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Journal articles on the topic 'AI Scalability'

Author: Grafiati

Published: 7 June 2025

Last updated: 2 August 2025

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1

Pamadi, Vishesh Narendra, and Pushpa Singh. "Edge AI vs Cloud AI: A Comparative Study of Performance Latency and Scalability." International Journal of Research in Modern Engineering & Emerging Technology 13, no. 3 (2025): 13–35. https://doi.org/10.63345/ijrmeet.org.v13.i3.2.

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The increasing need for real-time decision-making and low-latency processing in AI applications has created interest in the comparative study of Edge AI and Cloud AI. Cloud AI, with its high computational power and scalability, has been the choice for data-intensive applications. But with the introduction of IoT devices and the growing need for instant data processing, Edge AI, which processes data locally on devices close to the data source, has become a potential alternative. This shift in paradigm, however, introduces new challenges in balancing the computational power of edge devices with

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Naresh, Lokiny, and Dondlapally Rakesh. "Scalability and Load Balancing in Cloud-Native DevOps with Artificial Intelligence." Journal of Scientific and Engineering Research 10, no. 11 (2023): 187–92. https://doi.org/10.5281/zenodo.13348822.

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In the era of digital transformation, Cloud-Native DevOps has become a cornerstone for modern software development, enabling organizations to achieve agility, efficiency, and scalability. This paper delves into the critical aspects of scalability and load balancing within Cloud-Native DevOps, with a specific focus on the integration of Artificial Intelligence (AI) to enhance these functionalities. Scalability is essential for applications to accommodate fluctuating workloads by dynamically adjusting resources, while load balancing ensures the efficient distribution of traffic across multiple s

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Choudhury, Amit, and Yuvaraj Madheswaran. "Enhancing Cloud Scalability with AI-Driven Resource Management." International Journal of Innovative Research in Engineering and Management 11, no. 5 (2024): 32–39. http://dx.doi.org/10.55524/ijirem.2024.11.5.5.

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This research paper aims at analyzing the factors that can help improve scalability of cloud by incorporating different machine learning algorithms in management of resources. Since controlling and managing cloud resources is becoming more challenging with compounded base requirements, the majority of conventional resource management solutions may not prove adequate. This research assesses the performance of five state-of-art machine learning techniques namely Reinforcement Learning, Long Short-Term Memory, Gradient Boosting Machines, Autoencoders and Neural Architecture Search in minimizing o

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Venkatesh, M. J. Guru. "Decentralizing AI Computing: A Study with IPFS and Public Peer-to-Peer Networks." International Journal for Research in Applied Science and Engineering Technology 12, no. 11 (2024): 331–44. http://dx.doi.org/10.22214/ijraset.2024.65051.

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Ipfs and public peer-to-peer (P2P) networks were adopted to make AI calculations more decentralized. Taking AI workloads over a decentralized network could bring better fault tolerance, guarantee data safety, and increased privacy. This research explores the challenges of centralized AI, such as data confidentiality, scalability, and accessibility, while discussing the promise of decentralized AI. Combining IPFS with decentralized systems improves scalability, data protection, and fault tolerance.

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Siva, Karthik Devineni. "AI Data Quality Copilots: Enhancing Intelligent Systems with Real-Time Data Integrity, Scalability, and Ethical AI Practices." Journal of Scientific and Engineering Research 11, no. 10 (2024): 8–26. https://doi.org/10.5281/zenodo.13991320.

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AI has proven to be a quickly growing field; hence, data quality is essential when attaining proper, equitable, and lasting AI solutions. Data quality is critically important as organizations rely more on AI for their decisions, training, and decisions. This paper aims to establish AI Data Quality Copilots; sophisticated systems focused on addressing data-related issues through automatic real-time data quality evaluation and enhancement. Future issues – data drift, privacy, and inclusion – as verified by AI Data Quality Copilots will remain non-triggering for the AI model's reliabi

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Oluwatumininu Anne Ajayi. "Scalability challenges in implementing artificial intelligence in supply chain networks." World Journal of Advanced Research and Reviews 15, no. 1 (2022): 858–61. https://doi.org/10.30574/wjarr.2022.15.1.0737.

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The integration of Artificial Intelligence (AI) in supply chain networks promises transformative improvements in operational efficiency, predictive accuracy, and risk mitigation. From demand forecasting to autonomous logistics, AI applications hold significant potential to redefine traditional supply chain paradigms. However, despite successful pilot implementations, the journey from local adoption to enterprise-wide and global deployment remains fraught with obstacles. This paper examines the multifaceted scalability challenges associated with AI integration in supply chains, analyzing techni

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Yijie Weng and Jianhao Wu. "Database management systems for artificial intelligence: Comparative analysis of postgre SQL and MongoDB." World Journal of Advanced Research and Reviews 25, no. 2 (2025): 2336–42. https://doi.org/10.30574/wjarr.2025.25.2.0586.

