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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 studies into the chemical nature, thermal behaviour, solvent role, reactivity and biological evaluation of Rigidin E A marine alkaloid with potent liver cancer inhibition
The current work includes theoretical studies of Rigidin E (marine alkaloid) molecule with the DFT technique and evaluation of its biological properties in silico. DFT calculations in different media were performed for the title molecule. Gradual changes were noticed in the properties of the title compound when subjected to solvation in polar solvents. Electron density distribution, interaction and excitation were demonstrated using topological studies (ELF, LOL, RDG, and charge transfer) done using Multiwfn software. From FMO analysis, methanol is the solvent in which the title compound has the highest band gap value (3.8972 eV) compared to other solvents, and in the gas phase it has a band gap value of 3.6886 eV. Theoretical UV studies show that n ->?* and n ->?* electronic transitions are significant in Rigidin E. In water, the title molecule has a first-order hyperpolarizability about 100 times that of the reference substance urea, indicating its powerful NLO potential in aqueous medium. ADMET profile was generated using online tools (ADMET lab 2.0, PreADMET, and SwissADME). For the title molecule, docking was done against select liver cancer targets using AutoDock Tools and the lowest binding affinity was obtained ?4.62 kcal/mol against 4H6J protein. 2023 Elsevier B.V. -
Computational study of charge transfer iso-surface in first three excited states, electron-hole transition effects, chemical nature and bond order analysis investigations of chrysogine
This work presents the theoretical DFT (Density Functional Theory) studies and the biological application of chrysogine, a marine alkaloid. Energy minimisation and additional DFT evaluations were performed for vacuum and solvent media. It has been observed that solvation with polar solvents has resulted in a slight variation in the molecule's properties. The Multiwfn software was employed to carry out various topological analyses. Among these, the charge transfer studies show that the second and third excited states are the most significant. From the reactivity analysis, the least energy gap (4.9624 eV) is obtained in water, indicating that chrysogine is most reactive in aqueous media. Theoretical UV studies show that the trends in ?max values correspond to n >?* and n >?* electronic transitions within the molecule. An increase in medium polarity has demonstrated in the MEP (Molecular Electrostatic Potential) maps an increase in the potential range from ?6.619 10?2 a.u. to 6.619 10?2 a.u. in the gas phase, to a sharp rise to ?8.036 10?2 a.u. to 8.036 10?2 a.u. in ethanol, ?8.098 10?2 a.u. to 8.098 10?2 a.u. in methanol, ?8.130 10?2 a.u. to 8.130 10?2 a.u. in DMSO, and ?8.127 10-2 a.u. to 8.127 10?2 a.u. in water. The most significant transition contributing to molecular stability from NBO (Natural Bond Orbital) analysis is: (O2-C9) ?* ? ?* (C7-C8) with the energy of 258.13 kcal mol?1. The ADMET profile for the molecule was assimilated with the help of online servers. The molecule was docked against lung cancer target proteins (PDB ID: 1NTK, 3QFB) using software such as AutoDock Tools and PyMOL. The respective illustrations and data were visualised using Discovery Studio Visualizer. Good binding affinities (?5.69 kcal mol?1 for 1NTK and ?6.64 kcal mol?1 for 3QFB proteins) and interactions were achieved with the selected targets. 2024 Elsevier B.V. -
Computational Study of MHD Nanofluid Flow with Effects of Variable Viscosity and Non-uniform Heat Generation
The thermodynamic study of an unsteady two-dimensional nanofluid flow through a permeable stretched sheet is looked at. Water is used as the primary fluid, along with four different nanoparticles, including copper (Cu), titanium dioxide (TiO2), copper oxide (CuO), and aluminium oxide (Al2O3). The heat transfer phenomenon is explained by an outside source. Additionally considered are the impacts of heat generation and absorption. A similarity transformation is used to convert the considered set of mathematical equations into a system of ODEs. The BVP4C method is then mathematically applied, coupled with shooting fashion. The results are given for cases involving copper nanoparticles. The effects of various physical parameters on the profiles of the dimensionless flow field, temperature, average entropy generation function, skin friction, and the Nusselt number are examined with illustrations and thorough explanations. As exceptional circumstances of the current inquiry, there is a strong agreement between the current conclusion and the findings of the other researchers. The average entropy generation number, temperature, and velocity profiles are shown to be strongly influenced by regulating factors. The authors conclude that the average entropy production number decreased in the existence of a temperature- and space-dependent heat source/sink, but it increased with increasing the viscosity parameter. 2023, The Author(s), under exclusive licence to Springer Nature India Private Limited. -
Computational techniques for sustainable green procurement and production
