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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. -
Government Support and Policies
Government interventions play a crucial role in nurturing technopreneurship and advancing sustainability. The proposed chapter explains the significance of governmental support in bridging the financial, infrastructural, and knowledge gaps that technopreneurs face. It categorizes the types of support into financial aid, regulatory frameworks, infrastructure development, and educational programs, providing a structured overview of each. A detailed analysis of policy frameworks that foster innovation and sustainability is presented, supported with global examples such as the United States Small Business Innovation Research (SBIR) program, Israels Innovation Authority, Indias Digital India Initiative and Germanys High-Tech Strategy 2025. These examples illustrate how strategic policies can catalyse technological advancements and economic growth. The chapter further includes case studies from diverse regions, showcasing successful policy implementations and their tangible impacts. These case studies offer practical insights and best practices, demonstrating how tailored policies can create robust technopreneurial ecosystems. Finally, the chapter addresses the challenges in policy implementation and offers recommendations for future directions, emphasizing the need for adaptive, inclusive, and collaborative policy approaches. This comprehensive exploration aims to provide policymakers, academicians, and technopreneurs with valuable knowledge on leveraging government support for sustainable technopreneurial success. 2025 selection and editorial matter, Rajender Kumar, Rahul Sindhwani, Raman Kumar, Punj Lata Singh, and J. Paulo Davim. -
MXene Composite-Based Nanogenerators and Applications
Energy harvesting modules are becoming increasingly vital for developing autonomous, self-powered microelectronic devices. MXenes, a class of two-dimensional (2D) transition metal carbides/nitrides, have recently gained attention as promising candidates for energy applications due to their excellent electrical conductivity, large specific surface area, and tunable properties. MXene-based nanogenerators (NGs) represent a cutting-edge advancement in energy harvesting technology, harnessing the unique properties of MXenes to enhance performance. Incorporating MXenes into composite materials facilitates efficient ion/electron transport and increases the surface charge density, leading to higher output performance. Additionally, MXenes abundant functional groups and tunable surface properties enable strong interactions with polymer matrices, resulting in composites with superior mechanical strength and flexibility. This book chapter delves into the various properties of MXenes, highlighting their importance in energy harvesting technology and their inherent piezoelectric properties. It also covers using MXene-based composite materials in NG technology, focusing on MXenepolymer, MXenemetalorganic framework (MOF), and MXenecarbon composites. Additionally, the chapter discusses the applications of these composite-based NGs in various fields, including wearable technology and biomedical devices. Finally, the chapter summarizes the recent advancements in this field and future aspects in enhancing the use of MXene in energy harvesting technology. 2026 Taylor & Francis Group, LLC. -
Machine Learning-Based Maternal and Child Mortality Rate Prediction Using Random Forest Algorithm
This research uses a variety of data sources such as maternal age, health records of the mother and/or child, socioeconomic status, medical history, or prenatal care, and details of health indicators to determine the factors most decisive in increasing mortality risks. This entails data acquisition, data cleaning, data transformation and selection, and model building with an example of algorithms such as logistic regression and random forest. The trained models are checked for accuracy and their resilience level is checked using methods like SHapley Additive exPlanations and Local Interpretable Model agnostic Explanations for interpretation. The model is presented in an easy interface that doctors and health practitioners could use to make early and relevant decisions. It keeps updating the performance of established models and is a crucial way of maintaining data security for compliance with the set regulations. The rationale for this project is to offer practical recommendations for healthcare technicians so that more lives of mothers and children could be saved and maternal/child mortality decreased. Random Forest, in particular, has demonstrated superiority due to its ensemble approach, which mixes many decision trees to improve forecast accuracy and robustness. This technique can handle huge datasets with increased dimensionality and effectively lowers the overfitting risk. Additionally, Random Forest improves generalization by averaging the outputs of numerous trees, making it more tolerant to data noise and fluctuation. What makes it superior to single decision tree models is that it can handle both numerical and categorical data and handle missing values without a substantial loss of accuracy. 2025 selection and editorial matter, Babita Singla, Kumar Shalender, Nripendra Singh, and Sandhir Sharma; individual chapters, the contributors. -
Reimagining Healthcare: The South African PPP Revolution
