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Littérature scientifique sur le sujet « Predictive Intelligence »

Auteur : Grafiati

Publié le 2 juin 2025

Mis à jour le 31 juillet 2025

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Articles de revues sur le sujet "Predictive Intelligence"

Alghamdi, Turki. "Prediction of Diabetes Complications Using Computational Intelligence Techniques." Applied Sciences 13, no. 5 (2023): 3030. http://dx.doi.org/10.3390/app13053030.

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Diabetes is a complex disease that can lead to serious health complications if left unmanaged. Early detection and treatment of diabetes is crucial, and data analysis and predictive techniques can play a significant role. Data mining techniques, such as classification and prediction models, can be used to analyse various aspects of data related to diabetes, and extract useful information for early detection and prediction of the disease. XGBoost classifier is a machine learning algorithm that effectively predicts diabetes with high accuracy. This algorithm uses a gradient-boosting framework an

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Şahin, Hüseyin. "Predictive UAV Battery Maintenance Planning with Artificial Intelligence." Journal of Aviation 9, no. 2 (2025): 260–69. https://doi.org/10.30518/jav.1546277.

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This research paper explores the use of artificial intelligence (AI) in the maintenance planning of electric batteries for unmanned aerial vehicles (UAVs). Traditional maintenance strategies are challenged by the impact on battery performance and the complexity of battery degradation, highlighting the importance of an AI-assisted predictive maintenance approach. The research predicts battery degradation using machine learning techniques, specifically Artificial Neural Networks (ANN) model, in combination with MATLAB's Remaining Useful Life (RUL) Prediction Toolbox. The AI model is designed to

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Zhang, Li, Song Wang, and Guo Jun Su. "Intelligence Predictive Control Study on Lime Rotary Kiln Temperature." Applied Mechanics and Materials 385-386 (August 2013): 848–51. http://dx.doi.org/10.4028/www.scientific.net/amm.385-386.848.

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For the non-linearity, large time lag characters of rotary kiln, we use intelligent predictive control method to control it. The prediction model, scrolling optimization and feedback adjustment are ultimate constituted the predictive control system each part. Gas flow measurement is used to realize rotary kiln`s temperature predictive control,and took NN-Model as prediction model to realize the intelligent forecast. The results of simulation show that this method has better stability and robustness than the traditional control method.

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Sugarwar, Kalyani S., and Santanu Sikdar. "Artificial Intelligence Applications in Predictive Healthcare Systems." Journal of Advances and Scholarly Researches in Allied Education 22, no. 01 (2025): 322–33. https://doi.org/10.29070/s2zg4656.

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Predictive systems made possible by artificial intelligence (AI) are revolutionising healthcare by allowing for more precise, rapid, and individualised medical procedures. Using data analytics, NLP, and machine learning algorithms, this article delves into the ways AI is being applied to predictive healthcare, specifically in the areas of illness risk prediction, treatment plan optimisation, and patient outcome improvement. Using massive datasets derived from genetic information, electronic health records, and real-time monitoring equipment, predictive algorithms seek out trends and outliers t

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Aleksić, Veljko, and Dionysios Politis. "Trait Emotional Intelligence and Multiple Intelligences as Predictors of Academic Success in Serbian and Greek IT Students." International Journal of Cognitive Research in Science, Engineering and Education (IJCRSEE) 11, no. 2 (2023): 173–85. http://dx.doi.org/10.23947/2334-8496-2023-11-2-173-185.

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Even though research on predicting the academic achievement of IT students is not scarce, the inclusion of trait emotional intelligence and multiple intelligences as predictive factors is somewhat novel. The research examined associations between identified profiles of trait emotional intelligence and multiple intelligences, and academic success in the sample of 288 IT students, 208 from Serbia and 80 from Greece. The results show that trait emotional intelligence and multiple intelligences profile both proved to be important predictors of academic success. Another predictor of IT students’ ac

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Chornous, Galyna O., and Viktoriya L. Gura. "Integration of Information Systems for Predictive Workforce Analytics: Models, Synergy, Security of Entrepreneurship." European Journal of Sustainable Development 9, no. 1 (2020): 83. http://dx.doi.org/10.14207/ejsd.2020.v9n1p83.

