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An Examination of Methodological Approaches for Segmentating Fetal Brain MRI Images - Analysis 2

An Examination of Methodological Approaches for Segmentating...

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2nd ieee international conference on Trends in Quantum computing and Emerging Business Technologies, TQCEBT 2024

作者： Gaikwad, Nilima Vijayalakshmi, S. Christ College Department of Science Pune India Christ University Data Science Department Lavasa Campus India

ISBN: (纸本)9798350384277

In today's world and in the country like India, Women's health needs more care. Especially the women's health during the pregnancy period plays a vital role in both the mother as well as the baby's care. As per a survey, among thousands three of them found to have fetal brain abnormalities. If these abnormalities are predicted at the early stage, then it will be an added advantage in saving both the life of mother and baby. During the pregnancy number of tests have to be performed to monitor fetal development. Tests like fetal ultrasound, Chorionic Villus Sampling, Amniocentesis, Fetal Echocardiogram, Fetal MRI imaging SCAN etc. The fetal brain abnormality can be predicted as well as treated at the early stage by analyzing the fetal brain MRI during the gestational period. Identifying abnormalities in fetal brain MRI images involves several essential steps, including image segmentation, analyzing images involves extracting distinctive features, refining their quality, identifying relevant patterns, and categorizing them based on specific criteria. The process of classification determines whether an abnormality is present or not. Analyzing images presents a complex undertaking owing to the diversity in shapes, spatial arrangements, and intensity levels within the images. This paper focuses on reviewing and comparing various segmentation techniques, highlighting their respective strengths and weaknesses. © 2024 ieee.

关键词： Image segmentation

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Research and Applications of Large Language Models for Converting Unstructured data into Structured data 3

Research and Applications of Large Language Models for Conve...

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3rd international conference on Cloud computing, Big data Application and Software Engineering, CBASE 2024

作者： Liu, Xiangfeng Sun, Jianting Lei, Aidi Zhu, Junzhi Co. Ltd Chengdu610073 China University of Electronic Science and Technology of China School of Information and Software Engineering Chengdu China

ISBN: (纸本)9798331506582

The rapid growth of data has led to a significant increase in unstructured data, such as text, audio, and images, which dominate modern information processing. However, the complexity of unstructured data presents challenges for automated analysis and processing. Converting unstructured data into structured formats is crucial for tasks like data mining, information extraction, and knowledge graph construction. Traditional methods that rely on manual rules or statistical models struggle with complex, context-dependent data. Recently, large language models (LLMs), such as GPT-4 and BERT, have demonstrated great potential in unstructured data processing due to their powerful natural language understanding and generation capabilities. This study explores the application of large language models in transforming unstructured data into structured formats. It begins by reviewing the limitations of traditional approaches and then presents a framework for unstructured data processing using LLMs, covering data preprocessing, information extraction, and structured representation. The study's experiments demonstrate the superior performance of LLMs in handling various types of unstructured data, particularly in tasks like named entity recognition, relation extraction, and contextual understanding. The results show that LLMs achieve higher accuracy and generalization while reducing reliance on manual rules. The paper discusses the strengths and limitations of this method, proposing future improvements, such as combining domain-specific knowledge for model optimization and expanding applications to multimodal data processing. The research highlights the promising role of large language models in converting unstructured data into structured formats. © 2024 ieee.

关键词： Structured Query Language

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SPECTRUM data GRAPH STRUCTURE LEARNING BASED ON DUAL-VIEW CONTRASTIVE LEARNING FOR SPECTRUM PREDICTION OF ISCC 49

SPECTRUM DATA GRAPH STRUCTURE LEARNING BASED ON DUAL-VIEW CO...

