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                "unstructured": "Valivarthi, D. T. (2020). Blockchain-powered AI-based secure HRM data management: Machine learning-driven predictive control and sparse matrix decomposition techniques. International Journal of Modern Electronics and Communication Engineering, 8(4), 9-22"
            },
            {
                "key": "ref2",
                "doi": "10.1002/bse.3034",
                "unstructured": "Naz, F., Agrawal, R., Kumar, A., Gunasekaran, A., Majumdar, A., & Luthra, S. (2022). Reviewing the applications of artificial intelligence in sustainable supply chains: Exploring research propositions for future directions. Business Strategy and the Environment, 31(5), 2400- 2423"
            },
            {
                "key": "ref3",
                "doi": "10.30574/wjaets.2020.1.1.0023",
                "unstructured": "Ayyadurai, R. (2020). Smart surveillance methodology: Utilizing machine learning and AI with blockchain for Bitcoin transactions. World Journal of Advanced Engineering Technology and Sciences, 1(1), 110-120"
            },
            {
                "key": "ref4",
                "unstructured": "Narla, S. (2020). Transforming smart environments with multi-tier cloud sensing, big data, and 5G technology. International Journal of Computer Science Engineering Techniques, 5(1), 1-15"
            },
            {
                "key": "ref5",
                "doi": "10.1109/iccit57492.2022.10055057",
                "unstructured": "Hossain, E., Rahman, W., Ekram, A. B., Abedin, R., Roni, N. A., Haque, E.,... & Masum, S. (2022, December). The hybrid machine learning model for next-generation ecofriendly traveling and guides to reduce carbon emissions. In 2022 25th International Conference on Computer and Information Technology (ICCIT) (pp. 436-441). IEEE"
            },
            {
                "key": "ref6",
                "unstructured": "Sareddy, M. R. (2020). Next-generation workforce optimization: The role of AI and machine learning. International Journal of Computer Science Engineering Techniques, 5(5), 1-15"
            },
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                "key": "ref7",
                "doi": "10.46243/jst.2020.v5.i5.pp237-252",
                "unstructured": "Bolla, R. L., & Bobba, J. (2020). Enhancing usability testing through A/B testing, AI-driven contextual testing, and codeless automation tools. Journal of Science and Technology, 5(5), 237-252"
            },
            {
                "key": "ref8",
                "doi": "10.2139/ssrn.4112747",
                "unstructured": "Eyo, E., Abbey, S., & Onyekpe, U. Xgboost Machine Learning Algorithm Utilised for Predictive Modelling of Bearing Capacity of Soils Stabilised by Cementitious Additives’-Enriched Eco-Friendly Pozzolans. Available at SSRN 4112747"
            },
            {
                "key": "ref9",
                "unstructured": "Alagarsundaram, P. (2020). Analyzing the covariance matrix approach for DDoS HTTP attack detection in cloud environments. International Journal of Cloud Security & Cyber Threat Analysis, 10(2), 1-15"
            },
            {
                "key": "ref10",
                "unstructured": "Valivarthi, D. T., Peddi, S., & Narla, S. (2021). Cloud computing with artificial intelligence techniques: BBO-FLC and ABC-ANFIS integration for advanced healthcare prediction Venkat Garikipati, Charles Ubagaram, Narsing Rao Dyavani, Bhagath Singh Jayaprakasam, Hemnath R: HYBRID AI MODELS AND SUSTAINABLE MACHINE LEARNING FOR ECO-FRIENDL Y LOGISTICS, CARBON FOOTPRINT REDUCTION, AND GREEN SUPPLY CHAIN OPTIMIZATION models. International Journal of Cloud Computing & AI in Healthcare, 9(3), 167-182"
            },
            {
                "key": "ref11",
                "doi": "10.1007/s40815-020-00979-7",
                "unstructured": "Yu, Z., & Khan, S. A. R. (2022). Green supply chain network optimization under random and fuzzy environment. International Journal of Fuzzy Systems, 24(2), 1170-1181"
            },
            {
                "key": "ref12",
                "unstructured": "Kethu, S. S. (2020). AI-enabled customer relationship management: Developing intelligence frameworks, AI-FCS integration, and empirical testing for service quality improvement. International Journal of Intelligent Business Systems, 8(2), 1-15"
