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Attitude of public towards higher education: Conceptual analysis /
Scholedge International Journal Of Multidisciplinary And Allied Studies, Vol.2, Issue 12, pp.19-28, ISSN No: 2394-336X. -
Audit Tenure, Audit Fee, and Audit Quality: Evidence from India
This paper examined the relationship between the tenure of the auditor and the audit quality of Indian companies, particularly in the wake of two significant regulations in the financial reporting, the implementation of Ind AS (the IFRS compliant accounting standards) and mandatory auditor rotation. Using Discretionary Accruals as a proxy for audit quality, the study took the data of all the companies listed on the NSE for 11 financial years, from 2009 2019 (totaling 8,171 firm-year observations). It deployed panel data regression with a random-effects model. The results showed that audit quality improved up to specific auditors tenure, particularly with the IFRS compliance and Big 4 auditors. The higher audit fee is positively significantly associated with lower earnings quality. The study suggested that mandatory auditor rotation might provide the full benefit only along with other regulations on IFRS, auditor reputation, and audit fee. The study provided an impetus to the regulators, audit fraternity, and companies to improve the relevance of financial statements. This is one of the first longitudinal studies examining the interaction effects of different audit regulations. Robustness checks with other proxies of audit quality provided the same results. 2022, Associated Management Consultants Pvt. Ltd.. All rights reserved. -
Augmentation of the energy storage potential by harnessing the defects of charcoal for supercapacitor application
The depletion of fossil fuel reserves coupled with an avalanche in the global energy demand has driven the need for developing facile techniques for energy storage devices to a large extent. Supercapacitors, has emerged as one of the most promising energy storage devices to address the demands of providing high energy density, quick charge discharge cycles and long cyclic stability. Although carbon based materials play an imperative role in the fabrication of electrode material of this device, the inherent defects are known to hinder the performance of the system. Even so, these defects can be engineered in a way to improve its overall functionality. The present work reports the tuning of the inherent defects of wood charcoal by surface functionalisation and doping via thermal annealing in order to incorporate substitutional impurities such as Nitrogen and Sulfur resulting in the improvement of the surface area and porosity of the system. The specific surface area of the system is observed to increase significantly from 4.2 m2/g of the bare material to 411.19 m2/g and 865.36 m2/g with the addition of Nitrogen and Sulfur respectively at a pyrolysis temperature of 900 C. Furthermore, the incorporation of Nitrogen exhibits a remarkable specific capacitance of 567 F/g and 193.24 F/g, and the addition of Sulfur exhibits 644 F/g and 255.1 F/g in the three-electrode and two-electrode systems respectively at a current density of 1 A/g. They also exhibit an energy density of 26.83 Whkg?1 and 17.36 Whkg?1 respectively with a capacitance retention of 88.5 % and 86.1 % for 5000 cycles. 2024 Elsevier Ltd -
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 Based Medical Education
The education in medical field requires both theoretical knowledge and practical knowledge. It is important for medical student to acquire effective practical skills. Since the students apply the theoretical knowledge in practical manner in human body. Human body is very volatile, gentle, and difficult system. If a student apply trial in the humans for practical knowledge, there may cause the human error which leads to death of the person. To avoid this, the proposed system 'Augmented Reality Based Medical Education' is useful. Augmented reality makes the learning process more interactive and interesting. It can reproduce specific circumstances that assist students to rehearse with virtual objects that look like the human body and organ. Like traditional learning, it does not require real patients. By this way, augmented reality prevents risk of human life. Medical education with augmented reality extensively provides real time experiences. It has low risks and also affordable. When any human error occurs, there is no human loss. So the human life can be prevented by the system. The proposed system is developed using tools like Unity which is the complete platform for the developing our application, Vuforia-developer portal, a tool to create image target and Blender which is used to create 3D objects. 2023 IEEE. -
Augmented Reality based Navigation for Indoor Environment using Unity Platform
This paper proposes an augmented reality (AR) navigation system developed for indoor environment. The proposed navigation system is developed using Unity platform which is usually used for developing gaming applications. The proposed navigation system without the aid of Global Positioning System (GPS) tracks users position and orientation accurately by making use of computer vision and image processing techniques. The user can navigate to the desired location using its user friendly and intuitive interface. The proposed system can be extended further to provide indoor navigational guidance within lager buildings such as malls, airports, universities and medical facilities. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024. -
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-Enabled IoT Devices for Wireless Communication
[No abstract available] -
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. -
Authentication of symmetric cryptosystem using anti-aging controller-based true random number generator
