Search and Recommendation Procedure with the Help of Artificial Intelligence

Aravind Reddy Nayani; Alok Gupta; Prassanna Selvaraj; Ravi Kumar Singh; Harsh Vaidya

doi:10.36676/jrps.v10.i4.1503

Authors

Aravind Reddy Nayani Independent Researcher, USA
Alok Gupta Independent Researcher, USA
Prassanna Selvaraj Independent Researcher, USA
Ravi Kumar Singh Independent Researcher, USA
Harsh Vaidya Independent Researcher, USA

DOI:

https://doi.org/10.36676/jrps.v10.i4.1503

Keywords:

Artificial Intelligence, Machine Learning, Natural Language Processing, Search Engines, Recommendation Systems, Collaborative Filtering, Content-Based Filtering, Deep Learning

Abstract

This comprehensive research paper examines the integration of Artificial Intelligence (AI) in search and recommendation systems, focusing on developments. The study delves into various AI techniques, including machine learning algorithms, natural language processing, and deep learning, and their applications in enhancing search procedures and recommendation systems. Through an extensive literature review, analysis of case studies, and examination of current challenges, this paper provides in-depth insights into the state-of-the-art AI-driven search and recommendation procedures. The research also discusses ethical considerations, future trends, and potential innovations in this rapidly evolving field, offering a holistic view of the subject matter for both industry professionals and academic researchers.

References

Carpineto, C., & Romano, G. (2012). A survey of automatic query expansion in information retrieval. ACM Computing Surveys, 44(1), 1-50. https://doi.org/10.1145/2071389.2071390 DOI: https://doi.org/10.1145/2071389.2071390

Goldberg, D., Nichols, D., Oki, B. M., & Terry, D. (1992). Using collaborative filtering to weave an information tapestry. Communications of the ACM, 35(12), 61-70. https://doi.org/10.1145/138859.138867 DOI: https://doi.org/10.1145/138859.138867

He, X., Liao, L., Zhang, H., Nie, L., Hu, X., & Chua, T. S. (2017). Neural collaborative filtering. In Proceedings of the 26th International Conference on World Wide Web (pp. 173-182). https://doi.org/10.1145/3038912.3052569 DOI: https://doi.org/10.1145/3038912.3052569

IKEA. (2017). IKEA Place AR app. https://highlights.ikea.com/2017/ikea-place/

Linden, G., Smith, B., & York, J. (2003). Amazon.com recommendations: Item-to-item collaborative filtering. IEEE Internet Computing, 7(1), 76-80. https://doi.org/10.1109/MIC.2003.1167344 DOI: https://doi.org/10.1109/MIC.2003.1167344

Liu, T. Y. (2009). Learning to rank for information retrieval. Foundations and Trends in Information Retrieval, 3(3), 225-331. https://doi.org/10.1561/1500000016 DOI: https://doi.org/10.1561/1500000016

Lops, P., De Gemmis, M., & Semeraro, G. (2011). Content-based recommender systems: State of the art and trends. In Recommender systems handbook (pp. 73-105). Springer. https://doi.org/10.1007/978-0-387-85820-3_3 DOI: https://doi.org/10.1007/978-0-387-85820-3_3

Lundberg, S. M., & Lee, S. I. (2017). A unified approach to interpreting model predictions. In Advances in Neural Information Processing Systems (pp. 4765-4774). https://papers.nips.cc/paper/7062-a-unified-approach-to-interpreting-model-predictions

McKinsey & Company. (2018). Notes from the AI frontier: Applications and value of deep learning. https://www.mckinsey.com/featured-insights/artificial-intelligence/notes-from-the-ai-frontier-applications-and-value-of-deep-learning

McMahan, H. B., Moore, E., Ramage, D., Hampson, S., & y Arcas, B. A. (2016). Communication-efficient learning of deep networks from decentralized data. arXiv preprintional conference on knowledge discovery and data mining (pp. 1135-1144). https://doi.org/10.1145/2939672.2939778 DOI: https://doi.org/10.1145/2939672.2939778

