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High-performance reconfigurable FET for a simple variable gain buffer amplifier design
Design and simulation of variable gain analog buffer amplifier using single gate reconfigurable field-effect transistor (SG-RFET) with strained silicon channel are proposed. The design simplicity makes SG-RFET device a potential candidate compared to the multi-gate RFET devices. The gain of the proposed configuration is varied by tuning the feedback voltage. The voltage gain of the proposed configuration can be tuned from 0.97V/V to 5V/V with an output load of 1 k?. The operational transconductance amplifier (OTA) using the SG-RFET device is used in the proposed buffer amplifier design. 2021 Informa UK Limited, trading as Taylor & Francis Group. -
The Employees Demographic Profile of Startups in India with Special Reference to Bangalore City: A Case Study
In the current scenario, startups play a pivotal role and exert a significant influence on the promotion of economic growth. Authorities perceive their substantial impact, considering factors such as job creation, economic development, and contributions to technological upgrading (Thornton and Assocham in Startups Indiaan overview, 2016; Jain in Int J Appl Res 2:152154, 2016; Sarangi in Why do most Indian startups fail? Computer Science and Engineering, Indian Institute of Technology, Delhi, 2015). There has been a noteworthy effort to promote entrepreneurship through the establishment and support of various incubation centers. Despite the positive reception and development initiatives, the alarming startup failure rates in India persist due to various reasons. Available data indicates that 12 states in India have 1000 recognized startups each, with Karnataka being particularly well-recognized for progress and brand creation, especially in the city of Bangalore. In light of this, the study aims to identify the demographic profile of employees in startups in India, with a special focus on Bangalore city. The Author(s), under exclusive license to Springer Nature Switzerland AG 2024. -
Assessing Individual Intention in Adoption of Green Loans for Solar Rooftop Projects
Amidst a global surge in environmental consciousness, this study investigates the adoption dynamics of green loans for solar rooftop projects, capitalizing on the increasing awareness of individuals seeking to embrace environmentally friendly technological innovations. Against the backdrop of escalating environmental concerns, this research explores the intricate relationships between financial considerations, heightened environmental awareness, regulatory support, and access to information in shaping individual intentions to adopt green loans for solar rooftop initiatives. Leveraging a Structural Equation Modeling (SEM) approach and a sample size of 225 respondents, this study provides nuanced insights into the factors influencing sustainable financial choices, contributing to the ongoing discourse on the intersection of green financing, renewable energy, and individual decision-making. The findings hold implications for policymakers, financial institutions, and individuals striving to align their financial practices with eco-conscious initiatives to pursue a sustainable future. The Author(s), under exclusive license to Springer Nature Switzerland AG 2024. -
Adoption of knowledge-graph best development practices for scalable and optimized manufacturing processes
Using data analytics to properly extracting insights that are in-line to the enterprises strategic goals is crucial for the business sustainability. Developing the most fitting context as a knowledge graph that answer related businesses questions and queries at scale. Data analytics is an integral main part of smart manufacturing for monitoring the production processes and identifying the potentials for automated operations for improved manufacturing performance. This paper reviews and investigates the best development practices to be followed for industrial enterprise knowledge-graph development that support smart manufacturing in the following aspects: Decision for intelligent business processes, data collection from multiple sources, competitive advantage graph ontology, ensuring data quality, improved data analytics, human-friendly interaction, rapid and scalable enterprise's architectures. Successful digital-transformation adoption for smart manufacturing as an enterprise knowledge-graph development with the capability to be transformed to data fabric supporting scalability of smart manufacturing processes in industrial enterprises. 2023 -
Work-Family Interface during the Age thirty Transition: Experiences of Women Professionals from the Corporate sector
