In software industry, project managers usually rely on their previous experience to estimate the number men/hours required for each software project. The accuracy of such estimates is a key factor ...for the efficient application of human resources. Machine learning techniques such as radial basis function (RBF) neural networks, multi-layer perceptron (MLP) neural networks, support vector regression (SVR), bagging predictors and regression-based trees have recently been applied for estimating software development effort. Some works have demonstrated that the level of accuracy in software effort estimates strongly depends on the values of the parameters of these methods. In addition, it has been shown that the selection of the input features may also have an important influence on estimation accuracy.
This paper proposes and investigates the use of a genetic algorithm method for simultaneously
(1) select an optimal input feature subset and
(2) optimize the parameters of machine learning methods, aiming at a higher accuracy level for the software effort estimates.
Simulations are carried out using six benchmark data sets of software projects, namely, Desharnais, NASA, COCOMO, Albrecht, Kemerer and Koten and Gray. The results are compared to those obtained by methods proposed in the literature using neural networks, support vector machines, multiple additive regression trees, bagging, and Bayesian statistical models.
In all data sets, the simulations have shown that the proposed GA-based method was able to improve the performance of the machine learning methods. The simulations have also demonstrated that the proposed method outperforms some recent methods reported in the recent literature for software effort estimation. Furthermore, the use of GA for feature selection considerably reduced the number of input features for five of the data sets used in our analysis.
The combination of input features selection and parameters optimization of machine learning methods improves the accuracy of software development effort. In addition, this reduces model complexity, which may help understanding the relevance of each input feature. Therefore, some input parameters can be ignored without loss of accuracy in the estimations.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UL, UM, UPCLJ, UPUK
Air pollution is one of the main environmental issues faced by most countries around the world. Forecasting air pollution occurrences is an essential topic in air quality research due to the increase ...in awareness of its association with public health effects, and its development is vital to managing air quality. However, most previous studies have focused on enhancing accuracy, while very few have addressed uncertainty analysis, which may lead to insufficient results. The fuzzy time-series model is a better option in air pollution forecasting. Nevertheless, it has a limitation caused by utilizing a random partitioning of the universe of discourse. This study proposes a novel Markov weighted fuzzy time-series model based on the optimum partition method. Fitting the optimum partition method has been done based on five different partition methods via two stages. The proposed model is first applied for forecasting air pollution using air pollution index (API) data collected from an air monitoring station located in Klang city, Malaysia. The performance of the proposed model is evaluated based on three statistical criteria, which are the mean absolute percentage error, mean squared error and Theil’s
U
statistic, using the daily API data. For further validation of the model, it is also implemented for benchmark enrolment data from the University of Alabama. According to the analysis results, the proposed model greatly improved the performance of air pollution index and enrolment prediction accuracy, for which it outperformed several state-of-the-art fuzzy time-series models and classic time-series models. Thus, the proposed model could be a better option for air quality forecasting for managing air pollution.
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EMUNI, FIS, FZAB, GEOZS, GIS, IJS, IMTLJ, KILJ, KISLJ, MFDPS, NLZOH, NUK, OBVAL, OILJ, PNG, SAZU, SBCE, SBJE, SBMB, SBNM, UKNU, UL, UM, UPUK, VKSCE, ZAGLJ
Air pollution is a worldwide problem faced by most countries across the world. Prediction of air pollution is crucial in air quality research since it is related to public health effects. The ...symmetry concept of fuzzy data transformation from a single point (crisp) to a fuzzy number is essential for the forecasting model. Fuzzy time series (FTS) is applied for predicting air pollution; however, it has a limitation caused by utilizing an arbitrary number of intervals. This study involves predicting the daily air pollution index using the FTS Markov chain (FTSMC) model based on a grid method with an optimal number of partitions, which can greatly develop the model accuracy for air pollution. The air pollution index (API) data, which was collected from Klang, Malaysia, is considered in the analysis. The model has been validated using three statistical criteria, which are the root mean (RMSE), the mean absolute percentage error (MAPE), and the Thiels’ U statistic. Also, the model’s validation has been investigated by comparison with some of the famous statistical models. The results of the proposed model demonstrated outperformed the other models. Thus, the proposed model could be a better option in air pollution forecasting that can be useful for managing air quality.
