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Enhanced Fetal Ultrasound Image Segmentation using Spatial Attention Mechanisms with UNet: SAUnet

Enhanced Fetal Ultrasound Image Segmentation using Spatial A...

引用

2024 IEEE International Conference on computer Vision and Machine Intelligence, CVMI 2024

作者： Verma, Harshita Patro, Bdk Rajkiya Engineering College Dept. of Computer Science and Engineering Kannauj India

ISBN: (纸本)9798350376876

Fetal Ultrasound (US) imaging is necessary to prenatal treatment, as it provides information on the health and development of the fetus. Accurate segmenting is essential but poses significant challenges due to the dynamic fetal anatomy. This paper proposes Unet with Spatial Attention (SAUnet), a novel deep learning architecture that integrates attention mechanisms into the Unet framework. Unlike traditional models, SAUnet dynamically emphasizes key anatomical structures while suppressing irrelevant background information, enhancing the model's focus on informative regions within the US images. This targeted approach leads to improved segmentation accuracy and robustness. Our method was evaluated on the HC18 Challenge dataset, achieving a mean absolute difference(±std) of 2.23 ± 2.41 mm in head circumference measure and a mean Dice similarity coefficient of 97.63 ± 1.85%, demonstrating high segmentation accuracy. Additionally, mean difference and Hausdorff distance are 1.06 ± 3.11 mm and 1.42 ± 0.93 mm respectively suggesting robust performance. Incorporation of spatial attention in Unet architecture marks a significant advancement in fetal US image segmentation, offering a lightweight and efficient model suitable for real-time clinical deployment. Benchmark comparisons highlight SAUnet's competitive performance, reinforcing its potential as a state-of-the-art solution for prenatal imaging tasks. © 2024 IEEE.

关键词： Semantic Segmentation

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TraceChainBD: Securing Supply Chains from Counterfeiting in Bangladesh with Blockchain and IoT Integration

TraceChainBD: Securing Supply Chains from Counterfeiting in ...

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2024 International Conference on Innovations in science, Engineering and Technology, ICISET 2024

作者： Karmoker, Sagar Khan, Md. Adib Lamia, Sanjida Alam Tanjid, Mobinul Ahasan Dihan, Shahriar Sadman Anik, Nur Adnan Chowdhury East West University Dept. of Computer Science and Engineering Dhaka Bangladesh

ISBN: (纸本)9798350355499

The scheme uses blockchain technology in Bangladesh's supply chain to eliminate counterfeit products, improve tax compliance, and build consumer trust. By ensuring product authenticity, it safeguards brand reputations and boosts government revenue. Manufacturers, distributors, and retailers can enter product data which will then generate QR codes. In case of receiving the product or checking its authenticity, they can scan these QR codes via their mobile or a dedicated IOT device and all the information will be stored in Blockchain. Since blockchain data is secure and immutable, it cannot be altered, consumers can easily check product details by scanning QR codes, and if a product is flagged as suspicious, the Mobile Court Management System can trace its origin. This blockchain-based approach ultimately helps prevent counterfeit goods from entering the market and enhances the government's ability to collect taxes. © 2024 IEEE.

关键词： Blockchain

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Anomaly Detection In Time Series data Using Reinforcement Learning, Variational Autoencoder, and Active Learning

Anomaly Detection In Time Series Data Using Reinforcement Le...

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2024 IEEE International Conferences of AI, science, Engineering, and Technology, AIxSET 2024

作者： Golchin, Bahareh Rekabdar, Banafsheh Portland State University Dept. Computer Science PortlandOR United States

ISBN: (纸本)9798350390995

A novel approach to detecting anomalies in time series data is presented in this paper. This approach is pivotal in domains such as data centers, sensor networks, and finance. Traditional methods often struggle with manual parameter tuning and cannot adapt to new anomaly types. Our method overcomes these limitations by integrating Deep Reinforcement Learning (DRL) with a Variational Autoencoder (VAE) and Active Learning. By incorporating a Long Short-Term Memory (LSTM) network, our approach models sequential data and its dependencies effectively, allowing for the detection of new anomaly classes with minimal labeled data. Our innovative DRL-VAE and Active Learning combination significantly improves existing methods, as shown by our evaluations on real-world datasets, enhancing anomaly detection techniques and advancing time series analysis. © 2024 IEEE.

关键词： Active learning

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Machine Learning Algorithms for Diabetes Diagnosis Prediction 24

Machine Learning Algorithms for Diabetes Diagnosis Predictio...

