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Journal articles on the topic 'AI Data Centers'

Author: Grafiati

Published: 7 June 2025

Last updated: 2 August 2025

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1

Dr.A.Shaji, George. "Redefining Data Centers for the AI Revolution." Partners Universal Multidisciplinary Research Journal (PUMRJ) 02, no. 01 (2025): 13–18. https://doi.org/10.5281/zenodo.14739520.

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Artificial intelligence (AI) is the next big step in technology and changing how businesses work. As businesses use AI more to innovate and stay ahead, their actual infrastructure also needs to change along with it. The rapid increase in AI tasks requires a new way of thinking about traditional data centers to provide better scale, efficiency, reliability, and sustainability. This research analyzes the pressures of reshaping modern data centers and the innovations in compute, storage, networking, resiliency, and sustainability defining the next generation of AI-ready facilities. We also examin

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Sachin Mishra. "The Evolution of Data Centers in the Age of AI." International Journal of Scientific Research in Computer Science, Engineering and Information Technology 10, no. 5 (2024): 363–68. http://dx.doi.org/10.32628/cseit24105107.

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This article explores the evolving landscape of data centers in the era of artificial intelligence (AI). It examines the exponential growth of the global data center market, driven by increasing data generation and AI adoption. The article discusses key technological developments in data centers, including enhanced operational efficiency through AI-powered systems, specialized hardware for AI workloads, advanced cooling technologies, and sustainability initiatives. It also delves into future prospects, such as increased capacity for complex AI tasks, real-time processing of massive datasets, a

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Madhura, G. K. "Quantum-Inspired Optimization and Resource Allocation in AI-Driven Data Centers and Edge Networks." Journal of Advances in Computational Intelligence Theory 7, no. 2 (2025): 19–25. https://doi.org/10.5281/zenodo.15228558.

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<em>This study presents a comprehensive review and synthesis of recent research advancements integrating quantum-inspired optimization and artificial intelligence (AI) in data centers and edge computing networks. With the exponential growth in data generation and the demand for real-time processing, AI-driven infrastructures face challenges in scalability, latency, and energy efficiency. Through the evaluation of 29 scholarly works authored or co-authored by Vinod Veeramachaneni, Srinivasa Rao Bittla, and Srimaan Yarram, the study highlights innovations across diagnostics in electrical systems

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Kanthed, Surbhi. "Energy-Saving Practices in Data Centers." International Journal of Multidisciplinary Research and Growth Evaluation 4, no. 5 (2023): 1149–55. https://doi.org/10.54660/.ijmrge.2023.4.5.1149-1155.

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Data centers are vital to the digital economy but consume significant amounts of electrical energy, highlighting the need for more sustainable operational practices. This paper examines contemporary energy-saving solutions for data centers, emphasizing empirical strategies rather than theoretical discussions. Key methods include advanced cooling approaches (e.g., hot/cold aisle containment, economizer-based “free cooling,” and liquid immersion cooling), server optimization techniques (such as virtualization, containerization, and intelligent power management), and improvements in power distrib

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Kishor Ingavale, Girish. "Zero-Water Cooling For Modern AI Data Centers." International Journal of Scientific Research and Engineering Trends 11, no. 3 (2025): 1–13. https://doi.org/10.61137/ijsret.vol.11.issue3.121.

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Zou, Jing, Peizhe Xin, Chang Wang, Heli Zhang, Lei Wei, and Ying Wang. "AI Services-Oriented Dynamic Computing Resource Scheduling Algorithm Based on Distributed Data Parallelism in Edge Computing Network of Smart Grid." Future Internet 16, no. 9 (2024): 312. http://dx.doi.org/10.3390/fi16090312.

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Massive computational resources are required by a booming number of artificial intelligence (AI) services in the communication network of the smart grid. To alleviate the computational pressure on data centers, edge computing first network (ECFN) can serve as an effective solution to realize distributed model training based on data parallelism for AI services in smart grid. Due to AI services with diversified types, an edge data center has a changing workload in different time periods. Selfish edge data centers from different edge suppliers are reluctant to share their computing resources with

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Oluwatosin Oladayo Aramide. "Architecting highly resilient AI Fabrics: A Blueprint for Next-Gen Data Centers." World Journal of Advanced Engineering Technology and Sciences 8, no. 1 (2023): 529–39. https://doi.org/10.30574/wjaets.2023.8.1.0049.