Robertson, S. E., & Walker, S. (1994). Some simple effective approximations to the 2-poisson model for probabilistic weighted retrieval. In SIGIR '94 (pp. 232-241). Springer. https://doi.org/10.1007/978-1-4471-2099-5_24 DOI: https://doi.org/10.1007/978-1-4471-2099-5_24

Sontag, D., Collins-Thompson, K., Bennett, P. N., White, R. W., Dumais, S., & Billerbeck, B. (2012). Probabilistic models for personalizing web search. In Proceedings of the fifth ACM international conference on Web search and data mining (pp. 433-442). https://doi.org/10.1145/2124295.2124348 DOI: https://doi.org/10.1145/2124295.2124348

Statista. (2019). Search engine market share worldwide. https://www.statista.com/statistics/216573/worldwide-market-share-of-search-engines/

Steck, H. (2018). Calibrated recommendations. In Proceedings of the 12th ACM conference on recommender systems (pp. 154-162). https://doi.org/10.1145/3240323.3240372 DOI: https://doi.org/10.1145/3240323.3240372

Zafar, M. B., Valera, I., Rogriguez, M. G., & Gummadi, K. P. (2017). Fairness constraints: Mechanisms for fair classification. In Artificial Intelligence and Statistics (pp. 962-970). PMLR. http://proceedings.mlr.press/v54/zafar17a.html

Bellapukonda, P., Vijaya, G., Subramaniam, S., & Chidambaranathan, S. (2024). Security and optimization in IoT networks using AI-powered digital twins. In Harnessing AI and Digital Twin Technologies in Businesses (p. 14). https://doi.org/10.4018/979-8-3693-3234-4.ch024

E. A. Banu, S. Chidambaranathan, N. N. Jose, P. Kadiri, R. E. Abed and A. Al-Hilali, "A System to Track the Behaviour or Pattern of Mobile Robot Through RNN Technique," 2024 4th International Conference on Advance Computing and Innovative Technologies in Engineering (ICACITE), Greater Noida, India, 2024, pp. 2003-2005, doi: 10.1109/ICACITE60783.2024.10617430.

Patil, Y. M., Abraham, A. R., Chaubey, N. K., Baskar, K., & Chidambaranathan, S. (2024). A comparative analysis of machine learning techniques in creating virtual replicas for healthcare simulations. In Harnessing AI and Digital Twin Technologies in Businesses (p. 12). https://doi.org/10.4018/979-8-3693-3234-4.ch002

Bellapukonda, P., Vijaya, G., Subramaniam, S., & Chidambaranathan, S. (2024). Security and optimization in IoT networks using AI-powered digital twins. In Harnessing AI and Digital Twin Technologies in Businesses (p. 14). https://doi.org/10.4018/979-8-3693-3234-4.ch024 DOI: https://doi.org/10.4018/979-8-3693-3234-4.ch024

E. A. Banu, S. Chidambaranathan, N. N. Jose, P. Kadiri, R. E. Abed and A. Al-Hilali, "A System to Track the Behaviour or Pattern of Mobile Robot Through RNN Technique," 2024 4th International Conference on Advance Computing and Innovative Technologies in Engineering (ICACITE), Greater Noida, India, 2024, pp. 2003-2005, doi: 10.1109/ICACITE60783.2024.10617430. DOI: https://doi.org/10.1109/ICACITE60783.2024.10617430

Patil, Y. M., Abraham, A. R., Chaubey, N. K., Baskar, K., & Chidambaranathan, S. (2024). A comparative analysis of machine learning techniques in creating virtual replicas for healthcare simulations. In Harnessing AI and Digital Twin Technologies in Businesses (p. 12). https://doi.org/10.4018/979-8-3693-3234-4.ch002 DOI: https://doi.org/10.4018/979-8-3693-3234-4.ch002

George, B., Oswal, N., Baskar, K., & Chidambaranathan, S. (2024). Innovative approaches to simulating human-machine interactions through virtual counterparts. In Harnessing AI and Digital Twin Technologies in Businesses (p. 11). https://doi.org/10.4018/979-8-3693-3234-4.ch018

Charaan, R. M. D., Chidambaranathan, S., Jothivel, K. M., Subramaniam, S., & Prabu, M. (2024). Machine learning-driven data fusion in wireless sensor networks with virtual replicas: A comprehensive evaluation. In Harnessing AI and Digital Twin Technologies in Businesses (p. 11). https://doi.org/10.4018/979-8-3693-3234-4.ch020

