Daily Archives: July 4, 2026

Authors: Bandaru Udayasree

Abstract: The rapid growth of e-commerce startups has created significant opportunities for innovation and economic development; however, a large proportion of these ventures fail due to inadequate financial planning and uncertain profitability. Accurate estimation of start-up capital requirements and early prediction of business profitability are therefore essential for entrepreneurs, investors, and financial institutions. This research presents a machine learning-based framework for estimating start-up capital and predicting the profitability of e-commerce startups using historical business and financial data. The proposed system analyzes critical parameters such as funding amount, investment history, operational expenses, revenue projections, market trends, and business characteristics to identify patterns associated with successful and profitable ventures. Multiple machine learning algorithms, including Decision Tree, Random Forest, Gradient Boosting, Logistic Regression, and Multi-Layer Perceptron (MLP), are trained and evaluated to determine the most effective prediction model. Data preprocessing techniques such as feature selection, handling missing values, and normalization are applied to improve model performance and reliability. Experimental results demonstrate that the proposed framework achieves high prediction accuracy, enabling data-driven decision-making for startup planning and investment evaluation. The developed system provides an intelligent decision support tool that assists entrepreneurs in estimating initial capital requirements, assessing business profitability, minimizing financial risk, and improving the likelihood of long-term business success in the competitive e-commerce ecosystem.

DOI: http://doi.org/10.61137/ijsret.vol.12.issue3.503

Published by: vikaspatanker

Authors: Dr. Lakshmi Kunhikrishnan, Dr.J.Elanchezhian, Dr.K.Karnavel, Mrs.P.Aruna, Dr.V.V.Rajasegharan, Mr. Dhianeshwar, J

Abstract: A proper irrigation system is considered as the back bone of agriculture. To provide a channelized irrigation system various irrigation setups and techniques are targeted to meet different yields and for different purposes. Cheyyur has two major types of crops cultivated in two different seasons with two different irrigation setups. The irrigation setups need to be changed whenever the crops are changed. These difficulties not only cause wastage of water but also decreases the overall yield of the crops. To improve the improvement facilities a hybridized setup was modelled fabricated and implements in and around the parts of Cheyyur district to improve their irrigation abilities. The proposed project was accepted by the department of science and technology. The hybridized model was designed and fabricated. Optimization of the flow of the suction pressure was conducted and simulation has been carried out using Genetic Algorithm and performance was analysed using Fminconalgorithm in MATLAB to hybridized and integrate the two different irrigation system. Modelling studies were carried out to improve the suction and the control flow rate of 986 litres per hour to 70 Litres per hours to the agricultural field. This applied optimization technique proves to improve the yield of the crops.

DOI: http://doi.org/10.5281/zenodo.21188506

Published by: vikaspatanker

Authors: Dr.Pavithra Lakshminarasimhan

Abstract: Early infancy is a critical period for brain development, where social interaction plays a foundational role in shaping communication, emotional regulation, and cognitive growth. With increasing shifts toward nuclear family systems and digital engagement, concerns have emerged regarding reduced caregiver-infant interaction. This paper explores the relationship between early social deprivation and developmental outcomes, particularly behaviours resembling autism. While autism is a neurodevelopmental condition with strong genetic underpinnings, this paper emphasises that environmental factors may influence developmental expression without causing autism, often leading to delays or autism-like presentations.

DOI: https://doi.org/10.5281/zenodo.21192947

Published by: vikaspatanker

Authors: Sarah Thompson, Robert Evans, Christopher Walker, Emma Robinson, Chaitanya Srinivas, Sai Nishil

Abstract: The rapid advancement of Artificial Intelligence (AI) is transforming enterprise customer relationship management (CRM) platforms by enabling intelligent automation, predictive analytics, and data-driven decision-making. Salesforce, as a leading cloud-based CRM platform, provides a robust ecosystem for integrating AI technologies that enhance business processes, customer engagement, and operational efficiency. This paper explores the integration of Artificial Intelligence into Salesforce ecosystems to support intelligent business automation across sales, marketing, customer service, and enterprise operations. It examines the role of AI-powered capabilities such as machine learning, natural language processing, predictive modeling, intelligent recommendations, and automated workflow management in optimizing organizational performance. The study also discusses architectural considerations, integration frameworks, data management strategies, security requirements, and governance mechanisms necessary for successful AI adoption within Salesforce environments. Furthermore, the paper analyzes the benefits, challenges, and implementation best practices associated with AI-driven automation initiatives, highlighting their impact on productivity, customer experience, and digital transformation objectives. The findings demonstrate that the strategic integration of AI technologies within Salesforce ecosystems enables organizations to achieve scalable automation, enhanced decision intelligence, improved customer-centric operations, and sustainable competitive advantage in an increasingly digital business landscape.

