Prediction Accuracy Of Developed Models Research Articles

Diagnostic performance of ultrasound-based artificial intelligence for predicting key molecular markers in breast cancer: A systematic review and meta-analysis.

Breast cancer (BC) diagnosis and treatment rely heavily on molecular markers such as HER2, Ki67, PR, and ER. Currently, these markers are identified by invasive methods. This meta-analysis investigates the diagnostic accuracy of ultrasound-based radiomics as a novel approach to predicting these markers. A comprehensive search of PubMed, EMBASE, and Web of Science databases was conducted to identify studies evaluating ultrasound-based radiomics in BC. Inclusion criteria encompassed research on HER2, Ki67, PR, and ER as key molecular markers. Quality assessment using Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) and Radiomics Quality Score (RQS) was performed. The data extraction step was performed systematically. Our meta-analysis quantifies the diagnostic accuracy of ultrasound-based radiomics with a sensitivity and specificity of 0.76 and 0.78 for predicting HER2, 0.80, and 0.76 for Ki67 biomarkers. Studies did not provide sufficient data for quantitative PR and ER prediction analysis. The overall quality of studies based on the RQS tool was moderate. The QUADAS-2 evaluation showed that the studies had an unclear risk of bias regarding the flow and timing domain. Our analysis indicated that AI models have a promising accuracy for predicting key molecular biomarkers' status in BC patients. We performed the quantitative analysis for HER2 and Ki67 biomarkers which yielded a moderate to high accuracy. However, studies did not provide adequate data for meta-analysis of ER and PR prediction accuracy of developed models. The overall quality of the studies was acceptable. In future research, studies need to report the results thoroughly. Also, we suggest more prospective studies from different centers.

Wavelet Decomposition and Machine Learning Technique for Predicting Occurrence of Spiders in Pigeon Pea

Influence of weather variables on occurrence of spiders in pigeon pea across locations of seven agro-climatic zones of India was studied in addition to development of forecast models with their comparisons on performance. Considering the non-normal and nonlinear nature of time series data of spiders, non-parametric techniques were applied with developed algorithm based on combinations of wavelet–regression and wavelet–artificial neural network (ANN) models. Haar wavelet filter decomposed each of the series to extract the actual signal from the noisy data. Prediction accuracy of developed models, viz., multiple regression, wavelet–regression, and wavelet–ANN, tested using root mean square error (RMSE) and mean absolute percentage error (MAPE), indicated better performance of wavelet–ANN model. Diebold Mariano (DM) test also confirmed that the prediction accuracy of wavelet–ANN model, and hence its use to forecast spiders in conjunction with the values of pest–defender ratios, would not only reduce insecticidal sprays, but also add ecological and economic value to the integrated pest management of insects of pigeon pea.

Modeling and dynamic simulation of thermal energy storage system for concentrating solar power plant

Thermal energy storage system in concentrating solar power plants can guarantee sustainable and stable electricity output in case of highly unstable solar irradiation conditions. In this paper, the lumped parameter method is used to develop the models of different thermal energy storage systems. In order to improve the reliability as well as the prediction accuracy of developed models, the charging/discharging process is firstly simulated, and then the dynamic characteristics of thermal energy storage systems are fully tested by imposing 15% step disturbance of mass flow. The results show that the charging/discharging characteristics of the three different thermal energy storage systems are almost the same, which have little to do with the storage mediums used in the systems. Besides, for the representative 1MWe solar parabolic trough power plant, a 15% step up disturbance of oil mass flow will result in a small increase (1.8% and 1.3% respectively) on the outlet temperature of oil and molten salt. On the other side, a 15% step down disturbance of oil mass flow will lead a 2.7% and 2.2% decrease on the outlet temperature of oil and molten salt respectively. In order to verify the validity of the proposed models, the simulation results are compared with both the design points and representative experimental data from the 1MWe solar parabolic trough power plant. The results show that the maximum relative error is not more than 1% when comparing with the design points and the maximum relative error is not more than 12% when comparing with the representative experimental data. Conclusions of this paper are good references for system design, control and commissioning of concentrating solar power plants.

