Daily Archives: June 22, 2026

Authors: Henry Watson, Megan Foster, Ryan Thompson, Elizabeth Walker, Chaitanya Srinivas, Akhilesh Achari

Abstract: The increasing demand for personalized customer experiences, real-time engagement, and data-driven business strategies has accelerated the adoption of Artificial Intelligence (AI) within Customer Relationship Management (CRM) systems. This research examines AI-Driven CRM Automation Architectures for Modern Enterprise Ecosystems, focusing on the integration of machine learning, predictive analytics, intelligent process automation, cloud computing, and generative AI technologies to enhance customer-centric operations. The proposed architectural framework enables organizations to automate customer interactions, optimize sales and marketing processes, improve service delivery, and generate actionable insights from large volumes of customer data. By leveraging AI-powered recommendation engines, natural language processing, customer behavior analytics, and automated workflow orchestration, enterprises can achieve higher operational efficiency, increased customer satisfaction, and improved decision-making capabilities. The study further explores key architectural components, scalability requirements, security considerations, integration strategies, and governance mechanisms necessary for deploying intelligent CRM platforms in complex enterprise environments. Additionally, it highlights the role of AI-driven automation in fostering business agility, strengthening customer relationships, and supporting digital transformation initiatives. The findings indicate that modern AI-enabled CRM architectures provide a scalable and adaptive foundation for intelligent enterprise ecosystems, enabling organizations to enhance customer engagement, drive sustainable growth, and maintain competitive advantage in an increasingly digital and customer-focused marketplace.

DOI: http://doi.org/10.5281/zenodo.20802461

Published by: vikaspatanker

Authors: Abigail Collins, Jonathan Price, Dr. Natalie Stewart, Michael Reed, Chaitanya Srinivas, Akhilesh Achari

Abstract: The rapid advancement of Artificial Intelligence (AI), machine learning, cloud computing, and intelligent automation is transforming traditional enterprises into autonomous, data-driven organizations. This research explores AI-Orchestrated Enterprise Platforms that leverage autonomous decision intelligence to optimize business operations, enhance strategic decision-making, and improve organizational agility. The proposed framework integrates AI-driven analytics, predictive modeling, knowledge graphs, large language models (LLMs), robotic process automation (RPA), and continuous feedback loops to enable real-time decision orchestration across enterprise environments. By combining contextual awareness, adaptive learning, and autonomous execution capabilities, these platforms can proactively identify opportunities, mitigate risks, and automate complex operational workflows with minimal human intervention. The study examines the architectural components, implementation strategies, benefits, and challenges associated with deploying AI-orchestrated enterprise ecosystems, including scalability, governance, security, explainability, and regulatory compliance. Furthermore, it highlights the role of decision intelligence in fostering resilient, self-optimizing, and intelligent enterprises capable of responding dynamically to evolving business conditions. The findings suggest that AI-orchestrated enterprise platforms represent a significant step toward autonomous digital enterprises, enabling enhanced operational efficiency, improved business outcomes, and sustainable competitive advantage in the era of intelligent automation.

DOI: http://doi.org/10.5281/zenodo.20797625

Published by: vikaspatanker

Authors: Olivia Parker, Rebecca Turner, Samantha Green, Katherine Lewis, Chaitanya Srinivas, Akhilesh Achari

Abstract: The emergence of Generative Artificial Intelligence (Generative AI) is transforming software engineering practices by introducing intelligent automation across the software development lifecycle. In cloud computing environments, where applications must continuously evolve to meet dynamic scalability, performance, security, and reliability requirements, traditional software engineering approaches often face challenges related to complexity, resource management, and rapid deployment demands. This research explores the concept of Autonomous Cloud Software Engineering Through Generative AI Technologies, a framework that leverages advanced AI models to automate software design, code generation, testing, deployment, monitoring, maintenance, and optimization processes within cloud platforms. By integrating large language models, machine learning algorithms, cloud-native architectures, and DevOps practices, the proposed approach enables intelligent decision-making, self-adaptive system behavior, and continuous software improvement with minimal human intervention. The framework facilitates automated requirement analysis, intelligent code synthesis, predictive defect detection, infrastructure optimization, and autonomous operational management, thereby enhancing development productivity and software quality. Furthermore, Generative AI-driven automation supports rapid innovation, reduces development costs, accelerates release cycles, and improves system resilience in highly distributed cloud environments. The study examines the architectural components, enabling technologies, implementation strategies, benefits, and challenges associated with autonomous cloud software engineering and highlights its potential to redefine the future of intelligent software development. The findings suggest that the convergence of Generative AI and cloud computing establishes a robust foundation for creating adaptive, scalable, and self-managing software ecosystems capable of meeting the evolving demands of modern digital enterprises.