The age thirty transition is a significant developmental transition in the lives of women. Past studies have found this to be a period of disruption and re-evaluation of choices before lasting commitments are made in work and family. This research is focused on studying the experiences of women professionals from the corporate sector as they negotiate the age thirty transition and the work-family interface during this transition period. It provides a current understanding of this transition in lives of women professionals in the context of urban India. Based in the paradigm of phenomenology, this study follows the IPA method. Semi-structured interviews were conducted with six women and the data was analyzed at individual as well as cross-case level. The findings indicate that approach of age thirty i.e. approximately between ages 25 and 31 is a transitional period as the young women enter and establish themselves in adult roles in work and family. The changes in their environment and social expectations interact with developmental changes within the ??self. Amidst unique individual experiences, the results suggest a common structure of the transition experience with distinct phases and an evolution of work-family interface across these phases. Keywords: age thirty transition, work-family interface -
Deep Convolution Neural Network for RBC Images
The suggested study's objectives are to develop an unique criterion-based method for classifying RBC pictures and to increase classification accuracy by utilizing Deep Convolutional Neural Networks instead of Conventional CNN Algorithm. Materials and Procedures A dataset-master image dataset of 790 pictures is used to apply Deep Convolutional Neural Network. Convolutional Neural Network and Deep Convolutional Neural Network comparison using deep learning has been suggested and developed to improve classification accuracy of RBC pictures. Using Gpower, the sample size was calculated to be 27 for each group. Results: When compared to Convolutional Neural Network, Deep Convolutional Neural Network had the highest accuracy in classifying blood cell pictures (95.2%) and the lowest mean error (85.8 percent). Between the classifiers, there is a statistically significant difference of p=0.005. The study demonstrates that Deep Convolutional Neural Networks perform more accurately than Conventional Neural Networks while classifying photos of blood cells[1]. 2022 IEEE. -
Stacked LSTM a Deep Learning model to predict Stock market
The goal of Stock Market Prediction is to forecast the future value of a company's financial stocks. The use of machine learning and deep learning technologies in stock market prediction technologies is a recent trend. Machine learning makes predictions based on the values of current stock market indices by training on their previous values in sequential timely order using the artificial neural network, while deep learning makes predictions based on the values of current stock market indices by training on their previous values in sequential timely order using the artificial neural network. 2022 IEEE. -
Brand together: How co-creation generates innovation and re-energizes brands /
Vels Management Journal, Vol-1 (2), pp. 98-100. ISBN-978-0-7494-6325-0 -
Strategic perspective of internal branding: A critical review /
European Journal of Business and Management, Vol.6, Issue 34, pp.98-105, ISSN No: 2222-1905 (Print), 2222-2839 (Online). -
Hybrid AI Talent Acquisition Model: An Opinion Mining and Topic based approach
Artificial Intelligence models have found their usage in the human resource domain. In this paper, job reviewers' opinions on online discussion boards have been captured. The relative importance of factors has been established through an extensive literature review. First, LDA Topic modelling by adopting PCA is performed on unstructured text data has been analyzed. Second, sentiment analysis using the Li-Hu method has been employed to understand job seekers' satisfaction with job portals. The proposed model, 'Hybrid AI Talent Acquisition Model,' follows a novel approach to streamlining the jobseeker opinion related to online outlets. 2022 IEEE. -
21st Century Teacher Educator
Golden Research Thoughts, Vol. 2, Issue 11, pp. 40-45, ISSN No. 2231-5063 -
Development of an efficient real-time H.264/AVC advanced video compression encryption scheme
Multimedia is the combination of media such as text, graphics, video clips, and audio files. In todays world, multimedia plays an important role in many applications that we use in our daily lives. It is used in educational software, animation, sound, and text, as well as multi-media software. H.264/AVC video compression is extremely efficient in terms of compression. Despite this, H.264/AVC requires a lot of processing and consumes a lot of power insdespite of the fact that its compression efficiency is lower than that of H.264/AVC. We examine the various methods of Video H.264 Advanced Video Compression Standard Encryption Schemes in this paper. The performance of all types of encryption techniques will be evaluated using parameters such as cost overhead, delay, and encryption quality. This will provide us with a detailed comparative analysis of video encryption schemes, allowing us to determine which one is far more efficient for H.264/AVC. 2021 Taru Publications. -
Nature's Lament: A Comparative Psychoanalytical Reading of Childhood Trauma in Select War Narratives
