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Technopreneurship in India: A Case Study Analysis of Success Factors and Challenges With Reference to Bangalore, Karnataka
This research examines the critical success factors and challenges in technopreneurship in India through an extensive case study analysis. Utilizing content coding techniques, qualitative data from emerging technopreneurs is systematically analysed to uncover key themes and patterns. The study focuses on four diverse technopreneurs from Bangalore, selected via purposeful sampling. Bangalore, known as the Silicon Valley of India, hosts a wide array of technopreneurial start-ups, creating a highly competitive environment that demands significant effort for sustainability. Hence Bangalore in the state of Karnataka has been set for the scope of the study. A structured interview with twenty questions has been used to collect the data from the technopreneurs. The interview schedule covered questions related to critical success factors, challenges faced, role of government in supporting technopreneurship, Market opportunities, Intellectual Property Rights (IPR), Role of education in technopreneurship, etc. Apart from questions related to technopreneurship few demographic questions such as: Name, age, area of residence, educational background, family support etc. were also covered. The study reveals that educational institutions play a pivotal role in shaping up the technopreneurs which is one of the common threads amongst all the four technopreneurs. In addition, all the four technopreneurs mentioned that to be a successful technopreneurs one has to be updated with the market trends and also take efforts to up skill oneself with the latest technologies and technical metamorphosis. In addition, technopreneurs have to be competitive where they have to hone their technical skills every day due to the global competition. Especially for a metropolitan city like Bangalore, technopreneurship is a challenge because there are already many existing tech-based giants. Hence, being focussed, confident, updated and courageous is the core mantra for successful technopreneurs. 2025 selection and editorial matter, Rajender Kumar, Rahul Sindhwani, Raman Kumar, Punj Lata Singh, and J. Paulo Davim. -
An Enhanced Transfer Learning Based Classification for Diagnosis of Skin Cancer
Skin cancer is the most commonly diagnosed and reported malignancy worldwide. To reduce the death rate from cancer, it is essential to diagnose skin cancer at a benign stage as soon as possible. To save lives, an automated system that can detect skin cancer in its earliest stages is necessary. For the diagnosis of skin cancer, various researchers have performed tasks using deep learning and transfer learning models. However, the existing literature is limited in terms of its accuracy and its troublesome and time-consuming process. As a result, it is critical to design an automatic system that can deliver a fast judgment and considerably reduce mistakes in diagnosis. In this work, a deep learning-based model has been designed for the identification of skin cancer at benign and malignant stages using the concept of transfer learning approach. For this, a pre-trained VGG16 model is improved by adding one flatten layer, two dense layers with activation function (LeakyReLU) and another dense layer with activation function (sigmoid) to enhance the accuracy of this model. This proposed model is evaluated on a dataset obtained from Kaggle. The techniques of data augmentation are applied in order to enhance the random-ness among the input dataset for model stability. The proposed model has been validated by considering several useful hyper parameters such as different batch sizes of 8, 16, 32, 64, and 128; different epochs and optimizers. The proposed model is working best with an overall accuracy of 89.09% on 128 batch size with the Adam optimizer and 10 epochs and outperforms state-of-the-art techniques. This model will help dermatologists in the early diagnosis of skin cancers. 2022 by the authors. Licensee MDPI, Basel, Switzerland. -
AI vs. traditional portfolio management: A study on Indian investors
This research chapter investigates the dynamics between artificial intelligence (AI) and traditional portfolio management strategies, specifically focusing on the attitudes and preferences of investors in the Indian market. The study aims to elucidate the comparative performance, risk-adjusted returns, and behavioral aspects associated with AI-driven portfolio management as opposed to traditional methods. Utilizing a methodology tailored to the unique characteristics of the Indian investment landscape, this research engages investors with varying degrees of experience in the stock market. Through a meticulous collection of data during October and November 2023, employing convenience sampling, the authors explore the factors influencing investor perceptions and decisions in adopting AI-based portfolio management strategies. These findings contribute to the existing discourse by shedding light on the role of trust, subjective norms, perceived usefulness, perceived ease of use, and attitudes as critical variables shaping the adoption of AI in portfolio management. 2024, IGI Global. All rights reserved. -
