Gotowe bibliografie tematyczne / Disease Prediction and Monitoring Modelling

Spis treści

  1. Artykuły w czasopismach
  2. Rozprawy doktorskie
  3. Książki
  4. Części książek
  5. Streszczenia konferencji
  6. Raporty organizacyjne

Gotowa bibliografia na temat „Disease Prediction and Monitoring Modelling”

Autor: Grafiati
Data publikacji: 12 października 2024
Data aktualizacji: 31 lipca 2025

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Artykuły w czasopismach na temat "Disease Prediction and Monitoring Modelling"

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Orakwue, Stella I., and Nkolika O. Nwazor. "Plant Disease Detection and Monitoring Using Artificial Neural Network." International Journal of Scientific Research and Management 10, no. 01 (2022): 715–22. http://dx.doi.org/10.18535/ijsrm/v10i1.ec01.

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Fungi have been identified as a major threat to crop production in the world. In this study, methods of improving the performance of plant disease detection and prediction using artificial neural network techniques are presented. The hyperspectral fungi dataset of 21 plant species were collected and trained using backpropagation algorithms of an artificial neural network to improve the conventional hyperspectral sensor. The system was modelled using self-defining equations and universal modelling diagrams and then implemented in the neural network toolbox in Matlab. The system was tested valid
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Wang, Y. P., N. H. Idris, F. M. Muharam, N. Asib, and Alvin M. S. Lau. "Comparison of different variable selection methods for predicting the occurrence of Metisa Plana in oil palm plantation using machine learning." IOP Conference Series: Earth and Environmental Science 1274, no. 1 (2023): 012008. http://dx.doi.org/10.1088/1755-1315/1274/1/012008.

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Abstract Monitoring and predicting the spatio-temporal distribution of crop pests and assessing related risks are crucial for effective pest management strategies. Machine learning techniques have shown potential in analysing agricultural data and providing accurate predictions. Variable selection plays a critical role in crop pest analysis by identifying the most informative and influential features that contribute to pest distribution and risk prediction. The current practice of choosing variable selection methods is mostly based on previous experience and may involve a certain degree of sub
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Sharma, V., S. K. Ghosh, and S. Khare. "A PROPOSED FRAMEWORK FOR SURVEILLANCE OF DENGUE DISEASE AND PREDICTION." International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLVIII-M-1-2023 (April 21, 2023): 317–23. http://dx.doi.org/10.5194/isprs-archives-xlviii-m-1-2023-317-2023.

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Abstract. Recurring outbreaks of dengue during past decades have affected public health and burdened resource constraint health systems across the world. Transmission of such diseases is a conjugation of various complex factors including vector dynamics, transmission mechanism, environmental conditions, cultural behaviours, and public health policies. Modelling and predicting early outbreaks is the key to an effective response to control the spread of disease. In this study, a comprehensive framework has been proposed to model dengue disease by integrating significant factors using different i
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KAIMI, I., and P. J. DIGGLE. "A hierarchical model for real-time monitoring of variation in risk of non-specific gastrointestinal infections." Epidemiology and Infection 139, no. 12 (2011): 1854–62. http://dx.doi.org/10.1017/s0950268811000057.

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SUMMARYThe AEGISS (Ascertainment and Enhancement of Disease Surveillance and Statistics) project uses spatio-temporal statistical methods to identify anomalies in the incidence of gastrointestinal infections in the UK. The focus of this paper is the modelling of temporal variation in incidence using data from the Southampton area in southern England. We identified and fitted a hierarchical stochastic model for the time series of daily incident cases to enable probabilistic prediction of temporal variation in risk, and demonstrated the resulting gains in predictive accuracy by comparison with a
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Vychuzhanin, Vladimir, Alexey Vychuzhanin, Olga Guzun, and Oleg Zadorozhny. "Mathematical modelling of eye condition in glaucoma: Approaches to parameter analysis and their interactions." Information Technology and Computer Engineering 22, no. 1 (2025): 10–19. https://doi.org/10.63341/vitce/1.2025.09.

