Showing papers by "Vignan University published in 2021"

Design of multi-epitope vaccine candidate against SARS-CoV-2: a in-silico study.

Abstract: The best therapeutic strategy to find an effective vaccine against SARS-CoV-2 is to explore the target structural protein. In the present study, a novel multi-epitope vaccine is designed using in silico tools that potentially trigger both CD4 and CD8 T-cell immune responses against the novel Coronavirus. The vaccine candidate was designed using B and T-cell epitopes that can act as an immunogen and elicits immune response in the host system. NCBI was used for the retrieval of surface spike glycoprotein, of novel corona virus (SARS-CoV-2) strains. VaxiJen server screens the most important immunogen of all the proteins and IEDB server gives the prediction and analysis of B and T cell epitopes. Final vaccine construct was designed in silico composed of 425 amino acids including the 50S ribosomal protein adjuvant and the construct was computationally validated in terms of antigenicity, allergenicity and stability on considering all critical parameters into consideration. The results subjected to the modeling and docking studies of vaccine were validated. Molecular docking study revealed the protein-protein binding interactions between the vaccine construct and TLR-3 immune receptor. The MD simulations confirmed stability of the binding pose. The immune simulation results showed significant response for immune cells. The findings of the study confirmed that the final vaccine construct of chimeric peptide could able to enhance the immune response against nCoV-19.

Transfer learning-based deep ensemble neural network for plant leaf disease detection

Abstract: Plant diseases are a vital risk to crop yield and early detection of plant diseases remains a complex task for the farmers due to the similar appearance in color, shape, and texture. In this work, authors have proposed an automatic plant disease detection technique using deep ensemble neural networks (DENN). Transfer learning is employed to fine-tune the pre-trained models. Data augmentation techniques include image enhancement, rotation, scaling, and translation are applied to overcome overfitting. This paper presents a detailed taxonomy on the performance of different pre-trained neural networks and presents the performance of a weighted ensemble of those models relevant to plant leaf disease detection. Further, the performance of the proposed work is evaluated on publicly available plant village dataset, which comprises of 38 classes collected from 14 crops. The performance of DENN outperform state-of-the-art pre-trained models such as ResNet 50 & 101, InceptionV3, DenseNet 121 & 201, MobileNetV3, and NasNet. Performance evaluation of the proposed model demonstrates that effective in categorizing various types of plant diseases that comparatively outperform pre-trained models.

Composite deep neural network with gated-attention mechanism for diabetic retinopathy severity classification

Abstract: Diabetic Retinopathy (DR) is a micro vascular complication caused by long-term diabetes mellitus. Unidentified diabetic retinopathy leads to permanent blindness. Early identification of this disease requires frequent complex diagnostic procedure which is expensive and time consuming. In this article, we propose a composite deep neural network architecture with gated-attention mechanism for automated diagnosis of diabetic retinopathy. The feature descriptors obtained from multiple pre-trained deep Convolutional Neural Networks (CNNs) are used to represent color fundus retinal images. Spatial pooling methods are introduced to get the reduced versions of these representations without loosing much information. The proposed composite DNN learns independently from each of these reduced representations through different channels and contributes to improving the model generalization. In addition, model also includes gated attention blocks which allows the model to emphasize more on lesion portions of the retinal images while reduced attention to the non-lesion regions. Our experiments on APTOS-2019 Kaggle blindness detection challenge reveal that, the proposed approach leads to improved performance when compared to the existing best models. Our empirical studies also reveal that, the proposed approach leads to more generalised predictions with multi-modal representations when compared to those of uni-modal representations. The proposed composite deep neural network model recorded an accuracy of 82.54% ( $$\uparrow $$ 2%), and a Kappa score of 79 ( $$\uparrow 9$$ points) for diabetic retinopathy severity level prediction.

Syntheses of N-Doped Carbon Quantum Dots (NCQDs) from Bioderived Precursors: A Timely Update

Abstract: The emergence of carbon quantum dots (CQDs) opens up new opportunities in different branches of science and technology primarily because of their conducive biocompatibility, tunable bandgaps, and u...

Screening of phytochemical compounds of Tinospora cordifolia for their inhibitory activity on SARS-CoV-2: an in silico study.

Abstract: In the present study, we explored phytochemical constituents of Tinospora cordifolia in terms of its binding affinity targeting the active site pocket of the main protease (3CL pro) of SARS-CoV-2 u...

