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Journal articles on the topic 'Deployment models'

Author: Grafiati

Published: 4 June 2021

Last updated: 25 July 2025

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1

Ravi, Shankar Koppula. "Databricks MLflow." Journal of Scientific and Engineering Research 8, no. 11 (2021): 134–45. https://doi.org/10.5281/zenodo.11232369.

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This paper examines MLflow, an open-source platform specifically designed to simplify the management of the machine learning lifecycle. It covers various aspects, such as experiment tracking, code packaging, and sharing and deployment of models. The paper focuses on the integration of MLflow with Databricks, emphasizing how this collaboration enhances automatic experiment tracking and provides easier access to data and models. This integration ultimately leads to more efficient and reproducible machine learning workflows. The paper thoroughly explores the four main components of MLflow: MLflow

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Reddy, Vonteru Srikanth, and Kumar Debasis. "Statistical Review of Health Monitoring Models for Real-Time Hospital Scenarios." International Journal on Recent and Innovation Trends in Computing and Communication 11, no. 7s (2023): 465–81. http://dx.doi.org/10.17762/ijritcc.v11i7s.7025.

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Health Monitoring System Models (HMSMs) need speed, efficiency, and security to work. Cascading components ensure data collection, storage, communication, retrieval, and privacy in these models. Researchers propose many methods to design such models, varying in scalability, multidomain efficiency, flexibility, usage and deployment, computational complexity, cost of deployment, security level, feature usability, and other performance metrics. Thus, HMSM designers struggle to find the best models for their application-specific deployments. They must test and validate different models, which incr

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Howick, R. S., and M. Pidd. "Sales force deployment models." European Journal of Operational Research 48, no. 3 (1990): 295–310. http://dx.doi.org/10.1016/0377-2217(90)90413-6.

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B. Patel, Prof Hiral, and Prof Nirali Kansara. "Cloud Computing Deployment Models: A Comparative Study." International Journal of Innovative Research in Computer Science & Technology 9, no. 2 (2021): 45–50. http://dx.doi.org/10.21276/ijircst.2021.9.2.8.

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Rimbaud, Loup, Frédéric Fabre, Julien Papaïx, et al. "Models of Plant Resistance Deployment." Annual Review of Phytopathology 59, no. 1 (2021): 125–52. http://dx.doi.org/10.1146/annurev-phyto-020620-122134.

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Owing to their evolutionary potential, plant pathogens are able to rapidly adapt to genetically controlled plant resistance, often resulting in resistance breakdown and major epidemics in agricultural crops. Various deployment strategies have been proposed to improve resistance management. Globally, these rely on careful selection of resistance sources and their combination at various spatiotemporal scales (e.g., via gene pyramiding, crop rotations and mixtures, landscape mosaics). However, testing and optimizing these strategies using controlled experiments at large spatiotemporal scales are

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Sriningsih, Riry, Muhammad Subhan, and Minora Longgom Nasution. "Analysis of torch deployment models." Journal of Physics: Conference Series 1317 (October 2019): 012013. http://dx.doi.org/10.1088/1742-6596/1317/1/012013.

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BUSHEHRIAN, OMID. "SOFTWARE PERFORMANCE ENGINEERING BY SIMULATED-BASED OBJECT DEPLOYMENT." International Journal of Software Engineering and Knowledge Engineering 23, no. 02 (2013): 211–21. http://dx.doi.org/10.1142/s0218194013500058.

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The object deployment of a distributed software has a great impact on its performance. In this paper an analytical model for performance evaluation of different object deployments, is presented. The key advantage of the proposed model over the traditional Queuing Network models is the usefulness in the deployment optimization when the search space is huge and automatic instantiation of Queuing performance models corresponding to an object deployment is costly. Since our model produces an optimal deployment corresponding to each input load separately, the runtime behavior of the software corres

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Abdulrahman, Sahar, and Markus Trengove. "Mitigated deployment strategy for ethical AI in clinical settings." BMJ Health & Care Informatics 32, no. 1 (2025): e101363. https://doi.org/10.1136/bmjhci-2024-101363.

