Volume 13 | Issue 2

 Abstract

Quadruple hypergeometric polynomials constitute a natural higher-dimensional extension of classical hypergeometric polynomials and arise in multivariate approximation theory, multiple orthogonality, and the analysis of systems of partial differential equations. Recent work on hypergeometric functions of several variables-especially those of Srivastava, Exton, and their generalizations-has established a variety of series, transformation, and integral representations for triple and quadruple hypergeometric functions. However, systematic integral representations tailored specifically to polynomial families of quadruple hypergeometric type remain comparatively underdeveloped. In this paper we introduce a class of quadruple hypergeometric polynomials M_n (x_1,x_2,x_3,x_4) arising from a truncated quadruple hypergeometric series, and we derive several integral representations of Euler- and Laplace-type. Our approach combines classical techniques based on Beta and Gamma integrals with operational methods and factorization of the Pochhammer symbol, extending ideas used earlier for triple hypergeometric functions and Exton-Srivastava quadruple functions. The resulting integral formulas provide new analytical tools for studying convergence, asymptotic behavior, and structural properties (such as orthogonality and generating functions) of these polynomials. In addition, we discuss numerical implications and outline how the derived representations can be exploited for efficient computation via multidimensional quadrature and Monte Carlo methods.

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 Keywords

quadruple hypergeometric polynomials; multiple hypergeometric functions; Euler-type integrals; Laplace-type integrals; Pochhammer symbol; Exton-Srivastava functions; multiple Beta integrals; multivariate special functions

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This paper aims to study Noun Derivation found in the Basavapuranamu of Palkuriki Somana. This is a descriptive study. Noun Derivations of Telugu, Sanskrit, etc. are classified based on language, meaning and syntactic relation with examples.

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Palkuriki Somana, Basavapuranamu, Saivism, Noun Derivation, Telugu, Sanskrit, syntactic relation.

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Adversarial attacks on machine learning (ML) models present significant risks to the integrity and reliability of cybersecurity systems. These attacks manipulate the input data to deceive ML models into making incorrect predictions or classifications, compromising the performance of security systems such as intrusion detection systems, malware detection, and fraud prevention. The vulnerability of ML models to such attacks threatens the overall effectiveness of automated security defences and can lead to severe consequences in both real-world applications and threat mitigation efforts.This paper addresses the risks posed by adversarial attacks on ML models within the context of cybersecurity. We propose a novel algorithm aimed at enhancing the robustness of ML models against adversarial manipulations. The proposed algorithm leverages advanced defence mechanisms such as adversarial training, gradient masking, and feature squeezing to detect and mitigate the effects of adversarial inputs. By augmenting the training process with adversarial examples and employing real-time detection strategies, the algorithm improves the model's ability to withstand malicious alterations while maintaining high accuracy.

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 Keywords

Machine learning, Cybersecurity, Cyber Threats, Malicious Attacks

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The present chapter inspects parity and disparity of the characteristic features of the fiction of Shobha De and Preeti Shenoy. Similarity between the write up of these two novelists is the emergence of new woman who has eventually liberated and carved a niche for herself. The dissimilarity is that the novels of Shobha De are based on Radical Feminism whereas the fiction of Preeti Shenoy is based on Liberal Feminism. Shobha De and Preeti Shenoy are more or less contemporaries. There are similarities in writing different women oriented themes between these two writers. They have emphasized emancipation, self and identity, empowerment of women and the evolution of new woman in India. These fiction writers have highlighted the impact of the rise of feminism in India. The emergence of new woman is depicted through their female protagonists. The conventional woman of India is totally different from the traditional woman of India. They have shown outstanding perceptiveness and compassionate vision into the poignant world of women. They have guided their female protagonists towards self-definition and self-realization. They have instilled in their female characters a good sense of feminine identity. Their books are about the search of women for their identity. It is an examination of the feminine consciousness, and an awareness of their lives' complications being women. Their writings highlight the role of protagonists in creating a place of their own.

