Real-time systems are safety/mission-critical computing systems which behave deterministically and give correct reactions to the inputs (or changes in the physical environment) in a timely manner. As in all computing systems, there is always a possibility of the presence of some faults in real-time systems. Due to its time-critical missions, a fault-tolerance mechanism should be constructed. Fault tolerance can be achieved by hardware, software or time redundancy and especially in safety-critical applications. There are strict time and cost constraints, which should be satisfied. This leaves us to the situation where constraints should be satisfied and at the same time, faults should be tolerated. In this paper, the basic concepts, terminology, history, features, and techniques of fault tolerance approach on real-time systems, are detailed and related works are reviewed for composing a good resource for the researchers.
In recent years, Smart City concept has gained tremendous attention, and it has a wide range of application environment for the researchers. In this concept, the use of smart buildings has very critical importance to improve the quality and interactivity of urban services. In these services security also has an important place, to set up a secure city platform in which reducing costs and ensuring sustainability are very trivial issues that need to be overcome. In this paper, we have demonstrated an autonomous aerial navigation system (by Unmanned Aerial Vehicles (UAVs)) for indoor mapping of smart buildings for the physical security control with the use of Unity3D simulation environment and Robot Operating System in a real-life scenario. In a real-world application, the security needs can be overcome with the use of lots of static sensors and communication platforms. However, with the use UAVs, it is aimed to decrease the cost and increase the efficiency of the system. The development process of such sophisticated UAVs can be improved with the help of simulations, by removing the complexity of a real-world scenario, thus reducing the costs of time and money. The surrounding environment of UAV is mapped with laser scanners (LIDAR sensors). The UAV navigates autonomously within the known and unknown areas to cover as much area as possible, thus explore the indoor environment. ROS, which has various tools to help robot developers and enthusiasts create robot applications that work on real robots is used for implementing the mapping and navigation operation. The experimental results showed that the proposed approach produces a good mapping for an indoor environment of smart buildings.
Congenital anomalies are seen at 1–3% of the population, probabilities of which are tried to be found out primarily through double, triple and quad tests during pregnancy. Also, ultrasonographical evaluations of fetuses enhance detecting and defining these abnormalities. About 60–70% of the anomalies can be diagnosed via ultrasonography, while the remaining 30–40% can be diagnosed after childbirth. Medical diagnosis and prediction is a topic that is closely related with e-Health and machine learning. e-Health applications are critically important especially for the patients unable to see a doctor or any health professional. Our objective is to help clinicians and families to better predict fetal congenital anomalies besides the traditional pregnancy tests using machine learning techniques and e-Health applications.
In this work, we developed a prediction system with assistive e-Health applications which both the pregnant women and practitioners can make use of. A performance comparison (considering Accuracy, F1-Score, AUC measures) was made between 9 binary classification models (Averaged Perceptron, Boosted Decision Tree, Bayes Point Machine, Decision Forest, Decision Jungle, Locally-Deep Support Vector Machine, Logistic Regression, Neural Network, Support Vector Machine) which were trained with the clinical dataset of 96 pregnant women and used to process data to predict fetal anomaly status based on the maternal and clinical data. The dataset was obtained through maternal questionnaire and detailed evaluations of 3 clinicians from RadyoEmar radiodiagnostics center in Istanbul, Turkey. Our e-Health applications are used to get pregnant women’s health status and clinical history parameters as inputs, recommend them physical activities to perform during pregnancy, and inform the practitioners and finally the patients about possible risks of fetal anomalies as the output.
In this paper, the highest accuracy of prediction was displayed as 89.5% during the development tests with Decision Forest model. In real life testing with 16 users, the performance was 87.5%. This estimate is sufficient to give an idea of fetal health before the patient visits the physician.
The proposed work aims to provide assistive services to pregnant women and clinicians via an online system consisting of a mobile side for the patients, a web application side for their clinicians and a prediction system. In addition, we showed the impact of certain clinical data parameters of pregnant on the fetal health status, statistically correlated the parameters with the existence of fetal anomalies and showed guidelines for future researches.
Software development effort estimation is a critical activity of the project management process. In this study, machine learning algorithms were investigated in conjunction with feature transformation, feature selection, and parameter tuning techniques to estimate the development effort accurately and a new model was proposed as part of an expert system. We preferred the most general-purpose algorithms, applied parameter optimization technique (GridSearch), feature transformation techniques (binning and one-hot-encoding), and feature selection algorithm (principal-component analysis). All the models were trained on the ISBSG datasets and implemented by using the scikit-learn package in the Python language. The proposed model uses a multilayer perceptron as its underlying algorithm, applies binding of the features to transform continuous features and one-hot-encoding technique to transform categorical data into numerical values as feature transformation techniques, does feature selection based on the principal component analysis method and performs parameter tuning based on the GridSearch algorithm. We demonstrate that our effort prediction model mostly outperforms the other existing models in terms of prediction accuracy based on the mean absolute residual parameter.