Relevant bibliographies by topics / Predictive adaptive response

Contents

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

Academic literature on the topic 'Predictive adaptive response'

Author: Grafiati
Published: 3 June 2025
Last updated: 31 July 2025

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Journal articles on the topic "Predictive adaptive response"

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Nettle, Daniel, Willem E. Frankenhuis, and Ian J. Rickard. "The evolution of predictive adaptive responses in humans: response." Proceedings of the Royal Society B: Biological Sciences 281, no. 1780 (2014): 20132822. http://dx.doi.org/10.1098/rspb.2013.2822.

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Wells, Jonathan C. K. "Response to Gluckman et al. and Bateson: predictive adaptive responses." Trends in Endocrinology & Metabolism 19, no. 4 (2008): 112. http://dx.doi.org/10.1016/j.tem.2008.02.003.

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Wells, Jonathan CK. "A critical appraisal of the predictive adaptive response hypothesis." International Journal of Epidemiology 41, no. 1 (2012): 229–35. http://dx.doi.org/10.1093/ije/dyr239.

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Williams, Thomas C., and Amanda J. Drake. "Preterm birth in evolutionary context: a predictive adaptive response?" Philosophical Transactions of the Royal Society B: Biological Sciences 374, no. 1770 (2019): 20180121. http://dx.doi.org/10.1098/rstb.2018.0121.

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Preterm birth is a significant public health problem worldwide, leading to substantial mortality in the newborn period, and a considerable burden of complications longer term, for affected infants and their carers. The fact that it is so common, and rates vary between different populations, raising the question of whether in some circumstances it might be an adaptive trait. In this review, we outline some of the evolutionary explanations put forward for preterm birth. We specifically address the hypothesis of the predictive adaptive response, setting it in the context of the Developmental Orig
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Gluckman, P. D., and M. A. Hanson. "Adult disease: echoes of the past." European Journal of Endocrinology 155, suppl_1 (2006): S47—S50. http://dx.doi.org/10.1530/eje.1.02233.

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Disease occurs if an environmental challenge exceeds the ability of an individual to mount an effective adaptive response to it. Evolution has selected genomically determined traits, which are optimal for a species to survive the historical environment. However, this adaptive ability to withstand an environmental challenge varies among individuals and is itself a phenotypic characteristic: how is this determined? We argue that maternal and placental cues that constrain prenatal development, induce offspring to develop predictive adaptive responses more suited to a deprived postnatal environmen
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D., Ganga, and Ramachandran V. "Adaptive prediction model for effective electrical machine maintenance." Journal of Quality in Maintenance Engineering 26, no. 1 (2019): 166–80. http://dx.doi.org/10.1108/jqme-12-2017-0087.

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Purpose The purpose of this paper is to propose an optimal predictive model for the short-term forecast of real-time non-stationary machine variables by combining time series prediction with adaptive algorithms to minimize the error and to improve the prediction accuracy. Design/methodology/approach The proposed model is applied for prediction of speed and controller set point of three-phase induction motor operating on closed loop speed control with AC drive and PI controller. At Stage 1, the trend of the machine variables has been extracted and added to auto-regressive moving average (ARMA)
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Liu, Jiaqi. "AI-Based Epidemic Spread Prediction and Public Health Response Optimization: A Systematic Study from Data Analysis to Policy Implementation." Applied and Computational Engineering 118, no. 1 (2024): 1–7. https://doi.org/10.54254/2755-2721/2025.18470.

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The paper discusses how AI can be used to predict epidemics and improve public health responses on a wide range of critical topics from disease prediction using data to policymaking. Even conventional epidemiological models, often constrained by parameters, find it difficult to adapt to rapidly evolving disease dynamics. Our method combines machine learning (ML) and deep learning (DL) algorithms, such as long short-term memory (LSTM) and reinforcement learning (RL), to dynamically anticipate infection peaks and outbreak hotspots. Using both time-series and spatial data, the hybrid CNN-LSTM mod
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Yin, Fang Chen, Geng Sheng Ma, Ya Feng Ji, Jia Xue Yu, De Hao Gu, and Dian Hua Zhang. "Fuzzy Adaptive Direct Generalized Predictive Control Algorithm in the Application of AWC Control." Advanced Materials Research 945-949 (June 2014): 2529–32. http://dx.doi.org/10.4028/www.scientific.net/amr.945-949.2529.

