## 7 Conclusions  
This research introduces a novel architecture that combines various prediction algorithms to tackle the challenges of stock value prediction with exceptional accuracy. Specifically focusing on gold datasets, the study aimed to forecast gold prices for investors. The input data encompassed gold datasets from reputable sources such as S&P, Yahoo, and NASDAQ, representing standard stock market data. The predictive framework employed \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Metric**} & \multicolumn{2}{c}{**Training Data**} & \multicolumn{2}{c}{**Test Data**} \\ \cline{2-5}  & **With VMD** & **W/O VMD** & **With VMD** & **W/O VMD** \\ \hline MAE & 3.64 & 4.1 & 4.3 & 5.6 \\ MSE & 20.5 & 26.1 & 31.6 & 56.9 \\ MAPE & 0.29 & 0.33 & 0.33 & 0.43 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Accuracy of price prediction phase. Figure 12: Effect of using the sentiment analysis module. natural language processing (NLP) to process sentiments extracted from social media feeds, long short-term memory (LSTM) networks to analyze historical data, variation mode decomposition (VMD) for feature selection, and artificial neural networks (ANNs) to make predictions. Additionally, the research integrated deep reinforcement learning (DRL) algorithms and deep Q networks (DQNs) to blend sentiments with other algorithms, enabling the prediction of the opening stock value for the next day based on the previous day's data. The processes developed for training and testing data were meticulously presented, forming the foundation of the prediction model. Comparative analysis was conducted with benchmark performance metrics, including the best stock benchmark, buy-and-hold benchmark, constant rebalanced portfolios, and DQN. Through rigorous evaluation, the proposed architecture demonstrated superior accuracy in performance metrics. Graphical representations were employed to showcase peaks indicating high values at specific times or on specific days, aligning with benchmark standards. The comparison clearly highlighted that the DQN outperformed existing algorithms, underscoring the potential of the proposed architecture to predict stocks with unparalleled precision. Future research, which could extend this research into real-time applications within dynamic environments, such as livestock markets, holds immense promise. Such applications could provide invaluable insights into the model's effectiveness and adaptability across different market scenarios. Moreover, the framework's generic nature, as demonstrated in this study, suggests its versatility for application across diverse products beyond gold. This versatility transforms the model into a powerful tool for traders and investors in various sectors. Subsequent studies focusing on real-time livestock market data not only stand to validate the framework's effectiveness but also pave the way for tailored adaptations customized to specific industries and the unique intricacies of each market. The proposed framework contains three main modules. Each module can be enhanced with different techniques. In the sentiment analysis module, the proposed framework used classification techniques to judge whether the sentence is positive or negative. However, another primary technique that can be used is the lexicon-based technique in which the language dictionary is used to make the sentiment analysis. In the price prediction module, the proposed framework considered the stock historical prices as a signal and used VMD as a signal-processing technique to decompose the signal into sub-signals and remove the signal noise. Several other signal-processing techniques can be used for noise removal. This area is open to research, and other signal-processing techniques may easily enhance this module if they exist. Finally, the decision-making is undertaken by the deep reinforcement network. Several DRL techniques can be utilized in this module, giving better or worse results than the implemented one. Methodology, A.L.A., S.M.E. and M.W.F. ; Software, A.L.A.