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The rapid evolution of artificial intelligence (AI) has amplified the need for efficient database management systems (DBMS) to handle the growing volume, variety, and velocity of data. PostgreSQL, a robust relational database, and MongoDB, a leading NoSQL solution, are two widely adopted DBMSs in AI applications, each offering unique advantages. This paper provides a comprehensive comparative analysis of PostgreSQL and MongoDB, focusing on their suitability for AI use cases. Key evaluation criteria include data modeling, query complexity, scalability, ACID compliance, indexing, and integration

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Meng, Xianghui. "Optimization of algorithmic efficiency in AI: Addressing computational complexity and scalability challenges." Applied and Computational Engineering 45, no. 1 (2024): 305–11. http://dx.doi.org/10.54254/2755-2721/45/20241637.

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This study explores the efficiency and scalability challenges present in artificial intelligence (AI) algorithms, with particular consideration given to computational complexity issues and optimization strategies. This guide reviews key algorithms such as gradient descent and genetic algorithms in depth to highlight their roles in increasing AI efficiency. Through an exhaustive literature review, this paper highlights significant advancements in algorithmic design - parallelization and optimized data structures are among those highlighted - while their application can be seen in diverse situat

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Aravind Nuthalapati. "Scaling AI Applications on the Cloud toward Optimized Cloud-Native Architectures, Model Efficiency, and Workload Distribution." International Journal of Latest Technology in Engineering Management & Applied Science 14, no. 2 (2025): 200–206. https://doi.org/10.51583/ijltemas.2025.14020022.

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Abstract: The rapid growth of Artificial Intelligence (AI) has increasefd the demand for scalable, efficient, and cost-effective computational infrastructure. Traditional on-premise systems face limitations in scalability, resource allocation, and cost efficiency, making cloud computing a preferred solution. This paper examines cloud-native architectures, including containerization, Kubernetes orchestration, serverless computing, and microservices, as key enablers of AI scalability. Modern approaches for optimizing AI models involve using quantization and pruning and knowledge distillation app

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Adekunle, Omotoso Kazeem, Eretan Gbenga Ologbon, and Salami Jamiu Abiodun. "Artificial Intelligence (AI) Adoption and Startup Success Rates." International Journal of Research and Innovation in Social Science VIII, no. XI (2024): 3032–43. https://doi.org/10.47772/ijriss.2024.8110235.

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This study investigates the relationship between Artificial Intelligence (AI) integration and critical startup success metrics, focusing on revenue growth and product development. Startups, often operating with limited resources, face challenges such as market competition, resource constraints, and innovation pressures. AI tools, including predictive analytics, personalization engines, and scalability platforms, have emerged as game-changers in addressing these issues. Leveraging a mixed-methods approach, this study evaluates AI’s impact on revenue growth through enhanced customer insights, dy

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Yuan, Fujiang, Zihao Zuo, Yang Jiang, et al. "AI-Driven Optimization of Blockchain Scalability, Security, and Privacy Protection." Algorithms 18, no. 5 (2025): 263. https://doi.org/10.3390/a18050263.

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With the continuous development of technology, blockchain has been widely used in various fields by virtue of its decentralization, data integrity, traceability, and anonymity. However, blockchain still faces many challenges, such as scalability and security issues. Artificial intelligence, with its powerful data processing capability, pattern recognition ability, and adaptive optimization algorithms, can improve the transaction processing efficiency of blockchain, enhance the security mechanism, and optimize the privacy protection strategy, thus effectively alleviating the limitations of bloc

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Bershchanskyi, Yevhen, Halyna Klym, and Yurii Shevchuk. "Containerized Artificial Intelligent System Design in Cloud and Cyber-Physical Systems." Advances in Cyber-Physical Systems 9, no. 2 (2024): 151–57. https://doi.org/10.23939/acps2024.02.151.

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The integration of Artificial Intelligence (AI) into cloud computing and Cyber-Physical Systems (CPS) is crucial for achieving efficiency, scalability, and real-time capabilities in modern ecosystems. Containerization enhances AI deployment by improving portability, resource efficiency, and system isolation. This article addresses key design considerations and challenges in implementing con- tainerized AI within cloud-native and CPS environments, focusing on scalability, fault tolerance, real-time respon- siveness, and security. Through research analysis and case studies, it explores strategie

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Timms, Alexander, Abigail Langbridge, Antonis Antonopoulos, Antonis Mygiakis, Eleni Voulgari, and Fearghal O'Donncha. "Agentic AI for Digital Twin." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 28 (2025): 29703–5. https://doi.org/10.1609/aaai.v39i28.35373.