引用

49th ieee international conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Li, Shuang Sun, Yaxiu Zhang, Zherui Zhang, Han Lin, Yun Harbin Engn Univ Coll Informat & Commun Engn Harbin Peoples R China

ISBN: (纸本)9798350374520;9798350374513

In the upcoming 6G era, an efficient spectrum prediction method is crucial for meeting the requirements of complex intelligent applications in wireless networks. This paper proposes a spectrum data graph structure learning method and a Multi-Scale Feature Fusion Graph Convolutional Network model (MFGCN) aimed at optimizing Integrated Sensing, Communication, and computing (ISCC) for spectrum analysis and management. Our method addresses the issue of noisy graph connections commonly found in existing graph-based spectrum prediction methods. By employing a dual-view graph structure contrastive learning approach, it enhances the consistency of the graph structure, thereby supporting the high reliability demands of 6G networks. The MFGCN model combines the learning of local and global signal correlations, using convolutional kernels of various scales to capture signal patterns and adapt to both short-term and longterm environmental changes. Experimental results demonstrate the model's efficacy in real-world spectrum analysis and prediction, offering novel 6G data utilization strategies.

关键词： Spectrum analysis of ISCC graph structure learning contrastive learning graph convolutional network

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Highway data information visualization tool based on *** and GeoJson 7

Highway data information visualization tool based on *** and...

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7th international conference on data Science and Information Technology, DSIT 2024

作者： Yang, Jie Qiu, Tun Gao, Mingkun Cai, Zhi Duan, Zhuo Northeastern University LNJT ATS Technology Co. Ltd. College Of Information Science and Engineering Shenyang China Southeast University LNJT ATS Technology Co. Ltd. School of Transportation Shenyang China Southwest Jiaotong University LNJT ATS Technology Co. Ltd. Shenyang China LNJT ATS Technology Co. Ltd. Shenyang China

ISBN: (纸本)9798350384093

With the rapid development of smart highways, effective visualization and intelligent analysis of data information are important means to improve management efficiency and gain insight into traffic trends. This paper proposes a highway data information visualization tool based on *** and GeoJSON, aiming to achieve intuitive display and dynamic interaction of massive traffic data. The tool designs visualization functions of highway networks, toll stations, service areas and vehicle trajectories through a variety of methods such as basic data of highway networks and dynamic data of vehicle trajectories, and supports real-time monitoring and data analysis. Studies have shown that the tool performs well in scenarios such as road network section display, vehicle trajectory restoration, and abnormal event monitoring, which helps traffic managers quickly understand complex data and thus optimize decision-making strategies. Experimental results verify the effectiveness of the tool and its application potential in highway management. © 2024 ieee.

关键词： D3.js data visualization GeoJSON highway traffic management

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Cybersecurity strategies for protecting smart grid in Residential building complex from attacks 2

Cybersecurity strategies for protecting smart grid in Reside...

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2nd ieee international conference on Advancements and Key Challenges in Green Energy and computing, AKGEC 2024

作者： Pritish, V. Kumar, Praneeth Annapurani, K. Choudhary, Anirban Srm Institute of Science and Technology Department of Networking and Communications Tamilnadu Kattankulathur India

ISBN: (纸本)9798350390179

The addition of digital technology to energy systems, smart grids have been created, which make power delivery more reliable, efficient, and long-lasting. But this progress comes with big security risks, especially in apartment buildings with lots of smart devices and monitors that are all connected to each other. This paper looks at important cybersecurity methods that can be used to protect smart grid infrastructure in residential settings from cyberattacks. Some of the main goals are to protect important assets, keep data private and secure, make sure communication networks are safe, and make sure systems can handle attacks. Strong access controls, ongoing monitoring, and effective incident response methods are all part of the suggested procedures that put a high focus on lowering risks and keeping the smart grid running. The study also stresses how important it is to follow rules, teach users, and encourage smart grid components to work together. By following these thorough cybersecurity steps, smart grid security and dependability in homes may be kept, which will eventually lead to a safer and more resilient energy environment. © 2024 ieee.

关键词： Power grids

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Generative Adversarial Network Art Generator for Sculpture Analysis

Generative Adversarial Network Art Generator for Sculpture A...