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The fast-growing advancement in AI technologies has resulted in huge loads on the data center architecture resulting in the need to create extremely resistant, and fault-tolerant AI fabrics. This paper looks at AI design principles and technologies necessitated in the construction of fault-tolerant AI infrastructures that can support complex, data-heavy workloads. The major technologies of VXLAN EVPN, RDMA and ultra-low latency interconnect like RoCEv2, NV Link and PCIe Gen5 are paramount to high availability, low latency and high throughput. This article reviews industrial best practice by ob

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Researcher. "LEVERAGING AI AND ML TO REVOLUTIONIZE ENERGY EFFICIENCY IN DATA CENTERS." International Journal of Computer Engineering and Technology (IJCET) 15, no. 4 (2024): 370–83. https://doi.org/10.5281/zenodo.13269832.

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This article explores the transformative impact of Artificial Intelligence (AI) and Machine Learning (ML) on energy efficiency in data centers. It examines various areas where these technologies drive significant improvements, including predictive analytics, dynamic cooling management, smart workload scheduling, automated peak shaving, real-time optimization, enhanced maintenance strategies, and holistic system integration. Implementing AI/ML solutions optimizes operational costs and contributes to sustainability efforts by reducing the overall carbon footprint of data center facilities. Throu

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Dhruvitkumar V Talati. "AI for self-adaptive cloud systems: Towards fully autonomous data centers." World Journal of Advanced Research and Reviews 25, no. 30 (2025): 333–40. https://doi.org/10.30574/wjarr.2025.25.3.0727.

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The increasing complexity of modern computing systems, coupled with growing demands for energy-efficient and cost-effective data centers, has driven the need for self-adaptive cloud systems. Advancements in artificial intelligence hold the promise of enabling fully autonomous data centers that can adapt to dynamic workloads, optimize resource utilization, and reduce environmental impact. This paper explores the applications of AI techniques in the context of self-adaptive cloud systems, highlighting the potential for AI-powered solutions to address key challenges in the design, operation, and

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Janardhanan, Harish. "AI-Driven Load Balancing for Energy-Efficient Data Centers." International Journal of Computer Trends and Technology 72, no. 8 (2024): 13–18. http://dx.doi.org/10.14445/22312803/ijctt-v72i8p103.

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Vaidya, Dakshaja Prakash. "AI-Augmented Green Cloud Infrastructure for Telecom Data Centers." European Journal of Computer Science and Information Technology 13, no. 33 (2025): 104–16. https://doi.org/10.37745/ejcsit.2013/vol13n33104116.

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This article presents a novel AI-augmented system for optimizing energy consumption in telecom-based cloud data centers while maintaining strict service level agreements. The article uniquely combines advanced time-series forecasting techniques with reinforcement learning to predict computational workloads and dynamically allocate resources in alignment with renewable energy availability. Unlike previous solutions that focus solely on hardware efficiency or isolated subsystems, the article provides comprehensive optimization across distributed telecom infrastructure, addressing the industry-sp

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Baraskar, Tejas Sudhakar. "Efficient Utilization of Energy Consumption in AI Data Centers: Balancing Sustainability and Performance." International Journal for Research in Applied Science and Engineering Technology 13, no. 6 (2025): 3788–92. https://doi.org/10.22214/ijraset.2025.72915.

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The exponential development of Artificial Intelligence (AI) technologies in the last ten years has pushed a corresponding need for computational infrastructure that can host enormous workloads. From deep learning model training on a large scale to real-time inference on millions of devices, AI workloads demand enormous processing power, usually residing in highly advanced and specialized data centers. These AI data centers—powered by thousands of CPUs, GPUs, and accelerators constitute the unseen but essential foundation of today's digital intelligence. But with this computational revolution c

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Anish Alex. "Sustainable energy strategies for data centers in the AI era." World Journal of Advanced Engineering Technology and Sciences 15, no. 2 (2025): 001–7. https://doi.org/10.30574/wjaets.2025.15.2.0471.