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Using the characteristics of prediction model, rolling optimization and feedback correction, a AWC system based on generalized predictive control was designed, and its control performance was simulated based on a hot strip continuous mill. The results show that generalized predictive controller achieves better control effects than the normal PID on response time and steady precision with matching model; when model mismatching is caused by inaccuracy of plastic coefficient and pure delay time, the normal PID is overshot or even oscillation, but the control performance of the generalized predict
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Li, Zhaobo, Yimin Deng, and Shuanglei Sun. "Adaptive Cruise Predictive Control Based on Variable Compass Operator Pigeon-Inspired Optimization." Electronics 11, no. 9 (2022): 1377. http://dx.doi.org/10.3390/electronics11091377.

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A vehicle adaptive cruise system can control the speed and the safe distance between vehicles rapidly and effectively, which is an integral part of an intelligent driver assistance system. Adaptive cruise predictive control algorithms based on variable compass operator pigeon-inspired optimization (PIO) and PSO are proposed to improve the time response characteristics of multi-objective adaptive cruise system predictive control. Firstly, a longitudinal kinematic model of an adaptive cruise system was established and linearly discretized. Secondly, the multi-objective optimal cost function and
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Li, Zhaobo, Yimin Deng, and Shuanglei Sun. "Adaptive Cruise Predictive Control Based on Variable Compass Operator Pigeon-Inspired Optimization." Electronics 11, no. 9 (2022): 1377. http://dx.doi.org/10.3390/electronics11091377.

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A vehicle adaptive cruise system can control the speed and the safe distance between vehicles rapidly and effectively, which is an integral part of an intelligent driver assistance system. Adaptive cruise predictive control algorithms based on variable compass operator pigeon-inspired optimization (PIO) and PSO are proposed to improve the time response characteristics of multi-objective adaptive cruise system predictive control. Firstly, a longitudinal kinematic model of an adaptive cruise system was established and linearly discretized. Secondly, the multi-objective optimal cost function and
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Dissertations / Theses on the topic "Predictive adaptive response"

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Sheth, Katha Janak. "Model predictive control for adaptive digital human modeling." Thesis, University of Iowa, 2010. https://ir.uiowa.edu/etd/884.

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We consider a new approach to digital human simulation, using Model Predictive Control (MPC). This approach permits a virtual human to react online to unanticipated disturbances that occur in the course of performing a task. In particular, we predict the motion of a virtual human in response to two different types of real world disturbances: impulsive and sustained. This stands in contrast to prior approaches where all such disturbances need to be known a priori and the optimal reactions must be computed off line. We validate this approach using a planar 3 degrees of freedom serial chain mecha
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Abraham, Etimbuk. "Adaptive supervisory control scheme for voltage controlled demand response in power systems." Thesis, University of Manchester, 2018. https://www.research.manchester.ac.uk/portal/en/theses/adaptive-supervisory-control-scheme-for-voltage-controlled-demand-response-in-power-systems(3e64537d-52c7-4eb5-87f2-b73fe920b9cb).html.

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Radical changes to present day power systems will lead to power systems with a significant penetration of renewable energy sources and smartness, expressed in an extensive utilization of novel sensors and cyber secure Information and Communication Technology. Although these renewable energy sources prove to contribute to the reduction of CO2 emissions into the environment, its high penetration affects power system dynamic performance as a result of reduced power system inertia as well as less flexibility with regards to dispatching generation to balance future demand. These pose a threat both
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Shannon, Roger. "Predictive adaptive responses in Drosophila melanogaster." Thesis, University of Southampton, 2011. https://eprints.soton.ac.uk/338975/.

