Investigation on performance of particle swarm optimization (PSO) algorithm based fuzzy inference system (PSOFIS) in a combination of CFD modeling for prediction of fluid flow

Recent advancements toward the incremsent of drug solubility using environmentally-friendly supercritical CO2: a machine learning perspective

Inadequate bioavailability of therapeutic drugs, which is often the consequence of their unacceptable solubility and dissolution rates, is an indisputable operational challenge of pharmaceutical companies due to its detrimental effect on the therapeutic efficacy. Over the recent decades, application of supercritical fluids (SCFs) (mainly SCCO2) has attracted the attentions of many scientists as promising alternative of toxic and environmentally-hazardous organic solvents due to possessing positive advantages like low flammability, availability, high performance, eco-friendliness and safety/simplicity of operation. Nowadays, application of different machine learning (ML) as a versatile, robust and accurate approach for the prediction of different momentous parameters like solubility and bioavailability has been of great attentions due to the non-affordability and time-wasting nature of experimental investigations. The prominent goal of this article is to review the role of different ML-based tools for the prediction of solubility/bioavailability of drugs using SCCO2. Moreover, the importance of solubility factor in the pharmaceutical industry and different possible techniques for increasing the amount of this parameter in poorly-soluble drugs are comprehensively discussed. At the end, the efficiency of SCCO2 for improving the manufacturing process of drug nanocrystals is aimed to be discussed.

ANN-based swarm intelligence for predicting expansive soil swell pressure and compression strength

This research suggests a robust integration of artificial neural networks (ANN) for predicting swell pressure and the unconfined compression strength of expansive soils (PsUCS-ES). Four novel ANN-based models, namely ANN-PSO (i.e., particle swarm optimization), ANN-GWO (i.e., grey wolf optimization), ANN-SMA (i.e., slime mould algorithm) alongside ANN-MPA (i.e., marine predators' algorithm) were deployed to assess the PsUCS-ES. The models were trained using the nine most influential parameters affecting PsUCS-ES, collected from a broader range of 145 published papers. The observed results were compared with the predictions made by the ANN-based metaheuristics models. The efficacy of all these formulated models was evaluated by utilizing mean absolute error (MAE), Nash-Sutcliffe (NS) efficiency, performance index ρ, regression coefficient (R2), root mean square error (RMSE), ratio of RMSE to standard deviation of actual observations (RSR), variance account for (VAF), Willmott's index of agreement (WI), and weighted mean absolute percentage error (WMAPE). All the developed models for Ps-ES had an R significantly > 0.8 for the overall dataset. However, ANN-MPA excelled in yielding high R values for training dataset (TrD), testing dataset (TsD), and validation dataset (VdD). This model also exhibited the lowest MAE of 5.63%, 5.68%, and 5.48% for TrD, TsD, and VdD, respectively. The results of the UCS model's performance revealed that R exceeded 0.9 in the TrD. However, R decreased for TsD and VdD. Also, the ANN-MPA model yielded higher R values (0.89, 0.93, and 0.94) and comparatively low MAE values (5.11%, 5.67, and 3.61%) in the case of PSO, GWO, and SMA, respectively. The UCS models witnessed an overfitting problem because the aforementioned R values of the metaheuristics were 0.62, 0.56, and 0.58 (TsD), respectively. On the contrary, no significant observation was recorded in the VdD of UCS models. All the ANN-base models were also tested using the a-20 index. For all the formulated models, maximum points were recorded to lie within ± 20% error. The results of sensitivity as well as monotonicity analyses depicted trending results that corroborate the existing literature. Therefore, it can be inferred that the recently built swarm-based ANN models, particularly ANN-MPA, can solve the complexities of tuning the hyperparameters of the ANN-predicted PsUCS-ES that can be replicated in practical scenarios of geoenvironmental engineering.

Theoretical investigations on modeling blood flow through vessel for understanding effectiveness of magnetic nanocarrier drug delivery systems

For cancer therapy, the focus is now on targeting the chemotherapy drugs to cancer cells without damaging other normal cells. The new materials based on bio-compatible magnetic carriers would be useful for targeted cancer therapy, however understanding their effectiveness should be done. This paper presents a comprehensive analysis of a dataset containing variables x(m), y(m), and U(m/s), where U represents velocity of blood through vessel containing ferrofluid. The effect of external magnetic field on the fluid flow is investigated using a hybrid modeling. The primary aim of this research endeavor was to construct precise and dependable predictive models for velocity, utilizing the provided input variables. Several base models, including K-nearest neighbors (KNN), decision tree (DT), and multilayer perceptron (MLP), were trained and evaluated. Additionally, an ensemble model called AdaBoost was implemented to further enhance the predictive performance. The hyper-parameter optimization technique, specifically the BAT optimization algorithm, was employed to fine-tune the models. The results obtained from the experiments demonstrated the effectiveness of the proposed approach. The combination of the AdaBoost algorithm and the decision tree model yielded a highly impressive score of 0.99783 in terms of R2, indicating a strong predictive performance. Additionally, the model exhibited a low error rate, as evidenced by the root mean square error (RMSE) of 5.2893 × 10-3. Similarly, the AdaBoost-KNN model exhibited a high score of 0.98524 using R2 metric, with an RMSE of 1.3291 × 10-2. Furthermore, the AdaBoost-MLP model obtained a satisfactory R2 score of 0.99603, accompanied by an RMSE of 7.1369 × 10-3.