In todays digital world, data protection is extremely important. Every companys data is a valuable asset, so its important to ensure that it's secured from outside threats. Information security is not only an effective but also a necessary element to protect data from unauthorized access. The confidentiality of any communication system is strengthening with the help of random number generators along with some analog circuitry. This type of analog models demands more power and area. So analog circuit-based hardware Random Number Generators (RNG) are least preferred over digital RNGs. To improve the security every industry depends on the one-time password (OTP). Which gives the security but generation of the OTP is very easy. Random number generator is used for the generation of OTP. Similarly, hacking such OTP is easier than creating them. This paper introduces the Anti-Aging controller TRNG, a highly stable high-performance random number generator Anti-Aging Cryptographically Secured True Random Number Generator (AACTRNG). Implementation of this work can be done by using TANNER EDA Tools and ModelSim-Altera 6.4a (Quartus-II 9.0) used for the simulation to retrieve random numbers. 2021, King Abdulaziz City for Science and Technology. -
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. -
Autism Spectrum Disorder: Automated Detection based on rs-fMRI images using CNN
Autism spectrum disorder (ASD) impacts approximately 1 in every 160 children globally and is classified as a neurodevelopmental condition. Image classification in neuroscience has advanced primarily due to convolutional neural networks (CNNs) and their capacity to provide better algorithms, more computing resources, and data. This study used a brain scan dataset to test the feasibility of utilizing CNN to detect ASD. Using functional connectivity patterns, the Autism Brain Imaging Exchange (ABIDE) data repository, which includes recordings of rest-state functional magnetic resonance imaging (rs-fMRI), the aim of using it was to distinguish between individuals who have Autism Spectrum Disorder (ASD) and those who are healthy controls. The proposed method effectively classified the two groups. According to the test findings, the suggested model has the ability to accurately detect ASD with a reliability rate of 92.22% when implemented on the ABIDE dataset using the CC200, CC400, and AAL116 brain atlases. The CNN model is computationally more efficient since it uses fewer parameters than other cutting-edge methods. 2023 IEEE. -
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. -
Auto-encoder Convolut?onal Neural Network (AECNN) for Apple Fruit Flower Detection
The yield estimation task altogether relies upon the way toward identifying and checking the quantity of fruits on trees. In production of fruit, basic yield the board choices are guided through the bloom frequency, i.e., the quantity of the flowers that are present in a plantation. The intensity of bloom technique is still commonly assessed by methods for human visual investigation. Mechanized PC vision frameworks for flower recognizable proof depend closely on designed procedures which function just under explicit conditions and with restricted execution. This work comprises four significant advances, (I) system preparing for Fully Convolutional Network (FCN), (ii) preprocessing, (iii) component extraction, (iv) division. Initially, a strategy for assessing high-resolution pictures with deep FCN on Graphics Processing Unit (GPU). Then, non-linear and linear algorithms are presented for lessening the image noise, so the exact flower identification can be ensured. The next phase of the work handles the highlight extraction for diminishing the quality of the prime assets which are needed for handling without compromising on data applicable. By applying Local Binary Pattern (LBP), surface example likelihood can be summed up into a histogram. At last, isolate an image with high resolution into sub patches, assess all patches with the help of AECNN, at that point apply the refinement calculation on acquired score maps to figure out the final version of the mask segmentation. Trial results are led utilizing two datasets on flower pictures of AppleA and AppleB. Results are estimated regarding the measurements like Precision (P) and Recall (R). The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024. -
Autoimmune diseases and an approach to type 1 diabetes analysis using PSO, K-means, and silhouette values
An estimated 50 million Americans suffer from autoimmune diseases, as per the report from AARDA (American Autoimmune Related Diseases Association). More than 30 million people suffer in India from type 1 diabetes. More than $100 billion is spent on healthcare for autoimmune diseases in America, more than for cancer healthcare. Host genes and environmental factors control autoimmune diseases, and typically they do not have any specific cure. This paper proposes an artificial intelligence-based framework for the initial prediction of autoimmune diseases. This work attempts to identify characteristics of autoimmune diseases, and it lists the commonly occurring autoimmune diseases, the organs attacked by them, and the different stages involved. It also seeks to identify ways to prioritize the severity of the patient's disease, for providing treatments based on the severity, with the goal of reducing the pressure on the healthcare sector. Type 1 diabetes is an autoimmune disease and identifying the risk associated with diabetes and other related health problems could help to improve health worldwide. This work proposes a framework while exploring autoimmune disease prediction using machine learning techniques. The autoimmune disease considered is type 1 diabetes. The usage of machine learning techniques can help to enhance patient care and early prediction. This research is an attempt to explore the possibilities and also to propose a framework for early prediction of type 1 diabetes. Clustering is performed using K-means and PSO K-means. Validation of the clusters is carried out using silhouette coefficient. 2024 Elsevier Inc. All rights are reserved including those for text and data mining AI training and similar technologies.