Daily Archives: June 17, 2026

Authors: Swami Siva Mahadev

Abstract: The adoption of Industry 4.0 technologies has increased the need for seamless integration between Manufacturing Execution Systems (MES) and Enterprise Resource Planning (ERP) platforms. SAP Digital Manufacturing (SAP DM), built on the SAP Business Technology Platform (BTP), provides a cloud-based solution for managing and optimizing manufacturing operations. While integration with SAP S/4HANA is supported through standardized mechanisms such as IDocs, APIs, and SAP Cloud Integration, integrating SAP DM with non-SAP ERP systems, including Oracle ERP Cloud, Microsoft Dynamics 365, and Infor CloudSuite, presents additional challenges related to data exchange, interoperability, and process synchronization. This paper proposes a unified four-layer integration framework for connecting SAP Digital Manufacturing with both SAP and non-SAP ERP systems. The framework focuses on master data synchronization, production order management, middleware architecture, security governance, and implementation strategy. By analyzing industry practices and documented integration approaches, the study demonstrates how organizations can establish a scalable and standardized manufacturing integration landscape. The paper also discusses future opportunities in event-driven architectures, artificial intelligence-based production planning, and digital twin technologies.

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Authors: Ogbonna Nnamuchi

Abstract: This paper introduces a formal framework utilizing mathematical functional analysis to bridge the gap between empirical sleep science and jurisprudence. By treating fatigue trajectories as functions within infinite-dimensional Banach spaces, we formalize how biomathematical fatigue inputs intersect with duty-of-care allocations within tort and regulatory systems, shifting the legal focus from rigid shift-hour compliance to systemic accountability.

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Authors: Shravani Phalke, Rajit Joshi, Raj Lohar, Bharti Dhote

Abstract: By facilitating natural, flexible, and context-aware communication across a variety of languages and cultural contexts, artificial intelligence (AI) has revolutionized human-computer interaction. Large language models have advanced, but chatbots still have difficulty identifying, interpreting, and reacting sympathetically to users' emotional states. As a result, they frequently provide generic responses that lack genuine resonance. This paper introduces Novachat, a full-stack AI chatbot designed to close this gap by combining multilingualism and sophisticated emotion intelligence into a scalable MERN-stack architecture. In order to provide human-like, contextually nuanced conversations in English, Hindi, Marathi, and other languages, Novachat's modular framework integrates sentiment analysis, emotion-adaptive response generation, and language detection. To ensure smooth real-time adaptability, each module functions as a microservice and communicates via orchestration driven by APIs. The study describes the system's overall architecture, emotional classification model, dataset organization, and quantitative performance assessment using metrics like System Usability Scale (SUS), emotion recognition accuracy, response relevancy, and user engagement latency. According to experimental results, Novachat generates sympathetic responses and detects emotions with high accuracy; a SUS score indicates strong user acceptance. The field is moving closer to AI systems that genuinely recognize and value the user's emotional experience as a result of these results, which validate Novachat's function as an efficient, inclusive, and emotionally engaging conversational platform.

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Authors: Narasimman S, Jayavarman V, Parandhaman P, Vasanth V, Umavathi. V

Abstract: Rising storage and computational capacities have led to the accumulation of voluminous datasets. These datasets contain insights that describe natural phenomena, usage patterns, trends, and other aspects of complex, real-world systems. We propose greedy K-NN (K-Nearest Neighbor) data allocation strategies (across the agents) that improve the probability of identifying data leakages. These methods do not rely on alterations of the released data (e.g., watermarks). In some cases, we can also inject “realistic but fake” data records to further improve our chances of detecting leakage and identifying the guilty party. Mining large data requires intensive computing resources and data mining expertise, which might be inaccessible to most of the users. With the regularly obtainable cloud computing resources, data mining tasks cannot be stimulated to the cloud or outsourced to the third party to save cost. In this new pattern, data and model confidentiality becomes the major unease to the data owner. Data owners have to understand the possible trade-offs among client-side costs, model quality, and confidentiality to justify outsourcing solutions. In this paper, we propose the RASP Boost framework to address these problems in confidential cloud-based learning. The RASP-Boost approach works with our previous developed Random Space Data Perturbation (RASP) method to protect data confidentiality and uses the boosting framework to conquer the complexity of learning high-class classifiers as of RASP disconcerted data. So, we have to build upsome cloud-client combined boosting algorithms. These algorithms need low client-side calculation and communication expenses. The client does not call for to stay online in the progression of learning models. So, we have methodically studied the confidentiality of data, model, and learning process under a realistic security model.

