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Computational statistics of data science for secured software engineering
The chapter focuses on exploring the work done for applying data science for software engineering, focusing on secured software systems development. With requirements management being the first stage of the life cycle, all the approaches that can help security mindset right at the beginning are explored. By exploring the work done in this area, various key themes of security and its data sources are explored, which will mark the setup of base for advanced exploration of the better approaches to make software systems mature. Based on the assessments of some of the work done in this area, possible prospects are explored. This exploration also helps to emphasize the key challenges that are causing trouble for the software development community. The work also explores the possible collaboration across machine learning, deep learning, and natural language processing approaches. The work helps to throw light on critical dimensions of software development where security plays a key role. 2021, IGI Global. -
Computational simulation of surface tension and gravitation-induced convective flow of a nanoliquid with cross-diffusion: An optimization procedure
The control of heat transfer in the hydromagnetic semiconductor crystal involves Marangoni convection with buoyancy forces. In this study, the conventional thermo-solutal Marangoni mixed flow model is modified by incorporating the solutal buoyancy effects that are significant in the flow phenomenon. The heat and mass transfer (HMT) characteristics of the Marangoni convective flow of a Cu ? H2O nanofluid subjected to the assisting/resisting buoyancy forces and cross-diffusion are numerically studied. The homogeneous single-phase nanoliquid model is used in conjunction with experimental data of dynamic viscosity and thermal conductivity. The Dufour and Soret effects are considered. Governing equations are solved using the finite difference-based algorithm. The problem is analyzed in a unified way considering the cases of buoyancy-assisted flow and buoyancy-opposed flow. The response surface methodology (RSM) based on the face-centered composite design (CCD) is used to optimize the heat and mass transfer rates. A multivariate regression model is proposed and authenticated prior to optimization. Additionally, sensitivity analysis is performed using the full quadratic regression model. The increase in the temperature profile is more significant due to the radiative heat flux than the inclined magnetic field. Heat transfer has a high sensitivity to the appearance of thermal radiation, while mass transfer has a high sensitivity to the Soret effect. Simultaneous optimization of HMT rates is achieved with the high level of thermal radiation and low levels of the cross-diffusion aspects. 2022 -
Computational screening of natural compounds from Salvia plebeia R. Br. for inhibition of SARS-CoV-2 main protease
The novel Severe Acute Respiratory Syndrome Coronavirus (SARS-CoV-2) has emerged to be the reason behind the COVID-19 pandemic. It was discovered in Wuhan, China and then began spreading around the world, impacting the health of millions. Efforts for treatment have been hampered as there are no antiviral drugs that are effective against this virus. In the present study, we have explored the phytochemical constituents of Salvia plebeia R. Br., in terms of its binding affinity by targeting COVID-19 main protease (Mpro) using computational analysis. Molecular docking analysis was performed using PyRx software. The ADMET and drug-likeness properties of the top 10 compounds showing binding affinity greater than or equal to ? 8.0kcal/mol were analysed using pkCSM and DruLiTo, respectively. Based on the docking studies, it was confirmed that Rutin and Plebeiosides B were the most potent inhibitors of the main protease of SARS-CoV-2 with the best binding affinities of ? 9.1kcal/mol and ? 8.9kcal/mol, respectively. Further, the two compounds were analysed by studying their biological activity using the PASS webserver. Molecular dynamics simulation analysis was performed for the selected proteinligand complexes to confirm their stability at 300ns. MM-PBSA provided the basis for analyzing the affinity of the phytochemicals towards Mpro by calculating the binding energy, and secondary structure analysis indicated the stability of protease structure when it is bound to Rutin and Plebeiosides B. Altogether, the study identifies Rutin and Plebeiosides B to be potent Mpro inhibitors of SARS-CoV-2. Graphic abstract: [Figure not available: see fulltext.] 2021, Society for Plant Research. -
Computational Modelling of Complex Systems for Democratizing Higher Education: A Tutorial on SAR Simulation