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Predictive Adaptive Responses are changes in development made in the perinatal period in response to maternally transmitted information, and a mismatch between the diet selected during human evolution and the contemporary Western diet can produce an adult phenotype characterised by weight gain, cardiovascular disease, hypertension and diabetes. In humans, most evidence is epidemiological. Using Drosophila melanogaster, the problem can be approached from an adaptive phenotypic plasticity perspective. Health effects in humans stem from predictive adaptations made to enhance fitness and so it mus
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Zhu, Zeyu. "Multi-Omics Stress Responses and Adaptive Evolution in Pathogenic Bacteria: From Characterization Towards Diagnostic Prediction." Thesis, Boston College, 2020. http://hdl.handle.net/2345/bc-ir:108912.

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Thesis advisor: Tim van Opijnen<br>Thesis advisor: Welkin Johnson<br>Pathogenic bacteria can experience various stress factors during an infection including antibiotics and the host immune system. Whether a pathogen will establish an infection largely depends on its survival-success while enduring these stress factors. We reasoned that the ability to predict whether a pathogen will survive under and/or adapt to a stressful condition will provide great diagnostic and prognostic value. However, it is unknown what information is needed to enable such predictions. We hypothesized that under a stre
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Novieto, Divine Tuinese. "Adapting a human thermoregulation model for predicting the thermal response of older persons." Thesis, De Montfort University, 2013. http://hdl.handle.net/2086/9489.

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A human thermoregulation model has been adapted for predicting the thermal response of Typical Older Persons. The model known as the Older Persons Model predicts the core body temperature and regulatory responses of the older people in environmental exposures of cold, warm and hot. The model was developed by modifying an existing dynamic human thermoregulation model using anthropometric and thermo-physical properties of older people. The Model defines the body as two interrelating systems of the body structure (passive system) and the control system of the central nervous system (active system
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Matys, Libor. "Prediktivní regulátory s principy umělé inteligence v prostředí MATLAB - B&R." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2008. http://www.nusl.cz/ntk/nusl-217557.

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Master’s thesis deals with problems of predictive control especially Model (Based) Predictive Control (MBPC or MPC). Identifications methods are compared in the first part. Recursive least mean squares algorithm is compared with identification methods based on neural networks. Next parts deal with predictive control. There is described creation MPC with summing element and adaptive MPC. There is also compared fixed setting PSD controller with MPC. Responses on disturbance and changes of parameters of controlled plant are compared. Comparing is made on simulation models in MATLAB/Simulink and o
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CELLAMARE, MATTEO. "Bayesian adaptive designs in multi-arm multi-stage clinical trials." Doctoral thesis, 2016. http://hdl.handle.net/11573/926665.

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Clinical trial seek to investigate novel treatments, asses the relative benefits of competing therapies, and establish optimal treatment combinations. Statistical models provide an explicit way to models patients response to a treatment, and make inference about the clinical utility of therapies which guides clinical decision making. Statistical designs for clinical trials are a formal procedure the aim to maximize the the quality of generated information on the performance of experimental treatment. We explore a particular class of clinical trial design called adaptive design, which allows m
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Tung, Chun-Wei, and 童俊維. "Prediction of adaptive T-cell immune response." Thesis, 2010. http://ndltd.ncl.edu.tw/handle/00937674842917994514.

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博士<br>國立交通大學<br>生物資訊及系統生物研究所<br>98<br>The development of computer-aided vaccine design systems is a goal of immunoinformatics that can largely accelerate the design of vaccines. Accurate prediction of adaptive T-cell immune response is the critical step to develop computer-aided vaccine design systems. The core of this study is to develop high-performance optimization algorithms for solving large-scale parameter optimization problems of bioinformatics to mine informative physicochemical properties from known experimental data for predicting immunogenic pathway. The development of these algorit
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Books on the topic "Predictive adaptive response"

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Brunner, Ronald D., and Amanda H. Lynch. Adaptive Governance. Oxford University Press, 2017. http://dx.doi.org/10.1093/acrefore/9780190228620.013.601.

