Daily Archives: August 5, 2025

Authors: Girish Venkataramana

Abstract: In an era of accelerating digital disruption, businesses must go beyond traditional planning to remain competitive and resilient. This article explores how integrating Artificial Intelligence (AI) across core business functions—strategy, operations, marketing, finance, and human resources—enables organizations to become future-proof. By leveraging AI for real-time insights, predictive analytics, and intelligent automation, companies can drive agility, cross-functional collaboration, and continuous innovation. The article also examines common challenges in AI adoption, including data quality, talent shortages, and ethical concerns, and offers practical strategies for overcoming them. Through industry use cases and emerging trends, it becomes clear that AI is not just a technological upgrade but a foundational shift in how organizations operate and make decisions. Future-ready businesses will be those that embed AI into their strategic core, fostering a culture of adaptation, learning, and long-term growth

DOI: https://doi.org/10.5281/zenodo.16743166

 

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Authors: Shanthi Eshwaran

Abstract: The Lean AI Startup represents a powerful evolution in how ventures are launched and scaled—combining the speed and frugality of lean startup principles with the intelligence and efficiency of Artificial Intelligence. This article explores how founders can validate ideas, build smart MVPs, automate business functions, and grow sustainably using AI from day one. By integrating accessible tools like no-code AI platforms, predictive analytics, and intelligent automation, startups can operate with minimal resources while delivering maximum value. The piece highlights how AI accelerates product development, improves decision-making, personalizes user experiences, and enables rapid iteration without large teams or inflated budgets. It also addresses potential pitfalls such as ethical concerns, over-reliance on automation, and data privacy. Featuring real-world examples, this guide illustrates that the future of entrepreneurship lies in building lean, data-driven, and highly scalable ventures. With the right approach, any founder can leverage AI to create efficient, impactful startups that thrive in a competitive digital economy.

DOI: https://doi.org/10.5281/zenodo.16742455

 

Published by: vikaspatanker

Authors: Randale Vaishnavi, Dr.Swaroopa Shastri

Abstract: Sub-sea image recovery is challenging because water has special optical properties, including absorption and scattering, that compromise the quality of the image. The captured images and videos frequently suffer from two displeasing problems: First, color distortion; and second, poor visibility. This is mainly because that the light is exponentially attenuated while penetrating through water and the strength of attenuation is color dependent. This study introduces a hybrid unsupervised method for underwater image restoration, integrating Support Vector Machines (SVM) with traditional Decision Tree algorithm. The suggested approach utilizes the capabilities of SVM in classification to improve the performance of Decision Tree methods in the restoration process. SVM is used in this approach to classify different underwater environments and conditions to facilitate more accurate utilization of restoration methods appropriate for each class. Decision Tree algorithm, in contrast, adjusts restoration parameters dynamically using the classifications given by the SVM. This hybrid model aims to improve color correction, contrast enhancement, and visibility restoration in underwater imagesResults show that the hybrid SVM and Decision Tree algorithm method surpasses classic Decision Tree algorithms based on visual quality and quantitative measures e.g., Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index (SSIM). The comparison of a SVM And Decision Tree Algorithm, The SVM accuracy is 92% and The Decision Tree Algorithm 98%,The highest accuracy is Decision Tree algorithm.

DOI: http://doi.org/10.5281/zenodo.16742906.

 

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Authors: Vignesh Arumugam

Abstract: – The intersection of artificial intelligence (AI) and preventative health is transforming how nutrition and wellness products are developed, delivered, and personalized. As consumer demand shifts toward proactive and personalized healthcare, AI enables the creation of smarter formulations, data-driven recommendations, and adaptive supplement protocols tailored to individual biology. From analyzing biomarker and microbiome data to predicting nutrient deficiencies in real time, AI tools are redefining the speed and accuracy of innovation in the wellness industry. This article explores how AI is revolutionizing product development, scaling personalization, optimizing supply chains, and reshaping business models within the health and nutrition sector. It also addresses the ethical and regulatory challenges of AI-driven health solutions, offering real-world case studies and future projections. Ultimately, the integration of AI is enabling a shift from generalized wellness offerings to continuous, personalized health optimization—unlocking new opportunities for entrepreneurs, clinicians, and consumers alike.