Engineering systems like Synthetic Aperture Radar (SAR) are complex systems and require multi-domain knowledge to understand. Teaching and learning SAR processing is intensive in terms of time and resources. It also requires software tools and computational power for preprocessing and image analysis. Extensive literature exists on computational models of SAR in MATLAB and other commercial platforms. Availability of computational models in open-source reproducible platforms like Python kernel in Jupyter notebooks running on Google Colaboratory democratizes such difficult topics and facilitates student learning. The model, discussed here, generates SAR data for a point scatterer using SAR geometry, antenna pattern, and range equation and processes the data in range and azimuth with an aim to generate SAR image. The model demonstrates the generation of synthetic aperture and the echo signal qualities as also how the pulse-to-pulse fluctuating range of a target requires resampling to align the energy with a regular grid. The model allows for changing parameters to alter for resolution, squint, geometry, radar elements such as antenna dimensions, and other factors. A successful learning outcome would be to understand where parameters need to be changed, to affect the model in a specific way. Factors affecting Range Doppler processing are demonstrated. Use of the discussed model nullifies use of commercial software and democratizes SAR topic in higher education. 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Computational modeling of heat transfer in magneto-non-Newtonian material in a circular tube with viscous and Joule heating
Numerous industrial and engineering systems, like, heat exchangers, chemical action reactors, geothermic systems, geological setups, and many others, involve convective heat transfer through a porous medium. The diffusion rate, drag force, and mechanical phenomenon are dealt with in the DarcyForchheimer model, and hence this model is vital to study the fluid flow and heat transport analysis. Therefore, numerical simulation of the DarcyForchheimer dynamics of a Casson material in a circular tube subjected to the energy losses due to the viscous heating and Joule dissipation mechanisms is performed. The novelty of the present investigation is to scrutinize the convective heat transport characteristics in a circular tube saturated with DarcyForchheimer porous matrix by utilizing the non-Newtonian Casson fluid. The flow occurs due to the elongation of the surface of a tube with a uniform heat-based source/sink. The similarity solution of the nonlinear problem was obtained using dimensionless similarity variables. The effects of operating parameters related to the flow phenomena are analyzed. Further, the friction factor and Nusselt number are also analyzed in detail. The present flow model ensures no flow reversal and acts as a coolant of the heated cylindrical surface; the existence of the magnetic field, as well as an inertial coefficient,acts as the momentum-breaking forces, whereas Casson fluidity buildsit. The Joule heating phenomenon enhances the magnitude of temperature. The thermal field of the Casson fluid is higher at the surface of the circular pipe due to convective thermal conditions. 2021 Wiley Periodicals LLC. -
Computational Model for Hybrid Job Scheduling in Grid Computing
Grid computing the job scheduling is the major issue that needs to be addressed prior to the development of a grid system or architecture. Scheduling is the users job to apropos resources in the grid environment. Grid computing has got a very wide domain in its application and thus induces various research opportunities that are generally spread over many areas of distributed computing and computer science. The cardinal point of scheduling is being attaining apex attainable performance and to satisfy the application requirements with computing resources at exposure. This paper posits techniques of using different scheduling techniques for increasing the efficacy of the grid system. This hybrid scheduler could enable the grid system to reduce the execution time. This paper also proposes an architecture which could be implemented ensuring the optimal results in the grid environment. This adaptive scheduler would possibly combine the pros of two scheduling strategies to produce a hybrid scheduling strategy which could cater the ever changing workload encountered by the gird system. The main objective of the proposed system is to reduce to overall job execution time and processor utilization time. 2020, Springer Nature Switzerland AG. -
Computational Methods to Predict Suicide Ideation among Adolescents
Suicide has been a prominent cause of death worldwide, regardless of age, sex, geography, and so on, and predominantly suicide among teens, increased as the years have passed. Suicide ideation, suicide risk, suicide attempts have been studied extensively, and the most common cause has been identified as depression, followed by familial concerns, hereditary factors, stress, avoidance fear, and a variety of other variables. When visited by a doctor, most adolescents are unaware of their mental state and hence do not take action on their own or are not assisted by family or peer members to overcome their fear of social stigma or the treatment they must undergo. According to popular belief, early treatment and detection are the most effective ways to reduce the risk of suicide. As a result, the focus of this study is to illustrate some of the computational strategies utilized in deep learning and machine learning fields to detect kids at risk of suicide 2022 IEEE. -
