Time series clustering is an unsupervised method of organizing homogeneous time series in groups based on certain similarity criteria. As a result, it can be an essential step in Exploratory Data Analysis (E...
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The importance of security in smart city IoT applications has continued to grow in recent years, especially when critical infrastructure is involved. State-of-the-art deep intrusion detection systems (Deep IDS) help d...
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Functional safety and reliability of electronic components have been becoming increasingly important over the last years and will continue to do so for the foreseeable future. Not least because of autonomous driving, ...
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The innovative technology Augmented Reality (AR) provides enormous potentials for companies. But due to the high technological complexity and the lack of expertise and experience in companies, the potentials are only ...
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ISO 26262 introduced the concept of Safety Element out of Context (SEooC), a generic element which is developed to be used across many applications, hence, saving a lot of time, effort, and cost. The objective of this...
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Testing complex systems is crucial for ensuring safety, especially in automated driving, where diverse data sources and variable environments pose challenges. Here, robust safety validation is critical but exhaustive ...
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ISBN:
(数字)9798331534677
ISBN:
(纸本)9798331534684
Testing complex systems is crucial for ensuring safety, especially in automated driving, where diverse data sources and variable environments pose challenges. Here, robust safety validation is critical but exhaustive n-way combinatorial testing is impractical due to the vast number of test cases. The STARS framework uses tree-based scenario classifiers to limit feature combinations in a given domain.
Nursing care jobs are predominantly described as burdensome. In particular, the workload and the time available to deal with it are in a state of imbalance. Nurses are very often under time pressure and have to make d...
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Maternal health is among the greatest challenges in the world, especially in rural areas as there lack medical practitioners, they do not have easily accessible publics clinics and transport is difficult. Therefore, h...
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ISBN:
(纸本)9783031770777
Maternal health is among the greatest challenges in the world, especially in rural areas as there lack medical practitioners, they do not have easily accessible publics clinics and transport is difficult. Therefore, high rates of maternal as well as infant morbidity and mortalities are recorded. This research utilizes Artificial Intelligence (AI) with machine learning algorithms to forecast and address maternal health hazards right at their onset stage. The current research utilizes the concept of AI along with many Machine Learning (ML) methods like the Ensemble Learning Model (ELM), Random Forest (RF), K-Nearest Neighbour (KNN), Decision-Tree (DT), XG-Boost (XGB), Cat Boost (CB), and Gradient Boosting (GB), along with Synthetic Minority Over-sampling Technique (SMOTE) algorithm used for dealing with the problem class imbalance within the data set. SMOTE algorithm is utilized for the dataset balancing process. The handling system involves refining data preprocessing with the help of feature engineering and robust data cleaning which makes sure that anomalies do not erode the reliability of the predictive model. The existing methods [1] used RF (90%), DT (87%), XGB (85%), CB (86%), and GB (81%) algorithms and were compared with the accuracies of the proposed models like Logistic Regression (LR), Ensemble Learning Bagging (ELB), Ensemble Learning Stacking (ELS), Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), and Gated Recurrent Unit (GRU). The existing methods used only imbalance dataset. The accuracies of the proposed models with using SMOTE algorithm (balanced dataset) are LR (61.33%), KNN (81%), ELB (92.33%), ELS (90.66%) CNN (40.67%), RNN (59.67%), LSTM (54%), GRU (56%) respectively. Among these methods, ELB achieved 92.33% of accuracy with using SMOTE algorithm using imbalanced dataset. Whereas the accuracies of the proposed models without using SMOTE algorithm (imbalanced dataset) are LR (66.09%), KNN (68.47%)
Single-board computers, such as Raspberry Pi, have enabled a wide variety of monitoring applications. Microcontroller development boards offer a similar functionality but at a lower cost. Still, they are not widely us...
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