Technical Paper on Stock Prediction using Machine Learning Algorithms

Abstract

In today\'s financial world, a stock market is quite important. The stock market\'s worth is an important part of today\'s global economic system. The stock market attracts a large number of people from all areas of life, whether corporate or academic. Because of the non-linear structure of the stock market, research is one of the world\'s hottest and most important topics. People invest in the stock market based on previous theory outcomes or assumptions. When it comes to forecasting, people typically look for ways or procedures to help them reduce risk and improve performance. As a result, in today\'s competitive stock market, stock value forecast is crucial. Critical and technical studies\' approach cannot guarantee the consistency and accuracy of estimations. As a result, machine learning technology has risen in popularity in stock forecasting over the last 5-6 years, with estimates based on current stock values that have been educated in terms of prior values. This research focuses on using LSTM (Long Short Time Memory) technology to forecast future market trends.

Introduction

I. INTRODUCTION

The stock market has a significant influence on a country's economic performance. Forecasting stock prices, on the other hand, has always been challenging due to the market's volatility character, which does not follow to defined norms. The unpredictability of the market frequently results in losses for investors who gamble by arbitrarily forecasting prices rather than making informed trades. According to studies, up to 90% of investors lose money on a single investment. The stock market operates on supply and demand principles, with a high demand for a given company's stock and a limited supply driving up the stock price. Low demand, on the other hand, can cause a drop in the stock price. Based on historical data and periodic chart patterns, market analysis can be used to forecast stock values, resulting in profitable trading and substantial profits for investors. As a result, it is necessary to assess market conditions and construct a stock price prediction system that allows investors to optimise their outcomes and portfolios.

II. LITRATURE SURVEY

M. Sreemalli, P. Chaitanya, and K. Srinivas [1] claim that support vector machines and artificial neural networks are often employed techniques for stock price forecasting. Input vectors can be mapped onto high-dimensional feature spaces using ANNs, which can then be used to build nonlinear class partitions using linear models. ANNs are especially promising for machine learning tasks including classification and prediction. The ARIMA approach can be used to model time series data. The 2015 Bank Nifty dataset was used in this study to forecast Nifty bank data using machine learning techniques such support vector machines, artificial neural networks, and autoregressive integrated moving average. In comparison to previous approaches, the neural network implementation required more time to finish calculations, while the support vector machine implementation had a greater error rate.

To overcome the difficulties in predicting stock values, Indu Kumar, Kiran Dogra, Chetna Utreja, and Premlata Yadav [2] created a machine learning technique. Five models were developed for the study, and their abilities to forecast market trends were compared. As supervised learning methods, these models use Support Vector Machine (SVM), Random Forest, K-Nearest Neighbour (KNN), Naive Bayes, and SoftMax. The study found that the Random Forest method performs better than other models for larger databases, whereas the Naive Bayesian Classifier is better suited for small databases. Feature extraction from a given database, supervised classification from a training database to a test database, and result evaluation make up the proposed architecture for real-world applications.  

For precise stock price estimation, Ishita Parmar, Navan Shu Agarwal, and Shirish Saxena [3] suggested employing LSTM-based regression and machine learning. The study took into account volume, open, low, high, and close data from a Yahoo Finance dataset. For simulation and overview purposes to improve prediction accuracy, they concentrated on data from a single organisation. The LSTM model is more effective in properly forecasting stock values, according to the results.

In order to generate strong demand and successful business, Mariam Moukalled, Wassim ElHaj, and Mohamad Jaber [4] presented an automated industry that incorporates various external tools like mathematics, machine learning, and innovation. They construct unique machine learning methods to accomplish this, taking into account the significance of master data, while developing and training a large number of deep learning models. The stock of Apple Inc. (AAPL) that had the highest accuracy was SVM (82.91%).The direction of today's closing price in relation to yesterday's closing price is predicted using recurrent neural networks (RNN), feed forward neural networks (FFNN), support vector machines (SVM), and vector regression (SVR). Two samples yielded data for four different 10-year periods: historical information obtained from Reuters and stock news

Roondiwala et al.'s [5] developed an LSTM RNN model using features like low, high, off, and on values to estimate the model of the Nifty index. Testing was conducted using the NIFTY 50's five-year history. After 500 training cycles, this yields a root mean square error of 0.0086. This piece, however, steers clear of consistency. The percentage increase in daily sales was used by the authors of this study to determine the RMSE. However, even though the number is zero, it is equal to RMSE divided by 100, or around 1% of the Nifty index.

