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Augmented intelligent water drops optimisation model for virtual machine placement in cloud environment
Virtual machine placement in cloud computing is to allocate the virtual machines (VMs) (user request) to suitable physical machines (PMs) so that the wastage of resources is reduced. Allocation of appropriate VMs to suitable and effective PMs will lead the service provider to be a better competitor with more available resources for affording a greater number of VMs simultaneously which in turn reflects with the growth in the economy. In this research work, an augmented intelligent water drop (IWD) algorithm is used for effectively placing VMs. The preliminary goal of this proposed work is to reduce the overall resource utilisation by packing the VMs to appropriate PMs effectively. The proposed IWD model is tested under the standard simulation process as it is given in the literature. Performance of IWD is compared with the existing techniques first fit decreasing, least loaded and ant colony optimisation algorithm. Performance analysis shows the significance of the proposed method over existing techniques. The Institution of Engineering and Technology 2020. -
Augmented reality for history education
Augmented Reality is live, direct or indirect view of a physical real world environment whose elements are augmented by personal computers (PC) that produces the information such as sound, video, designs or GPS data. This paper shows an instructive mobi le application based system model on Augmented Reality which is used to learn subjects like history through augmented videos. The objective of development of this system model is to make the learning interesting for the young generation. Unity 3D and Vufo ria Augmented Reality Software Development Kit (SDK) is used for the development of this model. The prime purpose of this application model is to enhance the learning process with digital technologies. This paper has step by step implementation instructions for the development of augmented reality modeling that can supplement the current teaching-learning environment to generate interest among young generation in less interesting subjects such as History, Geography, etc. 2018 Authors. -
Augmented Reality-Enabled Education for Middle Schools
Augmented reality acts as an add-on to teachers while teaching students, and this helps the teachers and students to have an interactive session. Augmented realitys usage in education is cited as one of the major changes in the educational sector. Thus, the work carried out makes a positive impact in the educational industry. Augmented reality provides features like image recogntion, motion tracking, facial recognition, plane detection, etc., to provide interactive sessions. Simultaneous localization and mapping and concurrent odometry and mapping have proved to be efficient algorithms for augmented reality on mobile devices. The work carried out allows students to view interactive newspapers while reading a specific article. It also allows them to view a dynamic three-dimensional model of the solar system on their smartphone using augmented reality. 2020, Springer Nature Singapore Pte Ltd. -
Augmented Reality-Enabled Instagram Game Filters: Key to Engaging Customers
The gamification concept is rapidly grabbing attention of different sectors in the current competitive business ecosystem. Companies are amalgamating game elements to enrich customer enhancement. However, empirical studies incorporating Augmented Reality (AR) elements in the same are lacking. Therefore, main objective of this research is to inspect elements of AR, impacting the customer brand engagement in game filters of Instagram. Drawing on S-D Logic the authors aim to explore the impact of gameful experience on creating customer engagement. The capability of Customer Brand Engagement (CBE) to influence Brand Satisfaction (BS) and Brand loyalty (BL) is also explored in the study. Convenient sampling method was adopted to gather 458 responses from Gen Z in India. Responses were gathered using self-administered questionnaire. Findings of the study expand CBE literature to a new technology and refines knowledge of relationship between AR and preexisting CBE dimensions (affective, cognitive and activation), leading to BS and BL. This study has some implications for managerial decision making in creating resilient and long-term relations with customers. 2021 Taylor & Francis Group, LLC. -
Augmented Reality: New Future of Social Media Influencer Marketing
The advent of social media as a marketing tool has transformed how businesses connect and share information about their brands with their consumers. Amplified consumer engagement has created novel relationships between consumers and companies. Peoples reliance on seeking information from other online users and reviews has increased, and this is where social media influencers play an important role in shaping consumers opinions. Augmented reality will revolutionize the influencer marketing environment due to its ability to engage consumers. This research involved an online survey with questions established on a 7-point Likert scale. Later, exploratory factor analysis was used to summarize data better to understand associations between dependent and independent variables. Later principal component analysis was espoused for the extraction process. Varimax rotation congregated 39 items into various factors. The Kaiser-Meyer-Olkin (KMO) test was administered to justify the adequacy of the sample. The findings suggest that augmented reality moderates user engagement and is the future of influencer marketing. 2023 MDI. -