Publicprivate partnerships (PPPs) are rapidly being recognized as transformative methods for improving healthcare delivery and funding, particularly in resource-constrained environments. Global and national policy frameworks, supported by organizations such as the African Development Bank (AfDB) and the World Health Organization (WHO), emphasize the potential for PPPs to fill significant gaps in healthcare infrastructure and service delivery. Countries across Africa, notably South Africa, Kenya, Nigeria, and Rwanda, have put in place national PPP frameworks that formalize partnerships in healthcare, focusing on risk sharing, accountability, and sustainability. South Africas National Treasury PPP Unit is a regional pioneer in promoting PPP development that balances private-sector innovation with governmental control. Such frameworks allow PPPs to mobilize private resources, enhance public spending efficiency, and provide access to high-quality healthcare, particularly in marginalized communities. Despite positive developments, PPPs in African healthcare confront hurdles due to fragmented legal frameworks and low institutional capacity to manage complicated contracts. The AfDBs 20212031 PPP strategic framework seeks to fill these gaps by providing African States with resources to establish enabling environments and prepare viable healthcare projects for the market. Diverse models in South Africa and other countries, such as Kenyas Managed Equipment Services (MES) and Ghanas BuildOperateTransfer (BOT) programs, show how adaptable PPPs can improve healthcare finance and delivery. However, current regulatory frameworks are complicated and often disconnected, emphasizing the need for unifying legal standards to assure transparency and accountability. This chapter highlights the insights that present a strong PPP model adapted to healthcare financing. It highlights the necessity of transparent systems, good risk management, and combining publicprivate expertise to handle current healthcare concerns. PPPs can improve healthcare accessibility and quality, increase patient satisfaction, and strengthen healthcare systems by promoting improved governance, policy consistency, and capacity building. Strategically honed, PPPs can drive long-term breakthroughs, positioning healthcare systems better to address the changing demands of African communities and beyond. 2026 selection and editorial matter, Wasswa Shafik, Adel Ben Youssef, Chithirai Pon Selvan and Pushan Kumar Dutta; individual chapters, the contributors. -
Leveraging AI and Machine Learning for Healthcare Accessibility: Enhancing Clinical Decision Support Systems in Rural Africa
Healthcare in rural Africa is hindered by resource scarcity, limited infrastructure, and a shortage of trained professionals, contributing to high mortality and morbidity rates. This study examines the transformative potential of artificial intelligence (AI) and machine learning (ML) in clinical decision support systems (CDSS) to address these challenges. Focusing on diseases prevalent in the region, such as malaria, HIV/AIDS, and noncommunicable illnesses like diabetes, the research develops and evaluates AI-enhanced CDSS to improve diagnostic accuracy, treatment planning, and healthcare accessibility. This research contributes a framework for deploying AI-driven CDSS in resource-limited settings, with implications for enhancing global health outcomes. 2026 selection and editorial matter, Wasswa Shafik, Adel Ben Youssef, Chithirai Pon Selvan and Pushan Kumar Dutta; individual chapters, the contributors. -
Enhancing cybersecurity with distributed models and sparse mixture of experts
[No abstract available] -
Hairy Root Engineering for Enhanced Production of Secondary Metabolites
[No abstract available] -
Artificial Intelligence and Machine Learning in Clinical Care: Revolutionizing Decision Support
The potential of artificial intelligence (AI) and machine learning to significantly modify clinical decision support is examined in this chapter. AI algorithms can use extensive databases of imaging outcomes, clinical trials, and medical records to identify complex patterns that lead to precise diagnoses, treatment plans, and progressively affected patient outcomes. A diagnosis includes evaluating the patients condition leveraging information gathered from multiple kinds of tests and their past medical history. AI-driven systems in the healthcare industry are constrained by the difficulty of handling tiny volumes and poor-quality medical data. A better prediction system for low-quality data and the analysis of unusual and sensitive medical cases can be analyzed by more powerful AI technologies. The chapter shows how AI-powered equipment is presently affecting healthcare. With excellent accuracy, device getting-to-know algorithms can examine medical images and identify potential abnormalities in X-rays, mammograms, or other imaging modes that a human might overlook. Furthermore, AI may review an affected persons records and show fitness facts to estimate a patients vulnerability to specific diseases, allowing for active intervention and preventative measures. The chapter concludes with critical tips for optimizing AIs complete range of applications in scientific care. To ensure the ideal and ethical application of these effective technologies, the responsibilities consist of defensive record safety and privacy, tackling algorithmic bias, and inspiring cooperation among clinical experts and AI developers. The healthcare zone can enter a modern section of using statistics to make educated selections through the implementation of AI and machine-gaining knowledge. 2025 selection and editorial matter, Rakesh Kumar and Meenu Gupta individual chapters, the contributors. -
Non-orthogonal multiple access wireless systems using deep learning
In 5G networks, non-orthogonal multiple access (NOMA) increases spectral efficiency and user capacity greatly by letting multiple users share the same time, frequency, and code resources. Wireless communication systems stand to benefit significantly from deep learning owing to its ability to model intricate patterns. This chapter centers around deep learning-NOMA integration with special attention given to areas like channel estimation, interference management, and dynamic resource allocation. Using advanced deep learning frameworks such as convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and deep reinforcement learning (DRL), this chapter demonstrates how NOMA system performance can be optimized to meet the stringent requirements of 5G and beyond networks. Moreover, this chapter also discusses the challenges associated with implementing deep learning in NOMA including computational complexity and data requirements, alongside future trends like federated learning and edge computing among others. The integration of these technologies promises improved network efficiency, reduced latency, and enhanced user experience, thereby making NOMA a fundamental technology in wireless communication evolution. 2025 selection and editorial matter, Mariyam Ouaissa, Mariya Ouaissa, Hanane Lamaazi, Khadija Slimani, Ihtiram Raza Khan, and B. Sundaravadivazhagan. -
