An Optimized Neural Network with Bat Algorithm for DNA Sequence Classification

Recently, many researchers have used nature inspired metaheuristic algorithms due to their ability to perform optimally on complex problems. To solve problems in a simple way, in the recent era bat algorithm has become famous due to its high tendency towards convergence to the global optimum most of the time. But, still the standard bat with random walk has a problem of getting stuck in local minima. In order to solve this problem, this research proposed bat algorithm with levy flight random walk. Then, the proposed Bat with Levy flight algorithm is further hybridized with three different variants of ANN. The proposed BatLFBP is applied to the problem of insulin DNA sequence classification of healthy homosapien. For classification performance, the proposed models such as Bat levy flight Artificial Neural Network (BatLFANN) and Bat levy Flight Back Propagation (BatLFBP) are compared with the other state-of-the-art algorithms like Bat Artificial Neural Network (BatANN), Bat back propagation (BatBP), Bat Gaussian distribution Artificial Neural Network (BatGDANN). And Bat Gaussian distribution back propagation (BatGDBP), in-terms of means squared error (MSE) and accuracy. From the perspective of simulations results, it is show that the proposed BatLFANN achieved 99.88153% accuracy with MSE of 0.001185, and BatLFBP achieved 99.834185 accuracy with MSE of 0.001658 on WL5. While on WL10 the proposed BatLFANN achieved 99.89899% accuracy with MSE of 0.00101, and BatLFBP achieved 99.84473% accuracy with MSE of 0.004553. Similarly, on WL15 the proposed BatLFANN achieved 99.82853% accuracy with MSE of 0.001715, and BatLFBP achieved 99.3262% accuracy with MSE of 0.006738 which achieve better accuracy as compared to the other hybrid models.

     

Emerging applications of nanomaterials in chemical enhanced oil recovery: Progress and perspective

In enhanced oil recovery, different chemical methods utilization improves hydrocarbon recovery due to their fascinating abilities to alter some critical parameters in porous media, such as mobility control, the interaction between fluid to fluid, and fluid to rock surface. For decades the use of surfactant and polymer flooding has been used as tertiary recovery methods. In the current research, the inclusion of nanomaterials in enhanced oil recovery injection fluids solely or in the presence of other chemicals has got colossal interest. The emphasis of this review is on the applicability of nanofluids in the chemical enhanced oil recovery. The responsible mechanisms are an increment in the viscosity of injection fluid, decrement in oil viscosity, reduction in interfacial and surface tension, and alteration of wettability in the rock formation. In this review, important parameters are presented, which may affect the desired behavior of nanoparticles, and the drawbacks of nanofluid and polymer flooding and the need for a combination of nanoparticles with the polymer are discussed. Due to the lack of literature in defining the mechanism of nanofluid in a reservoir, this paper covers majorly all the previous work done on the application of nanoparticles in chemical enhanced oil recovery at home conditions. Finally, the problems associated with the nano-enhanced oil recovery are outlined, and the research gap is identified, which must be addressed to implement polymeric nanofluids in chemical enhanced oil recovery.     

Emerging applications of nanomaterials in chemical enhanced oil recovery:Progress and perspective

In enhanced oil recovery, different chemical methods utilization improves hydrocarbon recovery due to their fascinating abilities to alter some critical parameters in porous media, such as mobility control, the interaction between fluid to fluid, and fluid to rock surface. For decades the use of surfactant and polymer flooding has been used as tertiary recovery methods. In the current research, the inclusion of nanomaterials in enhanced oil recovery injection fluids solely or in the presence of other chemicals has got colossal interest. The emphasis of this review is on the applicability of nanofluids in the chemical enhanced oil recovery. The responsible mechanisms are an increment in the viscosity of injection fluid, decrement in oil viscosity, reduction in interfacial and surface tension, and alteration of wettability in the rock formation. In this review, important parameters are presented,which may affect the desired behavior of nanoparticles, and the drawbacks of nanofluid and polymer flooding and the need for a combination of nanoparticles with the polymer are discussed. Due to the lack of literature in defining the mechanism of nanofluid in a reservoir, this paper covers majorly all the previous work done on the application of nanoparticles in chemical enhanced oil recovery at home conditions. Finally, the problems associated with the nano-enhanced oil recovery are outlined, and the research gap is identified, which must be addressed to implement polymeric nanofluids in chemical enhanced oil recovery.     

