Ronak Duggar

@geetauniversity.edu.in

I am Technical Trainer at Geeta University in Geeta Technical Hub Department
Geeta University

Ronak Duggar


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https://researchid.co/ronak_jain_19
8

Scopus Publications

141

Scholar Citations

6

Scholar h-index

4

Scholar i10-index

Scopus Publications

  • Exploring few-shot learning approaches for bioinformatics advancements
    Neha Bhati, Ronak Duggar, Abdullah Alzahrani
    Applying Machine Learning Techniques to Bioinformatics Few Shot and Zero Shot Methods, 2024
    This chapter delves deeply into few-shot learning, a rapidly developing area crucial in driving bioinformatics forward. First, the basics of few-shot learning are laid forth, emphasizing the field's applicability to bioinformatics. Case studies showcasing real-world applications in areas as varied as protein structure prediction, drug development, and genomic analysis provide a deep dive into several few-shot learning approaches like meta-learning and transfer learning. The chapter also provides an in-depth analysis of recent developments, highlights current difficulties, and suggests exciting new avenues for exploration. This chapter highlights the rising significance of few-shot learning in bioinformatics and provides insights into its potential to benefit biomedical research.
  • Crucial role of blockchain in quantum computing: enhancing security and trust
    Ronak Duggar, Nesma E. ElSayed
    Quantum Computing, 2024
    This chapter focuses on the dramatic progress of quantum computing and its far-reaching consequences for cybersecurity. Conventional cryptographic techniques are in jeopardy because quantum computing can transform computational capacity by using the unique features of qubits. Blockchain technology emerges as a ray of hope as quantum computers get closer to breaching of commonly used encryption methods. Blockchain's decentralized structure and unchangeable ledgers offer quantum-resistant defenses. Understanding the interconnected nature of quantum computing and blockchain is the focus of this chapter, highlighting blockchain's potential to bolster digital trust in the face of quantum uncertainty. Topics include quantum-resistant encryption, novel smart contracts, quantum key distribution, and the associated regulatory difficulties. Ultimately, readers need to understand the mutual benefits of blockchain technology and quantum computing, and the crucial part blockchain plays in sustaining digital trust amid the quantum revolution.
  • Advanced Real-Time Simulation Framework for the Physical Interaction Dynamics of Production Lines Leveraging Digital Twin Paradigms
    Neha Bhati, Narayan Vyas, Vishal Dutt, Ronak Duggar, Aradhya Pokhriyal
    Simulation Techniques of Digital Twin in Real Time Applications Design Modeling and Implementation, 2024
    This chapter delves into the cutting-edge world of advanced real-time simulation frameworks, focusing on how they can revolutionize the dynamics of production lines through the adoption of digital twin paradigms. This chapter provides an in-depth look at the ideas behind digital twins, how they are built, and why they are so crucial to the manufacturing process. It sheds more information on the complexities of physical interaction dynamics while stressing the difficulties of more conventional approaches. The advanced framework's design, principles, and practical implementation are the focus of this chapter, which is bolstered by examples drawn from actual applications. Finally, a future in which cutting-edge simulations power revolutionary production lines is envisioned, complete with its associated obstacles, future opportunities, and ethical considerations.
  • Empowering safety by embracing IoT for leak detection excellence
    Neha Bhati, Ronak Duggar, Abeer Saber
    Innovations in Machine Learning and Iot for Water Management, 2023
    Improvements in connectivity and data analysis enabled by the internet of things (IoT) are set to revolutionize various sectors, with a particular emphasis on making workplaces safer. Manual leak inspections, which can be both time-consuming and dangerous, are quickly being replaced by IoT-driven devices. These systems are more than just an improvement in technology; they usher in a new paradigm with their ability to monitor in real time, issue immediate alerts, and locate leaks with pinpoint accuracy. Because of the benefits that IoT provides, several sectors are making the switch from more traditional practices. Leak detection enabled by the internet of things represents a step toward safer, greener production. The promise of improved worker safety and environmental sustainability lies at the heart of the internet of things, which should be rapidly adopted by businesses.
  • Optimizing Irrigation Efficiency with IoT and Machine Learning: A Transfer Learning Approach for Accurate Soil Moisture Prediction
    Srinivasa Rao Burri, D. K. Agarwal, Narayan Vyas, Ronak Duggar
    2023 World Conference on Communication and Computing Wconf 2023, 2023
