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Cardiac Image Analysis for Accurate Heart Disease Diagnosis Using Deep Learning Techniques: A Comprehensive Review 1

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6th International Conference on Soft Computing and Signal Processing, ICSCSP 2023

作者： David Samuel Azariya, S. Mohanraj, V. Jothi, G. Jeba Emilyn, J. Akilandeswari, J. Department of Information Technology Sona College of Technology Tamil Nadu Salem India Department of Computer Application Sona College of Arts and Science Tamil Nadu Salem India

ISBN: (数字)9789819986286

ISBN: (纸本)9789819986279

Coronary illness is a leading cause of mortality worldwide. Prompt discovery and precise forecasts of coronary disease can help in forestalling and dealing with the sickness. Lately, profound learning strategies, e.g., convolution neural networks (CNNs) have shown promising outcomes in different clinical imaging errands, including coronary illness expectation. This review proposes a CNN-based approach for coronary illness expectation utilizing cardiovascular pictures. The proposed model comprises numerous convolution layers, pooling, and completely associated layers. The model is prepared on an enormous dataset of cardiovascular pictures and related marks for coronary illness presence or non-attendance. The prepared model is assessed on a different test dataset to evaluate its exactness, responsiveness, explicitness, and other execution measurements. Our outcomes show that the proposed CNN-based approach accomplishes high exactness and outflanks customary machine learning strategies for coronary illness expectation. The proposed model can be utilized as a device to help doctors in the early determination and treatment of coronary illness. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Electrocardiograms

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Secure Sentinel Leveraging Machine Learning for Fraud Detection in Blockchain Transactions 5

Secure Sentinel Leveraging Machine Learning for Fraud Detect...

引用

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

作者： Shirley, C.P. Thanga Helina, S. Berin Jeba Jingle, I. Saran, P. Absin, S.J. Karunya Institute of Technology and Sciences Dept. of Computer Science and Engineering Coimbatore India Kpr College of Arts Science and Research Department of Commerce with Computer Application Coimbatore India

ISBN: (数字)9798350389609

ISBN: (纸本)9798350389609

Blockchain technology's decentralized and transparent transaction platforms have changed a number of sectors. However, harmful activities like fraud and money laundering are also drawn to blockchain due to its distributed and unchangeable nature. The distinctive features of blockchain transactions frequently prove to be too much for conventional fraud detection techniques to handle. In order to identify fraud in blockchain transactions, this work suggests "Secure Sentinel,"a machine learning-based method based on the Isolation Forest concept. By separating anomalies in a binary tree structure, the Isolation Forest algorithm is highly effective in detecting abnormalities inside datasets. By utilizing this approach, our system is able to identify transactions on the blockchain that are suspicious or stray from the norm. The Isolation Forest model will be trained using attributes such transaction amount, frequency, source/destination addresses, and transaction time. The project's goal is to improve blockchain networks' security and integrity by offering a real-time fraud detection system. Users, miners, and regulators are among the blockchain stakeholders who can take proactive steps to reduce risks and preserve system confidence by automatically identifying potentially fraudulent transactions. We will use past blockchain data to test our model's performance and determine how well it detects different kinds of fraudulent activity in terms of accuracy, precision, recall, and efficiency. By using machine learning to protect blockchain ecosystems from new dangers, Secure Sentinel is a step in the right direction toward advancing the acceptance and sustainability of blockchain technology. © 2024 IEEE.

关键词： Adversarial machine learning

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Enhancing Thyroid Disease Diagnosis Through Machine Learning: A Classification-Based Approach 5

Enhancing Thyroid Disease Diagnosis Through Machine Learning...