            },
            {
                "key": "ref13",
                "doi": "10.3390/su13031551",
                "unstructured": "de la Torre, R., Corlu, C. G., Faulin, J., Onggo, B. S., & Juan, A. A. (2021). Simulation, optimization, and machine learning in sustainable transportation systems: models and applications. Sustainability, 13(3), 1551"
            },
            {
                "key": "ref14",
                "unstructured": "Nippatla, R. P. (2019). AI and ML-driven blockchain-based secure employee data management: Applications of distributed control and tensor decomposition in HRM. International Journal of Engineering Research & Science & Technology, 1(1), 1-15"
            },
            {
                "key": "ref15",
                "doi": "10.1007/s11356-022-18711-3",
                "unstructured": "Ahmed, M., Shuai, C., & Ahmed, M. (2022). Influencing factors of carbon emissions and their trends in China and India: a machine learning method. Environmental Science and Pollution Research, 29(32), 48424-48437"
            },
            {
                "key": "ref16",
                "doi": "10.3390/su132413663",
                "unstructured": "Lavercombe, A., Huang, X., & Kaewunruen, S. (2021). Machine learning application to ecofriendly concrete design for decarbonization. Sustainability, 13(24), 13663"
            },
            {
                "key": "ref17",
                "unstructured": "Kalusivalingam, A. K., Sharma, A., Patel, N., & Singh, V. (2022). Leveraging Reinforcement Learning and Genetic Algorithms for Enhanced Optimization of Sustainability Practices in AI Systems. International Journal of AI and ML, 3(9)"
            },
            {
                "key": "ref18",
                "unstructured": "Jadon, R. (2019). Enhancing AI-driven software with NOMA, UVFA, and Dynamic Graph Neural Networks for scalable decision-making. International Journal of Engineering Research & Science & Technology, 7(1)"
            },
            {
                "key": "ref19",
                "unstructured": "chahid, y., chahid, i., & benabdellah, m. (2022). a framework for reducing CO2 emissions and enhancing environmental sustainability protection using IoT and artificial intelligence. Journal of Theoretical and Applied Information Technology, 100(16)"
            },
            {
                "key": "ref20",
                "unstructured": "Kethu, S. S. (2020). AI-enabled customer relationship management: Developing intelligence frameworks, AI-FCS integration, and empirical testing for service quality improvement. International Journal of Customer Analytics and Service Innovation, 10(3), 1-15"
            },
            {
                "key": "ref21",
                "unstructured": "Jadon, R. (2019). Integrating particle swarm optimization and quadratic discriminant analysis in AI-driven software development for robust model optimization. International Journal of Engineering Research & Science & Technology, 19(1), 25-40"
            },
            {
                "key": "ref22",
                "doi": "10.1007/s11356-021-14852-z",
                "unstructured": "Wang, W., & Wang, J. (2021). Determinants investigation and peak prediction of CO2 emissions in China’s transport sector utilizing bio-inspired extreme learning machine. Environmental Science and Pollution Research, 28(39), 55535-55553"
            },
            {
                "key": "ref23",
                "unstructured": "Parthasarathy, K., & Ayyadurai, R. (2020). IoTdriven visualization framework for enhancing business intelligence, data quality, and risk management in corporate financial analytics. International Journal of Financial Data Science, 10(3), 1-15"
            },
            {
                "key": "ref24",
                "doi": "10.3390/su142012990",
                "unstructured": "Mansouri, E., Manfredi, M., & Hu, J. W. (2022). Environmentally friendly concrete compressive strength prediction using hybrid machine learning. Sustainability, 14(20), 12990"
            },
            {
                "key": "ref25",
                "unstructured": "Kadiyala, B. (2021). Data sharing through decentralized cultural co-evolutionary optimization and anisotropic random walks with isogeny-based hybrid cryptography. Journal of Science and Technology, 6(6), 231-245"
