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Decentralized Finance and Cross-Chain Interoperable Automated Market Maker - Using BlockChain 2

Decentralized Finance and Cross-Chain Interoperable Automate...

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2nd International Conference on Emerging Trends in Information Technology and engineering, ic-ETITE 2024

作者： Sarumathi, S. Saxena, Apoorv Raj Saxena, Yash Raj Sana, Mehreen S. Md Verma, Saritha Roy, Sneha Hkbk College of Engineering Dept. of Computer Science Engineering Bangalore India ClayStack Bangalore India

ISBN: (纸本)9798350328202

Decentralized Finance (DeFi) represents an alternative paradigm in financial infrastructure, operating a top the Ethereum Blockchain. DeFi leverages Automated Market Makers (AMMs) to facilitate the exchange of coins/tokens within Liquidity Pools (LPs). However, the current framework limits transactions to a single chain, resulting in prolonged processing times for cross-chain transactions. The principal drawbacks of existing AMMs include protracted transaction confirmation periods and constraints in single-chain functionality. This paper introduces Mixta, a web-based multi-chain interoperable AMM, aiming to address these limitations. Mixta empowers users to seamlessly exchange assets across different chains, ultimately reducing transaction confirmation times. © 2024 IEEE.

关键词： Finance

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Breast Cancer Detection with Machine Learning-A Review 2

Breast Cancer Detection with Machine Learning-A Review

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2nd International Conference on Sustainable Computing and Data Communication Systems, ICSCDS 2023

作者： Meghana, Karnam Nandal, Neha Tanwar, Rohit Goel, Lipika Chhabra, Gunjan Gokaraju Rangaraju Institute of Engineering and Technology Dept. of Computer Science and Engineering Hyderabad India University of Petroleum and Energy Studies Dept. of Computer Science and Engineering Dehradun India Graphic Era Hill University Dept. of Computer Science and Engineering Dehradun India

ISBN: (纸本)9781665491990

Malignancy in the breast is a significant public health concern, where timely identification is essential for effective treatment. Machine Learning (ML) and Deep Learning (DL) algorithms are potential tools for prompt detection og breast malignancy through examination of medical images such as mammograms. Convolutional neural networks (CNNs), transfer learning, and ensemble learning are some of the recent techniques being used in this field. Despite the advantages of ML and DL algorithms for breast cancer detection, there are still several challenges that need to be addressed. The lack of diversity in the datasets used to train algorithms is one major challenge, with many datasets based on specific populations that may not represent others. Highly annotated data is also limited in medical field. The objective of this study is to provide researchers with valuable insights and guidance. © 2023 IEEE.

关键词： Convolutional neural networks

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Prediction of Alzheimer's Disease using Deep Learning Algorithms 2

Prediction of Alzheimer's Disease using Deep Learning Algori...

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2nd International Conference on Applied Artificial Intelligence and Computing, ICAAIC 2023

作者： Praveen, P. Srilatha, Koleti Sathvika, Masabathini Nishitha, Edulapuram Nikhil, Madduri Sr University Dept. of Computer Science and Artificial Intelligence Warangal India Sr Engineering College Dept. of Computer Science Engineering Warangal India