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Clinical diagnostic tools can disadvantage subgroups due to poor model generalisability, which can be caused by unrepresentative training data. Practical deployment solutions to mitigate harm for subgroups from models with differential performance have yet to be established. This paper will build on existing work that considers a selective deployment approach where poorly performing subgroups are excluded from deployments. Alternatively, the proposed ‘mitigated deployment’ strategy requires safety nets to be built into clinical workflows to safeguard under-represented groups in a universal dep

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Vijayan, Naveen Edapurath. "Building Scalable MLOps: Optimizing Machine Learning Deployment and Operations." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 10 (2024): 1–9. http://dx.doi.org/10.55041/ijsrem37784.

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As machine learning (ML) models become increasingly integrated into mission-critical applications and production systems, the need for robust and scalable MLOps (Machine Learning Operations) practices has grown significantly. This paper explores key strategies and best practices for building scalable MLOps pipelines to optimize the deployment and operation of machine learning models at an enterprise scale. It delves into the importance of automating the end-to-end lifecycle of ML models, from data ingestion and model training to testing, deployment, and monitoring. Approaches for implementing

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Vinayak, Kalluri, and Rambabu Kodali. "Benchmarking the quality function deployment models." Benchmarking: An International Journal 20, no. 6 (2013): 825–54. http://dx.doi.org/10.1108/bij-07-2011-0052.

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Perakis, Anastassions N., and Nikiforos Papadakis. "Fleet deployment optimization models. Part 1." Maritime Policy & Management 14, no. 2 (1987): 127–44. http://dx.doi.org/10.1080/03088838700000015.

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Martín-Martínez, Francisco, Jaime Boal, Álvaro Sánchez-Miralles, Carlos Becker Robles, and Rubén Rodríguez-Vilches. "Technical deployment of aggregator business models." Heliyon 10, no. 9 (2024): e30101. http://dx.doi.org/10.1016/j.heliyon.2024.e30101.

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Swamy, Prasadarao Velaga. "Continuous Deployment of AI Systems: Strategies for Seamless Updates and Rollbacks." International Journal of Innovative Research in Engineering & Multidisciplinary Physical Sciences 6, no. 6 (2018): 1–8. https://doi.org/10.5281/zenodo.12805458.

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The deployment of artificial intelligence (AI) systems poses unique challenges compared to traditional software applications, primarily due to the dynamic nature of AI models and their sensitivity to data changes. Continuous deployment (CD) strategies play a crucial role in managing these complexities by enabling organizations to deploy, update, and manage AI models seamlessly and efficiently. This paper reviews key strategies for implementing CD in AI systems, focusing on seamless updates and robust rollback mechanisms. Strategies discussed include incremental deployment, A/B testing, canary

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Bollineni, Satyadeepak. "Implementing DevOps Strategies for Deploying and Managing Machine Learning Models in Lakehouse Platforms." International Journal of Multidisciplinary Research and Growth Evaluation 5, no. 4 (2024): 1367–71. https://doi.org/10.54660/.ijmrge.2024.5.4.1367-1371.

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The paper addresses the entry of DevOps into lakehouse platforms to lessen the deployment and administration of machine learning models. It intends to discover the successful practices that achieve faster deployments, better operational efficiency, and strong management of data-driven applications without technical jargon. Streamlining processes and increasing collaboration across development and operations teams take DevOps miles ahead in adaptability and efficiency with lakehouse platforms. The paper includes pragmatic implementations and transformational potential between these hybrid data

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Hungness, Derek, and Raj Bridgelall. "Model Contrast of Autonomous Vehicle Impacts on Traffic." Journal of Advanced Transportation 2020 (August 14, 2020): 1–10. http://dx.doi.org/10.1155/2020/8935692.

