Dr. Praveen Kumar Yadaw

Biography
Dr. Praveen Kumar Yadaw is an accomplished Assistant Professor at Kalinga University, Raipur, Chhattisgarh, India. With a rich and diverse academic and administrative background, Dr. Praveen Kumar Yadaw brings over 14 years of experience to the field, demonstrating a profound expertise in both research and educational administration.

Academic and Administrative Experience:

Dr. Praveen Kumar Yadaw has held several prestigious positions throughout their career. Most recently, they served as the Chief Controller of Examinations at Anjaneya University since July 2023. Prior to this role, they were the Controller of Examinations at Shree DAVARA University. Their previous roles also include serving as Assistant Controller of Examinations and Head of the Department in the School of Engineering & Technology at ISBM University. At ISBM University, Dr. Praveen Kumar Yadaw was instrumental in various administrative functions including curriculum design, research and development.

EDUCATION

Ph.D. (Luminescent)
M. tech
B. Tech.
M. Sc.

RESEARCH, TEACHING, or OTHER INTERESTS

Spectroscopy, Electrical and Electronic Engineering, Multidisciplinary, Signal Processing

FUTURE PROJECTS

Scopus Publications

Breaking Barriers: Strategies for Overcoming Hurdles in AI Implementation
Rajendra Kumar, Nivedita Singh, Rahul Baghel, Mukendra Kumar Sahu, Praveen Kumar Yadav, and Dusan Petkovic
CRC Press



Smart Energy Conservation in Homes using Fully Residual Convolutional Networks for Solar Power Forecasting
Praveen Kumar Yadaw, M. Amutha, V. Sidharthan, Gajanan Babu Kumbhar, T. Prabakaran, and Dharmendra Kumar
IEEE
DG is a power production strategy that enhances efficiency by reducing carbon demand peaks, emissions, and transmission losses through the deployment of multiple smaller on-site energy sources located within individual buildings. Although net metering aids in balancing energy supply and demand, scaling distributed generation across several houses to support intermittent renewable energy sources remains challenging. Furthermore, distributed generation is not economically attractive for widespread implementation given the existing energy price framework. To maximise Smart Energy Conservation and Solar Power Forecasting, meticulous data preparation is crucial for addressing these challenges. This paper reviews the methodologies and procedures employed in data preparation in prior studies. Our approach employs a FCN architecture, utilising ResNet101 as the backbone, with integrated upsampling skip links to enhance prediction accuracy. The proposed method achieves an accuracy of 96.56%, outperforming leading models and techniques in solar power forecasting. These findings underscore the importance of robust machine learning algorithms and meticulous data preparation for smart homes to optimise the use of renewable energy. Efficient distributed generation utilisation in residential applications facilitates smart energy conservation, hence enhancing energy sustainability and improving the accuracy of solar power forecasts.


Using Multisim in Digital Electronic Experiments for Teaching: A Practical Guide
Praveen Kumar Yadaw, Chandra Kumar Sahu, Rajendra Sahu, and Soma Rajwade
AIP Publishing



Up-Conversion Behavior of Er³⁺/Yb³⁺-Activated Gd<inf>2</inf>O<inf>3</inf> Phosphor for Magnetic Resonance Application
Praveen Kumar Yadaw, J. Mitrić, N. Romčević, Vikas Dubey, N. Kumar Swamy, M. C. Rao, and Ravindranadh Koutavarapu
Springer Science and Business Media LLC



Boosting Emergency Management: Leveraging Mobile Apps for Rapid Response to Crises
Rajendra Kumar, Manas Rathore, Chandra Kumar Sahu, Praveen Kumar Yadaw, and Soma Rajwade
Chapman and Hall/CRC



An Enhanced GJO-Pattern Search Algorithm to Solve the Optimal Power Dispatch Problem
Jugal Kishor, Chandra Kumar Sahu, Manoj Kumar Nigam, Sandeep Biswal, Praveen Kumar Yadaw, and Rajendra Kumar
IEEE
A critical optimization job in power system operation is the Optimal Power Dispatch (OPD) problem, which aims to minimize the total generation cost while meeting power demand and system constraints. The complicated, nonlinear, and non-convex character of ELD presents difficulties for traditional optimization techniques, especially when taking valve-point effects, forbidden operating zones, and the integration of renewable energy sources into account. In order to solve the ELD problem, this study provides an improved Grey Jackal Optimization-Pattern Search (GJO-PS) hybrid technique.


