Abstract Predicting stock price movements has been challenging yet crucial for investors and financial analysts. Fluctuations in stock prices are valuable economic indicators, providing insights into overall economic well-being, consumer confidence, and market sentiment. In this study, we evaluate the efficacy of three different machine and deep learning algorithms in anticipating stock price trends. We assess the performance of Logistic Regression,... Predicting stock price movements has been challenging yet crucial for investors and financial analysts. Fluctuations in stock prices are valuable economic indicators, providing insights into overall economic well-being, consumer confidence, and market sentiment. In this study, we evaluate the efficacy of three different machine and deep learning algorithms in anticipating stock price trends. We assess the performance of Logistic Regression, Random Forest Regression, and Long Short-Term Memory (LSTM) algorithms in forecasting whether a stock's price will rise or fall in the upcoming period, utilising historical stock price data as input features. Our findings demonstrate that while each algorithm exhibits varying degrees of predictive accuracy, LSTM networks stand out as they generally outperform Logistic Regression and Random Forest Regression in capturing the complex temporal dependencies inherent in stock price data. This suggests that LSTM networks, with their superior performance, hold significant promise as effective tools for stock price trend prediction, particularly in volatile and non-linear financial markets. This could be a game-changer in stock price prediction, instilling optimism about the future of stock market analysis. Read More Read Less

Abstract The combination of text analytics and machine learning technologies in various applications has emerged recently. Generative content creation platforms use machine learning, which is not only restricted to text but also considers other types of data, i.e., graphs, images, and knowledge bases. The Generative platforms such as ChatGPT and other tools focus on multi-modal knowledge extraction, a challenging area in... The combination of text analytics and machine learning technologies in various applications has emerged recently. Generative content creation platforms use machine learning, which is not only restricted to text but also considers other types of data, i.e., graphs, images, and knowledge bases. The Generative platforms such as ChatGPT and other tools focus on multi-modal knowledge extraction, a challenging area in Machine Learning. Advanced generative models generate large amounts of consolidated information with different characteristics to address data accessibility issues. ChatGPT and other generative mechanisms are extensively applied in various domains, the financial market being significant. Using state-of-the-art linguistic models, we analyze the potential of ChatGPT to predict stock price growth using Indian news headlines, offering a promising outlook for the financial market. The main challenge is determining whether a model originally developed for commonly understood language can predict the success of banks in a complex and dynamic market. The study, a pioneering effort, provides valuable insights into the benefits and implications of ChatGPT in the economy, shedding light on its market research, risk assessment and sentiment analysis capabilities. It is one of the first attempts to addition to its use in text explore the use of ChatGPT in the Indian economy. In analytics, ChatGPT holds promise as a valuable asset for financial professionals, providing flexibility and sensitivity analytics capabilities in navigating the challenges of a volatile industry. As the financial sector evolves, continued research and development to leverage the full potential of language models such as ChatGPT will help enhance market research and decision-making processes. Integrating AI-powered solutions in finance has exciting possibilities for real-time insights and adaptive analysis. Read More Read Less

Abstract Accelerated advances in network speed, reliability, and security have increased the demand for software and services to move from being stored and processed locally on user's devices to being managed by third parties that can be accessed through the network. This has led to the need to develop new software architectures and software architectures that meet these new requirements. One example... Accelerated advances in network speed, reliability, and security have increased the demand for software and services to move from being stored and processed locally on user's devices to being managed by third parties that can be accessed through the network. This has led to the need to develop new software architectures and software architectures that meet these new requirements. One example of software architecture is the recent emergence of a microservices architecture to meet online service provider's maintenance and scalability requirements. Microservices is an architecture style that focuses on breaking down a system into small, lightweight services that are intentionally created to perform a highly cohesive business function, and it is an evolution of the traditional service-oriented architecture style. The practical projection of microservices architecture in information engineering is several small applications that work together to provide an integrated service that appears to the external medium as a single application. Each of these applications has its own independent source code and deals with data sources that are different from the rest. As a result, it has an independent development team. Partial applications depend on each other to accomplish their functions, and therefore, any modification to one of the services will affect the work of the rest of the services; in large applications that require keeping the system always available, we must perform the process of publishing updates for parts of the system without stopping it completely, so we must monitor the impact of modifications and forecast system status before new updates are deployed. In achieving this prediction, we must know the dependencies between different services to arrive at a cognitive model representing the dependencies between services or applications. Read More Read Less