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Computational Methods for Detection and Recognition of Coronary Artery Stenosis in Angiogram Images
Coronary Artery Disease (CAD) is caused by stenosis of the coronary artery's lumen. This heart disease is one of the reasons for the highest mortality worldwide. This illness manifests as stenosis or plaque in the coronary arteries and causes atherosclerosis. It damages or clogs the heart arteries, causing a lack of blood flow to the heart muscles and leading to a heart attack. There are different medical modalities to diagnose the heart artery disease. A standard method used by the cardiologist to diagnose the severity of this disease is coronary angiography. An X-ray machine is used to capture the angiogram image at various angles during cardiac catheterization. Experts examine the data and offers different opinions. owever, most of the angiogram videos consist of unclear images with artifacts, and because of the complex structure of the arteries, medical experts fail to get accurate information about the damages and blockages in arteries. Based on the cardiologist's suggestions, a computational model is proposed as a secondary method to detect and recognize the stenosis level from the coronary angiogram images. The proposed model is Coronary Artery Stenosis Detection Using Digital Image Processing (CASDDIP). The proposed research model/framework can identify the stenosis in the cardiogram image with good accuracy of 98.06% precision. This proposed research experimentation can be compared with existing literature methods which outperforms compared to other methods using real time dataset. A dataset, such as angiogram videos and images of patients under varying age groups, is used to train the model. These videos are acquired from the healthcare center with due consent. The proposed CASDDIP model consists of four modules: Keyframe extraction and preprocessing Coronary Artery Segmentation Feature extraction and stenosis detection Initially, a novel keyframe extraction method is proposed to find the keyframe from the angiogram video. Followed by a hybrid segmentation method is presented in this research to extract the coronary artery region from the image. Further a method is proposed to detect the stenosis by extracting and fusing different features. Detected stenosis is categorized using the proposed stenosis level classification method. This CASDDIP model is a supporting tool to help the cardiologist during diagnosis. -
Computational investigation into the structure, effect of band gap energies, charge transfer, reactivity, thermal energies and NADPH inhibitory activity of a benzimidazole derivative
This work contains computational investigations of a benzimidazole derivative consisting of density functional theory, electronic structure and biological evaluation of a benzimidazole derivative. Density functional theory evaluation were conducted, starting from geometry optimisation, followed by the molecular electrostatic potential, spectral analyses, polarizability studies and thermodynamic analyses via the frequency calculations. Solvent frontier molecular orbital analyses, impact on the properties of the molecule were modelled with the IEFPCM model of solvation. Topological analyses helped to ascertain the molecule's electronic structure. Biological assessment included pharmacokinetic property evaluation and molecular docking. Pharmacokinetic descriptors were generated using online tools and the molecule was assessed for its efficacy as a drug molecule by comparing with the rules concerning drug-likeness and analysing the descriptors relating to absorption, distribution, metabolism, excretion and toxicity of the molecule. Docking of the molecule with the two targets, 7D3E and 3A1F, yielded a good binding energy of ?7.39 and ?5.81 kcal/mol respectively. 2024 Elsevier B.V. -
Computational investigation into the solvent effect, electron distribution, reactivity profile, pharmacokinetic properties and anti-cancer action of Hemimycalin C
This work consists of DFT studies and biological evaluation of the marine alkaloid Hemimycalin C. The DFT calculations include energy minimisation, reactivity analysis of the frontier molecular orbitals, electronic transition studies (UV spectra generation), molecular electrostatic potential colour map analysis (MEP), and natural bond orbitals (NBO) studies. Non-linear optical (NLO) properties estimation is also performed to obtain the first-order hyperpolarizability, mean polarizability and dipole moment of Hemimycalin C. The solvent methanol emerges as the most interesting among the polar solvents employed in this study, as it impacts the properties of Hemimycalin C to a significant extent. Multiwfn software is used for topological analyses, which include the calculation of Reduced Density Gradient (RDG), Localised Orbital Locator (LOL) maps), and Electron Localisation Function (ELF). The computed ADMET profile indicates that the molecule is a potent lead (drug candidate) as the medicinal chemistry parameters are mostly within the optimal range. The Ramachandran plots are also computed to show the stability and quality of the target proteins, by computation of the permitted psi and phi angles. The complexes of the ligand are docked using AutoDock Tools against blood cancer receptors to obtain good binding affinity values. 2025 Elsevier B.V. -
