A Study on Impact of FDI on Indian Economy– An Empirical Analysis
Authors:-Assistant Professor Dr. Pooja Kumari, Associate Professor Dr. R. Vennila
Abstract- A lot of debate is going on about the importance of foreign direct investment (FDI) in the process of growth has been hot in a number of nations, including India. The foundation and prerequisite for economic development and growth is investment. In addition to a country’s foreign exchange reserves, other factors that are essential to its health include exports, government revenue, financial status, the amount of available domestic savings, and the volume and caliber of foreign investment. The aim of this study is to analyze the impact of FDI on Indian Economy. To meet the objective of the study time series data is used 2005 to 2023. Variables used in the study are Foreign Direct Investment as dependent variable and Gross Domestic Capital Formation (GDCF), export, import, Gross Domestic Product (GDP), Foreign Exchange Reserve (FER) and Wholesale Price Index (WPI)as independent variables. Techniques used in this study are descriptive test, correlation and regression analysis. FDI exhibits a positive correlation with variables such as GDCF (0.44), Export (0.38), Import (0.42), GDP (0.44), and FER (0.44). This implies that a 1% increase in FDI corresponds to a corresponding degree of change in other variables. The correlation between WPI and FDI is negative, or – 0.22, indicating that the two variables go in different directions. The study concluded that FDI statically significantly impact on Indian economy.
Unveiling the Dynamics of Goods and Services Tax (GST) Collection Growth: A Comprehensive Analysis from Financial Year 2019-20 to 2023-24
Authors:-Research Scholar Anurag Singh Parihar, Associate Professor Dr. C.P. Gujar
Abstract- This research paper delves into the intricate dynamics of Goods and Services Tax (GST) collection growth in India over a five-year period, from the financial year 2019-20 to 2023-24. By leveraging comprehensive data analysis, the study aims to unravel the multifaceted factors influencing GST collection trends, including economic activities, policy changes, compliance rates, and administrative measures. The paper begins with an overview of the GST framework, followed by a detailed examination of annual and quarterly GST revenue data. Key economic indicators are correlated with GST collection to highlight the impact of macroeconomic conditions. Furthermore, the research incorporates a comparative analysis of sectoral contributions to GST revenue, identifying sectors with significant growth and those lagging behind. The findings reveal critical insights into the effectiveness of GST implementation and its adaptability to changing economic landscapes. This analysis not only underscores the strengths and weaknesses in the current GST system but also offers strategic recommendations for enhancing revenue mobilization and ensuring sustainable fiscal growth. By providing a holistic view of GST collection dynamics, this paper contributes to the ongoing discourse on tax policy and economic reform in India.
The Downsizing Announcement: The Cognitive Analysis in the IT Sector
Authors:-Assistant Professor Dr. Meghna Sharma
Abstract- This research analyses different types of downsizing announcements in the IT sector. A cognitive analysis has been done to understand various types of downsizing announcements and how IT employees perceive them. A descriptive analysis has been done on the methods adopted by the IT companies to announce downsizing such as media announcements, internal declaration, feedback after declaring the downsizing, and storytelling method. The research concluded that employees prefer the storytelling method in making the downsizing announcement wherein they get the opportunity to understand that they may find better options in their career path.
The Role of Salesforce CRM in Enhancing Patient Engagement
Authors: Mehira Krishnan
Abstract: In today’s rapidly evolving healthcare environment, patient engagement stands out as a critical factor for improving clinical outcomes, enhancing patient satisfaction, and achieving efficient health service delivery. The paradigm shift toward patient-centered care—combined with the increasing digitization of healthcare systems—demands robust technological solutions that foster collaboration, streamline communication, and empower patients in their health journeys. Salesforce Customer Relationship Management (CRM), originally designed for the corporate sector, has made significant inroads into healthcare, offering platforms and tools to facilitate seamless connectivity among patients, providers, and payers. This article explores the multifaceted role of Salesforce CRM in fostering deeper patient engagement through data-driven personalization, integrated care coordination, scalable communication channels, and robust patient support functionalities. By enabling healthcare organizations to centralize patient data, automate workflows, and tailor interventions, Salesforce CRM not only improves adherence and satisfaction but also supports proactive care models and population health initiatives. While challenges related to data privacy, integration, and cost must be addressed, the transformative potential of Salesforce CRM in nurturing a participatory and connected healthcare landscape is substantial. This comprehensive review underscores best practices, real-world case studies, technological enablers, and strategic frameworks for leveraging Salesforce CRM to enhance patient engagement and drive health system innovation.
