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Uncertainty-Aware Deep Learning for Segmenting Ultrasound Images of Breast Tumours

Uncertainty-Aware Deep Learning for Segmenting Ultrasound Im...

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2023 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2023

作者： Munia, Afsana Ahmed Hossain, Ibrahim Jalali, Seyed Mohammad Tabarisaadi, Pegah Rahman, Ashikur Nahavandi, Saeid Deakin University Institute for Intelligent Systems Research and Innovation VIC3216 Australia Bangladesh University of Engineering and Technology Dept. of Computer Science and Engineering Dhaka Bangladesh

ISBN: (纸本)9798350337020

Precise image segmentation is one of the dominant factors in disease diagnosis. A typical application is the segmentation of breast ultrasound images, allowing radiologists to suggest what to do next. After emerging deep learning technology especially convolutional neural networks (CNNs), the image segmentation model achieved state-of-the-art performance in various medical applications such as cancer detection and classification, lung node segmentation, cell segmentation and so on. However, despite these successes, a big question arises: to what extent is the model certain about the predicted result? Generally, most deep learning models focus on high accuracy but not on uncertainty of predicted results, which is not enough to make a critical real-life decision such as a disease diagnosis, where a wrong decision can be life-threatening. Hence for making a crucial decision, it is essential that the predicted result will provide not only accuracy but also estimate model uncertainty. Our contribution to this research is to build a system that predicts pixel-wise semantic segmentation and provides uncertainty estimation of the predicted results. It is achieved by adding a dropout layer during training and using Monte Carlo dropout in inference. We evaluate our model with the breast ultrasound image dataset (BUSI) and compare the results with a few other state-of-the-art methods where our method outperforms others in terms of IoU. © 2023 IEEE.

关键词： Breast cancer Deep learning Semantic segmentation Ultrasound images Uncertainty quantification

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Blockchain Technology: Evolution and Future Prospects 5

Blockchain Technology: Evolution and Future Prospects

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5th International Conference on Sustainable Communication Networks and Application, ICSCNA 2024

作者： Babu, A. Aalan Asha Judi, V. Jebamalar, G Belshia Prem Kumar, R. Nalini, M. Siva Subramanian, R. Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Dept. of CSE Chennai India Sathyabama Institute of Science and Technology Dept of Computer Science and Engineering Chennai India S A Engineering College Department of CSE Chennai India RMK College of Engg and Tech Dept of CSE India S A Engineering College Dept of CSE India

ISBN: (纸本)9798331530013

This research study explores the new advancements in blockchain technology and its applicability across diverse fields. Starting with the general introduction to blockchain basics and components, and then following several trends including scalability, interoperability, privacy and security, green blockchain, and decentralized finance (DeFi). The discussion explores further with the future developments and uses of blockchain technology such as in the field of finance and financial products, supply chain, healthcare, and governance structures, entertainment industries and so on. Further, this study analyses the major issues and obstacles related to the use of blockchain and their categorization into technical, legal, financial, and social. Some recommendations for further research are given focusing on the consensus algorithms, incorporation with AI technology, and interdisciplinary approaches. Finally, the survey provides an overview of the main findings on the integration of blockchain technology and its impact across different industries. This study gives a general understanding of the current state of development with significant implications for stakeholders, which supports the call for further research and cooperation in the field of blockchain. © 2024 IEEE.

关键词： Decentralized finance

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Identify Initial Nodes for Spreader in A New Diffusion Model Based on Topology (DBT) in Social Networks 9

Identify Initial Nodes for Spreader in A New Diffusion Model...

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9th International Conference on Web Research, ICWR 2023

作者： Khosravi, Ehsan Yazdanifar, Fatemeh Salimi, Nikoo Beni, Hamid Ahmadi Sharif University of Technology Dept. of Computer Engineering Tehran Iran Amirkabir University of Technology Dept. of Mathematics and Computer Science Tehran Iran Engineering and Naval Architecture University of Genoa Dept. of the Electrical Electronics Electronics and Telecommunication Genoa Italy Azarbaijan Shahid Madani University Dept. of Software Engineering Tabriz Iran