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The era of information economy leads to redesigning not only business models of organizations but also to rethinking the human resources paradigm to harness the power of state-of-the-art technology for Human Capital Management (HCM) optimization. Predictive analytics and computational intelligence will bring transformative change to HCM. This paper deals with issues of HCM optimization based on the models of predictive workforce analytics (WFA) and Business Intelligence (BI). The main trends in the implementation of predictive WFA in the world and in Ukraine, as well as the need to protect bus

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Susanu, Carolina, Anamaria Hărăbor, Ingrid-Andrada Vasilache, Valeriu Harabor, and Alina-Mihaela Călin. "Predicting Intra- and Postpartum Hemorrhage through Artificial Intelligence." Medicina 60, no. 10 (2024): 1604. http://dx.doi.org/10.3390/medicina60101604.

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Background and Objectives: Intra/postpartum hemorrhage stands as a significant obstetric emergency, ranking among the top five leading causes of maternal mortality. The aim of this study was to assess the predictive performance of four machine learning algorithms for the prediction of postpartum and intrapartum hemorrhage. Materials and Methods: A prospective multicenter study was conducted, involving 203 patients with or without intra/postpartum hemorrhage within the initial 24 h postpartum. The participants were categorized into two groups: those with intra/postpartum hemorrhage (PPH) and th

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Bi, Jinqiang, Hongen Cheng, Wenjia Zhang, Kexin Bao, and Peiren Wang. "Artificial Intelligence in Ship Trajectory Prediction." Journal of Marine Science and Engineering 12, no. 5 (2024): 769. http://dx.doi.org/10.3390/jmse12050769.

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Maritime traffic is increasing more and more, creating more complex navigation environments for ships. Ship trajectory prediction based on historical AIS data is a vital method of reducing navigation risks and enhancing the efficiency of maritime traffic control. At present, employing machine learning or deep learning techniques to construct predictive models based on AIS data has become a focal point in ship trajectory prediction research. This paper systematically evaluates various trajectory prediction methods, spanning classical machine learning approaches and emerging deep learning techni

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Abdulrazzq, Raghdah Adnan, Nisreen Mustafa Sajid, and Marwan Sabah Hasan. "Artificial intelligence-driven predictive maintenance in IoT systems." South Florida Journal of Development 5, no. 12 (2024): e4781. https://doi.org/10.46932/sfjdv5n12-030.

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The study looks at the application of AI-driven predictive maintenance in IoT systems. Predictive device failure, efficient reduction in system downtime, reduced maintenance costs, and overall efficiency in connected devices will be enabled through machine learning and deep learning algorithms. The AI models developed within this research were able to provide a prediction accuracy of 92%, while the traditional methods of maintenance were far behind at 78%. It resulted in a 35% reduction in system downtime and a 28% decrease in maintenance costs while reducing the error rate to 8%. The above re

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Kamel, H. "Artificial intelligence for predictive maintenance." Journal of Physics: Conference Series 2299, no. 1 (2022): 012001. http://dx.doi.org/10.1088/1742-6596/2299/1/012001.

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Abstract Maintenance constitutes an important share of modern industrial activities. Reliable operations rely on the adequate application on maintenance. However, in the present competitive environment, maintenance processes must be optimized so that they will be performed only when needed, otherwise resources will be needlessly wasted. This is in contrast to the conventional approach where maintenance is scheduled according to a time plan regardless of it is needed or not. This paper presents the application of artificial intelligence to create a model that can successfully predict the condit

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Thèses sur le sujet "Predictive Intelligence"

St, Clair Ronald Benjamin. "Predictive failure? Intelligence-gathering and the FLQ." Thesis, University of Ottawa (Canada), 2005. http://hdl.handle.net/10393/27045.

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It has been the Canadian federal government's consistent position that the decision to invoke the War Measures Act had been arrived at due to a lack of available intelligence on the FLQ. They contend that sufficient advance warning of the FLQ's intentions to kidnap political officials before the October Crisis of 1970 had not been provided, and they were not given adequate intelligence on the nature and capabilities of the threat during the crisis. Essentially, the argument asserts that there had been a failure on the part of the intelligence-community in Canada to predict the FLQ's future act

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Walton, Brien C. "Is emotional intelligence predictive or entrepreneurial success?" Thesis, University of Pennsylvania, 2017. http://pqdtopen.proquest.com/#viewpdf?dispub=10158700.