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The complexity of the shipping industry, dynamic operational drivers, and diverse data sources present significant scalability challenges for digital twins. Agentic Large Language Models (LLMs) augmented with external tools offer a promising solution to accelerate digital twin adoption. Using pre-trained knowledge and reasoning capabilities, these LLMs autonomously select optimal tools and data streams for user-specific queries, enabling language to serve as a universal interface between digital twins and various stakeholders, from technicians to fleet managers. This interface facilitates real

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Mahender Singh. "AI-Driven Cross-Blockchain Automation for Serverless Quantum Workflows." Journal of Information Systems Engineering and Management 10, no. 32s (2025): 79–91. https://doi.org/10.52783/jisem.v10i32s.5190.

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This research paper investigates the way that AI driven cross chain interoperability for blockchain technology has the transformative potential. It analyzes of performance, cost efficiency and scalability between classical and quantum approaches. Results demonstrate that Hybrid AI Driven is more efficient in terms of throughput, cost reduction, and scalability compared to other approaches used in blockchain, and thus can contribute to transforming blockchain into one of many available application options.

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Rajesh Kesavalalji. "Optimizing Distributed AI Workloads in Cloud Environments: A Hybrid Scheduling and Resource Allocation Approach." World Journal of Advanced Research and Reviews 23, no. 1 (2024): 3137–49. https://doi.org/10.30574/wjarr.2024.23.1.2030.

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Improving performance, scalability and cost efficiency of distributed AI workloads in cloud environment is impossible without optimization. Currently, traditional scheduling and resource allocation methods have shown that they are not able to meet the needs of resources and dynamic characteristics of the AI application. The research discussed in this study aims to identify hybrid scheduling techniques that incorporate static and dynamic strategies, heuristic based techniques, and AI based approaches, for example, reinforcement learning, to optimize work distribution. Furthermore, mechanisms of

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Shekhawat, Ashok Singh, and Happa Khan. "AI Enabled Cloud Computing Pipeline: Architectural Framework, Challenges and Future Directions." Turkish Journal of Computer and Mathematics Education (TURCOMAT) 11, no. 1 (2020): 1100–1104. http://dx.doi.org/10.61841/turcomat.v11i1.14408.

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Cloud computing has converted the landscape of cutting-edge IT infrastructure, presenting scalability and costefficiency. Simultaneously, synthetic intelligence (AI) has developed to enable machines to carry out duties that require humanlike intelligence. This studies paper explores the intersection of AI and cloud computing, focusing at the architectural framework of AI-enabled cloud computing pipelines. These pipelines encompass crucial levels along with statistics ingestion, preprocessing, version schooling, deployment, and tracking. Challenges on this area, along with records privacy, prot

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SERGIUS D, ROFINA. "AI-Powered Project Management and Reporting System." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 03 (2025): 1–9. https://doi.org/10.55041/ijsrem42174.

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Project management is a critical aspect of any organization, requiring efficient tracking of tasks, milestones, and team collaboration. Traditional project management systems often lack automation in reporting and data analysis. This paper presents an AI-powered Project Management and Reporting System that integrates MySQL for structured data storage, utilizes the LLM for AI-based report generation, and employs Python-docx for professional document creation. The system automates the generation of structured reports, enhances project tracking, and facilitates efficient communication among stake

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Surya Kiran, Arjun Kumar, and Swathi Chukkala. "Decentralized AI at the Edge: Federated Learning, Quantum Optimization and IoT Scalability." International Journal of Science and Research Archive 14, no. 3 (2025): 256–63. https://doi.org/10.30574/ijsra.2025.14.3.0633.

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Decentralized artificial intelligence (AI) at the edge marks a revolutionary evolution in computing, enabling efficient, privacy-preserving, and scalable solutions tailored for the Internet of Things (IoT). This paper integrates cutting-edge advancements in federated learning (FL), quantum optimization, and scalable IoT architectures to propose a cohesive framework for next-generation edge AI systems. We conducted an extensive literature review covering privacy-focused decentralized AI, quantum-enhanced optimization methods, and IoT system scalability. Our research highlights significant enhan

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Srinivasa, Chakravarthy Seethala. "Cloud and AI Convergence in Banking & Finance Data Warehousing: Ensuring Scalability and Security." European Journal of Advances in Engineering and Technology 9, no. 3 (2022): 190–92. https://doi.org/10.5281/zenodo.14168767.

