![]() Changing these constraints convexifies the merit function. The merit function thus defined has the same maxima under constraints yielding probability vectors. For the first approach (DPA), the a posteriori probability of a tentative labelling is generalised to a continuous labelling. In this paper, we present three optimisation techniques, Deterministic Pseudo-Annealing (DPA), Game Strategy Approach (GSA), and Modified Metropolis Dynamics (MMD), in order to carry out image classification using a Markov random field model. ![]() To improve marketing analysis results, we further show how to combine image features with other statistical data and how it can be done in a graphical user interface (GUI). This paper demonstrates the usage of low level image features for statistical purposes (e.g., clustering or multi-dimensional scaling). IMADAC, designed for experts as well as users without image processing background, combines statistical analysis on both, common statistical data (e.g., age or gender) and image processing methods. (Proceedings of the 2nd ACM international conference on multimedia retrieval, ICMR ’12, pp. Institute of Computer Science, Brandenburg University of Technology, Cottbus, 2012) and Zellhöfer et al. ![]() In this paper we introduce IMADAC, a statistical software in expansion of Naundorf et al. Common statistical tools like SPSS, SAS, R, MATLAB, or RapidMiner still provide none or insufficient image processing packages. Today, several thousand digital images are taken and published every day but not used for marketing purposes. The strongly growing number of available images reveals a great opportunity for a new age in the field of statistical analysis. This has made it more efficient in forecasting stochastic river flow behaviour compared to the other developed hybrid models. The unique architecture of developed SVR-GA due to the ability of the GA optimizer to tune the internal parameters of the SVR model provides a robust learning process. In addition, it was found to improve the accuracy in forecasting high flow events. The results revealed the potential of the hybridized support vector regression model with a genetic algorithm (SVR-GA) over the other ML forecasting models for monthly river flow forecasting using 90%-10% data division. Several statistical indicators are computed to verify the performance of the models. Three data division modeling scenarios were inspected including 70%-30%, 80%-20, and 90%-10%. ![]() The effectiveness of data division influence on the ML models process was investigated. Four different ML models were developed for river flow forecasting located in semiarid region, Iraq. This is owing to their capacity to simulate the complex phenomena associated with hydrological and environmental processes. The application of machine learning (ML) models in forecasting river flow has grown rapidly. The hydrological process has a dynamic nature characterised by randomness and complex phenomena. The authors use accuracy and Kappa result produced by methods to show how good the data is for prediction and the prediction number will be produced for further analysis For analyzing the suicide data, the methods that can be used such as deep learning, decision tree for modeling the data, and KNN to classify the data attributes into groups so it is easier to analyze. With RapidMiner, authors can compare datasets containing socio-economic information at a level that is appropriate to the year and country. Rapid-Miner is one of the well-known data mining tools and is used for data mining. Data mining involves exploring and analyzing large amounts of knowledge to seek out patterns for giant data. Data mining and machine learning have additionally been used to support the clinical management of suicide across medicine and analysis, medication management, and the activity of medical care delivery. Prediction analytics in big data helps to spot individuals in crisis to intervene with emotional support, crisis and psych educational resources, and alerts for emergency help. During this research, authors are going to take advantage of data mining in optimizing suicide risk prediction in each country occurring, and each generation which has committed suicide in each country. The present process of evaluating suicide risk is very subjective, which may limit the efficacy and accuracy of prediction efforts. Suicide may be a growing public health concern with a worldwide prevalence of roughly 800,000 deaths per annum.
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