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<p> There are more self-made, billionaire entrepreneurs than billionaires who simply inherited their fortunes, but the majority of startup ventures fail within five years. A possible factor in business success or failure could be the emotional intelligence (EI) level of the entrepreneur, defined broadly as the ability to perceive, interpret, and manage emotions. Although there is substantial literature on EI applications in established organizations, there are few empirical studies exploring the predictive value of EI in the context of success for startup entrepreneurs. The purpose of this stu

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Fayad, Ramzi. "Predictive tool monitoring system for metal machining using aritificial intelligence methods." Thesis, University of Nottingham, 2005. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.433992.

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Murphy, Killian. "Predictive maintenance of network equipment using machine learning methods." Electronic Thesis or Diss., Institut polytechnique de Paris, 2024. http://www.theses.fr/2024IPPAS013.

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Avec la montée en puissance des capacités de calcul nécessaires pour les méthodes plus développées d'Apprentissage Machine (ML), la Prédiction des Incidents Réseau (NFP:Network Fault Prediction) connait un regain d'intérêt scientifique. La capacité de prédire les incidents des équipements réseau est de plus en plus fréquemment identifiée comme un moyen efficace d'améliorer la fiabilité du réseau. Cette capacité prédictive peut être utilisée pour atténuer ou mettre en œuvre une maintenance prédictive en prévision des cas d'incidents réseau imminents. Cela pourrait contribuer à la mise en œuvre

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Espinoza, Espinoza Bertha Yrene, and Rivera Natalia Elizabeth Gutiérrez. "Sistema de información para la toma de decisiones, usando técnicas de análisis predictivo para la Empresa IASACORP International S.A." Bachelor's thesis, Universidad Ricardo Palma, 2015. http://cybertesis.urp.edu.pe/handle/urp/1271.

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En la actualidad, las empresas manejan una gran cantidad de información, el cual era inimaginable años atrás, la capacidad de recolectarla es muy impresionante. En consecuencia, para varias empresas esta información se ha convertido en un tema difícil de manejar. Diariamente, las empresas sea del sector, tipo o tamaño que sea, toman decisiones, las cuales la mayoría son decisiones estratégicas que pueden afectar el correcto funcionamiento de la empresa. Es aquí, donde ingresa una de las herramientas más mencionadas en el área de TI: Business Intelligence, este término se refiere al uso de dat

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Boots, Byron. "Spectral Approaches to Learning Predictive Representations." Research Showcase @ CMU, 2012. http://repository.cmu.edu/dissertations/131.

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A central problem in artificial intelligence is to choose actions to maximize reward in a partially observable, uncertain environment. To do so, we must obtain an accurate environment model, and then plan to maximize reward. However, for complex domains, specifying a model by hand can be a time consuming process. This motivates an alternative approach: learning a model directly from observations. Unfortunately, learning algorithms often recover a model that is too inaccurate to support planning or too large and complex for planning to succeed; or, they require excessive prior domain knowledge

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Mitchell-White, Kathleen. "Reflective thinking and emotional intelligence as predictive performance factors in problem-based learning situations." ScholarWorks, 2010. https://scholarworks.waldenu.edu/dissertations/788.

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Continued improvement of the training and preparation of Federal Bureau of Investigation (FBI) special agents is critical to the organization's ability to protect the national security of the United States. Too little attention has been paid to the factors that improve new agent trainees' (NATs) ability to learn and succeed in their training programs. Based on the theories of reflective thinking and emotional intelligence, this nonexperimental, correlational study explored predictors of NATs' (N = 183) performance in problem-based exercises as part of the 20-week training program. Self-report

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Colburn, andrea Adams. "The Predictive Value of Emotional Intelligence: using Emotional Intelligence to Predict Success in and Satisfaction with Romantic and Friendship Relationships and Career." W&M ScholarWorks, 1997. https://scholarworks.wm.edu/etd/1539626138.

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Nickens, Nicole M. "Impact of intersubtest scatter on predictive validity of WISC-III /." free to MU campus, to others for purchase, 2003. http://wwwlib.umi.com/cr/mo/fullcit?p3091949.

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Zarei, Anahita. "A novel assessment index and intelligent predictive models for orthodontics /." Thesis, Connect to this title online; UW restricted, 2007. http://hdl.handle.net/1773/6093.