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In the internet of things (IoT), high-dimensional time series data are generated continuously and recorded from different data sources; moreover, these time series are characterized by intrinsic ...changes known as concept drifts. Beside, decision-making in IoT applications may often involve multiple factors and criteria. Therefore, methods capable of handling high-dimensional non-stationary time series and many outputs are of great value in IoT applications. An important gap in the literature is the absence of fuzzy time series (FTS) multiple-input multiple-output (MIMO) methods. To fill this gap, we present a new methodology for forecasting high-dimensional non-stationary time series called MO-ENSFTS (multiple output embedding non-stationary fuzzy time series). MO-ENSFTS is a first-order MIMO multivariate model. We apply a combination of data embedding transformation and a non-stationary FTS model. We tested the proposed methodology on four real-world high-dimensional IoT time-series data sets. The proposed approach is a data-driven method, which is flexible and adaptable for many IoT applications. The computational results show that the proposed method outperforms recurrent neural networks, random forests and support vector regression methods, and is more parsimonious than deep learning methods.
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DOBA, EMUNI, FIS, FZAB, GEOZS, GIS, IJS, IMTLJ, IZUM, KILJ, KISLJ, MFDPS, NLZOH, NUK, ODKLJ, OILJ, PILJ, PNG, SAZU, SBCE, SBJE, SBMB, SBNM, UILJ, UKNU, UL, UM, UPUK, VKSCE, ZAGLJ
The COVID-19 pandemic due to the SARS-CoV-2 coronavirus has directly impacted the public health and economy worldwide. To overcome this problem, countries have adopted different policies and ...non-pharmaceutical interventions for controlling the spread of the virus. This paper proposes the COVID-ABS, a new SEIR (Susceptible-Exposed-Infected-Recovered) agent-based model that aims to simulate the pandemic dynamics using a society of agents emulating people, business and government. Seven different scenarios of social distancing interventions were analyzed, with varying epidemiological and economic effects: (1) do nothing, (2) lockdown, (3) conditional lockdown, (4) vertical isolation, (5) partial isolation, (6) use of face masks, and (7) use of face masks together with 50% of adhesion to social isolation. In the impossibility of implementing scenarios with lockdown, which present the lowest number of deaths and highest impact on the economy, scenarios combining the use of face masks and partial isolation can be the more realistic for implementation in terms of social cooperation. The COVID-ABS model was implemented in Python programming language, with source code publicly available. The model can be easily extended to other societies by changing the input parameters, as well as allowing the creation of a multitude of other scenarios. Therefore, it is a useful tool to assist politicians and health authorities to plan their actions against the COVID-19 epidemic.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
Time series forecasting is widely used to predict future values in several applications, such as climate, industries demand, stock markets and business strategies. Fuzzy set based time series (FTS) ...forecasting models are widely used to solve forecasting problems because of fuzzy logic’s ability to deal with uncertainties. But designing a FTS model can be a complex task since there are important hyperparameters to be defined that directly influence performance, mainly the number of fuzzy sets, model order, and membership function used. Therefore, this paper proposes ADPT2FTS, an interval type-2 FTS model that uses an Autonomous Data Partitioning Algorithm (ADP), capable of improving uncertainty handling and relying on an autonomous method for defining the number of fuzzy sets to be used. We also investigated how changing model order and membership function influenced forecasting accuracy to choose the best parameters to design the best performing model possible. ADPT2FTS performance was evaluated using two complex financial datasets: the European Brent Oil Prices (EBOP) and the Taiwan Capitalization Weighted Stock Index (TAIEX); and two benchmark time series in the literature: University of Alabama Enrollments and Yealy Sunspot. The proposed model was compared to “state-of-art” forecasting models in the literature, designed with several different working mechanisms such as type-1 and type-2 fuzzy logic, neural networks and regression algorithms. Results show that ADPT2FTS achieved better performance and lower error values in terms of RMSE and MAPE metrics.
