Dr Pavithra G
@dsce.edu.in
Associate Professor
Dayananda Sagar College of Engineering, Bangalore
Dr. Pavithra G. was born in Bangalore, Karnataka, India & received the B.E. Degree (Bachelor of Engg.) in Electronics & Communication Engineering stream (ECE) from VTU, Belgaum, M.Tech. Degree in ECE branch with specialization in RF Communications from the Jain University in First Rank (FCD) & gold medal and obtained her doctorate - Ph.D. in Engineering from the prestigious Visvesvaraya Technological University (VTU Belgaum) respectively. She has got a teaching (academic), research experience of more than 15+ years in various engineering colleges in the Karnataka state. She has worked in the levels of Lecturer-Asst. Prof. (15+) in the colleges where she was a faculty in the Dept. of Electronics & Communication Engg. apart from having a very good industrial experience gained during her UG/PG project, internships & work tenures. Currently, she is working as an Associate Professor in the Dept. of ECE of the Dayananda Sagar College of Engg., Bangalore.
EDUCATION
B.E. (Acharya Institute of Technology, Bangalore)
Electronics & Communication Engg.
Affiliated to VTU, Belgaum, Karnataka,
Year of passing – 2006 (2002-2006)
M.Tech. (Jain College of
RF Communications
Affiliated to Jain University, Bangalore, Karnataka
Year of passing – 2012, 1st Rank, Gold Medalist (2010-12)
Ph.D. (VTU) Registered in Dec. 2015 (
Image Processing
Completed Ph.D. in VTU – RRC, Belgaum, Karnataka
Feb. 2020 (4 years)
RESEARCH, TEACHING, or OTHER INTERESTS
Electrical and Electronic Engineering, Communication, Cancer Research, Artificial Intelligence
Scopus Publications
Scholar Citations
Scholar h-index
Scholar i10-index
Scopus Publications
K. Nanda Kiran, V. M. Kumari Ayushi Kishore, Vinod Kumar Malkapure, G. Pavithra, and T. C. Manjunath
AIP Publishing
B. S. Hari, Bhaskar Roy, M. Elangovan, S. Kaliappan, G. Pavithra, and T. C. Manjunath
AIP Publishing
C. Madan Kumar, Y. Mohan, G. Manvanth, B. R. Nagesh, G. Pavithra, and T. C. Manjunath
AIP Publishing
D. Anupriya, Ananth Agarwal, Akshat Kumar, Afira Ansari, G. Pavithra, and T. C. Manjunath
AIP Publishing
Sampada Viraj Dravid, Nazeer Shaik, Gurumeet C. Wadhawa, S. Kaliappan, G. Pavithra, and T. C. Manjunath
AIP Publishing
K. Madhushree, M. Keerthana, J. Poornima, R. Kavya, M. Padmavathi, G. Pavithra, and T. C. Manjunath
AIP Publishing
Vaishanavi Patil, V. K. Vaibhav, B. G. Thyagaraj, G. Suprith Gowda, G. Pavithra, and T. C. Manjunath
AIP Publishing
Sagaragouda, Sahana G. Malagali, C. H. Srujana, G. Pavithra, and T. C. Manjunath
AIP Publishing
Praveen Rathod, D. N. Kavya, Atul Gupta, S. Kaliappan, G. Pavithra, and T. C. Manjunath
AIP Publishing
J. Kamalakumari, A. Archudha, R. Kiruthikaa, N. Saranya, S. Kaliappan, G. Pavithra, and T. C. Manjunath
AIP Publishing
Pankaj Dumka, Rishika Chauhan, Dhananjay R. Mishra, Feroz Shaik, Pavithra Govindaraj, Abhinav Kumar, Chandrakant Sonawane, and Vladimir Ivanovich Velkin
Institute of Advanced Engineering and Science
<p>Chemical reaction balancing is a fundamental aspect of chemistry, ensuring the conservation of mass and atoms in reactions. This article introduces a specialized Python functions designed for automating the balancing of chemical reactions. Leveraging the versatility and simplicity of Python, the module employs advanced algorithms to provide an efficient and user-friendly solution for scientists, educators, and industry professionals. This article delves into the design, implementation, features, applications, and future developments of the Python functions for automated chemical reaction balancing. The functions thus developed were tested on some typical chemical reactions and the results are the same as that in the literature.</p>
Ravindranath C., Pavithra G., Kavana Salimath, Sandeep K.V., Sindhu Sree M., and T. C. Manjunath
IEEE
