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MRI Brain Tumor Segmentation and Classification using different deep learning models

MRI Brain Tumor Segmentation and Classification using differ...

引用

2024 International Conference on Advances in Data engineering and Intelligent Computing Systems, ADICS 2024

作者： Modi, Dhruvin R. Senthil Kumar, A.M. School Of Computer Science And Engineering Vellore Institute Of Technology Chennai India

ISBN: (纸本)9798350364828

Brain tumors have significant challenges in diagnostic precision due to their complex morphological subtleties evident in MRI (Magnetic Resonance Imaging) scans. Clinical Decision Support Systems (CDSS) have become indispensable in this regard, with MRI brain tumor segmentation and classification at their forefront. This comprehensive survey deals with the profound impact of deep learning techniques on MRI brain tumor analysis, particularly within the context of CDSS. Emphasizing the models validated against the various Dataset, the exploration ranges from the intricate convolutional processes of CNNs (Convolutional Neural Networks) to the advanced self-attention mechanisms offered by recent Transformer-based methodologies. The significance of 3D datasets is underscored, given their ability to offer detailed and holistic representations of brain tumors. Metrics such as DSC (Dice Similarity Coefficient), JI (Jaccard Index), and sensitivity rates serve as pivotal benchmarks to evaluate the efficiency of these deep learning models. Moreover, this survey investigates the performance trade-offs inherent within these methodologies, pointing towards potential avenues for further enhancement and adaptability. By synthesizing this expansive body of knowledge, the goal is to harmonize cutting- edge technological strides with tangible clinical utility, thereby refining diagnostic accuracy and forging a brighter traj ectory for patient care in the realm of brain tumor studies. © 2024 IEEE.

关键词： Magnetic resonance imaging

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KNEE OSTEOARTHRITIS DETECTION AN AI DRIVEN SEVERITY ESTIMATION 2

KNEE OSTEOARTHRITIS DETECTION AN AI DRIVEN SEVERITY ESTIMATI...

引用

2nd International Conference on computer Vision and Internet of Things, ICCVIoT 2024

作者： Adhitya, G. Sivabalakrishnan, M. School of Computer Science and Engineering Vellore Institute of Technology Chennai India

ISBN: (纸本)9781837242672

In this work, we use the Kellgren and Lawrence (KL) grading system to investigate deep learning models or the automated detection and classification of knee osteoarthritis (OA) from radiographs. For prompt care, early identification of knee arthritis is essential, and radiography is an affordable, widely available diagnostic method. We evaluated the accuracy, precision, recall, and F1 score of a number of cutting-edge models, including VGG16, Xception, DenseNet121, InceptionV3, ResNet50, and EfficientNet07. With testing accuracy of 96.90% and 95.90%, respectively, and strong F1 scores of 0.9690 and 0.9790, Xception and InceptionV3 outperformed the other models under evaluation, demonstrating robust performance and trustworthy generalization. While ResNet50, EfficientNet07, and an ensemble model had lower accuracy and F1 scores, indicating possible limitations, DenseNet121 and VGG16 also shown strong performance in dealing with the complex knee arthritis grading system. According to our research, Xception and InceptionV3 are very good at automatically classifying knee OA, providing potential instruments for clinical treatment planning and early identification. © The Institution of engineering & technology 2024.

关键词： Diagnostic radiography

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Comparative Analysis of Various Deep Learning Techniques for Weed Detection in Corn Crops 8

Comparative Analysis of Various Deep Learning Techniques for...

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8th International Conference on Electronics, Communication and Aerospace technology, ICECA 2024

作者： Jiveesha, Jiya Vinothini, A. Vellore Institute of Technology School of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350367904

Weed detection is an important process in increasing the crop yield and minimizing labor input in crop management. Automatic weed detection helps in enhancing precision agriculture. The objective of this study is to explore the application of deep learning techniques for automated weed detection. It compares the performance of four deep learning models: Standard CNN, Custom Residual CNN, VGG16, and EfficientNet B0. The models were trained with a publicly available corn dataset with 24,000 images with various image processing techniques such as normalization and data augmentation to increase the model's resilience. EfficientNet B0 performed better when compared to other models with an accuracy of 96% proving the higher capability for crop and weed classification. © 2024 IEEE.

