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At the Interface of Colonial Knowing and Unknowing: A Critical Reading of the Golden Camellia in Amitav Ghoshs River of Smoke
This paper is a critical reading of Amitav Ghoshs fictional representation of modes of acquisition, assimilation and dissemination of colonial knowledge in River of Smoke (2012). The paper highlights the cultural exchange of botanical and horticultural knowledge between Europe and China in the nineteenth century narrativized by Ghosh. The novel illustrates the significance of non-Eurocentric modes of conserving knowledge that would otherwise suffer from the violence of utilitarian models of European epistemology. The paper explicates how Ghosh represents the Chinese as successful in ensuring that the Golden Camelliaa rare flowering variety in Chinais preserved from falling prey to the profiteering logic of botanical expeditions and epistemic hegemony by European naturalists. Using Pramod Nayars imperial cosmopolitanism and Robert Proctors agnotology as critical frames, the paper maps Ghoshs fictional representation of Chinese horticulturists using botanical illustration to disable Europeans from accessing the Golden Camellia. By circulating the nonexistence of the plant variety as the truth, the Chinese horticulturalists in the novel prevent the Golden Camellia from being usurped and profiteered by European botanists and plant traders. The paper also establishes how Ghoshs work functions as a significant addition to works foregrounding the South-South connection in the South Asian literary imagination. 2020 South Asian Literary Association. -
Affective geographies and the anthropocene: Reading shubhangi swarups latitudes of longing
This paper is a critical reading of the affective and emotional geographies imagined in the Islands plot-line of Shubhangi Swarups novel Latitudes of Longing (2018). The paper argues that Swarup presents the case of a rethinking environmental aesthetics that conveys a deeper sense of space, time, and place. By creating an ambient poetics to negotiate human and non-human interconnectedness, the paper demonstrates the strength of novelistic traditions and their potential to generate an idea of affect that is transcorporeal as one not located only in the site of the human body, instead, emanating from a more nuanced interconnectedness between the human and the non-human world. Informed by affective ecocriticism and Zayin Cabots multiple ontologies approach that generates ecologies of participation, the paper closely reads the Islands section to establish how literary illustrations provide an instance to widen the horizons of environmental engagement and generate a narrative imagination that encompasses a larger ecosystem cutting across geological spacetimes in the Anthropocene. Swarups use of fiction is critically used to generate an ecoaesthetics that leads to a more informed ethical action towards recognizing the interconnectedness of living and non-living forms that create sustainable ecologies. 2021 Journal of Dharma: Dharmaram Journal of Religions and Philosophies (DVK, Bangalore), ISSN: 0253-7222. -
Detecting and Countering Misinformation Through NLP-Based Approach for Fake News Detection
The rapid expansion of digital media and the seamless transmission of information have raised serious concerns about the widespread dissemination of misinformation and fake news. Combatting this issue requires robust and effective techniques that can accurately detect and classify fake news. Natural language processing (NLP) approaches have emerged as powerful tools in this endeavor, leveraging advanced text classification algorithms to identify and counteract misinformation. This study includes NLP approaches for countering misinformation through text classification, with a specific focus on fake news detection. Leveraging natural language processing techniques, the project implements a text classification pipeline for identifying and distinguishing between genuine and fake news. The pipeline encompasses essential NLP steps such as tokenization and stop word removal. Traditional machine learning algorithms, such as the gradient boosting classifier, CatBoost classifier, random forest classifier, AdaBoost classifier, logistic regression, and SVM linear kernel are trained using the transformed data to classify news articles. This study explores feature engineering techniques and model evaluation to enhance the classification performance. Experimental results indicate the effectiveness of The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. -
Enhancing Retailer Auctions and Analyzing the Impact of Coupon Offers on Customer Engagement and Sales Through Machine Learning
Systems that use coupons have been used extensively to boost customer interaction on platforms having a digital component. We use causal machine learning techniques to determine the effect of an advertising intervention, especially a discount offer, on the bids of a shop. Discount shopping coupons are a popular tactic for increasing sales. The largest challenge for dealers is accurately anticipating the wants of their customers, and here is where they always struggle. Machine learning algorithms have been utilized by researchers to address a variety of problems. Selecting the right coupon is a challenging undertaking because every customer's behavior differs depending on the deal. Due to categorical data adjustments being necessary due to the majority of characteristics having missing values, the situation is made more difficult. The dataset is used to classify the dataset, and machine learning algorithms like logistic regression, random forest and SVM model, decision tree and naive bayes models are used to determine the correctness of the classification. 2023 IEEE. -
Predictive Machine Learning Approaches for Estimating Residential Rental Rates in India
As urban areas like Chennai and Bangalore witness a continuous surge in land and housing prices, accurately estimating the market value of houses has become increasingly crucial. This presents a formidable challenge, prompting a growing demand for an accessible and efficient method to predict house rental prices, ensuring dependable forecasts for future generations. In response to this need, this study delves into the core factors influencing rental prices, with a keen focus on location and area. Leveraging a dataset comprising ten essential features tailored for detecting Rental Price in Metropolitan cities, the research meticulously preprocesses the data using a Python library to ensure data cleanliness, laying a robust foundation for constructing the predictive model. Employing a diverse range of Machine Learning algorithms, including Random Forest, Linear Regression, Decision Tree Regression, and Gradient Boosting, the study evaluates their efficacy in forecasting rental prices. Notably, feature extraction underscores the significance of area and property type in shaping rental prices. In comparison with existing methodologies, this research adopts gradient boosting as its preferred approach, achieving the most satisfactory predictive outcomes. Evaluation metrics are meticulously analyzed to validate the model's performance. Through this comprehensive analysis, the study not only offers valuable insights into rental price prediction but also ensures a rigorous comparison with existing approaches, maintaining originality and relevance in addressing the pressing challenges of housing market dynamics. 2024 IEEE. -
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/