Neha Tyagi

Comprehensive Analysis on Access Control and Security Frameworks in DevOps Environments

Praveen Anugula, Neha Tyagi, Abhik Sengupta, Amber Rastogi et autres

DevOps represents the modern approach for software development that integrates development and operations teams to achieve faster and more reliable delivery of applications. It highlights automation, collaboration also Continuous Integration and Deployment (CI/CD) to improves productivity and software quality. However, the increasing …

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Graph Attention and Deep Reinforcement Learning-based Adaptive Threat Intelligence Framework for Robust AI Malware Detection

Neha Tyagi, Peruri Sudeshna, M. Meena, V. Mangaiyarkarasi et autres

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Policy-Guarded AI Model Execution in Zero-Trust Cloud Environments

Vishi Singh Bhatia, Abhik Sengupta, Harvendra Singh, Abhishek Kumar Verma et autres

This paper considers the role of Policy-Guarded Zero-Trust Execution (PG-ZTE) system to ensure AI safety in the cloud. It primarily dwells on safeguarding AI work against unauthorized access and security threats. In the conventional cloud security, the identity of the user is …

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Stock Market Price Prediction using Neural Prophet with Deep Neural Network

Navin Chhibber, Sunil Khemka, Neha Tyagi, Rohit Tewari et autres

Stock market price prediction is a significant interdisciplinary research domain that depends at the intersection of finance, statistics, and economics. Forecasting Accurately predicting stock prices has always been a focal point for various researchers. However, existing statistical approaches for time-series prediction often …

Stock Market Price Prediction using Neural Prophet with Deep Neural Network

Navin Chhibber, Sunil Khemka, Neha Tyagi, Rohit Tewari et autres

Stock market price prediction is a significant interdisciplinary research domain that depends at the intersection of finance, statistics, and economics. Forecasting Accurately predicting stock prices has always been a focal point for various researchers. However, existing statistical approaches for time-series prediction often …

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Stock Market Price Prediction using Neural Prophet with Deep Neural Network

Navin Chhibber, Sunil Khemka, Neha Tyagi, Rohit Tewari et autres

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Blockchain-based intrusion detection system for IIoT with the aid of XGBoost voting ensemble method

Nitin Verma, Neha Tyagi, Rupal Chaturvedi

The proliferation of digital technologies has made cybersecurity a paramount concern for organizations worldwide. Traditional Intrusion Detection Systems (IDS) face such as scalability, accuracy, and trustworthiness challenges. Blockchain-integrated cyber-security deepening AI (Artificial Intelligence) (BCIS-XGB-IDS) could deal with novel attacks in the current …

Adaptive intrusion detection in IoT networks: Leveraging honeypots and synthetic data for attack mitigation

Rupal Chaturvedi, Neha Tyagi, Nitin Verma

Internet of Things (IoT) devices are attracting more and more cyber threats, resulting in them being vulnerable to intrusion assaults as they are swiftly expanding. This research focuses on enhancing IoT network security by leveraging honeypot-based intrusion detection systems improved with synthetic …

Secure Cloud Storage Encryption using Twin Layer Security Scheme with Secure Hash Algorithm

Harvendra Singh, Amit Prasad, Mayank Atreya, Abhik Sengupta et autres

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Stock Market Price Prediction Using Neural Prophet with Deep Neural Network

Navin Chhibber, Sunil Khemka, Neha Tyagi, Rohit Tewari et autres

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PPO-RA: A Proximal Policy Optimization-Based Deep Reinforcement Learning Framework for Adaptive Resource Allocation in Cloud–Edge–Mist Environments

Neha Tyagi, Harvendra Singh, Amit Prasad, Ashish Prabhu Anand et autres

Cloud computing has emerged as a fundamental paradigm for providing scalable and on-demand computational resources. However, due to the complexity and heterogeneity of modern cloud-edge-mist environments, dynamic resource allocation remains a challenging problem. To address these issues, this research proposes a Deep …

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Machine Learning-Driven Aspect-Based Analysis of Sentiment to Make Strategic Inferences from Customer Feedback

Rashmi Mishra, Neha Tyagi, Monali Gulhane, Nitin Rakesh et autres

This study considers the potential application of Aspect-Based Sentiment Analysis (ABSA) to the retail industry for uncovering deeper insights from customers' views. This goes beyond the traditional methods of sentiment analysis. By the detection of specific product attributes, such as price, quality, …

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