Sustainable Development has become an inevitable need of the hour. This paper problematizes the trauma of children as represented in the narratives, Beasts of No Nation by Uzodinma Iweala and A Long Way Gone by Ishmael Beah. The incomprehensibility of trauma, it's varied representation in fiction, dissociation of child psyche, and its detrimental effect on children is substantiated using psychoanalytic theory of trauma proposed by Cathy Caruth and contemporary trauma theorists. The paper argues the atrocities children are forced to be involved into, causes profound trauma in themselves leading to, encumbering of sustainable developmental goals. A comparative study of interpretive textual analysis is employed to study the havoc the society endears as a result of war, that wrecks the child, hindering the overall sustainable development. As it voices out the voiceless trauma of children the paper also aims in divulging the decisive influence of the select literary narratives in sensitizing the society in achieving societal as well as environmental sustainability. The Electrochemical Society -
NN-SVM: a hybrid neural networksupport vector machine framework for accurate pneumonia detection from chest X-rays
We present neural network (NN)-support vector machine (SVM), hybrid NN-SVM framework for three-class pneumonia detection (normal, bacterial, and viral) from chest X-rays (CXRs). Pretrained NN backbone is fine-tuned for radiographic textures; global average pooling (GAP) yields embeddings that feed calibrated radial basis function (RBF)-SVM. Standardized preprocessing (resize, normalization) and class-aware augmentation are applied. We report accuracy, precision, recall, F1-score, area under the curve (AUC), confusion matrices, and per-class receiver operating characteristic (ROC). Statistical significance is assessed via DeLong (AUC), McNemar (accuracy), and paired bootstrap (F1-score). Gradient-weighted class activation mapping (grad-CAM) supports interpretability; external validation and domain adaptation (batch normalization re-estimation and temperature scaling) assess robustness. NN-SVM attains 97.46% accuracy with strong macro-F1 and AUC. Compared with SoftMax head, SVM improves margin separation and calibration. We present NN-SVM, hybrid deep learning approach that combines transfer-learned convolutional neural networks (CNNs) with SVM classifier to automatically diagnose pneumonia from CXRs into three clinically relevant categories: viral pneumonia, bacterial pneumonia, and normal. We use pre-trained CNN to extract robust image embeddings after standardized preprocessing (resizing and normalization) and train RBF-kernel SVM on resulting features. Performance is evaluated with accuracy, precision, recall, F1-score, and confusion matrices. On labeled CXR dataset, NN-SVM achieves 97.46% accuracy, demonstrating strong diagnostic capability that can reduce radiologist burden and support timely clinical decision-making. This is an open access article under the CC BY-SA license. https://creativecommons.org/licenses/by-sa/4.0/ -
Prediction of health insurance premium using bidirectional long short-term memory network with local interpretable model-agnostic explanations
This research proposes an application of deep learning techniques towards the prediction of insurance premiums using ConvLSTM, BI-LSTM, and CNN-LSTM models. Nowadays, Insurance is becoming more sophisticated, there is a need for better models that predict premiums so that risk factors that can be properly valued. The aim of this study is to improve the accuracy and reliability of insurance premium prediction using deep learning methods. The main challenge is the shallow traditional models, whose capturing of temporal dependencies is ineffective and results are not explainable resulting in very few stakeholders having any trust to the predictions. To solve this, this study compared three models: ConvLSTM model, BI-LSTM and CNN LSTM. Of these, the BI-LSTM model was the most effective because it was able to learn bidirectional sequential patterns. These patterns were enhanced using L2 regularization, dropout and dense layers to improve generalization. The dataset used comes from a Kaggle repository, which contained actual insurance data incorporating age, BMI, region and smoking as attributes. Results showed that BI-LSTM had performed the best as compare to other models in terms of accuracy and loss minimization. Important findings highlighted features such as age, smoking, and BMI as pivotal to estimating premiums. Also, to make the model explainable, we incorporated Explainable AI using LIME which delivers interpretable explanations by showing and visualizing the most important features for single predictions. 2026 selection and editorial matter, K. V. Sambasivarao, and Anasuya Sesha Roopa Devi Bhima; individual chapters, the contributors. All rights reserved. -
Processing low-cost feedstock's into high quality biodiesal with a novel chemical multi functional process intensifier method /
Patent Number: 202141060717, Applicant: Ravikumar R.