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This article addresses the critical challenge of integrating sustainable energy solutions into data centers amid the explosive growth of artificial intelligence and cloud computing. As computational demands intensify, data centers face unprecedented energy consumption challenges, necessitating innovative approaches to renewable energy adoption. The article examines diverse renewable energy sources, including solar, wind, geothermal, hydropower, and biomass, highlighting their applicability and efficiency in data center environments. Energy storage technologies and grid integration strategies a

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Sunday Adeola Oladosu, Adebimpe Bolatito Ige, Christian Chukwuemeka Ike, Peter Adeyemo Adepoju, Olukunle Oladipupo Amoo, and Adeoye Idowu Afolabi. "Revolutionizing data center security: Conceptualizing a unified security framework for hybrid and multi-cloud data centers." Open Access Research Journal of Science and Technology 5, no. 2 (2022): 086–76. https://doi.org/10.53022/oarjst.2022.5.2.0065.

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The rapid shift towards hybrid and multi-cloud environments has introduced significant security challenges for data centers, as traditional security models struggle to meet the demands of modern infrastructures. This review conceptualizes a unified security framework aimed at revolutionizing data center security in the context of hybrid and multi-cloud architectures. The proposed framework integrates on-premise and cloud security controls into a cohesive, scalable solution that addresses the complexities of modern data centers, ensuring robust protection against increasingly sophisticated cybe

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Ashok, Kumar Kalyanam. "Optimizing Data Centers through IoT A Comprehensive Overview (The Future of Connected Data Centers)." International Journal on Science and Technology 14, no. 4 (2023): 1–12. https://doi.org/10.5281/zenodo.14613855.

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IoT has actually revolutionized the data centers through introducing innovations related to automation, efficiency, and real-time decision-making. The article probes into how IoT has actually integrated into the data center to ensure its transformation to a very optimally operating entity. IoT data centers will leverage sensors, data analytics platforms, automated control systems, and edge computing to improve monitoring, resource allocation, and energy management. The benefits provided by the integration of IoT range from energy efficiency to reduced operational costs, along with better scala

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Liu, Hui, AbdusSalam Aljbri, Jie Song, Jinqing Jiang, and Chun Hua. "Research advances on AI-powered thermal management for data centers." Tsinghua Science and Technology 27, no. 2 (2022): 303–14. http://dx.doi.org/10.26599/tst.2021.9010019.

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17

Ravindran, B., Sunita Sarawagi, and Aditi Jain. "AI and data science centers in top Indian academic institutions." Communications of the ACM 65, no. 11 (2022): 94–97. http://dx.doi.org/10.1145/3556634.

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18

Makholm, Jeff D., and Laura T. W. Olive. "Data Center Problems." Climate and Energy 41, no. 4 (2024): 21–26. http://dx.doi.org/10.1002/gas.22431.

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“Hyperscale” data centers accompanying the rapid growth of artificial intelligence (AI) is one of the fastest growing uses of electricity in the United States and the rest of the world. Indeed, such growth, with the retirement of dispatchable fossil fuel electricity plants, drives persistent warnings about potential reliability problems for the US power system. As Federal Energy Regulatory Commission's (FERC's) Commissioner Mark Christie warned in late July, to the House Subcommittee on Energy, Climate, and Grid Security.

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Sunday Adeola Oladosu, Adebimpe Bolatito Ige, Christian Chukwuemeka Ike, Peter Adeyemo Adepoju, Olukunle Oladipupo Amoo, and Adeoye Idowu Afolabi. "AI-driven security for next-generation data centers: Conceptualizing autonomous threat detection and response in cloud-connected environments." GSC Advanced Research and Reviews 15, no. 2 (2023): 162–72. https://doi.org/10.30574/gscarr.2023.15.2.0136.

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The dynamic evolution of next-generation data centers, driven by cloud-native and hybrid architectures, has necessitated a paradigm shift in cybersecurity. Traditional security models, designed for static and on-premise environments, struggle to address the complexities of cloud-connected infrastructures and the rapidly evolving threat landscape. Emerging challenges, such as advanced persistent threats (APTs), ransomware, and insider attacks, demand sophisticated and adaptive security solutions. In this context, artificial intelligence (AI) emerges as a transformative technology capable of red

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Prudhvi Singirikonda. "Clean Energy Solutions in Data Centers: Leveraging Advanced Materials and AI for Sustainable DevOps Operations." International Journal for Research Publication and Seminar 14, no. 5 (2023): 512–20. http://dx.doi.org/10.36676/jrps.v14.i5.1542.