DOI: http://doi.org/10.5281/zenodo.21187848

Published by: vikaspatanker

Authors: Research Scholar Rakesh Kumar Khillan, Associate Professor Dr. Abhinav Shukla

Abstract: Background: In clinical machine learning, the task of feature selection is frequently stated as a step toward increased accuracy, but a smaller model can be just as useful as it can help to ease the burden of input and give a better global picture of the model. This study compared the performance-compactness trade-off between a full feature Random Forest and a Genetic Algorithm (GA) selected Random Forest in terms of their performance in binary classification of the recorded heart disease status. Data: A public structured dataset with 918 instances, 11 features and a binary target HeartDisease was used. The full-featured Random Forest employed all of the predictors. The binary chromosomes, population size of 20, number of generations of 10, tournament selection, two-point crossover, bit-flip mutation and fitness function of 20-fold Random Forest accuracy are used in a wrapper GA. A subset of 7 predictors was selected and compared to the full-feature model via 10 replications of 20-fold stratified cross-validation. Accuracy, precision, sensitivity, F1-score, ROC-AUC, predictor count and cross-validated permutation importance were measured. Results: The best repeated internal accuracy (87.11% ± 5.06%) and ROC-AUC (0.9285 ± 0.0396) was obtained by the full-feature Random Forest method. The GA-selected model reduced the predictor set from 11 to 7 (36.36%) and achieved accuracy of 83.67% ± 5.45% and ROC-AUC of 0.9075 ± 0.0439. The mean difference of accuracy between the two models in the paired accuracy was −3.44 percentage points in favor of the full-feature model. The largest mean decreases in validation ROC-AUC following permutation was from ST_Slope, followed by ChestPainType and Oldpeak. Conclusions: The evidence was not sufficient to support the assumption which led to the improvement of predictive accuracy through evolutionary features selection. On the contrary, GA has come up with a small, clinically identifiable prototype that had less intraclass discrimination. Thus, the full-featured versus the compact configuration are used in different ways: to maximize predictive performance versus to minimize both user input and global predictor transparency. Prior to clinical-use claims, the features should be fully nested and be externally validated.

DOI: https://doi.org/10.5281/zenodo.21187688

Published by: vikaspatanker

Authors: Ryan Peterson, William Nelson, Joseph Baker, Victoria Adams, Chaitanya Srinivas, Sai Nishil

Abstract: Customer Relationship Management (CRM) systems have become critical platforms for managing customer interactions, business processes, and organizational knowledge in modern enterprises. However, the growing volume, variety, and complexity of customer data often create challenges in deriving meaningful insights and supporting real-time decision-making. Knowledge Graphs have emerged as a powerful technology for representing, connecting, and analyzing heterogeneous data through semantic relationships, enabling organizations to uncover hidden patterns and contextual intelligence. This paper explores the integration of Knowledge Graphs into CRM systems to enhance real-time business intelligence and insight generation. It examines how knowledge graph architectures facilitate data integration, entity resolution, relationship discovery, and semantic reasoning across diverse enterprise information sources. The study further investigates the role of advanced analytics, artificial intelligence, machine learning, and graph-based querying techniques in transforming customer data into actionable business knowledge. Additionally, the paper discusses implementation frameworks, data governance strategies, scalability considerations, security requirements, and integration challenges associated with deploying knowledge graph technologies within CRM environments. The findings indicate that knowledge graph-enabled CRM systems significantly improve customer intelligence, predictive analytics, decision support, personalization, and operational efficiency by providing a unified and context-aware view of enterprise data. The research concludes that the strategic integration of knowledge graphs into CRM ecosystems establishes a robust foundation for intelligent business operations, real-time analytics, and data-driven digital transformation initiatives.