ISBN: (纸本)9798350399691

With the development of science and technology through social networks, the process of diffusion has become a controversial subject. In social networks, the process of diffusion is transmitted from one individual to another. The influence maximization problem, in which at least k nodes are chosen as the initial nodes of the spreader in the smallest amount of time, is one of the most critical aspects of the diffusion process. Furthermore, the initial nodes in the social network should generate the most influence on the other nodes in the network. However, in the algorithms proposed to solve the problem of influence maximization, the real-world factors which affect the influence probability in diffusion models are ignored for example, the influence probability is determined randomly. This problem causes influence maximization algorithms based on diffusion models to face the challenge of time-consuming Monte Carlo simulation. So, the RIM algorithm presented in this article which this method selects the initial nodes of the spreader according on their local reachability. In addition, Due to privacy in social networks, complete information about people such as gender, education, etc. is not available. For this reason, in the RIM algorithm, using the graph structure in social networks, the influence probability is calculated in the new DBT diffusion model. In addition, the RIM algorithm exhibits stable performance in the selection of the spreader's initial nodes in terms of influence spread, and execution time. © 2023 IEEE.

关键词： Topology

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Prediction of Irregular Bangladesh-EU Migration Trends Using Machine Learning Techniques 3

Prediction of Irregular Bangladesh-EU Migration Trends Using...

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3rd International Conference on Advancement in Electrical and Electronic engineering, ICAEEE 2024

作者： Islam, Md. Shariful Rahim, Md Abdur Podder, Nitun Kumar Hossain, Md. Najmul Hossain, Md Imran Pabna University of Scence and Technology Dept. of Computer Science and Engineering Pabna Bangladesh Pabna University of Scence and Technology Dept. of Electrical Electronic and Communication Engineering Pabna Bangladesh Pabna University of Scence and Technology Dept. of Inormation and Communication Engineering Pabna Bangladesh

ISBN: (纸本)9798350388282

Remittances are important economic contributors to a countrya's economy. It has increased national savings, lowered foreign exchange limits, and helped balance payments and development budgets. Migrant workers are the main source of these remittance revenues. Many workers worldwide leave their homes and move to different parts of the world for overseas employment. These workers are authorized and have documentation of their country of origin and final destination. Besides, many workers have migrated illegally globally, particularly to European Union (EU) countries via marine routes. However, illegal immigration leads to a loss of national revenue due to non-payment of taxes and social security contributions and problems with living circumstances and legal issues. Therefore, this paper aims to present a time series forecast of irregular migrants in EU countries from Bangladesh over the next five years. We used five methods to analyze this forecasting: ARIMA (an autoregressive integrated moving average), XGBoost Series, Decision Tree, Feed Forward Neural Network, and CatBoost Regressor. In addition, we used data from the European Border and Coast Guard Agency to perform the time series forecast. A field intervention is carried out to assess the gender, age, education, socioeconomic level, and skill profile of migrant workers. Moreover, a few case studies of migrant workers and a survey were conducted to investigate the underlying causes of irregular migration. Finally, this study explains the underlying cause of irregular migration and its repercussions. It can provide some ideas for decreasing the loss of human life and property by reducing irregular migration to help take essential actions in the home and destination countries. © 2024 IEEE.

关键词： Forecasting

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Project Elements: A computational entity-component-system in a scene-graph pythonic framework, for a neural, geometric computer graphics curriculum

Project Elements: A computational entity-component-system in...

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44th Annual Conference on European Association for computer Graphics, EUROGRAPHICS 2023

作者： Papagiannakis, G. Kamarianakis, M. Protopsaltis, A. Angelis, D. Zikas, P. Foundation of Technology Hellas - ICS Heraklion Greece University of Crete Dept. of Computer Science Heraklion Greece University of Western Macedonia Dept. of Electrical and Computer Engineering Kozani Greece University of Geneva Dept. of Computer Science Geneva Switzerland ORamaVR SA Geneva Switzerland ORamaVR SA Heraklion Greece

ISBN: (纸本)9783038682103

We present the Elements project, a lightweight, open-source, computational science and computer graphics (CG) framework, tailored for educational needs, that offers, for the first time, the advantages of an Entity-Component-System (ECS) along with the rapid prototyping convenience of a Scenegraph-based pythonic framework. This novelty allows advances in the teaching of CG: from heterogeneous directed acyclic graphs and depth-first traversals, to animation, skinning, geometric algebra and shader-based components rendered via unique systems all the way to their representation as graph neural networks for 3D scientific visualization. Taking advantage of the unique ECS in a a Scenegraph underlying system, this project aims to bridge CG curricula and modern game engines (MGEs), that are based on the same approach but often present these notions in a black-box approach. It is designed to actively utilize software design patterns, under an extensible open-source approach. Although Elements provides a modern (i.e., shader-based as opposed to fixed-function OpenGL), simple to program approach with Jupyter notebooks and unit-tests, its CG pipeline is not black-box, exposing for teaching for the first time unique challenging scientific, visual and neural computing concepts. © 2023 The Authors.