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The adoption of connected and autonomous vehicles (CAVs) is in its infancy. Therefore, very little is known about their potential impacts on traffic. Meanwhile, researchers and market analysts predict a wide range of possibilities about their potential benefits and the timing of their deployments. Planners traditionally use various types of travel demand models to forecast future traffic conditions. However, such models do not yet integrate any expected impacts from CAV deployments. Consequently, many long-range transportation plans do not yet account for their eventual deployment. To address

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Researcher. "MACHINE LEARNING MODELS IN PRODUCTION: A SYSTEMATIC FRAMEWORK FOR SCALABLE AND ROBUST DEPLOYMENT." International Journal of Research In Computer Applications and Information Technology (IJRCAIT) 15, no. 6 (2024): 1608–28. https://doi.org/10.5281/zenodo.14243017.

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This article presents a comprehensive framework for deploying and productizing machine learning models in real-world industrial settings, addressing the critical gap between laboratory development and production implementation. Through a systematic analysis of 47 enterprise-scale ML deployments across diverse industries, we identify key challenges and establish best practices for transforming experimental models into robust production systems. The methodology encompasses four primary dimensions: technical integration architecture, operational excellence, continuous monitoring systems, and

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So, C. J., C. A. Alfano, L. A. Riviere, and P. J. Quartana. "0273 Residual Sleep Difficulties During Reset Operations Predict Greater Post-Deployment Mental Health Difficulties in U.S. Soldiers: A Cross-Lagged Analysis." Sleep 43, Supplement_1 (2020): A104. http://dx.doi.org/10.1093/sleep/zsaa056.271.

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Abstract Introduction Military service is associated with a number of occupational stressors, including non-conducive sleeping environments, shift schedules, and extended deployments overseas. Service members who undergo combat deployments are at increased risk for mental health and sleep difficulties. Bidirectional associations between sleep and mental health difficulties are routinely observed, but the directional association of these difficulties from one deployment to the next has not been addressed. The purpose of this study was to examine whether residual sleep problems or mental health

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Ayodele Emmanuel Sonuga, Kingsley David Onyewuchi Ofoegbu, Chidiebere Somadina Ike, and Samuel Olaoluwa Folorunsho. "Deploying large language models on diverse computing architectures: A performance evaluation framework." Global Journal of Research in Engineering and Technology 2, no. 1 (2024): 018–36. http://dx.doi.org/10.58175/gjret.2024.2.1.0026.

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Deploying large language models (LLMs) across diverse computing architectures is a critical challenge in the field of artificial intelligence, particularly as these models become increasingly complex and resource-intensive. This review presents a performance evaluation framework designed to systematically assess the deployment of LLMs on various computing architectures, including CPUs, GPUs, TPUs, and specialized accelerators. The framework is structured around key performance metrics such as computational efficiency, latency, throughput, energy consumption, and scalability. It considers the t

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Eid, Mustafa I. M., Ibrahim M. Al-Jabri, and M. Sadiq Sohail. "Selection of Cloud Delivery and Deployment Models." International Journal of Decision Support System Technology 10, no. 4 (2018): 17–32. http://dx.doi.org/10.4018/ijdsst.2018100102.

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Research interests on cloud computing adoption and its effectiveness in terms of cost and time has been increasing. However, one of the challenging decisions facing management in adopting cloud services is taking on the right combinations of cloud service delivery and deployment models. A comprehensive review of literature revealed a lack of research addressing this selection decision problem. To fill this research gap, this article proposes an expert system approach for managers to decide on the right combination of service delivery and deployment model selection. The article first proposes a

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Kim, Kwang-Jae, Herbert Moskowitz, Anoop Dhingra, and Gerald Evans. "Fuzzy multicriteria models for quality function deployment." European Journal of Operational Research 121, no. 3 (2000): 504–18. http://dx.doi.org/10.1016/s0377-2217(99)00048-x.

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BALASUBRAMANIAN, KRISHNAKUMAR, ANIRUDDHA GOKHALE, YUEHUA LIN, JING ZHANG, and JEFF GRAY. "WEAVING DEPLOYMENT ASPECTS INTO DOMAIN-SPECIFIC MODELS." International Journal of Software Engineering and Knowledge Engineering 16, no. 03 (2006): 403–24. http://dx.doi.org/10.1142/s021819400600280x.

