The stock market is highly dynamic and difficult to predict due to continuous fluctuations influenced by economic conditions, investor sentiment, company performance, and global events. Traditional stock prediction systems mainly depend on single-agent models, which often suffer from limited analytical capability and reduced prediction reliability during volatile market conditions. This paper proposes a Multi-Agent AI-Based Stock Market Analysis and Investment outcome that integrates multiple intelligent agents, machine learning algorithms, deep learning models, and sentiment analysis techniques for improved stock market forecasting. The system combines market analysis, sentiment analysis, risk evaluation, technical analysis, and decision making agents to provide Buy, Hold, and Sell recommendations. Historical stock data is collected using Yahoo Finance APIs and processed using Random Forest, XGBoost, and Long Short Term Memory (LSTM) models for trend prediction. The paper achieved 89.42% accuracy using Random Forest, 92.15% accuracy using XGBoost, and 93.28% prediction performance using the LSTM model. The multi-agent framework also demonstrated higher analytical efficiency with decision agent accuracy of 95% and sentiment agent accuracy of 91%.
Natural disasters such as floods and landslides impose severe socioeconomic losses across vulnerable geographies. Accurate early prediction demands model capable of capturing both Spatial and a Temporal patterns embedded in environmental data. This paper presents a novel Attentio…
Machine learning (ML) and artificial intelligence (AI) will be substituting financial modeling and corporate strategy and will offer an opportunity to make data-driven, adaptive, and predictive decisions. This review examines the application of machine learning processes to finan…
Modern surveillance systems often struggle to detect and respond to threats in real time. This paper introduces a system that uses context-aware threat intelligence to detect weapons, recognize violent actions, analyze time-based patterns, and assess potential threats based on th…
Microplastic contamination in coastal ecosystems has emerged as a critical environmental issue with significant ecological, economic, and public health consequences. Conventional monitoring approaches rely heavily on field sampling and laboratory-based analysis, which are time-co…
Immersive digital experiences rely increasingly on high-quality 3D content, yet traditional 3D authoring demands specialized expertise, multi-camera capture rigs, and prolonged processing pipelines that remain out of reach for most developers. This paper presents a lightweight, e…
Predictive analytics has emerged as a vital component of contemporary educational data analysis, enabling higher education institutions to move from reactive evaluation to proactive academic planning. The increasing availability of digital academic records—such as attendance, int…