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Livres sur le sujet "Predictive Intelligence"

Seebacher, Uwe. Predictive Intelligence für Manager. Springer Berlin Heidelberg, 2021. http://dx.doi.org/10.1007/978-3-662-62776-1.

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Rekik, Islem, Ehsan Adeli, Sang Hyun Park, and Julia Schnabel, eds. Predictive Intelligence in Medicine. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-87602-9.

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Rekik, Islem, Ehsan Adeli, Sang Hyun Park, and Celia Cintas, eds. Predictive Intelligence in Medicine. Springer Nature Switzerland, 2022. http://dx.doi.org/10.1007/978-3-031-16919-9.

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Rekik, Islem, Ehsan Adeli, Sang Hyun Park, and Maria del C. Valdés Hernández, eds. Predictive Intelligence in Medicine. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-59354-4.

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Rekik, Islem, Gozde Unal, Ehsan Adeli, and Sang Hyun Park, eds. PRedictive Intelligence in MEdicine. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-00320-3.

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Rekik, Islem, Ehsan Adeli, and Sang Hyun Park, eds. Predictive Intelligence in Medicine. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-32281-6.

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Rekik, Islem, Ehsan Adeli, Sang Hyun Park, Celia Cintas, and Ghada Zamzmi, eds. Predictive Intelligence in Medicine. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-46005-0.

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Rekik, Islem, Ehsan Adeli, Sang Hyun Park, and Celia Cintas, eds. Predictive Intelligence in Medicine. Springer Nature Switzerland, 2025. http://dx.doi.org/10.1007/978-3-031-74561-4.

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Dahl, Erik J., and David Strachan-Morris. Predictive Intelligence for Tomorrow’s Threats. Routledge, 2025. https://doi.org/10.4324/9781003603856.

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Seebacher, Uwe. Predictive Intelligence for Data-Driven Managers. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-69403-6.

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Chapitres de livres sur le sujet "Predictive Intelligence"

Chen, Robert H., and Chelsea Chen. "Predictive Analytics." In Artificial Intelligence, 2nd ed. Chapman and Hall/CRC, 2024. http://dx.doi.org/10.1201/9781003463542-19.

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Seebacher, Uwe. "Predictive Intelligence im Überblick." In Predictive Intelligence für Manager. Springer Berlin Heidelberg, 2021. http://dx.doi.org/10.1007/978-3-662-62776-1_2.

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Seebacher, Uwe. "Das Predictive-Intelligence-Team." In Predictive Intelligence für Manager. Springer Berlin Heidelberg, 2021. http://dx.doi.org/10.1007/978-3-662-62776-1_8.

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Seebacher, Uwe. "Die Predictive Intelligence Fallstudien." In Predictive Intelligence für Manager. Springer Berlin Heidelberg, 2021. http://dx.doi.org/10.1007/978-3-662-62776-1_9.

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Seebacher, Uwe. "Das Predictive Intelligence Ökosystem." In Predictive Intelligence für Manager. Springer Berlin Heidelberg, 2021. http://dx.doi.org/10.1007/978-3-662-62776-1_3.

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Valerio, Luis G. "Toxicology and Artificial Intelligence." In Predictive Analytics for Toxicology. CRC Press, 2024. http://dx.doi.org/10.1201/9781003171904-5.

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Dahl, Erik J., and David Strachan-Morris. "Introduction – ‘Predictive intelligence for tomorrow's threats': is predictive intelligence possible?" In Predictive Intelligence for Tomorrow’s Threats. Routledge, 2025. https://doi.org/10.4324/9781003603856-1.

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Seebacher, Uwe. "The Predictive Intelligence Team." In Predictive Intelligence for Data-Driven Managers. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-69403-6_8.

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Seebacher, Uwe. "The Predictive Intelligence Ecosystem." In Predictive Intelligence for Data-Driven Managers. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-69403-6_3.

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Seebacher, Uwe. "Das Predictive-Intelligence-Reifegrad-Modell." In Predictive Intelligence für Manager. Springer Berlin Heidelberg, 2021. http://dx.doi.org/10.1007/978-3-662-62776-1_4.