Ayyavaraiah, M., Jeyakumar, B., Chidambaranathan, S., Subramaniam, S., Anitha, K., & Sangeetha, A. (2024). Smart transportation systems: Machine learning application in WSN-based digital twins. In Harnessing AI and Digital Twin Technologies in Businesses (p. 11). https://doi.org/10.4018/979-8-3693-3234-4.ch026

Venkatesan, B., Mannanuddin, K., Chidambaranathan, S., Jeyakumar, B., Rayapati, B. R., & Baskar, K. (2024). Deep learning safeguard: Exploring GANs for robust security in open environments. In Enhancing Security in Public Spaces Through Generative Adversarial Networks (GANs) (p. 14). https://doi.org/10.4018/979-8-3693-3597-0.ch009

P. V, V. R and S. Chidambaranathan, "Polyp Segmentation Using UNet and ENet," 2023 6th International Conference on Recent Trends in Advance Computing (ICRTAC), Chennai, India, 2023, pp. 516-522, doi: 10.1109/ICRTAC59277.2023.10480851.

Athisayaraj, A. A., Sathiyanarayanan, M., Khan, S., Selvi, A. S., Briskilla, M. I., Jemima, P. P., Chidambaranathan, S., Sithik, A. S., Sivasankari, K., & Duraipandian, K. (2023). Smart thermal-cooler umbrella (UK Design No. 6329357).approaches to simulating human-machine interactions through virtual counterparts. In Harnessing AI and Digital Twin Technologies in Businesses (p. 11). https://doi.org/10.4018/979-8-3693-3234-4.ch018 DOI: https://doi.org/10.4018/979-8-3693-3234-4.ch018

Charaan, R. M. D., Chidambaranathan, S., Jothivel, K. M., Subramaniam, S., & Prabu, M. (2024). Machine learning-driven data fusion in wireless sensor networks with virtual replicas: A comprehensive evaluation. In Harnessing AI and Digital Twin Technologies in Businesses (p. 11). https://doi.org/10.4018/979-8-3693-3234-4.ch020 DOI: https://doi.org/10.4018/979-8-3693-3234-4.ch020

Ayyavaraiah, M., Jeyakumar, B., Chidambaranathan, S., Subramaniam, S., Anitha, K., & Sangeetha, A. (2024). Smart transportation systems: Machine learning application in WSN-based digital twins. In Harnessing AI and Digital Twin Technologies in Businesses (p. 11). https://doi.org/10.4018/979-8-3693-3234-4.ch026 DOI: https://doi.org/10.4018/979-8-3693-3234-4.ch026

Venkatesan, B., Mannanuddin, K., Chidambaranathan, S., Jeyakumar, B., Rayapati, B. R., & Baskar, K. (2024). Deep learning safeguard: Exploring GANs for robust security in open environments. In Enhancing Security in Public Spaces Through Generative Adversarial Networks (GANs) (p. 14). https://doi.org/10.4018/979-8-3693-3597-0.ch009 DOI: https://doi.org/10.4018/979-8-3693-3597-0.ch009

P. V, V. R and S. Chidambaranathan, "Polyp Segmentation Using UNet and ENet," 2023 6th International Conference on Recent Trends in Advance Computing (ICRTAC), Chennai, India, 2023, pp. 516-522, doi: 10.1109/ICRTAC59277.2023.10480851. DOI: https://doi.org/10.1109/ICRTAC59277.2023.10480851

Athisayaraj, A. A., Sathiyanarayanan, M., Khan, S., Selvi, A. S., Briskilla, M. I., Jemima, P. P., Chidambaranathan, S., Sithik, A. S., Sivasankari, K., & Duraipandian, K. (2023). Smart thermal-cooler umbrella (UK Design No. 6329357).