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Sujugation,Oppression,Empowerment,Dissimilarity,Parity

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Theoretical Comparative Study of Mallakhamb and Yoga Practitioners

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Theoretical Comparative Study of Mallakhamb and Yoga Practitioners

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Methods, approaches and techniques for teaching diverse subjects at diverse places can be vary from teacher to teacher, age to age of learners and teachers, and easy to hard in relation to the levels when it comes to schooling and teaching English Language. In ancient times India has gurukul system of gaining knowledge of different kinds. In modern times the scenario has changed tremendously. Government took charge of education and it became public concern after independence but in the era of technology the traditional education needs changes in approach and techniques. With the principal objective of examining the digital pedagogy with multiple alternatives for teaching, the present research article has been attempted. It discusses the digital tools in short, emphasis on the digital pedagogy in New Education Policy, risks and danger in using the digital tools and most importantly the investigation into the problem of using digital pedagogy and its inevitable shift in the era of technology for teaching English Language. Digital tools, software, applications, digital platforms and their use and challenges on the part of the students as well as the teachers are the major concerns of the research article.

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Digital pedagogy, methods and approaches, inevitability of technology.

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The paper explores into the way of life adopted by the young adults influenced by the effects of K- wave also known as Korean wave of South Korea in small districts like Darjeeling and Kalimpong in West Bengal, India. The Korean culture has been a wave of cultural assimilation through digital age, especially Korean popular music, K-pop. The young adults who are part of the popular culture and has an impact on other social relations and structures of any given society, is the focal point of the paper. It looks into how adapting another culture has changed the lifestyle, perspectives, social relations and approaches towards life among these young adults. The paper primarily focuses on understanding nuances and experiences of the young adults and their personal accounts on how has K-wave impacted their lives and how do they perceive a culture which has been learnt through the medium of digital world. It intends to explore a basic understanding at a micro level on how the young adults relate to the Korean culture and their choices and preferences towards their own culture.

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K-wave, K-pop, social relations, impact, experiences

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Education, according to Swami Vivekananda, is not filling the mind with a lot of facts but changing the man by perfecting the mind. Perfecting and getting complete mastery of one's own mind is the ideal of education. It is not possible to change the mind by force. Nor is it possible to change it by one's own sweet will. One can change one's mind only by gaining complete mastery over one's own thoughts. The thoughts and the senses should be my servants not my masters. Then only it is possible to dispel evil. Education is a process of making man divine by the mastery over one's thoughts and senses. This is possible through the power of concentration. Concentration, according to Vivekananda, is the power of attachment of the mind to certain things. Concentration includes the power of detachment also.

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EDUCATION, YOGA, SWAMY VIVEKANDA

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Stress is a universal element and persons from nearly every walk of life have to face stress. Employers today are critically analyzing the stress management issues that contribute to lower job performance of employees originating from dissatisfaction and high turnover ultimately affecting both organizational goals as well as family life of employees. The main reason for stress appears to be either failure to balance between career goals and family responsibility. This imbalance ultimately leads to increased number of family disputes, divorce cases, failure at corporate level and so on. The aim of the research was to find out the relationship between independent variable - work-life balance and dependent variable - job stress. The findings of the study revealed that the incidence of job stress was directly associated with the level of work-life balance. The result of hypothesis testing rejected the null hypothesis of no association between these two variables meaning there by the acceptance of alternate hypothesis of a strong association between these two variables. At the same time result of correlation coefficient between these two variables was highly negative which was interpreted that high work life balance has less job stress establishing the fact that a proper work-life balance certainly lessens job stress. The incidence of job stress in the female employees of IT industry in Jodhpur was more as compared to the male employees of the same age group.

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Work life balance, Job stress, Job satisfaction, IT employees

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False advertising, involving the dissemination of deceptive or misleading claims, poses significant risks to consumer welfare, market integrity, and public trust. This research critically examines the criminal aspects of false advertising, differentiating them from civil remedies, and explores the evolution of regulatory frameworks aimed at protecting consumers. Drawing on legal statutes, judicial precedents, and scholarly literature, the study highlights how criminal liability serves as a potent deterrent against deliberate deceptive practices that cause substantial harm. Special focus is given to emerging challenges in the digital era, such as cross-border advertising crimes, artificial intelligence-driven ad targeting, and regulatory gaps that traditional frameworks struggle to address. Comparative analysis with international best practices underscores the need for more cohesive and adaptive regulatory models. Through case studies and doctrinal analysis, the research finds that stricter enforcement, technological integration, and increased public awareness are essential to counter the growing sophistication of false advertising strategies. The findings suggest a multi-dimensional approach combining legal reforms, regulatory vigilance, and global collaboration is critical for safeguarding consumer interests and ensuring fair market practices. This study provides valuable insights for policymakers, regulators, and legal practitioners committed to enhancing consumer protection in the evolving advertising landscape.