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Domain-specific models increase the level of abstraction used to develop large-scale component-based systems. Model-driven development (MDD) approaches (e.g., Model-Integrated Computing and Model-Driven Architecture) emphasize the use of models at all stages of system development. Decomposing problems using MDD approaches may result in a separation of the artifacts in a way that impedes comprehension. For example, a single concern (such as deployment of a distributed system) may crosscut different orthogonal activities (such as component specification, interaction, packaging and planning). To

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Piridi, Sarat, Satyanarayana Asundi, and Dr James C. Hyatt. "Cross-Environment Deployment Strategies for Power Platform Solutions – Investigating best practices for managing multi-environment deployments, from development to production, using managed environments and DevOps." International Journal of Advanced Engineering Research and Science 12, no. 4 (2025): 66–75. https://doi.org/10.22161/ijaers.124.8.

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The cross-environment deployment strategies for Power Platform solutions which include moving Power Platform solutions from development to production using managed environments and DevOps practices. The paper takes advantage of ten key academic and industry sources to evaluate frameworks, automation tools and governing models to streamline deployment and enhance system reliability. The measurable benefits of such case studies are reduced deployment time and improved accuracy. With this DevOps deployed throughout cloud and hybrid platform and agile methodology, it facilitates scalable and secur

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Beltrán, Fernando, Marlies Van der Wee, and Sofie Verbruggen. "A Comparative Analysis of Selected National and Regional Investment Initiatives That Seek to Achieve Broadband Expansion by Deploying NGA Networks." Journal of Information Policy 8, no. 1 (2018): 267–95. http://dx.doi.org/10.5325/jinfopoli.8.1.0267.

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Abstract Expectations about higher economic growth and the ever-increasing demand for higher bandwidth are driving the worldwide deployment of Next-Generation Access (NGA) networks. The paths followed to achieve this goal markedly vary, however, across different countries. This article offers a comparison of a handful of leading NGA deployments that rely on different investment models. We study the broadband national initiatives of New Zealand and Australia and a group of selected regional NGA deployments in Europe. While New Zealand's approach partially relies on a public–private partnership

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Wang, Zhigang, Liqin Tian, Lianhai Lin, and Yinghua Tong. "Lattice-Based 3-Dimensional Wireless Sensor Deployment." Journal of Sensors 2021 (August 17, 2021): 1–14. http://dx.doi.org/10.1155/2021/2441122.

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With the wide application of wireless sensor networks (WSNs) in real space, there are numerous studies on 3D sensor deployments. In this paper, the k -connectivity theoretical model of fixed and random nodes in regular lattice-based deployment was proposed to study the coverage and connectivity of sensor networks with regular lattice in 3D space. The full connectivity range and cost of the deployment with sensor nodes fixed in the centers of four regular lattices were quantitatively analyzed. The optimal single lattice coverage model and the ratio of the communication range to the sensing rang

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Ukani, Neema Amish, and Saurabh S. Chakole. "Empirical analysis of machine learning-based moisture sensing platforms for agricultural applications: A statistical perspective." Journal of Physics: Conference Series 2327, no. 1 (2022): 012026. http://dx.doi.org/10.1088/1742-6596/2327/1/012026.

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Abstract Modelling of accurate detection & estimation soil moisture sensors requires integration of various signal processing, filtering, segmentation, and pattern analysis methods. Sensing of moisture is generally performed via use of resistive, or capacitive materials, which change their parametric characteristics w.r.t. changes in moisture levels. These sensors are further classified depending upon capabilities of measurements, which include, volumetric sensors, soil water tensor sensors, electromagnetic sensors, time domain reflectometry (TDR) sensors, Neutron probe sensors, tensiomete

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Prapas, Ioannis, Behrouz Derakhshan, Alireza Rezaei Mahdiraji, and Volker Markl. "Continuous Training and Deployment of Deep Learning Models." Datenbank-Spektrum 21, no. 3 (2021): 203–12. http://dx.doi.org/10.1007/s13222-021-00386-8.