ISBN: (纸本)9781665456302

Throughout the world, at least five crore individuals are thought to have the disease of Alzheimer. This Alzheimer's disease is the most well-known and prevalent kind of dementia (AD). Since the modest but significant advancements in Alzheimer's disease (AD) treatment, the diagnostic emphasis has progressively switched to the precise identification of the disease's earliest stage. In numerous attempts at clinical classification, the difficulty of differentiating pre-clinical AD from changes associated with normal aging or developed AD has been recognized. Alzheimer's disease is a neurological and brain disorder that eventually makes it difficult to do even the most fundamental tasks. It gradually destroys memory and thinking skills. It is a neurological degenerative disorder that causes both brain cell shrinkage and degeneration. The most typical reason of dementia is a gradual loss of cognitive ability, behavioral, and social abilities, limiting their capacity to operate independently. It causes both brain cell shrinkage and degeneration. As people live longer, there are more concerns about aging. Damage to brain cells can be avoided with an early diagnosis of this disease. Early detection can considerably slow or stop the growth of this disease because there is no cure for it. To stop the progression of Alzheimer's disease, early detection is essential. As a result, experts can initiate preventive care as soon as possible. They call for prompt and precise Alzheimer's disease detection in its initial and most elusive stages. The only accurate approach for diagnosis is magnetic resonance imaging (MRI) brain imaging, although exams like the Mini-Mental State Examination (Fol stein 1975), or MMSE, are routinely used for earlier diagnosis. The main objective of this research work is to devise a technique for accurately identifying and staging diseases in magnetic resonance imaging (MRI). Deep learning excels at analyzing images and predicting diseases. A growing body o

关键词： Magnetic resonance imaging

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The Integration of Semantic and Structural Knowledge in Knowledge Graph Entity Typing

The Integration of Semantic and Structural Knowledge in Know...

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2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL 2024

作者： Li, Muzhi Hu, Minda King, Irwin Leung, Ho-Fung Dept. of Computer Science & Engineering The Chinese University of Hong Kong Hong Kong

ISBN: (纸本)9798891761148

The Knowledge Graph Entity Typing (KGET) task aims to predict missing type annotations for entities in knowledge graphs. Recent works only utilize the structural knowledge in the local neighborhood of entities, disregarding semantic knowledge in the textual representations of entities, relations, and types that are also crucial for type inference. Additionally, we observe that the interaction between semantic and structural knowledge can be utilized to address the false-negative problem. In this paper, we propose a novel Semantic and Structureaware KG Entity Typing (SSET) framework, which is composed of three modules. First, the Semantic Knowledge Encoding module encodes factual knowledge in the KG with a Masked Entity Typing task. Then, the Structural Knowledge Aggregation module aggregates knowledge from the multi-hop neighborhood of entities to infer missing types. Finally, the Unsupervised Type Re-ranking module utilizes the inference results from the two models above to generate type predictions that are robust to false-negative samples. Extensive experiments show that SSET significantly outperforms existing state-of-the-art methods. © 2024 Association for Computational Linguistics.

关键词： Knowledge graph

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Soil Monitoring Using Machine Learning Algorithms

Soil Monitoring Using Machine Learning Algorithms

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2024 International Conference on Smart Devices, ICSD 2024

作者： Agarwal, Jyoti Bhatt, Ishita Goyal, Sahaj Singh, Prabhdeep Graphic Era Deemed to be University Dept. of Computer Science and Engineering Dehradun India

ISBN: (纸本)9798350394412

Soil monitoring is important aspect of agriculture to grow good quality food. It is crucial aspect for famers and the environmental scientists as well. If soil is monitored properly then chances of good quality crop increased which reduces farmer's tension and increase their income. This research paper is a step towards applying machine learning algorithms for soil monitoring. Decision Tree Regression, Random Forest Regression, Gradient Boosting Regressor and Stacking Regressor are such algorithms. Comparison analysis of these techniques are also done in this paper. This work will be helpful for farmers and land managers to accurately predict about the soil fertility which will further help them to take decision which crop should be grow as per the soil condition. © 2024 IEEE.

关键词： Adversarial machine learning

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RT-Swap: Addressing GPU Memory Bottlenecks for Real-Time Multi-DNN Inference 30

RT-Swap: Addressing GPU Memory Bottlenecks for Real-Time Mul...