Computational investigation into structural, topological, electronic properties, and biological evaluation of spiro[1H-indole-3,2?-3H-1,3-benzothiazole]-2-one
The current work comprises theoretical studies on spiro[1H-indole-3,2?-3H-1,3-benzothiazole]-2-one employing density functional theory (DFT). The optimized structure and molecular geometry of the title compound were calculated. Topological studies were performed using Multiwfn 3.8, these include ELF, LOL and RDG studies to identify the main bonding regions and weak interactions in the molecule. Solvation effects were studied by taking different green solvents, using IEFPCM model. Solvation effects were investigated for electronic properties (HOMO-LUMO and UV), MEP, and NLO properties and some variation is observed in the behaviour of the title compound in gas and solvent phases. Natural bond orbital (NBO) calculations are performed to study the inter- and intra-molecular charge transfer and stability. Pharmacological evaluation comprising of drug-likeness, ADME, environmental toxicity properties using online tools such as SwissADME, Pre-ADMET, and GUSAR, to determine whether the molecule can be a potential drug candidate is performed. Finally, molecular docking against anti-melanoma targets whose Ramachandran plots have been depicted to determine the stability of the target proteins, with PyMOL, AutoDock Suite and Discovery Studio Visualizer, is carried out. 2022 Elsevier B.V. -
Computational Intelligence for Solving Contemporary Problems
The special issue contains research papers elaborating advancements in computational intelligence. Computational intelligence mimics the extraordinary capacity of the human intellect to assert and understand in an environment of uncertainty and impre-cision. Computational intelligence is new-age multidisciplinary artificial intelligence. The main goal of computational intelligence is to develop intelligent systems to solve real-world problems that are not modelled or too hard to model mathematically. 2024, Bentham Science Publishers. All rights reserved. -
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Computational Chemical Property Prediction and Anticancer Simulation of Heterocyclic Molecules
The Density Functional Theory (DFT) technique is popularly employed in establishing organic molecules' structural properties and reactivities. The B3LYP hybrid functional with the basis set 6-311G++(d,p) is utilised in the computational calculations with Gaussian 09W software. The DFT studies include energy minimisation (geometry optimisation), frontier molecular orbitals (FMO) analyses, theoretical UV spectral computation, natural bond orbital (NBO) evaluation, Topological analyses using Multiwfn 3.8 software are performed to evaluate the Pauli repulsion in atomic orbitals (as shown by ELF (Electron Localisation Function) maps), the areas of strong and weak pi-delocalisation in the molecules (as depicted in LOL (Localised Orbital Locator) maps) and the weak non-covalent intra-molecular interactions (as indicated in colour maps of RDG (Reduced Density Gradient)). Pharmacological evaluation is performed using SwissADME, ADMETLab 2.0, and PreADMET online tools. Molecular docking is performed using AutoDock Tools 1.5.6 with select anticancer target proteins to predict the bioactivity potential of the title molecules. The molecules studied in the work include a spiro compoun d, spiro[1H-indole-3,2-3H-1,3- benzothiazole]-2-one, a 2(3H)-furanone, 3,3,5-triphenylfuran-2(3H)-one, and a benzo[d]imidazole, 5,6-dichloro-1-cyclopentyl-2-(methylsulfinyl)-1H- benzimidazole. In addition, comparative studies are performed on the structure and reactivity of spirobrassinin derivatives, spirocyclic isatin-derivative analogues, and 3(2H)-furanones, and these three classes of molecules have already been predicted to possess anticancer properties in vitro. Interesting properties emerge in the preliminary theoretical investigations for these molecules, particularly in the FMO, the NLO and the molecular docking studies. The theoretical studies explore the reactivity, structure, and stability of the molecules under study, and biological evaluation examines their potential as lead compounds for cancer therapeutics. These studies can be further extended to include experimental validation and in vitro and in vivo tests to confirm further the efficacy of the anticancer action as well as the potential toxicity of the compounds. The theoretical investigations provide a database of information that could be useful for experimental scientists and medicinal chemists who primarily focus on drug design and discovery in their research so that they can narrow down the number of possible lead compounds from the vast chemical space of organic compounds that possess drug-like characteristics. -