Predictive modeling of turning operations under different cooling/lubricating conditions for sustainable manufacturing with machine learning techniques

Sustainable manufacturing is one of the most important and most challenging issues in present industrial scenario. With the intention of diminish negative effects associated with cutting fluids, the machining industries are continuously developing technologies and systems for cooling/lubricating of the cutting zone while maintaining machining efficiency. In the present study, three regression based machine learning techniques, namely, polynomial regression (PR), support vector regression (SVR) and Gaussian process regression (GPR) were developed to predict machining force, cutting power and cutting pressure in the turning of AISI 1045. In the development of predictive models, machining parameters of cutting speed, depth of cut and feed rate were considered as control factors. Since cooling/lubricating techniques significantly affects the machining performance, prediction model development of quality characteristics was performed under minimum quantity lubrication (MQL) and high-pressure coolant (HPC) cutting conditions. The prediction accuracy of developed models was evaluated by statistical error analyzing methods. Results of regressions based machine learning techniques were also compared with probably one of the most frequently used machine learning method, namely artificial neural networks (ANN). Finally, a metaheuristic approach based on a neural network algorithm was utilized to perform an efficient multi-objective optimization of process parameters for both cutting environment.

Machining optimization in rotary ultrasonic drilling of BK-7 through response surface methodology using desirability approach

In the present work, optical glass BK-7 was drilled by rotary ultrasonic machine. Response surface methodology was amalgamated with desirability approach to frame the experimental matrix and seek pragmatic solutions to cope with the real machining problems. Spindle speed, ultrasonic power and feed rate were extensively scrutinized to evaluate the machining proficiency in terms of material removal rate (MRR) and surface roughness (SR). Thereafter, ANOVA check was exercised to scrutinize the adequacy of developed SR and MRR models and cast light on the significant model terms with their impact intensity on responses. The feed rate was observed to be the most influential factor in determining the qualitative (SR) and quantitative (MRR) aspect of the machining process. The machined surface along with tool surface was examined by scanning electron microscope to shed light on the material fractography and tool wear. Processed surface topography revealed the concluding evidence of brittle fracture dominance along with few traces of plastic flow of material. Severe tool wear was observed in the initial stage of experimentation due to bond fracture and grain fracture. Confirmatory tests validated the prediction accuracy of developed models by keeping the error within 5% between the predicted and experimental value at 95% confidence level.

Hydrological flow rate estimation using artificial neural networks: Model development and potential applications

In present paper we develop a model of monthly river flow rate using artificial neural networks, based on the assumption that air temperature and precipitation predetermine the flow rate dynamics. In order to create a reliable prediction model, we used monthly observations made at Lim river basin in southwestern Serbia from 1950 to 2012, since results of this analysis could be significant for hydro-energy production at ``Potpeć" and ``Brodarevo" hydropower plants. Analysis was conducted using multilayer feed-forward perceptron with Levenberg–Marquardt learning algorithm and appropriate number of hidden neurons which provide the most reliable prediction accuracy. Analysis of derived models with different number of hidden nodes indicate that models are insensitive to the number of hidden units. Model with eight hidden nodes was chosen as the most reliable one, providing highest prediction accuracy (the highest values of determination coefficient and Nash–Sutcliffe coefficient). Predictive power of the developed model was tested against the recordings made in period 1991–2012, providing satisfying prediction accuracy. Moreover, Monte-Carlo simulation showed that prediction accuracy of developed models is robust against expected experimental error, confirming that derived models provide reliable predictions of flow rates, which could be used for water management plans and strategies. We also propose two potential applications of derived model: for predicting the future flow rate using the predefined climate models, and for forecasting the hydroenergy production, on the basis of the linear dependence of the observed flow rate and previously produced electric power. These application are verified for the regional climate model EBU-POM, for the period 2013–2100, and using the data on electric power production at hydro powerplant ``Potpeć".