Optimization of ultrasound-aided extraction of bioactive ingredients from Vitis vinifera seeds using RSM and ANFIS modeling with machine learning algorithm.

Plant materials are a rich source of polyphenolic compounds with interesting health-beneficial effects. The present study aimed to determine the optimized condition for maximum extraction of polyphenols from grape seeds through RSM (response surface methodology), ANFIS (adaptive neuro-fuzzy inference system), and machine learning (ML) algorithm models. Effect of five independent variables and their ranges, particle size (X1: 0.5-1 mm), methanol concentration (X2: 60-70% in distilled water), ultrasound exposure time (X3: 18-28 min), temperature (X4: 35-45 °C), and ultrasound intensity (X5: 65-75 W cm-2) at five levels (- 2, - 1, 0, + 1, and + 2) concerning dependent variables, total phenolic content (y1; TPC), total flavonoid content (y2; TFC), 2, 2-diphenyl-1-picrylhydrazyl free radicals scavenging (y3; %DPPH*sc), 2,2'-azino-bis(3-ethylbenzothiazoline-6-sulfonic acid) free radicals scavenging (y4; %ABTS*sc) and Ferric ion reducing antioxidant potential (y5; FRAP) were selected. The optimized condition was observed at X1 = 0.155 mm, X2 = 65% methanol in water, X3 = 23 min ultrasound exposure time, X4 = 40 °C, and X5 = 70 W cm-2 ultrasound intensity. Under this situation, the optimal yields of TPC, TFC, and antioxidant scavenging potential were achieved to be 670.32 mg GAE/g, 451.45 mg RE/g, 81.23% DPPH*sc, 77.39% ABTS*sc and 71.55 µg mol (Fe(II))/g FRAP. This optimal condition yielded equal experimental and expected values. A well-fitted quadratic model was recommended. Furthermore, the validated extraction parameters were optimized and compared using the ANFIS and random forest regressor-ML algorithm. Gas chromatography-mass spectroscopy (GC-MS) and liquid chromatography-mass spectroscopy (LC-MS) analyses were performed to find the existence of the bioactive compounds in the optimized extract.

Optimization of ultrasound-assisted extraction of bioactive chemicals from Hemidesmus indicus (L.) R.Br. using response surface methodology and adaptive neuro-fuzzy inference system.

This study was designed to optimize the ultrasound-assisted extraction (UAE) of bioactive chemicals from Hemidesmus indicus (L.) R.Br. through RSM (response surface methodology) and ANFIS (adaptive neuro-fuzzy inference system). The effect of four independent parameters, methanol concentration (X1: 55-65%), temperature (X2: 30-40 °C), time (X3: 15-20 min) and particle size (X4: 0.5-1.00 mm) at five levels (- 2 ,- 1, 0, + 1, + 2) with respect to dependent parameters, total polyphenols content (TP) (y1), total flavonoids content (TF) (y2), %DPPHsc (y3), %ABTSsc (y4) and %H2O2sc (y5) were selected. The optimal extraction condition was observed at X1 = 65%, X2 = 40 °C, X3 = 20 min and X4 = 0.5 mm; under this circumstance, y1 = 352.85 mg gallic acid equivalents (GA)/g, y2 = 300.204 mg rutin equivalents (RU)/g and their antioxidant potentials (y3 = 81.33%, y4 = 65.04%, and y5 = 71.01%) has been attained. ANFIS was used to compare and confirm the optimized extraction parameter values. Further, GC-MS and LC-MS were performed to investigate the bioactive chemicals present in the optimized extract. Supplementary Information: The online version contains supplementary material available at 10.1007/s10068-023-01351-9.

Survival study on deep learning techniques for IoT enabled smart healthcare system.

Purpose: The paper is to study a review of the employment of deep learning (DL) techniques inside the healthcare sector, together with the highlight of the strength and shortcomings of existing methods together with several research ultimatums. Our study lays the foundation for healthcare professionals and government with present-day inclinations in DL-based data analytics for smart healthcare. Methods: A deep learning-based technique is designed to extract sensor displacement effects and predict abnormalities for activity recognition via Artificial Intelligence (AI). The presented technique minimizes the vanishing gradient issue of Recurrent Neural Networks (RNN), thereby reducing the time for detecting abnormalities with consideration of temporal and spatial factors. Proposed Moran Autocorrelation and Regression-based Elman Recurrent Neural Network (MAR-ERNN) introduced. Results: Experimental results show the feasibility of the proposed method. The results show that the proposed method improves accuracy by 95% and reduces execution time by 18%. Conclusion: MAR-ERNN performs well in the activity recognition of health status. Collectively, this IoT-enabled smart healthcare system is utilized by enhancing accuracy, and minimizing time and overhead reduction.