Analysing different appeals in Cadbury India commercials /
Appeals are an important aspect in the field of advertising that are used widely to promote or market a product by persuading its consumers. Television is one of the strongest medium that brings out the clarity of advertising appeals. This study aims to analyse the different appeals that are used by the brand Cadbury in India. -
Impact of investor sentiments on stock returns in India: An ESG perspective
This book chapter aims to explain how the investor sentiments in India impacts the returns of ESG stocks, further investigating how the stocks return gets affected positively or negatively by the behavior of investors after examining the ESG reports of a company. This study also provides a comparative analysis about the performance of ESG stocks over Non ESG stocks in the Indian Market. This includes understanding about the increased focus on ESG metrics which leads to superior stock performance, lower risk, or enhanced investor confidence compared to non- ESG investments. Hence, it provides us with a holistic picture of Indian market through an ESG perspective. 2025, IGI Global Scientific Publishing. All rights reserved. -
Modeling Popularity Evolution with Popularity-Augmented Graphs and Dynamic Bayesian PARAFAC
In recent years, social media has evolved as a significant platform for attracting new clients and customers. Every day, a wide range of new offers and products are shared over the social media platforms for buying, selling, promotions, etc., encouraging more and more social engagement. Therefore, it's important to predict high consumer engagement using past interactions. This study proposes a two-stage framework that integrates Popularity Augmented Social Graph construction with Dynamic Bayesian PARAFAC decomposition. The experiments were conducted on the open-source Behance project dataset, which contains interactions from over 85,000 users across 1,326 projects over 60 discrete time intervals. In the first stage, a Popularity Augmented Social Graph (PASG) is constructed using the popularity information. In the second stage, the graph is represented in tensor form and is factorized using Dynamic Bayesian PARAFAC (DBPF), which models latent relationships across users, content, and time. The performance of the model was evaluated using Mean Relative Error, Mean Absolute Error, Root Mean Squared Error, where it consistently outperformed the baseline methods. The results demonstrate the effectiveness of the proposed framework in providing a robust and scalable solution for popularity prediction in social media platforms. 2025 IEEE. -
Particle swarm optimization- based support vector regression for predictions: Approach and applications
For centuries, people have drawn inspiration from nature, and there is always more to learn and discover. The Particle Swarm Optimization (PSO) algorithm, a stochastic optimization algorithm based on population and inspired by the intelligent collective behavior of certain animals like fish schools or flocks of birds, is one of the most well-known nature-inspired algorithms presented in this work. As more was known about the fundamentals of this methodology, researchers produced new iterations to satisfy varying needs, new applications in diverse domains, theoretical research on the effects of different parameters, and a multitude of algorithm variations. PSO-support vector regression (SVR) is one such variant of this algorithm. SVR is a kind of Support Vector Machine (SVM) that solves regression problems. It seeks to identify a function that diverges from the actual values observed by no more than a given margin. The main idea is to retain the error under a certain threshold. PSO optimizes SVR parameters, including regularization, epsilon, and kernel parameters. This combination takes advantage of the strengths of both approaches. In this chapter, we will discuss the importance of the PSO-SVR algorithm in predicting the outcomes of real-world applications classified as healthcare, environmental, industrial, commercial, smart city, and other broad applications. 2025 selection and editorial matter, Sulabh Bansal, Aprna Tripathi, Shilpa Srivastava and Prem Prakash Vuppuluri; individual chapters, the contributors. -
Medical waste treatment device /
Patent Number: 354500-001, Applicant: Ila Anand. -
Optimizing Technology Innovation to Enhance Environmental Bonds for Improving Environmental Quality
This new incorporation of such high technologies transform the efficiency and effec tiveness of environmental bonds as a tool for sustainable finance. This is because most of the pitfalls that may characterize greenwashing, lack of transparency, and weak stakeholder engagement are taken care of by these technologies while under mining the impact of environmental bonds. For instance, accountability, trust and measurable environmental outcomes are ascertained through timely monitoring, and enhanced reporting standards. Blockchain blocks the alteration of data, while IoT promotes continuous environmental data monitoring; AI links to environ mental impact assessment through predictive analytics. Implementation of such technologies will require careful ethical consideration in terms of privacy of data and access. Technology adoption should be responsible to achieve its full potential in improving environmental quality and reaching the SDGs. The approach aligns corporate innovation with public sustainability objectives and facilitates real and social development. 2025 by IGI Global Scientific Publishing. All rights reserved. -