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Actes de conférences sur le sujet "Predictive Intelligence"

Panda, Ayush, Ayush Suman, Nilamadhab Mishra, et al. "Alzheimer's Disease Prediction using Advanced Predictive Intelligence Model." In 2024 International Conference on Intelligent Algorithms for Computational Intelligence Systems (IACIS). IEEE, 2024. http://dx.doi.org/10.1109/iacis61494.2024.10721920.

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Shankar, Metha, Sebastian Terence, Jude Immaculate, Anishin Raj M. M, and Vinoth Ewards. "Tata Motors Stock Price Prediction Using Predictive Artificial Intelligence." In 2024 10th International Conference on Advanced Computing and Communication Systems (ICACCS). IEEE, 2024. http://dx.doi.org/10.1109/icaccs60874.2024.10717037.

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Hosea, Pradeesh, and Sebastian Terence. "Review on Predictive Artificial Intelligence in Student Academic Prediction." In 2025 7th International Conference on Intelligent Sustainable Systems (ICISS). IEEE, 2025. https://doi.org/10.1109/iciss63372.2025.11076526.

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Rakhmanovich, Ibragimov Ulmas, Rami Ryad Hossein, Mustafa Albdairi, Qudratulla Omonov, and Balasubramaniam Kumaraswamy. "Predictive Analytics and Automation: Transforming Logistics with Artificial Intelligence with Blockchain Intelligence." In 2025 International Conference on Computational Innovations and Engineering Sustainability (ICCIES). IEEE, 2025. https://doi.org/10.1109/iccies63851.2025.11033145.

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Pali, Kalpana, and Laxmikant Tiwari. "Predictive Learning and Career Path Using Artificial Intelligence." In 2024 OPJU International Technology Conference (OTCON) on Smart Computing for Innovation and Advancement in Industry 4.0. IEEE, 2024. http://dx.doi.org/10.1109/otcon60325.2024.10688178.

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Srivastava, Sarthak, and Reena Antal. "Predictive Modeling of Liver Diseases using Artificial Intelligence." In 2024 4th International Conference on Soft Computing for Security Applications (ICSCSA). IEEE, 2024. https://doi.org/10.1109/icscsa64454.2024.00035.

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Mahmud, Shekhar, and Mustafa Kutlu. "An Explainable Artificial Intelligence Based Model Predictive Control Approach." In 2024 12th International Conference on Control, Mechatronics and Automation (ICCMA). IEEE, 2024. https://doi.org/10.1109/iccma63715.2024.10843891.

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Bokkena, Bhargava. "Enhancing IT Security with LLM-Powered Predictive Threat Intelligence." In 2024 5th International Conference on Smart Electronics and Communication (ICOSEC). IEEE, 2024. http://dx.doi.org/10.1109/icosec61587.2024.10722712.

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Garg, Apeksha, and Sudha Vemaraju. "Artificial Intelligence Applications in Predictive Maintenance for Sustainable Logistics." In 2024 IEEE 4th International Conference on ICT in Business Industry & Government (ICTBIG). IEEE, 2024. https://doi.org/10.1109/ictbig64922.2024.10911124.

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Mathur, Sandeep, Yazdani Hasan, Deepshikha Bhargava, Subhangee Bhattacharjee, and Ajay Rana. "Artificial Intelligence Based Predictive Analytics for Website Performance Optimization." In 2024 7th International Conference on Contemporary Computing and Informatics (IC3I). IEEE, 2024. https://doi.org/10.1109/ic3i61595.2024.10829168.

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Rapports d'organisations sur le sujet "Predictive Intelligence"

Walker, Cody, Vivek Agarwal, Linyu Lin, et al. Explainable Artificial Intelligence Technology for Predictive Maintenance. Office of Scientific and Technical Information (OSTI), 2023. http://dx.doi.org/10.2172/1998555.

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Smith, Timothy J. Predictive Network-Centric Intelligence: Toward a Total-Systems Transformation of Analysis and Assessment. Defense Technical Information Center, 2006. http://dx.doi.org/10.21236/ada608038.

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Willson. L51756 State of the Art Intelligent Control for Large Engines. Pipeline Research Council International, Inc. (PRCI), 1996. http://dx.doi.org/10.55274/r0010423.

