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检索条件"任意字段=2023 Intelligent Computing and Control for Engineering and Business Systems, ICCEBS 2023"

共 3241 条记录，以下是151-160 订阅

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Sniffing Detection Using Machine Learning

Sniffing Detection Using Machine Learning

引用

2023 intelligent computing and control for engineering and business systems, iccebs 2023

作者： Balamurugan, M. Vasanth, P. Grihith, S. Youkesh, G. Sri Sairam Engineering College Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350394580

Sniffing Detection using Machine Learning presents an innovative approach for detecting malicious Uniform Resource Locators (URLs) using the Random Forest Classifier. This system stands out with real-time adaptability, comprehensive feature extraction, and user customization. By integrating cutting-edge machine learning techniques, it offers a robust defense against the evolving landscape of malicious URLs, bridging the gap in cybersecurity solutions. This system's core is an advanced and adaptive URL identification and classification tool. The Random Forest Classifier excels in discerning intricate patterns within URLs, including structure, content, and behavior. Its adaptability and versatility make it an ideal choice for this multifaceted task. Real-time data integration ensures our model remains up-to- date with emerging threats. Additionally, the comprehensive feature extraction process contributes to accuracy, and user customization options adapt to specific needs. Compared to existing solutions, this system offers a holistic approach to malicious URL detection, positioning it as a forward-looking sentinel in the cybersecurity landscape. © 2023 IEEE.

关键词： Automated Response Cyber Threats Feature Extraction Machine Learning Algorithms Network Security Real-time Detection Security Infrastructure Sniffing Attacks URL Maliciousness User-friendly Interface

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Blind Cicerone

Blind Cicerone

引用

2023 intelligent computing and control for engineering and business systems, iccebs 2023

作者： Kavitha, V. Manimala, G. Kothai, S. Rashmi, T. Sri Sairam Engineering College Department of Computer Science and Engineering India

ISBN: (纸本)9798350394580

We propose a camera-based application that combines object detection and text extraction using OCR. It is a useful and adaptable system with wide range of potential applications which utilizes camera as its primary input source. This can be a smart phone camera, webcam, or any other camera-equipped device. The system performs real-time object detection on the camera frame, identifying objects and their precise locations. Bounding boxes are drawn around detected objects to visually indicate their positions. In addition to detecting objects, the system can also recognize and label the objects it identifies. This recognition can be based on a pre-trained model that associates objects with class labels. If text is detected within the camera frame, the system uses Optical Character Recognition (OCR) to extract and convert the text into machine- readable format. OCR algorithms are applied to recognize and extract printed or handwritten text. Once an object is detected and recognized, or if text is extracted, the system utilizes a Text-to- Speech (TTS) API to convert the recognized text or object label into spoken language. This enables the application to provide audible feedback to the user, enhancing accessibility. © 2023 IEEE.

关键词： bounding boxes class labels Object detection Optical Character Recognition (OCR) Text Extraction Text to Speech (TTS)

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Deep Learning-Based Tennis Shot Type Prediction Using Gait Analysis

Deep Learning-Based Tennis Shot Type Prediction Using Gait A...

引用

2023 intelligent computing and control for engineering and business systems, iccebs 2023

作者： Rashmi, A. Brindha, S. Sandhya, J. Lakshmi, G. Myvizhi Selvi, V. Gunavarshini, S. Madhanraj, P. Nithyasri, J. Sri Sairam Engineering College Department of Electronics and Communication Engineering India

ISBN: (纸本)9798350394580

This paper proposes a real-time tennis shot prediction system based on computer vision and gait analysis. This method uses the Position Pipeline to extract the tennis player's position markers from the webcam sequence. These positional signals are then used to calculate the player's gait characteristics, such as step length and cadence. A machine learning model based on gait measurements is then used to predict the type of shot the player will make. The expected recording style is displayed in real time on the webcam video. This technique was tested using a dataset of tennis players hitting different shots. The algorithm accurately predicts shot patterns 95% of the time. Tennis players who follow the recommended method can improve both their play and technique. It can also be used to develop new training programs and evaluate the effectiveness of different training philosophies. © 2023 IEEE.