Also, role of ML and ESPs [11-68] are becoming important in recent applications, recognition and control.

III. RECURRENT NEURAL NETWORK (RNN) AND LONG SHORT-TERM MEMORY (LSTM)

Long Short-Term Memory (LSTM) is a type of Recurrent Neural Network RNN that may capture input from earlier stages and use it to create future predictions [7]. An Artificial Neural Network (ANN) has three levels: the input layer, the hidden layers, and the output layer.

The number of nodes in the input layer in a NN with only one hidden layer is always dictated by the dimension of the data; nodes in the input layer connect to the hidden layer via ‘synapses'. The weight coefficient is the signal decision maker in every two-node relationship from (input to the hidden layer). features a parameter called weight that serves as the signal's decision maker. The learning process is inherently a continuous adjustment of weights; after learning is complete, the Artificial NN will have optimal weights for individual synapses.

The hidden layer nodes use the SoftMax function to apply a sigmoid or tangent hyperbolic (tanh) function to the sum of weights from the input layer, which is called the activation function. This transformation generates values with a minimised error rate between the train and test data. The values obtained after this transformation comprise our NN's output layer; however, they may not be the best output; in this case, a back propagation process will be used to target the optimal value of error; the back propagation process will connect the output layer to the hidden layer, sending a signal conforming to the best weight with the optimal error for the number of epochs determined. This method will be used again and again in an attempt to improve our forecasts and reduce prediction error.

Once this stage is completed, the model will be trained. A recurrent neural network (RNN) is a type of neural network (NN) that predicts future value based on a prior sequence of observations. This sort of NN learns data from earlier stages and forecasts future patterns.

The earlier stages of data need be remembered in order to anticipate and guess future values; in this case, the hidden layer works as a stock for prior information from the sequential data. The practise of forecasting future data by using elements from prior sequences is referred to as recurrent.

Because RNNs cannot hold long-term memory, the usage of Long Short-Term Memory (LSTM) based on "memory line" proved to be particularly beneficial in forecasting scenarios involving long-term data. In an LSTM, previous phases 1170 Adil Moghar et al. / Procedia Computer Science 170 (2020) 1168-1173 Adil MOGHAR and Mhamed HAMICHE/ Procedia Computer Science 00 (2020) 000-000 3 can be remembered using gates with a memory line integrated. The composition of LSTM nodes is depicted in diagram-1.

The capacity to memorise data sequences distinguishes LSTMs from other types of RNNs. Every LSTM node must have a set of cells responsible for storing passed data streams; the upper line in each cell connects the models as a transport line handing over data from the past to the present; and cell independence aids the model in disposing of a filter of add values from one cell to another. Finally, the sigmoidal neural network layer, which comprises the gates, drives the cell to an ideal value by discarding or allowing data to pass through. Each sigmoid layer contains a binary value (0 or 1), with 0 representing "let nothing pass through" and 1 representing "let everything pass through." The goal here is to gain control of the the state of each cell, the gates are controlled as follow.

Forget Gate returns a value between 0 and 1, with 1 indicating "completely keep this" and 0 indicating "do not keep this."

"Completely ignore this."

Memory Gate determines which fresh data is saved in the cell. First, a sigmoid layer called the "input door layer" determines which values will be altered. Following that, a tan layer generates a vector of fresh candidate values that could be used.

The Output Gate determines what each cell's output will be. The output value will be dependent on the cell status as well as the filtered and most recently added data.

Result And Conclusion

IV. TECHNICAL APPROACH

To retrieve historical stock price data from Yahoo Finance, you can use the yfinance library to specify the stock symbol and time period. The data is obtained as a pandas Data Frame containing OHLC prices and volume. Pre-processing involves handling missing values, detecting and handling outliers, normalizing the data, and performing feature engineering. Unnecessary columns or features can be removed. The pre-processed data is split into training and testing datasets, with time-based splitting ensuring the integrity of time series data. The data is then divided into input features and the target variable.

For machine learning training, the LSTM model is built using libraries like Keras and TensorFlow. The LSTM architecture consists of multiple layers, allowing for the capture of long-term dependencies in sequential data. Activation functions like ReLU introduce non-linearity, and the LSTM layers are connected to output or prediction layers. The model is compiled with a specified loss function and optimizer. Training involves fitting the model to the training data using the fit() function.