    This research aims to develop a Machine Learning model for predicting soil moisture levels, which may be used to construct smart irrigation systems. The model was evaluated and trained using data from the “Smart Irrigation System Dataset” made publicly available by the University of California, Irvine. A transfer-learned ResNet50 model is evaluated using various classification measures like accuracy, recall, precision, and area under the ROC curve (AUC). The proposed model has an AUC of 0.95, meaning it correctly identifies positive and negative samples 95% of the time. Moreover, the model’s performance is measured against that of other famous machine learning models like logistic regression, Support Vector Machines (SVM), K-Nearest Neighbours (KNN), random forests, decision trees, and naive Bayes, with the majority of these conventional models being outperformed. These findings have ramifications for researchers and engineers creating intelligent irrigation systems for precision agriculture.
  • Exploring the Potential of Deep Reinforcement Learning for Autonomous Navigation in Complex Environments
    Venkata Raghuveer Burugadda, Nitin Jadhav, Narayan Vyas, Ronak Duggar
    2023 7th International Conference on Computing Communication Control and Automation Iccubea 2023, 2023
    One of the most challenging problems in robotics and autonomous vehicles is autonomous navigation in complex and dynamic environments. Deep Reinforcement Learning (DRL), which enables agents to learn complicated behaviors autonomously through trial and error, has demonstrated that it has the potential to be an effective solution to this problem. By utilizing the Waymo open dataset and the Proximal Policy Optimisation (PPO) algorithm, this research paper aims to investigate the potential of DRL for autonomous navigation in complex environments. In the first step of this process, we conduct a literature review that focuses on numerous research that has studied the application of DRL for autonomous navigation in various settings. After that, we discuss our methodology, which entails utilizing PPO to instruct an agent navigating the Waymo dataset. According to the findings of our study, the trained agent can properly navigate through the environment, even when barriers and other dynamic elements are present. In addition, we assess our agent's performance using various criteria, such as the percentage of successful attempts, efficiency, and risk. According to our research's conclusions, DRL-based navigation systems have the potential to create genuinely autonomous systems that can navigate across surroundings that are both complicated and dynamic. In general, the findings of this study demonstrate how important it is to investigate the possibilities of DRL to find solutions to complex problems in the fields of robotics and autonomous cars.
  • Intelligent Healthcare: Using NLP and ML to Power Chatbots for Improved Assistance
    Rajasrikar Punugoti, Ronak Duggar, Risha Ranganath Dhargalkar, Neha Bhati
    2023 International Conference on Iot Communication and Automation Technology Icicat 2023, 2023
    This research examines the fundamental reasons chatbots exist, their functions, and their challenges. The applicability and consistency of the analysis are improved by the utilization of quantitative data that is gathered in real-time. The research also compares past techniques of creating chatbots with modern ones, highlighting how far chatbots have progressed from being able to merely engage in scripted scenarios to the advanced skills they have today thanks to end-to-end neural networks. Microsoft Research carried out the research and published it in the journal Science. The first paragraph of this essay presents a detailed examination of chatbots' roles, significance, and potential. This research sheds new insight into the concept of chatbots by investigating their development and many uses in greater detail than previously.
  • A Machine Learning Framework for Accurate Prediction of Parkinson's Disease from Speech Data
    Srinivasa Rao Burri, D. K. Agarwal, Narayan Vyas, Ronak Duggar
    Proceedings 2023 3rd International Conference on Innovative Sustainable Computational Technologies Cisct 2023, 2023
    Speech data can help Machine Learning (ML) systems diagnose Parkinson's disease (PD). This study predicted PD progression using the Parkinson's Progression Markers Initiative (PPMI) dataset and Deep Belief Networks (DBN). PD and healthy control voice inputs formed a Convolutional Neural Network (CNN) model. This DBN model was compared to others using numerous performance metrics. DBN outperformed other algorithms. It accurately and early detected PD. DBN-based models may enhance PD diagnosis. Complex neurodegeneration Parkinson's affects millions. Early diagnosis enhances medical therapy. Clinical evaluations and subjective judgments delay and misdiagnose traditional diagnostic methods. This study scientifically and data-driven diagnosed PD using the DBN model. Speech data can suggest physical and cognitive problems, which these algorithms can detect. This improves accuracy and permits early diagnosis before physical symptoms develop. This study suggests ML can transform PD diagnosis. PPMI-sized datasets help researchers increase model accuracy and reliability. Speech data lets doctors assess more. This research extends diagnosis. PD diagnosis and treatment improve disease management. Predicting PD progression using ML allows patient-specific treatment. This study indicates the DBN model can reliably and early diagnose Parkinson's disease using voice data. The DBN model shows ML can outperform other PD diagnosis approaches. These developments may enhance patient outcomes, illness management, and early interventions.