Linkages between Sustainability, Trade, and Geopolitics: The Green Subsidies Dilemma
Sustainability has become deeply rooted due to the nexus between trade and sustainability however, green subsidies is one area wherein the linkages between trade, sustainability and geopolitics, manifests the most. The subsidy race between Global North and South can be trade distortive, this has resulted in numerous WTO disputes. The author intends to establish the relationship green subsidies and ecological goals, followed by assessment of competitive dynamics between different trading entities pertaining green subsidies and also, the WTO redressal of disputes arising out of green subsidies. Herein, the author will conduct a comparative analysis of Green Subsidies regime in India, United States and European Union to ascertain the geo-political framing of sustainable trade. The overarching objective of this chapter is to ascertain the disguised power-based system camouflaged in the advocated rule based system of WTO as well as to provide recommendations for greening the Global trading system. 2025 by IGI Global Scientific Publishing. All rights reserved. -
Borders and Control: Negotiating Mobility, Security, and Rights in Digital Regimes
Despite millions crossing the border, the right of mobility has been restricted in lieu of states' discretion based on their sovereignty. This brings the authors to the age-old debate on mobility and border control. Amidst the tussle between the human right of mobility and the sovereign right of nation-states, the role of technology continues to influence the security lens of nation-states' borders. Recently, the EU, USA, Canada, Australia and other host countries installed digital border measures. A cross jurisdictional study of the above mentioned four jurisdictions highlight the surveillance and information-sharing system deployed at borders. The aim of the chapter is to assess the systemic efficiency of Digital Border Governance in Global North and South as means of securitization. Digital border governance brings an inevitable risk of abuse of data obtained at borders which can be remedied only via means of an inclusive regime built on the three pillars of fairness, transparency, and cooperation. 2026 by IGI Global Scientific Publishing. All rights reserved. -
Embodied Intelligence and the Phenomenology of Al
The intersection of artificial intelligence (Al), embodied cognition, and phenome-nology represents one of the most profound and complex inquiries in contemporary philosophy of mind. This chapter explores the epistemological and ontological dimensions of embodied intelligence and its relationship to the phenomenology of consciousness, with a specific focus on qualia-the subjective, first-person qualities of experience. By bridging computational models of intelligence with phenomeno-logical analyses of perception and awareness, the chapter advances an integrative framework that challenges reductionist and purely mechanistic views of cognition. It argues that consciousness, rather than emerging from disembodied computation, is fundamentally rooted in embodied, context-dependent, and experiential struc-tures. Drawing from phenomenological thinkers such as Husserl, Heidegger, and Merleau-Ponty, alongside contemporary Al paradigms including connectionism and enactivism. 2026 by IGI Global Scientific Publishing. All rights reserved. -
High-performance Zn(ii)-based coordination polymer as an electrode material for pseudocapacitive energy storage and hydrogen evolution
Recently, multifunctional materials for energy storage and production have been investigated to address diverse energy challenges. However, innovative methodologies focusing on the design and synthesis of novel materials remain essential to effectively tackle persistent challenges such as material degradation, high overpotentials, low conductivity, inferior cycling performance, elevated resistance, and high production costs. Working along these lines, we report a simplistic gram-scale synthesis, characterization, and excellent electrochemical behavior of a Zn(ii)-based coordination polymer (COP) abbreviated as Zn(DAB). It has been obtained in quantitative yields through a facile one-pot reaction between N4-ligand, 3,3?-diaminobenzidine (DAB), and Zn(ii) ions, derived from Zn(OAc)22H2O, at room temperature. The proposed structure of the COP was established through a series of standard spectroscopic and electron microscopic analyses. These methods unveiled the self-assembly of indefinitely long coordination strands, resulting in a two-dimensional (2D) layered structure. Zn(DAB), when probed for its electrochemical characteristics, reveals exemplary results. The material showed a high specific capacitance of 2091.4 F g?1, calculated at 1 A g?1 with 92% retention over 5000 charge-discharge cycles. Additionally, the COP also exhibited a subservient overpotential of 263 mV at a current density of 10 mA cm?2 for the hydrogen evolution reaction. These results highlight the promising potential of Zn(DAB) as a multifunctional electrode material for sustainable energy applications. 2025 The Royal Society of Chemistry. -
