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Synthesis, characterization and photophysical studies of a novel schiff base bearing 1, 2, 4-Triazole scaffold
A novel Schiff base derivative containing 1, 2, 4-triazole nucleus (TMPIMP) was synthesized from 4- [1,2,4] triazol-1-ylmethyl-phenylamine and salicylaldehyde in the presence of glacial acetic acid in an ethanolic medium. The synthesized compound was characterized by 1H-NMR, IR and UV spectral analysis. The excitation and emission spectra of triazolyl methyl phenyl imino methyl phenol (abbreviated as TMPIMP) were recorded in various solvents to investigate their solvatochromic behaviour. Dipole moments of the two electronic states of TMPIMP were calculated from solvatochromic spectral shifts. These were correlated with refractive index (?) and dielectric constant (?) of various solvents. Theoretical calculations were performed to estimate the excited state dipole moment on the basis of different solvent correlation methods, like the Bilot-Kawski, Bakhshiev, Lippert-Mataga, Kawski-Chamma-Viallet and Reichardt methods. The dipole moment in the excited state was found to be higher than that in the ground state due to a substantial redistribution of electron densities and charges. Using a multiple regression analysis, the solvent-solute interactions were determined by means of Kamlet Taft parameters (?, ?, ??). Computational studies were performed by Gaussian 09 W software using a time-dependent density functional theory (TD-DFT) in order to calculate the atomic charges and frontier molecular orbital energies in the solvent phase. The calculations indicated that the dipole moment of the molecule in an excited state is much higher than that in a ground state. The chemical stability of TMPIMP was determined by means of chemical hardness (?) using HOMO-LUMO energies. The reactive centers in the molecule were also identified by molecular electrostatic potential (MESP) 3D plots as a result of TD-DFT computational analysis. 2016 Elsevier B.V. All rights reserved. -
Estimation of ground state and excited state dipole moments of a novel Schiff base derivative containing 1, 2, 4-triazole nucleus by solvatochromic method
A novel schiff base derivative containing 1, 2, 4-triazole moiety (NBTMPA) has been synthesized from 4- [1, 2, 4] triazol-1-ylmethyl-phenylamine and 4-nitrobenzaldehyde in the presence of glacial acetic acid in an ethanolic medium. The absorbance and fluorescence spectra of (4-nitro-benzylidene)-(4- [1, 2, 4] triazol-1-ylmethyl-phenyl)-amine (NBTMPA) were recorded in various solvents to investigate their solvatochromic behaviour. Dipole moments of the two electronic states of NBTMPA were calculated from solvatochromic spectral shifts. These were correlated with the refractive index (n) and dielectric constant (?) of various solvents. Theoretical calculations were performed to estimate the excited state dipole moment on the basis of different solvent correlation methods, like the Bilot-Kawski, Bakhshiev, Lippert-Mataga, Kawski-Chamma-Viallet and Reichardt methods. The dipole moment in the excited state was found to be higher than that in the ground state due to a substantial redistribution of electron densities and charges. Using a multiple regression analysis, the solvent-solute interactions were determined by means of Kamlet Taft parameters (?, ?, ??). Computational studies were performed by Gaussian 09 W software using a time-dependent density functional theory (TDDFT) in order to calculate the atomic charges and frontier molecular orbital energies in the solvent phase. The calculations indicated that the dipole moment of the molecule in an excited state is much higher than that in a ground state. The chemical stability of NBTMPA was determined by means of chemical hardness (?) using HOMO-LUMO energies. The reactive centres in the molecule were also identified by molecular electrostatic potential (MESP) 3D plots as a result of a TDDFT computational analysis. 2015 Elsevier B.V. -
Thermal analysis of a radiative nanofluid over a stretching/shrinking cylinder with viscous dissipation
This study explores the impact of thermal radiation and viscous dissipation on the stagnation point flow of a copperwater nanofluid across a convective stretching/shrinking cylinder. The copper suspension in the base fluid water enables the fluid to conduct more heat by increasing its thermal conductivity. The mathematical model that governs the flow of Cu-H2O nanofluid is formulated by the system of partial differential equations (PDEs) which are then subjected to transformation by introducing suitable similarity variables so the system is transformed to the Ordinary Differential Equations (ODEs). These equations have been solved numerically via the bvp4c package in MATLAB. The outcomes have been signified graphically in the form of heat transfer rate, temperature, skin friction and velocity which are dependent on the concerning flow parameters. For each of these result, dual solutions have been produced which are conditional on the shrinking of cylinder. These results declare that the skin friction increases for the shrinking cylinder and decreases for the stretching cylinder whereas an opposite trend is seen for the rate of heat transfer. Similarly, heat transfer is found to be decreasing for the increase in both Biot and Eckert number. Meanwhile, the existence of greater values of curvature parameter causes to enhance both first and second solution of velocity as well as the temperature is augmenting with the increase in Eckert number and volume fraction of nano particles. 2022 Elsevier B.V. -