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Adaptive governance is defined by a focus on decentralized decision-making structures and procedurally rational policy, supported by intensive natural and social science. Decentralized decision-making structures allow a large, complex problem like global climate change to be factored into many smaller problems, each more tractable for policy and scientific purposes. Many smaller problems can be addressed separately and concurrently by smaller communities. Procedurally rational policy in each community is an adaptation to profound uncertainties, inherent in complex systems and cognitive constra
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Stuart, Philip E., Lam C. Tsoi, Caely A. Hambro, and James T. Elder. Genetics of psoriasis. Oxford University Press, 2018. http://dx.doi.org/10.1093/med/9780198737582.003.0005.

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Psoriasis is an immune-mediated inflammatory disease (IMID) characterized by skin inflammation, epidermal hyperplasia, increased risk of arthritis, and cardiovascular morbidity. Substantial evidence indicates that psoriasis is driven by abnormal interactions between cells of the innate and adaptive host defence systems, including keratinocytes, dendritic cells, and T-cells, resulting in a dysregulated immune response and markedly increased epidermal proliferation. The precise aetiology of psoriasis remains unknown. Here, we review how innate and adaptive host defence responses are regulated by
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Tsai, Jack, Natalie Jones, Robert H. Pietrzak, Ilan Harpaz-Rotem, and Steven M. Southwick. Susceptibility, Resilience, and Trajectories. Edited by Frederick J. Stoddard, David M. Benedek, Mohammed R. Milad, and Robert J. Ursano. Oxford University Press, 2018. http://dx.doi.org/10.1093/med/9780190457136.003.0019.

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Nearly everyone experiences a highly stressful or traumatic event during their lifetime. However, individual responses to such events vary widely from person to person. Some people respond with symptoms of anxiety, depression, acute stress, or posttraumatic stress disorder, yet others experience minimal or no psychiatric symptoms after trauma. What makes one person more susceptible and another more resilient to the negative effects of trauma? What are the different adaptive trajectories of trauma survivors and what determines their trajectory? These are some of the questions that are examined
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Book chapters on the topic "Predictive adaptive response"

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Naumenko, Maksym, Iryna Hrashchenko, Tetiana Tsalko, Svitlana Nevmerzhytska, Svitlana Krasniuk, and Yurii Kulynych. "Innovative technological modes of data mining and modelling for adaptive project management of food industry competitive enterprises in crisis conditions." In PROJECT MANAGEMENT: INDUSTRY SPECIFICS. TECHNOLOGY CENTER PC, 2024. https://doi.org/10.15587/978-617-8360-03-0.ch2.

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Developed in this research scientific and practical applied project solutions regarding Data Mining for enterprises and companies (on the example of food industry) involve the application of advanced cybernetic computing methods/algorithms, technological modes and scenarios (for integration, pre-processing, machine learning, testing and in-depth comprehensive interpretation of the results) of analysis and analytics of large structured and semi-structured data sets for training high-quality descriptive, predictive and even prescriptive models. The proposed by authors multi-mode adaptive Data Mi
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Iyiola-Tunji, Adetunji Oroye, James Ijampy Adamu, Paul Apagu John, and Idris Muniru. "Dual Pathway Model of Responses Between Climate Change and Livestock Production." In African Handbook of Climate Change Adaptation. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-45106-6_230.