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In the banking and finance sector, the integration of cloud computing and artificial intelligence (AI) technologies within data warehousing solutions is revolutionizing data management, processing, and security. This convergence is essential not only for handling complex datasets but also for meeting the growing demands for scalability and enhanced security—both critical to modern financial systems. This article examines how cloud-AI fusion addresses unique challenges in banking data warehousing, focusing on strategies to ensure scalability and secure sensitive financial data. By explori

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Naveen Kodakandla. "Scaling AI responsibly: Leveraging MLOps for sustainable machine learning deployments." International Journal of Science and Research Archive 13, no. 1 (2024): 3447–55. https://doi.org/10.30574/ijsra.2024.13.1.1798.

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As artificial intelligence (AI) has now become an integral part of many industries and amended their business processes, we have seen both technologies pushing innovation forward and at the same time posing some severe scaling issues with resource utilization and distributing access ethically. One can learn to execute AI systems at scale while optimizing performance, governance, and possibly sustainability simultaneously. In this article, Machine Learning Operations (MLOps) is investigated as a means to support sustainable (and ethical) scalability of AI through workflow streamlining, resource

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Umamaheswarareddy Chintam. "Optimizing EAI with AI and Cloud-Native Platforms : A Comparative Study of Popular Integration Frameworks." International Journal of Scientific Research in Computer Science, Engineering and Information Technology 11, no. 2 (2025): 405–15. https://doi.org/10.32628/cseit25112373.

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The integration of artificial intelligence with cloud-native platforms represents a transformative approach to Enterprise Application Integration, offering enhanced efficiency, scalability, and adaptability for modern businesses. This article examines the role of AI in optimizing EAI processes, with particular focus on its implementation across major cloud-native integration frameworks including SAP Cloud Platform Integration, MuleSoft, and Apache Camel. Through comparative analysis, this article evaluates how AI capabilities are embedded within these platforms to enhance data processing, work

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Researcher. "SCALABLE AI-DRIVEN MICROSERVICES ARCHITECTURES FOR DISTRIBUTED CLOUD ENVIRONMENTS." International Journal of Computer Engineering and Technology (IJCET) 15, no. 6 (2024): 154–68. https://doi.org/10.5281/zenodo.14053729.

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This article presents a comprehensive approach to designing scalable AI-driven microservices architectures for distributed cloud environments. It explores key challenges in integrating AI into distributed systems and proposes strategies for microservices design, deployment, and scaling of AI workloads. The article covers data pipeline optimization, security, and compliance considerations and presents a detailed case study of a scalable image recognition service. Through analysis of scalability, efficiency, and robustness, the proposed architecture demonstrates significant improvements over tra

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Venkataraman, Gangadharan. "Operational Excellence in Real-Time AI Systems: Observability, Experimentation, and Scalability." European Journal of Computer Science and Information Technology 13, no. 47 (2025): 61–74. https://doi.org/10.37745/ejcsit.2013/vol13n476174.

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Operational excellence in real-time AI systems requires sophisticated practices beyond model performance metrics. As organizations integrate AI deeper into critical business functions, the need for robust operational frameworks becomes paramount. This article presents key strategies for achieving production-grade reliability in AI systems through three essential pillars: observability, experimentation, and scalability. The observability section details techniques for monitoring both system health and model performance, including drift detection and integration with business metrics. The experi

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Srinivasa, Chakravarthy Seethala. "AI-Driven Modernization of Energy Sector Data Warehouses: Enhancing Performance and Scalability." European Journal of Advances in Engineering and Technology 10, no. 8 (2023): 90–92. https://doi.org/10.5281/zenodo.14168828.

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In recent years, the energy sector has experienced a transformative shift in data management, fueled by artificial intelligence (AI) and advanced data warehousing. As energy companies embrace more complex data sources—ranging from smart grid data to IoT-driven sensor networks—the need for robust, scalable, and high-performing data warehouses has intensified. This paper examines the implementation of AI in the modernization of energy data warehouses, focusing on performance optimization, data scalability, and operational efficiency. Using legacy-to-modernization frameworks and AI-dr

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Kumar, Dr R. G. Suresh. "AI-Chatbot for Disease Prediction." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 06 (2025): 1–8. https://doi.org/10.55041/ijsrem50664.