引用

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

作者： Verma, Shanti Modi, Hetal Desai, Jaynesh H Rami, Khyati Lokmanya Institute of Management and Computer Application Gujarat Technological University Gujarat Ahmedabad India Bhagwan Mahavir College of Computer Application Bhagwan Mahavir University Gujarat Surat India Vidyadeep University Gujarat Surat India SAL Institute of Technology & Engineering Research Department of Computer Science Gujarat Ahmedabad India

ISBN: (纸本)9798331530013

Thyroid disease has recently adopted the related classification which has been worthy in the contemporary system of healthcare delivery because of the advances in diagnostics. This work proposes an automated diagnostic system for thyroid disease using machine learning approach to improve the diagnostic accuracy of the disease. The presented model also uses the features selection and classifiers like SVM and Random Forest for the classification of different thyroid disorders including hypothyroidism, hyperthyroidism, and euthyroidism. Missing values in the dataset are treated to provide actual worth in the results while imbalance is also dealt with. The process involves feeding its input a well-constructed data set after which it is checked for its performance through a validation process. The performance of the model is evaluated based on thyroid disease related parameters and the findings show a better performance compared to conventional diagnosis techniques. In this study, the authors also present their future work on how to possibly incorporate this machine learning framework into clinical practice although the study shows how this framework will help the healthcare workers to make better decisions in relation to thyroid diseases. © 2024 IEEE.

关键词： Adversarial machine learning

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Digital Secure Storage for Healthcare Data Information Using Blockchain 3

Digital Secure Storage for Healthcare Data Information Using...

引用

3rd International Conference on Advance Computing and Innovative Technologies in Engineering, ICACITE 2023

作者： Naveenkumar, H. Ranjana, P. Hindustan Institute of Technology & Science Department of Computer Application Chennai603103 India Hindustan Institute of Technology & Science Department of Computer Science Engineering Chennai603103 India

ISBN: (纸本)9798350399264

Healthcare Blockchains grant access to a modern technique to accumulate healthcare information, achieve healthcare, and make a belief for medical records distribution and records combination in a decentralized unlock healthcare network surroundings. Even though the healthcare blockchain technologies have paid attention to extroverted benefits in addition to a concentration in assiduousness, management as well as university circles, the safety and confidentiality concerns stay behind the center of attention of deliberate while organizing Blockchains for information sharing across company operations and research collaboration in the healthcare sector. Medical data sharing via Blockchains are the subject of research that focuses on security and privacy needs. Together with technological solution techniques and strategies, the research provides a complete evaluation of the safety and confidentiality risks, requirements, and technology solutions. In the beginning, we discuss the security and privacy requirements for using healthcare Blockchains to share electronic medical records. Second, we group current initiatives into three reference blockchain procedure scenarios for the sharing of electronic medical records. We consider the technologies that can be used to implement this secure store for healthcare data properties in the two categories of healthcare Blockchains procedure scenarios, including anonymous signatures, and attribute-based encryption. Source to implement the access control for hospital, patient, doctor, admin. © 2023 IEEE.

关键词： Blockchain

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Multi-scale Feature Fusion for Enhanced Person Re-identification 3rd

Multi-scale Feature Fusion for Enhanced Person Re-identific...

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3rd International Conference on Machine Learning, Cloud Computing and Intelligent Mining, MLCCIM 2024

作者： Wang, XiaoHui Sun, Yulin Zou, Zhipeng Liang, Xiaoyang Sun, Zhenyu Liu, Wei Shandong Business Institute Yantai China Shandong Xuewen Information Technology Co. Ltd. Jining China Technology of Computer Application Yantai Engineering and Technology College Yantai China School of Computer and Artificial Intelligence Zhengzhou University Zhengzhou China