Advancing Surveillance Systems: Leveraging Sparse Auto Encoder for Enhanced Anomaly Detection in Image Data Security
Ravindra Changala, Praveen Kumar Yadaw, Mansoor Farooq, Mohammed Saleh Al Ansari, Veera Ankalu Vuyyuru, and S Muthuperumal
IEEE
In the realm of surveillance systems, ensuring robust anomaly detection capabilities is crucial for safeguarding against potential security breaches or hazardous incidents. This work uses the Sparse Autoencoder architecture to provide a unique method for improving anomaly identification in surveillance imagine information. The methodology begins with the collection of a comprehensive dataset, termed DCSASS, comprising videos from security cameras capturing a diverse range of abnormal and typical actions across various categories. Each video is meticulously labeled as normal or abnormal based on its content, facilitating the differentiation between routine operations and potential security threats. Individual frames extracted from the videos serve as image samples for subsequent model training and evaluation in anomaly detection tasks. In deep learning-driven anomaly identification networks, the preprocessing stage uses Min-Max Normalization to normalize pixel values, improving model stability and efficacy. Subsequently, the Sparse Autoencoder architecture, consisting of encoder and decoder components, is utilized for anomaly detection. The encoder, designed using convolutional neural network (CNN) architecture, extracts meaningful features from input images while inducing sparsity in learned representations through techniques like L1 regularization. Experimentation with various hyperparameters and architectural choices optimizes performance, with the decoder symmetrically mirroring the encoder's architecture to ensure accurate reconstruction of input images. The proposed approach outperforms existing methods such as CNN, RF and SVM, achieving 99% accuracy, making it 2.3% superior to other methods. Implemented in Python, our methodology demonstrates its efficacy in effectively capturing and reconstructing meaningful representations of input images, thereby enhancing anomaly detection capabilities in surveillance image data for improved security and safety measures.


Progress in DC-DC Converters: An Extensive Overview
Santosh Kumar, Akash Ingle, Sandeep Roy, Manju, Rajendra Kumar, and Praveen Kumar Yadaw
IEEE
Advancements in DC-DC converters have significantly enhanced the performance, functionality, and applicability of electronic systems across various sectors. This paper aims to provide an in-depth examination of recent innovations in DC-DC converters, encompassing their foundational principles, state-of-the-art advancements, and emerging trends. By exploring the pivotal role of DC-DC converters in diverse applications, the study seeks to elucidate the primary catalysts driving innovation in this domain and map out its evolutionary path. The comprehensive review delves into the evolution, fundamental concepts, categorizations, technological advancements, and emerging trends of DC-DC converters. Key areas of emphasis include the integration of wide bandgap semiconductors, enhanced circuit topologies, digital control techniques, high-frequency operations, and miniaturization. Furthermore, the paper evaluates prospective directions, comparative studies, and performance metrics within the DC-DC converter arena. Researchers, engineers, and industry professionals in power electronics stand to gain valuable insights from this synthesis of recent research endeavors. The scope of the review encompasses advanced circuit configurations, digital control strategies, wide bandgap semiconductor applications, performance assessment criteria, and potential avenues for future exploration. Ultimately, this comprehensive analysis aims to deepen our comprehension of DC-DC converters and their prospective impact on the future landscape of power electronics.


Influence of excitation wavelength on the down-conversion photoluminescence characteristics of Gd<inf>2</inf>O<inf>3</inf>:Er³⁺-Yb³⁺ phosphor
Praveen Kumar Yadaw, R.K. Padhi, Vikas Dubey, M.C. Rao, and N. Kumar Swamy
Elsevier BV



Instrumentation techniques for radiation dosimetry by using luminescence spectroscopy techniques
Jelena Mitrić
Elsevier



Mechanoluminescence behavior of rare-earth-activated phosphors
Abhijeet R. Kadam and Sanjay J. Dhoble
Elsevier



Spectrophotometric method for determining metribuzin herbicide with aniline in various environmental and agricultural samples

RECENT SCHOLAR PUBLICATIONS

Publications

Up-Conversion Behavior of Er3+/Yb3+-Activated Gd2O3 Phosphor for Magnetic Resonance Application