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EMUNI, FIS, FZAB, GEOZS, GIS, IJS, IMTLJ, KILJ, KISLJ, MFDPS, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, SBMB, SBNM, UKNU, UL, UM, UPUK, VKSCE, ZAGLJ
Time series classification (TSC) is a supervised task in which time series data are associated with predefined classes. Time ordering and correlations of the samples should be considered and ...observations can have different lengths. Therefore, learning compact and useful representations is an important approach for TSC. Fuzzy time series (FTS) methods have the ability to find temporal patterns and transition rules. Probabilistic weighted FTS (PWFTS) uses the empirical probabilities to find the model of a TS. Despite its good performance, this method has been used exclusively for forecasting problems. Therefore, in this study we propose a novel TSC method using the PWFTS representation learning weights as features in conjunction to a classifier algorithm. We show that PWFTS method is able to learn useful representations for time series in low computational time. Our algorithm was tested in 24 datasets available in the UCR TSC archive and compared with state-of-the-art methods: fuzzy cognitive maps, Rocket, and TS2Vec in combination to the classifiers random forest and support vector machine. The classification accuracy and execution time required to find the features were evaluated. The results show that our fuzzy-probabilistic representation learning method is similar in accuracy to the other methods, and the time consumption for PWFTS computation was two to five orders of magnitude faster than the other methods. The results suggest that our approach can be used in time sensitive applications and in systems with hardware limitations.
There is an urgent need to find consensus on screening, diagnosing and treating all degrees of dysglycemia that may occur during pregnancies in Brazil, considering that many cases of dysglycemia in ...pregnant women are currently not diagnosed, leading to maternal and fetal complications. For this reason the Brazilian Diabetes Society (SBD) and the Brazilian Federation of Gynecology and Obstetrics Societies (FEBRASGO), got together to introduce this proposal. We present here a joint consensus regarding the standardization of clinical management for pregnant women with any degree of dysglycemia, on the basis of current information, to improve medical assistance and to avoid related complications of dysglycemia in pregnancy to the mother and the fetus. This consensus aims to standardize the diagnosis among general practitioners, endocrinologists and obstetricians allowing the dissemination of information in basic health units, public and private services, that are responsible for screening, diagnosing and treating disglycemic pregnant patients.
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Fuzzy cognitive maps (FCMs) have demonstrated considerable success in time series forecasting and are adept at handling uncertainties and capturing the dynamics of complex systems. Nevertheless, ...challenges still remain in the handling of multivariate high-dimensional time series using a time-effective learning algorithm. This article introduces multiple-input multiple-output randomized high-order FCM (MRHFCM), a new methodology for predicting high-dimensional time series in multiple-input-multiple-output systems. MRHFCM represents a hybrid method that combines data embedding transformation, randomized high-order FCM (R-HFCM), and an echo state network. The core of MRHFCM involves a cascade of R-HFCMs termed the CR-HFCM model. Each CR-HFCM comprises three layers: 1) the input layer, 2) reservoir (internal layer), and 3) output layer. Notably, only the output layer is trainable, employing the least squares minimization algorithm. The weights within each subreservoir are randomly chosen and remain unchanged throughout the training procedure. Three real-world high-dimensional datasets are utilized to assess the performance of the proposed MRHFCM method. The results obtained reveal that our approach outperforms some existing baseline and state-of-the-art machine learning and deep learning forecasting techniques in terms of both accuracy and parsimony.
Seismic microzonation represents a basic tool for prevention activity planning and land management. An extensive and detailed microzonation study was performed with reference to the territory of the ...Municipality of Cavezzo, damaged during the seismic sequence hitting Emilia-Romagna Region, Northern Italy, in 2012. In this paper, we discuss the work carried out to characterize the spatial variability of ground motion amplification due to local soil conditions in the municipality area.
An inter- and multi-disciplinary approach is presented, involving geotechnical engineers, geophysicists, geologists and seismologists from different institutions, to thoroughly characterize the territory using complementary techniques with different level of resolution and confidence. A considerable amount of geomorphological, geological, hydrogeological, seismological, geotechnical and geophysical investigations was collected and processed for the purpose. A GIS-based (Geographic Information System) platform was initially setup to manage the gathered data, which now includes the results of about 1000 geotechnical and geophysical tests. Such an extended dataset was then used as a primary constraint for the creation of a comprehensive pseudo-3D geotechnical and seismo-stratigraphic model of the territory, consisting of a dense grid of one-dimensional vertical profiles to depict the variability of the soil properties over the area. The model was finally used as input for linear-equivalent ground response analysis. For the calculation of the amplification factors, special emphasis was given to the treatment and propagation of the uncertainties of the model parameters, whose different realizations have been accounted through a logic tree approach.
•The spatial variability of ground motion was analysed in Cavezzo, Italy.•Geological, seismological, geotechnical and geophysical analyses were performed.•A GIS platform was setup including about 1000 geotechnical and geophysical tests.•A pseudo-3d seismo-stratigraphic model was created to compute amplification factors.
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