Emerging Trends and the Future of Business Analytics
[No abstract available] -
Unveiling the Factors of Women Entrepreneurs on Social Media to Achieve Enterprise Sustainability
The research studies in the area of womens entrepreneurship (WE) has received more attention in the last decade due to its impact on bringing balanced development. On one hand, the growth of digital innovation has changed the landscape of entrepreneurship in emerging markets and on the other hand, the advocacy on business sustainability has increased. Prior studies are limited to understand the role of WE in this changing landscape. This study aims to identify the most relevant factors that influence the women entrepreneurs on social media to develop sustainable enterprise. An extensive literature review has been conducted to advance the knowledge on the WE and has been presented in form of a conceptual model to present a comprehensive perspective. Further, the research identifies social factors, psychological factors, resource factors, financial factor, firm-performance related factors, and technological factors. These factors are linked with entrepreneurial orientation among women on social media and therefore this helps in gaining sustainability. These study further present implications, strategies and agenda for future research in the area of WE. 2025 selection and editorial matter, Esra Sipahi Dongul, Serife Uguz Arsu, Richa Goel, and Tilottama Singh; individual chapters, the contributors. -
Securing patient information: A multilayered cryptographic approach in IoT healthcare
The increasing integration of devices utilising the of Internet of Things (IoT) in healthcare has resulted in the collection of an unparalleled volume of patient data. Personal identifiers, insurance information, medical history, and health monitoring measures are all included in a complete dataset. Ensuring security and privacy of IoT devices is crucial in the healthcare sector. The goal of this project is to combine steganography with three different cryptographic algorithms to develop a hybrid cryptographic technique. Among the algorithms under investigation are steganography, Caesar cipher, columnar transposition cipher, and one-time pad. Every encryption scheme uses three keys to encrypt patient data. The encrypted data is subsequently encoded into an image file through image-based steganography. To ensure confidentiality and authentication, an authorised user can decrypt the file through a designated decryption process, maintaining the integrity of patient data. 2025 selection and editorial matter, Keshav Kumar and Bishwajeet Kumar Pandey; individual chapters, the contributors. -
Quantum cryptography: An in-depth exploration of principles and techniques
Quantum cryptography is evolving in the field of data security and cryptographic research, as it offers a high level of security based on the principles of quantum mechanics. This chapter provides an extensive understanding and in-depth explanation about the basic concepts of the techniques implemented in quantum cryptography. The exploration of the fundamental concepts begins with elaboration on the foundational concepts of quantum mechanics, such as no-cloning, entanglement, superposition, and quantum state measurement, which are crucial for the better understanding of quantum cryptography. Further, the chapter delves more into the quantum key distribution (QKD) protocols such as BB84, BBM92, and B92. All the QKD protocols are analysed and compared based on the underlying principles and techniques. Furthermore, the importance and benefits of the integration of quantum cryptography with the traditional algorithms are also discussed. The chapter also aims to provide thorough study of quantum cryptography principles, challenges, and future directions along with a detailed comprehensive review of quantum cryptographic techniques. 2025 selection and editorial matter, Keshav Kumar and Bishwajeet Kumar Pandey; individual chapters, the contributors. -
Safeguarding the future through the prevention of cybercrime in the quantum computing era
Quantum computing is an emerging field that holds great promise for solving complex problems at an unprecedented speed by harnessing the principles of quantum mechanics. However, this disruptive technology also introduces new challenges, particularly in the realm of cybersecurity. Quantum computing can lead to cyberattacks such as cryptographic attacks, data breaches, blockchain vulnerabilities, social engineering, and phishing attacks. It is important to note that, at present, these risks are largely theoretical, as practical, large-scale quantum computers capable of breaking current cryptographic systems are not yet available. However, it is crucial for researchers, organisations, and policymakers to anticipate and address these potential threats in advance by developing quantum-resistant cryptographic algorithms, improving security protocols, and raising awareness about the evolving landscape of cyberthreats in the quantum computing era. There is a need for preparing safeguard measures form the quantum threat by investing in quantum-safe technologies, training cybersecurity professionals in quantum-resistant techniques, and fostering collaboration among industry, academia, and government entities. As quantum computing progresses, the landscape of cybercrime is expected to evolve, necessitating the development of robust laws to mitigate potential threats. The chapter aims at understanding the intersection of quantum computing and cybercrime, highlighting the potential implications and risks associated with quantum advancements in the context of cybersecurity. The chapter also emphasises the need for proactive measures and policies to mitigate the risks posed by quantum computing to cybersecurity. 2025 selection and editorial matter, Keshav Kumar and Bishwajeet Kumar Pandey; individual chapters, the contributors. -
