggle, overcoming limitations in accessing real financial data. through a meticulous process, the article demonstrates the power of Power BI in uncovering potential money laundering activities via visual analyses such as stacked column charts, line charts, pie charts, and clustered bar charts. the significance of appropriate data preparation, column selection, and visual representations is highlighted, offering a systematic approach for fraud detection in financial datasets. this research showcases the effectiveness of Power BI as a strategic tool, providing insights and methodologies that contribute to the ongoing discourse on fraud prevention in financial analytics. © 2024 Elsevier B.V.. All rights reserved.

关键词： Data handling

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An Analysis of Differences between Dark Pattern and Anti-Pattern to Increase Efficiency Application Design 7

An Analysis of Differences between Dark Pattern and Anti-Pat...

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7th international conference on Business and Industrial Research (ICBIR)

作者： Tiangpanich, Pumarin Nimkoompai, Apichaya Fac Informat Technol Immers Technol Lab Jyvaskyla Finland

ISBN: (纸本)9781665494755

As technology has grown faster than expected, marketing is starting to change from offline marketing to social media marketing. therefore, marketers have begun to use mobile applications to represent their brands and products to customers. However, to make it attractive, the user experience (UX) needs to step in to create the communication between brands and customers smoothly and use the user interface (UI) to represent it in simple and understandable ways. Nowadays information is principal for marketing. Lead to an organization trying to extract users' personal information without their consent, resulting in damage to the property of users via the Dark pattern of User Experience (UX). However, when designers design the application, they might notice that the design is having trouble or not having a good solution to deal with the problem that users might find. the design experience that can solve this problem is called Anti-patterns, in which approaches to common issues might appear obvious but are less than optimal in practice. While the dark pattern of user experience (UX) is deceptive, UX/UI design or inter- actions created with psychological knowledge is designed to mislead users to do something they did not intend to create value for the service that employs them. this research aims to create a solution for designers to use the right tools to create compelling artwork. Anti-patterns and dark patterns to understand and apply appropriately.

关键词： Anti-Pattern UI design Dark Pattern of UX Human-computer Interaction (HCI) Mobile Application User Experience (UX)

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Research on the Application of Kalman Filter Algorithm in Aircraft Trajectory Analysis 7

Research on the Application of Kalman Filter Algorithm in Ai...

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7th international conference on Intelligent Computing and Signal processing, ICSP 2022

作者： Zhang, Xianwei Yu, Weizhao Xi'an Shiyou University School of Computer Science Xi'an China

ISBN: (纸本)9781665478571

In recent years, the air traffic flow has increased exponentially. To ensure flight safety during the flight, it is necessary to monitor and predict the trajectory in real-time, and use the trajectory system to assist the controller to manage and monitor the aircraft. the main research content of this subject includes the trajectory prediction algorithm and its error analysis in trajectory prediction. In this paper, a domestic civil aviation trajectory data is collected as a sample, the geographic information system is used to process the sample data, a flight motion model is established based on the data, and the Kalman filtering algorithm is used to predict the sample data. the final result of the filtered trajectory is the same as the original trajectory., indicating that the Kalman filter is very suitable for trajectory prediction research. © 2022 IEEE.

关键词： Forecasting

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Grand Challenge of Image processing in Automatic Detection of Vehicles Running in Red Lights 7

Grand Challenge of Image Processing in Automatic Detection o...

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7th international conference on computer Graphics and Virtuality, ICCGV 2024

作者： Samonte, Mary Jane Wang, Yingqiang Tian, Xinzhi Wang, Qin School of Information Technology Mapua University 1191 Pablo Ocampo Sr. Extension Sta. Cruz Makati City1205 Philippines

ISBN: (数字)9781510679788

ISBN: (纸本)9781510679771

With more and more vehicles in the city, traffic violations are becoming more and more serious. Vehicle violations rely on manual interpretation, which is not only inefficient, but also leads to a backlog of data and inconsistent law enforcement standards. In response to this situation, this paper proposes an artificial intelligence- based algorithm for automatic interpretation of illegal behaviors of vehicles running red lights. this paper first discusses the modeling process of building a vehicle running a red light, including image data preprocessing, traffic light and vehicle recognition, vehicle red light detection, and license plate recognition;then an automatic interpretation algorithm for vehicle red light violations is designed. Finally, the actual traffic photos are used to test the algorithm. the experimental results show that the algorithm has a high recognition rate and can effectively automatically identify the illegal behavior of vehicles running red lights, so it can effectively solve the problem of low efficiency of manual judgment. © 2024 SPIE.

关键词： Artificial intelligence

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Deterministic Initialization of k-means Clustering by Data Distribution Guide 7

Deterministic Initialization of k-means Clustering by Data D...