Synthesized graphene oxide and fumed aerosil 380 dispersion stability and characterization with partially hydrolyzed polyacrylamide

Hydrolyzed polyacrylamide(HPAM)is a commonly used polymer for the chemicals,mining and refining processes of hydrocarbon but suffers from a persistent high-temperature instability problem.In contrast,the nanoparticle suspension remains a technical challenge because of the strong interactions of van der Waal forces within nanoparticles,which always encourage aggregation.This research sought to improve nanoparticles(NP)stability and polymer(HPAM)rheological properties to improved hydrocarbon recov-ery by utilizing synthesized graphene oxide(GO)nanosheets and fumed Aerosil 380 Silica oxide(SiO2).The aqueous nanocomposites based on HPAM-GO and HPAM-SiO2 in aqueous polymeric solutions have been developed,and its viscoelastic and static behaviour is studied.The results imply that by adding fumed silica NP,the viscoelastic behaviour of HPAM is marginally improved,particularly in high temper-atures and salinity,however,the inclusion of GO's significantly improves the viscosity and stability of the base polymer fluid at high temperatures.The Fourier data for the transformation of the infrared spectrum confirmed that the hydrogen bonding formed between HPAM carbonyl groups and silica NP surface sila-nol functionality and covalent interlinking of electrostatic h-bonding between HPAM and functional GO contributed to the improved stabilization and improved rheological performance that helps to recover high salinity and temperature hydrocarbons.     

Electrospun nanofibers from ferrocene-containing multiblock copolymers prepared via RAFT polymerization with F127 modified precursor

Electrospun nanofibers from copolymers composed of ferrocene side chains and PEO₁₀₀-PPO₆₅-PEO₁₀₀ (F127) were fabricated in chloroform and N,N–dimethyl formamide solutions (V/V = 95:5) at room temperature. Ferrocene-containing multiblock copolymers (PFcMA_n-F127-PFcMA_n) were synthesized through reversible addition-fragmentation chain transfer polymerization using s-1-dodecyl-s''- (α, α'-dimethyl-α''-acetic acid) trithiocarbonate (DDMAT) modified F127 with 2- (methacryloyloxy) ethyl ferrocencarboxylate (FcMA). The structures of as-obtained chemicals were characterized by FTIR, ¹H NMR, and ¹³C NMR. Additionally, the block copolymers molecular weight and polydispersity were measured using gel permeation chromatography. The electrochemical responsiveness of the polymers was investigated using cyclic voltammetry as well as the thermal stabilities of polymers and precursors were tested through DSC and TGA. The morphologies of electrospun fibers were observed through field emission scanning electronic spectroscopy and a fibrous diameter of 300–1100 nm was obtained. The results indicated that the suitable molecular weight of ferrocene -containing polymer would be processed in nanofibrous.     

Performance Evaluation of Deep Dense Layer Neural Network for Diabetes Prediction

Diabetes is one of the fastest-growing human diseases worldwide and poses a significant threat to the population’s longer lives. Early prediction of diabetes is crucial to taking precautionary steps to avoid or delay its onset. In this study, we proposed a Deep Dense Layer Neural Network (DDLNN) for diabetes prediction using a dataset with 768 instances and nine variables. We also applied a combination of classical machine learning (ML) algorithms and ensemble learning algorithms for the effective prediction of the disease. The classical ML algorithms used were Support Vector Machine (SVM), Logistic Regression (LR), Decision Tree (DT), K-Nearest Neighbor (KNN), and Naïve Bayes (NB). We also constructed ensemble models such as bagging (Random Forest) and boosting like AdaBoost and Extreme Gradient Boosting (XGBoost) to evaluate the performance of prediction models. The proposed DDLNN model and ensemble learning models were trained and tested using hyperparameter tuning and K-Fold cross-validation to determine the best parameters for predicting the disease. The combined ML models used majority voting to select the best outcomes among the models. The efficacy of the proposed and other models was evaluated for effective diabetes prediction. The investigation concluded that the proposed model, after hyperparameter tuning, outperformed other learning models with an accuracy of 84.42%, a precision of 85.12%, a recall rate of 65.40%, and a specificity of 94.11%.