Relevant bibliographies by topics / AI Scalability

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Book chapters
  4. Conference papers
  5. Reports

Academic literature on the topic 'AI Scalability'

Author: Grafiati
Published: 7 June 2025
Last updated: 16 July 2025

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Journal articles on the topic "AI Scalability"

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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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Dissertations / Theses on the topic "AI Scalability"

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Kaya, Ertay. "Providing Scalability For An Automated Web Service Composition Framework." Master's thesis, METU, 2010. http://etd.lib.metu.edu.tr/upload/12612033/index.pdf.

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In this thesis, some enhancements to an existing automatic web service composition and execution system are described which provide a practical significance to the existing framework with scalability, i.e. the ability to operate on large service sets in reasonable time. In addition, the service storage mechanism utilized in the enhanced system presents an effective method to maintain large service sets. The described enhanced system provides scalability by implementing a pre-processing phase that extracts service chains and problem initial and goal state dependencies from service descriptions.
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Book chapters on the topic "AI Scalability"

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Mishra, Abhishek. "Fundamentals of Scalability in AI." In Scalable AI and Design Patterns. Apress, 2024. http://dx.doi.org/10.1007/979-8-8688-0158-7_2.

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Rocki, Kamil, and Reiji Suda. "Parallel Monte Carlo Tree Search Scalability Discussion." In AI 2011: Advances in Artificial Intelligence. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-25832-9_46.

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Wajdi, Abdallah Ahmed, Houneida Sakly, Ramzi Guetari, and Naoufel Kraiem. "Fundamental Principles of AI Scalability in Healthcare." In Scalable Artificial Intelligence for Healthcare. CRC Press, 2025. https://doi.org/10.1201/9781003480594-2.

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Usatorre, Luis, Paula Morella, Iñigo Sedano, Sergio Clavijo, and Asier Aguayo. "AI Based Solutions for Manufacturing Mass Customization." In Lecture Notes in Mechanical Engineering. Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-86489-6_19.

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Abstract This paper analyses how to solve the challenges in the implementation of Mass Customization in manufacturing using Artificial Intelligence agents/services/tools. Considering that humans alone cannot cope with mass customization due to the huge amount of information, it is required AI based solutions that help humans to take decisions. We consider that those AI based solutions must communicate with other AI based solutions in order to obtain a holistic improvement (this is the Multi Agent System concept). More in detail, this paper addresses how to solve the challenges identified when AI based agents use external data coming from outside the company, so a Data Space to guaranteeing a secure data transaction and data ownership and sovereignty is required. This paper presents the solutions implemented in several projects to address the challenges created by the requirements on a-the implementation and scalability of AI based solutions in Manufacturing, b-for the implementation of multi-Agent AI-based systems and for c-implementing Data Spaces.
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Freeda, Adline R., R. Kanthavel, and A. Anju. "Scalability Issues in AI Computing in Large-Scale Networks." In Advances in Wireless Technologies and Telecommunication. IGI Global, 2024. http://dx.doi.org/10.4018/979-8-3693-6552-6.ch018.

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Artificial Intelligence, or simply AI, has witnessed tremendous growth in the modern world over these few years. As these AI applications start finding their way into increasing numbers of areas, the scalability aspect becomes an important criterion that decides whether such artificial intelligence applications succeed or not. Scalability in Artificial Intelligence computing over large-scale networks refers to all the issues occurring during scaling up AI systems by size and complexity to accommodate growing amounts of data, users, and computation. Consequently, scalability in large-scale networks is a considerable factor within the perspective of AI. These become terribly necessary in addressing cost, security, and performance-related issues to make the AI system efficient, robust, and scaling. This chapter will present an overview: infrastructure scaling, communication and coordination, frameworks and tools used for scalability, monitoring of performance and optimization, and challenges in scalability using AI in large-scale networks.
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Lewis, Jason Edward, Noelani Arista, Archer Pechawis, and Suzanne Kite. "Making Kin with the Machines." In Feminist AI. Oxford University PressOxford, 2023. http://dx.doi.org/10.1093/oso/9780192889898.003.0002.