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 Keywords

False Advertising, Consumer Protection, Criminal Liability, Regulatory Frameworks, Digital Advertising, Cross-border Advertising, Artificial Intelligence, Market Integrity, Judicial Response, Advertising Law

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 Abstract

Swami Vivekanad ke shaikshik vicharo ki bharatiya gyan parampara ke sandarbh mein prasangikta

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Swami Vivekanad ke shaikshik vicharo ki bharatiya gyan parampara ke sandarbh mein prasangikta

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The emergence of omnichannel marketing has significantly transformed the interaction between businesses and consumers by offering a cohesive and integrated customer experience across various digital and physical platforms. This study investigates the extent of omnichannel marketing adoption among businesses in Indore, a rapidly developing tier-2 city in central India, and its impact on consumer behaviour and business engagement. By employing correlation and multiple regression analysis on data collected from a sample of 150 respondents, the research examines the relationship between omnichannel strategies and key consumer metrics, including satisfaction, loyalty, personalization, and frequency of interaction. The findings reveal a statistically significant and positive correlation between omnichannel marketing and consumer satisfaction. Moreover, the regression results confirm that omnichannel adoption substantially enhances customer engagement, with satisfaction, loyalty, and frequency of interaction emerging as strong predictors. These insights underscore the strategic importance of omnichannel approaches for businesses seeking to build long-term consumer relationships and remain competitive in evolving urban markets.

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Omnichannel Marketing, Consumer Satisfaction, Customer Engagement, Business Strategy, Loyalty, Personalization, Digital Marketing, Indore, Tier-2 Cities, Regression Analysis

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Guru Ravidas Ji, on one hand, is a spiritual heritage of the Indian tradition, and on the other hand, he is a treasure in terms of intellectual thought and reflection. In my article, I have attempted to evaluate "Guru Ravidas Ji's contribution to Indian culture and society." In this context, I have tried to understand his spiritual legacy, his socially resistant discourses, and his critical views on the caste system. Along with this, I have also tried to mark his place in the cultural movement and define his position within the Bhakti movement.

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Caste-System, Bhagati Movement, Spiritual discourse, Social discourse

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The increasing rate and complexity of cyberattacks in the financial industry have required the implementation of more robust cybersecurity technologies. Traditional threat detection technologies are not adequate in managing the dynamic nature of such attacks, hence the need for more advanced systems. This study investigates the convergence of Observability-Driven Cybersecurity and Artificial Intelligence (AI) technologies and the use of technologies like AppDynamics towards advanced threat detection in financial IT systems. Despite the growing adoption of AI and observability technologies, there is a broad research gap on how such technologies can be synergistically combined to offer proactive security solutions in real-time, particularly in the high-risk and complex environment of financial institutions. Current systems are ineffective in detecting advanced persistent threats (APTs), insider threats, and in formulating attack plans until damage has been caused. This study seeks to bridge the gap by investigating how observability tools, like AppDynamics, can be combined with AI algorithms to enable early detection and prevention of cybersecurity threats. The study investigates real-time anomaly detection, predictive threat intelligence, and automated response features to boost security operations and response times. Through the utilization of performance monitoring, behavioral analytics, and machine learning technologies, this study proposes a holistic solution to boost the cybersecurity defenses. This study adds to the growing literature on the convergence of AI technologies and observability, offering practical suggestions for financial institutions seeking to upgrade their cybersecurity systems against increasingly sophisticated attacks.

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 Keywords

Observability-based security, artificial intelligence-based financial systems, AppDynamics, threat detection, real-time anomaly detection, predictive threat intelligence, machine learning, financial IT security, insider threats, advanced persistent threats, automated response systems, cybersecurity frameworks.