CovMnet-Deep Learning Model for classifying Coronavirus (COVID-19).

Diagnosing COVID-19, current pandemic disease using Chest X-ray images is widely used to evaluate the lung disorders. As the spread of the disease is enormous many medical camps are being conducted to screen the patients and Chest X-ray is a simple imaging modality to detect presence of lung disorders. Manual lung disorder detection using Chest X-ray by radiologist is a tedious process and may lead to inter and intra-rate errors. Various deep convolution neural network techniques were tested for detecting COVID-19 abnormalities in lungs using Chest X-ray images. This paper proposes deep learning model to classify COVID-19 and normal chest X-ray images. Experiments are carried out for deep feature extraction, fine-tuning of convolutional neural networks (CNN) hyper parameters, and end-to-end training of four variants of the CNN model. The proposed CovMnet provide better classification accuracy of 97.4% for COVID-19 /normal than those reported in the previous studies. The proposed CovMnet model has potential to aid radiologist to monitor COVID-19 disease and proves to be an efficient non-invasive COVID-19 diagnostic tool for lung disorders.

Aphrodisiac Performance of Bioactive Compounds from Mimosa pudica Linn.: In Silico Molecular Docking and Dynamics Simulation Approach.

Plants and their derived molecules have been traditionally used to manage numerous pathological complications, including male erectile dysfunction (ED). Mimosa pudica Linn. commonly referred to as the touch-me-not plant, and its extract are important sources of new lead molecules in drug discovery research. The main goal of this study was to predict highly effective molecules from M. pudica Linn. for reaching and maintaining penile erection before and during sexual intercourse through in silico molecular docking and dynamics simulation tools. A total of 28 bioactive molecules were identified from this target plant through public repositories, and their chemical structures were drawn using Chemsketch software. Graph theoretical network principles were applied to identify the ideal target (phosphodiesterase type 5) and rebuild the network to visualize the responsible signaling genes, proteins, and enzymes. The 28 identified bioactive molecules were docked against the phosphodiesterase type 5 (PDE5) enzyme and compared with the standard PDE5 inhibitor (sildenafil). Pharmacokinetics (ADME), toxicity, and several physicochemical properties of bioactive molecules were assessed to confirm their drug-likeness property. Molecular dynamics (MD) simulation modeling was performed to investigate the stability of PDE5-ligand complexes. Four bioactive molecules (Bufadienolide (-12.30 kcal mol-1), Stigmasterol (-11.40 kcal mol-1), Isovitexin (-11.20 kcal mol-1), and Apigetrin (-11.20 kcal mol-1)) showed the top binding affinities with the PDE5 enzyme, much more powerful than the standard PDE5 inhibitor (-9.80 kcal mol-1). The four top binding bioactive molecules were further validated for a stable binding affinity with the PDE5 enzyme and conformation during the MD simulation period as compared to the apoprotein and standard PDE5 inhibitor complexes. Further, the four top binding bioactive molecules demonstrated significant drug-likeness characteristics with lower toxicity profiles. According to the findings, the four top binding molecules may be used as potent and safe PDE5 inhibitors and could potentially be used in the treatment of ED.

Computer-aided diagnosis of COVID-19 from chest X-ray images using histogram-oriented gradient features and Random Forest classifier.

The decision-making process is very crucial in healthcare, which includes quick diagnostic methods to monitor and prevent the COVID-19 pandemic disease from spreading. Computed tomography (CT) is a diagnostic tool used by radiologists to treat COVID patients. COVID x-ray images have inherent texture variations and similarity to other diseases like pneumonia. Manually diagnosing COVID X-ray images is a tedious and challenging process. Extracting the discriminant features and fine-tuning the classifiers using low-resolution images with a limited COVID x-ray dataset is a major challenge in computer aided diagnosis. The present work addresses this issue by proposing and implementing Histogram Oriented Gradient (HOG) features trained with an optimized Random Forest (RF) classifier. The proposed HOG feature extraction method is evaluated with Gray-Level Co-Occurrence Matrix (GLCM) and Hu moments. Results confirm that HOG is found to reflect the local description of edges effectively and provide excellent structural features to discriminate COVID and non-COVID when compared to the other feature extraction techniques. The performance of the RF is compared with other classifiers such as Linear Regression (LR), Linear Discriminant Analysis (LDA), K-nearest neighbor (kNN), Classification and Regression Trees (CART), Random Forest (RF), Support Vector Machine (SVM), and Multi-layer perceptron neural network (MLP). Experimental results show that the highest classification accuracy (99. 73%) is achieved using HOG trained by using the Random Forest (RF) classifier. The proposed work has provided promising results to assist radiologists/physicians in automatic COVID diagnosis using X-ray images.