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AbstractDeep Learning (DL) has consistently surpassed other Machine Learning methods and achieved state-of-the-art performance in multiple cases. Several modern applications like financial and recommender systems require models that are constantly updated with fresh data. The prominent approach for keeping a DL model fresh is to trigger full retraining from scratch when enough new data are available. However, retraining large and complex DL models is time-consuming and compute-intensive. This makes full retraining costly, wasteful, and slow. In this paper, we present an approach to continuousl

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Boubrima, Ahmed, Walid Bechkit, and Herve Rivano. "Optimal WSN Deployment Models for Air Pollution Monitoring." IEEE Transactions on Wireless Communications 16, no. 5 (2017): 2723–35. http://dx.doi.org/10.1109/twc.2017.2658601.

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Kaskie, B. "The Widespread Deployment of Integrated Models of Care." Public Policy & Aging Report 23, no. 3 (2013): 1–9. http://dx.doi.org/10.1093/ppar/23.3.1a.

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Esposito, Richard A., Larry S. Monroe, and Julio S. Friedman. "Deployment Models for Commercialized Carbon Capture and Storage†." Environmental Science & Technology 45, no. 1 (2011): 139–46. http://dx.doi.org/10.1021/es101441a.

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Ng, ManWo. "Revisiting a class of liner fleet deployment models." European Journal of Operational Research 257, no. 3 (2017): 773–76. http://dx.doi.org/10.1016/j.ejor.2016.07.044.

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Bisman, Singh, Tuli Bhumika, and Kumar Rakesh. "Cloud computing: Virtualization, service models, and deployment options." i-manager’s Journal on Cloud Computing 11, no. 2 (2024): 28. https://doi.org/10.26634/jcc.11.2.21197.

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Cloud computing has completely transformed how users access and use applications, services, and data. This study provides a thorough analysis of cloud computing, including its history, key features, the role of virtualization in cloud environments, and various cloud service models. From the introduction of time-sharing in the 1960s to its widespread adoption in the 2000s, cloud computing has evolved significantly. The properties of cloud computing, such as resource pooling, on-demand self-service, measured service, resilience, and rapid flexibility, are examined in this research. Virtualizatio

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Luyembi, Tshiniama Honoré, and Mudinga Arsène Banza. "Comparative Study of Different Cloud Computing Deployment Models." International Journal of Innovative Science and Research Technology (IJISRT) 10, no. 2 (2025): 579–87. https://doi.org/10.5281/zenodo.14921249.

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Cloud Computing, often seen as a technological revolution, makes it possible to dematerialize information systems by making services accessible via a communication network, more often via the Internet. Its growth can be explained by its major advantages such as cost reduction, greater flexibility and independence from traditional physical infrastructures. We will compare the 4 cloud deployment models by showing how each model works. We will also detail the benefits and risks associated with each model.  The Study Focuses on these Four main Deployment models, Namely:  Public Cloud,  Private

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Stötzner, Miles, Steffen Becker, Uwe Breitenbücher, Kálmán Képes, and Frank Leymann. "Modeling Different Deployment Variants of a Composite Application in a Single Declarative Deployment Model." Algorithms 15, no. 10 (2022): 382. http://dx.doi.org/10.3390/a15100382.

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For automating the deployment of composite applications, typically, declarative deployment models are used. Depending on the context, the deployment of an application has to fulfill different requirements, such as costs and elasticity. As a consequence, one and the same application, i.e., its components, and their dependencies, often need to be deployed in different variants. If each different variant of a deployment is described using an individual deployment model, it quickly results in a large number of models, which are error prone to maintain. Deployment technologies, such as Terraform or

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Hieder, Inaam Abbas. "Compared to wireless deployment in areas with different environmentse." International Journal of Electrical and Computer Engineering (IJECE) 9, no. 2 (2019): 934–40. https://doi.org/10.11591/ijece.v9i2.pp934-940.