Krishnateja Shiva. (2024). Natural Language Processing for Customer Service Chatbots: Enhancing Customer Experience. International Journal of Intelligent Systems and Applications in Engineering, 12(22s), 155–164. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/6405

Ashutosh Tripathi, Low-Code/No-Code Development Platforms,

International Journal of Computer Applications (IJCA), 4(1), 2023, pp. 27–35.

https://iaeme.com/Home/issue/IJCA?Volume=4&Issue=1

Ashutosh Tripathi, Optimal Serverless Deployment Methodologies:

Ensuring Smooth Transitions and Enhanced Reliability, Face Mask Detection, Journal

of Computer Engineering and Technology (JCET) 5(1), 2022, pp. 21-28.

Tripathi, A. (2020). AWS serverless messaging using SQS. IJIRAE: International Journal of Innovative Research in Advanced Engineering, 7(11), 391-393. DOI: https://doi.org/10.26562/ijirae.2020.v0711.003

Tripathi, A. (2019). Serverless architecture patterns: Deep dive into event-driven, microservices, and serverless APIs. International Journal of Creative Research Thoughts (IJCRT), 7(3), 234-239. Retrieved from http://www.ijcrt.org

Kumar, A., Dodda, S., Kamuni, N., & Arora, R. K. (2024). Unveiling the impact of macroeconomic policies: A double machine learning approach to analyzing interest rate effects on financial markets. arXiv. https://arxiv.org/abs/2404.07225 DOI: https://doi.org/10.36227/techrxiv.171260001.17596245/v1

Suresh Dodda, Anoop Kumar, Navin Kamuni, et al. Exploring Strategies for Privacy-Preserving Machine Learning in Distributed Environments. TechRxiv. April 18, 2024.

DOI: 10.36227/techrxiv.171340711.17793838/v1 DOI: https://doi.org/10.36227/techrxiv.171340711.17793838/v1

Kumar, A., Ayyalasomayajula, M. M. T., Panwar, D., & Vasa, Y. (2024). Optimizing photometric light curve analysis: Evaluating Scipy's minimize function for eclipse mapping of cataclysmic variables. arXiv. https://doi.org/10.48550/arXiv.2406.00071

Kumar, A., Dodda, S., Kamuni, N., & Vuppalapati, V. S. M. (2024). The emotional impact of game duration: A framework for understanding player emotions in extended gameplay sessions. arXiv. https://doi.org/10.48550/arXiv.2404.00526 DOI: https://doi.org/10.1109/AIIoT58432.2024.10574558

Kumar, A. (2019). Implementation core business intelligence system using modern IT development practices (Agile & DevOps). International Journal of Management, IT and Engineering, 8(9), 444-464. https://doi.org/10.5281/zenodo.1234567

Krishnateja Shiva. (2024). Natural Language Processing for Customer Service Chatbots: Enhancing Customer Experience. International Journal of Intelligent Systems and Applications in Engineering, 12(22s), 155–164. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/6405

Krishnateja Shiva. (2022). Leveraging Cloud Resource for Hyperparameter Tuning in Deep Learning Models. International Journal on Recent and Innovation Trends in Computing and Communication, 10(2), 30–35. Retrieved from https://www.ijritcc.org/index.php/ijritcc/article/view/10980

Shiva, K., Etikani, P., Bhaskar, V. V. S. R., Palavesh, S., & Dave, A. (2022). The rise of robo-advisors: AI-powered investment management for everyone. Journal of Namibian Studies, 31, 201-214.

Etikani, P., Bhaskar, V. V. S. R., Choppadandi, A., Dave, A., & Shiva, K. (2024). Forecasting climate change with deep learning: Improving climate modeling accuracy. African Journal of Bio-Sciences, 6(14), 3903-3918. https://doi.org/10.48047/AFJBS.6.14.2024.3903-3918

Etikani, P., Bhaskar, V. V. S. R., Nuguri, S., Saoji, R., & Shiva, K. (2023). Automating machine learning workflows with cloud-based pipelines. International Journal of Intelligent Systems and Applications in Engineering, 11(1), 375–382. https://doi.org/10.48047/ijisae.2023.11.1.375

Etikani, P., Bhaskar, V. V. S. R., Palavesh, S., Saoji, R., & Shiva, K. (2023). AI-powered algorithmic trading strategies in the stock market. International Journal of Intelligent Systems and Applications in Engineering, 11(1), 264–277. https://doi.org/10.1234/ijsdip.org_2023-Volume-11-Issue-1_Page_264-277

Shiva, K., Etikani, P., Bhaskar, V. V. S. R., Mittal, A., Dave, A., Thakkar, D., Kanchetti, D., & Munirathnam, R. (2024). Anomaly detection in sensor data with machine learning: Predictive maintenance for industrial systems. J. Electrical Systems, 20-10s, 454–462.

