Financial Forecasting with Deep Learning Models Based Ensemble Technique in Stock Market Analysis , Chandrayani Rokde, Jagdish Chakole, Aishwarya Ukey International Journal of Information Engineering and Electronic Business, 2025 In recent years, deep learning techniques have emerged as powerful tools for analyzing and predicting complex patterns in sequential data across various fields.This study employs an ensemble of advanced deep learning models: Long Short-Term Memory (LSTM), Bi-Directional LSTM, Gated Recurrent Unit (GRU), LSTM Convolutional Neural Network (CNN), and LSTM with Self-Attention, to enhance prediction accuracy in time series forecasting.These models are applied to three distinct financial datasets: Tata Motors, HDFC Bank, and INFY.NS, we conduct a thorough comparative analysis to assess their performance.Utilizing K-fold cross-validation, we convert loss (MSE) into RMSE and MAPE, which help estimate accuracy .weachieved train accuracies of 97.46% for Tata Motors, 75.93% for INFY.NS, and 56.60% for HDFC Bank.Our empirical results highlight the strengths and limitations of each model within the ensemble framework and provide valuable insights into their effectiveness in capturing complex patterns in financial time series data.This research underscores the potential of deep learning-based ensemble techniques for improving stock price forecasting and offers significant implications for investors and the development of sophisticated trading and risk management systems.
A Novel Deep Convolutional Neural Network-based Trend Following Strategy for Stock Price Prediction 16th International Conference on Advances in Computing Control and Telecommunication Technologies Act 2025, 2025
Tutorial on Automated Trading using API Jagdish Chakole, Manish Kurhekar ACM International Conference Proceeding Series, 2023 Automation has played a significant role in many domains, and stock market trading is not an exception. Even retail traders can automate his/her trading strategy using API. A computer program doing trading is known as Algorithmic Trading. It eliminates inefficiency due to human emotions. Trading logic that decides when to buy and sell a stock is generally term as a trading strategy. The availability of large trading data has made it possible to automate the generation of dynamic trading strategies. Speed and accuracy are very vital aspects of profitable stock market trading. Algorithmic trading is far superior to manual trading in terms of speed and accuracy. In this tutorial, we demonstrate the automation of predefined trading strategies using Python API. We will also demonstrate the generation of trading strategies using Reinforcement learning, Deep learning, and various other domains in computer science. Validating the performance of any predefined trading strategy on historical data plays a significant role in its live performance and it is known as backtesting. Backtesting is the key feature of Algorithmic Trading. We will demonstrate backtesting of the trading strategies using a computer program on the Indian and American stock market data.
Analysis of Transfers Learning Techniques for Early Detection and Grading of Diabetic Retinopathy on Retinal Images Santosh Kumar Sahu, Ankush D. Sawarkar, Ajay Kumar Sahu, Akhil Anjikar, Amol P Bhopale, Jagdish Chakole Proceedings of 3rd International Conference on Advanced Computing Technologies and Applications Icacta 2023, 2023 Eye complications of diabetes include diabetic retinopathy (DR). It's caused by retinal blood vessel alterations. Working-age individuals' major cause of blindness is diabetic retinopathy. Early identification of diabetic retinopathy may prevent or postpone visual loss. Artificial intelligence (AI) can use computational image analysis methods to automatically identify and categorize diabetic retinopathy in retinal pictures. In this paper we study and analyze the different transfer learning models for early detection of diabetic retinopathy for retinal images. According to our findings, the accuracy of predictions ranges from 74% to 81%. It is worth noting that AI-based systems for diabetic retinopathy detection are still in the research phase, and more research is needed to evaluate their accuracy and effectiveness in real-world settings.
Convolutional Neural Network-based a novel Deep Trend Following Strategy for Stock Market Trading Ceur Workshop Proceedings, 2021
Trend following deep Q-Learning strategy for stock trading Jagdish Chakole, Manish Kurhekar Expert Systems, 2020 Computers and algorithms are widely used to help in stock market decision making. A few questions with regards to the profitability of algorithms for stock trading are can computers be trained to beat the markets? Can an algorithm take decisions for optimal profits? And so forth. In this research work, our objective is to answer some of these questions. We propose an algorithm using deep Q‐Reinforcement Learning techniques to make trading decisions. Trading in stock markets involves potential risk because the price is affected by various uncertain events ranging from political influences to economic constraints. Models that trade using predictions may not always be profitable mainly due to the influence of various unknown factors in predicting the future stock price. Trend Following is a trading idea in which, trading decisions, like buying and selling, are taken purely according to the observed market trend. A stock trend can be up, down, or sideways. Trend Following does not predict the stock price but follows the reversals in the trend direction. A trend reversal can be used to trigger a buy or a sell of a certain stock. In this research paper, we describe a deep Q‐Reinforcement Learning agent able to learn the Trend Following trading by getting rewarded for its trading decisions. Our results are based on experiments performed on the actual stock market data of the American and the Indian stock markets. The results indicate that the proposed model outperforms forecasting‐based methods in terms of profitability. We also limit risk by confirming trading actions with the trend before actual trading.
A secure approach for web based internet voting system using multiple encryption S.M. Jambhulkar, Jagdish B. Chakole, Praful R. Pardhi Proceedings International Conference on Electronic Systems Signal Processing and Computing Technologies Icesc 2014, 2014 Nowadays every things is becoming online, so human tendency has changed, they try to do every things from home using Internet. Election is also becoming online. But if we make voting system online the security is major concern. In our web based Internet voting system we are proving security to vote when it is travelling from voting client to voting server. Our main tool is the concept of multiple encryption and decryption.