Authentic leadership in a pandemic world: an exploratory study in the Indian context
Purpose: The purpose of this paper is to explore the strategies that helps leaders be authentic in order to be able to respond proactively and become effective in helping their organisations they lead in the context of the COVID-19 pandemic. Design/methodology/approach: Using a qualitative approach, 25 business leaders from diverse sectors were interviewed to understand what sustained them in an adverse context. Findings: Results reveal various dimensions of authentic leadership in a disruptive environment. Authentic leaders have to exhibit distinct behaviours that stems from re-examining oneself to reaffirming organisational purpose. Reimagining the work is emerged as the newer dimension to the authentic leadership considering the context of COVID-19. Practical implications: The results of the study provides insights for anyone leading organisations in today's disruptive business environment. The findings of this study can be used further to undertake quantitative studies to test professional relationships and understand the leadership strategies at different time frames. Originality/value: This paper addresses the strategies that leaders successfully follow to withstand the COVID crisis and highlights the different roles and behaviours that helped leaders to address the crisis confidently. 2022, Emerald Publishing Limited. -
Authentic Pride versus Hubristic Pride: Mediating Role of FoMO-directed Consumer Conformity Consumption Behaviour in Young Adults
Purpose-Sustainability is a word that has carried fame and prominence in the global conversation for the pro-environmental movement to protect the environment. Even making sustainable buying choices has been associated with individuals sense of identity in the socio-cultural sphere, especially when brands worldwide strongly promote them. This cross-sectional study aims to inquire if sustainability consciousness could impact consumers pride and, if yes, can fear of missing out (FoMO)-directed conformity consumption mediate the relationship between them or not. Method-Three standardised scales: the Sustainability Consciousness Questionnaire, 14-item Hubristic and Authentic Pride Scale and Consumer Consumption-FoMO Questionnaire, were administered to 18 to 35-year-old Indian young adults (N=204) recruited online to identify their levels of sustainability consciousness, hubristic and authentic pride and FoMO-directed consumer conformity consumption behaviour. Thus, convenient sampling was employed to collect the data for the study. The analysis involved Pearsons product-moment correlation followed by regression using SPSS software. Further, Sobels tests were conducted to verify the mediating roles of FoMO-directed consumer conformity consumption behaviour in relationships across sustainability consciousness and pride. Results Statistical analyses revealed that sustainable behaviours positively related to authentic pride with no mediating effects by FoMO-directed consumption behaviour. Similarly, sustainability attitudes are inversely associated with hubristic pride, but no mediating effects results were significant. On the other hand, sustainability knowingness was negatively related to hubristic pride, and the relationship was mediated significantly by certain but not all dimensions of FoMO. Conclusion-The study instilled empirical evidence for adaptive and maladaptive types of pride derived from sustainable orientation and the significant role of FoMO in strengthening hubristic pride. 2024 RJ4All. -
Author profiling: Age prediction of blog authors and identifying blog sentiment
Authorship profiling is about finding out different characteristic of an author like age, gender, native languages, education background etc., by finding out the patterns in their writing. Blog authors write about a lot of topics like purchase decisions, digital advertising, personality development, fitness, technology updates etc., and these authors play an influential role on its readers. In this paper, we are categorizing the blog authors in three different age groups based on the content available from the blog. Natural Language Toolkit (NLTK) is a set of libraries used for natural language processing to distinguish among the different writing pattern of the author based on the different age groups. NLTK helps to make analysis on the words of the blogs which is an important feature in our research. We also wanted to conduct sentiment analysis on the blog in order to understand the insight on how the author feels about the blog topic. Thus, we have used Nae Bayes Classifier for doing the analysis and considered two sentiments for the same: positive and negative. An average accuracy of 66.78% was achieved in predicting the age of authors. From the sentiment analysis we figured out that elder authors tend to have more positivity in their blogs as compared to younger authors. 2019, Institute of Advanced Scientific Research, Inc.. All rights reserved. -
