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All Journal International Journal of Power Electronics and Drive Systems (IJPEDS) International Journal of Informatics and Communication Technology (IJ-ICT) International Journal of Applied Power Engineering (IJAPE) Indonesian Journal of Electrical Engineering and Computer Science
Mishra, Debani Prasad
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Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering

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A painstaking analysis of various conventional and AI based MPPT approaches to the PV framework Rout, Kishrod Kumar; Mishra, Debani Prasad; Mishra, Sivkumar; Salkuti, Surender Reddy
International Journal of Power Electronics and Drive Systems (IJPEDS) Vol 14, No 4: December 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijpeds.v14.i4.pp2338-2346

Abstract

Electricity is the best technological advancement ever. Power is now used for everything in our culture because of how far it has come. Quiddity of life would not be possible without it. We know that the sole free hotspot for the PV module in our environmental factors is the sun. The PV cell changes sun-powered energy into electrical energy when the sun radiates on it. At the point when you produce power with the sunlight-based chargers, no ozone-harming substance emanations are placed into the climate. Since, the sun creates more energy than any manmade process at any point requires so as a result, in this article we will look at a variety of ways as well as a successful MPP strategy with high efficacy. It encompasses incremental conductance, perturbs & observe, and fuzzy logic approaches. A boost (DC-DC) converter ameliorates the likeness between a solar array and storage or power grid. In many solar-producing systems, DC/DC converters assist in surveilling the utmost power point by acting as a bridge betwixt load and solar panel. The intent of achieving the highest possible electricity can be done by calibrating the load to compare the current and voltage of a photovoltaic cell.
Solana blockchain technology: a review Mishra, Debani Prasad; Behera, Sandip Ranjan; Behera, Subhashis Satyabrata; Patro, Aditya Ranjan; Salkuti, Surender Reddy
International Journal of Informatics and Communication Technology (IJ-ICT) Vol 13, No 2: August 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijict.v13i2.pp197-205

Abstract

The introduction of a review article on the Solana blockchain is critical to setting the stage for the arguments and evidence to follow. This paragraph will provide context to the reader by discussing the current state of blockchain technology and introducing Solana as a potential solution. Blockchain technology has the potential for countless applications, ranging from financial transactions to secure data storage. However, existing blockchain systems suffer from scalability issues, were confirmation times and network congestion limit transaction volumes. This review paper on the Solana blockchain is valuable for those seeking an in-depth understanding of the design and efficacy. Given the increasing number of blockchain technologies available in the market, potential adopters face the challenge of selecting the most suitable blockchain network for their specific use case. A well-constructed review provides necessary information on the functioning of the technology, including its strengths and limitations. It also enables readers to compare various blockchain technologies and judge their suitability for their specific needs. Therefore, reviews like this one play a crucial role in helping to advance blockchain technology by driving the adoption of superior blockchain networks.
ChatGPT's effect on the job market: how automation affects employment in sectors using ChatGPT for customer service Mishra, Debani Prasad; Agarwal, Nandini; Shah, Dhruvi; Salkuti, Surender Reddy
International Journal of Informatics and Communication Technology (IJ-ICT) Vol 13, No 1: April 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijict.v13i1.pp116-122

Abstract

A significant language model called ChatGPT, created by OpenAI, has gained attention in artificial intelligence (AI) and natural language processing. This research paper aims to provide an in-depth analysis of ChatGPT and its potential impact on the future, including its limitations, pros and cons, and how it came to be. This paper first provides a brief overview of ChatGPT, including its architecture and training process, and how it differs from previous language models. It then delves into the model's limitations, such as its lack of common sense and susceptibility to discrimination or biases present in the data it was trained on. This paper also explores the potential benefits of ChatGPT, such as its ability to generate human-like text, its potential use in customer service, and its potential impact on the job market. The paper also discusses the ethical and social implications of ChatGPT, such as the potential for the model to perpetuate biases and the need for transparency and accountability in its deployment. Finally, the paper concludes by discussing the future of ChatGPT and similar language models and their potential impact on various industries and society as a whole. Overall, this research paper provides a comprehensive and nuanced survey of the AI tool ChatGPT and its potential impact on the future.
Alzheimer’s disease diagnosis using convolutional neural networks model Samanvi, Potnuru; Agrawal, Shruti; Mallick, Soubhagya Ranjan; Lenka, Rakesh Kumar; Palei, Shantilata; Mishra, Debani Prasad; Salkuti, Surender Reddy
International Journal of Informatics and Communication Technology (IJ-ICT) Vol 13, No 2: August 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijict.v13i2.pp206-213