Joint training of two-channel deep neural network for brain tumor classification

Abstract: Brain tumor recognition is a challenging task, and accurate diagnosis increases the chance of patient survival. In this article, we propose a two-channel deep neural network architecture for tumor classification that is more generalizable. Initially, local feature representations are extracted from convolution blocks of InceptionResNetV2 and Xception networks and are vectorized using proposed pooling-based techniques. An attention mechanism is proposed that allows more focus on tumor regions and less focus on non-tumor regions which eventually helps to differentiate the type of tumor present in the images. The proposed two-channel model allows joint training of two sets of tumor image representations in an end-to-end manner to achieve good generalization. Empirical studies on Figshare and BraT’S2018, benchmark datasets, reveal that our approach is superior in terms of generalization and simple in terms of number of layers compared to the existing complex models that follow fine-tuning of deep CNN models. Avoiding too much preprocessing and augmentation techniques, the proposed model sets new state-of-the-art scores on both the brain tumor datasets.

Bioconvection in a Convectional Nanofluid Flow Containing Gyrotactic Microorganisms over an Isothermal Vertical Cone Embedded in a Porous Surface with Chemical Reactive Species

Abstract: In this examination, a mathematical model is developed for Darcy free convection with reference to an isothermal vertical cone along with fixed apex half angle, pointing downward in a nanofluid-saturated porous medium. The aim of this methodology is to offer another sort of primary fluid containing nanoparticles and gyrotactic microorganism’s consistence of permeable medium, chemical reaction alongside convective boundary circumstance. The model presents design rules for improvement of imperative fabrication of fertilizer and polymer substance. The present model includes the gyrotactic microorganisms alongside nanoparticles, and cone is dependent on concentration of nanoparticles as well as density of motile microorganisms. Two important impacts Brownian motion as well as thermophoresis are also included in the present model for nanofluids. Reduced system of nonlinear differential equations is derived from governing partial differential of the present flow by using usual transformations along with Oberbeck–Boussinesq approximation. After that, this reduced system is solved numerically with the use of fifth-order Runge–Kutta method in conjunction with the shooting technique. Relevant outcomes are exhibited graphically and talked about quantitatively as for variety in the flow controlling parameters related to the present analysis. Mainly, the observations are bioconvection parameters will in general improve the concentration of the rescaled density of microorganisms and nanoparticles volume fraction and dimensionless motile microorganisms reduce with strong chemical reactions.

A technological overview & design considerations for developing electric vehicle charging stations

Abstract: In recent years, it is seen that there has been a huge expansion in the electric vehicles market aiming to reduce the impact of greenhouse gases. The deployment of an optimal and cost-effective electric vehicle charging stations similar to petrol/diesel stations with advanced control algorithms is necessary for the successful implementation. This review paper gives an overview of electric vehicles and various configurations about the design aspects of charging station. The charging stations are categorized on the basis of power utilized with various optimization algorithms, methods and future directions are presented to have an optimal design. And also, the highlights of grid connected combination of renewable energy based and grid connected, off-grid mode are summarized along with the future scope. Incorporation of renewable energy along with storage systems in the charging station can reduce the high load taken from the grid especially at peak times. By providing an overview of these key areas, the review study aims to provide a deep insight to the industry experts and researchers for future developments.

Nodal/Saddle Stagnation Point Slip Flow of an Aqueous Convectional Magnesium Oxide–Gold Hybrid Nanofluid with Viscous Dissipation

Abstract: In this analysis, convective heat transfer characteristics of a hybrid nanofluid mixture containing magnesium oxide (MgO) and gold (Au) nanoparticles are numerically studied. The impact of slip effects on nodal/saddle stagnation point boundary layer flow with viscous dissipation effect is mathematically modeled. The behavior of nanofluids is studied by employing Tiwari–Das nanofluid model. Pure water is the base fluid in this analysis. The governing partial differential equations with many independent variables are reduced to ordinary differential equations with one independent variable and then numerically solved by the Runge–Kutta–Fehlberg method with the desired accuracy. The outputs showed that MgO–Au/water hybrid nanofluid sharply raises the base fluid's thermal behavior. Results reveal that in the nodal and saddle point areas, the impact of higher slip effects significantly increases the local heat transfer rate.

Biodiesel Production From Lignocellulosic Biomass Using Oleaginous Microbes: Prospects for Integrated Biofuel Production.