            },
            {
                "key": "ref26",
                "doi": "10.1007/978-981-16-7920-9_32",
                "unstructured": "Deák, G., Georgescu, T., Bănică, C. K., Burlacu, I. F., Urloiu, I., & Zakarya, I. A. (2022, January). Green Smart System Based on AI for Ammonia and Hydrogen Eco-Friendly Use in Naval Transport from Protected Wetlands. In Proceedings of the 3rd International Conference on Green Environmental Engineering and Technology: IConGEET 2021, Penang, Malaysia (pp. 275-280). Singapore: Springer Nature Singapore"
            },
            {
                "key": "ref27",
                "unstructured": "Gattupalli, K. (2021). Revolutionizing customer relationship management with multi-modal AI interfaces and predictive analytics. Journal of Science and Technology, 6(1), 167-180"
            },
            {
                "key": "ref28",
                "unstructured": "Nippatla, R. P. (2019). AI and ML-driven blockchain-based secure employee data management: Applications of distributed control and tensor decomposition in HRM. International Venkat Garikipati, Charles Ubagaram, Narsing Rao Dyavani, Bhagath Singh Jayaprakasam, Hemnath R: HYBRID AI MODELS AND SUSTAINABLE MACHINE LEARNING FOR ECO-FRIENDL Y LOGISTICS, CARBON FOOTPRINT REDUCTION, AND GREEN SUPPLY CHAIN OPTIMIZATION Journal of Engineering Research & Science & Technology, 19(1), 1-15"
            },
            {
                "key": "ref29",
                "unstructured": "Swapna Narla (2021) AI-infused cloud solutions in CRM: Transforming customer workflows and sentiment engagement strategies. Journal Name, 15(1)"
            },
            {
                "key": "ref30",
                "doi": "10.1109/access.2021.3087415",
                "unstructured": "Ayyadurai, R. (2020). Big data analytics and demand-information sharing in e-commerce supply chains: Mitigating manufacturer encroachment and channel conflict. International Journal of Science, Engineering and Management, 14(2), 1-15"
            },
            {
                "key": "ref31",
                "doi": "10.1016/j.jclepro.2018.10.124",
                "unstructured": "Noh, J., & Kim, J. S. (2019). Cooperative green supply chain management with greenhouse gas emissions and fuzzy demand. Journal of Cleaner Production, 208, 1421-1435"
            },
            {
                "key": "ref32",
                "doi": "10.1016/j.jclepro.2020.125214",
                "unstructured": "Yachai, K., Kongboon, R., Gheewala, S. H., & Sampattagul, S. (2021). Carbon footprint adaptation on green supply chain and logistics of papaya in Yasothon Province using geographic information system. Journal of Cleaner Production, 281, 125214"
            },
            {
                "key": "ref33",
                "doi": "10.54660/.ijmrge.2023.4.1.751-759",
                "unstructured": "Anaba, D. C., Agho, M. O., Onukwulu, E. C., & Egbumokei, P. I. (2022). A conceptual model for integrating carbon footprint reduction and sustainable procurement in offshore energy operations. Fuel, 16, 4"
            },
            {
                "key": "ref34",
                "unstructured": "Kethu, S. S. (2019). AI-enabled customer relationship management: Developing intelligence frameworks, AI-FCS integration, and empirical testing for service quality improvement. International Journal of HRM and Organizational Behavior, Volume(Issue)"
            },
            {
                "key": "ref35",
                "unstructured": "Jadon, R. (2020). Improving AI-driven software solutions with memory-augmented neural networks, hierarchical multi-agent learning, and concept bottleneck models. International Journal of Modern Electronics and Communication Engineering, 8(2)"
            },
            {
                "key": "ref36",
                "unstructured": "Natarajan, D. R., & Purandhar, N. (2022). Advanced AI techniques in Autism Spectrum Disorder: Applying Hilbert-Huang Transform, Canonical Correlation Analysis, and Discrete Fourier Transform for precision diagnostics. International Journal of Engineering and Techniques, 8(2), 96-105"
            },
            {
                "key": "ref37",