DOI: http://doi.org/10.5281/zenodo.21187613

Published by: vikaspatanker

Authors: Jessica Allen, Nicole Green, Eric Campbell, Justin Perez, Chaitanya Srinivas, Sai Nishil

Abstract: Large Language Models (LLMs) are revolutionizing Customer Relationship Management (CRM) by enabling intelligent, context-aware, and human-like conversational interactions within enterprise platforms such as Salesforce. This research investigates the integration of LLM-driven conversational experiences into Salesforce environments to enhance customer engagement, streamline service operations, and support data-driven decision-making. By leveraging advanced natural language processing, generative AI, and real-time CRM data, conversational systems can provide personalized customer support, automate routine inquiries, assist sales and marketing teams, and improve overall customer experience. The study examines the architectural framework, implementation methodologies, and business value associated with embedding LLM capabilities into Salesforce applications. It also explores critical challenges including data privacy, security, regulatory compliance, model governance, scalability, and responsible AI deployment. Through an analysis of current enterprise practices and emerging technological trends, the research highlights how LLM-powered conversational interfaces improve operational efficiency, increase employee productivity, enhance customer satisfaction, and enable intelligent relationship management. The findings demonstrate that the adoption of generative AI within Salesforce represents a significant step toward next-generation CRM systems that deliver personalized, proactive, and highly responsive customer interactions while supporting organizational growth and digital transformation initiatives.

DOI: http://doi.org/10.5281/zenodo.21187231

Published by: vikaspatanker

Authors: Kevin Walker, Brian Scott, Steven Baker, Aaron Rivera, Chaitanya Srinivas, Sai Nishil

Abstract: The rapid growth of enterprise data assets has increased the need for intelligent data catalog systems that enable efficient data discovery, governance, quality management, and analytical decision-making. Traditional data catalogs often face challenges related to fragmented metadata, inconsistent data definitions, limited visibility across data ecosystems, and manual maintenance processes. This research explores how automated metadata integration and advanced analytics can enhance data catalog intelligence by creating a unified, dynamic, and context-aware framework for managing enterprise data assets. The study examines the role of metadata automation in capturing, classifying, enriching, and synchronizing metadata from diverse data sources, including databases, cloud platforms, data warehouses, business applications, and analytical systems. By leveraging artificial intelligence, machine learning, and metadata analytics, intelligent data catalogs can automatically identify data relationships, assess data quality, improve lineage tracking, and provide actionable insights for business users and data stewards. The research further investigates architectural components, implementation strategies, governance considerations, and scalability challenges associated with intelligent metadata-driven ecosystems. The findings demonstrate that automated metadata integration significantly improves data accessibility, operational efficiency, regulatory compliance, and decision support capabilities while reducing manual effort and governance complexity. As organizations continue to embrace data-driven transformation, intelligent data catalogs powered by automated metadata integration and analytics emerge as essential platforms for maximizing the value, usability, and strategic impact of enterprise data resources.

DOI: http://doi.org/10.5281/zenodo.21186870

Published by: vikaspatanker

Authors: Alexandra Price, Natalie Simmons, Gregory Foster, Stephanie Cook, Chaitanya Srinivas, Sai Nishil

Abstract: The growing complexity of customer interactions and the increasing demand for personalized services have accelerated the adoption of Artificial Intelligence (AI) in Customer Relationship Management (CRM) systems. This research explores the development of AI agent-driven context-aware recommendation systems designed to enhance intelligent customer relationship management through real-time personalization, predictive analytics, and automated decision support. By leveraging advanced AI agents, machine learning algorithms, natural language processing, and contextual data analysis, modern CRM platforms can generate highly relevant recommendations tailored to individual customer preferences, behaviors, purchase histories, and engagement patterns. Context-aware recommendation systems continuously analyze customer interactions across multiple channels to deliver personalized product suggestions, marketing content, service solutions, and engagement strategies that improve customer satisfaction and business performance. The study examines the architectural framework, operational mechanisms, and implementation strategies of AI-powered recommendation systems while addressing critical challenges related to data privacy, scalability, model accuracy, transparency, and ethical AI governance. Furthermore, the research highlights the role of autonomous AI agents in automating customer engagement processes, enhancing decision intelligence, and supporting proactive relationship management. The findings demonstrate that integrating context-aware AI recommendation capabilities into CRM environments significantly improves customer experience, operational efficiency, customer retention, and revenue generation, positioning intelligent recommendation systems as a key component of next-generation digital CRM ecosystems.

DOI: http://doi.org/10.5281/zenodo.21186715

Published by: vikaspatanker