Integrating intelligence: The convergence of computer science and engineering in cyber-physical systems
The dynamic and innovative paradigm known as cyber physical systems (CPSs) arises from the merging of digital technology and physical infrastructure. This chapter provides a thorough analysis of CPSs, covering the basic ideas, constituent parts, a range of applications, and their integration with more complex subjects. Fundamentally, CPSs represent the smooth fusion of computational and physical components, enabling real-time control, analysis, and monitoring. The fundamentals of CPSs are explained in this chapter, with a focus on how they facilitate the development of interconnected networks that can coordinate complicated tasks across multiple domains. A close examination of the complex interactions that occur between sensors, actuators, processors, and communication networks in CPS designs demonstrates how these components work together to gather, process, and distribute data. Furthermore, a wide range of industries, including infrastructure, manufacturing, transportation, and healthcare, are impacted by the diverse applications of CPSs. CPSs transform conventional processes, improving efficiency, safety, and production. Examples of these processes include intelligent healthcare devices that monitor patient vitals and smart transportation systems that optimise traffic flow. When CPSs are combined with more complex subjects, they become even more powerful, accelerating innovation and change in a variety of fields. By enabling CPSs to process and analyse data at network edges, edge computing can lower latency and bandwidth consumption. Algorithms for machine learning improve decision-making, allowing CPSs to adjust and gain knowledge from real-world data. By protecting CPSs from cyberattacks, security and resilience measures guarantee the availability and integrity of vital systems. Furthermore, human CPS contact opens up new collaborative paradigms and gives people the ability to communicate with intelligent systems in a natural way. To sum up, this chapter gives readers a thorough grasp of CPSs and how they have revolutionised contemporary life. It adds to the continuing conversation on CPS research, innovation, and implementation by clarifying their basic ideas, elements, applications, and integration with more complex subjects. With ongoing research and cooperation, CPSs have the potential to completely transform our world and bring in a new era of intelligence, creativity, and connectivity. 2025 selection and editorial matter, Kamal Upreti, Nishant Kumar, Mohammad Shabbir Alam, Mohammad Shahnawaz Nasir and Debabrata Samanta; individual chapters, the contributors. -
Exploring the impact of smart watches on health management for senior citizens: A qualitative study
The growing prevalence of chronic diseases and the aging population necessitate innovative health management solutions. The study presented in this chapter investigated the role of smart watches in managing health among senior citizens, focusing on usability, accessibility, and social support. Using a mixed-methods approach, involving semi-structured interviews and structured surveys with 12 senior citizens aged 60-69, we explored their experiences with using smart watches over 6-12 months. Our findings highlight that while smart watches provide significant health benefits, such as monitoring vital signs and promoting physical activity, usability challenges persist due to small text and complex interfaces. Social support from family is crucial for adoption and effective use. Our participants reported improved health outcomes, increased motivation for physical activity, and better communication with healthcare providers. However, privacy and security concerns, along with the need for customisable and user-friendly designs, were emphasised. This study underscores the potential of smart watches to enhance health management for senior citizens, advocating for targeted design improvements and robust data protection measures to maximise their benefits. 2025 selection and editorial matter, Kamal Upreti, Nishant Kumar, Mohammad Shabbir Alam, Mohammad Shahnawaz Nasir and Debabrata Samanta; individual chapters, the contributors. -
Gestational diabetes prediction using hybrid probabilistic machine learning models
[No abstract available] -
Bioinformatics Tools and Deep Learning for Plant High-Throughput Phenotyping and Phenomics
High-throughput phenotyping and phenomics are essential for advancing plant research and improving crop performance. The integration of bioinformatics tools and deep learning methodologies has transformed the way data is processed and analyzed in these fields. Bioinformatics tools facilitate the management and interpretation of large-scale genomic and phenotypic data, enabling researchers to extract valuable insights. Deep learning algorithms, particularly convolutional neural networks, have shown significant promise in automating the analysis of complex plant images and enhancing trait identification and prediction. This synergy between bioinformatics and deep learning accelerates the identification of key traits, improves the precision of phenotypic assessments, and supports the development of more resilient and productive crops. This chapter highlights how these advanced technologies contribute to more effective and scalable plant phenotyping and phenomics efforts. 2025 selection and editorial matter, Jen-Tsung Chen; individual chapters, the contributors.