Early Identification and Detection of Driver Drowsiness by Hybrid Machine Learning
Drunkenness or exhaustion is a leading cause of car accidents, with severe implications for road safety. More fatal accidents could be avoided if fatigued drivers were warned ahead of time. Several drowsiness detection technologies to monitor for signs of inattention while driving and notifying the driver can be adopted. Sensors in self-driving cars must detect if a driver is sleepy, angry, or experiencing extreme changes in their emotions, such as anger. These sensors must constantly monitor the driver's facial expressions and detect facial landmarks in order to extract the driver's state of expression presentation and determine whether they are driving safely. As soon as the system detects such changes, it takes control of the vehicle, immediately slows it down, and alerts the driver by sounding an alarm to make them aware of the situation. The proposed system will be integrated with the vehicle's electronics, tracking the vehicle's statistics and providing more accurate results. In this paper, we have implemented real-time image segmentation and drowsiness using machine learning methodologies. In the proposed work, an emotion detection method based on Support Vector Machines (SVM) has been implemented using facial expressions. The algorithm was tested under variable luminance conditions and outperformed current research in terms of accuracy. We have achieved 83.25 % to detect the facial expression change. 2013 IEEE. -
Plant Identification Using Fitness-Based Position Update in Whale Optimization Algorithm
Since the beginning of time, humans have relied on plants for food, energy, and medicine. Plants are recognized by leaf, flower, or fruit and linked to their suitable cluster. Classification methods are used to extract and select traits that are helpful in identifying a plant. In plant leaf image categorization, each plant is assigned a label according to its classification. The purpose of classifying plant leaf images is to enable farmers to recognize plants, leading to the management of plants in several aspects. This study aims to present a modified whale optimization algorithm and categorizes plant leaf images into classes. This modified algorithm works on different sets of plant leaves. The proposed algorithm examines several benchmark functions with adequate performance. On ten plant leaf images, this classification method was validated. The proposed model calculates precision, recall, F-measurement, and accuracy for ten different plant leaf image datasets and compares these parameters with other existing algorithms. Based on experimental data, it is observed that the accuracy of the proposed method outperforms the accuracy of different algorithms under consideration and improves accuracy by 5%. 2022 Tech Science Press. All rights reserved. -
Breast Cancer Detection in Mammography Images Using Deep Convolutional Neural Networks and Fuzzy Ensemble Modeling Techniques
Breast cancer has evolved as the most lethal illness impacting women all over the globe. Breast cancer may be detected early, which reduces mortality and increases the chances of a full recovery. Researchers all around the world are working on breast cancer screening tools based on medical imaging. Deep learning approaches have piqued the attention of many in the medical imaging field due to their rapid growth. In this research, mammography pictures were utilized to detect breast cancer. We have used four mammography imaging datasets with a similar number of 1145 normal, benign, and malignant pictures using various deep CNN (Inception V4, ResNet-164, VGG-11, and DenseNet121) models as base classifiers. The proposed technique employs an ensemble approach in which the Gompertz function is used to build fuzzy rankings of the base classification techniques, and the decision scores of the base models are adaptively combined to construct final predictions. The proposed fuzzy ensemble techniques outperform each individual transfer learning methodology as well as multiple advanced ensemble strategies (Weighted Average, Sugeno Integral) with reference to prediction and accuracy. The suggested Inception V4 ensemble model with fuzzy rank based Gompertz function has a 99.32% accuracy rate. We believe that the suggested approach will be of tremendous value to healthcare practitioners in identifying breast cancer patients early on, perhaps leading to an immediate diagnosis. 2022 by the authors. -
Improvement of Automatic Glioma Brain Tumor Detection Using Deep Convolutional Neural Networks