LSTM models are effective for stock price prediction due to their ability to learn patterns and relationships over time. They utilize memory cells to store and update information, enabling them to capture long-term dependencies. By stacking multiple LSTM layers, the model can learn hierarchical representations and complex patterns. The output layers map the learned features to the target variable, such as stock price.

To retrieve real-time stock price data from Yahoo Finance, the finance library can be used. The LSTM model, comprising stacked LSTM layers, captures patterns from historical stock price data. It utilizes memory cells to understand long-term dependencies. Fully connected dense layers map the learned features to the target variable. Predictions for future days are generated by initializing the model with real-time data and iterating over the specified time frame. The input data is updated, and the predicted values are reversed to obtain actual stock price predictions. These predictions can be visualized or presented for analysis and decision-making.

V. RESULT AND DISCUSSION

Our project encompassed a comprehensive analysis of machine learning algorithms for forecasting the performance of five distinct stocks: Reliance, TCS, NIFTY 50 BANK, Axis Bank, and Adani Power. We delved into the evaluation of several prominent algorithms, including SARIMA, ARIMA, LSTM, Moving Average, and SVM, to determine their efficacy in predicting stock market trends. The primary objective was to discern the reliability and predictive capabilities of these algorithms in capturing the intricacies of stock price movements.

Through rigorous assessment and meticulous evaluation, we obtained accuracy metrics for each algorithm, measured in percentages. These metrics served as a quantifiable indicator of the algorithms' performance in forecasting stock market trends. Our analysis encompassed an extensive range of data, enabling us to gain a comprehensive understanding of the algorithms' strengths and limitations. By examining the accuracy results, we were able to gauge the algorithms' proficiency in capturing and modelling the complex patterns inherent in stock price movements.

This study contributes valuable insights to the field of stock market prediction, aiding investors and market participants in making informed decisions. The findings from our analysis provide a basis for comparing and selecting the most suitable algorithm for forecasting stock performance, depending on the specific requirements and characteristics of the target stocks.

The graph displays the historical stock prices of Reliance from 18/4/2023 to 18/5/2023, focusing on the opening and closing prices. The blue line represents the opening prices, while the orange line represents the closing prices for each day. The data from this period is utilized to predict the stock prices for the next nine days.

The visualization provides insights into the trends and fluctuations in Reliance's stock prices during the specified timeframe. By observing the patterns in the opening and closing prices, investors and analysts can gain valuable information to make informed decisions regarding their investments. The predicted nine-day stock prices, represented by the blue and orange lines extending beyond the current timeframe, offer a glimpse into the anticipated future performance of Reliance's stock.

This analysis aids in understanding the potential trajectory of Reliance's stock price based on historical trends, facilitating strategic planning and risk assessment. It enables stakeholders to anticipate and react to market dynamics, supporting effective decision-making and maximizing investment opportunities.

The LSTM model is utilized to predict the stock prices for the next nine days using the price data of Reliance from April 18, 2023, to May 18, 2023. It is observed that as the amount of available data increases, the accuracy of the predictions improves. To enhance the accuracy and reliability of the predictions, a larger volume of data becomes essential.

By incorporating a larger dataset, the LSTM model can capture a more comprehensive range of market dynamics and patterns. This enables the model to identify and utilize relevant information effectively, resulting in more precise predictions. The inclusion of additional data facilitates a deeper understanding of historical trends, volatility patterns, and market behavior, contributing to enhanced forecasting accuracy.

Therefore, for improved prediction outcomes, it is advisable to incorporate a larger dataset encompassing a more extensive time period. This increased data volume enables the LSTM model to leverage a more robust foundation of information, leading to more reliable and accurate predictions of stock prices.

VI. ACKNOWLEDGEMENTS

We are very appreciative of the guidance and assistance we received from many people along the route to the project's completion since it was necessary for the performance and planned conclusion of this project. We would want to express our sincere gratitude. We appreciate everyone who helped us in this effort. Without their aid, we would not have been able to complete this job. We owe our success on this project solely to their guidance, oversight, and assistance, for which we are thankful. Dr. R.C. Jaiswal sir provided us with continual guidance and supervision, as well as constant availability, despite the fact that we encountered a variety of organisational issues when planning project activities and tasks.