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6th International Conference on Image, Video and Signal Processing, IVSP 2024

作者： Zambrana, Amanda Fanek, Loreen Carrera, Pablo Salar Ali, Md Liakat Narasareddygari, Mourya Reddy Dept. Of Computer Science And Physic Rider University NJ United States

ISBN: (纸本)9798400716829

Diabetes is one of the most common health conditions in the United States, affecting more than 37 million U.S. adults. Despite how many people are affected by this disease, a permanent cure does not currently exist. Therefore, it is very important for patients to get the proper diagnosis and treatment. If diabetes is not diagnosed and properly treated to control blood sugar levels, serious health problems can occur. Though undiagnosed cases are very dangerous, still 1 in 5 U.S. adults with diabetes do not know that they have it. Therefore, a method for early detection or prediction of the disease is imperative for healthcare workers to provide timely distribution of treatment. Presently, hospitals collect large volumes of patient data with various tests and provide appropriate treatment based on diagnoses. With big data analytics, hidden patterns can be found in the data that help to predict diagnosis outcomes. Such predictions can help lower the rate of undiagnosed patients. In our paper, we propose a prediction model for classification of diabetes based on various features of patient data. We developed and applied six machine learning algorithms on two diabetes data sets to predict diagnoses. We found that the best algorithms for prediction with our two data sets were Ridge Classifier with an accuracy of 83.12%, along with Random Forest and Decision Tree classifiers, both achieving accuracies of 95%. © 2024 ACM.

关键词： Forecasting

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Analysis of Ensemble Machine Learning Models for Fraud Detection

Analysis of Ensemble Machine Learning Models for Fraud Detec...

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2024 International Conference on Intelligent Systems for Cybersecurity, ISCS 2024

作者： Chaurasia, Siddharth Kesharwani, Satyam Sharma, Shanu Sharma, Swati Chugh, Bharti Kiet Group of Institutions Dept. of Computer Science and Engineering Ghaziabad India

ISBN: (纸本)9798350375237

Recent developments in electronic payment and e-commerce have led to a rise in digital fraud cases, including credit card fraud. Within the financial services industry, identifying credit card fraud is still a difficult task that can have significant effects on consumers' and financial institutions' reputations. Therefore, it is crucial to implement systems capable of detecting credit card fraud. This research utilizes the European Cardholder data Collection to assess the efficacy of ensemble machine learning techniques in identifying credit card fraud. The European cardholder data set is subjected to a rigorous assessment and evaluation of series of machine learning models, such as Gradient Boosting, Random Forest and CAT Boost, to detect fraudulent transactions. It has been noted that the dataset is unbalanced, which may indicate that the models' performance is not very ideal. The study suggests using data sampling strategies to achieve a balanced distribution of data across various algorithms that yield optimal outcomes. The main goal is to identify the model that performs best for machine learning classification problems by comparing the performance of different models. The results of this study will support further initiatives to improve credit card transaction security and lower financial losses brought on by fraud. © 2024 IEEE.

关键词： Losses

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Narrative Simplification in Human and AI Retelling

Narrative Simplification in Human and AI Retelling

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2024 IEEE International Conference on Big data, Bigdata 2024

作者： Tanaka, Yuuri Hashimoto, Yasuhiro The University of Aizu Dept. Computer Science & Engineering Aizu-Wakamatsu Japan

ISBN: (纸本)9798350362480

Human beings have long been regarded as "homo narrans,"employing storytelling to comprehend and communicate reality. This study investigates whether contemporary large language models (LLMs) can approximate the dept. and richness of human narrative abilities. Through an experiment comparing retellings by humans and various state-of-the-art LLMs on a dataset of man-made short stories, we examine how word count, vocabulary complexity, and readability evolve during the retelling process. We introduce the Flesch Reading Ease score as a quantitative measure of narrative simplicity. Our findings reveal an inverse correlation between narrative condensation and increased vocabulary density in both human and LLM retellings. While both humans and LLMs tend to condense stories, their approaches differ: humans generally maintain or improve readability, whereas LLMs, without specific instructions on persona-based retellings, often reduce readability. However, with appropriate prompts, LLMs can adjust their storytelling to more closely mimic human tendencies. We also identify a unique "adaptive retelling"ability in humans, who adjust readability based on the original story's readability. These insights advance our understanding of the "AI narrans"concept, highlighting both the current limitations and the potential of LLMs in achieving human-like storytelling capabilities. © 2024 IEEE.

关键词： AI narrans Flesch reading ease score large language models narrative Retelling

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Multi-Musical Instrument Recognition Neural Network 6

Multi-Musical Instrument Recognition Neural Network

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6th World Symposium on Artificial Intelligence, WSAI 2024

作者： Timouri, Bardia Aulakh, Ravdeep Cruz, Gathrean Dela British Columbia Institute of Technology Dept. of Computer Science Vancouver Canada

ISBN: (纸本)9798350374162

Artificial intelligence continues to evolve, particularly in the realms of natural language processing (LLMs), image generation, and task automation. Despite these advancements, multi-musical instrument recognition remains a challenging area with limited effective solutions. Addressing this, our research introduces an innovative methodology using a convolutional neural network (CNN) embedded within an artificial neural network framework. This method utilizes the OpenMIC-2018 dataset, meticulously refined for our purposes. We process entire songs by converting them into mel-spectrograms, which are instrumental in distinguishing subtle variations in pitch, dynamics, and timbre. Our approach sets a new benchmark in the field, adept.y identifying and categorizing up to 10 distinct musical instruments within complex audio recordings. It boasts an impressive F1 score of 56%. This significant achievement not only advances audio signal processing but also highlights the versatility and effectiveness of CNNs in handling sophisticated tasks like multi-instrument recognition. Our work serves as a stepping stone for future explorations in audio recognition, potentially paving the way for more nuanced and accurate audio analysis in various applications, from music production to sound engineering. © 2024 IEEE.