关键词： Convolutional neural networks

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Developing ML model for early detection and prediction of Alzheimer's disease using multi modal biomarkers

Developing ML model for early detection and prediction of Al...

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2024 IEEE International Students' Conference on Electrical, Electronics and computer science, SCEECS 2024

作者： Bajaj, Divyansh Renjith, P.N. Vellore Institute of Technology School of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350348460

The early identification and outlook of Alzheimer's disease (AD) are the primary focus of this review of the literature. It emphasizes the significance of early detection, the application of machine learning approaches for AD prediction, and the utilization of multimodal biomarkers from several data sources. The review also covers resolving flaws in AD prediction algorithms, privacy and ethical issues, and the usability of machine learning models. A synopsis of the state of the field's research and the significance of interdisciplinary cooperation in advancing machine learning and multimodal biomarkers for early AD detection are included in the conclusion. For the benefit of the millions of people afflicted by this illness, the primary goal is to make early AD identification a clinical reality. © 2024 IEEE.

关键词： AI Alzheimer EEG Machine learning Prediction

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SQL Injection Testing on Website using Sqlmap 2

SQL Injection Testing on Website using Sqlmap

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2nd IEEE International Conference on Trends in Quantum Computing and Emerging Business Technologies, TQCEBT 2024

作者： Subbulakshmi, T. Josiga, S. Brindha, S. Jishnu, S. Kumar, Adityan Sunil Vellore Institute of Technology School of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350384277

Websites handle the data of the webpages by storing them into a database and reading from it whenever needed. The data may include the content of the webpages or the users who use the website. Security of this data is one of the biggest concerns of websites. The websites need to make sure that the data does not fall into wrong hands. It needs to safeguard the private information of the users and prevent any type of leakage of information due to unauthorized access. In this article, SQL injection testing of a website is done using SQLMAP tool. © 2024 IEEE.

关键词： Information leakage

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Performance Improvement of Model View Controller based Applications through Linda's-Key

Performance Improvement of Model View Controller based Appli...

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2023 International Conference on Signal Processing, Computation, Electronics, Power and Telecommunication, IConSCEPT 2023

作者： Suhas Reddy, P. Anala, Jayanth Krishnamurthy, Vallidevi Balasubramanian, Surendiran Sujithra Kanmani, R. School of Computer Science and Engineering Vellore Institute of Technology Chennai India School of Computer Science and Engineering Vellore Institute of Technology Vellore India National Institute of Technology Puducherry Department of Computer Science and Engineering India

ISBN: (纸本)9798350312126

The Model-View-Controller (MVC) design pattern is widely used in software engineering for developing user interfaces. While MVC offers many benefits, handling data in a way that is efficient and effective can be a challenge. One approach to optimising the performance of MVC applications is converting lists to dictionaries. This paper discusses the benefits and drawbacks of this approach and presents the findings of recent research on this topic. The main advantage of converting lists to dictionaries is that it can improve the performance of MVC applications by offering faster access times and making code easier to read and maintain. However, there are drawbacks to this approach, such as increased memory usage and slower performance for certain operations. Several studies have been conducted on the performance of MVC applications when using lists versus dictionaries, with varying results. This paper overviews this research and highlights the implications for MVC development. © 2023 IEEE.

关键词： Data handling

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Tackling E-Waste Management Using Decentralized Marketplace Leveraging Ethereum Blockchain

Tackling E-Waste Management Using Decentralized Marketplace ...

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2024 International Conference on Signal Processing, Computation, Electronics, Power and Telecommunication, IConSCEPT 2024

作者： Allu, Himasri Bharti, Anjali Satapathy, Shashank Mouli School of Computer Science and Engineering Vellore Institute of Technology Tamil Nadu Vellore632014 India

ISBN: (纸本)9798331540685

This study explores the implementation of a decen-tralized marketplace using blockchain technology for e-waste management. Through stakeholder-driven validation using the proof of stake and on-chain attestation, the platform ensures transparency and accountability in the e-waste supply chain. Integrating the Meta Mask wallet facilitates secure transactions, enhancing user experience and adoption. Results demonstrate a 95% reduction in transaction confirmation time and a 25% cost reduction in Ethereum transactions compared to SWIFT transfers, enhancing economic viability and user engagement. Leveraging blockchain for e-waste management presents a promising avenue for addressing complex supply chain challenges and advancing toward a more sustainable future. © 2024 IEEE.