DOI: https://doi.org/10.5281/zenodo.16742377

 

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Authors: Bhavani Uyyala

Abstract: Workplace burnout is an escalating challenge in today’s high-speed, always-connected professional environments. Traditional stress management solutions often lack personalization, timeliness, and scalability, leaving many employees without effective support. Artificial Intelligence (AI) presents a powerful new avenue for identifying, preventing, and managing burnout through real-time insights and smart automation. By analyzing behavioral, biometric, and communication patterns, AI systems can detect early signs of stress, offer personalized recommendations, and automate routine tasks to reduce cognitive overload. From AI-powered wellness platforms and wearables to intelligent scheduling and sentiment analysis tools, these innovations enable proactive intervention before burnout escalates. However, the ethical use of such technology is critical—ensuring privacy, transparency, and consent remain central to implementation. This article explores how AI-driven tools are reshaping workplace wellness, helping individuals take control of their mental health while empowering organizations to create more sustainable, human-centered work cultures. As we look ahead, AI will not replace human care—it will enhance it, making resilience part of everyday work design.

DOI: https://doi.org/10.5281/zenodo.16742259

 

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Authors: Suresh Gollapudi

Abstract: – This article explores how businesses can drive sustainable growth by leveraging artificial intelligence (AI) across three core dimensions: AI-powered automation, predictive insights, and scalable tools. As markets grow increasingly complex and customer expectations evolve, traditional approaches to scaling are no longer sufficient. AI-powered automation helps reduce operational costs and boost efficiency by handling repetitive tasks. Predictive insights transform decision-making by forecasting outcomes and guiding strategic action, while scalable AI tools ensure that growth does not come at the expense of agility or manageability. The article presents real-world use cases and best practices, demonstrating how organizations—from startups to enterprises—can integrate AI into core functions, break down departmental silos, and build adaptive, future-ready business models. With a forward-looking view on ethical AI use and emerging trends such as generative AI and real-time analytics, the article provides a roadmap for unlocking business growth in a digitally-driven economy.

DOI: https://doi.org/10.5281/zenodo.16742282

 

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Authors: Revathi Bommakanti

Abstract: In today’s creator economy, side hustles are evolving into scalable, revenue-generating ventures. However, many solo entrepreneurs struggle to grow due to limited time, resources, and technical skills. This article explores how no-code AI tools empower side hustlers to automate repetitive tasks, analyze business data, and optimize performance—without needing to write a single line of code. From content generation and customer engagement to sales forecasting and financial tracking, no-code platforms are transforming how small businesses operate. Through practical tools, real-world examples, and common pitfalls to avoid, this guide offers a blueprint for turning passion projects into intelligent, self-sustaining businesses. By strategically integrating AI from the start, side hustlers can build smarter systems, make data-driven decisions, and free up time to focus on creativity, growth, and impact.

DOI: http://doi.org/10.5281/zenodo.16742147

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Authors: Harish Kumaran

Abstract: This article explores how AI-augmented analytics and real-time dashboards are transforming enterprise decision-making. As businesses face increasing complexity, data overload, and the need for instant responsiveness, traditional analytics methods fall short. AI-driven analytics enhances decision-making by automatically uncovering patterns, generating forecasts, and offering prescriptive recommendations, while real-time dashboards provide live visibility into key metrics and operations. Together, these technologies empower organizations to act with speed, precision, and agility. The article covers their core capabilities, integration strategies, implementation challenges, and the evolving role they play in modern business environments. It also looks ahead to the future of enterprise intelligence—where decisions are increasingly autonomous, collaborative, and insight-driven.

DOI: http://doi.org/10.5281/zenodo.16742123

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Authors: Priya Gopalakrishnan

Abstract: This article explores how artificial intelligence (AI) is revolutionizing the retail industry by enhancing personalization, demand forecasting, and inventory optimization. It discusses the limitations of traditional retail approaches and illustrates how AI enables data-driven strategies that improve customer engagement, operational efficiency, and profitability. By leveraging technologies such as machine learning, natural language processing, and predictive analytics, retailers can deliver customized experiences, anticipate market demand with greater accuracy, and optimize stock levels across supply chains. The article also outlines practical implementation strategies, highlights measurable business impacts, and offers a forward-looking perspective on the future of AI in retail.

DOI: http://doi.org/10.5281/zenodo.16742080

Published by: vikaspatanker