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Analyses of the Power Flow through Distributed Generator based on Unsynchronized Measurements
Based on measurements taken from the main substation and the connections between distributed generators and micro-grids that are not in sync, this study suggests a new way to look at the load flow of distributed generation. The conclusions are based on data from a distribution generatora's Load Flow Analysis that was not in sync. Distributed generation is what this approach is based on. Creating a strong communication system and using measurement data from the past are two ways to make this happen. This objective may be achieved with the use of previously gathered measurements. The time-tested backward-forward sweep method is the method of choice for analyzing power flow using unsynchronized data. This is the preferred approach. The angles of synchronization are likely to be unknowns that must be estimated. On a smart grid system with a large number of distributed generation and microgrids, a range of mathematical computations are conducted to verify the correctness of performance predictions produced by the suggested theory. The classic backward-forward sweep was shown to be the most effective method for analyzing power flow based on data that was not synchronized in many instances. This is the strategy that is presently being recommended. Because the angles of synchronization are presumed to be unknown, a mathematical equation must be devised to determine them. The Authors, published by EDP Sciences, 2024. -
Theory of planned behavior in predicting the construction of eco-friendly houses
Purpose: The present study aimed to explore the applicability of theory of planned behavior in construction of eco-friendly houses. Design/methodology/approach: Study utilized cross-sectional correlational research design, collected data from 269 adult house owners of Kerala, India, with the help of a self-report measures namely, attitude towards eco-friendly house construction, subjective norm, perceived behavioral control, behavioral intention to build eco-friendly houses, check list of eco-friendly house and socio-demographic data sheet. Descriptive statistics, Karl Pearson product moment correlation, confirmatory factor analysis and mediation analysis with the help of AMOS were used to describe the distribution of study variables and to test the research hypotheses and proposed model. Findings: Study revealed that behavioral intention to build eco-friendly house was the immediate and strongest predictor of actual behavior of constructing an eco-friendly house. Behavioral intention mediated the relationship of attitudinal variables, normative variables and control variables with the behavior of constructing eco-friendly houses. Research limitations/implications: The results vouched the applicability of theory of planned behavior as a comprehensive model in explaining the behavior of eco-friendly house construction. Practical implications: Results of the study iterates the utility of attitudinal, normative and control factors in enhancing the choice of constructing eco-friendly houses. The results can be applied to develop a marketing tool to enhance the behavior of choosing or constructing eco-friendly houses in the population. Originality/value: Role of conventional concrete construction in climate crisis is unquestioned, and adopting eco-friendly architecture is a potential solution to the impending doom of climate crisis. Behavioral changes play a significant role in the success of global actions to curb the climate crisis. Present study discusses the role of psychological variables in constructing eco-friendly houses. 2022, Emerald Publishing Limited. -
A multi-scale and rotation-invariant phase pattern (MRIPP) and a stack of restricted Boltzmann machine (RBM) with preprocessing for facial expression classification
In facial expression recognition applications, the classification accuracy decreases because of the blur, illumination and localization problems in images. Therefore, a robust emotion recognition technique is needed. In this work, a Multi-scale and Rotation-Invariant Phase Pattern (MRIPP) is proposed. The MRIPP extracts the features from facial images, and the extracted patterns are blur-insensitive, rotation-invariant and robust. The performance of classification algorithms like Fisher faces, Support Vector Machine (SVM), Extreme Learning Machine (ELM), Convolutional Neural Network (CNN) and Deep Neural Network (DNN) are analyzed. In order to reduce the time for classification, an OPTICS-based pre-processing of the features is proposed that creates a non-redundant and compressed training set to classify the test set. Ten-fold cross validation is used in experimental analysis and the performance metric classification accuracy is used. The proposed approach has been evaluated with six datasets Japanese Female Facial Expression (JAFFE), Cohn Kanade (CK +), Multi- media Understanding Group (MUG), Static Facial Expressions in the Wild (SFEW), Oulu-Chinese Academy of Science, Institute of Automation (Oulu-CASIA) and ManMachine Interaction (MMI) datasets to meet a classification accuracy of 98.2%, 97.5%, 95.6%, 35.5%, 87.7% and 82.4% for seven class emotion detection using a stack of Restricted Boltzmann Machines(RBM), which is high when compared to other latest methods. 2020, Springer-Verlag GmbH Germany, part of Springer Nature. -
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. -
Knots of the umbilical cord: Incidence, diagnosis, and management
Knot(s) of the umbilical cord have received emphasis because the clinical assessments and sonographic literature show a crucial role in fetal outcomes. The true umbilical cord knot could be a knot in a singleton pregnancy or an entanglement of two umbilical cords in monoamniotic twins. Clinical manifestations are almost silent, which can raise clinical challenges. They worsen outcomes, and the pathology can be easily missed during prenatal visits because ultrasonographers do not pay attention to the cord during an obstetric ultrasound scan. However, most medical centers now have ultrasound machines that improve fetal assessment. The umbilical cord should be routinely evaluated during a fetal assessment, and suspicion of an umbilical cord knot can be more frequently diagnosed and is detected only incidentally. Clinical outcome is usually good but depends on the knot's characteristics and if it is tight or loose. In this review, we discuss pathophysiology, the theories on formation, the main risk factors, ultrasound signs and findings, different opinions in the management, and features of pregnancy outcomes feature. 2024 International Federation of Gynecology and Obstetrics. -
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. -
BERT-Based Secure and Smart Management System for Processing Software Development Requirements from Security Perspective
Software requirements management is the first and essential stage for software development practices, from all perspectives, including the security of software systems. Work here focuses on enabling software requirements managers with all the information to help build streamlined software requirements. The focus is on ensuring security which is addressed in the requirements management phase rather than leaving it late in the software development phases. The approach is proposed to combine useful knowledge sources like customer conversation, industry best practices, and knowledge hidden within the software development processes. The financial domain and agile models of development are considered as the focus area for the study. Bidirectional encoder representation from transformers (BERT) is used in the proposed architecture to utilize its language understanding capabilities. Knowledge graph capabilities are explored to bind together the knowledge around industry sources for security practices and vulnerabilities. These information sources are being used to ensure that the requirements management team is updated with critical information. The architecture proposed is validated in light of the financial domain that is scoped for this proposal. Transfer learning is also explored to manage and reduce the need for expensive learning expected by these machine learning and deep learning models. This work will pave the way to integrate software requirements management practices with the data science practices leveraging the information available in the software development ecosystem for better requirements management. 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Application of machine intelligence-based knowledge graphs for software engineering
This chapter focuses on knowledge graphs application in software engineering. It starts with a general exploration of artificial intelligence for software engineering and then funnels down to the area where knowledge graphs can be a good fit. The focus is to put together work done in this area and call out key learning and future aspirations. The knowledge management system's architecture, specific application of the knowledge graph in software engineering like automation of test case creation and aspiring to build a continuous learning system are explored. Understanding the semantics of the knowledge, developing an intelligent development environment, defect prediction with network analysis, and clustering of the graph data are exciting explorations. 2021, IGI Global. -
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. -
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.