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Computers have become a vital part of the control of pipeline compressors and compressor stations. For many tasks, computers have helped to improve accuracy, reliability, and safety, and have reduced operating costs. Computers excel at repetitive, precise tasks that humans perform poorly - calculation, measurement, statistical analysis, control, etc. Computers are used to perform these type of precise tasks at compressor stations: engine / turbine speed control, ignition control, horsepower estimation, or control of complicated sequences of events during startup and/or shutdown. For other task

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Pasupuleti, Murali Krishna. AI-Driven Automation: Transforming Industry 5.0 withMachine Learning and Advanced Technologies. National Education Services, 2025. https://doi.org/10.62311/nesx/rr225.

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Abstract: This article delves into the transformative role of artificial intelligence (AI) and machine learning (ML) in shaping Industry 5.0, a paradigm centered on human- machine collaboration, sustainability, and resilient industrial ecosystems. Beginning with the evolution from Industry 4.0 to Industry 5.0, it examines core AI technologies, including predictive analytics, natural language processing, and computer vision, which drive advancements in manufacturing, quality control, and adaptive logistics. Key discussions include the integration of collaborative robots (cobots) that enhance hu

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Haßler, Björn, Wuxia Zhang, Olamide Eso, et al. Predictive models for classroom conditions in Tanzania – A literature review with a focus on Artificial Intelligence. Open Development & Education, 2024. http://dx.doi.org/10.53832/opendeved.1133.

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Vuyyuru, Tejaswini. Using Predictive Maintenance techniques and Business Intelligence to develop smarter factory systems for the digital age. Iowa State University, 2018. http://dx.doi.org/10.31274/cc-20240624-1566.

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Bozzo Hauri, Sebastián. The New Frontier of Civil Liability: Artificial Intelligence, Autonomy, and Consumer Protection. Carver University; Universidad Autónoma de Chile, 2025. https://doi.org/10.32457/bozzo2202599.

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Technological evolution has entered a phase that challenges the very foundations of private law. The emergence of systems based on artificial intelligence (AI)—particularly in their most recent form, so-called AI agents—compels a reassessment of the traditional framework of civil liability, especially in the field of consumer law. The trajectory of AI has followed a path marked by three distinct waves. The first wave was predictive AI, trained on historical data to anticipate future behavior, as seen in recommendation engines and segmentation models. The second wave introduced generative AI—su

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Pasupuleti, Murali Krishna. AI-Driven Marketing Innovations: Personalization and Ethics in the Digital Era. National Education Services, 2025. https://doi.org/10.62311/nesx/rr625.

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Abstract: This article explores the transformative impact of artificial intelligence (AI) on digital marketing, focusing on strategies for delivering personalized content and ensuring ethical advertising. By leveraging AI, marketers can now analyze consumer behavior with precision, enabling targeted content, automated ad placement, and real-time adjustments that enhance user engagement and conversions. The Article examines foundational AI techniques, such as recommendation engines, predictive analytics, and natural language processing, which drive personalization at scale. Additionally, it add

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Ndoye, Aïssatou, Khadim Dia, and Racine Ly. AAgWa Crop Production Forecasts Brief Series - Issue N.04. AKADEMIYA2063, 2023. http://dx.doi.org/10.54067/acpf.04.

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The Africa Agriculture Watch (AAgWa) Crop Production Forecast Brief 4 by AKADEMIYA2063 seeks to provide accurate production levels in Burkina Faso using the Africa Food Crop Production (AfCP) model. The AfCP, developed by AKADEMIYA2063, is an artificial intelligence (AI) based predictive model applied to remotely sensed bio-geophysical data to estimate crop yields and harvests before the harvesting period for nine crops across 47 African countries.

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Pasupuleti, Murali Krishna. AI and Quantum-Nano Frontiers: Innovations in Health, Sustainability, Energy, and Security. National Education Services, 2025. https://doi.org/10.62311/nesx/rr525.

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Abstract: This research report explores transformative advancements at the intersection of Artificial Intelligence (AI), Quantum Computing, and Nanotechnology, focusing on breakthrough innovations in health, sustainability, energy, and global security. By integrating quantum algorithms, AI-driven analytics, and advanced nanomaterials, this report highlights revolutionary solutions in precision medicine, predictive diagnostics, sustainable energy storage, universal water purification, and cybersecurity. Real-world case studies and emerging technologies such as graphene-based nanomaterials, quan

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