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AbstractThis chapter was aimed at evaluating the responses of livestock to fluctuations in climate and the debilitating effect of livestock production on the environment. Survey of livestock stakeholders (farmers, researchers, marketers, and traders) was carried out in Sahel, Sudan, Northern Guinea Savannah, Southern Guinea Savannah, and Derived Savannah zones of Nigeria. In total, 362 respondents were interviewed between April and June 2020. The distribution of the respondents was 22 in Sahel, 57 in Sudan, 61 in Northern Guinea Savannah, 80 in Southern Guinea Savannah, and 106 in Derived Sava
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van der Ark, L. Andries, and Niels Smits. "Computerized Adaptive Testing Without IRT for Flexible Measurement and Prediction." In Essays on Contemporary Psychometrics. Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-10370-4_19.

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AbstractIn education, testing procedures can be lengthy. The long duration takes up precious time and affects the quality of responses, possibly resulting in a biased diagnosis or wrong treatment. The problem can be reduced using computer adaptive testing (CAT). However, three issues prevent the use of traditional CAT: (1) the type of tests and questionnaires we focus on do not allow for the construction of large item banks, (2) the test data are usually not (approximately) unidimensional, and (3) the aim of the researchers may not only be measurement but also prediction. We propose a flexible
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Cave, Lisa A. "A Social-Cognitive Prediction of the Perceived Threat of Terrorism and Behavioral Responses of Terrorist Activities." In Foundations of Augmented Cognition. Advancing Human Performance and Decision-Making through Adaptive Systems. Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-07527-3_2.

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Illg, Christopher, and Oliver Nelles. "Adaptive Model Predictive Control with Regularized Finite Impulse Response Models." In ATHENA Research Book, Volume 1. University of Maribor Press, 2022. http://dx.doi.org/10.18690/um.3.2022.28.

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The use of regularized finite impulse response models allows to incorporate prior knowledge of the process. This can be used to decrease the variance error of an online parameter estimation and ensures a robust system identification. The online adapted model can be used to control time-variant or nonlinear processes. This approach is named adaptive model predictive control. The investigated method is tested on a nonlinear single tank simulation and is compared to an already established method.
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Selvakumar, P., S. Sasikala, Shweta Singh, Amitava Kar, Rajkumar Mandal, and T. C. Manjunath. "The Role of Machine Learning in Industrial Cybersecurity." In Advances in Computational Intelligence and Robotics. IGI Global, 2025. https://doi.org/10.4018/979-8-3373-3241-3.ch001.

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Transforming how industrial systems detect, mitigate, and respond to cyber threats. As industrial environments become increasingly interconnected through IoT, operational technology (OT), and cloud-based-driven cybersecurity solutions offer advanced capabilities, including real-time anomaly detection, predictive threat analysis, automated incident response, and adaptive learning, enabling organizations to transition from reactive to proactive both known and unknown cyber threats, significantly improving detection accuracy and reducing response times.This shift also enhances the scalability and
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Pandikumar, S., Pooja B S, Kalavathi M N, D. Mamatha, and Shweta . "AI-Driven Adaptive Operating System Interface for Personalized User Interaction." In Shaping the Digital Future: From Algorithms to Intelligence. QTanalytics India, 2025. https://doi.org/10.48001/978-81-980647-6-9-10.

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Traditional operating system (OS) interfaces require manual configuration, which can be inefficient. This explores the potential of AI-driven OS interfaces that dynamically adapt to user behaviour. By employing machine learning techniques, these interfaces can personalize user experiences, automate tasks, and optimize accessibility. This discusses various adaptive mechanisms, including intelligent UI adaptation, context-aware recommendations, voice and gesture control, smart notification management, personalized security, energy efficiency, and cross-device synchronization. As technology becom
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Swarup, D. Jyothi, S. Vidyasagar, V. Kalyanasundaram, A. Sujatha, A. Vijayakumar, and M. Sudhakar. "AI-Powered Smart Traffic Management in Intelligent Transportation Systems." In Urban Mobility and Challenges of Intelligent Transportation Systems. IGI Global, 2025. https://doi.org/10.4018/979-8-3693-7984-4.ch004.

