Self-Organizing Maps (SOMs) are a type of unsupervised neural network that map high-dimensional input data onto a lower-dimensional (typically two-dimensional) grid while preserving the topological relationships of the data. SOMs are widely used for clustering, visualization, dimensionality reduction, and pattern recognition. They are particularly effective in fields such as image processing, signal processing, bioinformatics, and market analysis.Self-Organizing Maps (SOMs) offer a wide range of research opportunities across domains such as visualization, clustering, anomaly detection, healthcare, and cybersecurity. These projects focus on advancing the theoretical foundations of SOMs, improving their scalability and adaptability, and applying them to solve real-world problems in various industries. Each project offers an exciting chance to contribute to both the academic understanding of SOMs and their practical applications.