Abstract

The global healthcare system and related fields are experiencing extensive transformations, taking inspiration from past trends to plan for a technologically advanced society. Neurodegenerative diseases are among the illnesses that are hardest to treat. Alzheimer’s disease is one of these conditions and is one of the leading causes of dementia. Due to the lack of permanent treatment and the complexity of managing symptoms as the severity grows, it is crucial to catch Alzheimer’s disease early. The objective of this study was to develop a convolutional neural network (CNN)-based model to diagnose early-stage Alzheimer’s disease more accurately and with less data loss than methods previously discovered. CNN, is adept at processing and recognising images and has been employed in various diagnostic tools and research in the healthcare sector, showing limitless potential. Convolutional, pooling and fully linked layers are the common layers that make up a CNN. In this paper, five CNN modelswere randomly chosen (ResNet, DenseNet, MobileNet, Inception, and Xception) and were trained. ResNet performed the best and was chosen to undergo additional modifications to improve accuracy to 95.5%. This was a remarkable achievement that made us hopeful for the performance of this model in larger datasets as well as other disease detection.
Cost optimization of electricity in energy storage system by dynamic programming Mishra, Debani Prasad; Routray, Bhabesh; Pattanayak, Nitesh; Shekhar, Priyansu; Behera, Pratyus Ranjan; Salkuti, Surender Reddy
International Journal of Applied Power Engineering (IJAPE) Vol 13, No 2: June 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijape.v13.i2.pp343-350

Abstract

This paper presents a dynamic programming solution for the cost optimization of an electric storage system. The objective is to minimize the total cost of meeting electricity demand over a specified time interval, considering energy constraints and costs. The proposed algorithm efficiently determines the optimal energy discharge and charge strategies for the storage system, resulting in reduced overall costs. The effectiveness and efficiency of the algorithm are demonstrated through various test cases, highlighting its potential for real-world applications in energy storage systems and electric grid management. It also provides an overview of different types of electrical storage systems, review recent research on optimization techniques for energy storage, and examines recent studies on the optimization of electrical storage systems for specific applications, such as peak load shaving and grid stability. Through this comprehensive analysis, we hope to shed light on the current state of the field and identify areas for further research and improvement.
Exploratory data analysis for electric vehicle driving range prediction: insights and evaluation Mishra, Debani Prasad; Kumar, Prince; Rai, Priyanka; Kumar, Ayush; Salkuti, Surender Reddy
International Journal of Applied Power Engineering (IJAPE) Vol 13, No 2: June 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijape.v13.i2.pp474-482

Abstract

One of the biggest challenges of electric vehicle (EV) users has been predicting the amount of driving time their vehicles will have on one battery charge. Planning a trip and reducing range anxiety depends on an accurate range estimate. This study aims to anticipate the EV driving range using machine learning methods. In this research, several regression models for predicting EV driving range will be developed and compared. A real-world dataset comprising various factors affecting EV range, such as power, trip distance, energy consumption, driving style, and environmental factors, is used for analysis. The dataset is preprocessed using exploratory data analysis methods to manage missing values, outliers, and categorical variables. The findings of this study contribute to the expanding area of EV range prediction and provide EV buyers, producers, and regulators with insightful information. The user experience can be improved, EV adoption can be boosted, and effective charging infrastructure design is made possible with accurate range prediction. The study also highlights the importance of model selection and data pretreatment in making accurate predictions.
Revolutionizing domestic solar power systems with IoT-enabled Blockchain technology Jhunjhunwalla, Drishana; Mishra, Debani Prasad; Hembram, Dashmat; Salkuti, Surender Reddy
International Journal of Applied Power Engineering (IJAPE) Vol 13, No 1: March 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijape.v13.i1.pp255-262

Abstract

Solar power systems in homes have become the need of the hour due to the crisis of fossil fuels. Also, it is a useful way of rural electrification and cutting down on running electricity costs. This paper discusses a 26-kW solar power system for powering homes along with IoT-based monitoring. The proposed system is expected to be low in cost and highly efficient. The system can also be used as a battery backup without solar power. The emergence of Blockchain technology is poised to revolutionize the sharing of information by providing a means of building trust in decentralized settings without the reliance on intermediaries. This technological breakthrough has the potential to transform several industries, including the internet of things (IoT). In addition to Blockchain, IoT has also been able to address some of its limitations by utilizing innovative technologies like big data and cloud computing. For security, Blockchain as a decentralized application will be used. Each block typically contains the transaction data, and power consumption data which can’t be tampered with even if changing all subsequent blocks, which is expensive to do so.
Fault detection and diagnosis of electric vehicles using artificial intelligence Mishra, Debani Prasad; Padhy, Somya Siddharth; Pradhan, Partha Sarathi; Gupta, Shubh; Senapati, Asutosh; Salkuti, Surender Reddy
International Journal of Applied Power Engineering (IJAPE) Vol 13, No 3: September 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijape.v13.i3.pp653-660