The roles of artificial intelligence techniques for increasing the prediction performance of important parameters and their optimization in membrane processes: A systematic review

Membrane-based separation processes has been recently of significant global interest compared to other conventional separation approaches due to possessing undeniable advantages like superior performance, environmentally-benign nature and simplicity of application. Computational simulation of fluids has shown its undeniable role in modeling and simulation of numerous physical/chemical phenomena including chemical engineering, chemical reaction, aerodynamics, drug delivery and plasma physics. Definition of fluids can be occurred using the Navier-Stokes equations, but solving the equations remains an important challenge. In membrane-based separation processes, true perception of fluid's manner through disparate membrane modules is an important concern, which has been significantly limited applying numerical/computational procedures such s computational fluid dynamics (CFD). Despite this noteworthy advantage, the optimization of membrane processes using CFD is time-consuming and expensive. Therefore, combination of artificial intelligence (AI) and CFD can result in the creation of a promising hybrid model to accurately predict the model results and appropriately optimize membrane processes and phase separation. This paper aims to provide a comprehensive overview about the advantages of commonly-employed ML-based techniques in combination with the CFD to intelligently increase the optimization accuracy and predict mass transfer and the unfavorable events (i.e., fouling) in various membrane processes. To reach this objective, four principal strategies of AI including SL, USL, SSL and ANN were explained and their advantages/disadvantages were discussed. Then after, prevalent ML-based algorithm for membrane-based separation processes. Finally, the application potential of AI techniques in different membrane processes (i.e., fouling control, desalination and wastewater treatment) were presented.

Comparing three types of data-driven models for monthly evapotranspiration prediction under heterogeneous climatic conditions

Evapotranspiration is one of the most important hydro-climatological components which directly affects agricultural productions. Therefore, its forecasting is critical for water managers and irrigation planners. In this study, adaptive neuro-fuzzy inference system (ANFIS) model has been hybridized by differential evolution (DE) optimization algorithm as a novel approach to forecast monthly reference evapotranspiration (ET0). Furthermore, this model has been compared with the classic stochastic time series model. For this, the ET0 rates were calculated on a monthly scale during 1995–2018, based on FAO-56 Penman–Monteith equation and meteorological data including minimum air temperature, maximum air temperature, mean air temperature, minimum relative humidity, maximum relative humidity & sunshine duration. The investigation was performed on 6 stations in different climates of Iran, including Bandar Anzali & Ramsar (per-humid), Gharakhil (sub-humid), Shiraz (semi-arid), Ahwaz (arid), and Yazd (extra-arid). The models’ performances were evaluated by the criteria percent bias (PB), root mean squared error (RMSE), normalized RMSE (NRMSE), and Nash-Sutcliff (NS) coefficient. Surveys confirm the high capability of the hybrid ANFIS-DE model in monthly ET0 forecasting; so that the DE algorithm was able to improve the accuracy of ANFIS, by 16% on average. Seasonal autoregressive integrated moving average (SARIMA) was the most suitable pattern among the time series stochastic models and superior to its competitors, ANFIS and ANFIS-DE. Consequently, the SARIMA was suggested more appropriate for monthly ET0 forecasting in all the climates, due to its simplicity and parsimony. Comparison between the different climates confirmed that the climate type significantly affects the forecasting accuracies: it’s revealed that all the models work better in extra-arid, arid and semi-arid climates, than the humid and per-humid areas.

Attribute reduction based scheduling algorithm with enhanced hybrid genetic algorithm and particle swarm optimization for optimal device selection

The applications of the Internet of Things in different areas and the resources that demand these applications are on the increase. However, the limitations of the IoT devices such as processing capability, storage, and energy are challenging. Computational offloading is introduced to ameliorate the limitations of mobile devices. Offloading heavy data size to a remote node introduces the problem of additional delay due to transmission. Therefore, in this paper, we proposed Dynamic tasks scheduling algorithm based on attribute reduction with an enhanced hybrid Genetic Algorithm and Particle Swarm Optimization for optimal device selection. The proposed method uses a rank accuracy estimation model to decide the rank-1 value to be applied for the decomposition. Then canonical Polyadic decomposition-based attribute reduction is applied to the offload-able task to reduce the data size. Enhance hybrid genetic algorithm and particle Swarm optimization are developed to select the optimal device in either fog or cloud. The proposed algorithm improved the response time, delay, number of offloaded tasks, throughput, and energy consumption of the IoT requests. The simulation is implemented with iFogSim and java programming language. The proposed method can be applied in smart cities, monitoring, health delivery, augmented reality, and gaming among others.