Bhaskar, V. V. S. R., Etikani, P., Shiva, K., Choppadandi, A., & Dave, A. (2019). Building explainable AI systems with federated learning on the cloud. Journal of Cloud Computing and Artificial Intelligence, 16(1), 1–14.

Ogeti, P., Fadnavis, N. S., Patil, G. B., Padyana, U. K., & Rai, H. P. (2022). Blockchain technology for secure and transparent financial transactions. European Economic Letters, 12(2), 180-192. http://eelet.org.uk

Vijaya Venkata Sri Rama Bhaskar, Akhil Mittal, Santosh Palavesh, Krishnateja Shiva, Pradeep Etikani. (2020). Regulating AI in Fintech: Balancing Innovation with Consumer Protection. European Economic Letters (EEL), 10(1). https://doi.org/10.52783/eel.v10i1.1810 DOI: https://doi.org/10.52783/eel.v10i1.1810

Krishnateja Shiva, Pradeep Etikani, Vijaya Venkata Sri Rama Bhaskar, Savitha Nuguri, Arth Dave. (2024). Explainable Ai for Personalized Learning: Improving Student Outcomes. International Journal of Multidisciplinary Innovation and Research Methodology, ISSN: 2960-2068, 3(2), 198–207. Retrieved from https://ijmirm.com/index.php/ijmirm/article/view/100

Dave, A., Shiva, K., Etikani, P., Bhaskar, V. V. S. R., & Choppadandi, A. (2022). Serverless AI: Democratizing machine learning with cloud functions. Journal of Informatics Education and Research, 2(1), 22-35. http://jier.org

Dave, A., Etikani, P., Bhaskar, V. V. S. R., & Shiva, K. (2020). Biometric authentication for secure mobile payments. Journal of Mobile Technology and Security, 41(3), 245-259.

Saoji, R., Nuguri, S., Shiva, K., Etikani, P., & Bhaskar, V. V. S. R. (2021). Adaptive AI-based deep learning models for dynamic control in software-defined networks. International Journal of Electrical and Electronics Engineering (IJEEE), 10(1), 89–100. ISSN (P): 2278–9944; ISSN (E): 2278–9952

Narendra Sharad Fadnavis. (2021). Optimizing Scalability and Performance in Cloud Services: Strategies and Solutions. International Journal on Recent and Innovation Trends in Computing and Communication, 9(2), 14–21. Retrieved from https://www.ijritcc.org/index.php/ijritcc/article/view/10889

Varun Nakra. (2023). Enhancing Software Project Management and Task Allocation with AI and Machine Learning. International Journal on Recent and Innovation Trends in Computing and Communication, 11(11), 1171–1178. Retrieved from https://www.ijritcc.org/index.php/ijritcc/article/view/10684

Arth Dave, Lohith Paripati, Venudhar Rao Hajari, Narendra Narukulla, & Akshay Agarwal. (2024). Future Trends: The Impact of AI and ML on Regulatory Compliance Training Programs. Universal Research Reports, 11(2), 93–101. Retrieved from https://urr.shodhsagar.com/index.php/j/article/view/1257

Joel lopes, Arth Dave, Hemanth Swamy, Varun Nakra, & Akshay Agarwal. (2023). Machine Learning Techniques And Predictive Modeling For Retail Inventory Management Systems. Educational Administration: Theory and Practice, 29(4), 698–706. https://doi.org/10.53555/kuey.v29i4.5645

Varun Nakra, Arth Dave, Savitha Nuguri, Pradeep Kumar Chenchala, Akshay Agarwal. (2023). Robo-Advisors in Wealth Management: Exploring the Role of AI and ML in Financial Planning. European Economic Letters (EEL), 13(5), 2028–2039. Retrieved from https://www.eelet.org.uk/index.php/journal/article/view/1514