关键词： Open source software

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Mutual Information on Low-rank Matrix for Effective Intrusion Detection 4

Mutual Information on Low-rank Matrix for Effective Intrusio...

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4th International Conference on Sustainable Technologies for Industry 4.0, STI 2022

作者： Jannat, Hasin E Rahman, Md Mahbubur Dey, Samrat Kumar Islam, A F M Mohaimenul Ict Division Dept of Ict Dhaka Bangladesh Dept of Computer Science and Engineering Bangladesh Gazipur Bangladesh

ISBN: (纸本)9781665490450

With the popularity of the internet expanding, security is a top concern nowadays. computer networks are becoming more and more prone to cyberattacks and other dangers due to the massive volume of data flow in various formats. Finding significant information to detect intrusions becomes more difficult as a result. In this data-driven world, dimensionality reduction is a crucial procedure for intrusion detection. For academics, an intriguing subject is choosing relevant traits from a big amount of irrelevant and associated data to accurately detect alerts/*** of the most popular statistical analysis-based methods for dimensionality reduction is principal component analysis (PCA), however, it has certain limitations. In this paper, we addressed this limitations of PCA and showed that mutual information of low-rank matrix is an effective subspace detection techniques as well as gives a better classification performance. © 2022 IEEE.

关键词： Intrusion detection

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An Experimental Study on No-Reference Image Quality Assessment of Image Datasets 7

An Experimental Study on No-Reference Image Quality Assessme...

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7th IEEE International conference for Convergence in Technology, I2CT 2022

作者： Magotra, Varun Rohil, Mukesh Kumar Bhartiya Vidya Bhavans Sardar Patel Institute of Technology Dept. of Computer Engineering Mumbai India Birla Institute of Technology and Science Dept. of Computer Science Rajasthan Pilani India

ISBN: (纸本)9781665421683

Image quality assessment algorithms are of various types depending upon the the method they are employing. Over the years, these algorithms have found various applications like image compression, image restoration, image transmission etc. Image quality assessment algorithms have played a significant in these fields by appropriately evaluating the quality of the images. Based on the structure of these algorithms, in this paper, we further explored the application of Image Quality Assessment algorithm especially Non-Reference Algorithm in the world of AI. In this paper, we have showed how the Non-reference IQA algorithms can be used to assess the quality of the images in the data that are being used in for training the deep learning models. In our study, we specially focused on the datasets that are being used in the training of the deep learning models that are used in the healthcare sector. We have showed how non-reference algorithms can be used to create a more quality data so that the efficiency of the automated systems which heavily relies on the data quality can be increased. © 2022 IEEE.

关键词： Image compression

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All-in-Focus Imaging using qshiftN DTCWT and LP in the Frequency Partition Domain 9

All-in-Focus Imaging using qshiftN DTCWT and LP in the Frequ...

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9th International Conference on Computing for Sustainable Global Development, INDIACom 2022

作者： Rama Mohan, C. Kiran, S Vasudeva Kumar, A. Ashok Visvesvaraya Technological University Dept. of Computer Science and Engineering Belgaum India Ysr Engineering College of Yvu Dept. of Computer Science and Engineering Proddatur India Nmam Institute of Technology Dept. of Information Science and Engineering Nitte India Ysr Engineering College of Yvu Department of Physics Proddatur India