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AI-powered smart traffic management changes the intelligent transportation systems with optimized urban growth, reduces congestion, and increases safety on roads through the adoption of advanced technologies such as machine learning, computer vision, and timing using real internal data analysis. Thus, AI provides dynamic changes in traffic, prediction for maintenance,. and facilitates effective incident response. The chapter explores the integration of AI into traffic signal optimization, autonomous vehicle programming, and adaptive maneuvers. It encompasses key highlights, including predictiv
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Ed-Daakouri, Ikram, Mustapha El Hissoufi, and Lhoussaine Alla. "Generative AI and Intelligent Processing of Customer Oppositions." In Advances in Computational Intelligence and Robotics. IGI Global, 2025. https://doi.org/10.4018/979-8-3693-8332-2.ch020.

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Generative Artificial Intelligence is transforming customer objection management by enabling personalized, real-time, and adaptive interactions. This chapter examines the role of generative AI in addressing objections through predictive modeling, leveraging historical data, and sentiment analysis to enhance response relevance and customer satisfaction. Ethical considerations, including transparency, bias reduction, and human oversight, are discussed to ensure responsible AI implementation. A proposed framework integrates AI's adaptability with hybrid human-AI collaboration, highlighting its ef
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Achanta, Saroja V. B. N. H. "AI in Public Services." In Advances in Public Policy and Administration. IGI Global, 2025. https://doi.org/10.4018/979-8-3693-8372-8.ch005.

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Artificial Intelligence (AI) is transforming public services across sectors. In healthcare, AI-driven tools enhance diagnostics, predictive analytics, and personalized treatments. Machine learning algorithms predict disease outbreaks and optimize patient care. Education benefits from AI through adaptive learning platforms, which tailor content to individual students, boosting engagement and academic performance. In transportation, AI streamlines traffic management optimizes routes, and enables autonomous vehicles, reducing congestion and accidents. Public safety uses AI in crime prevention, su
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Conference papers on the topic "Predictive adaptive response"

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Omamo, Amos, and Joseph Imathiu. "Modeling Resilience in Cybersecurity: a Systems Dynamics Approach to Predictive Threat Response and Adaptive Security Policies." In 2025 IST-Africa Conference (IST-Africa). IEEE, 2025. https://doi.org/10.23919/ist-africa67297.2025.11060506.

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Wang, Xiangyu, and Yifan Wu. "Real-Time Braking Habit Prediction and Adaptive Response System: Enhancing Vehicle Braking Experience." In 2024 4th International Conference on Electronic Information Engineering and Computer Science (EIECS). IEEE, 2024. https://doi.org/10.1109/eiecs63941.2024.10799963.

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Illg, Christopher, Tim Decker, Jonas Thielmann, and Oliver Nelles. "Adaptive Model Predictive Control with Finite Impulse Response Models." In 31. Workshop Computational Intelligence. KIT Scientific Publishing, 2021. http://dx.doi.org/10.58895/ksp/1000138532-10.

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Conte, Francesco, Stefano Massucco, Federico Silvestro, Diego Cirio, and Marco Rapizza. "Demand Response by Aggregates of Domestic Water Heaters with Adaptive Model Predictive Control." In 2023 IEEE Power & Energy Society General Meeting (PESGM). IEEE, 2023. http://dx.doi.org/10.1109/pesgm52003.2023.10252833.

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Popentiu vladicescu, Florin, and Albu Razvandaniel. "A COMPARATIVE STUDY FOR WEB SERVICES RESPONSE TIME PREDICTION." In eLSE 2013. Carol I National Defence University Publishing House, 2013. http://dx.doi.org/10.12753/2066-026x-13-107.

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Web-based learning communities could significantly benefit from having as feature adaptive systems. Those models take advantage of the knowledge and experiences of community affiliates and utilize it to better serve each person. Learning is naturally a practice directly associated to sociability. Conventional learning involves the development and operation of a community. Many research studies offer evidence that "strong feelings of community may not only increase persistence in courses but may also increase the commitment to group goals, cooperation among members, satisfaction with group effo
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Lichtenwalner, Peter F., Gerald R. Little, Lawrence E. Pado, and Robert C. Scott. "Adaptive Neural Control for Active Flutter Suppression." In ASME 1996 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 1996. http://dx.doi.org/10.1115/imece1996-0947.