DOI: http://doi.org/10.5281/zenodo.20797458

Published by: vikaspatanker

Authors: Samantha Green, Richard Morgan, Katherine Lewis, Benjamin Scott, Chaitanya Srinivas, Akhilesh Achari

Abstract: Modern cloud-native applications increasingly rely on microservice architectures to achieve scalability, flexibility, and resilience. However, the growing complexity of distributed environments presents significant challenges in performance management, resource allocation, fault detection, and service coordination. This paper proposes an intelligent self-optimizing microservice framework driven by autonomous feedback loops that continuously monitor, analyze, and adapt system behavior in real time. The framework integrates feedback-driven control models, artificial intelligence techniques, and automated decision-making mechanisms to dynamically optimize service performance, resource utilization, and operational reliability. By leveraging continuous feedback from runtime metrics, system events, and workload patterns, the proposed approach enables proactive adaptation to changing environmental conditions and application demands without human intervention. The study investigates key architectural components, optimization strategies, and autonomous control mechanisms that support self-healing, self-scaling, and self-configuring capabilities within microservice ecosystems. Experimental analysis demonstrates notable improvements in response time, throughput, fault tolerance, and infrastructure efficiency when compared with conventional static management approaches. The results indicate that autonomous feedback-driven optimization provides a robust foundation for developing intelligent, adaptive, and resilient microservice-based systems capable of meeting the demands of modern cloud and edge computing environments.

DOI: http://doi.org/10.5281/zenodo.20797329

Published by: vikaspatanker

Authors: Jennifer Roberts, Rebecca Turner, Victoria Hughes, Richard Morgan, Chaitanya Srinivas, Akhilesh Achari

Abstract: Cloud-native architectures have become the foundation of modern digital applications due to their scalability, flexibility, resilience, and ability to support continuous deployment across distributed computing environments. However, the increasing complexity of microservices, containers, orchestration platforms, and dynamic workloads introduces significant challenges in maintaining system reliability and preventing service disruptions. Traditional reactive maintenance approaches often fail to identify potential failures before they impact application performance and user experience. This research presents a predictive failure analysis framework for reliability engineering in cloud-native architectures that leverages predictive analytics, machine learning algorithms, real-time monitoring, and intelligent fault detection mechanisms to proactively identify and mitigate system failures. The proposed approach continuously analyzes operational metrics, infrastructure logs, service dependencies, and workload patterns to detect anomalies, forecast potential failures, and recommend corrective actions before critical incidents occur. By integrating predictive models with cloud-native reliability engineering practices, the framework supports automated fault diagnosis, resource optimization, resilience enhancement, and service continuity. The study explores key architectural components, predictive techniques, reliability metrics, and implementation strategies for building highly available and fault-tolerant cloud-native systems. Experimental evaluation demonstrates improvements in failure prediction accuracy, system uptime, response performance, and operational efficiency compared to conventional monitoring methods. The findings indicate that predictive failure analysis provides a robust foundation for developing intelligent, adaptive, and resilient cloud-native infrastructures capable of supporting the growing demands of modern enterprise applications and distributed computing ecosystems.