The work presented in this research article enables a significant advancement in gardening methodologies, leveraging an Internet of Things (IoT) - enabled automated plant watering system that underscores the paramount importance of efficiency and sustainability. The developed system, orchestrated by an 8-bit ATmega328P microcontroller, integrates resistive soil moisture sensor and humidity and temperature sensors (DHT11) to furnish real-time information. The watering technique of the developed system is further refined through the integration of a typed-1 fuzzy based logical based controllers, which is utilized to regulate its operation. With the integration of a Wi- Fi module, users gain the ability to monitor water distribution remotely through a smartphone application. The utilization of Thing Speak cloud technology streamlines data transfer and analysis, thereby enhancing the overall performance of the system on a full scale. The developed automated plant watering system not only conserves water and fosters optimal plant growth but also aligns seamlessly with advanced technological trends in the farming sector. It exemplifies a harmonious fusion of ecological awareness and cutting-edge IoT developments.
Ajay Ajay, , , , , , , Ajay Kumar, Krishan Kant Singh Gautam, Pratibha Deshmukh,et al.
ASPG Publishing LLC
This research compares federated and centralized learning paradigms to discover the best machine learning privacy-model accuracy balance. Federated learning allows model training across devices or clients without data centralization. It's innovative distributed machine learning. Keeping data on individual devices reduces the hazards of centralized data storage, improving user privacy and security. However, centralized learning concentrates data on a server, which raises privacy and security problems. It evaluates two learning approaches using simulated data in a simple regression problem framework. Federated learning seems to be as accurate as centralized learning while protecting privacy. The paper also shows how federated learning works in popular machine learning frameworks like TensorFlow Federated. This research shows that federated learning protects privacy while producing accurate machine learning models. It challenges the idea that machine learning must constantly choose between privacy and accuracy. Empirical facts and theoretical ideas from this study advance machine learning methodology discussions. In the digital era, it promotes privacy-conscious, dispersed learning frameworks.
Bhawani Sankar Panigrahi, S. Artheeswari, Wasim Khan, Pavithra G, Dr. Sushil Kumar Pathak, and R Bharanidharan
IEEE
The revolutionary role that artificial intelligence (AI) plays in the field of astrophysics, with a particular emphasis on the automated detection of celestial objects and anomaly detection. The conventional approaches to identifying celestial objects and detecting anomalies are becoming increasingly laborious as the amount of data collected by astronomers continues to grow at an exponential rate. The utilization of sophisticated machine learning algorithms allows for the demonstration of the effectiveness of artificial intelligence in automating these operations, thereby considerably improving the efficiency and accuracy of the identification of celestial objects. Not only does the proposed model make the classification of stars, galaxies, and other celestial entities more user-friendly, but it also performs exceptionally well when it comes to identifying anomalies, such as unexpected patterns within the data or rare occurrences in the field of astrophysics. Astrophysicists are able to efficiently explore large databases by leveraging the power of artificial intelligence (AI). This not only enables them to gain a more comprehensive understanding of the universe but also makes it easier for them to recognize significant celestial phenomena in a timely manner. The findings of this make a contribution to the ongoing synergy that exists between artificial intelligence and astrophysics. They also pave the path for new discoveries and deeper insights into the complexities of the cosmos.
