Cognitive RPA: A Framework for Hybridizing Artificial Intelligence with Robotic Process Automation in Enterprise Systems (Published)
This article investigates the convergence of Artificial Intelligence (AI) and Robotic Process Automation (RPA) as a hybrid approach to overcome current limitations in automated processing of unstructured, non-routine business tasks. While traditional RPA excels at rule-based, repetitive processes, it struggles with the ambiguity and complexity inherent in decision-intensive workflows. Through a methodological framework combining theoretical analysis and empirical case studies across multiple industries, this article examines how AI technologies—specifically natural language processing, computer vision, and cognitive computing—can be architecturally integrated with RPA to create more adaptable and intelligent automation systems. The article identifies key integration patterns, implementation challenges, and organizational considerations for successful deployment of hybrid AI-RPA solutions. Findings suggest that properly orchestrated AI-RPA systems demonstrate significant capabilities in handling complex document processing, contextual decision-making, and exception management that neither technology could effectively address independently. The article contributes both theoretical insights into the evolution of intelligent automation and practical guidance for organizations seeking to extend automation beyond structured processes into knowledge-intensive domains.
Keywords: Artificial Intelligence, cognitive automation, intelligent decision support, natural language processing, robotic process automation
Revolutionizing Enterprise Resource Planning Through AI Integration: A Technical Deep Dive (Published)
The integration of Large Language Models (LLMs) in Enterprise Resource Planning (ERP) systems represents a transformative advancement in business process automation. The implementation focuses on four key areas: dynamic data querying through natural language processing, automated workflow communications, intelligent error management, and conversational AI integration. These innovations have revolutionized how organizations interact with their ERP systems, enabling intuitive data access, streamlined workflows, proactive error handling, and enhanced user experiences. The adoption of LLM-enhanced ERP solutions has demonstrated substantial improvements in operational efficiency, system reliability, and user satisfaction while reducing manual intervention and processing times across various industry sectors.
Keywords: Conversational AI, LLM-enhanced ERP systems, error management, natural language processing, workflow automation
Robust detection of LLM-generated text through transfer learning with pre-trained Distilled BERT model (Published)
Detecting text generated by large language models (LLMs) is a growing challenge as these models produce outputs nearly indistinguishable from human writing. This study explores multiple detection approaches, including a Multi-Layer Perceptron (MLP), Long Short-Term Memory (LSTM) networks, a Transformer block, and a fine-tuned distilled BERT model. Leveraging BERT’s contextual understanding, we train the model on diverse datasets containing authentic and synthetic texts, focusing on features like sentence structure, token distribution, and semantic coherence. The fine-tuned BERT outperforms baseline models, achieving high accuracy and robustness across domains, with superior AUC scores and efficient computation times. By incorporating domain-specific training and adversarial techniques, the model adapts to sophisticated LLM outputs, improving detection precision. These findings underscore the efficacy of pretrained transformer models for ensuring authenticity in digital communication, with potential applications in mitigating misinformation, safeguarding academic integrity, and promoting ethical AI usage.
Keywords: Classifier, GenAI, detection, fine tuning, large language models, machine learning, natural language processing, pretraining