关键词： Ethereum

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Detecting AI-Generated Deep Fakes Using ResNext CNN and LSTM-Based RNN: A Robust Approach for Real-Time Video Manipulation Detection

Detecting AI-Generated Deep Fakes Using ResNext CNN and LST...

引用

International Conference on. Cryptology and Network Security with Machine Learning, ICCNSML 2023

作者： Dhar, Akanksha Agrawal, Ekansh School of Computer Science and Engineering Vellore Institute of Technology Tamil Nadu Vellore632014 India

ISBN: (纸本)9789819706402

The increasing computational power has greatly empowered deep learning algorithms, making the creation of highly realistic and virtually undetectable synthetic videos, commonly known as deep fakes, remarkably simple. These deep fakes, often featuring convincing face swaps, can be employed in situations such as generating political turmoil, fabricating terrorism events, distributing revenge porn, and engaging in blackmail. In this study, we introduce an innovative deep learning-based technique designed to effectively differentiate between AI-generated fake videos and genuine ones. Our approach excels at automatically identifying manipulated videos, including those involving content replacement and reenactment deep fakes. Our endeavor involves leveraging Artificial Intelligence (AI) to combat its own creations. The central component of our system utilizes a ResNext Convolutional Neural Network (CNN) to extract features from individual frames of videos. These features are then used to train a Long Short-Term Memory (LSTM)-based Recurrent Neural Network (RNN). This RNN helps classify videos by determining if they have been manipulated, such as being deepfakes, or if they are genuine recordings. To ensure our model performs well with real-time data and reflects real-world situations, we evaluate it on a large and balanced dataset created by combining various existing datasets like FaceForensic++, Deepfake Detection Challenge, and Celeb-DF. Furthermore, we showcase that our system achieves competitive results using a straightforward yet effective approach. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Long short-term memory

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Community Detection within the US House based on Policy Voting 9

Community Detection within the US House based on Policy Voti...

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9th International Conference on science, technology, engineering and Mathematics, ICONSTEM 2024

作者： Kumar Singh, Sanskriti Sanjay Sinha, Apoorv Kumar Nair, Atul Nathezhtha, T. School of Computer Science And Engineering Vellore Institute of Technology Chennai India

ISBN: (纸本)9798350365092

With an increase in the rise of divisive politics that are often contrarian to the core beliefs and manifesto of political parties, it is become imperative for political parties and candidates to find a solid middle ground where they both compromise. However, this compromise cannot be integrated at time of voting which is why it is very important to cluster defectors. Clustering analysis is instrumental in the process of understanding possible relationships between defectors and their original party. For instance, if a defector consistently votes against his own, they simply wouldn't be part of it but that is not the case. Defections usually happen due to a specific policy or more importantly, the domain of a political policy. As such, seeing where the defector concurs with the opposition is desired objective of the proposed study. The proposed model utilizes the Louvain Walk as aclustering methodology due to its focus on modularity measures to understand the vitality of a node to a particular subnetwork and cluster while also putting an emphasis on the cardinality of its relationship with those amongst its cluster. Due to existing target labels, it'll also be uncovered what policies and which states cause the most defections and how they interact with each other which can only be done bygraph analysis. © 2024 IEEE.

关键词： Defects

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Block-Chain Enhanced Patient Triage System

Block-Chain Enhanced Patient Triage System

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2024 International Conference on Electrical, Electronics and Computing Technologies, ICEECT 2024

作者： Arjun, P. Geethan, Daniel Anandha Kanniga Devi, R. Sanjay K, S. Vellore Institute of Technology School of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350378092

In today's fast-paced healthcare environment, efficient management of patient records and seamless patient-provider communication is crucial. This study proposes a comprehensive solution integrating blockchain technology (Ethereum), frontend web interfaces, natural language processing (NLP), and adverse drug event detecting functions. Through a user-friendly *** interface, patients input health data securely, while NLP automates medical information extraction from conversations. ADE detectors identify drug interactions and adverse events, enhancing care and safety. Ethereum integration via Metamask ensures secure record storage, with IPFS storage and CIDs enabling encrypted data sharing. A smart contract on Ethereum automates data management for transparency and accountability. © 2024 IEEE.

关键词： Records management

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