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The application of Robot Framework for Acceptance Test Driven Development (ATDD) in functional testing of enterprise applications represents a transformative approach to quality assurance and software development. By integrating automated testing with ATDD practices, development teams can foster early collaboration among stakeholders, ensuring that functional requirements are clearly understood and met from the inception of a project. This methodology leverages the versatility and extensibility of the Robot Framework to design and execute comprehensive test suites that simulate real-world user scenarios in enterprise environments. The framework's keyword-driven approach simplifies test case creation, promotes reusability, and enhances the maintainability of test scripts, even in complex systems. Moreover, the adoption of ATDD facilitates continuous feedback, allowing developers to rapidly identify and resolve discrepancies between expected and actual system behaviors. This results in reduced defect rates and improved overall software quality. The integration of these practices also supports agile development cycles by aligning testing activities with iterative delivery and continuous integration pipelines. Consequently, organizations can achieve faster time-to-market while maintaining high standards of functionality and reliability. This study explores the benefits, challenges, and best practices associated with implementing Robot Framework for ATDD, providing insights into effective strategies for optimizing test automation in enterprise settings. It further examines case studies that demonstrate measurable improvements in efficiency and quality assurance outcomes, underlining the framework's capacity to meet the evolving demands of modern software development. Ultimately, the adoption of Robot Framework for ATDD not only broadens test coverage but also fosters continuous improvement and sustainable software quality.

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Robot Framework, ATDD, Functional Testing, Enterprise Applications, Test Automation, Quality Assurance, Agile Development, Continuous Integration, Software Testing, Automation Tools

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The rapid expansion of digital ecosystems has placed Application Programming Interfaces (APIs) at the core of modern connectivity, driving both innovation and security challenges. This abstract explores the future of API security by investigating emerging trends, advanced technologies, and adaptive strategies that are essential for safeguarding digital interactions. As organizations increasingly rely on cloud-based and microservices architectures, the complexity and number of attack vectors have grown exponentially. Traditional security measures are proving insufficient in the face of sophisticated cyber threats. In response, the integration of artificial intelligence and machine learning is revolutionizing threat detection by enabling real-time analysis and automated responses to potential vulnerabilities. Moreover, the adoption of zero-trust security models and adaptive authentication mechanisms is reshaping how sensitive data is protected, ensuring that every access request is rigorously verified. The evolving regulatory landscape further reinforces the need for proactive risk management and compliance-driven security strategies. Through an evaluation of contemporary case studies and industry best practices, this work outlines a multi-layered approach to API security that emphasizes continuous monitoring, periodic vulnerability assessments, and the implementation of robust encryption techniques. Ultimately, the convergence of innovative technologies and strategic foresight is setting the stage for a resilient API security framework. This paper offers actionable insights for security professionals, developers, and policymakers committed to enhancing the integrity and reliability of digital infrastructures in an increasingly interconnected world.

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API Security, Future Trends, Emerging Technologies, Machine Learning, Zero-Trust, Adaptive Authentication, Regulatory Compliance

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 Abstract

Real-Time Data Streaming with Spark and Kafka represents a pivotal approach in modern data analytics, where instantaneous processing and rapid insights are crucial. This methodology leverages Apache Spark's distributed computing capabilities and Apache Kafka's robust messaging framework to seamlessly ingest, process, and analyze voluminous data streams in real time. The integration of these technologies facilitates the handling of complex data pipelines, enabling organizations to derive actionable insights from continuous data flows generated by various sources such as social media, sensors, and transactional systems. Spark's in-memory computing accelerates data processing, while Kafka provides high-throughput, fault-tolerant messaging that ensures data reliability and scalability. Together, they address challenges such as latency, data consistency, and system resilience. The system architecture supports dynamic scaling and can accommodate fluctuating workloads, making it adaptable to different operational requirements. Additionally, real-time processing enables proactive decision-making by promptly identifying trends and anomalies, which is essential in sectors like finance, healthcare, and telecommunications. The paradigm also encourages the use of stream processing frameworks and machine learning models, enhancing predictive analytics and automated responses. Overall, the synergy between Spark and Kafka not only improves performance but also offers a cost-effective and flexible solution for real-time data analytics, paving the way for future innovations in big data technology and distributed systems. This abstract encapsulates the significance, operational mechanics, and transformative potential of real-time data streaming in today's digital landscape.