ISBN: (纸本)9789380544441

Image fusion utilizes fusion rules to combine multifocus images of two or more images of the same scene, which produces a fully focused image. The fusion of images provides vital information about the objects, which is more useful for visual perception and medical diagnosis. Directional selectivity and shift invariance characteristics are found to be very important in the image fusion process. Due to poor shift invariance and directionality, traditional wavelet-based fusion methods create ringing distortions in the fused image. In this paper, a novel multifocus image fusion algorithm based on the quarter shift dual tree complex wavelet transform (qshiftN DTCWT) and laplacian pyramid (LP) in the frequency partition (FP) domain is proposed. Firstly, the source images are decomposed into row and column coefficients with a frequency factor in the FP domain. Secondly, due to its directional and shift invariant characteristics, row and column images are collectively fused with qshiftN DTCWT, which proved to be a very efficient multiresolution transform for image fusion. Finally, to increase the effectiveness of the qshiftN DTCWT in the FP-based method, the LP algorithm is used to produce an all-in-focus image. The result of the proposed strategy reveals the good quality of the fused image due to its better directionality and also reduces artifacts with its shift invariance property. The statistical results also show a better performance of the proposed methodology than that of traditional fusion methodologies. © 2022 Bharati Vidyapeeth, New Delhi.

关键词： Image quality

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Neural Network for Musical Data Mining for Phrase Boundary Detection

Neural Network for Musical Data Mining for Phrase Boundary D...

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2023 IEEE Symposium Series on Computational Intelligence, SSCI 2023

作者： Henel, Daniel Mazur, Aleksander Retajczyk, Marcin Adrian, Weronika T. Kluza, Krzysztof Horzyk, Adrian Agh University of Krakow Dept. of Applied Computer Science Krakow Poland Agh University of Krakow Dept. of Biocybernetics and Biomedical Engineering Krakow Poland

ISBN: (纸本)9781665430654

The surge of interest in artificial intelligence systems has sparked new directions of research in the realm of music data analysis. At the same time, the exploration and exploitation of intrinsic musical structures and sequences, a timeless endeavor, continue to captivate scholars and practitioners alike. In this context, the fusion of computational techniques with music analysis emerges as a natural progression. One of the pivotal crossroads in this convergence is the identification of musical phrase boundaries, pivotal demarcations that underpin the organizational fabric of a musical composition. This article pioneers an inventive approach to address the challenge of detecting these musical phrase boundaries, harnessing the power of artificial neural networks. However, the innovation does not stop there;the pinpointed phrase boundaries undergo a comprehensive dissection utilizing pattern mining techniques. The focus of this analysis is on unveiling recurrent motifs and classifying phrases into coherent clusters, predicated on the repetitions and similarities exposed through these neural network-driven techniques. This exploration was conducted using an extensive repository of folk songs, a treasure trove of foundational musical expressions that indelibly shape the stylistic contours of musical compositions. We claim that the presented approach not only opens avenues for penetrating and nuanced analysis, but also enriches our comprehension of the intricate interplay of musical components and their manifestations inspired by neural networks. © 2023 IEEE.

关键词： computational intelligence data mining music analysis musical phrases detection neural networks

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Can We Trust the Actionable Guidance from Explainable AI Techniques in Defect Prediction? 32

Can We Trust the Actionable Guidance from Explainable AI Tec...

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32nd IEEE International Conference on Software Analysis, Evolution and Reengineering, SANER 2025

作者： Lee, Gichan Ju, Hansae Lee, Scott Uk-Jin Hanyang University Dept. of Computer Science & Engineering Ansan South Korea Hanyang University Dept of Applied Artificial Intelligence Ansan South Korea

ISBN: (纸本)9798331535100

Despite advances in high-performance Software Defect Prediction (SDP) models, practitioners remain hesitant to adopt them due to opaque decision-making and a lack of actionable insights. Recent research has applied various explainable AI (XAI) techniques to provide explainable and actionable guidance for SDP results to address these limitations, but the trustworthiness of such guidance for practitioners has not been sufficiently investigated. Practitioners may question the feasibility of implementing the proposed changes, and if these changes fail to resolve predicted defects or prove inaccurate, their trust in the guidance may diminish. In this study, we empirically evaluate the effectiveness of current XAI approaches for SDP across 32 releases of 9 large-scale projects, focusing on whether the guidance meets practitioners' expectations. Our findings reveal that their actionable guidance (i) does not guarantee that predicted defects are resolved;(ii) fails to pinpoint modifications required to resolve predicted defects;and (iii) deviates from the typical code changes practitioners make in their projects. These limitations indicate that the guidance is not yet reliable enough for developers to justify investing their limited debugging resources. We suggest that future XAI research for SDP incorporate feedback loops that offer clear rewards for practitioners' efforts, and propose a potential alternative approach utilizing counterfactual explanations. © 2025 IEEE.

关键词： Actionable Analytics Explainable AI Model-Agnostic Techniques Software Defect Prediction

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