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30th IEEE Real-Time and Embedded Technology and applications Symposium, RTAS 2024

作者： Kang, Woosung Lee, Jinkyu Lee, Youngmoon Oh, Sangeun Lee, Kilho Chwa, Hoon Sung DGIST Dept. of Electrical Engineering and Computer Science Korea Republic of Dept. of Computer Science and Engineering Korea Republic of Hanyang University Dept. of Robotics Korea Republic of Ajou University Department of Software and Computer Engineering Korea Republic of School of AI Convergence Soongsil University Korea Republic of

ISBN: (纸本)9798350358414

The increasing complexity and memory demands of Deep Neural Networks (DNNs) for real-Time systems pose new significant challenges, one of which is the GPU memory capacity bottleneck, where the limited physical memory inside GPUs impedes the deployment of sophisticated DNN models. This paper presents, to the best of our knowledge, the first study of addressing the GPU memory bottleneck issues, while simultaneously ensuring the timely inference of multiple DNN tasks. We propose RT-Swap, a real-Time memory management framework, that enables transparent and efficient swap scheduling of memory objects, employing the relatively larger CPU memory to extend the available GPU memory capacity, without compromising timing guarantees. We have implemented RT-Swap on top of representative machine-learning frameworks, demonstrating its effectiveness in making significantly more DNN task sets schedulable at least 72% over existing approaches even when the task sets demand up to 96.2% more memory than the GPU's physical capacity. © 2024 IEEE.

关键词： Deep neural networks

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Comparative Study of Machine Learning Algorithms for Feature Extraction and Prediction of Bacterial Leaf Blight Disease in Paddy Fields 8

Comparative Study of Machine Learning Algorithms for Feature...

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8th International Conference on Inventive Systems and Control, ICISC 202

作者： Krishna, Penugonda Seetha Rama Nagarajan, S. Annamalai University Dept. of Computer Science Engineering Tamilnadu Chidambaram India Government College of Engineering Srirangam Department of Computer Science and Engineering Trichy620012 India

ISBN: (纸本)9798350386578

Paddy farming plays a critical role in global food security, yet diseases like Bacterial Leaf Blight (BLB) pose significant threats to crop yield and quality. Early detection and accurate diagnosis are essential for effective disease management. This research study investigates machine learning methods for feature extraction and prediction of BLB in paddy fields. Utilizing a comprehensive dataset of paddy leaf images, several machine learning models including Support Vector Machines (SVM), Random Forest (RF), k-Nearest Neighbours (kNN), and Gradient Boosting Machine (GBM) were trained and evaluated. Performance metrics such as accuracy, precision, recall, and F1-score were employed to assess the effectiveness of each model. The comparative analysis discusses the strengths and limitations of each method, focusing on those that demonstrate superior capability in early and accurate detection of BLB. These findings provide valuable insights for agricultural scientists and practitioners to enhance disease management strategies using modern machine learning techniques. By leveraging this knowledge, specialists can optimize interventions and improve agricultural practices to mitigate the impact of diseases like BLB on paddy farming and ensure global food security. © 2024 IEEE.

关键词： Adversarial machine learning

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Data Mining Techniques in Financial Fraud Detection 1

Data Mining Techniques in Financial Fraud Detection

引用

1st IEEE International Conference on Advances in Computing, Communication and Networking, ICAC2N 2024

作者： Rathore, Rachna Sharma, Yash Ambika, P. Upadhyay, Amit Kumar Mahajan, Samriti Kumar, Rupendra Jiwaji University Dept. of Mathematics Gwalior India CSE-APEX Chandigarh India Meenakshi A.H.E.R Dept. of Commerce Chennai India Mangalayatan University Dept. of Computer Engineering & Applications Aligarh India Lingayas Vidyapeeth University Dept. of Commerce & Management Faridabad India JS University Dept. Computer Science Shikohabad India