ISBN: (纸本)9789819624676

In the realm of surveillance and security, person re-identification (Re-ID) stands as a critical challenge, particularly across non-overlapping camera networks. The essence of this challenge lies in the accurate and efficient identification of individuals by leveraging features captured from multiple perspectives. Addressing this, we introduce the Contextual Multi-Scale Network (Asy-CMSNet), a novel framework designed to enhance the Re-ID process through the integration of asymmetrical convolution and an innovative loss function, the Enhanced TriHard (E-TriHard). Unlike traditional approaches that rely on the stacking of convolutional layers, Asy-CMSNet employs a sophisticated mechanism that allows for the nuanced extraction and integration of multi-scale features, thereby optimizing the identification process across diverse pedestrian appearances. Central to our framework is the notion that effective person Re-ID necessitates the consideration of both global and localized pedestrian details, ranging from broad clothing characteristics to subtle accessories, which our model adeptly captures and utilizes. Through extensive experimentation across several benchmark datasets, Asy-CMSNet demonstrates superior performance in person Re-ID challenges, highlighting a substantial progression in the area. Our findings not only set new performance benchmarks but also pave the way for future research into more efficient, scalable, and robust person Re-ID systems. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Benchmarking

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Research on Virtual Restoration and 3D Digital of Qutan Temple Mural 6

Research on Virtual Restoration and 3D Digital of Qutan Temp...

引用

6th IEEE International Conference on Sensing, Diagnostics, Prognostics, and Control, SDPC 2022

作者： Yujuan, Ma Xiaojing, Liu Qinghai University Department of Computer Technology and Application Xining China

ISBN: (数字)9781665469869

ISBN: (纸本)9781665469869

A neural network virtual restoration method based on the restoration of ancient murals and a large number of artificial synthetic murals is proposed to solve the problem of unstructural degradation (fading, blurring and noise) in Qutan temple murals. This method uses the visual Restomer network, adds a large number of data pairs of synthetic virtual murals and original pictures as the training data set of mural restoration, and improves the visual effect of high-resolution image restoration such as murals. The experimental results show that this method performs well in mural restoration, and achieves good restoration results in unstructural degradation restoration of murals. In order to better display the excellent mural culture of Qutan temple, this paper further uses the graphic element layering technology, arranges the story script according to the mural content, and uses the k-frame technology to animate the layered graphic elements, which realizes the three-dimensional digital animation display of the mural of Qutan temple, and vividly deduces the essence of the mural content. © 2022 IEEE.

关键词： Image reconstruction

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NOMA in Flexible Optical Access Network: Principle, Advantage and Challenges 21

NOMA in Flexible Optical Access Network: Principle, Advantag...

引用

21st International Conference on Optical Communications and Networks, ICOCN 2023

作者： Chai, Jia Feng, Nan Ma, Mingyi Li, Zhe Li, Shaobo Beijing Institute of Computer Technology and Application China The 54th Research Institute of Cetc Shijiazhuang050081 China Hebei Key Laboratory of Photonic Information Technology and Application Shijiazhuang050081 China

We summarize the current principle, advantage, and challenges of power domain non-orthogonal multiple access technology which exploited the benefits of different channel gains of passive optical network to improve the... 详细信息

ISBN: (纸本)9798350343502

关键词： Passive optical networks

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An Overview of Clustering Algorithms for Credit Card Fraud Detection

An Overview of Clustering Algorithms for Credit Card Fraud D...

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2024 International Conference on Optimization Computing and Wireless Communication, ICOCWC 2024

作者： Jebaraj, N.R. Solomon Shekhawat, Jyoti Gupta, Rupal Department of Computer Science and Information Technology Bangalore India Computer Science & Application Vivekananda Global University Jaipur India Teetthanker Mahaveer University Department of Computing Sciences and Information Technology Uttar Pradesh Moradabad India

