Programs offered by academic institutions in higher education need to meet specific standards that are established by the appropriate accreditation bodies. Curriculum mapping is an important part of the curriculum management process that is used to document the expected learning outcomes, ensure quality, and align programs and courses with industry standards. Semantic web languages can be used to express and share common agreement about the vocabularies used in the domain under study. In this paper, we present an approach based on ontology for curriculum mapping in higher education. Our proposed approach is focused on the creation of a core curriculum ontology that can support effective knowledge representation and knowledge discovery. The research work presents the case of ontology reuse through the extension of the curriculum ontology to support the creation of micro-credentials. We also present a conceptual framework for knowledge discovery to support various business use case scenarios based on ontology inferencing and querying operations.

Background/Objectives: Our study investigates the primary characteristics of managerial decision-making processes in the public hospital units in Romania, particularly in the Northeast region. This research aims to delineate the decision-making model applied by managers in these units, considering the multitude of legislative, economic, technical, ethical, and organizational changes prompted by the pandemic. Methods: A mixed-method research approach was utilized, combining semi-structured interviews and autoethnography, to capture experiences, attitudes, perceptions, motivations, and ethical considerations of decision-makers within the healthcare system. Results: The findings revealed that managerial decisions in public hospitals were influenced by unique elements such as the vulnerability and support needs of patients, the absence of a clear hierarchy, the personalized nature of healthcare services, the complexity of care processes, and the use of advanced technology. External factors, notably political and economic influences, alongside internal ethical dilemmas, significantly impacted decision making. Conclusions: This study identifies the reliance on evidence-based decision making and a consultative managerial style as key to addressing these challenges. This research contributes theoretically by comparing decision-making models and practically by identifying a decision-making model that includes forms, techniques, and tools that could guide managers in decision making in Romanian public hospitals.

The electrocardiogram (ECG) is the most common non-invasive used method for monitoring the heart condition. Combined with other available patient medical data, the manual analysis and evaluation of ECG data become labour intensive and prone to errors. With the increased amount of available digital medical data, there is a need for proper methods to support medical practitioners in the decision-making process. These practitioners base their diagnostic decisions on standardized procedures, combined with field experience. In this paper, we present the conceptual design for an approach to knowledge discovery of ECG data based on ontologies and rules learning using Learning Classifier Systems (LCS). Ontologies can provide a platform and application-independent representation of knowledge based on the patient's medical data. Furthermore, rule-based reasoning provides a mechanism for discovering new knowledge. LCS provide a tool for automatically discovering new rules that are maximally general and can support sequential decision-making process. The use of LCS and rule-based reasoning provide a mechanism for encoding existing and new knowledge that can improve the efficiency of personalized medical treatment.

Personalized medicine aims to understand the underlying relationships between the multitudes of factors affecting a patient's health and provide physicians with an evidence-based approach to customize the treatment based on patient-specific characteristics. Machine-learning techniques can examine available data and discover relationships and patterns that may not be explicitly expressed within the data. In this case, physicians can use this knowledge for hypothesis testing and conduct investigations into the possible conditions that affect the patients' health. The benefits of personalized medicine include improved patient satisfaction, reduced length of hospitalization, enhanced treatment outcomes, and increased overall efficiency of the health care system. In this paper, we present an approach based on Learning Classifier Systems (LCS) to automatically discover rules that can support medical decision-making in evaluating the patient's heart condition. LCS are considered adaptive rule-based systems that can evolve a set of classifiers called rules based on a learning component that assigns credit to existing rules and an evolutionary component that helps discover new ones. The proposed approach is based on the implementation of an accuracy-based LCS that has been modified to support rules learning for personalized medical decision-making. The experimental results in the case study section provide a proof of concept for rules learning based on ECG data to discover rules that can support physicians in the medical decision-making process.

Desertification is the impoverishment of fertile land, caused by various factors and environmental effects, such as temperature and humidity. An appropriate Internet of Things (IoT) architecture, routing algorithms based on artificial intelligence (AI), and emerging technologies are essential to monitor and avoid desertification. However, the classical AI algorithms usually suffer from falling into local optimum issues and consuming more energy. This research proposed an improved multi-objective routing protocol, namely, the efficient quantum (EQ) artificial rabbit optimisation (ARO) based on edge computing (EC) and a software-defined network (SDN) concept (EQARO-ECS), which provides the best cluster table for the IoT network to avoid desertification. The methodology of the proposed EQARO-ECS protocol reduces energy consumption and improves data analysis speed by deploying new technologies, such as the Cloud, SDN, EC, and quantum technique-based ARO. This protocol increases the data analysis speed because of the suggested iterated quantum gates with the ARO, which can rapidly penetrate from the local to the global optimum. The protocol avoids desertification because of a new effective objective function that considers energy consumption, communication cost, and desertification parameters. The simulation results established that the suggested EQARO-ECS procedure increases accuracy and improves network lifetime by reducing energy depletion compared to other algorithms.