Abstract: Biodiesel is an eco-friendly, renewable, and potential liquid biofuel mitigating greenhouse gas emissions. Biodiesel has been produced initially from vegetable oils, non-edible oils, and waste oils. However, these feedstocks have several disadvantages such as requirement of land and labor and remain expensive. Similarly, in reference to waste oils, the feedstock content is succinct in supply and unable to meet the demand. Recent studies demonstrated utilization of lignocellulosic substrates for biodiesel production using oleaginous microorganisms. These microbes accumulate higher lipid content under stress conditions, whose lipid composition is similar to vegetable oils. In this paper, feedstocks used for biodiesel production such as vegetable oils, non-edible oils, oleaginous microalgae, fungi, yeast, and bacteria have been illustrated. Thereafter, steps enumerated in biodiesel production from lignocellulosic substrates through pretreatment, saccharification and oleaginous microbe-mediated fermentation, lipid extraction, transesterification, and purification of biodiesel are discussed. Besides, the importance of metabolic engineering in ensuring biofuels and biorefinery and a brief note on integration of liquid biofuels have been included that have significant importance in terms of circular economy aspects.

Deep convolution feature aggregation: an application to diabetic retinopathy severity level prediction

Abstract: Diabetic retinopathy (DR) is one of the main causes of loss of vision and blindness in humans across the world. DR is usually found in patients suffering from diabetes for a long period. Automation of DR diagnosis rescues many people from going blind by identifying the disease at the early stages. In this work, we introduce a robust model for DR severity level prediction by leveraging features extracted from pre-trained models to represent DR images. The activation filter values from multiple convolution blocks of VGG-16 are extracted and aggregated using pooling and fusion methods. The aggregation module produces a compact, informative, and discriminative representation of the retinal images by removing noisy and redundant features using pooling and fusion approaches. These feature representations are fed to the proposed DNN architecture to identify the severity level of DR. On the benchmark Kaggle APTOS 2019 contest dataset, our proposed method sets a new state-of-the-art result with an accuracy of 84.31% and an AUC 97. Experimental studies reveal that the proposed model exhibits superior performance compared with the existing models, especially in the case of severe and proliferate stage DR images.

Enhanced transport properties of graphene oxide based cement composite material

Abstract: This study is aimed to evaluate the transport properties of a cement composite incorporated with graphene oxide (GO). GO is a 2-dimensional nano material, enriched with reactive oxygen functional groups which made it as a suitable reactive material with cementitious materials. The GO-cement composite was prepared by adding GO in four different proportions such as 0.01%, 0.02%, 0.03%, and 0.04% by weight of cement. In the durability aspect water absorption, water sorptivity, resistance to acid attack, mechanical properties, thermal analysis, chloride migration coefficient, and chloride penetration depth were examined experimentally for GO-cement composite specimens and compared with plane cement composite specimens. Experimental results indicated that 14.5%, 30%, and 40.47% degradation in water absorption capacity, chloride migration coefficient, and chloride penetration depth of cement composite specimens with 0.03% addition of graphene oxide than normal cement composite specimens at 28 days of curing. Additionally, significant decrement in the sorptivity coefficient of cement composite was also achieved at 0.03% of GO addition. Mechanical properties were enhanced by 77.70% in flexural strength at 0.03% of GO addition and 47.61% in compressive strength at 0.04% of GO addition in cement composite at 28 days of curing. SEM analysis of the test samples exhibited the pore filled structure of the cement composites with GO addition. In addition, all GO-cement composite specimens exhibited greater resistance to an acidic environment and negligible loss in strength and weight was observed than normal cement composite specimens at 28 days immersion in 2% and 5% concentrations of both H2SO4 and HCl acidic solutions. Thermal resistance was increased in the terms of compressive strength in all GO-cement composite mixes gradually by increasing the concentration of GO content from 0.01% to 0.04% by weight of cement. It can be concluded that an effective improvement in transport properties of cement composite was achieved by the incorporation of graphene oxide nano material.

Nanotechnology: an emerging approach to combat COVID-19.

Abstract: The recent outbreak of coronavirus disease (COVID-19) has challenged the survival of human existence in the last 1 year. Frontline healthcare professionals were struggling in combating the pandemic situation and were continuously supported with literature, skill set, research activities, and technologies developed by various scientists/researchers all over the world. To handle the continuously mutating severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) requires amalgamation of conventional technology with emerging approaches. Nanotechnology is science, engineering, and technology dealing at the nanoscale level. It has made possible the development of nanomaterials, nano-biosensors, nanodrugs, and vaccines for diagnosis, therapy, and prevention of COVID-19. This review has elaborately highlighted the role of nanotechnology in developing various detection kits such as nanoparticle-assisted diagnostics, antibody assay, lateral flow immunoassay, nanomaterial biosensors, etc., in detection of SARS-CoV-2. Similarly, various advancements supervene through nanoparticle-based therapeutic drugs for inhibiting viral infection by blocking virus attachment/cell entry, multiplication/replication, and direct inactivation of the virus. Furthermore, information on vaccine development and the role of nanocarriers/nanoparticles were highlighted with a brief outlining of nanomaterial usage in sterilization and preventive mechanisms engineered to combat COVID-19 pandemic.