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Vinod Kumar Jangala
Affiliation	Sr Java  Developer EXPERIENCE Scadea Software Solutions,Texas
Email-Id:	vinodkumarjangala01@gmail.com
Publication: Patents: Soil Testing Equipment for Agriculture Design Number: 6522825. AI Software Performance Monitoring and Optimization Computing Device Design Number: 6501050. Books: AI-Enabled Java Microservices Architecture: Design, Security, and Cloud-Native Deployment. Publications: Jangala, V. K. AgriIntegrixSensor: An integrity-driven intelligent sensing framework for precision agriculture. Web of Semantics: Journal of Interdisciplinary Science, 20 2025. Jangala, V. K. Authentication and authorization mechanisms in Java-based systems. International Journal of Contemporary Research in Multidisciplinary, 3(1) 2024. Jangala, V. K. Comparative analysis of REST and GraphQL APIs in large scale enterprise applications. International Journal of Contemporary Research in Multidisciplinary, 2(1) 2023 Jangala, V. K. AI-enabled Java microservices architecture: Design, security, and cloudnative deployment 2023. Jangala, V. K. Automated data reconciliation framework for enterprise risk management systems. International Journal of Trend in Research and Development, 9(1), 164–169 2022

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Sravika Koukuntla
Affiliation	Full stack Developer, Richardson, Texas.
Email-Id:	sravikakoukuntla01@gmail.com
Publication: Patents: Edge-Enabled Pedestrian Safety Sensor Device Design Number: 6523094   Training and Evaluation Computer Device Design Number: 6500775 . Books: Design and migration of large-scale enterprise applications to cloud-native microservices architectures: A case study. International Journal of Engineering Technology Research & Management. Publications: Koukuntla, S. Performance optimization of full-stack applications using reactive frontend and backend integration. International Journal of Contemporary Research in Multidisciplinary, 4(2) 2025. Koukuntla, S. A novel edge-enabled pedestrian safety behavior sensor for predictive collision prevention. Best Journal of Innovation in Science, Research and Development, 4(2), 22 2025. Koukuntla, S. A self-adaptive architecture for full-stack applications using micro-frontends and cloud-native microservices. International Journal of Research and Analytical Reviews (IJRAR) 2024. Koukuntla, S. Modern full-stack engineering: Designing scalable micro-frontend and cloudnative microservices applications 2024 Koukuntla, S. Micro-frontend architecture for scalable and maintainable enterprise web applications: An empirical architectural evaluation. International Journal of Economy and Innovation, 32 2023

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Authors: Samarth Kumbhar, Viraj Rajendra Patil, Hemant Prashant Chandegave, Vivek Nagargoje

Abstract: For many years beginners tend to invest in stocks and face loss due to volatile nature of markets, or lack of informed decisions like trusting investment through word of mouth, this leads to discouragement from investment in stock market. InvestAi is a platform designed for beginners who are looking to enter the world of Stocks, platform is AI driven forecasting and analysis system designed to help users understand stocks and predictions using “explainable” machine learning techniques. The system aims to increase financial literacy and increase Informed investment decisions via explainable Ai (X AI) and interactive visuals. It also features sentiment analysis of news and also explains how it links or affects a particular stock.

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Vinay Kumar Reddy Vangoor
Affiliation	MetaSoftTech Solutions LLC, Chandler, AZ, USA Client: American Express, Phoenix, AZ, USA Role: System Administrator.
Email-Id:	vinaykumarreddyvangoor@gmail.com
Publication:  Books: Intelligent Autonomous Infrastructure: AI-Driven Self-Evolving Enterprise Systems and DevOps Intelligence. Publications: Vangoor, V. K. R.  Next-gen access control: Blockchain-powered biometric authentication 2025. Vangoor, V. K. R. Predictive cybersecurity for quantum-era data centers using artificial intelligence analytics. International Journal of Scientific Development and Research, 10(9), 16 2025. Madunuri, R., Ravi, C. S., Chitta, S., Bonam, V. S. M., & Vangoor, V. K. R. Machine learning-based anomaly detection for enhancing cybersecurity in financial institutions. In Proceedings of the Asian Conference on Intelligent Technologies (ACOIT) (pp. 1–8) 2024. Vangoor, V. K. R. Intelligent post-quantum cryptography deployment in enterprise Linux infrastructure using machine learning. South Asian Journal of Engineering and Technology, 14(6), 9 2024 Vangoor, V. K. R. Reinforcement learning-based virtual machine orchestration for hybrid OpenStackVMware cloud environments. International Journal of Economy and Innovation, 41, 10 2023

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