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Abstract This survey explores the role of AI-powered medical chatbots in disease prediction, with a focus on addressing the limitations of earlier models based on Long Short-Term Memory (LSTM) networks. These earlier systems often suffer from restricted disease coverage and suboptimal prediction accuracy. To overcome these issues, the use of Recurrent Neural Networks (RNNs) is proposed, as they offer improved handling of sequential patient data and the ability to generate more precise health-related responses. The review also highlights the importance of integrating comprehensive disease datas

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Shreyam Dutta Gupta. "EDRA: A hybrid architecture for scalable and real-time AI applications." International Journal of Science and Research Archive 13, no. 2 (2024): 3724–34. https://doi.org/10.30574/ijsra.2024.13.2.2611.

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Scalability, low latency, and fault tolerance are critical requirements for modern software systems, especially for AI-powered applications like chatbots and real-time fraud detection. Modern systems often fail to balance real-time processing with scalability, leading to bottlenecks and poor fault tolerance. Traditional architectures, such as Request-Driven (RD) and Event-Driven (ED) models, provide limited scalability and fault tolerance under dynamic workloads. To address these limitations, this paper evaluates a widely used hybrid model, Event-Driven Request Architecture (EDRA), that integr

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Richardson, Nicholas, Srinikhita Kothapalli, Abhishake Reddy Onteddu, RamMohan Reddy Kundavaram, and Rajasekhar Reddy Talla. "AI-Driven Optimization Techniques for Evolving Software Architecture in Complex Systems." ABC Journal of Advanced Research 12, no. 2 (2023): 71–84. https://doi.org/10.18034/abcjar.v12i2.783.

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This work uses AI-driven optimization to improve software design in complex systems by addressing scalability, flexibility, and performance while balancing conflicting goals. AI methods, including machine learning, reinforcement learning, and evolutionary algorithms, are studied to optimize architectural design and adaption in dynamic situations. The research synthesizes literature, case studies, and technical reports to assess AI-driven methodologies and find gaps in current practices using secondary data. AI approaches improve software system flexibility, scalability, and efficiency, especia

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Vidhan, Shah, Wan Boyan, and Chandrashekhar Shiva. "Scaling Complexity Developing Large-Scale AI-Driven Products for Web, Mobile, and Voice Applications." Sarcouncil Journal of Engineering and Computer Sciences 4, no. 1 (2025): 26–32. https://doi.org/10.5281/zenodo.15053020.

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The rapid proliferation of artificial intelligence (AI) has revolutionized the development of large-scale products across web, mobile, and voice platforms. This study explores the challenges and strategies associated with scaling AI-driven applications, focusing on performance optimization, resource utilization, and ethical considerations. Through a mixed-methods approach, we analyzed 50 AI-driven products, evaluating key metrics such as latency, scalability, user satisfaction, and algorithmic fairness. Results revealed significant differences across platforms, with voice applications demonstr

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Soldati, Pablo, Euhanna Ghadimi, Burak Demirel, Yu Wang, Mathias Sintorn, and Raimundas Gaigalas. "Approaching AI-native RANs through generalization and scalability of learning." Ericsson Technology Review 2023, no. 3 (2023): 2–12. http://dx.doi.org/10.23919/etr.2023.10068317.

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Malhotra, Shubham. "AI-DRIVEN AUTOMATION IN DEVOPS: ENHANCING SCALABILITY AND OPERATIONAL EFFICIENCY." International Journal of Multidisciplinary Research and Technology 6, no. 1 (2025): 29–31. https://doi.org/10.5281/zenodo.14913006.

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Akaash Vishal Hazarika and Mahak Shah. "Blockchain-based Distributed AI Models: Trust in AI model sharing." International Journal of Science and Research Archive 13, no. 2 (2024): 3493–98. https://doi.org/10.30574/ijsra.2024.13.2.2598.

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This paper explores the intersection of blockchain technology and dis- tributed artificial intelligence (AI) models. We analyze how blockchain can be utilized to secure AI model sharing and training processes in distributed environments, thereby enhancing trust and accountability. Key concepts such as decentralized model training, data provenance, incentivization mechanisms, and the role of smart contracts are discussed in detail. Moreover, the paper examines ethical implications and regulatory challenges inherent in this integration. Potential future research directions are outlined, emphasiz

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Researcher. "ARTIFICIAL INTELLIGENCE IN DATA INTEGRATION: ADDRESSING SCALABILITY, SECURITY, AND REAL-TIME PROCESSING CHALLENGES." International Journal of Engineering and Technology Research (IJETR) 9, no. 2 (2024): 130–44. https://doi.org/10.5281/zenodo.13735941.