Autism spectrum disorder detection using brain MRI image enabled deep learning with hybrid sewing training optimization
Autism spectrum disorder (ASD) is brain enabled disorder representing behaviors in a repetitive manner and social deficits. In this paper, ASD is diagnosed using brain magnetic resonance imaging (MRI) enabled deep learning with a hybrid optimization algorithm. Also, the hybrid optimization algorithm utilized is hybrid sewing training optimization (HSTO) which trains ZFNet for ASD detection. Pre-processing of the MRI image is done by Wiener filter and the filtered image is fed for region of interest extraction. Moreover, pivotal region extraction is carried out by the proposed HSTO, which is finally allowed for ASD detection by ZFNet. The proposed HSTO is formed by combining sewing training-based optimization and hybrid leader-based optimization. Furthermore, the performance of HSTO_ZFNet is found by five performance metrics of accuracy with 95.7%, true negative rate with 92.6%, true positive rate with 93.7%, false negative rate with 68.7%, and false positive rate with75.9%. 2023, The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature. -
Auto-diagnosis of covid-19 using lung ct images with semi-supervised shallow learning network
In the current world pandemic situation, the contagious Novel Coronavirus Disease 2019 (COVID-19) has raised a real threat to human lives owing to infection on lung cells and human respiratory systems. It is a daunting task for the researchers to find suitable infection patterns on lung CT images for automated diagnosis of COVID-19. A novel integrated semi-supervised shallow neural network framework comprising a Parallel Quantum-Inspired Self-supervised Network (PQIS-Net) for automatic segmentation of lung CT images followed by Fully Connected (FC) layers, is proposed in this article. The proposed PQIS-Net model is aimed at providing fully automated segmentation of lung CT slices without incorporating pre-trained convolutional neural network based models. A parallel trinity of layered structure of quantum bits are interconnected using an N -connected second order neighborhood-based topology in the suggested PQIS-Net architecture for segmentation of lung CT slices with wide variations of local intensities. A random patch-based classification on PQIS-Net segmented slices is incorporated at the classification layers of the suggested semi-supervised shallow neural network framework. Intensive experiments have been conducted using three publicly available data sets, one for purely segmentation task and the other two for classification (COVID-19 diagnosis). The experimental outcome on segmentation of CT slices using self-supervised PQIS-Net and the diagnosis efficiency (Accuracy, Precision and AUC) of the integrated semi-supervised shallow framework is found to be promising. The proposed model is also found to be superior than the best state-of-the-art techniques and pre-trained convolutional neural network-based models, specially in COVID-19 and Mycoplasma Pneumonia (MP) screening. 2013 IEEE. -
Automated and Interpretable Fake News Detection With Explainable Artificial Intelligence
Fake news is a piece of misleading or forged information that affects society, business, governments, etc., hence is an imperative issue. The solution presented here to detect fake news involves purely using rigorous machine learning approaches in implementing a hybrid of simple yet accurate fake text detection models and fake image detection models to detect fake news. The solution considers the text and images of any news article, extracted using web scraping, where the text segment of a news article is analyzed using an ensemble model of the Nae Bayes, Random Forest, and Decision Tree classifier, which showed improved results than the individual models. The image segment of a news article is analyzed using only a Convolution Neural Network, which showed optimal accuracy similar to the text model. To better train the text models, data preprocessing and aggregation methods were used to combine various fake-real news datasets to have ample amounts of data. Similarly, the CASIA dataset was used to train the image model, over which Error Level Analysis was performed to detect fake images. model results are represented as confusion matrices and are measured using various performance metrics. Also, to explain predictions from the hybrid model, Explainable Artificial Intelligence is used. 2024 Taylor & Francis Group, LLC. -
Automated Brain Imaging Diagnosis and Classification Model using Rat Swarm Optimization with Deep Learning based Capsule Network
