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Recognition of Valid QR Codes with Machine Learning 13

Recognition of Valid QR Codes with Machine Learning

引用

13th IEEE International Conference on Communication Systems and Network Technologies, CSNT 2024

作者： Minocha, Arshia Goyal, Aman Gandhi, Rashmi Amity School of Engineering and Technology Amity University Auup Computer Science and Engineering Uttar Pradesh Noida India

ISBN: (纸本)9798350305463

The widespread use of Quick Response (QR) codes has made QR codes an attractive target for cyberattacks, posing a security and privacy concern. Quick Response (QR) codes have revolutionized marketing strategies by providing a fast and efficient way to connect offline and online platforms. However, the security issues arise as the usage of QR codes. This paper introduces an automatic detector that identifies QR code images and lets the user know whether it is malicious or benign. To achieve this, a dataset of 10,000 original and malicious QR code images (both) is taken. Convolutional Neural Network (CNN) is employed to classify original and malicious codes. Additionally, histogram density analysis is incorporated to enhance feature identification, leading to improved classification accuracy. This paper implemented ResNet50, MobilenetV3 and InceptionV3 CNN models for evaluating the abilities to predict the malicious codes. The InceptionV3 model emerges as the most promising, attaining an accuracy of 98.13%, offering an efficient and accurate solution for document forensics and counterfeit detection. © 2024 IEEE.

关键词： Convolutional neural networks

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Cloud-Based Object Detection Model Using Amazon Rekognition 1

引用

International Conference on Recent Developments in Cyber Security, ReDCySec 2023

作者： Singh, Darshita Arora, Deepak Department of Computer Science and Engineering Amity School of Engineering and Technology Lucknow Amity University Uttar Pradesh India

ISBN: (数字)9789819998111

ISBN: (纸本)9789819998104

Object identification is a well-known research subject in the field of computer vision, with various applications like surveillance, autonomous driving, and robotics. The integration of machine learning with cloud computing has enabled organizations to automate many procedures and tasks, cut costs, and boost efficiency. With the help of a wide range of machine learning (ML) services offered by cloud computing platforms like Amazon Web Services (AWS), Microsoft Azure, and Google Cloud Platform (GCP), organizations may take advantage of ML’s potential without the need for specialized equipment or costly staff. A cloud-based ML service called Amazon Rekognition offered by Amazon Web Services is a powerful tool for object identification. Through this paper, the authors offer a study on the application of Amazon Rekognition for object detection and recognition. The idea is to detect objects in the provided images using machine learning and deep learning algorithms provided by Amazon Rekognition. The effectiveness of Amazon Rekognition in recognizing objects in images is precisely examined by the authors, who compare the discovered objects with state-of-the-art object detection algorithms and then provide the result with a corresponding confidence percentage. Experimental results show that Amazon Rekognition handles object detection tasks well, achieving a good balance between accuracy and speed. It is an effective tool for object detection with high average precision and recall values for many object categories. However, accuracy may vary depending on the complexity of the objects in the image, the lighting conditions, and other factors. Amazon Rekognition is a managed service that makes use of encryption, access control, compliance, monitoring, and logging. While the infrastructure and security are handled by AWS, it’s crucial to incorporate security best practices within the application for maximum security. It is important for developers to carefully evaluate the perf

关键词： Object detection

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Analytical Approach Towards Cybersecurity Through AI-Enabled Threat Intelligence 11

Analytical Approach Towards Cybersecurity Through AI-Enabled...

引用

11th International Conference on Reliability, Infocom Technologies and Optimization, ICRITO 2024

作者： Singh, Anurag Kanishka Dubey, Sanjay Kumar Amity School of Engineering and Technology Amity University Department of Computer Science and Engineering Uttar Pradesh Noida India

ISBN: (纸本)9798350350357

A growing variety of cyber hazards are appearing as the world becomes more and more digital. The sophistication and rate of innovation of emerging threats puts organizations of all sizes at danger, and traditional cybersecurity solutions frequently fall short of keeping up. Threat intelligence powered by artificial intelligence, or AI-TI, offers a fresh and cutting-edge method for tackling cybersecurity issues. To find patterns and anomalies that could point to a cyber-attack, AI-TI solutions can examine massive amounts of data from a variety of sources, including as internal networks, security logs, and open threat intelligence feeds. Then, with the use of this information, security teams can be made aware of prospective dangers and assist in the creation of defense measures. Compared to conventional cybersecurity solutions, AI-TI systems have a number of benefits: Automation: AI-TI systems have the potential to automate a number of threat detection and response-related duties, freeing up security staff to concentrate on more strategic projects. Continuous improvement: AI-TI systems have the ability to continuously learn and get better with time, which enables them to stay on top of the most recent threats. Advanced threat detection: AI-TI systems can be used to identify and react to threats that are too sophisticated or subtle for traditional approaches to identify. © 2024 IEEE.