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This article examines the pivotal role of Artificial Intelligence (AI) in addressing data integration challenges faced by large enterprises. As organizations grapple with an ever-increasing volume and diversity of data sources, AI and Machine Learning (ML) technologies are emerging as critical solutions for efficient data management. The article explores three primary forms of data integration—consolidation, virtualization, and propagation—and their significance in contemporary data environments. It analyzes how AI techniques are revolutionizing data mapping, quality enhanceme

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Jamaludin, Haris, Unang Achlison, and Nur Rokhman. "Enhancing AI Model Accuracy and Scalability Through Big Data and Cloud Computing." Journal of Technology Informatics and Engineering 3, no. 3 (2024): 296–307. https://doi.org/10.51903/jtie.v3i3.203.

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Data's exponential growth and cloud computing advancements have significantly impacted artificial intelligence (AI) model development. This study investigates how big data techniques integrated with cloud computing enhance the scalability and accuracy of AI models across sectors such as healthcare, business, and cybersecurity. Adopting a qualitative methodology, the research examines secondary data from 2020–2024, including case studies and literature. Key findings reveal that cloud computing enables large-scale data processing with significant efficiency, achieving average speeds of 20–45 sec

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Boya Marqas, Ridwan, Saman M. Almufty, Prof Dr ENGİN AVCI, and Renas R. Asaad. "Optimizing Artificial Intelligence Systems for Real-World Applications." International Journal of Scientific World 11, no. 1 (2025): 40–47. https://doi.org/10.14419/xxc0jx38.

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The optimization of Artificial Intelligence (AI) systems is critical for improving performance, scalability, and adaptability across various real-world applications. This paper explores key optimization techniques, including algorithmic enhancements, hardware acceleration, software tools, and data preprocessing. Challenges such as resource constraints, domain-specific requirements, and ethical concerns are analyzed. Case studies in healthcare, finance, manufacturing, and autonomous systems demonstrate notable improvements in accuracy, efficiency, and scalability. A systematic framework is prop

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R Charmika, R Navya, C Vijay, G Madhusudhan, L Bhavana, and M Maheshkumar. "AI-Driven Automation for Green Buildings and Sustainable Agriculture: Enhancing Efficiency, Scalability, and Resource Management." International Research Journal of Innovations in Engineering and Technology 09, Special Issue (2025): 97–103. https://doi.org/10.47001/irjiet/2025.inspire16.

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The integration of artificial intelligence (AI), machine learning (ML), and the Internet of Things (IoT) is transforming green buildings and sustainable agriculture by enhancing energy efficiency, predictive maintenance, and resource optimization. This study presents an AIdriven framework incorporating Ensemble Learning, Transfer Learning, and Federated Learning to improve decision-making while ensuring privacy and scalability. Edge AI and IoT enable real-time automation, reducing cloud dependency and operational costs. AI-powered HVAC systems optimize energy use in smart buildings, while AI-I

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Surendra Vitla. "The Future of Identity and Access Management: Leveraging AI for Enhanced Security and Efficiency." Journal of Computer Science and Technology Studies 6, no. 3 (2024): 136–54. https://doi.org/10.32996/jcsts.2024.6.3.12.

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As organizations face increasingly complex security challenges, the integration of Artificial Intelligence (AI) in Identity and Access Management (IAM) systems has emerged as a transformative solution. This paper explores the multifaceted role of AI in enhancing IAM systems, focusing on key capabilities such as anomaly detection, continuous improvement, scalability, regulatory compliance, and access management processes. AI-driven systems enhance security by enabling real-time anomaly detection, adaptive learning, and automated responses to evolving threats. They improve scalability and perfor

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Researcher. "THE INTEGRATION OF GENERATIVE AI IN RPA FOR ENHANCED INSURANCE CLAIMS PROCESSING." Journal of Advanced Research Engineering and Technology (JARET) 3, no. 2 (2024): 38–52. https://doi.org/10.5281/zenodo.14274780.

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This research explores the integration of generative AI with Robotic Process Automation (RPA) to optimize insurance claims processing. By autonomously generating and processing complex claims, generative models streamline the handling of intricate scenarios [1]. The use of synthetic data for training RPA systems allows for greater accuracy in managing rare or nuanced claims, improving scalability and operational efficiency [2]. The study demonstrates how combining generative AI with RPA can significantly increase efficiency, accuracy, and scalability, optimizing insurance operations while redu

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Nida, Bhanu Raju. "The transformative impact of data products: The foundation for scalability and AI success." International Journal of Multidisciplinary Research and Growth Evaluation 6, no. 1 (2025): 1902–6. https://doi.org/10.54660/.ijmrge.2025.6.1-1902-1906.

