University education has become an integral and basic part of most people preparing for working life. However, placement of students into the appropriate university, college, or discipline is of paramount importance for university education to perform its role. In this study, various explainable machine learning approaches (Decision Tree [DT], Extra tree classifiers [ETC], Random forest [RF] classifiers, Gradient boosting classifiers [GBC], and Support Vector Machine [SVM]) were tested to predict students’ right undergraduate major (field of specialization) before admission at the undergraduate level based on the current job markets and experience. The DT classifier predicts the target class based on simple decision rules. ETC is an ensemble learning technique that builds prediction models by using unpruned decision trees. RF is also an ensemble technique that uses many individual DTs to solve complex problems. GBC classifiers and produce strong prediction models. SVM predicts the target class with a high margin, as compared to other classifiers. The imbalanced dataset includes secondary school marks, higher secondary school marks, experience, and salary to select specialization for students in undergraduate programs. The results showed that the performances of RF and GBC predict the student field of specialization (undergraduate major) before admission, as well as the fact that these measures are as good as DT and ETC. Statistical analysis (Spearman correlation) is also applied to evaluate the relationship between a student’s major and other input variables. The statistical results show that higher student marks in higher secondary (hsc_p), university degree (Degree_p), and entry test (etest_p) play an important role in the student’s area of specialization, and we can recommend study fields according to these features. Based on these results, RF and GBC can easily be integrated into intelligent recommender systems to suggest a good field of specialization to university students, according to the current job market. This study also demonstrates that marks in higher secondary and university and entry tests are useful criteria to suggest the right undergraduate major because these input features most accurately predict the student field of specialization.

Objective: This research is being conducted to develop a technological solution for mentally distorted students. Though the mental health of university students is known globally as a momentous public health matter. Academicals and social stresses are playing quite a negative role in university student's life, especially in forms of mental illness like stress, depression, and anxiety. These mental health issues are becoming a major constraint towards their studies and career. Method: Psychologist used different scales to measure a level of mental disorder. However, to measure such a disease level, we are working on a knowledge-based expert system that will be used to compute its level among the students who are affiliated with technological studies. Mostly psychologist does psychotherapy and use other instruments to cure such a patient for which they must have to visit the psychologist. However, if the psychologist is not available especially in remote areas then the expert system can be used as reciprocal. In order to make our expert system more validate and authentic the knowledge of psychological expert will be used under the process of development. Results: Data from 500 technological University Students are collected from one of the universities in Sialkot, Pakistan. Almost more than 200 students remained clear minded and fall under the normal state of depression and 122 students in case of Anxiety remained normal. 206 students out of 500 were responders to the abnormal stage of anxiety on a 5-point scale from an average of 4.5 points. On the basis of this, an expert system is being designed to facilitate the students. Conclusion: As per results, 30%-35% students were in the range of abnormality. Therefore, we are further going to develop an evaluation mechanism by using technological ways so that an expert system can replace a psychologist.

To understand the complex nature of the human brain, network science approaches have played an important role. Neural signaling and communication form the basis for studying the dynamics of brain activity and functions. The neuroscientific community is interested in the network architecture of the human brain its simulation and for prediction of emergent network states. In this chapter we focus on how neurosignaling and communication is playing its part in medical psychology, furthermore, we have also reviewed how the interaction of network topology and dynamic models of a brain network.