Conclusion

Low demand, on the other side, may result in a reduction in stock price. Market analysis can be used to forecast stock values based on historical data and periodic chart patterns, resulting in profitable trading and considerable gains for investors. As a result, it\'s critical to examine market conditions and build a stock price prediction system that helps investors to maximise their outcomes and portfolios. This research offers an RNN built on LSTM to anticipate future values for both axis bank and dependence assets; the results of our model have been promising. The test results show that our model can track the evolution of opening prices for both assets. We will aim to find the optimal settings for bout data length and number of training epochs that better suit our assets and maximise our prediction accuracy in the future.

References

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C. and Taher Saraf, “ Stock Price Prediction using Machine Learning”, Journal of Emerging Technologies and Innovative Research (JETIR), Open Access, Peer Reviewed and refereed Journal, Indexed in Google Scholar, Microsoft Academic, CiteSeerX, Thomson Reuters, Mendeley : reference manager, ISSN-2349-5162, Impact Factor:7.95, Volume 9, Issue 9 pp. e33-e41, September 2022. [26] Jaiswal R. C. and Ritik Manghani, “Pneumonia Detection using X-rays Image Preprocessing”, Journal of Emerging Technologies and Innovative Research (JETIR), Open Access, Peer Reviewed and refereed Journal, Indexed in Google Scholar, Microsoft Academic, CiteSeerX, Thomson Reuters, Mendeley : refer-ence manager, ISSN-2349-5162, Impact Factor:7.95, Volume 9, Issue 9 pp. c653-c662, September 2022. [27] Jaiswal R. C. and Apoorva Ushire, “ Real Time Water Monitoring System Using NodeMCU ESMP8266 ”, Journal of Emerging Technologies and Innovative Research (JETIR), Open Access, Peer Reviewed and refereed Journal, Indexed in Google Scholar, Microsoft Academic, CiteSeerX, Thomson Reuters, Mende-ley : reference manager, ISSN-2349-5162, Impact Factor:7.95, Volume 9, Issue 9 pp. c1-c8, September 2022. [28] Jaiswal R. C. and Firoz Saherawala, “ Smart Glasses ”, Journal of Emerging Technologies and Innovative Research (JETIR), Open Access, Peer Reviewed and refereed Journal, Indexed in Google Scholar, Microsoft Academic, CiteSeerX, Thomson Reuters, Mendeley : reference manager, ISSN-2349-5162, Impact Factor:7.95, Volume 9, Issue 8 pp. f393-f401, August 2022. [29] Jaiswal R. C. and Asawari Walkade, “ Denial of Service Detection and Mitigation ”, Journal of Emerging Technologies and Innovative Research (JETIR), Open Access, Peer Reviewed and refereed Journal, Indexed in Google Scholar, Microsoft Academic, CiteSeerX, Thomson Reuters, Mendeley : reference man-ager, ISSN-2349-5162, Impact Factor:7.95, Volume 9, Issue 5 pp. f108-f116, May 2022. [30] Jaiswal R. C. and Fiza Shaikh, “ Augmented Reality based Car Manual System ”, Journal of Emerging Technologies and Innovative Research (JETIR), Open Access, Peer Reviewed and refereed Journal, Indexed in Google Scholar, Microsoft Academic, CiteSeerX, Thomson Reuters, Mendeley : reference manager, ISSN-2349-5162, Impact Factor:7.95, Volume 9, Issue 5 pp. c326-c332, May 2022. [31] Jaiswal R. 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C. and Shreyas Nazare, “ IoT Based Home Automation System”, Journal of Emerging Technologies and Innovative Research (JETIR), Open Ac-cess, Peer Reviewed and refereed Journal, ISSN-2349-5162, Impact Factor:7.95, Volume 8, Issue 11 pp. 151-153, November 2021. [36] Jaiswal R. C. and Prajwal Pitlehra, “Credit Analysis Using K-Nearest Neighbours’ Model”, Journal of Emerging Technologies and Innovative Research (JETIR), Open Access, Peer Reviewed and refereed Journal, ISSN-2349-5162, Impact Factor:7.95, Volume 8, Issue 5, pp. 504-511, May 2021. [37] Jaiswal R. C. and Rohit Barve, “Energy Harvesting System Using Dynamo”, Journal of Emerging Technologies and Innovative Research (JETIR), Open Ac-cess, Peer Reviewed and refereed Journal, ISSN-2349-5162, Impact Factor:7.95, Volume 8, Issue 5, pp. 278-280, May 2021. [38] Jaiswal R. 