One potential research topic in the field of computer science and data mining using machine learning could be exploring the use of deep learning models for anomaly detection in large datasets. Anomaly detection is an important problem in many industries, including finance, healthcare, and security. Deep learning models, such as autoencoders and convolutional neural networks, have shown promising results in detecting anomalies in various types of data, including images, time series, and transactional data. However, there are still many challenges to overcome, such as dealing with imbalanced data, handling noisy or missing data, and scaling to large datasets. A research study in this area could investigate the effectiveness of different deep learning models for anomaly detection, compare their performance to traditional methods, and propose new techniques for improving accuracy and efficiency.
I give my proposal for a research topic, research thesis, thesis concept in the area of your interest:
Research Context: In recent years, the scale of various economic, financial, social, health, food, energy, nature, climate, etc. crises is increasing. As a result, the importance of improving crisis management techniques and using new ICT information technologies and Industry 4.0 for this purpose is growing. The importance of improving risk management processes using new Industry 4.0 technologies, including but not limited to i.e. Big Data Analytics and Artificial Intelligence, is also growing.
Accordingly, the research topic may address the following issue: The application of selected ICT information technologies, Industry 4.0, the technologies of the current fourth technological revolution, including Big Data Analytics, machine learning, deep learning, artificial intelligence to improve risk management systems, early warning systems within the framework of crisis management, and the improvement of forecasting models used to predict abnormal situations, events of special risk increases, emergencies, specific types of disasters, etc.
I would like to invite you to join me for scientific cooperation on this issue,