Extraction of bioactive compounds from Psidium guajava leaves and its utilization in preparation of jellies

Abstract: Psidium guajava L. (guava) is predominantly grown throughout the world and known for its medicinal properties in treating various diseases and disorders. The present work focuses on aqueous extraction of bioactive compounds from the guava leaf and its utilization in the formulation of jelly to improve the public health. The guava leaf extract has been used in the preparation of jelly with pectin (1.5 g), sugar (28 g) and lemon juice (2 mL). The prepared guava leaf extract jelly (GJ) and the control jelly (CJ, without extract) were subjected to proximate, nutritional and textural analyses besides determination of antioxidant and antimicrobial activities. GJ was found to contain carbohydrate (45.78 g/100 g), protein (3.0 g/100 g), vitamin C (6.15 mg/100 g), vitamin B3 (2.90 mg/100 g) and energy (120.6 kcal). Further, the texture analysis of CJ and GJ indicated that both the jellies showed similar properties emphasizing that the addition of guava leaf extract does not bring any change in the texture properties of jelly. GJ exhibited antimicrobial activity against various bacteria ranging from 11.4 to 13.6 mm. Similarly, GJ showed antioxidant activity of 42.38% against DPPH radical and 33.45% against hydroxyl radical. Mass spectroscopic analysis of aqueous extract confirmed the presence of esculin, quercetin, gallocatechin, 3-sinapoylquinic acid, gallic acid, citric acid and ellagic acid which are responsible for antioxidant and antimicrobial properties.

A Short-Term Solar Photovoltaic Power Optimized Prediction Interval Model Based on FOS-ELM Algorithm

Abstract: Solar energy conversion efficiency has improved by the advancement technology of photovoltaic (PV) and the involvement of administrations worldwide. However, environmental conditions influence PV power output, resulting in randomness and intermittency. These characteristics may be harmful to the power scheme. As a conclusion, precise and timely power forecast information is essential for the power networks to engage solar energy. To lessen the negative impact of PV electricity usage, the offered short-term solar photovoltaic (PV) power estimate design is based on an online sequential extreme learning machine with a forgetting mechanism (FOS-ELM) under this study. This approach can replace existing knowledge with new information on a continuous basis. The variance of model uncertainty is computed in the first stage by using a learning algorithm to provide predictable PV power estimations. Stage two entails creating a one-of-a-kind PI based on cost function to enhance the ELM limitations and quantify noise uncertainty in respect of variance. As per findings, this approach does have the benefits of short training duration and better reliability. This technique can assist the energy dispatching unit list producing strategies while also providing temporal and spatial compensation and integrated power regulation, which are crucial for the stability and security of energy systems and also their continuous optimization.

Exploring the Crosstalk between Inflammation and Epithelial-Mesenchymal Transition in Cancer

Abstract: Tumor cells undergo invasion and metastasis through epithelial-to-mesenchymal cell transition (EMT) by activation of alterations in extracellular matrix (ECM) protein-encoding genes, enzymes responsible for the breakdown of ECM, and activation of genes that drive the transformation of the epithelial cell to the mesenchymal type. Inflammatory cytokines such as TGFβ, TNFα, IL-1, IL-6, and IL-8 activate transcription factors such as Smads, NF-κB, STAT3, Snail, Twist, and Zeb that drive EMT. EMT drives primary tumors to metastasize in different parts of the body. T and B cells, dendritic cells (DCs), and tumor-associated macrophages (TAMs) which are present in the tumor microenvironment induce EMT. The current review elucidates the interaction between EMT tumor cells and immune cells under the microenvironment. Such complex interactions provide a better understanding of tumor angiogenesis and metastasis and in defining the aggressiveness of the primary tumors. Anti-inflammatory molecules in this context may open new therapeutic options for the better treatment of tumor progression. Targeting EMT and the related mechanisms by utilizing natural compounds may be an important and safe therapeutic alternative in the treatment of tumor growth.

