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Detection and Analysis of Autism Spectrum Disorder Using Random Forest Classifier 2

Detection and Analysis of Autism Spectrum Disorder Using Ran...

引用

2nd International conference on Recent Trends in Microelectronics, Automation, Computing, and communications Systems, ICMACC 2024

作者： Zambre, Ganesh Albhar, Harshad Masli, Toukir Patil, Sarita G H Raisoni College of Engineering and Management Department of Computer Engineering Pune India

ISBN: (纸本)9798350366570

Autism Spectrum Disorder (ASD) diagnosis is difficult due to its complex and diverse symptomatology. This study focuses on using machine learning, specifically the Random Forest algorithm, to identify ASD. We use a large dataset including behavioural and demographic characteristics to pre-process, select features, and assess the model's performance in differentiating ASD cases. Our findings emphasize the Random Forest classifier's predictive powers and its potential to aid in early ASD diagnosis in clinical settings, potentially improving intervention outcomes. © 2024 IEEE.

关键词： Adversarial machine learning

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Lanturn: Measuring Economic security of Smart Contracts Through Adaptive Learning 23

Lanturn: Measuring Economic Security of Smart Contracts Thro...

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30th acm SIGSAC conference on computer and communications security (acm CCS)

作者： Babel, Kushal Javaheripi, Mojan Ji, Yan Kelkar, Mahimna Koushanfar, Farinaz Juels, Ari Cornell Tech IC3 New York NY 10044 USA Univ Calif San Diego La Jolla CA 92093 USA

ISBN: (纸本)9798400700507

We introduce Lanturn: a general purpose adaptive learning-based framework for measuring the cryptoeconomic security of composed decentralized-finance (DeFi) smart contracts. Lanturn discovers strategies comprising of concrete transactions for extracting economic value from smart contracts interacting with a particular transaction environment. We formulate the strategy discovery as a black-box optimization problem and leverage a novel adaptive learning-based algorithm to address it. Lanturn features three key properties. First, it needs no contract-specific heuristics or reasoning, due to our black-box formulation of cryptoeconomic security. Second, it utilizes a simulation framework that operates natively on blockchain state and smart contract machine code, such that transactions returned by Lanturn's learning-based optimization engine can be executed on-chain without modification. Finally, Lanturn is scalable in that it can explore strategies comprising a large number of transactions that can be reordered or subject to insertion of new transactions. We evaluate Lanturn on the historical data of the biggest and most active DeFi Applications: Sushiswap, UniswapV2, UniswapV3, and AaveV2. Our results show that Lanturn not only rediscovers existing, well-known strategies for extracting value from smart contracts, but also discovers new strategies that are previously undocumented. Lanturn also consistently discovers higher value than evidenced in the wild, surpassing a natural baseline computed using value extracted by bots and other strategic agents.

关键词： MEV Machine Learning Optimization Decentralized Finance Cryptoeconomics Smart Contract security Tool

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Research on the application of artificial intelligence in computer network technology based on the era of big data 2

Research on the application of artificial intelligence in co...

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2nd International conference on Artificial Intelligence and Autonomous Robot Systems, AIARS 2023

作者： Chen, Xiangge Tianjin University of Technology Tianjin China

ISBN: (纸本)9798350324358

With the arrival of the era of big data, the value of artificial intelligence technology has become more and more prominent, and its application in computer network technology has become more extensive. In practice, the application of artificial intelligence in computer network technology can greatly improve the level of security protection of computer networks, and can improve the level of network management, and can better play the advantages of computer network technology. In this regard, this paper firstly introduces big data and artificial intelligence, and analyses the necessity of applying artificial intelligence in computer network technology, and on this basis proposes the specific application of artificial intelligence in computer network technology. © 2023 IEEE.

关键词： Big data

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An Efficient Cluster Head Selection Method for Enhanced security and Energy Optimization in Wireless Sensor Networks 2

An Efficient Cluster Head Selection Method for Enhanced Secu...