关键词： Convolutional Neural Network Deep learning Image processing Tensor flow Web Application

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Navigating the Metaverse: Trends and Opportunities in 2023

Navigating the Metaverse: Trends and Opportunities in 2023

引用

2023 intelligent computing and control for engineering and business systems, iccebs 2023

作者： Arya, Ashima Dhanaraj, Rajesh Kumar Juneja, Sapna Shilpa Kiet Group of Institutions Ghaziabad India Pune India Chandigarh University Mohali India

ISBN: (纸本)9798350394580

The idea of the Metaverse, which entails employing immersive technologies like virtual reality (VR), augmented reality (AR), and others to extend the bounds of the physical world, has drawn a lot of interest. However, there is currently no consensus over the precise nature of the Metaverse, leading to inconsistent writings. By examining previous studies, this research article seeks to offer a thorough knowledge of the Metaverse. It looks at the Metaverse's trends, traits, difficulties, and prospects, especially in relation to numerous application disciplines. The report also proposes the Metaverse Research Model, a conceptualization and clarification of core Metaverse concepts that provides helpful direction for future research. © 2023 IEEE.

关键词： AR VR Metaverse

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Performance Analysis of Machine Learning Classification for Parkinson's Disease from Biomedical Audio Data

Performance Analysis of Machine Learning Classification for ...

引用

2023 intelligent computing and control for engineering and business systems, iccebs 2023

作者： Santhosh Kumar, C. Suagnthi, A. Sarmiladevi, N. Indhuja, A. Venkatesan, G. Sona College of Technology Department of Information Technology Salem India KGiSL Institute of Technology Department of Cse Coimbatore India Kongunadu College of Engineering and Technology Department of Ai & Ds Trichy India Mahendra College of Engineering Department of Cse Salem India Saveetha Engineering College Department of Civil Engineering Chennai India

ISBN: (纸本)9798350394580

Millions of people throughout the world suffer from the crippling neurological ailment known as Parkinson's disease (PD). While there is no cure for PD, early recognition and intervention can improve a patient's quality of life. The use of machine learning for the categorization of Parkinson's disease using biomedical audio data has gained popularity in recent years. In this study, we employ classification algorithms to categorize Parkinson's disease patients using a clinical, genetic, and audio dataset. In this paper, we analyze the effectiveness of various classification algorithms for detecting Parkinson's disease, such as Logistic Regression, k-NN, decision tree, random forest, and an ensemble model employing AdaBoost classifier. Our results demonstrate that the accuracy of the decision tree classifier algorithm outperforms the other algorithms by 92.59% on our dataset. In particular, the study highlights the potential use of algorithms for machine learning in identifying and diagnosing the early stages of Parkinson's disease. © 2023 IEEE.

关键词： Classification Machine Learning Parkinson's disease

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Smart Safety Device

Smart Safety Device

引用

2023 intelligent computing and control for engineering and business systems, iccebs 2023

作者： Rajapandian, B. Premalatha, S. Archana, B.S. Kanishka, K. Mahalakshmi, S. Jayaraj, Shreya Sri Sairam Engineering College Department of Electronics and Instrumentation Engineering Chennai India

ISBN: (纸本)9798350394580

This abstract outlines a women safety device designed to enhance personal security and instill confidence in women by addressing the growing concerns of safety in today's world. The device incorporates a range of innovative features such as real-time GPS tracking, emergency alert systems, self-defense tools, and community support, all seamlessly integrated into a compact, user-friendly wearable. With the increasing importance of women this device aims to provide a practical and reliable solution to ensure peace of mind and safety for women in various situations. © 2023 IEEE.

关键词： emergency alert systems GPS tracking user-friendly wearable

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Smart Glasses for Disabled People

Smart Glasses for Disabled People

引用

2023 intelligent computing and control for engineering and business systems, iccebs 2023

作者： Srividya, K. Anand, A.K. Jayasree, M. Sharmila, M. Hamshini, R.R. Sri Sairam Engineering College Department of Electronics & Instrumentation Chennai44 India

ISBN: (纸本)9798350394580

This paper introduces an innovative assistive technology aimed at improving the quality of life for individuals with visual impairments. This project presents a system that combines the capabilities You Only Look Once [6] (YOLO) deep learning algorithm with the ESP32 [7] microcontroller to create Object Detection Glasses. These glasses employ real-time object detection to provide users with auditory feedback about their surroundings, enabling them to navigate and interact with their environment more independently. The first task of the glasses is to take pictures of the object and store it as a snap. The second task is that they compare the captured image to the existing image. In order to convert the text into speech, it used Text to Speech technology (TTS). The picture will be taken by ESP 32 with the perfect size the image will be displayed and then by using the speaker the object will be detected. The integration of YOLO [6] and ESP32 [7] offers a cost-effective and efficient solution for enhancing accessibility and inclusivity, promoting greater autonomy and safety for individuals with visual disabilities. © 2023 IEEE.