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Abstract In this essay, the authors argue that heterogeneous Indigenous perspectives are the best way of approaching Artificial Intelligence’s (AI’s) ‘circle of relationships’ between human and non-human actors. Where AI design is often ‘human-centred’, this essay radically refuses the idea of a separate human species whose priorities trump that of its environment. Instead, it emphasises reciprocal and respectful interspecies relationships. In doing so, it holds feminism to its pledge to contest extractive capitalist logic by reframing AI as ʻĀIna’, or as land but not territory. The essay contests the industry’s emphasis on unfettered growth and scalability by urging for ‘good growth’ based on the knowledges and governing practices of Kānaka Maoli, Cree, and Lakota people.
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Gladston, Angelin, T. R. Harish, and J. V. Keerthivasan. "AutoGen AI." In Advances in Computational Intelligence and Robotics. IGI Global, 2025. https://doi.org/10.4018/979-8-3373-0370-3.ch004.

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In the rapidly evolving digital landscape, the ability to efficiently generate high-quality content and functional code is crucial for businesses and developers aiming to stay competitive. AutoGen AI harnesses the power of Gemini AI to automate and enhance the entire content and code creation process, streamlining workflows and delivering superior results. Users provide their specifications and requirements through a React-based front end, designed to offer an intuitive and interactive experience, making the system accessible to users of varying technical expertise. Gemini AI generates customized textual content or functional code that aligns with the specified criteria that are ready for immediate review and implementation, whether it's content creation or code generation. Additionally, AutoGen AI integrates secure data management and real-time synchronization using MongoDB, ensuring seamless collaboration and data security. By focusing on productivity and scalability, AutoGen AI redefines digital content and code creation, offering a powerful solution for businesses.
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Devan, Karthigayan. "Automating AI Infrastructure." In Advances in Educational Technologies and Instructional Design. IGI Global, 2024. https://doi.org/10.4018/979-8-3693-7723-9.ch008.

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This chapter explores the automation of AI infrastructure within scalable and optimized cloud platforms, specifically tailored for the realm of smart education. As educational institutions increasingly leverage AI technologies to enhance learning experiences, the need for robust and efficient cloud solutions becomes paramount. This chapter delves into the critical components of automated AI infrastructure, including cloud architecture, resource management, and deployment strategies. By examining best practices for automation, scalability, and optimization, the chapter provides a comprehensive guide for educators and administrators looking to implement AI-driven solutions in their institutions. Additionally, real-world case studies illustrate the successful application of these principles in smart education settings, highlighting the transformative impact of automation on educational outcomes. The insights presented aim to equip stakeholders with the knowledge to harness AI effectively, ensuring sustainable and adaptive learning environments.
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Tyagi, Himanshu, Pawan Kumar Goel, Akansha Gautam, Abhishek Agarwal, and Sandhya Samant. "AI and Blockchain Synergy." In Advances in Computational Intelligence and Robotics. IGI Global, 2025. https://doi.org/10.4018/979-8-3373-3241-3.ch012.

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This chapter explores the convergence of AI and blockchain in industrial automation, highlighting advancements in security, efficiency, and transparency. It outlines AI's role in predictive maintenance, anomaly detection, and intelligent decision-making, alongside blockchain's strengths in data integrity, tamper-proof records, and automated compliance. A multi-layered architecture is proposed for seamless integration, addressing scalability, complexity, and legal hurdles. Real-world use cases include secure predictive maintenance, fraud detection, decentralized identity, and supply chain automation. Emerging trends such as federated learning, AI-powered smart contracts, and zero-trust architectures are also discussed, showcasing the transformative potential of combining AI and blockchain.
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Jana, Olivia, and Shivnath Ghosh. "AI-Driven Predictive Analytics." In Advances in Medical Technologies and Clinical Practice. IGI Global, 2025. https://doi.org/10.4018/979-8-3373-0690-2.ch006.

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Early detection is essential for managing glaucoma, a leading cause of irreversible blindness that often progresses without noticeable symptoms until significant vision loss has occurred. This study examines the application of Convolutional Neural Networks (CNNs), an emerging deep learning technology, for automating and enhancing glaucoma diagnosis. By analysing retinal fundus and optical coherence tomography (OCT) images, CNNs effectively detect structural changes such as optic nerve cupping and retinal nerve fibre layer thinning with high accuracy. Compared to traditional diagnostic methods, CNNs offer advantages including enhanced sensitivity, specificity, automation, and scalability. This research underscores the potential of integrating Deep Learning based, CNN systems into clinical workflows, paving the way for improved glaucoma screening and management.
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Conference papers on the topic "AI Scalability"

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Chowdhury, Pradipto, Swati Mishra, Swayam Srivastava, et al. "Scalability and AI: An Insight on Software Project Management." In 2024 6th International Conference on Computational Intelligence and Networks (CINE). IEEE, 2024. https://doi.org/10.1109/cine63708.2024.10881438.

