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Enhancing Transaction Security through Iris Recognition 4

Enhancing Transaction Security through Iris Recognition

引用

4th International Conference on Expert Clouds and Applications, ICOECA 2024

作者： Arepalli, Gopi Suresh Bhavana, Paluvai Krishna, Yakkanti Venkata Sai Surendrababu, Chittibomma Lakireddy Bali Reddy College of Engineering Dept. of computer Science and Engineering Mylavaram India Dept. of Computer Science and Engineering Mylavaram India

ISBN: (纸本)9798350385793

UPI frauds have led to increased use of biometrics for personal identification to ensure security and accuracy. Biometric UPI based on iris recognition technology improves customer service by providing a safe and paperless environment. This system replaces the existing UPI pin and affords protection to the UPI payment from cyber-attacks. During enrollment, genuine user's iris samples and UPI ID are retained in the database, and transactions are completed by capturing and matching iris patterns by using G-6 iris recognizer and biometric Framework. This innovative technology has revolutionized the way to make payments and has significantly reduced the risk of fraudulent activities. With biometric UPI, customers can enjoy a seamless experience when making transactions. The use of biometrics also ensures that there is no chance of identity theft or unauthorized access to sensitive information. © 2024 IEEE.

关键词： Authentication Protocol

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AAF-SCM: An Authenticated Framework for Supply Chain Management

AAF-SCM: An Authenticated Framework for Supply Chain Managem...

引用

2024 IEEE International Conference on Intelligent Systems and Advanced Applications, ICISAA 2024

作者： Roy, Prince Kumar, Rajneesh Morshed, Md Neaz Fairleigh Dickinson University Dept. of Applied Computer Science United States Dept. of Computer Science & Engineering India

ISBN: (纸本)9798331539948

The process of a supply chain involves a series of activities that distribute, assemble, and manage products, transferring them from the supplier to the end consumer. It operates as a complex, interconnected network regulated by supply and demand. Security in supply chain management is a crucial aspect of its overall security measures, focusing primarily on maintaining essential end-to-end protection. Without this, several attacks can be used to manipulate data and valuable information from the entire chain of networks. This paper explains how encrypted intermediates can secure the entire SCM by validating each intermediate using public-private key encryption. We also demonstrate and assess how the proposed AAF-SCM model can aid in tracing security breaches or unauthorized data tampering. We also validate this model against nefarious attacks like Fabrication, Assertion, Deletion, and Modification. Finally, we propose opportunities to develop innovative systems that enhance security through intermediary steps. © 2024 IEEE.

关键词： Supply chain management

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Evaluating Various Techniques for Twitter Sentiment Analysis for Election Results 1

Evaluating Various Techniques for Twitter Sentiment Analysis...

引用

1st International Conference on Pioneering Developments in computer science and Digital Technologies, IC2SDT 2024

作者： Mishra, Prerna Punia, Divya Sikka, Geeta Sharma, Ajay K Sikka, Kushagra NIT Delhi Dept. of Computer Science and Engineering India University of Florida Dept. of Computer Science and Engineering Gainsville United States

ISBN: (纸本)9798350365016

The Indian General Election of 2019 is proof of the rise in political content on social media platforms, reflecting user attitudes and behaviors towards the electoral process. This paper examines the collective behavior of Twitter users during the election period and assesses the feasibility of election prediction through social media analysis in India. This work presents weekly sentiment analysis on election-related Twitter content. It analyzes how social media content can be used as a reliable factor of public sentiment during elections and has recourse to be a predictive parameter alongside other factors. The simulation results show that VADER outperforms various state-of-the-art techniques with a mean absolute error of 1.345 for candidate-wise predictions, significantly outperforming Textblob (5.445), Pattern analyzer (5.135), and pre-trained BERT (28.335) while yielding similar results for party-wise and alliance-wise analyses. © 2024 IEEE.

关键词： Tweets

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Automated Exam Script Checking using Zero-Shot LLM and Adaptive Generative AI

Automated Exam Script Checking using Zero-Shot LLM and Adapt...

引用

2024 IEEE International Conference on Computing, Applications and Systems, COMPAS 2024

作者： Himi, Shinthi Tasnim Tanzila Monalisa, Natasha Sultana, Shirin Afrin, Anika Hasib, Khan Md Bangladesh University of Business and Technology Dept. of Computer Science and Engineering Dhaka Bangladesh Jahangirnagar University Dept. of Computer Science and Engineering Dhaka Bangladesh Brac University Dept. of Computer Science and Engineering Dhaka Bangladesh

ISBN: (纸本)9798331529765

The manual grading of exam scripts is a labor-intensive process, often plagued by subjectivity and inconsistency. This study presents an innovative approach to automating the evaluation of exam scripts using the Large Language Model (LLM) integrated with Zero-Shot Learning (ZSL) and Generative AI. Our system leverages the capabilities of GPT-4 to generate and evaluate answers, applying similarity measures to ensure accuracy and fairness in grading. The model's adaptability through ZSL allows it to assess new questions without additional training. The system also incorporates iterative refinement techniques to enhance the quality of standard answers over time. Performance evaluations demonstrate a low error rate, with an average relative error of 1.29% for annotated questions and 1.67% for non-annotated questions compared to human graders. The performance underscores the automated system's transformative potential in revolutionizing educational assessments, offering a consistent, fair, and highly efficient alternative to traditional grading methods. © 2024 IEEE.