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This paper examines how artificial intelligence (AI) and advanced engineering materials contribute to reduced energy consumption in data centers, a key consideration for sustainable DevOps. The topic of study is to use AI to introduce efficiency within data center operations such as maintenance, workload distribution, and cooling systems. Furthermore, the paper discusses the effectiveness of integrating different materials within buildings, including phase change materials and cutting-edge thermal management approaches to minimize energy utilization. Simulation findings reveal the potential to

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Lv, Zhihan, Liang Qiao, Sahil Verma, and Kavita. "AI-enabled IoT-Edge Data Analytics for Connected Living." ACM Transactions on Internet Technology 21, no. 4 (2021): 1–20. http://dx.doi.org/10.1145/3421510.

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As deep learning, virtual reality, and other technologies become mature, real-time data processing applications running on intelligent terminals are emerging endlessly; meanwhile, edge computing has developed rapidly and has become a popular research direction in the field of distributed computing. Edge computing network is a network computing environment composed of multi-edge computing nodes and data centers. First, the edge computing framework and key technologies are analyzed to improve the performance of real-time data processing applications. In the system scenario where the collaborativ

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DiCostanzo, Dominic J., Ahmet S. Ayan, Sachin R. Jhawar, Theodore T. Allen, and Emily S. Patterson. "Machine Learning Data Pipeline for the Democratization of AI." Proceedings of the International Symposium on Human Factors and Ergonomics in Health Care 12, no. 1 (2023): 120–24. http://dx.doi.org/10.1177/2327857923121029.

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The use of artificial intelligence continues to increase. In healthcare, there has been a recent increase in AI applications to real-time individual patient clinical care, as opposed to population-based research or quality improvement efforts. However, the expertise to evaluate and implement these solutions is limited and often congregates in academic medical centers, creating barriers to adoption for smaller community and rural centers. Lowering the barrier to entry for innovative tools can help address disparities in patient outcomes due to access and other urban/rural contributors. We descr

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Chung, Hyo-Jin. "Based on the experience of early childhood teachers using AI analysis of perceptions and needs of class execution." korean Jouranl of Early Childhood Education 27, no. 2 (2025): 119–41. https://doi.org/10.15409/riece.2025.27.2.5.

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In this study, we analyzed the classes of early childhood teachers with experience in utilizing AI and their awareness and needs for AI-based classes. To this end, we conducted a survey of 215 teachers of 3-5 year-old children in kindergartens and daycare centers in Seoul, Gyeonggi-do, and Incheon who had experience in using AI for classes, and the collected data was analyzed using SPSS 27.0. The results of the study were as follows: First, early childhood teachers’ AI-based classes were most often about providing assistance with learning activities. In terms of institution type, kindergartens

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Deepika Bhatia. "GenAI Chips in Cloud Data Centers: Driving Efficiency at Scale." International Journal of Scientific Research in Computer Science, Engineering and Information Technology 11, no. 2 (2025): 3891–97. https://doi.org/10.32628/cseit25112705.

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Integrating Generative AI (GenAI) chips in cloud data centers marks a transformative advancement in managing artificial intelligence workloads and computational efficiency. This comprehensive article explores the revolutionary impact of these specialized processors on cloud infrastructure, focusing on three key areas: advanced cooling technologies, power management innovations, and applications in cloud computing. It examines how liquid cooling systems and immersion cooling technologies are revolutionizing thermal management in data centers while Dynamic Voltage Scaling (DVS) systems are optim

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Vinod, Veeramachaneni. "Optimizing Renewable Energy Integration in AI-Driven Data Centers Using Quantum Algorithms." Journal of Network Security and Data Mining 8, no. 1 (2024): 36–48. https://doi.org/10.5281/zenodo.14168045.

