Modern Human Resource Management (HRM) for knowledge-based enterprises must consider competencies of the workforce on a more detailed level of description than in the past. With this more complex description and the requirement to exchange information between different organizations in networked enterprises it becomes necessary to standardize the description of competencies and other related concepts. We have developed an ontology containing concepts of HRM for two different projects: a meta-search engine for searching for jobs in job portals and for a university competence management system. We present the requirements derived from the two projects and describe the design of the ontology. This ontology is characterized by its integration of job descriptions, concepts for evaluating competencies on different levels and evidences for competencies. The definition is also aligned with the HR-XML approach of defining competence profiles.

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The integration of the Internet of Things with machine learning in different disciplines has benefited from recent technological advancements. In medical IoT, the fusion of these two disciplines can be extremely beneficial as it allows the creation of a receptive and interconnected environment and offers a variety of services to medical professionals and patients. Doctors can make early decisions to save a patient's life when disease forecasts are made early. IoT sensor captures the data from the patients, and machine learning techniques are used to analyze the data and predict the presence of the fatal disease i.e., diabetes. The goal of this research is to make a smart patient's health monitoring system based on machine learning that helps to detect the presence of a chronic disease in patient early and accurately. For the implementation, the diabetic dataset has been used. In order to detect the presence of the fatal disease, six different machine learning techniques are used i.e., Support Vector Machine (SVM), Logistic Regression, Artificial Neural Network (ANN), Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), and Long Short-Term Memory (LSTM). The performance of the proposed model is evaluated by using four evaluation metrics i.e., accuracy, precision, recall, and F1-Score. The RNN outperformed remaining algorithms in terms of accuracy (81%), precision (75%), and F1-Score (65%). However, the recall (56%) for ANN was higher as compared to SVM and logistic regression, CNN, RNN, and LSTM. With the help of this proposed patient's health monitoring system, doctors will be able to diagnose the presence of the disease earlier.