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Liu, Xiang. "Evolution of Optical Network for Ubiquitous AI." In Optical Fiber Communication Conference. Optica Publishing Group, 2025. https://doi.org/10.1364/ofc.2025.th3a.1.

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We review the emerging optical network evolution trends to support ubiquitous AI by providing sufficient capacity, latency, flexibility, scalability and reliability, while maximally reusing modern network architectures such as C-RAN and OXC-based 3D mesh connection.
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Selvam, Muthu, and Kishan B. S. "AI-Powered Cloud Computing for Performance Optimization and Scalability in Distributed Systems." In 2025 International Conference on Computing for Sustainability and Intelligent Future (COMP-SIF). IEEE, 2025. https://doi.org/10.1109/comp-sif65618.2025.10969950.

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Latha, Y. L. Malathi, Sajjan Choudhuri, Abhinaya Reddy Jannepally, Yogita D. Bhise, Togaru Swathi, and Sivakoteswara rao Katta. "Optimizing Cloud Resource Management Through AI Enhancing Efficiency and Scalability in Modern Computing Environments." In 2024 International Conference on Artificial Intelligence and Emerging Technology (Global AI Summit). IEEE, 2024. https://doi.org/10.1109/globalaisummit62156.2024.10947796.

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Kok, Arvid, Antonio Carvalho, and Michael Street. "Exploring Shared Large Language Models: Early Insights into Scalability and Efficiency in AI Assistant and Agent Deployment." In 2025 International Conference on Military Communication and Information Systems (ICMCIS). IEEE, 2025. https://doi.org/10.1109/icmcis64378.2025.11048102.

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Dou, Kai, Zekai Fan, Yuhan Zhang, Wenhan Wu, Feng Ye, and Jun Chen. "Exploring the Use of Large AI Models for Floor Live Load Surveys: A Preliminary Study." In IABSE Symposium, Tokyo 2025: Environmentally Friendly Technologies and Structures: Focusing on Sustainable Approaches. International Association for Bridge and Structural Engineering (IABSE), 2025. https://doi.org/10.2749/tokyo.2025.2435.

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<p>Reliable live load values are essential for the reliability design of civil structures. Due to the inherent randomness of live loads, collecting a large amount of sample data is crucial for accurate load modeling. Traditional survey methods typically rely on sampling to investigate floor live loads, which poses several challenges, such as low efficiency, high costs, and privacy concerns. Additionally, weighing large indoor items on-site, such as heavy furniture or household appliances, presents significant difficulties, further limiting the accuracy of load surveys.</p><p>
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Raponi, Antonello, and Zoltan Nagy. "CompArt: Next-Generation Compartmental Models for Complex Systems Powered by Artificial Intelligence." In The 35th European Symposium on Computer Aided Process Engineering. PSE Press, 2025. https://doi.org/10.69997/sct.186609.

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Compartmental models are widely used to simplify the analysis of complex fluid dynamics systems, yet subjective compartment definitions and computational constraints often limit their applicability. The CompArt algorithm introduces an AI-driven framework that automates compartmentalization in Computational Fluid Dynamics (CFD) simulations, optimizing both accuracy and efficiency. By leveraging unsupervised clustering techniques such as Agglomerative Clustering, CompArt identifies coherent flow regions based on velocity and turbulent kinetic energy dissipation rate, ensuring a data-driven, phys
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Semenova, Nadezhda, Johnny Moughames, Xavier Porte, Muamer Kadic, Laurent Larger, and Daniel Brunner. "Scalability and noise in (photonic) hardware neural networks." In AI and Optical Data Sciences II, edited by Ken-ichi Kitayama and Bahram Jalali. SPIE, 2021. http://dx.doi.org/10.1117/12.2585745.

