Breast diseases are a group of diseases that appear in different forms. An entire group of these diseases is breast cancer. This disease is one of the most important and common diseases in women. A machine learning system has been trained to identify specific patterns using an algorithm in a machine learning system to diagnose breast cancer. Therefore, designing a feature extraction method is essential to decrease the computation time. In this article, a two-dimensional contourlet is utilized as the input image based on the Breast Cancer Ultrasound Dataset. The sub-banded contourlet coefficients are modeled using the time-dependent model. The features of the time-dependent model are considered the leading property vector. The extracted features are applied separately to determine breast cancer classes based on classification methods. The classification is performed for the diagnosis of tumor types. We used the time-dependent approach to feature contourlet sub-bands from three groups of benign, malignant, and health control test samples. The final feature of 1200 ultrasound images used in three categories is trained based on k-nearest neighbor, support vector machine, decision tree, random forest, and linear discrimination analysis approaches, and the results are recorded. The decision tree results show that the method’s sensitivity is 87.8%, 92.0%, and 87.0% for normal, benign, and malignant, respectively. The presented feature extraction method is compatible with the decision tree approach for this problem. Based on the results, the decision tree architecture with the highest accuracy is the more accurate and compatible method for diagnosing breast cancer using ultrasound images.
Understanding the drug solubility behavior is likely the first essential requirement for designing the supercritical technology for pharmaceutical processing. Therefore, this study utilizes different machine learning scenarios to simulate the solubility of twelve non-steroidal anti-inflammatory drugs (NSAIDs) in the supercritical carbon dioxide (SCCO2). The considered NSAIDs are Fenoprofen, Flurbiprofen, Ibuprofen, Ketoprofen, Loxoprofen, Nabumetone, Naproxen, Nimesulide, Phenylbutazone, Piroxicam, Salicylamide, and Tolmetin. Physical characteristics of the drugs (molecular weight and melting temperature), operating conditions (pressure and temperature), and solvent property (SCCO2 density) are effectively used to estimate the drug solubility. Monitoring and comparing the prediction accuracy of twelve intelligent paradigms from three categories (artificial neural networks, support vector regression, and hybrid neuro-fuzzy) approves that adaptive neuro-fuzzy inference is the best tool for the considered task. The hybrid optimization strategy adjusts the cluster radius of the subtractive clustering membership function to 0.6111. This model estimates 254 laboratory-measured solubility data with the AAPRE = 3.13%, MSE = 2.58 × 10–9, and R2 = 0.99919. The leverage technique confirms that outliers may poison less than four percent of the experimental data. In addition, the proposed hybrid paradigm is more reliable than the equations of state and available correlations in the literature. Experimental measurements, model predictions, and relevancy analyses justified that the drug solubility in SCCO2 increases by increasing temperature and pressure. The results show that Ibuprofen and Naproxen are the most soluble and insoluble drugs in SCCO2, respectively.
The dew point pressure (DPP) is a crucial thermodynamic property for gas reservoir performance evaluation, gas/condensate characterization, reservoir development and management, and downstream facility design. However, dew point pressure measurement is an expensive and time-consuming task; its estimation using the thermodynamic approaches has convergency problems, and available empirical correlations often provide high uncertainty levels. In this paper, the hybrid neuro-fuzzy connectionist paradigm is developed using 390 literature measurements. The adaptive neuro-fuzzy inference system (ANFIS) topology, including the training algorithm and cluster radius (radii), was determined by combining trial-and-error and statistical analyses. The hybrid optimization algorithm and radii=0.675 are distinguished as the best characteristics for the ANFIS model. A high value of observed R2 = 0.97948 confirms the excellent performance of the designed approach for calculating the DPP of retrograde gas condensate reservoirs. Furthermore, visual inspections and statistical indices are employed to compare the ANFIS reliability and available empirical correlations. The results showed that the ANFIS model is more accurate than the well-known empirical correlations and previous intelligent paradigms in the literature. The designed ANFIS model, the best empirical correlation, and the most accurate intelligent paradigm in the literature present the absolute average relative deviation (AARD) of 1.60%, 11.25%, 2.10%, and, respectively.
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