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<em>The increasing demand for energy efficiency and sustainability in AI-driven data centers has led to a growing interest in integrating renewable energy sources. However, the intermittent nature of renewables poses significant challenges to energy management and resource optimization. This paper presents a novel framework employing quantum algorithms to optimize renewable energy integration in AI data centers. By leveraging the computational advantages of quantum computing, the proposed methodology enhances energy distribution, load balancing, and storage management in real-time. Quantum-bas

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Nathany, Deepika. "Energy-Efficient Data Centers: A Supply Chain Approach to Sustainability." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 05, no. 06 (2021): 1–7. https://doi.org/10.55041/ijsrem8912.

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The increasing global demand for data storage and processing due to the explosion of artificial intelligence and cloud computing has led to the exponential growth in energy consumption of data centers. Traditional data centers incur high operational costs due to their inefficient infrastructure and power intensive cooling methods. This research explores the role of supply chain management in optimizing energy efficiency in data centers and how optimizing supply chains can contribute to energy efficiency and reduction in carbon footprint. It examines sustainable practices, including modular des

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Shankar, Sahana. "Enhancing Energy Efficiency in AI-Powered Data Centers: Challenges and Solutions." International Journal of High School Research 6, no. 11 (2024): 93–98. https://doi.org/10.36838/v6i11.15.

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Libri, Antonio, Andrea Bartolini, and Luca Benini. "pAElla: Edge AI-Based Real-Time Malware Detection in Data Centers." IEEE Internet of Things Journal 7, no. 10 (2020): 9589–99. http://dx.doi.org/10.1109/jiot.2020.2986702.

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Vatse, Aditi. "Integration, GTM, and Metrics: AI Voice Call BOT for Outgoing Call Centers." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 05 (2025): 1–8. https://doi.org/10.55041/ijsrem48328.

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The emergence of artificial intelligence (AI) has profoundly reshaped numerous sectors, with outbound call centers among the most significantly affected. Industries such as insurance, healthcare, and financial services rely heavily on outbound communication to collect data, confirm information, and enhance customer engagement [1]. In response to the growing need for operational efficiency and improved customer interactions, AI voice call BOTs have emerged as a transformative solution, automating repetitive tasks, streamlining communication workflows, and providing 24/7 support [2]. This resear

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Mhatre, Anand Laxman. "Generative AI for Health Care Contact Center." Journal of Artificial Intelligence & Cloud Computing 2, no. 1 (2023): 1–3. http://dx.doi.org/10.47363/jaicc/2023(2)e183.

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Contact centers in healthcare facilities face a myriad of challenges. Amongst them are the high workload due to numerous patient inquiries, the inability to respond to queries fast, the inability to effectively collect and process patient data, and increasing operational costs to meet the growing workload. Generative AI is an emerging technology that promises to address these issues. The technology can automate engagements hence reducing workloads and improving response time to inquiries. It also reduces operational costs and offers big data benefits. This document discusses the applications a

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Kazancı, Nevra, Erçin Tevfik Öztuncel, and Metin Akuş. "AI-Based Call Center Management." European Journal of Research and Development 4, no. 4 (2024): 338–51. https://doi.org/10.56038/ejrnd.v4i4.593.

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Call centers today operate within complex ecosystems where surveillance technology, digitalization, and process automation are pivotal. These advancements enable multi-channel communication, personalized service, and proactive customer support. Unlike traditional models centered solely on phone interactions, modern call centers leverage digital tools to enhance operational efficiency. A significant innovation lies in the application of image processing techniques, including face recognition algorithms. These technologies automate tasks, minimizing human intervention and optimizing workflow. In

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Chauhan, Mamta. "Geothermal Energy Integration in Data Centers: A Pathway to Carbon-Neutral and AI-Optimized Cooling Systems." International Journal of Science and Research (IJSR) 14, no. 3 (2025): 1701–4. https://doi.org/10.21275/sr25328043456.

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Nagaraj M, Raghavendra M Y, and Ameena Firdous Nikhat. "Scalable and secure network architectures for next-generation data centers." World Journal of Advanced Research and Reviews 10, no. 1 (2021): 397–406. http://dx.doi.org/10.30574/wjarr.2021.10.1.0114.