Earlier identification of brain tumor (BT) is essential to increase the survival rate of the patients. The commonly used imaging technique for BT diagnosis is magnetic resonance imaging (MRI). Automated BT classification model is required for assisting the radiologists to save time and enhance efficiency. The classification of BT is difficult owing to the non-uniform shapes of tumors and location of tumors in the brain. Therefore, deep learning (DL) models can be employed for the effective identification, prediction, and diagnosis of diseases. In this view, this paper presents an automated BT diagnosis using rat swarm optimization (RSO) with deep learning based capsule network (DLCN) model, named RSO-DLCN model. The presented RSO-DLCN model involves bilateral filtering (BF) based preprocessing to enhance the quality of the MRI. Besides, non-iterative grabcut based segmentation (NIGCS) technique is applied to detect the affected tumor regions. In addition, DLCN model based feature extractor with RSO algorithm based parameter optimization processes takes place. Finally, extreme learning machine with stacked autoencoder (ELM-SA) based classifier is employed for the effective classification of BT. For validating the BT diagnostic performance of the presented RSO-DLCN model, an extensive set of simulations were carried out and the results are inspected under diverse dimensions. The simulation outcome demonstrated the promising results of the RSO-DLCN model on BT diagnosis with the sensitivity of 98.4%, specificity of 99%, and accuracy of 98.7%. 2023 World Scientific Publishing Company. -
Automated epileptic seizure classification using adaptive fast Fourier transform with non-uniform sampling and improved deep belief network
In automated brain-computer interaction (BCI), EEG signals are essential. This research uses AI to detect epileptic seizures, employing data from the BONN dataset (UCI), CHB-MIT dataset (physionet server), and Bangalore EEG Epilepsy Dataset (BEED). The goal is to develop an automated system for accurate seizure detection using adaptive fast Fourier transform with non-uniform sampling (AIFFT-NS) and an improved deep belief network (IDBN) model to enhance classification accuracy. The AIFFT-NS model serves as a channel for transforming spectro-temporal data. Using various EEG datasets, a number of extensive experiments are carried out, resulting in the validation of the efficacy of the proposed approach. High accuracy metrics, with 96.16% for the BEED dataset, 99.41% for the BONN dataset, and 96.31% for the CHB-MIT dataset, represent the evidentiary outcomes. This study emphasises the critical function of AI-facilitated spectro-temporal EEG analysis within the domain of medical diagnostics, going beyond the realm of automated seizure onset classification. Copyright 2024 Inderscience Enterprises Ltd. -
Automated lung cancer T-Stage detection and classification using improved U-Net model
Lung cancer results from the uncontrolled growth of abnormal cells. This research proposes an automated, improved U-Net model for lung cancer detection and tumor staging using the TNM system. A novel mask-generation process using thresholding and morphological operations is developed for the U-Net segmentation process. In the pre-processing stage, an advanced augmentation technique and contrast limited adaptive histogram equalization (CLAHE) are implemented for image enhancement. The improved U-Net model, enhanced with an advanced residual network (ARESNET) and batch normalization, is trained to accurately segment the tumor region from lung computed tomography (CT) images. Geometrical parameters, including perimeter, area, convex area, solidity, roundness, and eccentricity, are used to find precise T-stage of lung cancer. Validation using performance metrics such as accuracy, specificity, sensitivity, precision, and recall shows the proposed hybrid method is more accurate than existing approaches, achieving a staging accuracy of 94%. This model addresses the need for a highly accurate automated technique for lung cancer staging, essential for effective detection and treatment. 2024 Institute of Advanced Engineering and Science. All rights reserved. -
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. -
Automated segmentation and classification of nuclei in histopathological images
Various kinds of cancer are detected and diagnosed using histopathological analysis. Recent advances in whole slide scanner technology and the shift towards digitisation of whole slides have inspired the application of computational methods on histological data. Digital analysis of histopathological images has the potential to tackle issues accompanying conventional histological techniques, like the lack of objectivity and high variability. In this paper, we present a framework for the automated segmentation of nuclei from human histopathological whole slide images, and their classification using morphological and colour characteristics of the nuclei. The segmentation stage consists of two methods, thresholding and the watershed transform. The features of the segmented regions are recorded for the classification stage. Experimental results show that the knowledge from the selected features is capable of classifying a segmented object as a candidate nucleus and filtering out the incorrectly identified segments. Copyright 2022 Inderscience Enterprises Ltd. -
Automated testbed and real-time port analysis for reconfigurable inputoutput boards