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Araújo, Thiago, and Philippe Navaux. "Scalability of the ARM Nvidia Grace Superchip for Deep Learning Applications." In Escola Regional de Alto Desempenho da Região Sul. Sociedade Brasileira de Computação - SBC, 2025. https://doi.org/10.5753/eradrs.2025.6822.

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The advance of Artificial Intelligence (AI) has heightened the demand for computational resources, presenting challenges in scalability and energy efficiency. The low-power ARM architecture is a promising option for AI workloads. This study assesses the scalability of the ARM Nvidia Grace Superchip in Deep Learning (DL) using a model that predicts referrals for severe diabetic retinopathy. We analyze performance in terms of speedup and energy consumption across 1 to 144 cores. Although speedup increases with more threads, the gains are less pronounced at higher core counts due to resource satu
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Verheijen, Floris, Xander Lub, and Ran Zhang. ""Dear Applicant, the AI Will See you Now!" Job Applicants' Reactions to AI-Enhanced Selection." In 38th Bled eConference. University of Maribor Press, 2025. https://doi.org/10.18690/um.fov.4.2025.13.

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As artificial intelligence (AI) reshapes hiring, organizations increasingly rely on AI-enhanced selection methods such as chatbot-led interviews and algorithmic resume screening. While AI offers efficiency and scalability, concerns persist regarding fairness, transparency, and trust. This qualitative study applies the Artificially Intelligent Device Use Acceptance (AIDUA) model to examine how job applicants perceive and respond to AI-driven hiring. Drawing on semi-structured interviews with 15 professionals, the study explores how social influence, anthropomorphism, and performance expectancy
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Reports on the topic "AI Scalability"

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Pasupuleti, Murali Krishna. Neuromorphic Nanotech: 2D Materials for Energy-Efficient Edge Computing. National Education Services, 2025. https://doi.org/10.62311/nesx/rr325.

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Abstract The demand for energy-efficient, real-time computing is driving the evolution of neuromorphic computing and edge AI systems. Traditional silicon-based processors struggle with power inefficiencies, memory bottlenecks, and scalability limitations, making them unsuitable for next-generation low-power AI applications. This research report explores how 2D materials, such as graphene, transition metal dichalcogenides (TMDs), black phosphorus, and MXenes, are enabling the development of neuromorphic architectures that mimic biological neural networks for high-speed, ultra-low-power computat
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Pasupuleti, Murali Krishna. Decentralized Creativity: AI-Infused Blockchain for Secure and Transparent Digital Innovation. National Education Services, 2025. https://doi.org/10.62311/nesx/rrvi125.

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Abstract The convergence of artificial intelligence (AI) and blockchain technology is transforming the creative economy by enabling secure, transparent, and decentralized innovation in digital content creation, intellectual property management, and monetization. Traditional creative industries are often constrained by centralized platforms, opaque copyright enforcement, and unfair revenue distribution, which limit the autonomy and financial benefits of creators. By leveraging blockchain’s immutable ledger, smart contracts, and non-fungible tokens (NFTs), digital assets can be authenticated, to
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Pasupuleti, Murali Krishna. Stochastic Computation for AI: Bayesian Inference, Uncertainty, and Optimization. National Education Services, 2025. https://doi.org/10.62311/nesx/rriv325.

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Abstract: Stochastic computation is a fundamental approach in artificial intelligence (AI) that enables probabilistic reasoning, uncertainty quantification, and robust decision-making in complex environments. This research explores the theoretical foundations, computational techniques, and real-world applications of stochastic methods, focusing on Bayesian inference, Monte Carlo methods, stochastic optimization, and uncertainty-aware AI models. Key topics include probabilistic graphical models, Markov Chain Monte Carlo (MCMC), variational inference, stochastic gradient descent (SGD), and Bayes
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Pasupuleti, Murali Krishna. Sustainable and Affordable Global Housing via AI and Advanced Materials. National Education Services, 2025. https://doi.org/10.62311/nesx/rr425.

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Abstract: The global housing crisis demands innovative solutions that balance affordability, sustainability, and technological advancement. This research report explores the transformative role of Artificial Intelligence (AI) and advanced materials in addressing housing shortages, reducing environmental impact, and improving construction efficiency. AI-driven generative design, smart automation, and predictive analytics optimize resource allocation and enhance structural safety, while advanced materials such as 3D-printed composites, self-healing concrete, aerogels, and bio-based materials rev
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Pasupuleti, Murali Krishna. Smart Nanomaterials and AI-Integrated Grids for Sustainable Renewable Energy. National Education Services, 2025. https://doi.org/10.62311/nesx/rr1025.

