Mukta Bhatele
Biographic Data
| ID | 10126942 |
|---|---|
| NAME | Mukta Bhatele |
| GIVEN NAMES | Mukta |
| FAMILY NAME | Bhatele |
| SIGNATURE | BHATELE M |
| AFFILIATIONS | Amicable Knowledge Solution University |
| VERIFIED | No |
| TOTAL WORKS | 6 |
| TOTAL CITATIONS | 0 |
| WORKS AS AUTHOR | 6 |
| WORKS AS EDITOR | 0 |
| FIRST PUBLICATION YEAR | 2024 |
| LATEST PUBLICATION YEAR | 2024 |
| H-INDEX | 0 |
A Machine Learning Approach for Predictive Analysis of Traffic Flow
Traffic congestion is a critical issue affecting urban areas globally, leading to significant economic and social costs. Predictive traffic flow analysis has emerged as a promising solution to mitigate congestion and enhance transportation efficiency. This paper proposes a machine learning approach for predictive analysis of traffic flow, leveraging the wealth of available data from various sources such as traffic sensors, GPS devices, and traffi…
Secure Dicom Image Communication
The envisioned system addresses the multifaceted challenges in DICOM image communication by implementing sophisticated digital watermarking algorithms that are resilient against advanced deep learning attacks, ensuring data integrity and bolstering security in healthcare imaging systems. By harmonizing the balance between data protection and patient privacy within legal frameworks, and using privacy-preserving methods, the system adheres to compl…
Enhancing Reliability and Efficiency of Detecting Malicious Data Injection in Wireless Sensor Networks
Wireless sensor networks (WSNs) have gained significant attention in recent years due to their versatile applications ranging from wildlife tracking to environmental monitoring. However, WSNs are susceptible to various challenges such as hardware failures, communication link faults, and malicious attacks, which can significantly affect their reliability and performance. In this paper, we focus on the issue of malicious data injection in WSNs and …
Enhanced Ai Security With DWT Watermarking and Hybrid Anomaly Detection Framework (Hadf)
This paper presents a novel approach to enhancing security in artificial intelligence systems through the fusion of Discrete Wavelet Transform (DWT)--based watermarking with a Hybrid Anomaly Detection Framework (HADF). Traditional watermarking techniques often struggle to withstand various attacks in digital environments, especially in the context of AI systems where the stakes are high. In response, the proposed framework combines the robustness…
The Role of Deep Learning in Exploring Traffic Prediction Techniques
This research paper delves into the pivotal role of deep learning in advancing traffic prediction techniques. With urban traffic management becoming increasingly intricate, accurate short-term traffic prediction remains a cornerstone for effective congestion mitigation and transportation planning. Leveraging the capabilities of deep learning methodologies, this study systematically explores various deep learning models and their applications in p…
Enhancing Medical Image Security Through RGB and Yuv Color Based Advanced Triple Watermarking Techniques
This paper delves into the exploration and evaluation of diverse triple watermarking embedding techniques specifically tailored for medical imaging. In the healthcare sector, where the confidentiality and integrity of medical images are paramount, implementing robust security measures without compromising image quality is crucial. The study focuses on triple watermarking methods that incorporate three layers of security, aiming to enhance the pro…
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A Machine Learning Approach for Predictive Analysis of Traffic Flow
Traffic congestion is a critical issue affecting urban areas globally, leading to significant economic and social costs. Predictive traffic flow analysis has emerged as a promising solution to mitigate congestion and enhance transportation efficiency. This paper proposes a machine learning approach for predictive analysis of traffic flow, leveraging the wealth of available data from various sources such as traffic sensors, GPS devices, and traffi…
Secure Dicom Image Communication
The envisioned system addresses the multifaceted challenges in DICOM image communication by implementing sophisticated digital watermarking algorithms that are resilient against advanced deep learning attacks, ensuring data integrity and bolstering security in healthcare imaging systems. By harmonizing the balance between data protection and patient privacy within legal frameworks, and using privacy-preserving methods, the system adheres to compl…
Enhancing Reliability and Efficiency of Detecting Malicious Data Injection in Wireless Sensor Networks
Wireless sensor networks (WSNs) have gained significant attention in recent years due to their versatile applications ranging from wildlife tracking to environmental monitoring. However, WSNs are susceptible to various challenges such as hardware failures, communication link faults, and malicious attacks, which can significantly affect their reliability and performance. In this paper, we focus on the issue of malicious data injection in WSNs and …
Enhanced Ai Security With DWT Watermarking and Hybrid Anomaly Detection Framework (Hadf)
This paper presents a novel approach to enhancing security in artificial intelligence systems through the fusion of Discrete Wavelet Transform (DWT)--based watermarking with a Hybrid Anomaly Detection Framework (HADF). Traditional watermarking techniques often struggle to withstand various attacks in digital environments, especially in the context of AI systems where the stakes are high. In response, the proposed framework combines the robustness…
The Role of Deep Learning in Exploring Traffic Prediction Techniques
This research paper delves into the pivotal role of deep learning in advancing traffic prediction techniques. With urban traffic management becoming increasingly intricate, accurate short-term traffic prediction remains a cornerstone for effective congestion mitigation and transportation planning. Leveraging the capabilities of deep learning methodologies, this study systematically explores various deep learning models and their applications in p…
Enhancing Medical Image Security Through RGB and Yuv Color Based Advanced Triple Watermarking Techniques
This paper delves into the exploration and evaluation of diverse triple watermarking embedding techniques specifically tailored for medical imaging. In the healthcare sector, where the confidentiality and integrity of medical images are paramount, implementing robust security measures without compromising image quality is crucial. The study focuses on triple watermarking methods that incorporate three layers of security, aiming to enhance the pro…
Computer Science (6 works) · Artificial Intelligence (5 works) · Computer security (3 works) · Advanced Steganography and Watermarking Techniques (2 works) · Computer vision (2 works) · Digital watermarking (2 works) · Machine learning (2 works) · Network Security and Intrusion Detection (2 works) · Traffic control and management (2 works) · Traffic Prediction and Management Techniques (2 works)