关键词： Artificial intelligence

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Analyzing the Impact of AI Adoption on Job Market Dynamics and Employment Trends Using a Synthetic Dataset 13

Analyzing the Impact of AI Adoption on Job Market Dynamics a...

引用

13th International Conference on System Modeling and Advancement in Research Trends, SMART 2024

作者： Chole, Vikrant Sahu, Santosh Moolchandani, Jhankar Kumar, Ashok Kumar, Rahul Shukla, Anshita Amity School of Engineering and Technology Amity University Madhya Pradesh Department of Computer Science and Engineering Gwalior India

ISBN: (纸本)9798350380583

This paper aims at establishing the effects of adopting artificial intelligence in the job market based on an artificially generated set of 500 jobs. In the following analysis, the impacts of AI Adoption levels on salary distributions, job growth, as well as the risks of job automation are examined with the help of such statistical methods as multiple linear regression, logistic regression, classification algorithms. The results show a positive relationship between AI utilization and job creation in industries where AI augments human work but reveal higher automation threats for industries with repetitive activities. Also, AI adoption corresponds to higher wages particularly in the advanced skill jobs but the jobs at high automation risk enjoy wage premium due to job insecurity. This paper acknowledges the fact that AI adoption is both a benefit and a threat that calls for careful executive actions on best strategies for managing changes in the workforce. Such future work should replicate these effects using actual data as well as religious and ethical effects on a wider scale. © 2024 IEEE.

关键词： Multiple linear regression

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Advanced Predictive Modelling for Bitcoin and Ethereum Price Dynamics 7

Advanced Predictive Modelling for Bitcoin and Ethereum Price...

引用

7th International Conference on Contemporary Computing and Informatics, IC3I 2024

作者： Waseem, Mohd Singh, Shailendra Narayan Amity School of Engineering and Technology Amity University Department of Computer Science and Engineering Uttar Pradesh Noida India

ISBN: (纸本)9798350350067

Over the last decade, Bitcoin and Ethereum have become cryptocurrencies that have attracted the attention of the financial world with their potential for business transactions and the use of new blockchain technology. This study is dedicated to predicting Bitcoin and Ethereum price trends from 2014 to 2024, covering the main period of growth and change in the cryptocurrency market. This research aims to develop a good forecasting model through a comprehensive analysis of historical price data, market trends, economic progress and macroeconomy. It uses techniques such as machine learning, time series analysis, and sentiment analysis to try to predict future price movements of Bitcoin more accurately and Ethereum. By revealing the fundamental principles influencing cryptocurrency prices, this research leads to a deeper understanding of the dynamics driving digital assets and their impact on the financial sector. © 2024 IEEE.

关键词： Ethereum

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Machine Learning-Based Prediction of Gold Prices Using Economic Indicators 13

Machine Learning-Based Prediction of Gold Prices Using Econo...

引用

13th International Conference on System Modeling and Advancement in Research Trends, SMART 2024

作者： Kumar, Rahul Moolchandani, Jhankar Shukla, Anshita Sahu, Santosh Thada, Vikas Chole, Vikrant Amity School of Engineering and Technology Amity University Madhya Pradesh Department of Computer Science and Engineering Gwalior India

ISBN: (纸本)9798350380583

In furtherance to this, this research work focuses on the role of applying machine learning techniques in predicting the price of Gold using price data along with three macroeconomic variables. Thirteen models were tested in order to determine which of the three models: They also established that among the seven algorithms, namely linear regression, random forest regressor, and gradient boosting regressor, is the most useful in predicting gold price. Gradient Boosting Regressor was the optimized of all to tremendous accuracy with the help of the used algorithm that let it learn complex and non-linear features in the data inputs. They proved that the recent month and year price data, long term moving average and US Dollar Index (DXY) are the variables which affect the gold prices. The results confirm the necessity to include a broad set of economic variables in the model and the information can be reputable and valuable to investors and policy makers. Nevertheless, it could be proposed that future work would try to incorporate more features and even try to apply deep learning as an effort to improve the accuracy of the envisioned predictions. © 2024 IEEE.

关键词： Adversarial machine learning

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Eye Disease Detection Using CNN, ResNet, and VGG16 3rd

Eye Disease Detection Using CNN, ResNet, and VGG16

引用

3rd International Conference on Universal Threats in Expert Applications and Solutions, UNI-TEAS 2024

作者： Bano, Sofiya Agrawal, Bhawana Bhardwaj, Shweta Department of Computer Science and Engineering Amity School of Engineering and Technology Amity University Uttar Pradesh Noida India

ISBN: (纸本)9789819751457

This chapter deals with the application of deep learning models, specifically convolutional neural networks (CNNs), ResNet, VGG16, and VGG19, in the domain of eye disease detection. Early and accurate diagnosis of eye conditions is critical for preserving vision and preventing irreversible damage. Our research involves collecting and preprocessing ophthalmic images, customizing and training deep learning models, and rigorously evaluating their performance across a diverse dataset. We also provide a practical case study demonstrating the real-world application of these AI models in clinical contexts, showcasing their potential to expedite diagnosis and support healthcare professionals. The report concludes with insights into future research directions, recognizing the evolving landscape of AI in healthcare and its promising role in improving eye disease detection. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Convolutional neural networks

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Comparative Analysis of Machine Learning Models for Diabetes Prediction 13

Comparative Analysis of Machine Learning Models for Diabetes...

