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In brain cancer, a biopsy as an invasive procedure is needed in order to differentiate between malignant and benign brain tumor. However, in some cases, it is difficult or harmful to perform such a procedure, to the brain. The aim of this study is to investigate a new method in maximizing the probability of brain cancer type detection without actual biopsy procedure. The proposed method combines both image and statistical analysis for tumor type detection. It employed image filtration and segmentation of the target region of interest with MRI to assure an accurate statistical interpretation of the results. Statistical analysis was based on utilizing the mean, range, box plot, and testing of hypothesis techniques to reach acceptable and accurate results in differentiating between those two types. This method was performed, examined and compared on actual patients with brain tumors. The results showed that the proposed method was quite successful in distinguishing between malignant and benign brain tumor with 95% confident that the results are correct based on statistical testing of hypothesis.

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Streamflow prediction is vital to control the effects of floods and mitigation. Physical prediction model often provides satisfactory results, but these models require massive computational work and hydrogeomorphological variables to develop a prediction system. At the same time, data-driven prediction models are quick to apply, easy to handle, and reliable. This study investigates a new hybrid model, the wavelet bootstrap quadratic response surface, for accurate streamflow prediction. Wavelet analysis is a well-known time-frequency joint analysis technique applied in various fields like biological signals, vibration signals, and hydrological signals. The wavelet analysis is used to denoise the time series data. Bootstrap is a nonparametric method for removing uncertainty that uses an intensive resampling methodology with replacement. The authors analyzed the results of the studied models with different statistical metrics, and it has been observed that the wavelet bootstrap quadratic response surface model provides the most efficient results.

Reliable streamflow prediction is vital to improving river operations, flood avoidance, water supply, and water resources management. Recently, response surface models have been launched in reservoir inflow prediction due to their potential to model composite nonlinear behaviour. Authors develop a hybrid model, wavelet quadratic response surface for reservoir inflow prediction in Chenab river basin, Pakistan. Wavelet transform has extensive applications in the field biomedical, engineering, and hydrology. Discrete wavelet transform technique discloses the structure of nonstationary signals. A proper and careful selection of mother wavelet ensure the best performance of wavelet transform. The choice of a suitable wavelet function participates in implementing the wavelet function used in response surface based models for reliable prediction. The performance of the proposed model is checked on different performance indices for model evaluation. The new developed model, wavelet quadratic response surface, depicts excellent results than other studied models.