Akhil Mittal, Pandi Kirupa Gopalakrishna Pandian. (2023). Adversarial Machine Learning for Robust Intrusion Detection Systems. International Journal on Recent and Innovation Trends in Computing and Communication, 11(11), 1459–1466. Retrieved from https://www.ijritcc.org/index.php/ijritcc/article/view/10918

Akhil Mittal, Pandi Kirupa Gopalakrishna Pandian. (2024). Deep Learning Approaches to Malware Detection and Classification. International Journal of Multidisciplinary Innovation and Research Methodology, ISSN: 2960-2068, 3(1), 70–76. Retrieved from https://ijmirm.com/index.php/ijmirm/article/view/94

Mittal, A., & Pandian, P. K. G. (2022). Anomaly detection in network traffic using unsupervised learning. International Journal on Recent and Innovation Trends in Computing and Communication, 10(12), 312. https://www.ijritcc.org

Akhil Mittal. (2024). Machine Learning-Based Phishing Detection: Improving Accuracy and Adaptability. International Journal of Intelligent Systems and Applications in Engineering, 12(22s), 587–595. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/6524

Pavan Ogeti. (2024). Benefits and Challenges of Deploying Machine Learning Models in the Cloud. International Journal of Intelligent Systems and Applications in Engineering, 12(22s), 194–209. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/6409

Pavan Ogeti, Narendra Sharad Fadnavis, Gireesh Bhaulal Patil, Uday Krishna Padyana, Hitesh Premshankar Rai. (2022). Blockchain Technology for Secure and Transparent Financial Transactions. European Economic Letters (EEL), 12(2), 180–188. Retrieved from https://www.eelet.org.uk/index.php/journal/article/view/1283

Ogeti, P., Fadnavis, N. S., Patil, G. B., Padyana, U. K., & Rai, H. P. (2023). Edge computing vs. cloud computing: A comparative analysis of their roles and benefits. Volume 20, No. 3, 214-226.

Fadnavis, N. S., Patil, G. B., Padyana, U. K., Rai, H. P., & Ogeti, P. (2020). Machine learning applications in climate modeling and weather forecasting. NeuroQuantology, 18(6), 135-145. https://doi.org/10.48047/nq.2020.18.6.NQ20194

Narendra Sharad Fadnavis. (2021). Optimizing Scalability and Performance in Cloud Services: Strategies and Solutions. International Journal on Recent and Innovation Trends in Computing and Communication, 9(2), 14–21. Retrieved from https://www.ijritcc.org/index.php/ijritcc/article/view/10889

Gireesh Bhaulal Patil. (2022). AI-Driven Cloud Services: Enhancing Efficiency and Scalability in Modern Enterprises. International Journal of Intelligent Systems and Applications in Engineering, 10(1), 153–162. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/6728

Padyana, U. K., Rai, H. P., Ogeti, P., Fadnavis, N. S., & Patil, G. B. (2023). AI and Machine Learning in Cloud-Based Internet of Things (IoT) Solutions: A Comprehensive Review and Analysis. Integrated Journal for Research in Arts and Humanities, 3(3), 121–132. https://doi.org/10.55544/ijrah.3.3.20

Patil, G. B., Padyana, U. K., Rai, H. P., Ogeti, P., & Fadnavis, N. S. (2021). Personalized marketing strategies through machine learning: Enhancing customer engagement. Journal of Informatics Education and Research, 1(1), 9. http://jier.org

Padyana, U. K., Rai, H. P., Ogeti, P., Fadnavis, N. S., & Patil, G. B. (2023). AI and Machine Learning in Cloud-Based Internet of Things (IoT) Solutions: A Comprehensive Review and Analysis. Integrated Journal for Research in Arts and Humanities, 3(3), 121–132. https://doi.org/10.55544/ijrah.3.3.2 DOI: https://doi.org/10.55544/ijrah.3.3.20

Padyana, U. K., Rai, H. P., Ogeti, P., Fadnavis, N. S., & Patil, G. B. (2024). Predicting disease susceptibility with machine learning in genomics. Letters in High Energy Physics, 2024(20).