A method and apparatus for producing a cost-effectively purified biodiesel product from feedstocks are provided. It is possible to utilize both a crude feedstock pretreatment process and a free fatty acid refining process in certain implementations before transesterification and the creation of crude biodiesel and glycerin. When it comes to biodiesel refining, there are several options. As a result of these operations, a pure biodiesel product that meets various commercial requirements may be produced. -
Effect of multiwalled carbon nanotube alignment on the tensile fatigue behavior of nanocomposites
The one-dimensional structure of carbon nanotubes makes them highly anisotropic, making them to possess unusual mechanical properties, and hence employed as promising nanofiller for the composite structures. However, various carbon nanotube properties are not completely utilized when they are used as reinforcement in composites due to inadequate and immature processing techniques. In the present work, an attempt has been made to utilize the strong anisotropic nature of multi-walled carbon nanotubes (MWCNTs) for improving the fatigue life of nanocomposites only by considering a very low weight percentage (<0.5 wt%). The anisotropy of MWCNTs was imparted into the nanocomposites by aligning them in the epoxy matrix with DC electric field during composite curing. Nanocomposites were made for three MWCNT loadings (0.1, 0.2, and 0.3 wt%). The tensile fatigue behavior was investigated under stress control by applying cyclic sinusoidal load with the frequency range of 13 Hz and stress ratio, R = 0.1. The specimens were tested for the fatigue load until the failure or 1E+05 cycles. The fractured surfaces were examined through scanning electron microscope to analyze the fatigue fracture behavior. A small weight percentage of MWCNT loading (0.2 wt%) into the polymer composite has enhanced on an average 13% to 15% fatigue life, which is encouraging to develop the low cost, improved fatigue life composite structures. Also, the energy dissipation mechanism in MWCNT dispersed nanocomposites has shown a reduced crack propagation rate. The Author(s) 2017. -
Digital education for a resilient new normal using artificial intelligenceapplications, challenges, and way forward
As society and technology advance to meet Industry 4.0 requirements, the educational system has also undergone many transformative changes in the past decade. Education is regarded as one of the most important tools for developing individuals, families, businesses, and the economy. New digital technologies are making a great revolution by transforming all aspects of education in teaching, learning, assessment, and feedback. The COVID-19 pandemic has led to the proliferation of digital education and its replacement of traditional education in the educational system. The developments in artificial intelligence (AI) are indispensable in all sectors, including education. AI-integrated learning helps management, teachers, students, parents, and other stakeholders gain insight into their performance to impact the process positively. This chapter aims to throw light on the emerging need and technologies used for digital education and to examine the role of AI in education with examples from the perspectives of teaching, learning, and assessment in the new normal. The application of AI in education and its effectiveness is explored through six publicly available datasets along with strengths, weaknesses, opportunities, challenges, and the future of digital education. This chapter discusses several examples and benefits of AI applications that enhance the educational experience and also emphasizes the need to align it with technology and curriculum to achieve the intended learning outcomes. 2023 Elsevier Ltd. All rights reserved. -
A research on the use of VFX in Indian film director Shankar's films /
Shankar is a renowned Tamil film director popular for his technically brilliant films. He is well known for his big budgeted films, which are outstanding. The research consists of an in depth analysis of the use of visual effects in all eleven films made by the director using various parameters. The parameters that are considered are title sequence, song sequence; fight sequence, romantic/, dramatic sequence, comedy sequence and similarity of visual effects (VFX) in scenes.