Computational techniques are used to generate, solve, analyze, explain, or manage any simple or complex task. The use of environmentally responsible techniques to meet demand for resources, commodities, utilities, and services is known as green procurement. Computational technique in green procurement and production is one of the components of sustainable procurement, along with a commitment to social responsibility and good corporate behavior. Some solutions for this kind of issue are low-maintenance, energy-efficient, and long-lasting. Several experts and researchers provided their findings on the environmental impact of ICT with the use of computational techniques. Also, the importance of energy-efficient information technology for environmentally conscious and feasible information technology is a hot topic because a computer faces environmental challenges at every stage of its life, from development to use to disposal. Due to changing environmental conditions, corporations have prioritized carbon emissions in procurement and transportation, which have the highest carbon impact. To encourage potential suppliers to adopt environmentally friendly practices, green criteria should be introduced into public procurement. Environmentally friendly corporate practices and environmental conservation are considered significant tools through public procurement. Techniques for green procurement and production procedures have recently been correlated with the concept of computational techniques of green procurement and production, owing to the increased emphasis on the concept of computational approaches. For eco-friendly procurement and production operations, computational approaches are inculcated and presented in the same way that they are for green procurement and manufacturing. From this perspective, this chapter presents a methodology for merging computational techniques into green procurement and production in public procurement in the form of green computing. 2024 by Elsevier Inc. All rights reserved, including those for text and data mining, AI training, and similar technologies. -
Computational techniques to study the dynamics of generalized unstable nonlinear Schringer equation
In this paper, a more general form of unstable nonlinear Schringer equation which describe the time evolution of disturbances in marginally stable or unstable media is studied. A new modification of the Sardar sub-equation method is discussed and employed to retrieve solitons and other solutions of the suggested nonlinear model. A variety of solutions, including bright solitons, dark solitons, singular solitons, combo bright-singular solitons, periodic, exponential, and rational solutions are provided with considerable physical perspective. Using the q-homotopy analysis algorithm in combination with the Laplace transform, we present the approximate solutions of the bright and dark solitons, including the physical nature of the attained solutions. The computation complexity and results indicate that the given techniques are simple, effective, uncomplicated, and that they may be used to a wide range of unstable and stable nonlinear evolution equations encountered in mathematics, mathematical physics, and other applied disciplines. 2022 -
Computationally Efficient Machine Learning Methodology for Indian Nobel Laureate Classification
A computationally efficient methodology for Indian Nobel Laureate classification is proposed in this study, emphasizing the optimization of image categorization through supervised learning techniques. Leveraging advancements in Convolutional Neural Networks (CNNs), the research aims to enhance the efficiency and precision of image classification tasks. The study utilizes Logistic Regression for dataset analysis, initially employing browser extensions for mass downloading categorized image data. Haar cascade classifiers are then used for data wrangling, focusing on facial, nose, and mouth recognition. Following this, feature engineering through wavelet transformation reduces image dimensionality, preparing the dataset for the chosen ML model, Logistic Regression. The primary focus is to simplify technology for improved image categorization. Support Vector Machines (SVM), Random Forest, and Logistic Regression are examined, with Logistic Regression emerging as the most effective model, achieving an accuracy rate of 87.5%. A thorough evaluation using Confusion Matrices reveals Logistic Regression's superior performance in classifying images of Indian Nobel laureates. A strategic up-sampling approach is implemented to address dataset inconsistencies, ensuring balanced representation across classes. The Haar wavelet transform is then applied for feature extraction, optimizing the dataset for ML models. The dataset is split into training and testing sets (80-20), and the three models are trained and evaluated for accuracy. Logistic Regression proves to be the best performer, offering insights into prominent leaders' identification. The research offers a detailed pipeline for data preprocessing, feature engineering, and model assessment, culminating in a robust image categorization system. Logistic Regression emerges as a reliable method for biographical picture identification, demonstrating superior accuracy over SVM and Random Forest. This research underscores the importance of efficient and accurate image classification methodologies for practical applications in real-world scenarios, particularly in recognizing influential leaders. 2024 IEEE. -
Computationally efficient wavelet domain solver for florescence diffuse optical tomography