This article introduces automatic brain tumor detection from a magnetic resonance image (MRI). It provides novel algorithms for extracting patches and segmentation trained with Convolutional Neural Network (CNN)'s to identify brain tumors. Further, this study provides deep learning and image segmentation with CNN algorithms. This contribution proposed two similar segmentation algorithms: one for the Higher Grade Gliomas (HGG) and the other for the Lower Grade Gliomas (LGG) for the brain tumor patients. The proposed algorithms (Intensity normalization, Patch extraction, Selecting the best patch, segmentation of HGG, and Segmentation of LGG) identify the gliomas and detect the stage of the tumor as per taking the MRI as input and segmented tumor from the MRIs and elaborated the four algorithms to detect HGG, and segmentation to detect the LGG works with CNN. The segmentation algorithm is compared with different existing algorithms and performs the automatic identification reasonably with high accuracy as per epochs generated with accuracy and loss curves. This article also described how transfer learning has helped extract the image and resolution of the image and increase the segmentation accuracy in the case of LGG patients. Copyright 2022, Mary Ann Liebert, Inc., publishers 2022. -
A Hybrid AES with a Chaotic Map-Based Biometric Authentication Framework for IoT and Industry 4.0
The Internet of Things (IoT) is being applied in multiple domains, including smart homes and energy management. This work aims to tighten security in IoTs using fingerprint authentications and avoid unauthorized access to systems for safeguarding user privacy. Captured fingerprints can jeopardize the security and privacy of personal information. To solve privacy- and security-related problems in IoT-based environments, Biometric Authentication Frameworks (BAFs) are proposed to enable authentications in IoTs coupled with fingerprint authentications on edge consumer devices and to ensure biometric security in transmissions and databases. The Honeywell Advanced Encryption Security-Cryptography Measure (HAES-CM) scheme combined with Hybrid Advanced Encryption Standards with Chaotic Map Encryptions is proposed. BAFs enable private and secure communications between Industry 4.0s edge devices and IoT. This works suggested schemes evaluations with other encryption methods reveal that the suggested HAES-CM encryption strategy outperforms others in terms of processing speeds. 2023 by the authors. -
P-ROCK: A Sustainable Clustering Algorithm for Large Categorical Datasets
Data clustering is crucial when it comes to data processing and analytics. The new clustering method overcomes the challenge of evaluating and extracting data from big data. Numerical or categorical data can be grouped. Existing clustering methods favor numerical data clustering and ignore categorical data clustering. Until recently, the only way to cluster categorical data was to convert it to a numeric representation and then cluster it using current numeric clustering methods. However, these algorithms could not use the concept of categorical data for clustering. Following that, suggestions for expanding traditional categorical data processing methods were made. In addition to expansions, several new clustering methods and extensions have been proposed in recent years. ROCK is an adaptable and straightforward algorithm for calculating the similarity between data sets to cluster them. This paper aims to modify the algorithm by creating a parameterized version that takes specific algorithm parameters as input and outputs satisfactory cluster structures. The parameterized ROCK algorithm is the name given to the modified algorithm (P-ROCK). The proposed modification makes the original algorithm more flexible by using user-defined parameters. A detailed hypothesis was developed later validated with experimental results on real-world datasets using our proposed P-ROCK algorithm. A comparison with the original ROCK algorithm is also provided. Experiment results show that the proposed algorithm is on par with the original ROCK algorithm with an accuracy of 97.9%. The proposed P-ROCK algorithm has improved the runtime and is more flexible and scalable. 2023, Tech Science Press. All rights reserved. -
Performance Analysis of Machine Learning Algorithms for Classifying Hand Motion-Based EEG Brain Signals
Brain-computer interfaces (BCIs) records brain activity using electroencephalogram (EEG) headsets in the form of EEG signals; these signals can be recorded, processed and classified into different hand movements, which can be used to control other IoT devices. Classification of hand movements will be one step closer to applying these algorithms in real-life situations using EEG headsets. This paper uses different feature extraction techniques and sophisticated machine learning algorithms to classify hand movements from EEG brain signals to control prosthetic hands for amputated persons. To achieve good classification accuracy, denoising and feature extraction of EEG signals is a significant step. We saw a considerable increase in all the machine learning models when the moving average filter was applied to the raw EEG data. Feature extraction techniques like a fast fourier transform (FFT) and continuous wave transform (CWT) were used in this study; three types of features were extracted, i.e., FFT Features, CWT Coefficients and CWT scalogram images. We trained and compared different machine learning (ML) models like logistic regression, random forest, k-nearest neighbors (KNN), light gradient boosting machine (GBM) and XG boost on FFT and CWT features and deep learning (DL) models like VGG-16, Dense-Net201 and ResNet50 trained on CWT scalogram images. XG Boost with FFT features gave the maximum accuracy of 88%. 2022 CRL Publishing. All rights reserved. -
Mathematical foundations based statistical modeling of software source code for software system evolution