关键词： Zero-shot learning

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Multiple Approaches Towards Authentication Using Keystroke Dynamics 2

Multiple Approaches Towards Authentication Using Keystroke D...

引用

2nd International Conference on Machine Learning and Data engineering, ICMLDE 2023

作者： Arsh, Aditya Kar, Nirmalya Das, Smita Deb, Subhrajyoti Dept. of Computer Science & Engineering NIT Agartala Tripura India Dept. of Computer Science & Engineering ICFAI University Tripura India

In the realm of global computer security, safeguarding sensitive data and computer systems stands as an ongoing challenge. Striking the delicate balance between convenient access for legitimate users and thwarting imposter attacks is imperative. Traditional authentication methods, primarily reliant on username and password schemes, have been the longstanding cornerstone of cyber system authentication. Yet, this conventional approach is marred by inherent vulnerabilities, including password exchange, shoulder surfng, brute force attacks, dictionary attacks, speculation, phishing, and emerging threats. In response to these shortcomings, biometrics technology has emerged as a reliable alternative for authentication and verification, with Keystroke Dynamics being a well-established method. This research advances Keystroke Dynamics-based authentication through cutting-edge technologies, including machine learning, deep learning, and neural networks. A quantitative assessment demonstrates remarkable results - Machine Learning achieves 99.9% accuracy with the Random Forest model, Deep Learning combines CNN with GRU to reach 99.31% accuracy, and the DBN model registers at 98.01%. In the realm of neural networks, Bi-CNN and FFM-NN excel, with accuracy rates of 96.8% and 94.7%, respectively. These findings lay the foundation for robust, secure authentication systems, ensuring the protection of sensitive data and computer assets in our interconnected world. © 2024 Elsevier B.V.. All rights reserved.

关键词： Authentication

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5G User Equipment (UE) Positioning and Localization Estimation using Machine Learning

5G User Equipment (UE) Positioning and Localization Estimati...

引用

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

作者： Survase, Sanket Diwan, Raman Lal, Kumari Nidhi Kumar, Shishupal VNIT Nagpur Dept. of Computer Science and Engineering Nagpur India IIIT Nagpur Dept. of Computer Science and Engineering Nagpur India

ISBN: (纸本)9798350378092

The emergence of 5G technology has ushered in a new era of connectivity, making communications faster and cheaper. One of the main challenges of 5G networks is accuracy and efficiency, which are important for many applications such as driver less cars, smart cities and virtual reality. This research paper explores the integration of machine learning techniques to improve the accuracy and reliability of 5G infrastructure. The plan uses rich data generated by 5G networks, combining various parameters such as signal strength, flight time and angle of arrival. Machine learning algorithms are used to process this data and create good models for the correct location. The system is designed to adapt to dynamic and complex urban environments where traditional methods often encounter limitations. The main contribution of this research includes the development of a new machine learning-based distribution algorithm suitable for 5G networks, a comprehensive evaluation of performance comparison with the existing system, and evaluation of recommendations for feasibility and implementation. The results showed significant improvements in accuracy and reliability, demonstrating the potential of machine learning to revolutionize the 5G workplace. © 2024 IEEE.

关键词： Adversarial machine learning

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Privacy-Preserving Speech Analysis- A Review 6

Privacy-Preserving Speech Analysis- A Review

引用

6th International Conference on Computational Intelligence and Communication Technologies, CCICT 2024

作者： Kumar, Pankaj Shukla, Mayank Malik, Medha Dept. of Computer Science and Engineering ABES Engineering College Ghaziabad India Dept. of Computer Science and Engineering ABES Engineering College Ghaziabad India

ISBN: (纸本)9798350374629

Speech-recognition technology is an essential component of many smart IoT devices. To make these systems secure and privacy-preserving, it is important to consider outsourcing speech-recognition functionality to a third-party storage provider. By removing the speech-recognition process from the device itself, the amount of personal data stored on the device is minimized. Additionally, the provider can ensure the strictest security standards are met when the audio data is stored, including the use of encryption and access control mechanisms. Furthermore, the provider can also ensure that the speech-recognition process is done using the latest technologies to ensure accurate recognition of the audio data. To ensure the end-user's privacy is kept intact, the provider should allow only authorized users to access the audio data. Additionally, the provider should also enable data access logging and audit trails allowing for the tracking of any access to the data. By outsourcing speech-recognition services to a third-party, smart IoT devices can ensure the highest levels of privacy and security while still enabling users to get the most out of their device. The server processes the encrypted data to generate recognition results, such as user commands and voice printing, without compromising the privacy of the user's voice data. To enhance voice data privacy, federated learning can be employed in the design of these models. This approach enables distributed training of the model without the need to transmit the user's voice data to a central server. The adoption of this approach in the IoT industry is gaining momentum as it offers the potential to create smarter and more secure IoT devices that inspire customer trust. ©2024 IEEE.