In the computational world, automation plays a vital role in every aspect. The idea of developing and testing Reconfigurable InputOutput (RIO) Boards automatically without manual interaction is a challenging task. Initially, testing was done manually which takes a lot of time and also requires human interaction. With the proposed idea one can reduce human interaction and testing time with smart automated design setup. This testing includes testing of various functionalities of the RIO boards. This can be done by testing the features of each port with its pin configuration. To be more precise, in this process an automated testbed is designed and algorithms are proposed to verify the features of each pin such as Digital Read, Digital Write, Analog Read and Analog Write of the RIO boards along with the motor pins. Thus, the proposed method makes the setup simple, without any complications by giving the instructions to perform the testing process for each board without human interaction. Results show that the proposed method can reduce time consumption 95%, human interaction by 95% and increase testing accuracy to 87%. 2020 Informa UK Limited, trading as Taylor & Francis Group. -
Automatic Diagnosis of Autism Spectrum Disorder Detection Using a Hybrid Feature Selection Model with Graph Convolution Network
A neurodevelopmental disorder is called an autism spectrum disorder (ASD) that influences a persons assertion, interaction, and learning abilities. The consequences and severity of symptoms of ASD will vary from person to person; the disorder is mainly diagnosed in children aged 15years and older, and its symptoms may include unusual behaviors, interests, and social challenges. If it is not resolved at this stage, it will become severe in the coming days. So, in this manuscript, we propose a way to automatically tell if someone has ASD that works well by using a combination of feature selection and deep learning. Four phases comprise the proposed model: preprocessing, feature extraction, feature selection, and prediction. At first, the collected images are given to the preprocessing stage to remove the noise. Then, for each image, three types of features are extracted: the shape feature, texture feature, and histogram feature. Then, optimal features are selected to minimize computational complexity and time consumption using a new technique based on a combination of adaptive bacterial foraging optimization (ABFO), support vector machines-recursive feature elimination (SVM-RFE), minimum redundancy and maximum relevance (mRMR). Then, the graph convolutional network (GCN) classifier uses the selected features to identify an image as normal or autistic. According to the research observations, our models accuracy is enhanced to 97.512%. 2023, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. -
Automatic Skin Lesion SegmentationA Novel Approach of Lesion Filling through Pixel Path
Abstract: Lesion segmentation is a vital step in a melanoma recognition system. Many algorithms were developed for the efficient skin lesion segmentation. Most of them fails to realize a perfect segmentation. This paper proposes a novel, fully automatic system, for the lesion segmentation in dermatograms. The proposed approach executes in two steps. Selection of root seed is the first step. All the lesion pixels in the dermatogram are identified during the second step. Traversal through a predefined lesion pixel path ensures the reachability of all lesion pixels irrespective of the possible lesion discontinuity. The proposed algorithm is tested with two publically available dataset, PH2 and images of ISBI2016 challenge. Out of the six evaluation parameters, the proposed method shows the best values for specificity, accuracy, Hammuode distance and XOR. This confirms the merit of the proposal with respect to existing popular methods. 2020, Pleiades Publishing, Ltd. -
AUTOMATION OF TEST CASE PRIORITIZATION: A SYSTEMATIC LITERATURE REVIEW AND CURRENT TRENDS
An Important stage in software testing is designing a test suite [18]. The test case repository consists of a large number of test cases. However, only a portion of these test cases would be relevant and can find bugs. Test case prioritization(TCP) is one such technique that can substantially increase the cost-effectiveness of the testing activity. Using test case prioritization, more relevant test cases can be captured and tested in the earlier stages of the testing phase. The objective of the study is to understand different techniques used and a systemic study on the effectiveness of these approaches. The Literature consists of a few relevant articles introducing novel techniques for test case prioritization between 2008 and 2022. Studies show that parameters that are considered for test case prioritization are important. Hence, the current article also focuses on the parameters considered in the literature. 40% of the articles used in the literature review use different test case information as parameters. A systemic review and analysis of data sets involved in the literature are evaluated in the study. The review also focuses on the different approaches used for comparing the newly introduced approach and reveals a novel approach for prioritization. 2023 Little Lion Scientific. All rights reserved.