P. Horsley Solomon, Balachandra Pattanaik, Ojasvi Pattanaik, Pavithra G, D. Elamvazhudhi, and Bazani Shaik
IEEE
The process of enhancing electronic circuits in order to improve energy efficiency is currently being made more efficient with the application of artificial intelligence (AI). In this day and age, where energy consumption is a big problem, the exploitation of techniques driven by artificial intelligence has shown to be an indispensable tool in the construction of electronic devices that reduce power consumption without sacrificing performance. This is achieved through the deployment of ways that are driven by artificial intelligence. Within the scope of this, the application of machine learning techniques is being researched for the purpose of analyzing and modifying circuit designs. There are several other variables that are taken into consideration, such as voltage, current, and the specifications of the components. The purpose of this research is to make a significant contribution to the development of electronic systems that are efficient in terms of energy consumption by utilizing circuit optimization that is driven by artificial intelligence capabilities. Because of this, the research will be able to satisfy the growing need for technology that is favourable to the environment. These discoveries not only offer valuable insights into the synergy that exists between artificial intelligence and the design of electronics, but they also pave the way for future improvements in technology that are aware of the amount of energy that is being consumed.
Bhawani Sankar Panigrahi, Balachandra Pattanaik, Ojasvi Pattanaik, S. B G Tilak Babu, Pavithra G, and Bazani Shaik
IEEE
Itaddresses the challenges that are associated with dynamic power management in embedded systems by utilizing techniques that are derived from the discipline of reinforcement learning (RL). Given the increasing complexity of embedded systems and the need for solutions that are efficient for energy consumption, RL is a potential technology that can dynamically optimize power utilization. This is particularly pertinent in light of the requirement for solutions that are efficient for energy consumption. The dynamic workload fluctuations that are inherent in embedded systems are taken into consideration in this research, which studies the integration of RL algorithms to control power levels in real time in an adaptive manner. This research also takes into account the variable workloads that are endemic to embedded systems. To evaluate the efficacy of RL-based dynamic power management strategies in comparison to traditional methods, with a specific emphasis on the potential for enhanced energy efficiency and system responsiveness, the goal of this is to evaluate the effectiveness of these strategies. The findings not only contribute to the improvement of understanding of the use of reinforcement learning in the setting of embedded systems, but they also provide insights into the utility of RL in meeting the evolving power management requirements in contemporary computing environments.
Gali Nageswara Rao, C. Gunasundari, S. B G Tilak Babu, Pavithra G, Vijay Kumar Dwivedi, and Bazani Shaik
IEEE
Artificial intelligence (AI) and its revolutionary effects on the medication development process. The project explores the potential integration of artificial intelligence tools into the different phases of drug development using state-of-the-art computational methods. This project’s overarching goal is to assess how well target selection, lead compound optimization, and toxicity prediction are served by data analytics, predictive modelling, and machine learning algorithms. This research aims to analyze contemporary uses and breakthroughs in artificial intelligence (AI) to better understand how it improves drug discovery pipeline efficiency and accuracy. Beyond this, it delves into the challenges and opportunities of AI-driven drug discovery, with an emphasis on finding fresh approaches to old biomedical problems. The dynamic environment at the crossroads of AI and pharmaceutical sciences is better-understood thanks to this comprehensive analysis. It opens the door to more efficient drug development processes and the development of new therapeutic approaches.
Bhawani Sankar Panigrahi, Angelina Royappa, Dr Sandeep Monga, H. Geetha, Pavithra G, and Bazani Shaik
IEEE
The use of deep learning techniques in the field of picture recognition for the purpose of identifying electronic components. Because of the growing complexity and variety of electronic devices, it is essential for manufacturing, maintenance, and quality control to be able to identify components in a way that is both efficient and accurate. This is accomplished through the utilization of deep learning algorithms, the utilization of convolutional neural networks (CNNs), and advanced image processing techniques in order to improve recognition skills. The suggested model displays higher accuracy in differentiating diverse electronic components, overcoming problems such as differences in size, orientation, and lighting conditions. Moreover, the model succeeds in overcoming these challenges effectively. Based on the findings, it appears that deep learning-based image recognition provides a strong solution for automating the identification process in electronic component analysis. This, in turn, contributes to better efficiency and reliability in the electronics industry. This research makes a contribution to the expanding field of computer vision and highlights the potential of deep learning in the advancement of electrical component identification systems.