Uday Krishna Padyana, Hitesh Premshankar Rai, Pavan Ogeti, Narendra Sharad Fadnavis, & Gireesh Bhaulal Patil. (2024). Server less Architectures in Cloud Computing: Evaluating Benefits and Drawbacks. Innovative Research Thoughts, 6(3), 1–12. https://doi.org/10.36676/irt.v10.i3.1439 DOI: https://doi.org/10.36676/irt.v10.i3.1439

Rai, H. P., Ogeti, P., Fadnavis, N. S., Patil, G. B., & Padyana, U. K. (2024). AI-based forensic analysis of digital images: Techniques and applications in cybersecurity. Journal of Digital Economy, 2(1), 47-61.

Hitesh Premshankar Rai, Pavan Ogeti, Narendra Sharad Fadnavis, Gireesh Bhaulal Patil, & Uday Krishna Padyana. (2024). Integrating Public and Private Clouds: The Future of Hybrid Cloud Solutions. Universal Research Reports, 8(2), 143–153. https://doi.org/10.36676/urr.v9.i4.1320

Hitesh Premshankar Rai, Pavan Ogeti, Narendra Sharad Fadnavis, Gireesh Bhaulal Patil, & Uday Krishna Padyana. (2024). Integrating Public and Private Clouds: The Future of Hybrid Cloud Solutions. Universal Research Reports, 8(2), 143–153. https://doi.org/10.36676/urr.v9.i4.1320 DOI: https://doi.org/10.36676/urr.v9.i4.1320

Ugandhar Dasi. (2024). Developing A Cloud-Based Natural Language Processing (NLP) Platform for Sentiment Analysis and Opinion Mining of Social Media Data. International Journal of Intelligent Systems and Applications in Engineering, 12(22s), 165–174. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/6406

Dasi, U., Singla, N., Balasubramanian, R., Benadikar, S., & Shanbhag, R. R. (2024). Ethical implications of AI-driven personalization in digital media. Journal of Informatics Education and Research, 4(3), 588-593.

Nikhil Singla. (2023). Assessing the Performance and Cost-Efficiency of Serverless Computing for Deploying and Scaling AI and ML Workloads in the Cloud. International Journal of Intelligent Systems and Applications in Engineering, 11(5s), 618–630. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/6730

Challa, S. S., Tilala, M., Chawda, A. D., & Benke, A. P. (2019). Investigating the use of natural language processing (NLP) techniques in automating the extraction of regulatory requirements from unstructured data sources. Annals of PharmaResearch, 7(5), 380-387.

Chaturvedi, R., & Sharma, S. (2024). Implementing Predictive Analytics for Proactive Revenue Cycle Management. Journal for Research in Applied Sciences and Biotechnology, 3(4), 74–78. https://doi.org/10.55544/jrasb.3.4.9 DOI: https://doi.org/10.55544/jrasb.3.4.9

Chaturvedi, R., Sharma, S., Pandian, P. K. G., & Sharma, S. (2024). Leveraging machine learning to predict and reduce healthcare claim denials. Zenodo. https://doi.org/10.5281/zenodo.13268360

Ritesh Chaturvedi. (2023). Robotic Process Automation (RPA) in Healthcare: Transforming Revenue Cycle Operations. International Journal on Recent and Innovation Trends in Computing and Communication, 11(6), 652–658. Retrieved from https://www.ijritcc.org/index.php/ijritcc/article/view/11045

Chaturvedi, R., & Sharma, S. (2022). Assessing the Long-Term Benefits of Automated Remittance in Large Healthcare Networks. Journal for Research in Applied Sciences and Biotechnology, 1(5), 219–224. https://doi.org/10.55544/jrasb.1.5.25 DOI: https://doi.org/10.55544/jrasb.1.5.25

Chaturvedi, R., & Sharma, S. (2022). Enhancing healthcare staffing efficiency with AI-powered demand management tools. Eurasian Chemical Bulletin, 11(Regular Issue 1), 675-681. https://doi.org/10.5281/zenodo.13268360

Dr. Saloni Sharma, & Ritesh Chaturvedi. (2017). Blockchain Technology in Healthcare Billing: Enhancing Transparency and Security. International Journal for Research Publication and Seminar, 10(2), 106–117. Retrieved from https://jrps.shodhsagar.com/index.php/j/article/view/1475