Browsing by Author "Mohan, Senthilkumar"

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Citation Count: 1
Enabling business sustainability for stock market data using machine learning and deep learning approaches
(Springer, 2024) Divyashree, S.; Joshua, Christy Jackson; Md, Abdul Quadir; Mohan, Senthilkumar; Abdullah, A. Sheik; Mohamad, Ummul Hanan; Ahmadian, Ali
This paper introduces AlphaVision, an innovative decision support model designed for stock price prediction by seamlessly integrating real-time news updates and Return on Investment (ROI) values, utilizing various machine learning and deep learning approaches. The research investigates the application of these techniques to enhance the effectiveness of stock trading and investment decisions by accurately anticipating stock prices and providing valuable insights to investors and businesses. The study begins by analyzing the complexities and challenges of stock market analysis, considering factors like political, macroeconomic, and legal issues that contribute to market volatility. To address these challenges, we proposed the methodology called AlphaVision, which incorporates various machine learning algorithms, including Decision Trees, Random Forest, Na & iuml;ve Bayes, Boosting, K-Nearest Neighbors, and Support Vector Machine, alongside deep learning models such as Multi-layer Perceptron (MLP), Artificial Neural Networks, and Recurrent Neural Networks. The effectiveness of each model is evaluated based on their accuracy in predicting stock prices. Experimental results revealed that the MLP model achieved the highest accuracy of approximately 92%, outperforming other deep learning models. The Random Forest algorithm also demonstrated promising results with an accuracy of around 84.6%. These findings indicate the potential of machine learning and deep learning techniques in improving stock market analysis and prediction. The AlphaVision methodology presented in this research empowers investors and businesses with valuable tools to make informed investment decisions and navigate the complexities of the stock market. By accurately forecasting stock prices based on news updates and ROI values, the model contributes to better financial management and business sustainability. The integration of machine learning and deep learning approaches offers a promising solution for enhancing stock market analysis and prediction. Future research will focus on extracting more relevant financial features to further improve the model's accuracy. By advancing decision support models for stock price prediction, researchers and practitioners can foster better investment strategies and foster economic growth. The proposed model holds potential to revolutionize stock trading and investment practices, enabling more informed and profitable decision-making in the financial sector.
Citation Count: 0
Enabling business sustainability for stock market data using machine learning and deep learning approaches (AUG, 10.1007/s10479-024-06118-x, 2024 )
(Springer, 2024) Divyashree, S.; Joshua, Christy Jackson; Quadir, Md Abdul; Mohan, Senthilkumar; Abdullah, A. Sheik; Mohamad, Ummul Hanan; Ahmadian, Ali
[No Abstract Available]
Citation Count: 0
A novel approach for self-driving car in partially observable environment using life long reinforcement learning
(Elsevier, 2024) Quadir, Md Abdul; Jaiswal, Dibyanshu; Mohan, Senthilkumar; Innab, Nisreen; Sulaiman, Riza; Alaoui, Mohammed Kbiri; Ahmadian, Ali
Despite ground-breaking advancements in robotics, gaming, and other challenging domains, reinforcement learning still faces significant challenges in solving dynamic, open-world problems. Since reinforcement learning algorithms usually perform poorly when exposed to new tasks outside of their data distribution, continuous learning algorithms have drawn significant attention. In parallel with work on lifelong learning algorithms, there is a need for challenging environments, properly planned trials, and metrics to measure research success. In this context, a Deep Asynchronous Autonomous Learning System (DAALS) is proposed in this paper for training a selfdriving car in a partially observable environment for real-world scenarios in a continuous state-action space. To cater to three different use cases, three different algorithms were used. To train their agents for learning and upgrading discrete state policies, DAALS used the Asynchronous Advantage Stager Reviewer (AASR) algorithm. To train its agent for continuous state spaces, DAALS also uses an Extensive Deterministic Policy Gradient (EDPG) algorithm. To train the agent in a lifelong form of learning for partially observable environments, DAALS uses a Deep Deterministic Policy Gradient Novel Lifelong Learning Algorithm (DDPGNLLA). The system offers flexibility to the user to train the agents for both discrete and continuous state-action spaces. Compared to previous models in continuous state-action spaces, Deep deterministic policy gradient lifelong learning algorithm outperforms previous models by 46.09%. Furthermore, the Deep Asynchronous Autonomous System tends to outperform all previous reinforcement learning algorithms, making our proposed approach a real-world solution. As DAALS has tested on number of different environments it provides the insights on how modern Artificial Intelligence (AI) solutions can be generalized making it one of the better solutions for AI general domain problems.