Bhawani Sankar Panigrahi, Thiyagarajan T, M. Tamilselvi, S. B G Tilak Babu, Pavithra G, and Bazani Shaik
IEEE
The use of deep learning techniques for the purpose of improving fault detection in industrial machinery. It is of the utmost importance to have defect detection mechanisms that are both reliable and effective, since the complexity of industrial processes continues to increase. In this paper, the implementation of deep learning algorithms is investigated. These algorithms make use of neural networks to understand complex patterns and anomalies that are present in data coming from machinery. There are many different models that are being researched to see whether or not they are effective in detecting defects at early stages, limiting downtime, and eliminating costly interruptions. These models include convolutional neural networks (CNNs) and recurrent neural networks (RNNs). For the purpose of this , the performance of various methodologies is evaluated over a wide range of industrial situations, taking into consideration issues such as the variability of sensor data and noise. The findings demonstrate the promise of deep learning as a significant tool for enhancing defect detection skills, thereby paving the way for industrial equipment systems that are more reliable and resilient.
Bhawani Sankar Panigrahi, Manjunath H R, Amar Chipade, S. B G Tilak Babu, Pavithra G., and Beporam Iftekhar Hussain
IEEE
It is very important to be able to guess how strong weld joints will be because it affects the structural stability and performance of the parts that have been welded. In recent years, artificial neural networks (ANNs) have become useful for predicting complicated relationships in many fields, such as engineering and materials science. Artificial Neural Networks are the main focus of this research to guess how strong weld parts will be when they are made using various welding methods. The research entails gathering empirical data from a variety of welding processes, including arc welding, resistance welding, and laser welding, on a wide range of materials and joint configurations. Input variables such as welding current, voltage, speed, material characteristics, and joint shape are included in the files. The result of the testing processes is the strength of the weld bond. A neural network is utilised to create prediction models that accurately depict the complex patterns and non-linear connections seen in welding data. The network structure is carefully improved by considering factors such as Number of layers, neurons per layer, and activation functions. The acquired data is fed into the network throughout the training phase, and the weights are modified repeatedly to lower the frequency of incorrect predictions. To test and confirm the models that were created, different datasets that were not used during the training phase are employed. The accuracy, precision, and generality of artificial neural network (ANN) models in various welding processes are tested. Through comparison studies, it is demonstrated that Artificial Neural Networks (ANNs) outperform standard empirical models in predicting the strength of weld joints. The results show that artificial neural networks can accurately and reliably guess how strong weld parts will be for a wide range of welding methods. The new models are better than the old empirical models. This shows that artificial neural networks (ANNs) could be useful for improving and making sure the quality of welding processes. The results of this research help us understand better the complex relationships that determine how strong weld parts are. They also lay the groundwork for using AI in welding technology.
A Chandrashekhar, Amit Rawate, A. Dhanamathi, Rakeshnag Dasari, Rajeshri Pravin Shinkar, and Pavithra G
IEEE
The application of Deep Reinforcement Learning (DRL) to enhance the autonomous navigation capabilities of drones in environments that are complex and congested. The complications involved with guiding drones across restricted places, overcoming obstructions, and navigating among clutter are addressed in this article. Through the utilization of DRL techniques, the framework that has been proposed gives drones the ability to learn and alter their navigation strategies on their own through the process of trial and error, hence optimization of real-time decision-making. The incorporation of deep neural networks for the processing of sensory data makes it possible for drones to comprehend their environment, which in turn makes it easier for them to make educated judgments with regard to safe navigation. In order to demonstrate the potential of DRL in boosting the autonomy and robustness of drone navigation systems in demanding environments, the effectiveness of the technique is evaluated using simulations and experiments conducted in the real world. The findings make a contribution to the development of autonomous drone technology, which has implications for a variety of applications including environmental monitoring, search and rescue operations, and surveillance.