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2nd International conference on Self Sustainable Artificial Intelligence Systems, ICSSAS 2024

作者： Kandasamy, Kathirvel Shanmugam, Hemalatha Karpagam Academy of Higher Education Department of Computer Science TamilNadu Coimbatore India

ISBN: (纸本)9798350368413

Wireless Sensor Networks (WSNs) consist of sensors that transmit data wirelessly, making energy consumption a critical factor in the overall transmission process. Detecting malicious nodes in WSNs is a significant challenge, which can be addressed through the selection of cluster heads (CHs). This research introduces an efficient method for CH selection, considering limiting factors such as maximum power, throughput, and minimal spread to the base station (BS). However, there is a possibility that cluster heads could become malicious. To mitigate this, malicious nodes are identified and removed using a Net Credit Score. The new method is evaluated based on parameters such as Packet Delivery Ratio, Delay, Energy Consumption, and Throughput Ratio, demonstrating its efficiency. © 2024 IEEE.

关键词： Sensor nodes

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2nd International conference on Deep Sciences for Computing and communications, IcondeepCom 2023

2nd International Conference on Deep Sciences for Computing ...

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2nd International conference on Deep Sciences for Computing and communications, IcondeepCom 2023

ISBN: (纸本)9783031689079

The proceedings contain 82 papers. The special focus in this conference is on Deep Sciences for Computing and communications. The topics include: Digital Identity System for Real World Asset Using Blockchain;blockchain-Driven Framework for Fake Product Detection;blockchain Powered Real-Estate Management System;a Block-Chain Based Decentralized Mechanism to Ensure the security of Electronic Voting System Using Solidity Language;a Secure Persistent Health Care System Using Blockchain Smart Contract;multimodal Fake News Detection Using Deep Learning Techniques;maritime Human Drowning Detection Using Intelligent Deep Learning Algorithm;development of a Pothole Detection System Using Deep Learning Techniques and Depth Estimation;machine Learning and Deep Learning Algorithms for Breast Cancer Prediction;leaf Disease Detection Using Deep Learning Approach;Robust Traffic Sign Recognition Using CNN YOLOv5 Model;Fusion Emotion Prediction Using the CEFER Algorithm;performance Analysis of Various Machine Learning Techniques for Mental Health Tracking;Chronic Kidney Disease (CKD) Detection Analysis Using Machine Learning;Revolutionizing MS Rehabilitation with Digital Twins and Machine Learning: A Promising Path to Precision Medicine;empirical Evaluation of Machine Learning Techniques for Car Price Prediction;water Quality Analysis Using Machine Learning Techniques;design and Development of Human Identification and Obstacle Detection System for Blind Using Machine Learning;effective Parkinson Disease Detection and Prediction Using Voting Classifier in Machine Learning;inflow and Infiltration Water Problem Detection Using Machine Learning;crop Recommendation and Production Prediction;a Comparison of Cox Model and Machine Learning Techniques in the High-Dimensional Survival Data;Smart Farming: Using IoT and AI to Improve Crop Yield in Aeroponics System.

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GROTTO: Screaming fast (2+1)-PC for Z2n via (2,2)-DPFs 30

GROTTO: Screaming fast (2+1)-PC for Z2n via (2,2)-DPFs

引用

30th acm SIGSAC conference on computer and communications security (acm CCS)

作者： Storrier, Kyle Vadapalli, Adithya Lyons, Allan Henry, Ryan Univ Calgary Calgary AB Canada IIT Kanpur Kanpur Uttar Pradesh India