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Mathematical modelling of physiological processes is a key component of intelligent medical systems, as it describes disease mechanisms in greater detail and contributes to early diagnosis. This study presents an analytical model for assessing eye health, incorporating key ophthalmological parameters: intraocular pressure (IOP), perfusion coefficient (Pperf), best-corrected visual acuity (BCVA), visual field index (VFI), retinal nerve fibre layer thickness (RNFL), and neuroretinal rim area (Rim_area). The study aimed to develop a model that can accurately evaluate the nonlinear interactions be
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Alodat, Iyas. "Analysing and predicting COVID-19 AI tracking using artificial intelligence." International Journal of Modeling, Simulation, and Scientific Computing 12, no. 03 (2021): 2141005. http://dx.doi.org/10.1142/s1793962321410051.

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In this paper, we will discuss prediction methods to restrict the spread of the disease by tracking contact individuals via mobile application to individuals infected with the COVID-19 virus. We will track individuals using bluetooth technology and then we will save information in the central database when they are in touch. Monitoring cases and avoiding the infected person help with social distance. We also propose that sensors used by people to obtain blood oxygen saturation level and their body temperature will be used besides bluetooth monitoring. The estimation of the frequency of the dis
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Dixon, Giles, Hannah Thould, Matthew Wells, et al. "A systematic review of the role of quantitative CT in the prognostication and disease monitoring of interstitial lung disease." European Respiratory Review 34, no. 176 (2025): 240194. https://doi.org/10.1183/16000617.0194-2024.

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BackgroundThe unpredictable trajectory and heterogeneity of interstitial lung disease (ILDs) make prognostication challenging. Current prognostic indices and outcome measures have several limitations. Quantitative computed tomography (qCT) provides automated numerical assessment of CT imaging and has shown promise when applied to the prognostication and disease monitoring of ILD. This systematic review aims to highlight the current evidence underpinning the prognostic value of qCT in predicting outcomes in ILD.MethodsA comprehensive search of four databases (Medline, EMCare, Embase and CINAHL
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Liebenstund, Lisa, Mark Coburn, Christina Fitzner, et al. "Predicting experimental success: a retrospective case-control study using the rat intraluminal thread model of stroke." Disease Models & Mechanisms 13, no. 12 (2020): dmm044651. http://dx.doi.org/10.1242/dmm.044651.

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ABSTRACTThe poor translational success rate of preclinical stroke research may partly be due to inaccurate modelling of the disease. We provide data on transient middle cerebral artery occlusion (tMCAO) experiments, including detailed intraoperative monitoring to elaborate predictors indicating experimental success (ischemia without occurrence of confounding pathologies). The tMCAO monitoring data (bilateral cerebral blood flow, CBF; heart rate, HR; and mean arterial pressure, MAP) of 16 animals with an ‘ideal’ outcome (MCA-ischemia), and 48 animals with additional or other pathologies (subdur
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Eswaran, Sarojini, Bharathiraj L.T, and Jayanthi S. "Modelling of ambient air quality, Coimbatore, India." E3S Web of Conferences 117 (2019): 00002. http://dx.doi.org/10.1051/e3sconf/201911700002.

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Air pollution is dispersion of the particulates, biological molecules, or other harmful materials into the Earth’s atmosphere, possibly causing diseases. Air pollutants can be either particles, liquids or gaseous. In the recent era, air pollution has become a major environmental issue because of the enhanced anthropogenic activities such as burning fossil fuels, natural gases, coal and oil, industrial process, advanced technologies and motor vehicles. The proposed project focused on air pollution study of North Coimbatore region (11° 0’ 16.4016’’ N and 76° 57’ 41.8752’’ E), Tamilnadu, India. T
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Cheng, Yunyun, Rong Cheng, Ting Xu, Xiuhui Tan, and Yanping Bai. "Machine Learning Techniques Applied to COVID-19 Prediction: A Systematic Literature Review." Bioengineering 12, no. 5 (2025): 514. https://doi.org/10.3390/bioengineering12050514.

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COVID-19 was one of the most serious global public health emergencies in recent years, and its extremely fast spreading speed had a profound negative impact on society. A comprehensive analysis and prediction of COVID-19 could lay a theoretical foundation for monitoring and early warning systems. Since the outbreak of COVID-19, there has been an influx of research on predictive modelling, with artificial intelligence (AI) techniques, particularly machine learning (ML) methods, becoming the dominant research direction due to their superior capability in processing multidimensional datasets and
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Rozprawy doktorskie na temat "Disease Prediction and Monitoring Modelling"

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Nimlang, Nanlok Henry. "Modélisation et prévision du risque de maladie à l'aide de la télédétection et du SIG : Application aux cas de paludisme au Nigeria." Electronic Thesis or Diss., IMT Mines Alès, 2024. http://www.theses.fr/2024EMAL0004.