关键词： ESP32 TTS YOLO

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Fuel Monitoring in Bike Vehicles Based on IoT

Fuel Monitoring in Bike Vehicles Based on IoT

引用

2023 intelligent computing and control for engineering and business systems, iccebs 2023

作者： Yuvaraj, G. Nithya, M. Shiny, A. LakshmiNarayanan, S. Vinothini, A. Selva Vignesh, S.M. Sri Sairam Engineering College Department of Computer Science and Engineering Chennai India Veltech Multitech Dr.Rangarajan Dr.Sakunthala Engineering College Department of Artificial Intelligence & Data Science Avadi India

ISBN: (纸本)9798350394580

In this work, we explore the feasibility of automatically dispensing gasoline using a prepaid RFID card. Individual RFID cards loaded with predetermined quantities will be provided to each user. The gasoline station's dispenser has an RFID reader that can scan the card and update the available balance on an LCD screen. The user inputs the required fuel quantity via keypad, and the system then determines how long the electrical fuel pump needs to run for and begins distributing the gasoline. When the system detects that the target level has been reached, it will immediately turn off the pump. It utilizes a Raspberry Pi module to alert the user through their smartphone. In addition, there is a fire sensor and a fuel purity sensor in this system to prevent fires and ensure clean fuel. As a result, the gasoline bunk management system will be decentralized and need fewer people to operate. © 2023 IEEE.

关键词： ADC 0804 buzzer fuel level sensor GSM/GPRS module Raspberry pi sim808

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Virtualization Using Docker Container for Reproducible Desktop Environment

Virtualization Using Docker Container for Reproducible Deskt...

引用

2023 intelligent computing and control for engineering and business systems, iccebs 2023

作者： Manikandan, J. Kirubakaran, K. Yuvanesh, N. Dhino Bilgates, J. Haresh, S. Sri Sairam Engineering College Ece Chennai India

ISBN: (纸本)9798350394580

To overcome the conventional and traditional technique of sharing data by sending a file through a hard drive or even sending a file across the internet requires a third-party program or a medium (hardware). To eliminate the intermediary channel that we use to transmit and receive data (hardware and software for file sharing), we are streaming a desktop to the client's browser so they can access a common OS among them using Docker Container. The desktop that is being streamed to our browser (client side) is kept in a private container that can only be accessed with the credentials that we have (hostname, password). The hardware components may be modified to our liking, such as increasing the RAM capacity, storage size, and container lifetime. This allows us to commercialize it as a product that contains all of the peripheral software and can be deployed wherever we want, for example. Simply put, our concept is inspired by Google Suite, but instead of distributing online apps, we are attempting to stream a whole operating system through a browser. Which will provide enough storage as requested by the client. © 2023 IEEE.

关键词： Docker container Google suite Hardware RAM Software Webapp

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Animal Intrusion Detection And CropProtection Using Yolo And Sound Devices

Animal Intrusion Detection And CropProtection Using Yolo And...

引用

2023 intelligent computing and control for engineering and business systems, iccebs 2023

作者： Kothai, R. Jastin Samuel, M. Siva, S. Agathiyan, M. Kabilan, J. Sri Sairam Engineering College Department of Eee India

ISBN: (纸本)9798350394580

The YOLO algorithm redefines object detection by framing it as a regression problem. Instead of sliding window approaches, YOLO divides an image into a grid and assigns bounding boxes to objects within grid cells. Each bounding box prediction also includes class probabilities, making YOLO a single-pass approach that efficiently localizes and classifies objects simultaneously. © 2023 IEEE.

关键词： Coexistence Conservation Crop protection Human-elephant conflicts LED spark simulations Non-invasive solution Sound devices YOLO software

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