Estrogen induced proliferation of mutant cells is a growth signal hallmark of breast cancer. Fluorescent molecule that can tag Estrogen Receptor (ER) can be effectively used for detecting cancerous tissue at an early stage. A novel targetspecific NIRf dye conjugate aimed at measuring ER status was synthesized by ester formation between 17-? estradiol and a hydrophilic derivative of ICG, cyanine dye, bis-1,1-(4-sulfobutyl) indotricarbocyanine-5-carboxylic acid, sodium salt. In-vitro studies provided specific binding on ER+ve [MCF-7] cells clearly indicating nuclear localization of the dye for ER+ve as compared to plasma level staining for MDAMB-231. Furthermore, cancer prone cells showed 4.5-fold increase in fluorescence signal intensity compared to control.; A model of breast phantom was simulated to study the in-vivo efficiency of dye with the parameters of dye obtained from photo-physical and in-vitro studies. The excitation (754 nm) and emission (787 nm) equation are solved independently using parallel processing strategies. The results were obtained by carrying out wavelet transformation on forward and the inverse data sets. An improvisation of the Information content of system matrix was suggested in wavelet domain. The inverse problem was addressed using LevenbergMarquardt (LM) procedure with the minimization of objective function using Tikhonov approach. The multi resolution property of wavelet transform was explored in reducing error and increasing computational efficiency. Our results were compared with the single resolution approach on various parameters like computational time, error function, and Normalized Root Mean Square (NRMS) error. A model with background absorption coefficient of 0.01 mm-1 with anomalies of 0.02 mm-1 with constant reduced scattering of 2.0 mm for different concentration of dye was compared in the result. The reconstructed optical properties were in concurrence with the tissue property at 787 nm. We intend our future plans on in-vivo study on developing a complete instrumentation for imaging a target specific lipophilic dye. Springer International Publishing Switzerland 2014. -
Computer Assisted Unsupervised Extraction and Validation Technique for Brain Images from MRI
Magnetic Resonance Imaging (MRI) of human is a developing field in medical research because it assists in considering the brain anomalies. To identify and analyze brain anomalies, the research requires brain extraction. Brain extraction is a significant clinical image handling method for quick conclusion with clinical perception for quantitative assessment. Automated methods of extracting brain from MRI are challenging, due to the connected pixel intensity information for various regions such as skull, sub head and neck tissues. This paper presents a fully automated extraction of brain area from MRI. The steps involved in developing the method to extract brain area, includes image contrast limited using histogram, background suppression using average filtering, pixel region growing method by finding pixel intensity similarity and filling discontinuity inside brain region. Twenty volumes of brain slices are utilized in this research method. The outcome is achieved by this method is approved by comparing with manually extracted slices. The test results confirm the performance of this strategy can effectively section brain from MRI. 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Computer modelling of trace SO2 and NO2 removal from flue gases by utilizing Zn(ii) MOF catalysts
SO2 and NO2 capture and conversion have been investigated via density functional theory (DFT) and grand canonical Monte Carlo (GCMC) simulations using a novel hydrogen-bonded 3D metal-organic framework (MOF) containing a Zn(ii) centre and a partially fluorinated (polar -CF3) long-chain dicarboxylate ligand with a melamine (basic -NH2) co-ligand. Initially, computational single-component isotherms have been determined for SO2 and NO2 gases. These simulations have shown exothermic adsorption enthalpies of ?36.4 and ?28.6 kJ mol?1 for SO2 and NO2, respectively. They have also indicated that SO2 has a high affinity for polar -CF3 and basic -NH2 binding sites of the ligand in the framework pore walls. The lower adsorption capacity of NO2 compared with SO2 is due to weaker electrostatic interactions with the framework. Furthermore, MOF adsorbent selectivity for removing trace amounts of SO2 and NO2 in flue gases has been estimated through the co-adsorption of ternary gas mixtures (SO2/CO2/N2 and NO2/CO2/N2). Together with DFT, the climbing image nudged elastic band (CI-NEB) method has been used for investigating the plausible mechanisms for HbMOF1 catalyzed cycloadditions of SO2 and NO2 with epoxides leading to the formation of cyclic sulphites and nitrates, respectively. 2023 The Royal Society of Chemistry. -
Computer simulation of diesel fueled engine processes using matlab and experimental investigations on research engine
The depletion of conventional fuel source at a fast rate and increasing environmental pollution have motivated extensive research in combustion modeling and energy efficient engine design. In the present work, a computer simulation incorporating progressive combustion model using thermodynamic equations has been carried out using MATLAB to evaluate the performance of a diesel engine. Simulations at constant speed and variable load have been carried out for the experimental engine available in the laboratory. For simulation, speed and Air/Fuel ratios, which are measured during the experiment, have been used as input apart from other geometrical details. A state-of-the-art experimental facility has been developed in-house. The facility comprises of a hundred horsepower water cooled eddy current dynamometer with appropriate electronic controllers. A normal load test has been carried out and the required parameters were measured. A six gas analyzer was used for the measurement of NOx, HC, CO2, O2, CO and SOx. and a smoke meter was used for smoke opacity. The predicted Pressure-Volume (PV) diagram was compared with measurements and found to match closely. It is concluded that the developed simulation software could be used to get quick results for parametric studies. Copyright 2017 ASME. -