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Tout au long de la thèse, les objectifs de recherche, les buts et les questions de recherche énoncés sont suivis d'analyses détaillées, de processus et de méthodes utilisés pour les atteindre. Ceci se matérialise par des contributions visant à répondre aux questions de recherche en fonction de leurs méthodes et résultats respectifs. Dans ce mémoire, les contributions présentées sont principalement classées en deux domaines principaux : l'identification des paramètres des facteurs de risque spatiaux (écologiques, météorologiques, socio-économiques et épidémiologiques) et l'analyse. L'analyse gé
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Marmara, Vincent Anthony. "Prediction of Infectious Disease outbreaks based on limited information." Thesis, University of Stirling, 2016. http://hdl.handle.net/1893/24624.

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The last two decades have seen several large-scale epidemics of international impact, including human, animal and plant epidemics. Policy makers face health challenges that require epidemic predictions based on limited information. There is therefore a pressing need to construct models that allow us to frame all available information to predict an emerging outbreak and to control it in a timely manner. The aim of this thesis is to develop an early-warning modelling approach that can predict emerging disease outbreaks. Based on Bayesian techniques ideally suited to combine information from diff
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Memedi, Mevludin. "Mobile systems for monitoring Parkinson's disease." Doctoral thesis, Örebro universitet, Institutionen för naturvetenskap och teknik, 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:du-13797.

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A challenge for the clinical management of Parkinson's disease (PD) is the large within- and between-patient variability in symptom profiles as well as the emergence of motor complications which represent a significant source of disability in patients. This thesis deals with the development and evaluation of methods and systems for supporting the management of PD by using repeated measures, consisting of subjective assessments of symptoms and objective assessments of motor function through fine motor tests (spirography and tapping), collected by means of a telemetry touch screen device. One ai
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Ahmed, Siraj. "Prediction of Rate of Disease Progression in Parkinson’s Disease Patients Based on RNA-Sequence Using Deep Learning." Thesis, Université d'Ottawa / University of Ottawa, 2020. http://hdl.handle.net/10393/41411.

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The advent of recent high throughput sequencing technologies resulted in an unexplored big data of genomics and transcriptomics that might help to answer various research questions in Parkinson’s disease(PD) progression. While the literature has revealed various predictive models that use longitudinal clinical data for disease progression, there is no predictive model based on RNA-Sequence data of PD patients. This study investigates how to predict the PD Progression for a patient’s next medical visit by capturing longitudinal temporal patterns in the RNA-Seq data. Data provided by Parkinson P
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Cresswell, Mark Philip. "Developing an integrated approach to epidemic forecasting, through the monitoring and prediction of meteorological variables associated with disease." Thesis, University of Liverpool, 2001. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.250341.

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Kavanagh, Madeline. "The Rational Design of LRRK2 Inhibitors for Parkinson's Disease." Thesis, The University of Sydney, 2013. http://hdl.handle.net/2123/9762.

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Parkinson’s disease is a chronic neurodegenerative disorder that affects 1-2% of the world’s population over the age of 65. Current treatments that reduce the severity of symptoms cause numerous side-effects and lose efficacy over the course of disease progression. Leucine-rich repeat kinase 2 (LRRK2) is a novel drug target for the development of disease modifying therapeutics for Parkinson’s disease. LRRK2 mutants have elevated kinase activity and, as such, chemical inhibitors have therapeutic potential. The physiological benefits that arise from chemically inhibiting LRRK2 have been proven t
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Revie, James Alexander Michael. "Model-based cardiovascular monitoring in critical care for improved diagnosis of cardiac dysfunction." Thesis, University of Canterbury. Mechanical Engineering, 2013. http://hdl.handle.net/10092/7876.