关键词： Speech recognition

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NLP Powered Restaurant Chatbot for Exceptional Customer Engagement 2

NLP Powered Restaurant Chatbot for Exceptional Customer Enga...

引用

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

作者： Goyal, Diya Guru Prasad, M.S. Garg, Ishika Tewari, Shashank Pandu N, K. Asha, P.N. Dept. of Computer Science and Engineering Dehradun India Srinivas University Institute of Engineering & Technology Dept. of Computer Science and Engineering Surathkal India REVA University School of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350371314

Technology is very volatile in nature, in a way that it keeps on changing daily. Restaurants are no different in the effects of this changing scenario. The primary objective of this research paper is to explore the historical trajectory of restaurants, analyze the current trends shaping the industry, and delve into the potential of chatbot integration to overcome existing challenges. Chatbots can play a pivotal role in enhancing the performance of businesses as per the research done. An integrated Dialog Flow application has been proposed, incorporating Rule-based grammar matching and M.L matching algorithms. This proposed work is trying to make NLP-based chatbots which may expedite the endeavor of businesses to produce and become profitable. © 2024 IEEE.

关键词： Python

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Land Cover Extraction from Satellite Imagery using U-Net Model 18

Land Cover Extraction from Satellite Imagery using U-Net Mod...

引用

18th INDIAcom;11th International Conference on Computing for Sustainable Global Development, INDIACom 2024

作者： Rani, K. Pushpa Sreedevi, Pogula Dastagiriah, C. Rao, K. Srinivas Subbaraju, G.V. Padmavathi, Jasti Mlr Institute of Technology Dept. of Computer Science and Engineering Hyderabad India G. Pulla Reddy Engineering College Dept. of Computer Science and Engineering Kurnool India Anurag University Dept. of Computer Science and Engineering Hyderabad India Scetw Dept. of Computer Science and Engineering Hyderabad India Hyderabad India

ISBN: (纸本)9789380544519

Satellite imagery offers extensive information that can be used for a variety of societal applications, from the number of buildings in a metropolis to the land cover types of a specific area. However, extracting such information is a difficult task. In the past, images were scanned manually by domain experts to extract features like buildings, infrastructure, and vegetation, which would take weeks or even months depending on the image size. To address this challenge, we trained our model using U-Net architecture, a deep learning model, because of its advantages in multiscale feature learning and faster processing. Data augmentation is done on the dataset in order to enhance the amount of data and prevent overfitting artificially. The trained model achieved an F1 score of 92% and an impressive precision score of 95%. This automated method can be scaled to large sets of data, reducing the need for human labeling. © 2024 Bharati Vidyapeeth, New Delhi.

关键词： Data augmentation Deep learning Machine learning Multiscale feature learning U-Net architecture

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Smart Agritech : An Enhanced Solution for Advanced Plant Disease Detection, Remediation, and Data-Driven Agriculture 2

Smart Agritech : An Enhanced Solution for Advanced Plant Dis...

引用

2nd IEEE International Conference on Interdisciplinary Approaches in Technology and Management for Social Innovation, IATMSI 2024

作者： Rekha, K.S. Haridas, Aakash Dept. of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350360523

Plant diseases can have a detrimental effect on crop yield and quality, posing significant challenges to global food security. Early and accurate disease detection is essential for timely intervention and mitigation. In this study, we propose a novel approach for plant disease detection using a deep learning method based on a state-of-the-art convolutional neural network, ResNet9 architecture. The ResNet9 model is trained on a diverse dataset of plant images, including both healthy and diseased plants, representing different plant species and diseases. Our method leverages the power of deep learning to automatically learn discriminative features from plant images, enabling it to distinguish between healthy and diseased plants with a high degree of accuracy. We evaluate the performance of our model on a real-world dataset of plant images, and the results demonstrate its effectiveness in disease detection. The model achieves a high accuracy rate, indicating its potential for field deployment to assist farmers in monitoring and managing plant health. In conclusion, our study presents a promising solution for automated plant disease detection, providing farmers and agronomists with a practical tool to identify and combat plant diseases early, ultimately contributing to improving crop productivity and food security. © 2024 IEEE.

关键词： Convolutional neural networks

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