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As demand for high-performance, efficient, and secure data center operations rises, traditional network architectures are increasingly inadequate for modern digital ecosystems. Emerging technologies such as cloud computing, AI, IoT, and big data have overwhelmed existing infrastructures, driving the need for innovative solutions. This paper examines advancements in scalable frameworks, specifically Software-Defined Networking (SDN) and Network Function Virtualization (NFV). SDN centralizes control for dynamic traffic management, while NFV virtualizes network services to enhance flexibility and

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Dibie, Emmanuel. "The Future of Renewable Energy: Ethical Implications of AI and Cloud Technology in Data Security and Environmental Impact." Journal of Advances in Mathematics and Computer Science 39, no. 10 (2024): 62–73. http://dx.doi.org/10.9734/jamcs/2024/v39i101935.

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The increasing integration of artificial intelligence (AI) and cloud technology into renewable energy systems presents a significant opportunity to enhance the efficiency, reliability, and cost-effectiveness of energy production, distribution, and management. These technologies enable real-time data analysis, predictive maintenance, and improved decision-making, essential for managing variable renewable energy sources. However, the ethical implications, such as data security, privacy concerns, and the environmental footprint of cloud infrastructure, remain underexplored. This paper addresses t

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Polu, Omkar Reddy. "AI-DRIVEN PROGNOSTIC FAILURE ANALYSIS FOR AUTONOMOUS RESILIENCE IN CLOUD DATA CENTERS." INTERNATIONAL JOURNAL OF CLOUD COMPUTING 2, no. 2 (2024): 27–37. https://doi.org/10.34218/ijcc_02_02_003.

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Aishwarya Natarajan. "The Hydro-Digital Paradox: Water Scarcity in the Age of Artificial Intelligence." Journal of Computer Science and Technology Studies 7, no. 7 (2025): 500–505. https://doi.org/10.32996/jcsts.2025.7.7.55.

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The development of artificial intelligence is rapidly accelerating, and the demand for computational infrastructure is similarly growing. While these rapidly expanding data centers, known as hyperscale data centers, are of crucial importance for both instruction and operation of advanced AI models, the environmental consequences of this water consumption remain largely invisible to end users. While data centers remain a small fraction of total water consumption, increasing AI capabilities have led to massive increases in water consumption, in many cases, amounting to water consumption similar

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Karthikeyan Selvarajan. "AI-powered big data platforms for enterprise analytics." World Journal of Advanced Engineering Technology and Sciences 15, no. 1 (2025): 2151–61. https://doi.org/10.30574/wjaets.2025.15.1.0441.

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This article presents a comprehensive analysis of AI-powered big data platforms that are revolutionizing enterprise-scale analytics across industries. The article examines the architectural evolution from traditional data warehouses to modern lakehouse paradigms, detailing how artificial intelligence integration transforms core data platform capabilities, including ingestion, storage, processing, and security. The article demonstrates quantifiable performance improvements, with organizations achieving reductions in processing time and cost efficiency gains compared to conventional systems. Thr

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Liu, Xutong. "Scalable and Robust Online Learning for AI-powered Networked Systems." ACM SIGMETRICS Performance Evaluation Review 52, no. 3 (2025): 39–42. https://doi.org/10.1145/3712170.3712183.

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In today's world of pervasive connectivity and intelligent technologies, modern networked systems-ranging from sprawling data centers to large-scale Internet of Things (IoT) systems-have grown by leaps and bounds, unlocking numerous transformative services, like smart cities, immersive mixed reality, and generative artificial intelligence (AI). Traditional network optimization and resource allocation methods-built around static models-are increasingly unable to keep up with the evolving demands of these largescale environments. AI-driven solutions are emerging as game-changers, enabling networ

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Badrinath, Goteti, and Arpita Gupta. "A survey on ransomware detection using AI models." International Journal of Informatics and Communication Technology (IJ-ICT) 14, no. 3 (2025): 1085. https://doi.org/10.11591/ijict.v14i3.pp1085-1094.