Lanthanide-based coordination polymers: a fluorometric Frontier in explosive sensing
In the pursuit of public safety, environmental protection, and counter-terrorism, significant advancements have been made in explosive detection techniques. However, challenges such as limited sensitivity, poor selectivity, and high operational costs remain, particularly for trace-level detection. In this study, we present a simple and scalable synthesis of lanthanide-based coordination polymers (Ln-COPs), denoted as Ho(DAB) and Tb(DAB), formed through the coordination of Ho(iii) and Tb(iii) ions, respectively, with the organic linker 3,3?-diaminobenzidine (DAB). Spectroscopic and electron microscopic analyses confirm their two-dimensional planar structure, resulting from the self-assembly of infinitely long polymeric strands. These luminescent Ln-COPs demonstrate exceptional performance as sensors for detecting both nitroaromatic and non-nitroaromatic explosives via fluorescence quenching. Notably, Tb(DAB) exhibits a remarkable limit of detection of 7.7 M for TNP. Furthermore, mechanistic insights into the quenching process are explored. These results underscore the sensitivity and practical applicability of Ln-COPs in advanced explosive detection systems. This journal is The Royal Society of Chemistry, 2025 -
Green Approach for the Large-Scale Synthesis of a MetalOrganic Framework Derived From Perylene and Copper: A Fluorometric Sensor for Sm (III)
The detection of samarium (Sm) and its isotopes/ions is gaining significant importance across various fields, including nuclear energy, materials science, environmental monitoring, and biomedicine. Accurate and sensitive detection methods are crucial for ensuring safety, quality control, and compliance with regulatory standards. Despite the growing need, existing detection strategies often face challenges such as being time-consuming, inaccurate, cumbersome, and expensive. Surprisingly, fluorometric sensing has been explored minimally for Sm detection. In this study, we introduce a novel approach for detecting Sm (III) ions using a fluorescent metalorganic framework (MOF), Cu-PTC, synthesized via a one-pot, green, room temperature method. This represents the first reported use of a fluorescent MOF for Sm (III) detection. The Cu-PTC MOF was synthesized using the potassium salt of perylene-3,4,9,10-tetracarboxylic acid and Cu (II) ions from Cu (OAc)2. Structural analysis revealed the formation of a 3D network with well-defined pores. Photophysical characterization confirmed the absorption and emission properties of Cu-PTC. The MOF was successfully employed for the selective detection of Sm (III) ions, with a detection limit of 6.53 ?M. 2025 John Wiley & Sons Ltd. -
Synergistic pseudocapacitive behavior of Cr2CTx MXene and Cu-PTC MOF in CM-II: an Electroactive composite
The increasing global energy demand and a shift towards sustainable energy solutions necessitate the development of advanced energy storage devices, with supercapacitors emerging as key candidates. Achieving high energy density without compromising power density remains a critical challenge, underscoring the need for novel materials with robust pseudocapacitive behavior. This study introduces a novel electroactive composite, CM-II, composed of Cr2CTx MXene and a Cu-based MOF, Cu-PTC. The synthesis, structural, and morphological characterization of CM-II is extensively detailed. Cr2CTx MXene provides a conductive scaffold, while Cu-PTC contributes redox-active sites and porosity, creating a synergistic combination that enhances charge storage. The pseudocapacitive performance of CM-II has been systematically evaluated, with a specific capacitance of 3035 F g?1 and a long cycle-life with a capacitance retention of 96% after 5000 cycles, showcasing its potential as a high-performance material for next-generation supercapacitors. 2025 The Author(s). Published by IOP Publishing Ltd. -
CuNi-PTC metal-organic framework: unveiling pseudocapacitive energy storage and water splitting capabilities
Metal-organic frameworks (MOFs), owing to their distinctive structural properties and customizable functionalities, have been garnering significant attention in the pursuit of advanced energy storage and conversion technologies. In this work, a bimetallic MOF, CuNi-PTC, has been synthesized through a straightforward method. Investigations reveal its potential as a high-performance electrode material for supercapacitors and as an electrocatalyst for water splitting. The CuNi-PTC MOF features a large specific surface area, hierarchical porosity, and strong structural stability, as evidenced by spectroscopic and electron microscopy analyses. As a supercapacitor electrode material, CuNi-PTC delivers an impressive specific capacitance of 1066.24 F g?1 at a current density of 1 A g?1, along with excellent cycling stability, retaining 94% of its capacity after 5000 charge-discharge cycles. Additionally, the electrocatalytic performance of CuNi-PTC for both the hydrogen evolution reaction (HER) and the oxygen evolution reaction (OER) was assessed, showing overpotentials of 212 mV for the HER and 380 mV for the OER at a current density of 10 mA cm?2, along with exceptional long-term durability. 2025 RSC. -