ISBN: (纸本)9798400700507

We introduce GROTTO, a framework and C++ library for space- and time-efficient (2 + 1)-party piecewise polynomial (i.e., spline) evaluation on secrets additively shared over Z(2n). GROTTO improves on the state-of-the-art approaches based on distributed comparison functions (DCFs) in almost every metric, offering asymptotically superior communication and computation costs with the same or lower round complexity. At the heart of GROTTO is a novel observation about the structure of the "tree" representation underlying the most efficient distributed point functions (DPFs) from the literature, alongside an efficient algorithm that leverages this structure to do with a lightweight DPF what state-of-the-art approaches require comparatively heavyweight DCFs to do. Our open-source GROTTO implementation supports dozens of useful functions out of the box, including trigonometric and hyperbolic functions with their inverses;various logarithms;roots, reciprocals, and reciprocal roots;sign testing and bit counting;and over two dozen of the most common univariate activation functions from the deep-learning literature.

关键词： Multiparty computation distributed point functions

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Enhancing Cloud security with Deep Learning-Based Intrusion Detection in Cloud Computing Environments 2

Enhancing Cloud Security with Deep Learning-Based Intrusion ...

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2nd International conference on Advancement in Computation and computer Technologies, InCACCT 2024

作者： Aswini Devi, T. Jain, Arpit Koneru Lakshmaiah Education Foundation Department of Computer Science & Engineering AP Vaddeswaram India GRIET Hyderabad India

ISBN: (纸本)9798350371314

Cloud computing (CC) offers a wide range of on-demand resources and services over the internet. However, due to the inherent vulnerability of the cloud's dispersed architecture, guaranteeing the privacy and security of cloud assets and resources remains a tough task. Data and service protection in cloud systems is a continuous and serious concern. This article presents a fresh solution to this problem by utilizing Deep Learning (DL) techniques for intrusion detection in cloud computing systems. The present research analyses the strategies used by several Intrusion Detection Systems (IDS). As network infrastructures expand, so do security risks, increasing the need for dependable intrusion detection. Developers have created numerous intrusion detection systems (IDS) in response to the expanding security and privacy issues that saturate modern computer networks. Improving the datasets used for training and testing these security solutions requires equal focus as building defense systems. Improved datasets significantly enhance the detection capabilities of both offline and online intrusion detection models. This essay contributes to the growing corpus of research on CC security by conducting a thorough examination of publicly available network-based IDS datasets. It emphasizes the need to incorporate cutting-edge DL approaches into IDS for increased security. As cloud computing continues to transform the digital world, the findings of this study have major implications for safeguarding sensitive data and key services in cloud-based ecosystems. Furthermore, they provide solutions to the current anomaly detection issues caused by insufficient normal patterns in training data. The detection of accuracy information for two, five, and twenty-three classes and soft-max regression (SMR) feature learning perform similarly for 5-class and 23-class categorization, and it was also found that STL completed all categorization categories with above 98% accuracy. © 2024 IEEE.

关键词： Intrusion detection

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An AI-Enabled Smart Healthcare Application for Remote Patient Monitoring System 2

An AI-Enabled Smart Healthcare Application for Remote Patien...

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2nd International conference on Recent Trends in Microelectronics, Automation, Computing, and communications Systems, ICMACC 2024

作者： Abdullah, A Sheik Pooja, A. Poovithazhan, K. Anjali, K. Kavya, R. School of Computer Science and Engineering Vellore Institute of Technology Chennai India Vellore Institute of Technology Chennai India

ISBN: (纸本)9798350366570

This research presents a groundbreaking healthcare application aimed at revolutionizing patient care and improving the overall healthcare experience. The application is developed through an extensive research and development process that leverages cutting-edge technologies to address critical challenges faced by healthcare providers and patients alike. By seamlessly integrating advanced features and functionalities, this application holds the potential to revolutionise the way healthcare is given and received. This, outlines a health care application that aims to improve patient care, facilitate interaction between the healthcare professionals and patients and enhance overall healthcare management. The application provides a comprehensive platform for patients, doctors and healthcare providers to access and manage medical records, schedule appointments, receive personalized health recommendations, and securely communicate in real-time. It incorporates advanced features such as AI-powered symptom analysis, medication reminders, and telemedicine capabilities. The application's user-friendly interface, combined with robust data privacy and security measures, ensures a seamless and confidential healthcare experience for all users. By leveraging technology, this health care application strives to revolutionize the way healthcare is delivered and accessed, ultimately improving patient outcomes and healthcare efficiency. © 2024 IEEE.