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Data centers and cloud environments are compromised as they are at great risk from ransomware attacks, which attack data integrity and security. Through this survey, we explore how AI, especially machine learning and deep learning (DL), is being used to improve ransomware detection capabilities. It classifies ransomware types, highlights active groups such as Akira, and evaluates new DL techniques effective at real-time data analysis and encryption handling. Feature extraction, selection methods, and essential parameters for effective detection, including accuracy, precision, recall, F1-score

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Teja, G., and Siva Prasad. "Revolutionizing Datacenter Sustainability: AI-Power ed Innovations for Water-Efficient Cooling Systems." International Journal of Artificial Intelligence, Machine Learning and Intelligent Systems 1, no. 1 (2025): 1–9. https://doi.org/10.46610/ijaimlis.2025.v01i01.001.

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The exponential growth of data-driven technologies puts astonishing demands on data centers, which massively consume energy and water resources. Traditional cooling systems in data centers rely mainly on water as the primary coolant source, presenting significant environmental challenges and resource scarcity issues. This paper will discuss the potential role of AI in evolving data center cooling systems toward improving water efficiency without sacrificing operational reliability. Applying such innovations, created using predictive analytics, real-time monitoring, and optimization algorithms,

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Jyothsna, Devi Dontha. "Cybersecurity Best Practices For Industrial Automation in Smart Data Centers." International Journal on Science and Technology 12, no. 4 (2021): 1–10. https://doi.org/10.5281/zenodo.14752361.

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This paper explores the best practices for cybersecurity in industrial automation systems within smart data centers, focusing on the protection of critical infrastructure and the prevention of cyber threats. As industries increasingly adopt automation technologies, securing industrial control systems and data centers from cyberattacks has become a top priority. This paper identifies common vulnerabilities, assesses the potential impact of security breaches, and proposes practical cybersecurity strategies. It further discusses the role of IoT, AI, and machine learning in enhancing security fram

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Metwally, Ahmed Sayed M., Yazeed Alhumaidan, Saad Alzahrani, and Mohamed H. Abdelati. "Advanced frameworks for data privacy and ethical considerations in AIpowered library management." International Journal of ADVANCED AND APPLIED SCIENCES 12, no. 5 (2025): 97–108. https://doi.org/10.21833/ijaas.2025.05.010.

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Implementing artificial intelligence (AI) and blockchain technology in management systems transforms traditional libraries into advanced information centers that are data-driven and effectively managed. While these technologies enhance efficiency and operational capabilities, they also present two critical challenges: data privacy and ethical concerns. This study examines the role of AI and blockchain in library management, focusing on issues related to data privacy and ethical challenges that arise from their use. It also offers best practices to ensure safe implementation. The research adopt

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Boschee, Pam. "Comments: Grabbing the Brass Ring To Power the Demand for Data Centers and Generative AI." Journal of Petroleum Technology 76, no. 05 (2024): 8–9. http://dx.doi.org/10.2118/0524-0008-jpt.

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_ “There’s no way to get there without a breakthrough.” These were the words of OpenAI’s CEO Sam Altman at a sideline meeting with Bloomberg at the World Economic Forum (WEF) in Davos, Switzerland, in January, referring to the energy required to power generative AI, data centers, cloud computing, and to support required equipment and infrastructure. As industries swiftly transition into a fresh era of digital revolution, spearheaded by the fast adoption and advancement of generative AI technology, the demand for energy to power data centers and required infrastructure skyrockets. Research firm

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Berkowitz, Gale, Sara Peterson, Eva Marie Smith, Timothy Taylor, and Claire Brindis. "Community and treatment program challenges for chemically dependent American Indian and Alaska Native women." Contemporary Drug Problems 25, no. 2 (1998): 347–71. http://dx.doi.org/10.1177/009145099802500205.

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Alcohol and other drug (AOD) use is a serious and growing problem among American Indian and Alaska Native (AI/AN) women. In addition, there is wide variation across communities in AOD use patterns, access to treatment for substance use, and access to other needed health services. An evaluation study was conducted to document the needs of AI/AN women in AOD treatment and the treatment services and community factors that both facilitate and impede recovery at nine IHS-funded treatment centers. The data illuminate the challenges posed to the treatment centers, and how communities influence the su

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Gopi, Krishna Kalpinagarajarao. "Balancing AI Innovation and Data Privacy in Oracle Cloud-Based Health Systems." International Journal of Innovative Research in Engineering & Multidisciplinary Physical Sciences 13, no. 1 (2025): 1–15. https://doi.org/10.5281/zenodo.14785713.