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In the mobile phone system, it is highly desirable to estimate the loss of the track not only to improve performance but also to achieve an accurate estimate of financial feasibility; the inaccurate estimate of track loss either leads to performance degradation or increased cost. Various models have been introduced to accurately estimate the path loss. One of these models is the Okomura / Hata model, which is recommended for estimating path loss in cellular systems that use micro cells. This system is suitable for use in a variety of environments. This study examines the comparison of path los

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Liagkou, Vasiliki, George Fragiadakis, Evangelia Filiopoulou, Vagia Kyriakidou, Christos Michalakelis, and Mara Nikolaidou. "Comparing the Cost of IaaS and CaaS Services." International Journal of Technology Diffusion 13, no. 1 (2022): 1–11. http://dx.doi.org/10.4018/ijtd.315632.

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Cloud computing environments allow businesses to deploy applications in a fast and scalable way. Infrastructure-as-a-service (IaaS) and container-as-a-service (CaaS) models can be adopted for the deployment of cloud-based applications. The current paper presents a specific near-real-world scenario of a cloud-based application, deployed by the two aforementioned cloud models. The deployment cost differs between the cloud models and relies on the number of utilized resources, which is driven by the user demand. Since the cost is a major importance factor that finally determines the adoption of c

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Toluwase Peter Gbenle, Abraham Ayodeji Abayomi, Abel Chukwuemeke Uzoka, Oyejide Timothy Odofin, Oluwasanmi Segun Adanigbo, and Jeffrey Chidera Ogeawuchi. "Developing an AI Model Registry and Lifecycle Management System for Cross-Functional Tech Teams." International Journal of Scientific Research in Science, Engineering and Technology 11, no. 4 (2024): 442–56. https://doi.org/10.32628/ijsrset25121179.

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This paper presents a comprehensive solution for managing AI models across their lifecycle through the development of an AI model registry and lifecycle management system. As AI continues to play a crucial role across industries, the complexity of managing models—from development to deployment—presents significant challenges, especially within cross-functional teams. These challenges include issues such as model versioning, metadata management, deployment inconsistencies, and communication breakdowns among data scientists, engineers, and business stakeholders. The proposed system addresses the

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Reingle Gonzalez, Jennifer M., Stephen A. Bishopp, Katelyn K. Jetelina, Ellen Paddock, Kelley Pettee Gabriel, and M. Brad Cannell. "Does military veteran status and deployment history impact officer involved shootings? A case–control study." Journal of Public Health 41, no. 3 (2018): e245-e252. http://dx.doi.org/10.1093/pubmed/fdy151.

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AbstractBackgroundDespite veterans’ preference hiring policies by law enforcement agencies, no studies have examined the nature or effects of military service or deployments on health outcomes. This study will examine the effect of military veteran status and deployment history on law enforcement officer (LEO)-involved shootings.MethodsTen years of data were extracted from Dallas Police Department records. LEOs who were involved in a shooting in the past 10 years were frequency matched on sex to LEOs never involved in a shooting. Military discharge records were examined to quantify veteran sta

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Elkhatib, Yehia. "Building Cloud Applications for Challenged Networks." Communications in Computer and Information Science 514 (November 21, 2015): 1–10. https://doi.org/10.1007/978-3-319-25043-4_1.

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Cloud computing has seen vast advancements and uptake in many parts of the world. However, many of the design patterns and deployment models are not very suitable for locations with challenged networks such as countries with no nearby datacenters. This paper describes the problem and discusses the options available for such locations, focusing specifically on community clouds as a short-term solution. The paper highlights the impact of recent trends in the development of cloud applications and how changing these could better help deployment in challenged networks. The paper also outlines the c

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Nissen, Lars R., Karen-Inge Karstoft, Mia S. Vedtofte, et al. "Cognitive ability and risk of post-traumatic stress disorder after military deployment: an observational cohort study." BJPsych Open 3, no. 6 (2017): 274–80. http://dx.doi.org/10.1192/bjpo.bp.117.005736.

