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Nowadays, embedded systems are comprised of heterogeneous multi-core architectures, i.e., CPUs and GPUs. If the application is mapped to an appropriate processing core, then these architectures provide many performance benefits to applications. Typically, programmers map sequential applications to CPU and parallel applications to GPU. The task mapping becomes challenging because of the usage of evolving and complex CPU- and GPU-based architectures. This paper presents an approach to map the OpenCL application to heterogeneous multi-core architecture by determining the application suitability and processing capability. The classification is achieved by developing a machine learning-based device suitability classifier that predicts which processor has the highest computational compatibility to run OpenCL applications. In this paper, 20 distinct features are proposed that are extracted by using the developed LLVM-based static analyzer. In order to select the best subset of features, feature selection is performed by using both correlation analysis and the feature importance method. For the class imbalance problem, we use and compare synthetic minority over-sampling method with and without feature selection. Instead of hand-tuning the machine learning classifier, we use the tree-based pipeline optimization method to select the best classifier and its hyper-parameter. We then compare the optimized selected method with traditional algorithms, i.e., random forest, decision tree, Naïve Bayes and KNN. We apply our novel approach on extensively used OpenCL benchmarks, i.e., AMD and Polybench. The dataset contains 653 training and 277 testing applications. We test the classification results using four performance metrics, i.e., F-measure, precision, recall and R 2 . The optimized and reduced feature subset model achieved a high F-measure of 0.91 and R 2 of 0.76. The proposed framework automatically distributes the workload based on the application requirement and processor compatibility. [ABSTRACT FROM AUTHOR]

Cervical cancer is one of the deadliest diseases in women worldwide. It is caused by long-term infection of the skin cells and mucosal cells of the genital area of women. The most disturbing thing about this cancer is the fact that it does not show any symptoms when it occurs. In the diagnosis and prognosis of cervical cancer disease, machine learning has the potential to help detect it at an early stage. In this paper, we analyzed different supervised machine learning techniques to detect cervical cancer at an early stage. To train the machine learning model, a cervical cancer dataset from the UCI repository was used. The different methods were evaluated using this dataset of 858 cervical cancer patients with 36 risk factors and one outcome variable. Six classification algorithms were applied in this study, including an artificial neural network, a Bayesian network, an SVM, a random tree, a logistic tree, and an XG-boost tree. All models were trained with and without a feature selection algorithm to compare the performance and accuracy of the classifiers. Three feature selection algorithms were used, namely (i) relief rank, (ii) wrapper method and (iii) LASSO regression. The maximum accuracy of 94.94% was recorded using XG Boost with complete features. It is also observed that for this dataset, in some cases, the feature selection algorithm performs better. Machine learning has been shown to have advantages over traditional statistical models when it comes to dealing with the complexity of large-scale data and uncovering prognostic features. It offers much potential for clinical use and for improving the treatment of cervical cancer. However, the limitations of prediction studies and models, such as simplified, incomplete information, overfitting, and lack of interpretability, suggest that further efforts are needed to improve the accuracy, reliability, and practicality of clinical outcome prediction. [ABSTRACT FROM AUTHOR]