关键词： Electronic health record

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Post-quantum cryptography for Multiple high-resolution millimeter wave images for enhanced security in IOT Networks 2

Post-quantum cryptography for Multiple high-resolution milli...

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2nd International conference on Advancement in Computation and computer Technologies, InCACCT 2024

作者： Govindhan, P. Kumar, K. Puducherry Technological University Electronics and Communication Pondicherry India

ISBN: (纸本)9798350371314

In the current era, the exponential rise in security threats is a significant concern, extending beyond the realm of the Internet of Things (IoT). The data being transferred and stored requires a robust encryption and decryption mechanism. The existing system utilizes the SHA algorithm and various encryption methods to secure image data. However, with the evolution of next-generation networks, quantum techniques pose a potential threat to decrypting existing cryptographic methodologies. These quantum techniques can extract data from the internet today and process it in the future. To address this looming cybersecurity challenge, this paper proposes a novel technique - a post-quantum cryptographic method designed to withstand quantum attacks in the near future. Specifically, the paper introduces a wavelength multiplexing embedded post-quantum technique capable of encrypting multiple high-resolution Millimeter-wave images. Each image, representing a pixel value of 4160 horizontally and 3120 vertically, contributes to the overall security of the method. The proposed wavelength multiplexing method is compared with the BBM92 protocol in terms of processing speed, confidentiality, integrity, and nonrepudiation. Post quantum cryptographic method works in the quantum computers are fast, reliable in qubits ensures authentication, verify the identity of a system in IOT networks make the scope to support the future networks working in quantum environment and simulation environment shows a promising result in terms of encrypt time, and sum rate of the cryptographic system models. © 2024 IEEE.

关键词： Quantum cryptography

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Research on security resource allocation algorithm of edge computing based on mobile blockchain 2

Research on security resource allocation algorithm of edge c...

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2nd IEEE International conference on Electrical, Automation and computer Engineering, ICEACE 2024

作者： Wang, Yuhang Chungbuk National University Cheongju Korea Republic of

ISBN: (纸本)9798350368208

Blockchain technology is predicted to be one of the key technologies of 6G cellular mobile communication due to its advantages of decentralization, anonymity, openness and immutability. Blockchain technology based on mobile communications can protect privacy and save costs. However, due to problems such as fixed and energy consumption of traditional blockchain scenarios, the mobile blockchain generated by mobile mining in the future may become a mainstream technology and be applied in all aspects. In mobile communication, mobile devices cannot meet the huge computing power requirements of mobile blockchain due to power supply and memory problems. Mobile users can offload mining tasks to edge computing servers while moving blockchain in a communication environment requires refactoring the system security model to be applied to collaborative processing of offloading tasks in mobile edge computing. Based on traditional blockchain and mobile edge computing, this paper first proposes hypotheses about mobile blockchain, analyzes the attack model of traditional blockchain, forms a computing power alliance, and builds a security model based on smart contracts. The security guarantee of mobile blockchain is formed from three aspects: the difficulty value of personal computing power is increased, the security based on contract accounts is improved, and the success probability of fork attack is reduced. In the process of resource allocation, auction algorithm is used to construct the price utility function to solve the problem of social welfare maximization. The simulation results verify that the security increases with the increase of sequestration funds and sequestration time, and the success rate of fork attack decreases with the increase of the number of verifiers. Compared with traditional methods, the combined optimization algorithm has certain advantages in utility sum and average return, and its security is also higher than other algorithms. After the emergence of mobi

关键词： Mobile edge computing

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