                "unstructured": "Sareddy, M. R. (2020). Next-generation workforce optimization: The role of AI and machine learning. International Journal of Computer Science Engineering Techniques, 5(5), 1-12"
            },
            {
                "key": "ref38",
                "unstructured": "Sareddy, M. R., & Hemnath, R. (2019). Optimized federated learning for cybersecurity: Integrating split learning, graph neural networks, and hashgraph technology. International Journal of Cybersecurity and Intelligent Systems, 7(3), 43-"
            },
            {
                "key": "ref39",
                "doi": "10.1007/s40815-020-00979-7",
                "unstructured": "Yu, Z., & Khan, S. A. R. (2022). Green supply chain network optimization under random and fuzzy environment. International Journal of Fuzzy Systems, 24(2), 1170-1181"
            },
            {
                "key": "ref40",
                "doi": "10.1016/j.jclepro.2019.118984",
                "unstructured": "government intervention, green investment, and customer green preferences in the petroleum industry. Journal of Cleaner Production, 246, 118984"
            },
            {
                "key": "ref41",
                "unstructured": "Kethu, S. S. (2021). AI-Driven Intelligent CRM Framework: Cloud-Based Solutions for Customer Management, Feedback Evaluation, and Inquiry Automation in Telecom and Banking. Journal of Science and Technology, 6(3), 253-271"
            },
            {
                "key": "ref42",
                "unstructured": "Samudrala, V. K., Rao, V. V., Pulakhandam, W., & Karthick, M. (2022). Enhancing urban management systems with advanced hybrid AI models: Integrating federated learning, deep neural networks, and predictive analytics for greater sustainability. International Journal of Multidisciplinary Educational Research, 11(1)"
            },
            {
                "key": "ref43",
                "unstructured": "Sareddy, M. R. (2020). Next-generation workforce optimization: The role of AI and machine learning. International Journal of Computer Science and Engineering, 5(5)"
            },
            {
                "key": "ref44",
                "doi": "10.62643/ijerst.2022.v18.i04.pp73-86",
                "unstructured": "Alagarsundaram, P. (2022). Symmetric keybased duplicable storage proof for encrypted data in cloud storage environments: Setting up an integrity auditing hearing. International Journal of Engineering Research & Science & Technology, 18(4)"
            },
            {
                "key": "ref45",
                "unstructured": "Gollavilli, V. S. B. H. (2021). Convergence of Blockchain, IoT, and Big Data: Driving innovations in e-commerce ecosystems. International Journal of Management Research & Review, 11(2), 1–10"
            },
            {
                "key": "ref46",
                "unstructured": "Narla, S., & Purandhar, N. (2021). AI-infused cloud solutions in CRM: Transforming customer workflows and sentiment engagement strategies. International Journal of Advanced Science, Engineering and Management, 15(1), 57-68"
            },
            {
                "key": "ref47",
                "unstructured": "Boyapati, S., & Kaur, H. (2021). Bridging the urban-rural divide: A data-driven analysis of internet inclusive finance in the e-commerce era. International Journal of Engineering & Science Research, 11(1), 167–186. Venkat Garikipati, Charles Ubagaram, Narsing Rao Dyavani, Bhagath Singh Jayaprakasam, Hemnath R: HYBRID AI MODELS AND SUSTAINABLE MACHINE LEARNING FOR ECO-FRIENDL Y LOGISTICS, CARBON FOOTPRINT REDUCTION, AND GREEN SUPPLY CHAIN OPTIMIZATION"
            },
            {
                "key": "ref48",
                "unstructured": "Jadon, R. (2020). Optimized machine learning pipelines: Leveraging RFE, ELM, and SRC for advanced software development in AI applications. International Journal of Computer Science Engineering Techniques, 8(3), 45-58"
            },
            {
                "key": "ref49",
                "unstructured": "Ayyadurai, R. (2022). Transaction security in e-commerce: Big data analysis in cloud environments. International Journal of Engineering Research, 10(4), 176"
            },
            {
                "key": "ref50",
                "doi": "10.62643/ijerst.2022.v18.i04.pp73-86",