Feature and Subfeature Selection for Classification Using Correlation Coefficient and Fuzzy Model

Abstract: This article presents an analysis of data extraction for classification using correlation coefficient and fuzzy model. Several traditional methods of data extraction are used for classification that could not provide sufficient information for further step of data analysis on class. It needs refinement of features data to distinguish a class that differs from a traditional class. Thus, it proposes the feature tiny data (subfeature data) to find distinguish class from a traditional class using two methods such as correlation coefficient and fuzzy model to select features as well as subfeature for distinguishing class. In the first approach, the correlation coefficient methods with gradient descent technique are used to select features from the dataset and in the second approach, the fuzzy model with supreme of minimum value is considered to get subfeature data. As per the proposed model, some features (i.e., three features from the acoustic dataset, two features from the QCM dataset, and eight features from the audit dataset, etc.) and subfeatures (as per threshold value like 20 for acoustic; 10 for QCM, and 20 for audit, etc.) are selected based on correlation coefficient as well as fuzzy methods, respectively. Further, the probability approach is used to find the association and availability of subfeature data from the dimensional reduced database. The experimental results show the proposed framework identifies and selects both feature and subfeature data with the effectiveness of the new class. The comparison results of several classifiers on several datasets are explained in the experimental section.

Smart Phishing Detection in Web Pages using Supervised Deep Learning Classification and Optimization Technique ADAM

Abstract: Phishing is the process of portraying malignant web pages in the place of genuine web pages to obtain important and delicate information from the end-user. Nowadays phishing is considered as one of the most serious threats to web security. Most of the existing techniques for phishing detection use Bayesian classification for differentiating malignant web pages from genuine web pages. These methods work well if a dataset contains less no of web pages and they provide accuracy up to 90 percent. In recent years the size of the web is increasing tremendously and the existing methods have not provideda good enough accuracy for large datasets. So this paper proffers an innovative approach to identify phishing websites using hyperlinks available in the source code of the HTML page in the corresponding website. The proposed method uses a feature vector with 30 parameters to detect malignant web pages. These features are used in training the supervised Deep Neural Network model with Adam optimizer for differentiating fraudulent websites from genuine websites. The proposed deep learning model with Adam Optimizer uses a Listwise approach to classify phishing websites and genuine websites. The performance of the proposed approach is decent when compared to other traditional machine learning approaches like SVM, Adaboost, AdaRank. The results show that the proposed approach provides more accurate results in detecting phishing websites.

COVID-19 diagnosis system by deep learning approaches.

Abstract: The novel coronavirus disease 2019 (COVID-19) has been a severe health issue affecting the respiratory system and spreads very fast from one human to other overall countries. For controlling such disease, limited diagnostics techniques are utilized to identify COVID-19 patients, which are not effective. The above complex circumstances need to detect suspected COVID-19 patients based on routine techniques like chest X-Rays or CT scan analysis immediately through computerized diagnosis systems such as mass detection, segmentation, and classification. In this paper, regional deep learning approaches are used to detect infected areas by the lungs' coronavirus. For mass segmentation of the infected region, a deep Convolutional Neural Network (CNN) is used to identify the specific infected area and classify it into COVID-19 or Non-COVID-19 patients with a full-resolution convolutional network (FrCN). The proposed model is experimented with based on detection, segmentation, and classification using a trained and tested COVID-19 patient dataset. The evaluation results are generated using a fourfold cross-validation test with several technical terms such as Sensitivity, Specificity, Jaccard (Jac.), Dice (F1-score), Matthews correlation coefficient (MCC), Overall accuracy, etc. The comparative performance of classification accuracy is evaluated on both with and without mass segmentation validated test dataset.

An IoT-based bin level monitoring system for solid waste management

Abstract: Improper disposal of solid waste that impacts human health and pollutes the environment, arising a need for successful and necessary collection of waste materials. However, most trash bins placed in cities can be seen overflowing due to traditional or inefficient waste management approaches. Therefore, a real-time remote monitoring system is needed to alert the level of garbage in bins to the relevant authority for immediate waste clearance. This paper presents the development and validation of a self-powered, simple connect, IoT solution to monitor the unfilled level of trash bins from a central monitoring station. The end sensor nodes of the developed IoT system are called Bin Level Monitoring Unit (BLMU) which are installed in every trash bin where the unfilled level needs to be monitored. Every BLMU measures the unfilled level of the trash bins and transmits it to a wireless access point unit (WAPU). Each WAPU receives the unfilled level data from several BLMUs and uploads it to the central server for storage and analysis. The waste collection authority can view and analyze the unfilled level of each bin using a smart graphical user interface. The following important experiments were carried out to validate the developed system: (1) the developed bin level monitoring system was tested by filling a trash bin with solid waste at various levels, and the corresponding unfilled level of the trash bin was monitored using the smart graphical user interface. (2) The life expectancy of the BLMU was evaluated as approximately 434 days. (3) The maximum transmission distance between a BLMU and a WAPU is 119 m. (4) The cost of a developed trash bin is 107 USD. Based on the results achieved, the developed trash bins can be suitable for smart cities.