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[44] Jaiswal R.C. and Aashay Pawar, “Stock Market Study Using Supervised Machine Learning”, International Journal of Innovative Science and Research Technology (IJISRT), Open Access, Peer Reviewed and refereed Journal , ISSN: 2456-2165; IC Value: 45.98; SJ Impact Factor:6.253, Volume 5 Issue I, pp. 190-193, Jan 2020. [45] Jaiswal R.C. and Deepali Kasture, “Pillars of Object-Oriented System”, International Journal for Research in Applied Science & Engineering Technology (IJRASET), Open Access, Peer Reviewed and refereed Journal , ISSN: 2321-9653; IC Value: 45.98; SJ Impact Factor:7.177, Volume 7 Issue XI, pp. 589-591, Nov 2019. [46] Jaiswal R.C. and Yash Govilkar, “A Gesture Based Home Automation System”, International Journal for Research in Applied Science & Engineering Technol-ogy (IJRASET), Open Access, Peer Reviewed and refereed Journal, ISSN: 2321-9653; IC Value: 45.98; SJ Impact Factor:7.177, Volume 7 Issue XI, pp. 501-503, Nov 2019. [47] Jaiswal R.C. and Onkar Gagare, “Head Mounted Display”, International Journal for Research in Applied Science & Engineering Technology (IJRASET), Open Access, Peer Reviewed and refereed Journal, ISSN: 2321-9653; IC Value: 45.98; SJ Impact Factor:7.177, Volume 7 Issue XI, pp. 535-541, Nov 2019. [48] Jaiswal R.C. and Nehal Borole, “Autonomous Vehicle Prototype Development and Navigation using ROS”, International Journal for Research in Applied Science & Engineering Technology (IJRASET), Open Access, Peer Reviewed and refereed Journal, ISSN: 2321-9653; IC Value: 45.98; SJ Impact Factor:7.177, Volume 7 Issue XI, pp. 510-514, Nov 2019. [49] Jaiswal R.C. and Vaibhav Pawar, “Voice and Android Application Controlled Wheelchair”, Journal of Emerging Technologies and Innovative Research (JE-TIR), Open Access, Peer Reviewed and refereed Journal, ISSN-2349-5162, Volume 6, Issue 6, pp. 635-637, June 2019. [50] Jaiswal R.C. and Shreya Mondhe, “ Waste Segregation & Tracking”, International Journal for Research in Applied Science & Engineering Technology (IJRA-SET), Open Access, Peer Reviewed and refereed Journal , ISSN: 2321-9653; IC Value: 45.98; SJ Impact Factor:7.429, Volume 8, Issue 5, pp. 2085-2087, May 2019. [51] Jaiswal R.C. and Shreya Mondhe, “Stock Market Prediction Using Machine Learning & Robotic Process Automation”, Journal of Emerging Technologies and Innovative Research (JETIR), Open Access, Peer Reviewed and refereed Journal, ISSN-2349-5162, Volume 6, Issue 6, pp. 926-929, February 2019. [52] Jaiswal R.C. and Samruddhi Sonare, “Smart Supervision Security System Using Raspberry Pi ”, Journal of Emerging Technologies and Innovative Research (JETIR), ISSN-2349-5162, Volume 6, Issue 4, pp. 574-579, April 2019. [53] Jaiswal R.C. and Manasi Jagtap, “Automatic Car Fragrance Dispensing System”, International Journal of Research and Analytical Reviews (IJRAR), ISSN-2349-5138, Volume 6, Issue 1, pp. 315-319, March 2019. [54] Jaiswal R.C. and Sumukh Ballal, “Scalable Healthcare Sensor Network”, Journal of Emerging Technologies and Innovative Research (JETIR), ISSN-2349-5162, Volume 6, Issue 2, pp. 350-354, February 2019. [55] Jaiswal R.C. and Samruddhi Sonare, “Multiple Camera Based Surveillance System Using Raspberry Pi”, International Journal of Research and Analytical