Abstract

Electric vehicle (EV) performance is greatly influenced by the motor drive system's stability, efficiency, and safety. With the increased usage of electric vehicles, fault detection and diagnostics (FDD) of the motor drive system has become an important topic of research. In recent years, there has been a lot of interest in artificial intelligence (AI) approaches employed in FDD. This paper provides an overview of the application of AI in defect detection for electric vehicles. The FDD method is divided into two steps: feature extraction and fault classification. Feature extraction involves identifying relevant parameters or characteristics from the EV's sensors and signals, enabling the AI system to capture meaningful patterns. Subsequently, fault classification employs AI algorithms to categorize and identify specific faults based on the extracted features, facilitating efficient diagnosis and maintenance of EVs. In the realm of EVs, the combination of AI techniques and FDD has the potential to improve performance, reliability, and safety while enabling proactive maintenance and reducing downtime. Using machine learning and deep learning, we can detect the fault in the system before it starts damaging our EV.
Strategic Deployment of EV Charging Infrastructure: An In-Depth Exploration of Optimal Location Selection and CC-CV Charging Strategies Mishra, Debani Prasad; Nayak, Pranav Swaroop; Kumar, Aman; Salkuti, Surender Reddy
International Journal of Informatics and Communication Technology (IJ-ICT) Vol 14, No 1: April 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijict.v14i1.pp259-267

Abstract

The continued expansion of the electric vehicle (EV) market necessitates strategic planning for the placement of charging stations to ensure efficient access and utilization of electric infrastructure. This paper presents a comprehensive review of the critical factors in optimizing the selection of EV charging station locations, along with the implementation of Constant Current-Constant Voltage (CC-CV) charging models. The study addresses the challenges and opportunities in identifying the most effective locations for charging stations to accommodate the growing demand for sustainable transportation. Furthermore, it examines the benefits of adopting CC-CV charging models to improve the charging process, achieving a balance between charging speed and battery longevity. Through this analysis, the review aims to provide valuable insights to stakeholders involved in the development and expansion of EV charging infrastructure, thereby supporting the transition to a more sustainable and extensive electric mobility ecosystem.
Efficient blockchain based solution for secure medical record management Mishra, Debani Prasad; Rajeev, B; Mallick, Soubhagya Ranjan; Lenka, Rakesh Kumar; Salkuti, Surender Reddy
International Journal of Informatics and Communication Technology (IJ-ICT) Vol 14, No 1: April 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijict.v14i1.pp59-67

Abstract

Electronic medical records (EMRs) have become a key player in the healthcare ecosystem contributing to the assessment of ailments, the choice of the treatment avenue, and the delivery of services. However, there is consideration of EMR storage whereby centralized storage leads to increased security and privacy issues in the patient’s record. In this paper, we proposed a blockchain and interplanetary file system (IPFS) based prototype model for EMR management. It provides a smart contract-enabled decentralized storage platform where healthcare data security, availability, and access management are prioritized. This model also employs cryptographic techniques to protect sensitive healthcare data. Finally, the model is evaluated in a realistic scenario. The experimental results demonstrate that compared to the current systems, the proposed prototype model outperforms them in terms of efficiency, privacy, and security.
Co-Authors Agarwal, Nandini Agrawal, Shruti Behera, Niharika Behera, Pratyus Ranjan Behera, Sandip Ranjan Behera, Subhashis Satyabrata Bhardwaj, Lakshay Biswal, Prabin Das, Subha Ranjan Dash, Arul Kumar Gupta, Shubh Hembram, Dashmat Jhunjhunwalla, Drishana Kumar, Aman Kumar, Ayush Kumar, Pavan Kumar, Prince Lenka, Rakesh Kumar Mallick, Soubhagya Ranjan Mishra, Sivkumar Nayak, Pranav Swaroop Ojha, Prajna Jeet Padhy, Somya Siddharth Palei, Shantilata Patro, Aditya Ranjan Pattanayak, Nitesh Pradhan, Partha Sarathi Pradhan, Rudranarayan Priyadarshini, Ananya Rai, Niti Rani rai, Priyanka Rajeev, B Rout, Kishrod Kumar Routray, Bhabesh Sahay, Sanchita Sahoo, Arun Kumar Sahu, Jayanta Kumar Sahu, Smruti Susmita Salkuti, Surender Reddy Samal, Sarita Samanvi, Potnuru Satapathy, Swayamjyoti Senapati, Asutosh Senapati, Rudranarayan Sethy, Sai Kanha Shah, Dhruvi Shekhar, Priyansu Singh, Anurag Singh, Saksham Subudhi, Umamani Yagyna Duthsharma, Rampa Sri Sai