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2024 international conference on Communication, computing and Internet of Things, IC3IoT 2024

作者： Kalita, Swagat Subhash Mahajan, Parag Sharanya, S. Srm Institute Of Science And Technology Data Science And Business System School Of Computing Chennai India

ISBN: (纸本)9798350352689

This study examines computer-generated sculpture using a hybrid architecture of Generative Adversarial Networks (GANs) and Convolutional Neural Networks (CNNs). CNNs are essential for visual data analysis and processing, while GANs iteratively blur the line between synthesis and authenticity. A dynamically interacting generator and discriminator network form the GAN framework. The discriminator, trained to distinguish authentic sculptures from computer-generated ones, guides the generator toward greater realism and complexity with each iteration. This study analyzes the complicated dynamics of GAN-enabled sculpture production, examining several elements that affect the result. We intend to illuminate the complex relationship between composition, texture, and shape of digitally generated sculptures by analyzing sculptures using GANs and CNNs. We want to understand their formation methods as well as their aesthetic value. We also consider how GAN-generated sculptures may be transformed beyond traditional artistic usage. The sculptures offer unique opportunities for artistic expression, scholarly study, and practical use. They can enhance art, education, architectural design, and historical preservation. This thorough inquiry using GANs and CNNs aims to push the limitations of traditional art creation and explore unknown sculpture interpretation and analysis zones. © 2024 ieee.

关键词： Generative adversarial networks

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Fededrated Learning: Research, Challenges, and Future Directions 5

Fededrated Learning: Research, Challenges, and Future Direct...

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5th ieee international conference on computing, Power, and Communication Technologies, IC2PCT 2024

作者： Kaswan, Kuldeep Singh Dhatterwal, Jagjit Singh Yadav, Rishika Malik, Kiran Tripathi, Aanjey Mani Galgotias University School of Computing Science and Engineering UP Greater Noida India Artificial Intelligence and Data Science Koneru Lakshmaiah Education Foundation AP Guntur India Graphic Era Hills University Department of Computer Science and Engineering Uttarakhand Dehradun India Mriem Department of Computer Science and Engineering Rohtak India Galgotias University School of Computing Science and Engineering Greater Noida India

ISBN: (纸本)9798350383522

Federated Learning, a disruptive and novel aspect of machine learning, is at the forefront of decentralized, privacy-conscious data processing. This in-depth review study navigates the complex environment of Federated Learning Ecosystems, attempting to extract the core of the topic while shining light on its research horizons, pressing difficulties, and promising approaches. Our trip begins with a look at the fundamental concepts of Federated Learning, emphasizing its importance in the landscape of distributed machine learning paradigms. We track its development via historical milestones and key contributions, offering context for the field's increasing research. This study focuses on the many features of Federated Learning research, including privacy-preserving techniques, communication protocols, aggregation approaches, and real-world applications. We aggregate major results and advances within these domains, highlighting notable authors and their contributions. However, the route to Federated Learning's full fulfillment is plagued with difficulties, including questions about privacy, communication efficiency, and scalability. In the future, we will enlighten the growing paths of Federated Learning research, forecasting trends, providing insights into overcoming present problems, and visualizing its integration into diverse fields. To summarize, this review article serves as a guidepost for scholars, practitioners, and enthusiasts wanting to understand the vast domain of Federated Learning. It captures the core of this dynamic discipline, offers strategic counsel for resolving its obstacles, and encourages optimism about its bright future within the domain of distributed machine learning paradigms. © 2024 ieee.

关键词： data Analysis Distributed Environments Edge computing Federated Learning Machine Learning

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Building a Secure and Scalable Finance Chatbot: Client-Server Architecture, Load Balancing, and TLS Security 4

Building a Secure and Scalable Finance Chatbot: Client-Serve...