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7th international conference on Digital Arts, Media and technology, DAMT 2022 and 5th ECTI Northern Section conference on Electrical, Electronics, computer and Telecommunications Engineering, NCON 2022

作者： Sirikayon, Chaloemphon thammano, Arit King Mongkut's Institute of Technology Ladkrabang Faculty of Information Technology Computational Intelligence Laboratory Bangkok Thailand

ISBN: (纸本)9781665495103

Clustering by the k-means is the most widely used method because of its ease of use. But the disadvantage of the k-means algorithm is that it relies on a random initialization. therefore, the results obtained from each clustering are not stable depending on the starting point, affecting the results obtained in other applications. this paper, therefore, presents a method for determining the initialization of the k-means algorithm using the Data Distribution Guide (DDG). And use it as an aid in determining the starting point without random. Make the results of clustering always equal. And from the experimental results, We found that the accuracy obtained from clustering using the initialization from this method was good. Compared to the commonly used initialization designation. © 2022 IEEE.

关键词： K-means clustering

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An Agent-based Detection Method of Condition Progression Trends on Mobile for Hypertensive Diabetic Patients 7

An Agent-based Detection Method of Condition Progression Tre...

引用

7th international conference on information technology, InCIT 2023

作者： Sanon, Sutussa Temdee, Punnarumol Mae Fah Luang University Computer and Communication Engineering for Capacity Building Research Center School of Information Technology Chiang Rai Thailand

ISBN: (纸本)9798350358698

Diabetes and hypertension are prevalent chronic conditions on a global scale. there exists a correlation between them, rendering them more prone to transpire concurrently. the proposed mobile application provides users with a proactive self-monitoring capability by detecting tendencies of condition progression. In this study, there exist three distinct categories of condition progression trends that can be identified, namely positive, negative, and neutral condition progression trends. In order to provide a detection mechanism, a collaboration among three distinct types of agents takes place. Content agents are responsible for managing the incoming data, interpreter agents handle the preprocessing of the data, and aggregator agents are tasked with creating the detection rules. More specifically, three types of content agents are used in this study including the personal agent, clinical agent, and behavior agent respectively. the result of the simulation indicates that the proposed detection method achieves an accuracy rate of 93.67%. © 2023 IEEE.

关键词： Software agents

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Classification Of Rock Slices Based On SKNet 7

Classification Of Rock Slices Based On SKNet

引用

7th international conference on Intelligent Computing and Signal processing, ICSP 2022

作者： Yang, Xiao Fu, Xian Zhang, Shu Hubei Normal University School of Computer and Information Engine Huangshi China

ISBN: (纸本)9781665478571

When classifying rocks in the field of petroleum mining and beneficiation, factors such as rock type, particle size, and interstitial spacing make the rock classification task complicated and low in accuracy. When manual classification is used, the workload is large and it is easily affected by subjective factors. this paper proposes a rock multi-classification method based on Selective Kernel Networks (SKNet). the method first builds a Residual Network (ResNet) model to classify rock slice images, and the accuracy of this method reaches 89.5%. At the same time the Gradient-weighted Class Activation Map (Grad-Cam) is used to visualize the model and increase the interpretability of the neural network. Finally, SKNet is constructed on the existing ResNet101 model to improve the accuracy of classification. After many experiments, it is finally accurate the rate reached 95.3%. In this experiment, the central-northern rock slice images of the Lhasa terrane are used to complete the experiments and tests. the experimental results show that using SKNet to classify rock slices can achieve faster and more accurate classification results. © 2022 IEEE.

关键词： Rocks

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Migration and Tuning of Software Prefetching for Sunway Multi-Core Processor 7

Migration and Tuning of Software Prefetching for Sunway Mult...

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7th international conference on Intelligent Computing and Signal processing, ICSP 2022

作者： Gao, Xiuwu Jiang, Jun Huang, Liangming Wei, Hongmei Jiangnan Institute of Computing Technology Wuxi China National Research Center of Parallel Computer Engineering and Technology Beijing China

ISBN: (纸本)9781665478571

Data prefetching is a widely used technique to alleviate "memory wall"problem by fetching the data that may be touched in the near future in advance. Generally, data prefetching is classified into hardware prefetching and software prefetching. Compared to hardware prefetching, software prefetching is more flexible, and typically achieves higher prefetch accuracy. Currently, Sunway multiple-core processor only supports hardware prefetching. To study how software prefetching perform on Sunway processor, in this paper, we first migrate the software data prefetching in the GCC complier to Sunway processor. then we tune the loop-level prefetching cost model according to Sunway processor's hardware features. Finally, we conduct experiments to evaluate the effectiveness of the tuned software prefetching. Results show that, compared to the baseline where no prefetching is applied, software prefetching delivers an average speedup of 1.08x (up to 2.46x) and 1.16x (up to 1.88x) for SPECint 2006 and SPECfp 2006 benchmark suite, respectively. Moreover, software prefetching outperforms hardware prefetching for both benchmark suites. this demonstrates the efficacy of software data prefetching. © 2022 IEEE.

关键词： Degradation Costs Multicore processing Prefetching Computational modeling Benchmark testing Signal processing

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