Surface Modification and Enhanced Catalytic Interface in Bifunctional Montmorillonite for the Synthesis of Novel Thiazolo[3,2-a]pyrimidine-6-Carboxylates
This study centers on the modification of montmorillonite (MMT) through the incorporation of pillaring agents, specifically ceria (CeO2) and zirconia (ZrO2), using a straightforward synthesis technique. The resulting catalyst is thoroughly characterized by employing various standard spectroscopic and electron microscopic methods to verify its structural and compositional integrity. Moreover, temperature-programmed desorption (TPD) is utilized to assess and quantify the acidic properties of the catalyst. The modified MMT catalyst is then applied in the ultrasonic-assisted one-pot synthesis of novel thiazolo[3,2-a]pyrimidine-6-carboxylates. This approach allowed for the efficient production of these compounds, which are subsequently characterized by 1H NMR, 13C NMR, and High-Resolution Mass Spectrometry (HRMS) to confirm their structures. Additionally, the study elucidates the mechanistic role of ultrasonication in enhancing the synthesis process, highlighting the way sonic energy improves reactant dispersion, accelerates reaction rates, and facilitates high-yield formation of the target heterocycles. 2025 Wiley-VCH GmbH. -
Performance Evaluation of Machine Learning Models for Detecting Vulnerabilities in Internet of Things Network
Security threats and attacks are a growing concern in the field of Internet of Things (IoT) infrastructure. Internet-based automated network application models are used across various domains; commensurately, different security vulnerabilities and anomaly attacks are also increased at the same level. These attacks could cause failures in IoT infrastructure and network systems. In the modern world, Machine Learning (ML) models support various predictive analyses, providing more accurate results for future forecasting in various fields. In this article, we compare existing classical Machine Learning (ML) algorithms supported by Artificial Intelligence (AI) to evaluate and predict the performance and accuracy of different vulnerabilities in IoT infrastructure. We considered and compared the results of Logistic Regression (LR), Support Vector Machine (SVM), Decision Tree (DT), Random Forest (RF), and Artificial Neural Network (ANN) using publicly available datasets. Through this evaluation, we obtained an accuracy of 99.4% from DT, RF, and ANN. Additionally, RF demonstrated a highest accuracy of F1 is 0.994 and lowest STD variance is 0.014 than compared models in the selected dataset. 2026, Dr. Yashwant Research Labs Pvt. Ltd. All rights reserved. -
Eco-conscious consumers green real estate decisions in India: the role of social commerce
Purpose: The primary purpose of this paper is to examine the role of perceived trust, information quality, positive word of mouth and societal norms toward real estate purchase intention. The study also examines how pro-environmental self-identity mediates the relationship between positive word of mouth and real estate purchase intent, as well as between societal norms and real estate purchase intention. This research aims to delve into these intricate dynamics through a multidimensional lens. Design/methodology/approach: The research employs existing scholarly works and measurable variables evaluated through a five-point Likert scale, hypothesis testing and mediation analysis to examine the proposed framework. A structured survey comprising six sections was administered, yielding 385 valid responses. The data analysis process included the use of confirmatory factor analysis and structural equation modelling techniques. Findings: The analysis indicates that pro-environmental self-identity has the most significant influence on real estate purchase intention, closely followed by positive word of mouth. Incorporating eco-friendly themes in marketing campaigns significantly boosts purchase intentions. However, perceived trust does not significantly impact purchase intentions. Other factors, such as information quality and societal norms, also play significant roles, underscoring the importance of understanding the complex dynamics shaping consumer decisions in the real estate market. Research limitations/implications: This research exclusively targets responses from young consumers in specific regions of India. Future studies should aim for a more extensive geographic scope, encompassing a diverse global population for a broader understanding of the subject. Originality/value: Based on previous literature, this study is the first to identify the elements influencing the inclination to buy environmentally friendly real estate through social commerce. 2025, Emerald Publishing Limited.