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Generalized Ricci solitons on Riemannian manifolds admitting concurrent-recurrent vector field
Let (M,g) be a Riemannian manifold admitting a concurrent-recurrent vector field ?. We prove that if the metric g is a generalized Ricci soliton such that the potential field V is a conformal vector field, then M is Einstein. Next we show that if the metric of M is a gradient generalized Ricci soliton, then either of these three occurs: (i) ?? is invariant along gradient of potential function; (ii) M is Einstein; (iii) the potential vector field is pointwise collinear to concurrent-recurrent vector field ?. Finally, we investigate gradient generalized Ricci soliton on a Riemannian manifold (M,g) admitting a unit parallel vector field, and in this case we show that if g is a non-steady gradient generalized Ricci soliton, then the Ricci tensor satisfies Ric=-??{g-?????}, where ?? is the canonical 1-form associated to ?. 2022, The Author(s), under exclusive licence to The Forum DAnalystes. -
Generalized Vertex Induced Connected Subsets of a Graph
Vol.2 (May), 61-68 -
Generalized viscoelastic flow with thermal radiations and chemical reactions
Background: A generalized model of mathematical nature is considered to address the viscoelastic flow problem using fractional derivatives. Control/freedom of the flow mechanism is achieved with these derivatives. In simulations of industrial interest, more variations are available with fractional derivatives when compared with ordinary derivatives. Relaxation times are incorporated to handle the abrupt changes in the flow domain. Fluid flow is carried out under the influence of thermal radiations and when a heat source or sink is present. Chemical reactions of the first order are observed in the mathematical modeling of the flow. Methods: Flow is induced with the movement of the lower surface while applying force on the x-plane. Simulations of the governing mathematical problem are computed with the combination of finite element and finite difference algorithms. Significant Findings: It is noted that velocity, temperature, and concentration change with the variation of fractional order derivatives which was not possible with the classical derivatives. Moreover, with greater relaxation times, velocity, temperature, and concentration remained at a lower level. The modeled mechanism can be considered to avoid costly trials in chemical and polymer casting industries. 2023 Elsevier B.V. -
Generation a: Life perspectives, potentials, challenges and future of neurodiverse stars in India
In India, one in 500 people (Balaji, 2019) are diagnosed with ASD. Around 40 non-governmental organizations cater to Autism Spectrum Disorder (ASD) children; out of that, a few organizations focus on adults providing them vocational training to make them employable. One such exercise was initiated by SAP Labs India, a leading software company, and Enable India, an Indian NGO where they developed a focused training program for people with ASD and placed them in vital technical jobs in SAP Labs India (Karwa, 2016). First, we peek into the lifestyles of a few successful neurodiverse rock stars in India and their journey from becoming aware of their profile to establishing a career and becoming a role model to other people with ASD. Second, we present the autism landscape in India. Third, we explore the organizations that have hired people with ASD, their policies connected to neurodiversity, and the organizations that give training and support. Fourth, we present the potentials and the challenges people with ASD face. Fifth and final, we cover the role of different stakeholders to foster support and up-skill people with ASD for better community development. 2022 by Emerald Publishing Limited. All rights reserved. -
Generation of Dynamic Table Using Magic Square to Enhance the Security for the ASCII CODE Using RSA
The efficiency of any cryptosystem not only depends on the speed of the encryption and decryption processes but also on its ability to produce different ciphertexts for the same plaintext. RSA, the public key cryptosystem, is the most famous and widely accepted cryptosystem, but it has some security vulnerabilities because it produces the same ciphertext for identical plaintexts occurring in several places. To enhance the security of RSA, magic square-based encoding models have been proposed in the literature. Although magic square-based encoding models have been proposed, they are static. Thus, this paper introduces a dynamic-based magic square with RSA, where encryption and decryption are performed using numbers generated from the magic square instead of ASCII values. Unlike the static magic square, the proposed dynamic magic square allows users to specify the starting and ending numbers in any position rather than fixed positions. In the proposed dynamic magic square generation, different 4 4 magic square templates are created, and 16 16 magic squares are generated from them. Experimental results clearly demonstrate the improved security of RSA. The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024. -
Generative AI and its impact on creative thinking abilities in higher education institutions