Reviews (IJRAR), ISSN-2348-1269, Volume 6, Issue 1, pp. 1635-1637, February 2019. [56] Jaiswal R.C. and Reha Musale, “Application of Digital Signature to Achieve Secure Transmission”, International Journal for Research in Applied Science & Engineering Technology (IJRASET),ISSN: 2321-9653; IC Value: 45.98; SJ Impact Factor:6.887, Volume 7 Issue II, pp. 150-153, February 2019. [57] Jaiswal R.C. and Himanshu Mithawala, “Automatic Gate Monitoring System”, Journal of Emerging Technologies and Innovative Research (JETIR), ISSN-2349-5162, Volume 6, Issue 1,pp. 88-94, January 2019. [58] Jaiswal R.C. and Bernard Lewis, “Dynamic Runway and Gate Terminal Allocation for Flights”, Journal of Emerging Technologies and Innovative Research (JETIR), UGC approved Journal, ISSN-2349-5162, Volume 5, Issue 12, December 2018. [59] Jaiswal R.C. and Sakshi Jain, “Text Search Engine”,‘, Journal of Emerging Technologies and Innovative Research (JETIR), UGC approved Journal ISSN-2349-5162, Volume 5, Issue 11, November 2018. [60] Jaiswal R.C. and Arti Gurap, “Design of Different Configurations of Truncated Rectangular Microstrip Patch Antenna For 2.4 GHz And 1.6 GHz ‘, Journal of Emerging Technologies and Innovative Research (JETIR),UGC Approved Journal, ISSN-2349-5162, Volume 5, Issue 10, October 2018. [61] Jaiswal R.C. and Atharva Mahindrakar, “ Mine Warfare and Surveillance Rover”, International Journal for Research in Applied Science & Engineering Tech-nology (IJRASET), ISSN: 2321-9653; IC Value: 45.98; SJ Impact Factor:6.887, Volume 6 Issue III, March 2018. [62] Jaiswal R.C. and Saloni Takawale “Multi-Client Server Communication Enhancement through Intranet”, International Journal for Research in Applied Sci-ence & Engineering Technology (IJRASET), ISSN: 2321-9653; UGC approved Journal, IC Value: 45.98; SJ Impact Factor :6.887, Volume 6 Issue 1, January 2018. [63] Jaiswal R.C. and Nikita Kakade, “Skin disease detection and classification using Image Processing Techniques”, Journal of Emerging Technologies and Inno-vative Research (JETIR), ISSN-2349-5162; UGC approved Journal:5.87, Volume 4, Issue 12, December 2017. [64] Jaiswal R.C. and Nikita Kakade, “OMR Sheet Evaluation Using Image Processing”, Journal of Emerging Technologies and Innovative Research (JETIR), ISSN-2349-5162; UGC approved Journal:5.87, Volume 4, Issue 12, December 2017. [65] Jaiswal R.C. and Swapnil Shah, “Customer Decision Support System”, International Research Journal of Engineering and Technology (IRJET), e-ISSN: 2395-0056; p-ISSN: 2395-0072; UGC approved Journal, SJ Impact Factor:5.181, Volume: 04 Issue: 10 | Oct -2017. [66] Jaiswal R.C. and Ketan Deshpande, “IOT Based Smart City: Weather, Traffic and Pollution Monitoring System”, International Research Journal of Engineer-ing and Technology (IRJET), e-ISSN: 2395-0056; p-ISSN: 2395-0072; UGC approved Journal, SJ Impact Factor:5.181, Volume: 04 Issue: 10 | Oct -2017. [67] Jaiswal R.C. and Vipul Phulphagar, “Arduino Controlled Weight Monitoring With Dashboard Analysis”, International Journal for Research in Applied Science & Engineering Technology (IJRASET), ISSN: 2321-9653; UGC approved Journal, IC Value: 45.98; SJ Impact Factor:6.887, Volume 5 Issue XI November 2017. [68] Jaiswal R.C. and Siddhant Sribhashyam, “Comparison of Routing Algorithms using Riverbed Modeler”, International Journal of Advanced Research in Com-puter and Communication Engineering (IJARCCE), ISSN: (Online) 2278-1021; online) 2278-1021 ISSN (Print) 2319 5940; UGC approved Journal, Impact Factor 5.947Vol. 6, Issue 6, June 2017.

Copyright

Copyright © 2023 Shivprasad Dhule, Himanshu Dhas, Pratik Chavan, Dr. R. C. Jaiswal. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.