Source code is the heart of the software systems; it holds a wealth of knowledge that can be tapped for intelligent software systems and leverage the possibilities of reuse of the software. In this work, exploration revolves around making use of the pattern hidden in various software development processes and artifacts. This module is part of the smart requirements management system that is intended to be built. This system will have multiple modules to make the software requirements management phase more secure from vulnerabilities. Some of the critical challenges bothering the software development community are discussed. The background of Machine Learning approaches and their application in software development practices are explored. Some of the work done around modeling the source code and approaches used for vulnerabilities understanding in software systems are reviewed. Program representation is explored to understand some of the principles that would help in understanding the subject well. Further deeper dive into source code modeling possibilities are explored. Machine learning best practices are explored inline with the software source code modeling. 2022 the Author(s), licensee AIMS Press. -
The realist approach for evaluation of computational intelligence in software engineering
Secured software development must employ a security mindset across software engineering practices. Software security must be considered during the requirements phase so that it is included throughout the development phase. Do the requirements gathering team get the proper input from the technical team? This paper unearths some of the data sources buried within software development phases and describes the potential approaches to understand them. Concepts such as machine learning and deep learning are explored to understand the data sources and explore how these learnings can be provided to the requirements gathering team. This knowledge system will help bring objectivity in the conversations between the requirements gathering team and the customer's business team. A literature review is also done to secure requirements management and identify the possible gaps in providing future research direction to enhance our understanding. Feature engineering in the landscape of software development is explored to understand the data sources. Experts offer their insight on the root cause of the lack of security focus in requirements gathering practices. The core theme is statistical modeling of all the software artifacts that hold information related to the software development life cycle. Strengthening of some traditional methods like threat modeling is also a key area explored. Subjectivity involved in these approaches can be made more objective. 2021, The Author(s), under exclusive licence to Springer-Verlag London Ltd. part of Springer Nature. -
Software Systems Security Vulnerabilities Management by Exploring the Capabilities of Language Models Using NLP
Security of the software system is a prime focus area for software development teams. This paper explores some data science methods to build a knowledge management system that can assist the software development team to ensure a secure software system is being developed. Various approaches in this context are explored using data of insurance domain-based software development. These approaches will facilitate an easy understanding of the practical challenges associated with actual-world implementation. This paper also discusses the capabilities of language modeling and its role in the knowledge system. The source code is modeled to build a deep software security analysis model. The proposed model can help software engineers build secure software by assessing the software security during software development time. Extensive experiments show that the proposed models can efficiently explore the software language modeling capabilities to classify software systems' security vulnerabilities. 2021 Raghavendra Rao Althar et al. -
Automated Risk Management Based Software Security Vulnerabilities Management
An automated risk assessment approach is explored in this work. The focus is to optimize the conventional threat modeling approach to explore software system vulnerabilities. Data produced in the software development processes are better leveraged using Machine Learning approaches. A large amount of industry knowledge around security vulnerabilities can be leveraged to enhance current threat modeling approaches. Work done here is in the ecosystem of software development processes that use Agile methodology. Insurance business domain data are explored as a target for this study. The focus is to enhance the traditional threat modeling approach with a better quantitative approach and reduce the biases introduced by the people who are part of software development processes. This effort will help bridge multiple data sources prevalent across the software development ecosystem. Bringing these various data sources together will assist in understanding patterns associated with security aspects of the software systems. This perspective further helps to understand and devise better controls. Approaches explored so far have considered individual areas of software development and their influence on improving security. There is a need to build an integrated approach for a total security solution for the software systems. A wide variety of machine learning approaches and ensemble approaches will be explored. The insurance business domain is considered for the research here. CWE (Common Weaknesses Enumeration) mapping from industry knowledge are leveraged to validate the security needs from the industry perspective. This combination of industry and company data will help get a holistic picture of the software system's security. Combining the industry and company data helps lay down the path for an integrated security management system in software development. The risk management framework with the quantitative threat modeling process is the work's uniqueness. This work contributes toward making the software systems secure and robust with time. 2013 IEEE. -