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BackgroundStudies of the association between pre-deployment cognitive ability and post-deployment post-traumatic stress disorder (PTSD) have shown mixed results.AimsTo study the inflence of pre-deployment cognitive ability on PTSD symptoms 6–8 months post-deployment in a large population while controlling for pre-deployment education and deployment-related variables.MethodStudy linking prospective pre-deployment conscription board data with post-deployment self-reported data in 9695 Danish Army personnel deployed to different war zones in 1997–2013. The association between pre-deployment cogni

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Dunbar, Christopher R., Mark S. Riddle, Kristen Clarkson, et al. "1104. Deployment-Associated Infectious Gastroenteritis and Associations With Irritable Bowel Syndrome, Post-Traumatic Stress Disorder, and Combat Stress: A Retrospective Cohort Study Among Deployed United States Military Personnel." Open Forum Infectious Diseases 5, suppl_1 (2018): S331. http://dx.doi.org/10.1093/ofid/ofy210.938.

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Abstract Background Previous studies have shown an association between post-traumatic stress disorder (PTSD) and the development of irritable bowel syndrome (IBS) in deployed service members. Deployment places soldiers at risk for chemical, physical, psychological, and infectious stressors. Acute stress can alter the gastrointestinal barrier leading to gut barrier dysfunction, which is an independent risk factor for infectious gastroenteritis (IGE). We sought to assess if there was an association between IBS and PTSD in military deployed in support of recent and ongoing military operations. Me

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Mokale, Mahesh. "Automated Debugging and Deployment for High-Performance Telecom Applications." International Scientific Journal of Engineering and Management 02, no. 11 (2023): 1–8. https://doi.org/10.55041/isjem00206.

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Abstract: High-performance telecom applications require efficient debugging and deployment strategies to ensure reliability, scalability, and seamless operations. These applications operate within highly complex and distributed environments where even minor failures or inefficiencies can result in significant service disruptions, financial losses, and customer dissatisfaction. Given the critical role telecom applications play in enabling global communication networks, minimizing downtime, optimizing system performance, and maintaining operational continuity is a top priority for telecom servic

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Gonnade, Priyanka, and Sonali Ridhorkarb. "Empirical analysis of decision recommendation models for various processes from a pragmatic perspective." Multidisciplinary Reviews 7, no. 8 (2024): 2024159. http://dx.doi.org/10.31893/multirev.2024159.

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Decision recommendation models allow researchers and process designers to identify and implement high-efficiency processes in ambiguous situations. These models perform multipara metric analysis on the given process sets to recommend high-quality decisions that assist in improving process-based efficiency levels. A wide variety of models have been proposed by researchers for the implementation of such recommenders, and each of them varies in terms of their functional nuances, applicative advantages, internal operating characteristics, contextual limitations, and deployment-specific future scop

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Gonnade, Priyanka, and Sonali Ridhorkar. "Empirical Analysis of Decision Recommendation Models for Various Processes from A Pragmatic Perspective." British Journal of Multidisciplinary and Advanced Studies 4, no. 6 (2023): 20–49. http://dx.doi.org/10.37745/bjmas.2022.0358.

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Decision recommendation models allow researchers and process designers to identify & implement high-efficiency processes under ambiguous situations. These models perform multiparametric analysis on the given process sets in order to recommend high quality decisions that assist in improving process-based efficiency levels. A wide variety of models are proposed by researchers for implementation of such recommenders, and each of them varies in terms of their functional nuances, applicative advantages, internal operating characteristics, contextual limitations, and deployment-specific future s

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Kompally, Venkata Srinivas. "A Review of Large Language Models in Edge Computing: Applications, Challenges, Benefits, and Deployment Strategies." International journal of data science and machine learning 05, no. 01 (2025): 300–322. https://doi.org/10.55640/ijdsml-05-01-25.