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4th international conference on Sustainable Expert Systems, ICSES 2024

作者： Reddy, T. Anisha Akhilesh, S. Manogna, Sodum Bhaskaran, Sreebha Kavitha, C.R. Amrita School of Computing Amrita Vishwa Vidyapeetham Dept of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798331540364

This research introduces a new and improved model for a financial chatbot based on deep learning, machine learning, and natural language processing to provide suitable financial services and increase the financial literacy of users. It utilizes a detailed financial database, another complex machine learning structure for market forecasts., and natural language processing to parse user questions. Moreover, there is the customer resource management feature that applies machine learning in the analysis of financial data and natural language processing for understanding the user's intent. The paper also looks into the use of transport layer security (TLS) encryption to secure the flow of the sensitive information, the data that is to be transmitted from the chatbot server to the user clients. By integrating the dimensions of concepts such as financial analysis, personal finance advice, and data transmission, this research provides an extensive and secure approach to improving one's financial situation. © 2024 ieee.

关键词： Financial data processing

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MCASM: A Framework Cloud_Mashup Based on the Concept of Assisted Search Model for Managing data as a Service (DaaS) 1

MCASM: A Framework Cloud_Mashup Based on the Concept of Assi...

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1st international conference on Innovative and Intelligent Information Technologies, IC3IT 2024

作者： Belabed, Mourad Dennai, Abdeslem Moussaoui, Nabil Khair, Younes Tahri Mohamed University Ccai Research Team SGRELab Bechar Algeria

ISBN: (纸本)9798331541064

New web technologies have made it easier to create, use, share and recycle online resources over time. Cloud computing provides the ability to create multiple applications and software systems to satisfy complex user needs while improving the utilization of shared resources and information. The concept of 'mashups' was created through this technological advancement, which combines datasets and services from different sources into a single website. This makes cooperation between websites and web services easier. However, the abundance of data available poses challenges, including sifting through irrelevant information due to the vast volume of online data and the criteria used for searching Thus, the organization and management of results data are not taken into consideration. To tackle these issues, this article proposes the use of cloud Mashup applications employing an Assisted Search Model (ASM)[1]. By querying DaaS-Cloud (data as a Service-Cloud) services, with the possibility of integrating other Clouds by applying the notion of InterCloud, this approach termed MCASM, enables the creation of cloud mashups that utilize ASM concepts. This service provides users with access to a broad spectrum of data sources and enhances the relevance of data selection based on specified parameters. © 2024 ieee.

关键词： Cloud platforms

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Leveraging Deep Autoencoders for Security in Big data Framework: An Unsupervised Cloud computing Approach 2

Leveraging Deep Autoencoders for Security in Big Data Framew...

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2nd international conference on Distributed computing and Optimization Techniques, ICDCOT 2024

作者： Roy, Sourav Nijaguna, G.S. Shankar, Smitha Sharath Adnan, Myasar Mundher Umaeswari, P. Department of Electronics and Communication Engineering Bangalore India S.E.A. College of Engineering and Technology Department of Information Science and Engineering Bangalore India Nitte Meenakshi Institute of Technology Dept of Ece Bengaluru India The Islamic University Najaf Iraq R.M.K. Engineering College Department of Computer Science and Business Systems Tamil Nadu Kavaraipettai India

ISBN: (纸本)9798350382952

Abnormalities recognition in bank transaction big data is the number one issue for stability of financial security system. Due to the rate digital transactions are increasing it is vital to have effective ways. Encryption with deep autoencoder model should be explored as it involves trained neural networks that learn such patterns from the complex transaction data. The following paper demonstrates application of anomaly detection using deep autoencoders in the banking big data transactions. It focuses on the theoretical bases, network design, preparedness and the testing measures for deep autoencoders. On the other hand, it solves problems such as high dimensionality and imbalanced dataset. This research paper shows deep autoencoders effectiveness in deep learning and how the network identifies different fraudulent big data transactions, money laundry and unauthorized access. It also encompasses recent developments of cloud environments and future methods using deep autoencoders including the fact that constant search for new possible solutions is a must. The insights delivered contribute to the discourse in financial security community, which incorporates researchers, practitioners, and policymakers involved in anomaly detection in cloud. © 2024 ieee.

关键词： Cryptography

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