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Real-Time Data Streaming; Apache Spark; Apache Kafka; Big Data Analytics; In-Memory Computing; Distributed Systems; Data Pipelines

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This study explores the integration of artificial intelligence and machine learning into large-scale Oracle Cloud ERP deployments to revolutionize enterprise operations. By incorporating advanced analytics, predictive modeling, and process automation, these technologies transform conventional ERP systems into dynamic platforms that enhance efficiency and decision-making capabilities. The infusion of AI enables real-time data processing and sophisticated insights, which lead to improved resource allocation, optimized workflows, and proactive risk management. Machine learning algorithms, through continuous learning from historical and real-time data, empower the system to adapt to evolving business needs and forecast trends with heightened accuracy. This integration not only minimizes manual intervention but also enhances data quality and operational scalability across complex organizational structures. Case studies illustrate substantial cost reductions, heightened productivity, and strategic agility in environments where Oracle Cloud ERP systems are bolstered by AI-driven solutions. The paper also addresses implementation challenges, including data integration issues, cybersecurity risks, and the need for robust change management strategies to support technological advancements. In conclusion, the research underscores the transformative potential of AI and machine learning in modernizing ERP systems, paving the way for more resilient, efficient, and competitive business operations in an increasingly digital marketplace. This study provides a framework for organizations looking to harness these technologies, offering insights into both the benefits and the practical considerations essential for successful deployment.

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Oracle Cloud ERP, AI integration, machine learning, digital transformation, process automation, predictive analytics, enterprise resource planning

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Generative artificial intelligence (AI) is revolutionizing how businesses leverage vast data lakes to support critical decision-making processes. Data lakes, characterized by their ability to store diverse and large-scale datasets, pose significant challenges in extracting actionable insights efficiently. Generative AI, encompassing technologies such as generative adversarial networks (GANs) and large language models (LLMs), addresses these challenges by automating complex data management tasks, enhancing data quality, and generating predictive insights. This literature review investigates recent advancements and applications of generative AI within large-scale data lakes, highlighting its transformative impact on business analytics, risk management, and operational optimization. Through synthesizing findings from current research, the review explores practical implementations, evaluating benefits such as accelerated insight generation, improved scalability, and enhanced interpretability of complex datasets. Moreover, this paper discusses critical considerations including data governance, ethical implications of AI-generated insights, and integration challenges within existing enterprise systems. Emphasis is placed on how generative AI facilitates more responsive and agile decision-making frameworks, ultimately enhancing competitive advantage and strategic responsiveness in dynamic market conditions. The synthesis aims to provide a comprehensive understanding for business leaders, data scientists, and IT professionals on effectively deploying generative AI capabilities to maximize value extraction from extensive corporate data resources.

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Generative AI, data lakes, business analytics, large-scale datasets, decision-making, GANs, predictive insights

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The exponential growth of data and the development of artificial intelligence (AI) have generated huge data lakes, which provide organizations big, unstructured data sets. Although data lakes have the promise to provide useful insights to organizations, organizations are not able to use these assets effectively for strategic decision-making. The objective of this study is to explore the application of generative AI on big data lakes to generate meaningful insights and create embeddings to enhance business intelligence (BI) operations. A significant research gap exists in the practical implementation of generative AI models to unstructured data in data lakes, especially with regard to efficiency in processing and scalability. Most existing methodologies focus heavily on traditional data processing techniques and do not have the attention of overcoming the complexity and size of data involved in data lakes. Moreover, although AI has been implemented with smaller and structured data sets with significant success, applying AI on big, heterogeneous data sets requires new strategies to facilitate proper interpretation of data and actionable results. This research provides a conceptual model incorporating generative AI models with data lakes to recover contextual embeddings and generate insights in support of better-informed business decision-making. Identifying the importance of feature extraction through automated methods, anomalies, and advanced predictive models, this research is an attempt to address the prevalent vacuum of awareness surrounding the cost-efficient large-scale applications of AI to analyze real-time data. Last but not least, this research is about enhancing decision-making capabilities, cutting costs, and promoting organizational response effectiveness in emerging data ecosystems.

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Generative AI, data lakes, business intelligence, insight generation, embeddings, unstructured data, predictive modeling, scalable AI solutions, decision-making optimization, data processing, anomaly detection, feature extraction.

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