Design and Development of Artificial Intelligence Knowledge Processing System for Optimizing Security of Software System
Software security vulnerabilities are significant for the software development industry. Exploration is conducted for software development industry landscape, software development eco-system landscape, and software system customer landscape. The focus is to explore the data sources that can provide the software development team with insights to act upon the security vulnerabilities proactively. Across these modules of software landscape, customer landscape, and industry landscape, data sources are leveraged using artificial intelligence approaches to identify the security insights. The focus is also on building a smart knowledge management system that integrates the information processed across modules into a central system. This central intelligence system can be further leveraged to manage software development activities proactively. In this exploration, machine learning and deep learning approaches are devised to model the data and learn from across the modules. Architecture for all the modules and their integration is also proposed. Work helps to envision a smart system for Artificial Intelligence-based knowledge management for managing software security vulnerabilities. 2023, Crown. -
Detection of Forest Fire Using Modified LSTM Based Feature Extraction with Waterwheel Plant Optimisation Algorithm Based VAE-GAN Model
A crucial natural resource that directly affects the ecology is forests. Forest fires have become a noteworthy problem recently as a result of both natural and man-made climatic changes. A smart city application that uses a forest fire discovery technology based on artificial intelligence is provided in order to prevent significant catastrophes. A major danger to the environment, animals, and human lives is posed by forest fires. The early detection and suppression of these fires is crucial. This work offers a thorough method for detecting forest fires using advanced deep learning (DL) algorithms. Preprocessing the forest fire dataset is the initial step in order to improve its relevance and quality. Then, to enable the model to capture the dynamic character of forest fire data, long short-term memory (LSTM) networks are used to extract useful feature from the dataset. In this work, weight optimisation in LSTM is performed using a Modified Firefly Algorithm (MFFA), which enhances the model's performance and convergence. The Variational Autoencoder Generative Adversarial Networks (VAEGAN) model is used to classify the retrieved features. Furthermore, every DL model's success depends heavily on hyperparameter optimisation. The hyperparameters of an VAEGAN model are tuned in this research using the Waterwheel Plant Optimisation Algorithm (WWPA), an optimisation technique inspired by nature. WPPA uses the idea of plant growth to properly tune the VAEGAN's parameters, assuring the network's peak fire detection performance. The outstanding accuracy (ACC) of 97.8%, precision (PR) of 97.7%, recall (RC) of 96.26%, F1-score (F1) of 97.3%, and specificity (SPEC) of 97.5% of the suggested model beats all other existing models, which is probably owing to its improved architecture and training techniques. Copyright: 2024 The authors. This piece is published by IIETA and is approved under the CC BY 4.0 license. -
Hybrid nanofluid flow over a vertical rotating plate in the presence of hall current, nonlinear convection and heat absorption
An exact analysis has been carried out to study a problem of the nonlinear convective flow of hybrid nanoliquids over a vertical rotating plate with Hall current and heat absorption. Three different fluids namely CuAl2O3H2Ohybrid nanofluid, Al2O3H2O nanofluid and H2O basefluids are considered in the analysis. The simulation of the flow was carried out using the appropriate values of the empirical shape factor for five different particle shapes (i.e., sphere, hexahedron, tetrahedron, column and lamina). The governing PDEs with the corresponding boundary conditions are non-dimensionalised with the appropriate dimensionless variables and solved analytically by using LTM (Laplace transform technique). This investigation discusses the effects of governing parameters on velocity and temperature fields in addition to the rate of heat transfer. The numeric data of the density, thermal conductivity, dynamic viscosity, specific heat, Prandtl number and Nusselt number for twelve different hybrid nanofluids at 300 K is presented. The temperature profile of hybrid nanoliquid is larger than nanoliquid for same volume fraction of nanoparticles. Also, the glycerin-based nanoliquid has a high rate of heat transfer than engine oil, ethylene glycol and water-based nanoliquids in order. 2018 by American Scientific Publishers All rights reserved. -