Computer Vision Based Automatic Margin Computation Model for Digital Document Images
Margin, in typography, is described as the space between the text content and the document edges and is often essential information for the consumer of the document, digital or physical. In the present age of digital disruption, it is customary to store and retrieve documents digitally and retrieve information automatically from the documents when necessary. Margin is one such non-textual information that becomes important for some business processes, and the demand for computing margins algorithmically mounts to facilitate RPA. We propose a computer vision-based text localization model, utilizing classical DIP techniques such as smoothing, thresholding, and morphological transformation to programmatically compute the top, left, right, and bottom margins within a digital document image. The proposed model has been experimented with different noise filters and structural elements of various shapes and size to finalize the bilateral filter and lines and structural elements for the removal of noises most commonly occurring due to scans. The proposed model is targeted towards text document images and not the natural scene images. Hence, the existing benchmark models developed for text localization in natural scene images have not performed with the expected accuracy. The model is validated with 485 document images of a real-time business process of a reputed TI company. The results show that 91.34 % of the document images have conferred more than 90 % IoU value which is well beyond the accuracy range determined by the company for that specific process. 2023, Crown. -
Computer Vision for Real-Time Nutrition Scanning in Polycystic Ovary Syndrome Management
PCOS stands for polycystic ovary syndrome and it is widely known to be one of the leading causes of insulin resistance and hormonal imbalance among women within the reproductive age spanning from 8 to 13%. Although integrating a healthy diet is a crucial step while managing PCOS, the outdated and frustrating method of manually food logging slows the process and still lacks accuracy. Improving performance feedback is one of the key areas to ensure adherence to a healthy plan. This is the driving reason behind the development of 'NutriScan-PCOS.' In the PCOS focused Streamlit App, the PCOS diet MobileNet application nutrition model helps to do just that. It is trained on 4000 images of 80 diverse Indian foods for quick identification and portion size estimation with OpenCV. The driving function of the app is to track meals and overall nutrition value, scoring on a dynamic scale on 5 parameters focused on the impact on PCOS. It is designed to be user-centric, with a clear green-yellowred traffic system to ensure guidance is instantaneous and easy. NutriScan-PCOS runs on a Streamlit interface with options like photo upload, live camera, barcode scan, and daily logs-making it easy and practical for everyday use. 2026 IEEE. -
Computer-based Intelligence and Security
There is a massive increase in the incidence of cyberattacks day by day in the modern enterprise environment. Although humans are behind this task due to the rapid growth in the incidence human intervention is unable to control this. Therefore, something more than human intervention is required to have a check over it. With cyberattacks evolving at a rapid pace and with the increase in the use of devices in todays world, Artificial Intelligence (AI) and Machine Learning (ML) can help to have a check over cybercrime incidences, automate the process of detection of threat, and handle these in a better way as compared to the conventional methods used for controlling cybercrime and cyberattack. AI and ML has shown good results in data information security as these technologies are capable of analyzing a large and wide variety of data and can track threats related to cyberattacks that may cause phishing attacks. As these technologies are capable of learning and improving from past experiences, they can even predict and tell the new variety of attacks that may occur in the coming days. This chapter describes the use of AI in controlling cyberattacks and cybercrime and the expert views on this matter. 2024 selection and editorial matter, S. Vijayalakshmi, P. Durgadevi, Lija Jacob, Balamurugan Balusamy, and Parma Nand; individual chapters, the contributors. -
Computerized grading of brain tumors supplemented by artificial intelligence
For effective diagnosis of health conditions, there is a need to process medical images to obtain meaningful information. The diagnosis of brain tumors begins with magnetic resonance imaging (or MRI) scan. This is followed by segmentation of the medical images so obtained which can prove cumbersome if it were to be performed manually. Determining the best approach to do segmentation remains challenge among multiple computerized approaches. This paper combines both the identification and classification of tumors from the MRI results and is backed by a cloud-based framework to provision the same. The phase of extraction of features includes the utilization of a Hadoop framework and Gabor filter along with variations in terms of orientation and scale. Artificial bee colony algorithm and support vector machine classifier have been used to designate the degree of optimal features and categorize the same. The grading of brain tumors from MRI images can be fulfilled by the aforementioned approach. The said approach is believed to deliver promising results in terms of accuracy, which has also been verified experimentally. 2019, Springer-Verlag GmbH Germany, part of Springer Nature. -