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Abstract Under a joint research and development effort conducted by McDonnell Douglas Aerospace (MDA) and the National Aeronautics and Space Administration, Langley Research Center (NASA LaRC), a neural network-based adaptive control system has been developed and demonstrated for active wing flutter suppression. The adaptive control system, which uses a neural network embedded within a Model Predictive Control (MPC) framework, is referred to as Neural Predictive Control (NPC). During Phase II of the Adaptive Neural Control of Aeroelastic Response (ANCAR) program, the NPC system was applied for
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Li, Jitao, Zijian Wu, and Xiaojin Huang. "Research on Adaptive Updating Method of Nuclear Power Plant Transient Models Based on Concept Drift." In 2024 31st International Conference on Nuclear Engineering. American Society of Mechanical Engineers, 2024. http://dx.doi.org/10.1115/icone31-134670.

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Abstract The transient in Nuclear Power Plant (NPP) can be considered as the process of the system transitioning from one condition to another. By monitoring the time series data during the operation of NPP and utilizing data-driven machine learning approach to build classification and prediction models to identify and predict the transient, it can provide early warnings of abnormalities, assist operators in making decisions in advance when accidents are in the development stage, thereby improving the safety of the NPP. Traditional data-driven machine learning method for transient identificati
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Wang, Hungjen, Daniel D. Frey, and Gordon M. Kaufman. "Bayesian Analysis of Adaptive One-Factor-at-a-Time Experimentation." In ASME 2007 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. ASMEDC, 2007. http://dx.doi.org/10.1115/detc2007-34926.

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This paper considers the problem of achieving improvements through adaptive experimentation. To limit the focus we consider only design spaces with discrete two-level factors. We prove that, in a Bayesian framework, one factor at a time experimentation is an optimally efficient response to step by step accrual of sample information. We derive Bayesian predictive distributions for experimentation outcomes given natural conjugate priors. Using an example based on fatigue life of weld repaired castings, we show how to use our results.
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ME, Lavanya, Shiyamala Devi G, Shuba S, Sneha K, and Sofiya S. "Adaptive Learning Management System." In International Conference on Recent Trends in Computing & Communication Technologies (ICRCCT’2K24). International Journal of Advanced Trends in Engineering and Management, 2024. http://dx.doi.org/10.59544/pqyl6304/icrcct24p39.

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Adaptive Management Systems (ALMS) are an evolving class of educational technology designed to deliver personalized, data driven learning experiences. Traditional Learning Management Systems (LMS) typically provide static content, offering limited flexibility to adapt to individual learner needs. In contrast, ALMS utilize artificial intelligence (AI) and machine learning algorithms to analyse student data and dynamically adjust instructional materials, pacing, and assessments to better match each student’s unique strengths, weaknesses, and learning preferences. This paper explores the structur
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Trišović, Nataša R., Tamas Mankovits, and Ana S. Petrović. "PREDICTIVE REANALYSIS IN STRUCTURAL DYNAMICS." In 10th International Congres of the Serbian Society of Mechanics. Serbian Society of Mechanics, Belgrade, 2025. https://doi.org/10.46793/icssm25.159t.

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Predictive reanalysis has emerged as a vital computational strategy in structural dynamics, enabling efficient updates of structural response predictions following minor modifications in geometry, material properties, or boundary conditions, without resorting to full re-computation. Traditionally rooted in finite element methods, reanalysis techniques have evolved through the integration of Artificial Intelligence (AI) models, offering unprecedented speed and adaptability in dynamic system assessments. This paper provides a comprehensive overview of predictive reanalysis approaches, with an em
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Reports on the topic "Predictive adaptive response"

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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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