Generative AI technologies such as ChatGPT have started gaining increased popularity among higher education institutions. Students, as well as teaching professionals, can utilize these tools for various academic purposes due to the immense benefits they provide by way of customization of data generated and ease of access to data. However, this chapter seeks to analyze how such tools may impact students' creative thinking ability. It also analyses the drawbacks faced by teachers after implementation of such tools. The methodology adopted for the study was two surveys: one administered to gather students' opinions and the other for understanding teachers' perspectives. The analysis of the data collected shows that the over-reliance of students on such generative AI tools might hinder students' ability to think creatively to some extent. The chapter also suggests some of the strategies that can be adopted by teachers to ensure students' capabilities are assessed accurately. 2024, IGI Global. All rights reserved. -
Generative AI and the Future of Cyber Threats: Building Resilient, Trustworthy Defenses
Generative AI is transforming cybersecurity, introducing autonomous, adaptive threats that challenge traditional defences. Capable of producing realistic content, mimicking behaviour, and scaling deceptive attacks, GAI reshapes phishing, malware, deepfakes, and social engineering. Vulnerabilities in AI- generated code and synthetic data demand proactive, AI- driven countermeasures. This chapter explores XAIs role in transparency and trust, highlights emerging technologies for intrusion detection and predictive modelling, and emphasises ethical design, verification, and collaboration to build resilient infrastructures against next- generation intelligent cyber threats. 2026 by IGI Global Scientific Publishing. -
Generative AI for Healthcare Security: Addressing Privacy Challenges through Anomaly Detection in Healthcare Communications
Cybersecurity within the healthcare sector is paramount due to the sensitive nature of patient data and critical healthcare services. This chapter explores the role of Generative AI (GAI), particularly using BERT embeddings and the Isolation Forest algorithm, in enhancing cybersecurity measures. It begins by discussing the significance of cybersecurity in healthcare and the potential threats healthcare organizations face, emphasizing the need for robust security measures to protect patient data and ensure uninterrupted healthcare services. The chapter provides an overview of GAI and its applications in cybersecurity, focusing on its ability to detect anomalies in healthcare communications. A detailed case study demonstrates the practical implementation of GAI techniques for anomaly detection in healthcare emails, highlighting the effectiveness of BERT embeddings and Isolation Forest in identifying potential security breaches. Furthermore, the chapter discusses the broader implications of generative AI in healthcare cybersecurity, addressing privacy concerns and ethical considerations. The findings underscore the importance of integrating advanced AI technologies with robust privacy-preserving measures to safeguard patient data while promoting technological innovation in healthcare cybersecurity. 2025 selection and editorial matter, Anoop V.S., Suhasini Verma, Usharani Hareesh Govindarajan. -
Generative AI for Next-Generation Recommender Systems: Architectures, Applications, and Future Directions
The recommender systems have become a must in delivering personalized experiences across digital platforms. Still, traditional approaches, such as collaborative and content-based filtering, suffer from some inherent limitations: data sparsity, scalability, and dynamic user adaptation. In this context, generative AI emerges as a game-changing solution empowered by state-of-the-art models like variational autoencoders (VAEs), generative adversarial networks (GANs), and transformers to overcome the above-mentioned limitations. These models make possible the synthesis of user-item interaction data, uncovering latent patterns and providing context-aware recommendations, thereby redefining personalization in recommender systems. This chapter provides a detailed survey on the role of generative AI in recommender systems, their components, architectures, and applications. Case studies in e-commerce, entertainment, and education provide insights into how generative models help drive personalization, tackle the cold-start problem, and adapt dynamically to the evolution of user behaviors. Nevertheless, open issues regarding computational complexity, privacy protection, and ethical considerations remain. To address these, the chapter outlines the future enhancements in the areas of federated learning for privacy-preserving collaboration, multimodal data integration for holistic user profiling, and explainable AI frameworks to foster transparency and trust. Bridging these gaps would let generative AI-driven recommenders further revolutionize personalization, scalability, and inclusiveness, opening up a way to innovative solutions across the board in various industries. 2026 by John Wiley & Sons Inc. All rights reserved. -
Generative AI in Action: Empirical Case Studies on Startup Innovation and Entrepreneurial Decision-Making
Generative artificial intelligence (AI) is quickly reshaping the world of entrepreneurship and offers startups more opportunities than ever before to innovate and make strategic decisions, as well as to operate more effectively. The chapter is a synthesis of 50 recent academic articles that critically examine how generative AI, specifically large language models and creative automation systems, is transforming the business model design process and venture execution. It discusses two significant directions, one the effect of generative AI in startup innovation of quick product ideation, bespoke customer service and scalable solutions and the other the effect that AI will have on entrepreneurial decision-making, with AI-based analytics and support systems informing resource allocation and market perspective. Based on empirical evidence and practical case studies, the chapter offers practical recommendations to successful adoption and sets the research directions in the future to allow the full implementation of the transformative abilities of generative AI in the startup world. 2026 by IGI Global Scientific Publishing. All rights reserved. -