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Artificial Intelligence (AI) technologies are transforming the healthcare industry through major innovations in patient care, operational efficiency and decision-making. As a strong platform to integrate AI-driven solutions into health systems, Oracle Cloud Infrastructure (OCI) provides scalability in computing power, advanced analytics, and comprehensive data management. Though healthcare hugely benefits from these advances in AI, data security, privacy, and compliance are other sides of the coin, particularly given stringent legislation such as GDPR, HIPAA, and regional health data protectio

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Nath, Sonjoy Kumar. "A TECHNIQUE FOR PROVIDING SECURED UNINTERRUPTED SERVICES OF A DATA CENTER." international journal of advanced research in computer science 15, no. 2 (2024): 15–20. http://dx.doi.org/10.26483/ijarcs.v15i2.7055.

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This paper presents a comprehensive technique for ensuring the secured, uninterrupted operation of data centers, addressing the dual challenges of resilience against disruptions and robust security against cyber-physical threats. By integrating advanced cryptographic methods, dynamic access control, AI-based anomaly detection, and sustainable power solutions, the proposed approach offers a novel framework for enhancing data center reliability and security. The study synthesizes multidisciplinary research, including cybersecurity, power engineering, and environmental sustainability, to construc

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Lu, Zhaojie, Rhibull Tang, Zijia Wan, and Zetao Ye. "Mitigating the Environmental Impact of High-Performance Computing Data Centers." International Journal of Energy 6, no. 3 (2025): 9–18. https://doi.org/10.54097/y0yb9r53.

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While High Performance Computing (HPC) is critical for scientific breakthroughs and AI development, its growing energy use (often dependent on fossil fuels) requires a detailed environmental impact analysis. This study addresses the ever-increasing ecological challenges posed by high-performance computing (HPC) data centers by developing a model to quantify and predict their energy consumption and carbon emissions. Our model estimates the annual energy consumption of HPC data centers globally using data on the size and capacity of small and massive data centers. It distinguishes between theore

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Bedir, Galip, Ibrahim Benek, Eda Yuca, and Ismail Donmez. "Gifted Students' Perceptions of Artificial Intelligence through Drawings: A Perspective from Science and Art Centers." Journal of Education in Science, Environment and Health 11, no. 2 (2025): 126–39. https://doi.org/10.55549/jeseh.785.

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Artificial Intelligence (AI) emerges as the development of computer systems and software that imitate human abilities and perform human-like tasks. Understanding what gifted students think about this system that includes deep cognitive abilities is considered important. Based on this premise, this study examines the perceptions of gifted students towards the concept of AI. The research was conducted using phenomenological design, a qualitative research method. The data of the research were collected from 50 gifted students enrolled at a Science and Art Center and selected through a convenience

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Liu, Pengcheng, Wei Ji, Qiang Liu, and Xuwei Xue. "AI-Assisted Failure Location Platform for Optical Network." International Journal of Optics 2023 (February 1, 2023): 1–10. http://dx.doi.org/10.1155/2023/1707815.

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In the paper, we applied the customized AI module to the OTDR device and, combined with the optical power monitoring module, realized the AI-assisted optical network fault location mechanism for the high-density interconnection scenario of data centers. The mechanism can make full use of the data from optical links. Based on the link data, the AI module can predict the links that may fail, and then the target links will be monitored by the optical power module. The mechanism can quickly locate and respond to faulty links. Through the test, the introduction of an AI model can improve the averag

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Ebby Darney, P. "A Review on Artificial Intelligence Chip." December 2022 1, no. 1 (2022): 99–109. http://dx.doi.org/10.36548/rrrj.2023.1.009.

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As chipmakers design different types of chips to enable Artificial Intelligence (AI) applications, the adoption of AI chips has increased recently. To support applications based on deep learning, AI chips have inbuilt AI acceleration and are created with a specialized architecture. One of the key drivers boosting the market's expansion is the increasing integration of AI processors in data centers. The major significance of using AI chips when compared with traditional ICs are fast computational integration and large bandwidth. This study summarizes the need of the AI chips and its functionali

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