7,087 research outputs found

    Specification-Driven Predictive Business Process Monitoring

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    Predictive analysis in business process monitoring aims at forecasting the future information of a running business process. The prediction is typically made based on the model extracted from historical process execution logs (event logs). In practice, different business domains might require different kinds of predictions. Hence, it is important to have a means for properly specifying the desired prediction tasks, and a mechanism to deal with these various prediction tasks. Although there have been many studies in this area, they mostly focus on a specific prediction task. This work introduces a language for specifying the desired prediction tasks, and this language allows us to express various kinds of prediction tasks. This work also presents a mechanism for automatically creating the corresponding prediction model based on the given specification. Differently from previous studies, instead of focusing on a particular prediction task, we present an approach to deal with various prediction tasks based on the given specification of the desired prediction tasks. We also provide an implementation of the approach which is used to conduct experiments using real-life event logs.Comment: This article significantly extends the previous work in https://doi.org/10.1007/978-3-319-91704-7_7 which has a technical report in arXiv:1804.00617. This article and the previous work have a coauthor in commo

    Thirty Years of Machine Learning: The Road to Pareto-Optimal Wireless Networks

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    Future wireless networks have a substantial potential in terms of supporting a broad range of complex compelling applications both in military and civilian fields, where the users are able to enjoy high-rate, low-latency, low-cost and reliable information services. Achieving this ambitious goal requires new radio techniques for adaptive learning and intelligent decision making because of the complex heterogeneous nature of the network structures and wireless services. Machine learning (ML) algorithms have great success in supporting big data analytics, efficient parameter estimation and interactive decision making. Hence, in this article, we review the thirty-year history of ML by elaborating on supervised learning, unsupervised learning, reinforcement learning and deep learning. Furthermore, we investigate their employment in the compelling applications of wireless networks, including heterogeneous networks (HetNets), cognitive radios (CR), Internet of things (IoT), machine to machine networks (M2M), and so on. This article aims for assisting the readers in clarifying the motivation and methodology of the various ML algorithms, so as to invoke them for hitherto unexplored services as well as scenarios of future wireless networks.Comment: 46 pages, 22 fig

    A Classifying Procedure for Signaling Turning Points

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    A Hidden Markov Model (HMM) is used to classify an out of sample observation vector into either of two regimes. This leads to a procedure for making probability forecasts for changes of regimes in a time series, i.e. for turning points. Instead o maximizing a likelihood, the model is estimated with respect to known past regimes. This makes it possible to perform feature extraction and estimation for different forecasting horizons. The inference aspect is emphasized by including a penalty for a wrong decision in the cost function. The method is tested by forecasting turning points in the Swedish and US economies, using leading data. Clear and early turning point signals are obtained, contrasting favourable with earlier HMM studies. Some theoretical arguments for this are given.Business Cycle; Feature Extraction; Hidden Markov Switching-Regime Model; Leading Indicator; Probability Forecast.

    Development and Investigation of Cost-Sensitive Pruned Decision Tree Model for Improved Schizophrenia Diagnosis

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    Schizophrenia is often characterized by delusions, hallucinations, and other cognitive difficulties, affects approximately seventy million adults globally. This study presents a cost-sensitive pruned Decision Tree J48 model for fast and accurate diagnosis of Schizophrenia. The model implements supervised learning procedures with 10-fold cross-validation resampling method and utilizes unstructured filter to replace missing values in the data with the modal values of corresponding features. Features are selected using Pearson’s correlation on hot-coded data to detect redundancy in data. Cost matrix is designed to minimize the tendencies of the J48 algorithm to predict false negative outcomes. This consequently reduces the error of the model in diagnosing a Schizophrenia candidate as free from the disease. The model is found to significantly diagnose Schizophrenia with 78% accuracy, 89.7% sensitivity, 57.4% specificity and Area under the Receiver Operator Characteristic (ROC) curve of 0.895. The ROC curve is also seen to distinguish Schizophrenia from other conditions with similar symptoms. These results show the potential of machine-learning models for quick, effective diagnosis of schizophrenia
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