American International Journal of Sciences and Engineering Research <p><strong>Aims and Scope</strong></p> <p>American International Journal of Sciences and Engineering Research is a <em>Double-Blind Peer-Reviewed Refereed International Journal </em>published by the American Center of Science and Education. The journal accepts article submissions by e-mail (<a href=""></a>). The subject areas include, but are not limited to the following fields:-</p> <ul> <li>Fisheries Science, Aquaculture;</li> <li>Aquatic environmental monitoring, investigation, and assessment;</li> <li>Aquatic pollution and remediation; </li> <li>Oceanography, Limnology, Aquatic biology; </li> <li>Aquatic chemistry, Aquatic ecology; </li> <li>Aquatic toxicology, Hydrology, Geology; </li> <li>Dynamics of aquatic ecosystems; </li> <li>Water treatment, Ocean engineering, Hydraulic engineering; </li> <li>Environmental economics and management;</li> <li>Conservation and utilization of aquatic resources</li> <li>Technical, non-technical, strategic, operational, and managerial topics of the civil aviation sectors.</li> <li>Climate change</li> <li>Ecology and sustainable development</li> <li>Waste and water management</li> <li>Renewable and sustainable energy</li> <li>Environmental technologies</li> <li>Green construction and sustainable development</li> <li>Sustainable land development</li> <li>Environmental economics and policy</li> <li>Urban planning and development</li> <li>Social sciences and humanities</li> <li>Social impact assessment</li> <li>Sustainable agricultural systems</li> <li>Environmental physics,</li> <li>environmental chemistry,</li> <li>environmental economics,</li> <li>environmental management,</li> <li>environmental engineering &amp; technology,</li> <li>environmental health, ecological and environmental protection</li> <li>air and water pollution, solid waste, noise, recycling, natural resources, climate change, biodiversity, and so on.</li> <li>Agriculture &amp; Manufacturing</li> <li>Food processing</li> <li>Foodservice &amp; Regulation</li> <li>Labor and education on food production</li> <li>Marketing, Wholesale and distribution</li> <li>Research &amp; Development of food technology</li> <li>Financial Services in the modern food industry </li> <li>Food policy matters, food quality, food nutrition, food safety, etc.</li> <li>It also publishes papers concerning obesity research, food processing and control technologies, food chemistry</li> <li>Theories and methods of safety science, engineering, and technology</li> <li>Safety management, monitoring and supervision, and occupational health</li> <li>Safety assessment and risk analysis</li> <li>Safety economics</li> <li>Safety psychology and education</li> <li>Fire and blast safety and smoke control</li> <li>Safety in the coal mine, Machinery, and civil engineering</li> <li>Drug, healthcare, and patient safety</li> <li>Food safety</li> <li>Construction and environment safety</li> <li>Traffic and transportation safety</li> <li>Chemical health and safety of hazardous materials</li> <li>Synchronization Protocols and Algorithms</li> <li>Security Protocols and Algorithms</li> <li>QoS Protocols and Algorithms</li> <li>Ad-Hoc and Sensor Network Protocols and Algorithms</li> <li>Content Delivery Networks Protocols and Algorithms</li> <li>P2P Protocols and Algorithms</li> <li>Cluster-Based Protocols and Algorithms</li> <li>Real-Time Protocols and Algorithms</li> <li>Wireless Protocols and Algorithms</li> <li>MAC Protocols and Algorithms for Wired Networks</li> <li>Mobile wireless internet protocols and algorithms</li> <li>Delay-Tolerant protocols and algorithms</li> <li>Mesh network protocols and algorithms</li> <li>Protocols and algorithms for Voice over IP delivery</li> <li>Cognitive Radio Network Protocols and Algorithms</li> <li>Monitoring and management protocols and algorithms</li> <li>Optical networking protocols and algorithms</li> <li>Scalable Network Protocols and Algorithms</li> <li>Protocols and algorithms for Green Computing and Resource Allocation</li> <li>Power Efficient and Energy Saving Network Protocols and Algorithms</li> <li>Routing Protocols and Algorithms</li> <li>Tree-based Protocols and Algorithms</li> <li>Distributed/Decentralized Algorithms for Networks</li> <li>Fault-tolerant Protocols and Algorithms</li> <li>Protocols and algorithms for Mobile and Dynamic Networks</li> <li>Cross-Layer Collaborative Protocols and Algorithms</li> <li>Formal methods and cryptographic algorithms for communication</li> <li>Multimedia Network Protocols and Algorithms</li> <li>Network Protocols and Algorithms for Context-Aware and Semantic Networks</li> <li>Localized Network Protocols and Algorithms</li> <li>Transport Layer Protocols</li> <li>Smart grid Protocols and Algorithms</li> <li>Network Protocols Simulation Techniques</li> <li>Protocols and Algorithms for Mobile and Vehicular Ad Hoc Networks</li> <li>Cloud Computing Network Protocols and Algorithms</li> <li>Artificial Intelligence Algorithms Applied to Network Protocols</li> <li>Protocols and algorithms for IPTV delivery</li> </ul> <p> </p> <p style="text-align: justify; color: #ff0000;"><em><strong>It is important to mention that all the works are showed without any kind of payment. All of them are published free from payments or taxes.</strong></em></p> American Center of Science and Education en-US American International Journal of Sciences and Engineering Research 2641-0303 STOCK MARKET PRICE PREDICTION USING MACHINE LEARNING TECHNIQUES <p style="text-align: justify;"><em>Predicting stock market prices is a challenging task in the financial sector, where the Efficient Market Hypothesis (EMH) posits the impossibility of accurate prediction due to the inherent uncertainty and complexity of stock price behaviour. However, introducing Machine Learning algorithms has shown the feasibility of stock market price forecasting. This study employs advanced Machine Learning models that can predict stock price movements with the right level of accuracy if the correct parameter tuning and appropriate predictor models are developed. In this research work, the LSTM model, which is a type of Recurrent Neural Network (RNN), time series forecasting Facebook Prophet algorithm and Random Forest Regressor model have been implemented on 10 Dhaka Stock Market (DSEbd) listed companies and six international giants for predicting the stock and forecasting the future price. The dataset of domestic companies is extracted from the graphical representation of the DSEbd website, and the international companies' dataset is imported from Yahoo Finance. In this experiment, Facebook Prophet demonstrates a long period of forecasting with reasonable accuracy, capturing daily, weekly, and yearly seasonality, including holiday effects for market trend analysis. Remarkably, the LSTM model exhibits significant accuracy, yielding the best results with evaluation metrics, including RMSE (0.35), MAPE (0.50%), and MAE (0.30). The experimental results underscore the efficiency of LSTM for future stock forecasting, observed over 15 days of upcoming market prices. A comparison of the results shows that the LSTM model efficiently forecasts the next day's closing price.</em></p> <p style="text-align: justify;"><strong>JEL Classification Codes: </strong>H54, P42, G17, C88.</p> Mahfuz Islam Khan Jabed Copyright (c) 2024 Mahfuz Islam Khan Jabed 2024-02-03 2024-02-03 7 1 1 6 10.46545/aijser.v7i1.308