Computing isogeny on Edwards curves for quantum safe cryptography
In recent years, cryptographic research has seen a surge of interest in post-quantum cryptography driven by the potential threat that quantum computers pose to traditional public-key cryptosystems. Isogeny-based cryptography is a promising method in post-quantum cryptography, relying on the computational challenge of calculating isogenies, which are specific mappings between elliptic curves. The efficiency of isogeny computations is vital for real-world cryptographic applications. However, computing isogenies, especially with large parameters, can be very resource intensive. To overcome this challenge, we purpose an efficient method for computing odd-degree isogenies on certain form of an elliptic curves by employing an auxiliary coordinate. Our work appears to bridge the gap in computational efficiency for odd-degree isogenies, especially in terms of reducing the complexity of the isogeny computations when compared to traditional affine and projective methods. The derived formula is more efficient than affine and projective cases. We also analyse the algebraic complexity of these calculations and compare them to alternative formulae. Additionally, we evaluate the runtimes for isogeny computation across different prime numbers and compare them with other elliptic curve model to check the performance. At last, we suggest potential avenues for future work. Bharati Vidyapeeth's Institute of Computer Applications and Management 2025. -
Concentration-dependent luminescence characterization of terbium-doped strontium aluminate nanophosphors
The present investigation describes the synthesis of luminescent terbium-doped strontium aluminate nanoparticles emitting bright green light, which were synthesized through a solid-state reaction method assisted by microwave radiation. Various samples containing different concentrations of Tb were synthesized, and an analysis of their structural and morphological features was conducted using powder x-ray diffraction, Fourier transform infrared spectroscopy and field emission scanning electron microscopy. The band gaps of the samples were determined utilizing the KubelkaMunk method. The quenching mechanism observed was identified to be due to dipoledipole interaction using the Dexter theory. The optimized sample with a terbium concentration of 4at.% has a luminescence lifetime of 1.05 ms with 20.62% quantum efficiency. The results of this study indicate that the terbium-doped strontium aluminate fluorescent nanoparticles exhibit promising potential for a wide range of applications, including bioimaging, sensing and solid-state lighting. 2024 John Wiley & Sons Ltd. -
Concept Drift Detection for Social Media: A Survey
The research over information retrieval from social media data has progressed for streaming data since the last decade. Recently, academic researchers have witnessed users' changing topics, trends, and intent on social media. This change of information with time takes into account the temporal attribute for real-time data, and thus, advances in this domain are exponentially growing. Although concept drift is still not explored due to a shortage of available datasets, concept drift for social media is minimally explored. This manuscript makes attempts to identify the types of concept drift for social media data, discuss the historical perspective of concept drift on social media, and enlist the possible research directions. 2021 IEEE. -
Concept Mapping of Issues of Students Life in University
The undergraduate student body forms around 85.9% of the total number of students enrolled in India, which is a significant population. It has become imperative to understand the issues that these students face during their undergraduate years as a precursor to developing mechanisms and strategies to enable student progress, both academically and developmentally. This study aimed at developing a concept map to outline the various aspects and issues of the undergraduate students life in India utilizing the concept mapping method. Data from participants (n = 141) at different phases was analysed resulting in 49 unique life issues and aspects and 8 clusters. The emerging issues have relevance and implications for teachers, parents, administrators and other stakeholders in structuring and developing services targeted towards undergraduate students in India. 2015, National Academy of Psychology (NAOP) India. -
Conceptual comprehension analysis of a student using soft cosine measure
Knowledge is the substantial wealth of a man and he possesses an innate thirst to acquire it. Knowledge embodies facts or ideas acquired through study, investigation and observation or experience. In this context, technology with its varied techniques comprising endless algorithms in natural language processing (NLP) plays an imperative role in the pursuit of knowledge. Inferences thus gathered are a clear pointer to the content teaching of students. Soft cosine measure algorithm is used in this analysis process to provide an answer regarding the grasping ability of each student with optimum learner participation and creativity. After each lecture, students have to upload their corresponding notes and this in turn would be compared with the teacher's lecture notes. The soft cosine computation gives individual results, on how much each student has comprehended a concept. This new methodology is a much awaited contribution of the educational field. 2022 Author(s).