关键词： Spectrographs

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Deciphering Deception: Unmasking Fake News in Multilingual Contexts 5

Deciphering Deception: Unmasking Fake News in Multilingual C...

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5th IEEE International Conference on Computing, Power, and Communication Technologies, IC2PCT 2024

作者： Agarwal, Ashutosh Singh, Yash Pratap Rai, Vipin Galgotias University Dept. of Computer Science & Engineering Greater Noida India

ISBN: (纸本)9798350383522

A major global worry in the ever-expanding digital ecosystem is the spread of incorrect information. The ability to recognize false news in non-English languages is still lacking, despite great advancements in the identification of false news in English. This research looks at the unique opportunities and problems that these languages bring. It offers a thorough assessment and comparative analysis of existing approaches, highlighting their advantages and disadvantages. By doing comparative study of various differ techniques we have found that Multilingual-Fake Model currently works the best. The results of the study highlight how critical it is to use inclusive and linguistically varied tactics in order to successfully counter disinformation. It emphasizes how crucial it is to create trustworthy tools and processes that can adjust to the subtleties of many languages and their socio-cultural settings. It highlights how crucial it is to comprehend these components completely in order to create detection methods that are more potent. The study admits that the dissemination of incorrect information is an issue that is deeply rooted in the social environment of linguistic groups and transcends language borders. A discussion of possible future study areas closes the report. In a multilingual culture, this study marks a substantial breakthrough toward the development of more thorough and efficient techniques for spotting bogus news. © 2024 IEEE.

关键词： Bidirectional Transformer fake news multi-lingual

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Performance Evaluation of Off-Chain Storage Systems in Blockchain Environments

Performance Evaluation of Off-Chain Storage Systems in Block...

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2024 IEEE International Conference on Information Technology, Electronics and Intelligent Communication Systems, ICITEICS 2024

作者： Puneetha, R. Tanuja, R. Manjula, S.H. Bangalore University Uvce Dept. of Computer Science and Engg Bengaluru India

ISBN: (数字)9798350382693

ISBN: (纸本)9798350382693

The Internet of Things(IoT) generates an exponential amount of data, and the data is heterogeneous and dynamic. The data has to be processed skillfully to unearth meaningful information from it. But, it is challenging for a human mind to process this data. Hence, data is stored either on a centralized server or on cloud platforms for future analytics. Whereas, centralized storage methods pose challenges with the underlying design being consolidated into a single authorization. Centralization leads to data misuse or manipulation and single-point failures. As a result, there was a paradigm shift towards emerging technologies such as Blockchain-based solutions. Blockchain technology has consistently attracted the attention of researchers and technological giants as an inevitable solution to the problems faced by centralized authorization and in enhancing the security and privacy of the data. However, storing massive amounts of data on Blockchain is not a viable solution since it is expensive and redundant. The widely accepted and researched resolution to this problem is offloading non-transactional data onto a decentralized storage widely on an InterPlanetary File System (IPFS). In this work, we analyze the security and privacy risks of storing sensitive data such as healthcare data, on IPFS by employing rigorous real-time data loads and retrievals on IPFS nodes in a private network setup, Docker environment. The performance is evaluated on the grounds of data availability and integrity. The results show the trend that IPFS is not a feasible solution for storing and transacting sensitive data to an extent. © 2024 IEEE.

关键词： Authorization Performance evaluation data privacy Scalability Telecommunication traffic Medical services Real-time systems

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Enhanced Security Measures for Image Privacy: Encryption Perspectives 2

Enhanced Security Measures for Image Privacy: Encryption Per...

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2nd International Conference on Intelligent data Communication Technologies and Internet of Things, IDCIoT 2024

作者： Omshith, R. Balaji, Vemisetti Pavan Rahul, Rayapudi Venkata Kumaran, U. Amrita School of Computing Dept. of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350327533

A key technology in information security, picture encryption is intended to safeguard the integrity and confidentiality of digital photographs. Using a variety of cryptographic techniques and algorithms, the original image is transformed into an encrypted format throughout this process, making it more difficult for unauthorized users to view or understand the content. Secure communication, picture sharing, medical imaging, and many more fields requiring the protection of sensitive visual data find widespread use in image encryption. A key technology in information security, picture encryption is intended to safeguard the integrity and confidentiality of digital photographs. It also discusses the difficulties and potential paths for picture encryption, highlighting the rising demand for reliable, effective, and secure image encryption solutions in a world that is becoming more and more networked and data-driven. Advanced Encryption Standard (AES) is more secure than (Exclusive OR) XOR, Fernet. © 2024 IEEE.

关键词： Advanced Encryption Standard Confidentiality Encryption Exclusive OR (XOR) Fernet Integrity

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