DOI: http://doi.org/10.5281/zenodo.20797204

Published by: vikaspatanker

Authors: Jidnyasa Bagul, Pradnya Dhavan, Project Guide Dr. Nandini Dhole

Abstract: In modern institutions and organizations, effective communication of information is essential, yet traditional notice boards often fail to provide timely updates and accessibility. This project presents a Voice-Based Notice Board system that leverages speech recognition and text-to-speech technologies to automate the process of publishing and delivering notices. The system allows authorized users to input notices through voice commands, which are then converted into text using speech-to-text processing. The processed information is stored in a cloud-based database and can be displayed on a digital screen as well as broadcasted through audio output using text-to-speech synthesis. The integration of Internet of Things (IoT) technology ensures real-time updates and remote accessibility. The proposed system is built using Raspberry Pi, along with a microphone module, speaker system, and display unit. Python is used as the primary programming language, incorporating various speech processing libraries for accurate voice recognition and natural audio output. This solution enhances accessibility, reduces manual effort, and ensures faster dissemination of information. It is particularly useful in environments such as educational institutions, offices, hospitals, and public spaces, where quick and efficient communication is crucial. The system also provides scope for future enhancements, including multilingual support and mobile application integration. Overall, the Voice-Based Notice Board offers a smart, efficient, and user-friendly alternative to traditional notice systems by combining automation, cloud computing, and voice interaction technologies.

Published by: vikaspatanker

Authors: Bharti Saxena, Rupali Chaure, Ashish Chourey, Mohit Singh Tomar

Abstract: Here we introduce an empirical exploration of a real-time Hybrid Deep Learning model for Age and Gender Recognition (HDL-AGR) based on facial images collected from multiple unconstrained scenarios. Estimate age and gender from facial images is a classic computer vision problem with applications ranging from human-computer interaction, intelligent surveillance, personalized marketing to healthcare screening. Most existing approaches are limited by low accuracy on far-side age groups, extreme sensitivity to lighting and occlusion, and extreme computational overhead that would preclude real-time deployment. The proposed HDL-AGR framework consists of a backbone (which has been defined as a modified EfficientNet-B4 convolutional base), attention module (Transformer-based), and an output head (dual-branch, trained jointly for age regression and gender classification) to be tuned up to date. The model is trained and evaluated with five benchmark datasets UTKFace, IMDB-WIKI, Adience, CACD and Fair Face containing over the 845K annotated images. Empirical results: HDL-AGR achieves. (i) A new state-of-the-art Mean Absolute Error (MAE) of 3.94 years in age estimation, along with an unprecedented gender classification accuracy of 97.2% and (ii) Operates at an inference speed of 54 frames per second on standard GPU hardware – outperforming all compared peer methods in the process. The contribution of each architectural component is confirmed through ablation studies. Conclusion: Our results identify HDL-AGR as a strong, efficient, and practically deployable approach for online recognition of facial attributes.

DOI: http://doi.org/10.5281/zenodo.20792037

Published by: vikaspatanker

Authors: Radhika Kulkarni, Tejal Mungase, Prof. Shradha Pawar

Abstract: Choosing the right career path and the right job opportunity has become increasingly difficult in a labour market where industry requirements evolve faster than academic curricula and where the sheer volume of job postings makes manual evaluation impractical for most candidates. This paper presents the AI-Based Career Advisor, an intelligent system designed to help individuals understand how well their resume aligns with a target job description, identify missing skills, and receive concrete, personalized guidance for improving their employability. The system combines a supervised machine learning model with natural language processing and large language model components to deliver this guidance in a single, integrated workflow. At its core is a resume–job description fit classifier trained on 6,241 real-world resume–job pairs sourced from a public dataset, using TF-IDF based feature engineering across 10,012 dimensions. Six candidate algorithms — Logistic Regression, Naive Bayes, Support Vector Machine, Random Forest, a Neural Network, and XGBoost — were trained and compared, with XGBoost emerging as the best-performing model after hyperparameter optimization, achieving 78.14% test accuracy and an 89.57% ROC-AUC score. The system further incorporates a hybrid skill-extraction pipeline built on spaCy's named entity recognition and phrase matching, a GPT-4-based resume enhancement module accessed through LangChain, and supporting modules for learning-resource and project-idea recommendation. The complete pipeline is deployed as an interactive Streamlit web application, giving users real-time predictions and actionable career feedback. This paper discusses the motivation, design, methodology, and evaluation of the system, and outlines directions for extending it into a more comprehensive career guidance platform.

Published by: vikaspatanker