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Abstract: The transition to sustainable and intelligent renewable energy systems is being driven by advancements in smart nanomaterials and AI-integrated smart grids. Nanotechnology has enabled the development of high-performance energy materials, such as graphene, perovskites, quantum dots, and MXenes, which enhance the efficiency, durability, and scalability of renewable energy solutions. Simultaneously, AI-driven smart grids leverage machine learning, deep learning, and digital twins to optimize energy distribution, predictive maintenance, and real-time load balancing in renewable energy ne
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Pasupuleti, Murali Krishna. Next-Generation Extended Reality (XR): A Unified Framework for Integrating AR, VR, and AI-driven Immersive Technologies. National Education Services, 2025. https://doi.org/10.62311/nesx/rrv325.

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Abstract: Extended Reality (XR), encompassing Augmented Reality (AR), Virtual Reality (VR), and Mixed Reality (MR), is evolving into a transformative technology with applications in healthcare, education, industrial training, smart cities, and entertainment. This research presents a unified framework integrating AI-driven XR technologies with computer vision, deep learning, cloud computing, and 5G connectivity to enhance immersion, interactivity, and scalability. AI-powered neural rendering, real-time physics simulation, spatial computing, and gesture recognition enable more realistic and adap
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Pasupuleti, Murali Krishna. Automated Smart Contracts: AI-powered Blockchain Technologies for Secure and Intelligent Decentralized Governance. National Education Services, 2025. https://doi.org/10.62311/nesx/rrv425.

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Abstract: Automated smart contracts represent a paradigm shift in decentralized governance by integrating artificial intelligence (AI) with blockchain technologies to enhance security, scalability, and adaptability. Traditional smart contracts, while enabling trustless and automated transactions, often lack the flexibility to adapt to dynamic regulatory frameworks, evolving economic conditions, and real-time security threats. AI-powered smart contracts leverage machine learning, reinforcement learning, and predictive analytics to optimize contract execution, detect fraudulent transactions, and
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Pasupuleti, Murali Krishna. Quantum Intelligence: Machine Learning Algorithms for Secure Quantum Networks. National Education Services, 2025. https://doi.org/10.62311/nesx/rr925.

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Abstract: As quantum computing and quantum communication technologies advance, securing quantum networks against emerging cyber threats has become a critical challenge. Traditional cryptographic methods are vulnerable to quantum attacks, necessitating the development of AI-driven security solutions. This research explores the integration of machine learning (ML) algorithms with quantum cryptographic frameworks to enhance Quantum Key Distribution (QKD), post-quantum cryptography (PQC), and real-time threat detection. AI-powered quantum security mechanisms, including neural network-based quantum
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Pasupuleti, Murali Krishna. Quantum-Enhanced Machine Learning: Harnessing Quantum Computing for Next-Generation AI Systems. National Education Services, 2025. https://doi.org/10.62311/nesx/rrv125.

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Abstract Quantum-enhanced machine learning (QML) represents a paradigm shift in artificial intelligence by integrating quantum computing principles to solve complex computational problems more efficiently than classical methods. By leveraging quantum superposition, entanglement, and parallelism, QML has the potential to accelerate deep learning training, optimize combinatorial problems, and enhance feature selection in high-dimensional spaces. This research explores foundational quantum computing concepts relevant to AI, including quantum circuits, variational quantum algorithms, and quantum k
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Pasupuleti, Murali Krishna. Optimal Control and Reinforcement Learning: Theory, Algorithms, and Robotics Applications. National Education Services, 2025. https://doi.org/10.62311/nesx/rriv225.

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Abstract: Optimal control and reinforcement learning (RL) are foundational techniques for intelligent decision-making in robotics, automation, and AI-driven control systems. This research explores the theoretical principles, computational algorithms, and real-world applications of optimal control and reinforcement learning, emphasizing their convergence for scalable and adaptive robotic automation. Key topics include dynamic programming, Hamilton-Jacobi-Bellman (HJB) equations, policy optimization, model-based RL, actor-critic methods, and deep RL architectures. The study also examines traject
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