引用

13th International Conference on System Modeling and Advancement in Research Trends, SMART 2024

作者： Shukla, Anshita Kumar, Rahul Chole, Vikrant Moolchandani, Jhankar Sahu, Santosh Kumar, Ashok Amity School of Engineering and Technology Amity University Department of Computer Science and Engineering Madhya Pradesh Gwalior India

ISBN: (纸本)9798350380583

This paper aims at comparing performance of various machine learning models for the task of predicting diabetes using one of the public datasets. The research assesses the big data analysis in the determination of the possibilities of risk of diabetes and the preferred models of Logistic Regression and KNearest Neighbors (KNN). Performance measures including accuracy, precision and recall were used in assessing the performance of the two models. The study do reveals the high level of potential of both the models yet reveals that Logistic Regression just edges a bit ahead of KNN in terms of accuracy and precision. These results show the potential of using machine learning in hospital diagnosis and specifically in diagnosis of Chronic diseases such as diabetics. © 2024 IEEE.

关键词： Adversarial machine learning

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Cloud-Based Integrated Real-Time Twitter Grievance Redressal with AWS EC2 and RDS Using Machine Learning Approach for Enhanced Security

Cloud-Based Integrated Real-Time Twitter Grievance Redressal...

引用

International Conference on Recent Developments in Cyber Security, ReDCySec 2023

作者： Chauhan, Shambhavi Arora, Deepak Department of Computer Science and Engineering Amity School of Engineering and Technology Amity University Uttar Pradesh Lucknow India

ISBN: (纸本)9789819998104

Social media platforms, especially Twitter, have become a popular channel for customers to express their grievances related to products or services. Companies need to have an efficient grievance redressal system in place to address these complaints in real-time to ensure customer satisfaction and loyalty. This research paper presents the development of a cloud-based integrated real-time Twitter grievance redressal system using Amazon Web Services (AWS) and machine learning approach. The proposed system uses AWS cloud infrastructure to lever the huge volume of dataset created by tweets and machine learning algorithms to classify and prioritize them based on their severity. The system includes a web application that allows the grievance redressal team to view, categorize, and respond to the tweets efficiently. The efficiency of the planned system is evaluated via a case study. The results show that the system can effectively handle a huge volume of tweets and improve the grievance redressal process. The system’s response time is significantly reduced, and the team can prioritize the tweets based on their severity and importance, leading to better customer satisfaction. Data security is a critical aspect of the proposed real-time application as it will be handling sensitive data of the users. Therefore, security measures such as encryption, MFA, and disaster recovery must be properly implemented and configured, in order to ensure the security of data of the suggested grievance redressal system. The suggested system has achieved an accuracy of 89.5%. This research paper contributes to the development of efficient social media grievance redressal systems using cloud infrastructure and machine learning algorithms. The proposed system can be easily integrated into existing customer relationship management systems, making it a viable solution for companies of all sizes. The system’s ability to handle real-time data and provide quick responses can improve customer trust and

关键词： Sentiment analysis

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Optimization for IoT Jobs in Multi-Cloud Computing Environments

Optimization for IoT Jobs in Multi-Cloud Computing Environme...

引用

2024 IEEE International Conference on Signal Processing and Advance Research in Computing, SPARC 2024

作者： Atre, Anshul Singh, Kamlesh Kumar Chaurasia, Brijesh Kumar Amity University Amity School of Engineering & Technology Department of Computer Science & Engineering UP Lucknow India Amity University Amity School of Engineering & Technology Department of Electronics Communications and Engineering UP Lucknow India Pranveer Singh Institute of Technology Department of Computer Science & Engineering Kanpur India

ISBN: (纸本)9798350385199

Effective network communication is essential in the current digital era, and cloud computing (CC) and the Internet of Things (IoT) are significant aspects of daily life. Accelerating and lowering the latency of data transfers to the cloud for processing, analysis, and storage is the aim of cloud computing. IoT integration and job scheduling are the two most challenging issues in the multi-cloud computing environment because of the diverse and numerous IoT devices. The significant objectives of cloud computing-based IoT optimization are to reduce the total time needed to complete all input operations, determine which resources are required for the Message Queuing Telemetry Transport (MQTT), and other crucial protocols used in this work to display delay and evaluate the performance of different jobs at different IoT nodes and bit counts. Results and analysis show the viability of cloudSim is successfully achieved with the use of delayed quality of communication of jobs for different nodes. © 2024 IEEE.

关键词： Cloud platforms

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