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In an increasingly data-driven world, data products serve as the foundation for scalability and are critical to the success of Artificial Intelligence (AI) systems. These products transform raw data into valuable insights, tools, and automated processes that empower organizations to thrive in complex, competitive environments. This paper examines the transformative role of data products, emphasizing their contributions to scalability, AI development, business intelligence, process optimization, and regulatory compliance. With their ability to drive innovation, enhance decision-making, and futu

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Abdelhamid, Mohamed Moetez, Layth Sliman, and Raoudha Ben Djemaa. "AI-Enhanced Blockchain for Scalable IoT-Based Supply Chain." Logistics 8, no. 4 (2024): 109. http://dx.doi.org/10.3390/logistics8040109.

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Purpose: The integration of AI with blockchain technology is investigated in this study to address challenges in IoT-based supply chains, specifically focusing on latency, scalability, and data consistency. Background: Despite the potential of blockchain technology, its application in supply chains is hindered by significant limitations such as latency and scalability, which negatively impact data consistency and system reliability. Traditional solutions such as sharding, pruning, and off-chain storage introduce technical complexities and reduce transparency. Methods: This research proposes an

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Hussain, Nurudeen Yemi, Faith Ibukun Babalola, Eseoghene Kokogho, and Princess Eloho Odio. "A Robust Model for Integrating Artificial Intelligence into Financial Risk Management: Addressing Compliance, Accuracy, and Scalability Issues." International Journal of Research and Innovation in Social Science IX, no. II (2025): 3651–68. https://doi.org/10.47772/ijriss.2025.9020283.

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The integration of Artificial Intelligence (AI) into financial risk management has transformed the industry by enabling real-time analysis, enhanced decision-making, and predictive insights. However, challenges related to compliance with regulatory frameworks, the accuracy of AI models, and the scalability of these solutions persist. This study proposes a robust model that systematically integrates AI into financial risk management while addressing these critical issues. The model combines machine learning (ML) algorithms, natural language processing (NLP), and explainable AI (XAI) techniques

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Gaurav Naresh Mittal. "Leveraging Artificial Intelligence to Optimize ETL Pipelines: Enhancing Efficiency, Accuracy, and Scalability." International Journal of Scientific Research in Computer Science, Engineering and Information Technology 11, no. 2 (2025): 46–55. https://doi.org/10.32628/cseit251112385.

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This article explores the transformative impact of Artificial Intelligence on Extract, Transform, Load (ETL) processes in modern enterprise data management. The article examines how AI technologies enhance data extraction through intelligent discovery systems, unstructured data processing, and adaptive web scraping capabilities. It investigates the role of AI in data transformation, including automated cleansing, smart mapping, and real-time quality monitoring. The article further analyzes AI's contribution to optimizing data loading through intelligent scheduling and adaptive performance tuni

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Vinnakota, Santosh. "Automating ETL with AI." Journal of Software Engineering and Simulation 11, no. 3 (2025): 23–29. https://doi.org/10.35629/3795-11032329.

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The traditional Extract, Transform, Load (ETL) process plays a vital role in data management. However, it is often plagued by inefficiencies, high maintenance costs, and performance bottlenecks. This paper explores how artificial intelligence (AI) and machine learning (ML) can automate and optimize ETL processes. We discuss AI-driven approaches for data ingestion, transformation, and loading, illustrating their impact on performance, scalability, and accuracy. Additionally, we provide a framework for implementing AI-driven ETL automation and evaluate real-world use cases demonstrating signific

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Kulikova, Elena, Vladimir Sulimin, and Vladislav Shvedov. "Artificial intelligence for ambient air quality control." E3S Web of Conferences 419 (2023): 03011. http://dx.doi.org/10.1051/e3sconf/202341903011.

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Air quality, integral to public health and environmental stability, necessitates innovative solutions for effective monitoring and control. Existing methodologies are often limited in their predictive accuracy, scalability, and cost-effectiveness. This paper explores the potential of Artificial Intelligence (AI) in transforming ambient air quality control. We conduct an in-depth review of current AI applications, examining various models’ strengths and weaknesses in predicting and controlling air quality. These include machine learning, deep learning, and other AI methodologies. Real-world cas

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Beltramini, Enrico. "The fish is only as big as the pond it swims in: theological perspectives on post-scaling AI." Theology 128, no. 4 (2025): 266–74. https://doi.org/10.1177/0040571x251355920.