ISBN: (纸本)9798350383348

This paper offers a top-stage view of clustering algorithms that can be used for Clustering algorithms, which are robust and bendy techniques for uncovering patterns and anomalies in large datasets. Those algorithms can be used for grouping transactions with capabilities together with place, hour, gadgets bought, card holder, or each different characteristic related to the marketing. For credit card fraud detection, clustering algorithms are famous answers due to their capability to discover anomalous clusters that could represent fraudulent transactions. High-quality, well-known clustering algorithms and ok-way, hierarchical, and fuzzy clustering are mentioned as their particular packages. The paper also offers a few case studies concerning the usage of clustering algorithms for credit card fraud detection. This case research suggests real-global examples of how clustering algorithms may be implemented for credit rating card information. In the end, the paper outlines the demanding situations and possibilities of clustering algorithms. It is essential for banking structures, as unauthorized use of credit scorecards results in widespread economic losses. Detection of fraud is often predicated on gadgets mastering algorithms in conjunction with clustering algorithms. Clustering algorithms are the unsupervised getting-to-know method used to group patterns of facts based on similarity to find ability fraud. Famous clustering algorithms embody okay-manner, Gaussian aggregate models, and Self-Organizing Maps. Those algorithms are trying to differentiate legitimate clients from those committing fraud by figuring out clusters of ordinary behavior. Ok-manner uses an iterative manner to group data in keeping with Euclidean distance measures. Gaussian aggregate models use a probabilistic method to classify kinds of records and may incorporate preceding information approximately the statistics location if you want to generate extra accurate clusters. Self-organizing maps use a

关键词： aggregate detection Exceptional fraudulent functions

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A Comparative Analysis of User Experience and Educational Outcomes in Math Memory Games

A Comparative Analysis of User Experience and Educational Ou...

引用

2024 International Conference on computer Engineering, Network and Intelligent Multimedia, CENIM 2024

作者： Martin, Nicholas Nathanael, Raffel Jeriyanto, Doni Hermawan, Eric Savero Fung, Janshen Panggabean, Francisco Maruli School of Computer Science Bina Nusantara University Game Application & Technology Program Computer Science Department Jakarta Indonesia Master of Computer Science Binus Graduate Program Bina Nusantara University Jakarta Indonesia

ISBN: (纸本)9798350368802

This research explores multiple varieties of effective and child-friendly learning approaches for mathematics and memorization among young children in today's increasingly digital age considering their specific developmental needs. This type of approach includes in-person teaching, engaging online videos, interactive textbooks, and innovative educational games. This research employs a detailed comparative analysis, considering both 'User Experience' and measurable learning outcome factors, and spans a range of age groups from preschoolers to elementary school children. Through highlighting the strengths and weaknesses of each method, this research aims to inspire the adoption of efficient, child-centric, and novel teaching techniques by educators and institutions, thereby advancing the quality of education in Indonesia. The results show that using serious game in education presents high usefulness score alongside positive user experience, suggesting its viability for educational purposes. © 2024 IEEE.

关键词： Contrastive Learning

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Exploring Deep Learning Techniques for Automated Cataract Detection in Digital Camera Imagery

Exploring Deep Learning Techniques for Automated Cataract De...

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2024 International Conference on Advances in Modern Age Technologies for Health and Engineering Science, AMATHE 2024

作者： Kamepalli, Sujatha Veeranjaneyulu, N. Bodapati, Jyostna Devi Vignan's Foundation For Science Technology And Research Dept. Department Of Information Technology And Computer Application Guntur India Vignan's Foundation For Science Technology And Research Dept. Department Of Advnced Computer Science And Engineering Guntur India

ISBN: (纸本)9798350371567

Cataracts, a prevalent eye condition impacting vision, demand accurate and prompt identification for effective risk reduction and blindness prevention. Traditional methods of cataract diagnosis rely heavily on manual examination by ophthalmologists, which can be time-consuming and subject to human error. With the advent of digital imaging technology, there is an opportunity to enhance the efficiency of cataract detection. The deep neural network models VGG19 and ResNet-50 are recommended in this paper for automatic cataract identification on digital camera images. To optimize network performance, adjustments to loss and activation functions were made, incorporating minimal kernels, reduced training parameters, and layers. This resulted in a ResNet-50 model with significantly decreased computational cost and average operating time compared to the cutting-edge models. The proposed ResNet 50 model achieved an average accuracy of 93.41%, outperforming the VGG 19 model, which had an accuracy of 91.13%. This research contributes to the evolution of cataract detection methodologies, emphasizing the potential of deep learning models, particularly ResNet-50, in improving diagnostic accuracy and operational efficiency for enhanced eye health outcomes. © 2024 IEEE.

关键词： Digital cameras

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