RECENT SCHOLAR PUBLICATIONS

  • 5 Crucial role of blockchain in quantum computing: enhancing security and trust
    R Duggar, NE ElSayed
    Quantum Machine Learning: Quantum Algorithms and Neural Networks, 79 , 2024
    2024
  • Advanced Real‐Time Simulation Framework for the Physical Interaction Dynamics of Production Lines Leveraging Digital Twin Paradigms
    N Bhati, N Vyas, V Dutt, R Duggar, A Pokhriyal
    Simulation Techniques of Digital Twin in Real‐Time Applications: Design … , 2024
    2024
    Citations: 2
  • Empowering Safety by Embracing IoT for Leak Detection Excellence
    N Bhati, R Duggar, A Saber
    Innovations in Machine Learning and IoT for Water Management, 231-251 , 2024
    2024
    Citations: 8
  • Exploring few-shot learning approaches for bioinformatics advancements
    N Bhati, R Duggar, A Alzahrani
    Applying Machine Learning Techniques to Bioinformatics: Few-Shot and Zero … , 2024
    2024
    Citations: 9
  • A machine learning framework for accurate prediction of Parkinson's disease from speech data
    SR Burri, DK Agarwal, N Vyas, R Duggar
    2023 3rd international conference on innovative sustainable computational … , 2023
    2023
    Citations: 28
  • Exploring the potential of deep reinforcement learning for autonomous navigation in complex environments
    VR Burugadda, N Jadhav, N Vyas, R Duggar
    2023 7th International conference on computing, communication, control and … , 2023
    2023
    Citations: 26
  • Optimizing irrigation efficiency with iot and machine learning: A transfer learning approach for accurate soil moisture prediction
    SR Burri, DK Agarwal, N Vyas, R Duggar
    2023 World Conference on Communication & Computing (WCONF), 1-6 , 2023
    2023
    Citations: 52
  • Intelligent healthcare: Using nlp and ml to power chatbots for improved assistance
    R Punugoti, R Duggar, RR Dhargalkar, N Bhati
    2023 International Conference on IoT, Communication and Automation … , 2023
    2023
    Citations: 16

MOST CITED SCHOLAR PUBLICATIONS

  • Optimizing irrigation efficiency with iot and machine learning: A transfer learning approach for accurate soil moisture prediction
    SR Burri, DK Agarwal, N Vyas, R Duggar
    2023 World Conference on Communication & Computing (WCONF), 1-6 , 2023
    2023
    Citations: 52
  • A machine learning framework for accurate prediction of Parkinson's disease from speech data
    SR Burri, DK Agarwal, N Vyas, R Duggar
    2023 3rd international conference on innovative sustainable computational … , 2023
    2023
    Citations: 28
  • Exploring the potential of deep reinforcement learning for autonomous navigation in complex environments
    VR Burugadda, N Jadhav, N Vyas, R Duggar
    2023 7th International conference on computing, communication, control and … , 2023
    2023
    Citations: 26
  • Intelligent healthcare: Using nlp and ml to power chatbots for improved assistance
    R Punugoti, R Duggar, RR Dhargalkar, N Bhati
    2023 International Conference on IoT, Communication and Automation … , 2023
    2023
    Citations: 16
  • Exploring few-shot learning approaches for bioinformatics advancements
    N Bhati, R Duggar, A Alzahrani
    Applying Machine Learning Techniques to Bioinformatics: Few-Shot and Zero … , 2024
    2024
    Citations: 9
  • Empowering Safety by Embracing IoT for Leak Detection Excellence
    N Bhati, R Duggar, A Saber
    Innovations in Machine Learning and IoT for Water Management, 231-251 , 2024
    2024
    Citations: 8
  • Advanced Real‐Time Simulation Framework for the Physical Interaction Dynamics of Production Lines Leveraging Digital Twin Paradigms
    N Bhati, N Vyas, V Dutt, R Duggar, A Pokhriyal
    Simulation Techniques of Digital Twin in Real‐Time Applications: Design … , 2024
    2024
    Citations: 2
  • 5 Crucial role of blockchain in quantum computing: enhancing security and trust
    R Duggar, NE ElSayed
    Quantum Machine Learning: Quantum Algorithms and Neural Networks, 79 , 2024
    2024