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There is a significant need for a computer-aided modeling, effective information analysis and ontology knowledge base models to support both special needs children and care providers. As this research work correlated to the symmetry scope, it proposes an innovative generic smart knowledge-based “School Care Coordination System” (SCCS), which is established on a novel holistic six-layered data management model. The development of the Smart-SCCS adopts a methodology of ontology engineering to transform the given theoretical unstructured special educational needs and disabilities (SEND) code of practice into a comprehensive knowledge representation and reasoning system. The intended purpose is to deliver a system that can coordinate and bring together education, health and social care services into a single application to meet the needs of children and young people (CYP) with SEND. Moreover, it enables coordination, integration and monitoring of education, health and social care activities between different actors (formal, informal and CYP in the education sector) involved in the school care process network to provide personalized care interventions based on a predefined care plan. The developed ontology knowledge-based model has been proven efficient and solved the enormous difficulties faced by schools and local authorities on a daily basis. It enabled the coordination of care and integration of information for CYP from different departments in health, social care and education. The developed model has received significant attention with great feedback from all the schools and the local authorities involved, showing its efficiency and robustness.

Depression is a global disorder with serious consequences. With more depression-related data and improved machine learning, it may be possible to build intelligent systems that can detect depression early on. This research uses the burns depression checklist as the gold standard for diagnosing depression and the support vector machine, decision tree, and light gradient boosting method as algorithms to create models capable of diagnosing depression on a data-set of 604 surveyed participants. This research demonstrates the efficiency of machine learning algorithms within the field of mental health. This paper serves to increase the body of knowledge by training insufficiently researched algorithms on a commonly used depression detection data-set with the goal of reaching or surpassing the level of performance seen in current research. This experimental research has found the decision tree classifier to be the best approach for predicting depression with an accuracy of 95.66% while that of the support vector machine classifier and the light gradient boosting classifier are 91.48% and 94.58%, respectively. The techniques presented in this paper perform better than those being used in current machine learning research. This research study may support the clinicians in determining what attributes are most crucial in diagnosis of depressed individuals as well as improve the health of the general populace.

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