Generative AI-human collaboration in higher education: Applications, challenges, and strategies
The advent of GenAI has brought about substantial progress and prospects in diverse sectors, including education. We are witnessing significant progress in this field of artificial intelligence, with the emergence of chatbots such as ChatGPT and the proliferation of remarkably realistic AI-generated graphics. Generative AI, as an emerging technology, has the potential to bring significant and transformative improvements to education. Generative AI encourages higher education institutions to embrace and utilize the potential of these technologies to enhance several aspects such as student experience, faculty workload, intellectual property, etc. This chapter has explored the application of generative AI in the context of higher education, in light of its increasing prevalence. Although generative artificial intelligence offers a great deal of promise to improve education, the technology is not entirely devoid of difficulties. The chapter also discusses challenges and strategies related to generative AI in higher education. 2024 by IGI Global. -
Generative AI: fuelling e-commerce revenue growth
Generative AI (GenAI) is a novel technology that has transformed businesses across various industries. E-commerce companies are increasingly using cutting-edge technologies, such as GenAI, that could drive revenue growth. This study leverages the lens of social-technical systems and dynamic capabilities theories to examine how e-commerce companies adopt GenAI and its subsequent impact on their revenue generation capabilities. This study investigated the factors influencing the adoption of GenAI in e-commerce companies. This study considers both Social factorstop management support, organizational readiness, competitive pressure, and vendor support) and Technical factors (security concerns, technological readiness, and AI explainability). Further, it investigates how the use of GenAI affects the development of technological capabilities and leads to financial performance, particularly in sales growth, revenue generation, profitability, and cost reduction. To validate the proposed model, we surveyed e-commerce company managers and analyzed the collected data using PLS-SEM. These findings offer valuable insights for e-commerce companies and their managers, enabling them to leverage GenAI for revenue generation. This novel study makes a significant contribution to the academic understanding of GenAI adoption and how it can be adopted for successful revenue generation. The Author(s), under exclusive licence to Springer Nature Limited 2025. -
Generative Artificial Intelligence and Academic Integrity: Transforming Authorship and Research Standards
Generative AI (GAI) is fundamentally altering the academic authorship and research ethics landscape by sourcing multiple long-held beliefs of originality, intellectual labor, and academic accountability. The rise of tools such as ChatGPT, Gemini, and Claude in the writing, analysis, and creation of knowledge creates an urgent need for Higher Education to redefine the meaning of ethical authorship. This paper seeks to examine how Generative AI has changed many of the long-standing academic traditions, highlighting dependence on technology and analyze the inconsistencies in the AI Policies at institutions of higher education worldwide. By utilizing qualitative and normative methods from deontological and virtue ethics, this study compares the policies of Harvard University and University of Cambridge and highlights the best practices for responsible use and integration of AI technologies. The chapter will also propose a human-centered, accountable, and transparent AI collaboration model that protects academic integrity while supporting continued technological advancement 2026 IGI Global Scientific Publishing. -
GeneRiskCalc: a web-based tool for genetic risk association analysis in casecontrol studies
Background: Genetic association studies play a pivotal role in identifying disease-associated variants, but researchers face challenges in performing essential calculations like HardyWeinberg equilibrium testing, odds ratios, and confidence intervals due to reliance on manual methods or multiple software tools. We aimed to develop GeneRiskCalc, an integrated web-based platform that simplifies genetic association analysis by automating HardyWeinberg equilibrium assessment, odds ratios with confidence interval calculation, and visual data presentation in casecontrol studies. Using an HTML/CSS/JavaScript framework, we developed online software with three core functionalities: (1) automated HWE evaluation, (2) odds ratio with 95% confidence interval computation with statistical validation, and (3) dynamic Forest Plot generation for data visualization. The tool was designed with an intuitive interface to minimize prerequisite statistical expertise. Results: The tool, named the Genetic Risk Association Calculator (GeneRiskCalc), demonstrated high computational accuracy in HWE testing (?2 validation) and association metrics (odds ratio and confidence interval). The results were cross-validated against established statistical methods, confirming their reliability. Furthermore, the integrated Forest Plotter enabled immediate visualization of effect sizes across multiple genetic models, facilitating a comprehensive interpretation of genetic associations. Conclusion: By integrating essential analytical steps into a single platform, the GeneRiskCalc, streamlines genetic epidemiology workflows, addressing key challenges in data analysis. Its user-friendly interface enhances accessibility, promotes reproducibility, and accelerates research in genetic association studies. The tool is freely available at GeneRiskCalc (https://sites.google.com/view/GeneRiskCalc/home?authuser=0). The Author(s) 2025. -
Genetic Algorithm-Based Optimization ofUNet forBreast Cancer Classification: A Lightweight andEfficient Approach forIoT Devices
IoT devices are widely used in medical domain for detection of high blood sugar and life threatening disease such as cancer. Breast cancer is one of the most challenging type of cancer which not only affects women but in some cases men also. Deep learning is one of the widely used technology which provides efficient classification of cancerous lumps but it is not useful for IoT devices as the devices lack resources such as storage and computation. For the suitability in IoT devices, in this work, we are compressing UNet, the popular semantic segmentation technique, for the pixel-wise classification of breast cancer. For compressing the deep learning model, we use genetic algorithm which removes the unwanted layers and hidden units in the existing UNet model. We have evaluated the proposed model and compared with the existing model(s) and found that the proposed compression technique suppresses the storage requirement to 77.1%. Additionally, it also improves the inference time by 3.82without compromising the accuracy. We conclude that the primary reason of inference time improvement is the requirement of less number of weight and bias by the proposed model. The Author(s), under exclusive license to Springer Nature Switzerland AG 2024. -
Genetic Algorithms for Graph Theoretic Problems
[No abstract available] -
Genetic Algorithms for Wireless Network Security
[No abstract available] -
Genetic and cytogenetic screening of autistic spectrum disorder: Genotype-phenotype profiles
Autism, a pervasive developmental disorder typically characterized by repetitive behaviour, social skills deficit (or a deficit in social communication), speech and language impairments. Our prime focus is to analyze the clinical features and phenotypical behavioural changes using the diagnostic and statistical manual of mental disorders, fourth edition, text revision (DSM IV-TR), and locating the biomarkers associated with specific autistic characters using karyotyping and fluorescence in situ hybridization (FISH) techniques. The prevalence rate of the neurexin 1 (NRXN1) gene polymorphism was also assessed in the current study. The study group involved 196 samples with 98 autistics, and equal age-matched (2) controls based on their birth order and carrier. The participants include 35.2% males (n = 69) and 14.8% females (n = 29). The autistic and control participants were categorized based on their ages as group I (<12 yrs) with n = 62; males n = 41 (20.9%); females n = 21 (10.7%) and group II (?12 yrs)-n = 36; males n = 28 (14.2%); females n = 08 (4.08%). Karyotyping was done for autism participants (n = 98) and the results showed that 90% of autistic participants were either the only child or the first child with a low perception and frequency in both the groups. Subsequently, we carried out the FISH assay on participants (n = 37) with higher DSM-IV TR score (?30). Only 30 FISH tests were negative for subtelomeric deletions with NRXN1 polymorphism genotypic frequency as 62.50%, 25% and 25% for A/A, A/G and G/G genotype respectively. Our study suggests the link between a haplotype with clinical signs of autism for the single nucleotide sequence (SNP rs9636391) and links autistic characters and gene among autistic children according to their birth order, age and gender in India. 2021 Elsevier B.V. -
Genetic causes of male infertility and emerging potential of stem cell therapeutics
Infertility has haunted mankind from ancient times. In 40 to 50% of the cases resulting in failure to conceive, the cause can be attributed to male infertility. The rates of infertility have risen in the past few decades among both among men and women. The reasons can be attributed to life style changes and environmental pollution apart from genetic causes. Scientists have found out few possible causes of male infertility and there are few treatment measures currently being carried out. The success of these depends on the primary cause of the infertility problem. In the past few decades, a number of genes playing roles in different stages of sperm development and differentiation were functionally analysed. This knowledge coupled with the advancements in stem cell technology and in vitro gametogenesis has led to new frontiers in possible therapeutic measures of male infertility, especially those with genetic causes. The present study focuses on the causes and treatment of male infertility focussing mainly on important genetic regulations and emerging stem cell-based therapeutics. 2021 World Research Association. All rights reserved.