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This article examines the far-reaching implications of the Chinchilla Paper and related insights for artificial intelligence (AI) scalability, existential risk narratives, and theological reflections on AI. The Chinchilla Paper, a landmark study by DeepMind, disrupts traditional assumptions about computational power as the key to AI advancement, revealing instead that AI’s potential is fundamentally constrained by the finite availability of high-quality, human-generated training data. This insight reframes discussions on AI scalability, casting doubt on existential risk narratives that envisio

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Faisal, Reena, Samira Kamran, Obah Tawo, Carl Amekudzi, Martins Awofadeju, and Beryl Fonkem. "Strategic use of AI for Enhancing Operational Scalability in U.S. Technology Startups and Fintech Firms." International Journal of Scientific Research and Modern Technology 2, no. 12 (2023): 10–22. https://doi.org/10.5281/zenodo.14555146.

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Artificial Intelligence (AI) in the existing business systems has a huge potential to enhance the operational scalability of the technology start-ups and the fintech companies. This paper presents the long-term effects of AI-powered process automation on the operational phenomenon and efficiency of American technology start-ups and fintech firms. This study fills the gap in literature by measuring the effects of AI adoption over time on the productivity, cost savings, and overall operating performance. This paper begins with an introduction. The introduction first sets the need for operational

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l Yadav1, Seja. "AI-Integrated Chatbot for Business Automation." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 03 (2025): 1–9. https://doi.org/10.55041/ijsrem42608.

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This research paper analyzes chatbot integration in agrochemical websites through two case studies: Agrolifes Chat Assistant, chatbot, and Agrolifes Chemical India, platform incorporating chatbot functionalities within an enterprise re- source planning (ERP) system. The study explores how these chatbots assist customers with inquiries, product navigation, and tailored recommendations while evaluating their technical architecture, design, and usability . The Agrolifes Chat Assistant acts as a conversational interface for direct user engagement, handling product-related queries and guiding websi

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Arjunan, Prabu. "Cloud Storage for AI: Making Informed Decisions." Journal of Artificial Intelligence & Cloud Computing 3, no. 3 (2024): 1–2. https://doi.org/10.47363/jaicc/2024(3)e219.

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This work provides an in-depth investigation into the features of cloud storage solutions that are specifically designed to support AI tasks. Companies are embracing AI technologies rapidly, and these have scalability as one of the priority needs for secure storage systems.

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Kamalendar Reddy Kotha, Sai Charan Tokachichu, and Sudheer Chennuri. "AI and Machine Learning in Enhancing Scalability and Efficiency of Integrated E-commerce and ERP Systems." International Journal of Scientific Research in Computer Science, Engineering and Information Technology 10, no. 5 (2024): 254–64. http://dx.doi.org/10.32628/cseit24105108.

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This article explores the transformative potential of Artificial Intelligence (AI) and Machine Learning (ML) in enhancing the integration of E-commerce platforms with Enterprise Resource Planning (ERP) systems. As E-commerce experiences explosive growth and ERP systems become increasingly complex, businesses face significant challenges in maintaining scalability and efficiency. We examine how AI and ML can optimize various aspects of these integrated systems, from intelligent automation and predictive analytics to anomaly detection and decision support. Through case studies and analysis of cur

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Deeti, Vinay Kumar. "A Strategic Framework for AI, Machine Learning, and Generative AI Adoption in AWS Cloud Environments." Asian Journal of Multidisciplinary Research & Review 5, no. 4 (2024): 42–56. https://doi.org/10.5281/zenodo.15549889.

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Integration of Artificial Intelligence (AI), Machine Learning (ML), and Generative AI (GenAI) with AWS cloud environments has created a model shift in enterprise digital transformation strategies. The objective of this paper is to propose a comprehensive strategic framework which guides the systematic adoption of these technologies at the same time highlighting architectural consideration, model lifecycle orchestration, cost-optimization, scalability, and regulatory compliance.

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Kalava, Sudheer Peddineni. "Best AI Framework Guide: Build Production-Ready Agents That Work." International Scientific Journal of Engineering and Management 03, no. 12 (2024): 1–9. https://doi.org/10.55041/isjem02191.

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Artificial Intelligence (AI) frameworks serve as the foundation for building scalable, production-ready AI agents that enable automation, decision-making, and intelligent interactions. This paper explores the architecture, core components, and best practices for selecting, deploying, and optimizing AI agent frameworks. Additionally, it addresses security considerations, compliance standards, performance enhancements, and real-world integration strategies for enterprise adoption. Keywords Artificial Intelligence, AI Agents, Automation, Scalability, Security, Optimization, Enterprise AI

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