Championing inclusion: Understanding lgbt diversity and social support in the workplace
Purpose : This study investigated the impact of LGBT diversity management practices on the acceptance of LGBT employees by non-LGBT peers in Indian organizations. Based on classical social support theory and signaling theory, the study focused on how social support from co-workers and supervisors influenced this relationship. Methodology : Data were collected by surveying 546 employees across nine tech parks in the Indian IT/ITES sector. Partial Least Square (PLS) predictions and structural equation modeling (SEM) were conducted using Smart PLS version 4. Mediation and moderation analyses were also performed. Findings : The results exhibited that LGBT diversity management positively affected the acceptance of LGBT peers in the workplace (? = 0.298; t = 6.314; p = 0.00). Supervisor support was a complementary mediator (VAF = 0.33), while co-worker support moderated the association (? = 0.514; t = 15.916; p = 0.00). Practical Implications : The study presented managerial acumen regarding how social support from supervisors and co-workers enriched the efficacy of diversity management approaches. These outcomes were predominantly pertinent for organizations considering adopting an all-encompassing place of work for LGBT employees. Originality : This investigation delivered a distinctive offering by inspecting the role of social support in LGBT diversity management among the Indian IT segment. While based in Bengaluru, the study encouraged further investigation into additional businesses and geographies. 2024, Associated Management Consultants Pvt. Ltd.. All rights reserved. -
Time allocation between paid and unpaid work among men and women: An empirical study of indian villages
The paper examines the time allocation between paid work (wage earning or self-em-ployed work generally termed as employment work) and unpaid (domestic chores/care work generally termed as non-employment work) along with wage rates, imputed earnings, and occupational structure among men and women and according to different social groups to establish the extent to which the rural labour market is discriminated by sex and social group. The major objective of the paper is to show the differential in wage income between men and women in farm and non-farm activities. The paper also shows the division of time between employment and non-employ-ment activities by men and women. The paper uses high-frequency data and applies econometric techniques to know the factors behind time allocation among different activities across gender. The study finds that males spend more hours on employment work and work at a higher wage rate than females. As a result, a vast monetary income gap between men and women is observed, even though women worked more hours if employment and non-employment activities are jointly taken into consideration. Time spent on employment work and non-employment (mainly domestic chores) has been found to vary significantly due to social identity, household wealth, land, income, educa-tion, and skill. The segregation of labour market by sex was evident in this study, with men shifting to non-farm occupations with greater monetary returns and continued dependence on womens farm activities. Enhancing the ownership of land and other assets, encouraging womens participation particularly among minorities, and improving health are some of the policy recommendations directed from this study to enhance participation in employment work and shifting towards higher wage income employment. 2021 by the authors. Licensee MDPI, Basel, Switzerland. -
Perspectives about Illness, Attitudes, and Caregiving Experiences among Siblings of Persons with Schizophrenia: A Qualitative Analysis
Background: Siblings of persons diagnosed with schizophrenia (SPS) are one among the major sources of support for persons with schizophrenia. There is a dearth of psychosocial literature on SPS in India. This qualitative study explored the perspectives about the illness, attitudes, and caregiving experiences of SPS. Materials and Methods: Qualitative audio-recorded interviews were conducted with 15 SPS, purposively selected from a tertiary mental health hospital of Southern India. A general inductive approach was adopted to analyze the qualitative data. Results: Four broad themes were identified from qualitative data analysis. (1) SPS described several explanatory models of mental illness in terms of causal attributions and treatment care. (2) They had expressed emotion toward their ill siblings, such as criticality, hostility, and emotional over-involvement. (3) They experienced objective and subjective burden while caring for their ill sibling. In spite of all these, (4) they were part of their ill siblings' care in terms of ensuring regular follow-ups and drug adherence and supported their livelihood. They coped up with adaptive as well as maladaptive strategies. Conclusion: SPS provide significant support to their affected siblings. However, they do have non-biomedical models of mental illness and negative attitudes toward patients and experience burden. Hence, psychosocial interventions may help SPS while caregiving for their affected siblings. 2019 Indian Psychiatric Society - South Zonal Branch.