Computational Aspects of Business Management with Special Reference to Monte Carlo Simulation
Business management is concerned with organizing and efficiently utilizing resources of a business, including people, in order to achieve required goals. One of the main aspects in this process is planning, which involves deciding operations of the future and consequently generating plans for action. Computational models, both theoretical and empirical, help in understanding and providing a framework for such a scenario. Statistics and probability can play an important role in empirical research as quantitative data is amenable for analysis. In business management, analysis of risk is crucial as there is uncertainty, vagueness, irregularity, and inconsistency. An alternative and improved approach to deterministic models is stochastic models like Monte Carlo simulations. There has been a considerable increase in application of this technique to business problems as it provides a stochastic approach and simulation process. In stochastic approach, we use random sampling to solve a problem statistically and in simulation, there is a representation of a problem using probability and random numbers. Monte Carlo simulation is used by professionals in fields like finance, portfolio management, project management, project appraisal, manufacturing, insurance and so on. It equips the decision-maker by providing a wide range of likely outcomes and their respective probabilities. This technique can be used to model projects which entail substantial amounts of funds and have financial implications in the future. The proposed chapter will deal with concepts of Monte Carlo simulation as applied to Business Management scenario. A few specific case studies will demonstrate its application and interpretation. 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG. -
Computational approach of artificial neural network
This paper makes an attempt to predict the movement of the stock price for the following day using Artificial Neural Network (ANN). For the purpose of this research, two companies from each industry have been chosen that is, TATA Motors and Honda Motors from the Automobile industry and Cadila Pharmaceuticals Ltd. and Glenmark Pharmaceuticals from the Pharmaceutical industry. The historical prices of these companies were collected and by using Artificial Neural Network (ANN), the movement of the stock price for the next day is predicted. 2017 IEEE. -
Computational and experimental investigation on biological and photophysical properties of high yielded novel aryl-substituted pyrazolone analogue
A series of new aryl-substituted pyrazolone derivatives 5(a-h) were synthesised via the Baylis-Hillman acetate reaction with pyrazolones and tested for antifungal, antibacterial, and antioxidant properties in vitro. Among the tested molecules 5d and 5e show good in vitro antifungal and antibacterial activities due to the presence of fluorine, which enhances the absorption rate by increasing lipid solubility and improves the pharmacological activity. It is also evident from the results obtained from structure-activity relationship (SAR) studies. Further, the photophysical properties of synthesized compounds were theoretically estimated using the ab-intio technique. The ground state optimization and HOMOLUMO energy levels are calculated using the DFT-B3LYP-6-311 basis set. Using the theoretically estimated HOMOLUMO value, global chemical reactivity descriptor parameters are estimated, and the result shows that compounds 5d and 5e have a higher electronegative and electrophilicity index than other molecules. Overall results suggest that, fluorine substituted pyrazolone derivatives show good photophysical, SAR, and biological properties. 2022 Elsevier B.V. -
COMPUTATION OF b-CHROMATIC TOPOLOGICAL INDICES OF SOME GRAPHS AND ITS DERIVED GRAPHS
The two fastest-growing subfields of graph theory are graph coloring and topological indices. Graph coloring is assigning the colors/values to the edges/vertices or both. A proper coloring of the graph G is assigning colors/values to the vertices/edges or both so that no two adjacent vertices/edges share the same color/value. Recently, studies involving Chromatic Topological indices that dealt with different graph coloring were studied. In such studies, the vertex degrees get replaced with the colors, and the computation is carried out based on the topological index of our choice. We focus on b-Chromatic Zagreb indices and b-Chromatic irregularity indices in this work. This paper discusses the b-Chromatic Zagreb indices and b-Chromatic irregularity indices of the gear graph, star graph, and its derived graphs such as the line and middle graph. 2023, RAMANUJAN SOCIETY OF MATHEMATICS AND MATHEMATICAL SCIENCES. All rights reserved.