ISBN: (纸本)9798350356816

In today's world, financial fraud poses a real threat to global economies, necessitating robust detection systems. Utilizing data mining methods in fraud detection has emerged as a new approach, offering powerful tools, especially with large financial transaction datasets. This paper explores various Data Mining techniques applied in Financial Fraud Detection, including decision trees, neural networks, and clustering methods, highlighting their effectiveness in identifying fraud. As decision trees can easily classify & predict transactions based on this historical data, it helps to recognize fraudulent activities. Neural networks can improve the accuracy of predicting fraudulent transactions by detecting intricate patterns, they are capable & adaptable for learning even odd more. Data points that are close together with each other end up in the same cluster - revealing outliers such as fraud;which would appear to be a point very distant from its neighbours. The adoption of these methodologies within financial systems prevents fraud before a crime has been committed, allowing for the resources & time spent on manual investigation to be reallocated. In addition, the development of machine-learning solutions & further enhancements in artificial intelligence allows for specialized fraud prevention methods to evolve. This abstract emphasises the importance of data mining for financial security which also points at some on-going research in this area. © 2024 IEEE.

关键词： Support vector machines

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Parametric Cyber Defense: A Sophisticated Machine Learning Architecture for Advanced Intrusion Detection and Threat Classification 5

Parametric Cyber Defense: A Sophisticated Machine Learning A...

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

作者： Lakshmi, S. Maalan, M.R. Kishore Kumar, R. St. Joseph's College of Engineering Dept. of Computer Science Engineering Chennai India

ISBN: (数字)9798350389609

ISBN: (纸本)9798350389609

In contemporary network environments, effective countermeasures against cybersecurity threats require highly effective Intrusion Detection Systems (IDS). This paper proposes an advanced methodology to enhance IDS by employing parametric analysis on network traffic characteristics. Through careful data selection and preprocessing from a comprehensive dataset, data integrity is maintained, with features such as packet size and transfer duration being leveraged. Machine learning algorithms, including Decision Tree, Extra Tree, and xgBoost, are applied to detect and classify various cyberattack categories. In addition, advanced feature selection and dimensionality reduction techniques are utilized to improve model efficiency. Extensive experimentation demonstrates superior accuracy in identifying and classifying cyber threats while reducing the occurrence of false positives. The proposed framework strengthens cybersecurity infrastructures through the integration of advanced machine learning techniques to address evolving threats. © 2024 IEEE.

关键词： Adversarial machine learning

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A Survey on Blockchain-Based Certificate Authentication System: From Traditional to Digital 4

A Survey on Blockchain-Based Certificate Authentication Syst...

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4th International Conference on Ubiquitous Computing and Intelligent Information Systems, ICUIS 2024

作者： Saraswathi, R Vijaya Vasavi Swathi, Meda Venkata Punwatkar, Sakshi Manoj Konduru, Shreya Sami, Mohammed Abdul Vnr Vignana Jyothi Institute of Engineering and Technology Dept. Of Computer Science and Engineering Hyderabad India

ISBN: (纸本)9798331529635

This research study pertains to the essential problems of certificate authentication within academic organizations. It concentrates on the major issues of forgery and inefficiencies as well as administrative bottlenecks that arise in conventional systems. The usual ways, which are based on one authority organization, are likely to be compromised, move slowly on verification, and have increased overhead costs. Blockchain technology is a distributed way of doing things without a middleman, and thus it is able to improve security, transparency, and the immutability of educational records. The article identifies the shortcomings of the present blockchain-based implementations with the focus on scalability, interoperability with legacy infrastructure, and high transaction costs. In the paper, an experiment on Ethereum-based smart contracts as well as the Secure Hash Algorithm (SHA-256) and InterPlanetary File System (IPFS) technologies shows the potential of these technologies to a certificate validation system by providing cryptographic integrity and, therefore, data storage that cannot be altered. The proposed work introduces a decentralized architecture that uses smart contracts for certificate issuing and verification to some extent. By integrating cryptographic hashing mechanisms, the system provides real-time, verifiable, and immutable credential records, addressing both fraud prevention and operational inefficiencies. © 2024 IEEE.

关键词： Ethereum

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