                "unstructured": "Alagarsundaram, P. (2022). Symmetric keybased duplicable storage proof for encrypted data in cloud storage environments: Setting up an integrity auditing hearing. International Journal of Engineering Research & Science & Technology, 18(4)"
            },
            {
                "key": "ref51",
                "doi": "10.1109/access.2020.3000139",
                "unstructured": "Yallamelli, A. R. G., & Sambas, A. (2022). An optimized case-based reasoning approach with MAML and K-Means clustering for AI-driven multi-class workload prediction in autonomic cloud databases and data warehouse systems. ISAR International Journal of Mathematics and Computing Techniques, 7(2), 1-"
            },
            {
                "key": "ref52",
                "unstructured": "Narla, S. (2020). Transforming smart environments with multi-tier cloud sensing, big data, and 5G technology. International Journal of Computer Science Engineering Techniques, 5(1), 1-12"
            },
            {
                "key": "ref53",
                "unstructured": "Gudivaka, R. K., Gudivaka, R. L., & Gudivaka, B. R. (2019). Robotics-driven swarm intelligence for adaptive and resilient pandemic alleviation in urban ecosystems: Advancing distributed automation and intelligent decision-making processes. International Journal of Modern Electronics and Communication Engineering, 7(4), 9-21"
            },
            {
                "key": "ref54",
                "unstructured": "Kethu, S. S. (2020). AI and IoT-driven CRM with cloud computing: Intelligent frameworks and empirical models for banking industry applications. International Journal of Modern Electronics and Communication Engineering, 8(1), 54-67"
            },
            {
                "key": "ref55",
                "doi": "10.30574/wjaets.2020.1.1.0023",
                "unstructured": "Ayyadurai, R. (2020). Smart surveillance methodology: Utilizing machine learning and AI with blockchain for Bitcoin transactions. World Journal of Advanced Engineering Technology and Sciences, 1(1), 110-120"
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                "unstructured": "Valivarthi, D. T., & Purandhar, N. (2021). Blockchain-enhanced HR data management: AI and ML applications with distributed MPC, sparse matrix storage, and predictive control for employee security. International Journal of Advanced Science, Engineering and Management, 15(4), 1-15"
            },
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            },
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                "key": "ref59",
                "unstructured": "Allur, N. S. (2020). Big data-driven agricultural supply chain management: Trustworthy scheduling optimization with DSS and MILP techniques. Journal of Current Science & Humanities, 8(4), 1-16"
            },
            {
                "key": "ref60",
                "doi": "10.62643/ijerst.2022.v18.i04.pp73-86",
                "unstructured": "Alagarsundaram, P. (2020). Symmetric keybased duplicable storage proof for encrypted data in cloud storage environments: Setting up an integrity auditing hearing. International Journal of Cloud Security & Data Management, 10(3), 1-15"
            },
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                "key": "ref61",
                "unstructured": "Basani, D. K. R. (2021). Leveraging robotic process automation and business analytics in digital transformation: Insights from machine learning and AI. International Journal of Engineering Research & Science & Technology, 17(3), 115-133"
            },
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                "key": "ref62",
                "unstructured": "Allur, N. S. (2020). Enhanced performance management in mobile networks: A big data framework incorporating DBSCAN speed anomaly detection and CCR efficiency assessment. International Journal of Advanced Mobile Networks, 8(4), 1-15"
            },
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                "key": "ref63",
                "unstructured": "Dondapati, K. (2020). Integrating neural networks and heuristic methods in test case prioritization: A machine learning perspective. International Journal of Engineering & Science Research, 10(3), 49-61"
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