Nihal Raj, Jolly Masih, Krishnamoorthy Selvaraj, Pavithra G, and E.V. Ramkumar
IEEE
Through the widespread use of ICTs, the convergence of the Internet of Things (IoT) and Artificial Intelligence (AI) has sparked a revolutionary transformation in rural farming. This study delves at the dynamic interplay of IoT, AI, and ICTs as they pertain to digital agriculture and their far-reaching effects on rural areas. The study's findings show that Internet-of-Things (IoT) sensors can monitor things like soil moisture, temperature, and humidity in real-time, and that artificial intelligence (AI) algorithms can interpret this data to deliver predictive insights that optimize resource allocation and boost crop yields. These learnings are shared through an ICT platform, which helps to close the digital divide and give rural farmers more agency. In order to promote broad use of technology in rural regions, it is crucial to have policies and incentives in place that are helpful to do so. Privacy of data and reducing prejudice are two ethical factors that contribute to fair results. Collectively, these results show how much progress can be made towards ending rural poverty, improving food security, reducing the digital gap, fostering sustainability, and propelling broad economic expansion. Collaboration across disciplines, model improvement, cost-effectiveness, and innovative applications are key steps for the future of rural community technology development.
Manoj Ram Tammina, Bhargavi Posinasetty, Prabha Shreeraj Nair, Santosh Kumar, Pavithra G, and Harpreet Kaur
IEEE
When applied to healthcare, machine learning ushers in a new age of data-driven medical practice that holds great promise for better patient outcomes and individualized treatment. However, this evolution isn't without significant difficulties, such as the difficulty of striking a balance between patient confidentiality and data use. In this study, we use -Differential Privacy as a privacy-protecting technique and a number of machine learning models to quantify the value of the data collected. Our research demonstrates a subtle trade-off, where more stringent privacy safeguards often result in less useful data, and vice versa. We stress the need for ethical frameworks, patient permission, and flexible privacy restrictions as means of negotiating this space. Achieving responsible and successful machine learning-enabled healthcare calls for a number of future steps, including optimization of privacy settings, adoption of federated learning, data ownership through blockchain, validations in the actual world, and extensive ethical advice.
S. Aslam, Madhavi Shamkuwar, S. B G Tilak Babu, J Suresh, Pavithra G, and Natrayan L
IEEE
The aircraft industry has long used cutting-edge technologies to ensure equipment security, dependability, and performance. In aerospace applications, fault tolerance-the ability of a system to perform reliably despite errors or failures-is essential. Developing devices that work well even when things go wrong is vital. Using a Smart Multiphase Power Converter (SMPC) and a Model Reference Adaptive Control (MRAC) algorithm, this abstract improves aircraft fault tolerance. Multiphase power converters are increasingly used in airplanes due to their high power density, fault tolerance, and efficiency. The MRAC algorithm controls this operation. MRAC continuously optimizes system settings to match the reference model. MRAC algorithms adapt aeronautical control systems to maintain peak performance despite external interference, changing operational parameters, and broken parts. Combining the SMPC and MRAC algorithms in aerospace fault-tolerant machine design has many benefits. First, it checks the system's state and adjusts the controller parameters to account for any deviations from the ideal, ensuring great reliability. In conclusion, smart multiphase power converters and model reference adaptive control can improve aeronautical fault-tolerant machine architecture. This technology benefits the aircraft sector with its fault tolerance, efficiency, and adaptability. As aerospace technology advances, imaginative solutions are essential for safe and reliable future missions in Earth's atmosphere and beyond. Space operations can be safer and more efficient because to this study's implications for machinery design that can withstand extreme circumstances.