A Review Of Bioinformatics Algorithms For Gene Expression Analysis
Authors: Kabir Suryavanshi
Abstract: Gene expression analysis is a cornerstone of modern molecular biology, enabling the elucidation of gene functions, regulatory mechanisms, and disease associations. With the exponential growth of high-throughput sequencing technologies, such as microarrays and RNA-Seq, vast datasets are now available, presenting both an unprecedented opportunity and a computational challenge. Bioinformatics algorithms have risen to meet these demands by providing accessible and scalable methods for data normalization, feature selection, clustering, classification, and pathway analysis. This review presents a comprehensive overview of the key algorithms used in gene expression analysis, discussing their theoretical foundations, practical applicability, and comparative strengths. Emphasis is placed on the transition from traditional statistical methods to contemporary machine learning approaches, highlighting how each has contributed to unraveling complex biological phenomena. Emerging issues, such as data heterogeneity, batch effects, and the integration of multi-omics datasets, are examined alongside the innovative algorithmic solutions developed to tackle them. Furthermore, the impact of algorithmic advances on translational research, including biomarker discovery, drug development, and personalized medicine, is discussed. By critiquing the evolution of bioinformatics tools and their roles in gene expression analysis, this article aims to guide researchers in selecting and applying the most appropriate algorithms for their specific investigative goals, while also identifying areas for future development. Ultimately, as biological research grows increasingly data-driven, the synergy between algorithm development and gene expression analysis will continue to deepen our understanding of functional genomics and disease etiology.
Data Integration Challenges In Modern Healthcare Systems
Authors: Tara Manjrekar
Abstract: In recent years, the proliferation of health data generated across multiple points of care has presented both unprecedented opportunities and unique challenges for modern healthcare systems. Efficient data integration is vital for improving clinical outcomes, research analytics, and administrative processes. Yet, obstacles such as data heterogeneity, disparate standards, inconsistent formats, siloed information systems, and privacy concerns impede seamless information flow. As healthcare moves toward value-based and patient-centered models, the integration of structured and unstructured data from electronic health records (EHR), laboratory systems, wearable devices, and population health databases becomes crucial. This article explores the technological, ethical, and organizational complexities of healthcare data integration. It underscores how legacy infrastructure, varying interoperability standards, and evolving regulatory requirements complicate the harmonization of vast datasets. Additionally, the article addresses the role of artificial intelligence (AI), cloud computing, and blockchain in streamlining data integration, while discussing socio-technical barriers and best practices for implementation. Ultimately, robust data integration strengthens evidence-based medicine, advances precision healthcare, and empowers patients and providers. However, it requires multidisciplinary strategies, sustainable investments, and consistent policy evolution to address technical and ethical challenges. The analysis concludes with recommendations for future innovation and collaboration to realize the true potential of integrated healthcare data systems.
A Multi-Agent System For Extracting And Querying Financial Documents
Authors: Sagar Gupta
Abstract: Financial documents—10-K/10-Q filings, earnings releases, prospectuses, invoices, and bank statements—combine dense prose, complex tables, and heterogeneous layouts. We propose a multi-agent architecture that orchestrates document ingestion, layout-aware extraction, knowledge graph construction, retrieval-augmented reasoning, and auditable question answering. Our design integrates advances in agentic LLM collaboration (AutoGen, CAMEL), reasoning-and-acting prompts (ReAct), and document AI (LayoutLMv3, Donut) with domain standards (FIBO ontology, ISO 20022 messaging) and public data interfaces (SEC EDGAR, XBRL). We specify components, routing policies, evaluation protocols (FinQA, TAT-QA), and governance for factuality, lineage, and compliance. This paper contributes: (1) a reference architecture and coordination patterns for financial-document intelligence, (2) a schema-aligned extraction pipeline that yields structured, explainable facts, and (3) a reproducible evaluation plan emphasizing numeric reasoning, table/text fusion, and citation fidelity.
