Following years of research, the processes driving schizophrenia (SZ) genesis, recur rence, symptomatically, and therapy remain a mystery. One of the explanations for this condition could be a lack of proper analytic methods to cope with the variety and complexity of SZ. Deep learning algorithms’ (a branch of artificial intelligence (AI) inspired by the nervous system) extraordinary precision in classification and prediction tasks has changed a wide range of scientific domains and is fast penetrating SZ research. Deep learning has the potential to assist doctors in the prediction, dia gnosis, and treatment of SZ. A thorough literature review was conducted to examine existing research on the use of deep learning in the study of psychosis for diagnosis, as well as the use of electroencephalographic data and signal classification. In this study, we propose the suggested methodology; acquisition, pre-processing of the data; extracting the features; dimension reduction; artificial intelligence classifiers. The outcomes are expected to be positive or negative, and different evaluation metrics will be applied. Results from the study are expected to show that the proposed AI classifiers were able to achieve high accuracy in detecting schizophrenia. The findings of this research have important implications for improving early detection and treatment outcomes for individuals with schizophrenia. Future research should focus on further improving the performance of the proposed AI classifiers and exploring their potential for real-world application.

In recent years, technology and computers have evolved rapidly, allowing tasks to be performed with minimal complexity and effort, but it has also led to an increase in cyber-attacks, such as data breaches and identity theft. This highlights the necessity of effective methods for detecting abnormal logins and protecting sensitive information. The objective of this study is to develop an advanced abnormal logins detection system using machine learning algorithms. To address the limitations of existing approaches, the proposed algorithm incorporates additional contextual information and adopts a hybrid approach that combines machine learning and network analysis. The CERT r4.2 dataset is utilized for evaluation purposes. The performance of the developed algorithm is assessed through experiments, showcasing a high accuracy rate and effective detection of various insider threat scenarios. Comparative analysis demonstrates its superiority over existing approaches, underscoring its potential in enhancing security measures in web applications. Furthermore, this project’s findings highlight its significance in safeguarding sensitive data, mitigating identity theft risks, and promoting resilient infrastructure. The project aligns with the United Nations Sustainable Development Goals, specifically SDG 9 (Industry, Innovation, and Infrastructure) and SDG 11 (Sustainable Cities and Communities), as well as the Saudi Vision 2030. In conclusion, the advanced abnormal logins detection system showcases the potential of machine learning algorithms and hybrid methodologies in addressing cybersecurity challenges. The project’s implications extend to enhancing data protection, mitigating identity theft risks, and contributing to the sustainable development goals outlined by international and national initiatives.

Education is an important factor of a civilization and has been of importance throughout history. The right education results in a fruitful future of change, development, and globalization for coming generations. Thus, it is of great importance to constantly re-evaluate the techniques used in modern education systems with the aim of continually improving the provision and quality of education. In a time where technology thrives to support human development in numerous ways, machine learning can be a great asset to the education system. Through the application of machine learning on educational data through prediction, universities and educational institutions will be able to improve teaching and learning outcomes, as well as provide the right support for the different types of students at the institution. The main objective of this project is to provide an accurate machine learning model that can predict students’ performance early on to support them on their journeys. This project will be fulfilled by continuous research, working closely with large numbers of data, and experimenting with different algorithms to reach the best results possible. In this paper Random Forest resulted in being the best performing algorithm for the intended model.