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Large Language Models (LLMs) have achieved very good success in natural language processing, but deployment of these powerful models on edge computing devices across all domains presents unique challenges. This paper reviews the state of LLMs in edge computing, focusing on four key aspects: their emerging applications across various sectors, the technical challenges of running LLMs on resource-constrained edge devices, the potential benefits of bringing LLM capabilities closer to data sources, and effective deployment strategies to enable LLMs at the edge. We also discuss on how LLM edge deplo

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Baldini, Edoardo, Stefano Chessa, and Antonio Brogi. "Estimating the Environmental Impact of Green IoT Deployments." Sensors 23, no. 3 (2023): 1537. http://dx.doi.org/10.3390/s23031537.

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The Internet of Things (IoT) is demonstrating its huge innovation potential, but at the same time, its spread can induce one of highest environmental impacts caused by the IoT industry. This concern has motivated the rise of a new research area aimed at devising green IoT deployments. Our work falls in this research area by contributing to addressing the problem of assessing the environmental impact of IoT deployments. Specifically, we propose a methodology based on an analytical model to assess the environmental impact of an outdoor IoT deployment powered by solar energy harvesting. The model

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Hedenus, F., N. Jakobsson, L. Reichenberg, and N. Mattsson. "Historical wind deployment and implications for energy system models." Renewable and Sustainable Energy Reviews 168 (October 2022): 112813. http://dx.doi.org/10.1016/j.rser.2022.112813.

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Fan, Tie Gang, Gui Fa Teng, and Li Min Huo. "Minimum Cost Programming Models of Nodes Deployment for WSNs." Applied Mechanics and Materials 719-720 (January 2015): 696–701. http://dx.doi.org/10.4028/www.scientific.net/amm.719-720.696.

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WSNs can cover a wide range of application. Node deployment is a fundamental factor in determining the connectivity, coverage, lifetime and cost of WSNs. This paper focuses on the cost of network that satisfies some constraints (coverage, connectivity and lifetime). In order to satisfy the connectivity and coverage, we use the regular hexagonal cell architecture. We present a new metric, the Cost Per Unit Area and Lifetime, to be objective function. Three programming models are proposed under different scenarios. For reasons of space, we present briefly the method to solve above models and som

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Castro, Diogo, Prasanth Kothuri, Piotr Mrowczynski, Danilo Piparo, and Enric Tejedor. "Apache Spark usage and deployment models for scientific computing." EPJ Web of Conferences 214 (2019): 07020. http://dx.doi.org/10.1051/epjconf/201921407020.

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This talk is about sharing our recent experiences in providing data analytics platform based on Apache Spark for High Energy Physics, CERN accelerator logging system and infrastructure monitoring. The Hadoop Service has started to expand its user base for researchers who want to perform analysis with big data technologies. Among many frameworks, Apache Spark is currently getting the most traction from various user communities and new ways to deploy Spark such as Apache Mesos or Spark on Kubernetes have started to evolve rapidly. Meanwhile, notebook web applications such as Jupyter offer the ab

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Yang, Zhaojing, Min Xu, Xuecheng Tian, Yong Jin, and Shuaian Wang. "Optimal Deployment of Container Weighing Equipment: Models and Properties." Applied Sciences 14, no. 17 (2024): 7798. http://dx.doi.org/10.3390/app14177798.

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Container weighing is crucial to the safety of the shipping system and has garnered significant attention in the maritime industry. This research develops a container weighing optimization model and validates several propositions derived from this model. Then, a case study is conducted on ports along the Yangtze River, and the sensitivity analysis of the model is provided. We report the following findings. First, the model can be solved efficiently for large-scale optimization problems. Second, as the number of weighing machines increases, the container weighing mode changes—from selectively w

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Sivasamy, K., C. Arumugam, S. R. Devadasan, R. Murugesh, and V. M. M. Thilak. "Advanced models of quality function deployment: a literature review." Quality & Quantity 50, no. 3 (2015): 1399–414. http://dx.doi.org/10.1007/s11135-015-0212-2.

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