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Cardiovascular disease is a large problem in the intensive care unit (ICU) due to its high prevalence in modern society. In the ICU, intensive monitoring is required to help diagnose cardiac and circulatory dysfunction. However, complex interactions between the patient, disease, and treatment can hide the underlying disorder. As a result, clinical staff must often rely on their skill, intuition, and experience to choose therapy, increasing variability in care and patient outcome. To simplify this clinical scenario, model-based methods have been created to track subject-specific disease and tr
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Van, Zyl Jacobus. "Modelling chaotic systems with neural networks : application to seismic event predicting in gold mines." Thesis, Stellenbosch : University of Stellenbosch, 2001. http://hdl.handle.net/10019.1/4580.

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Thesis (MSc (Computer Science))-- University of Stellenbosch, 2001.<br>ENGLISH ABSTRACT: This thesis explores the use of neural networks for predicting difficult, real-world time series. We first establish and demonstrate methods for characterising, modelling and predicting well-known systems. The real-world system we explore is seismic event data obtained from a South African gold mine. We show that this data is chaotic. After preprocessing the raw data, we show that neural networks are able to predict seismic activity reasonably well.<br>AFRIKAANSE OPSOMMING: Hierdie tesis ondersoek die
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Westerlund, Per. "Condition measuring and lifetime modelling of disconnectors, circuit breakers and other electrical power transmission equipment." Doctoral thesis, KTH, Elektroteknisk teori och konstruktion, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-214984.

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The supply of electricity is important in modern society, so the outages of the electric grid should be few and short, especially for the transmission grid. A summary of the history of the Swedish electrical system is presented. The objective is to be able to plan the maintenance better by following the condition of the equipment. The risk matrix can be used to choose which component to be maintained. The risk matrix is improved by adding a dimension, the uncertainty of the probability. The risk can be reduced along any dimension: better measurements, preventive maintenance or more redundancy.
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Zecchin, Chiara. "Online Glucose Prediction in Type-1 Diabetes by Neural Network Models." Doctoral thesis, Università degli studi di Padova, 2014. http://hdl.handle.net/11577/3423574.

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Diabetes mellitus is a chronic disease characterized by dysfunctions of the normal regulation of glucose concentration in the blood. In Type 1 diabetes the pancreas is unable to produce insulin, while in Type 2 diabetes derangements in insulin secretion and action occur. As a consequence, glucose concentration often exceeds the normal range (70-180 mg/dL), with short- and long-term complications. Hypoglycemia (glycemia below 70 mg/dL) can progress from measurable cognition impairment to aberrant behaviour, seizure and coma. Hyperglycemia (glycemia above 180 mg/dL) predisposes to invalidating p
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Książki na temat "Disease Prediction and Monitoring Modelling"

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National Symposium on Hydrology (India) (11th 2004 Roorkee, India). Water quality: Monitoring, modelling, and prediction. Edited by Jain C. K, Trivedi R. C, Sharma K. D, National Institute of Hydrology (India), and India. Central Pollution Control Board. Allied Publishers, 2004.

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Plant Virus Epidemics: Monitoring, Modelling and Predicting Outbreaks. Academic Press, 1986.

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(Editor), George D. McLean, Ronald G. Garrett (Editor), and William G. Ruesink (Editor), eds. Plant Virus Epidemics: Monitoring, Modelling and Predicting Outbreaks. Academic Press, 1986.

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Jain, C. K. Water Quality ; Monitoring, Modelling and Prediction. Allied Publishers Pvt. Ltd., 2004.

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Madhu, G., Sandeep Kautish, A. Govardhan, and Avinash Sharma, eds. Emerging Computational Approaches in Telehealth and Telemedicine: A Look at The Post-COVID-19 Landscape. BENTHAM SCIENCE PUBLISHERS, 2022. http://dx.doi.org/10.2174/97898150792721220101.

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This book gives an overview of innovative approaches in telehealth and telemedicine. The Goal of the content is to inform readers about recent computer applications in e-health, including Internet of Things (IoT) and Internet of Medical Things (IoMT) technology. The 9 chapters will guide readers to determine the urgency to intervene in specific medical cases, and to assess risk to healthcare workers. The focus on telehealth along with telemedicine, encompasses a broader spectrum of remote healthcare services for the reader to understand. Chapters cover the following topics: - A COVID-19 care s
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Części książek na temat "Disease Prediction and Monitoring Modelling"

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Urošević, Vladimir, Nikola Vojičić, Aleksandar Jovanović, et al. "BRAINTEASER Architecture for Integration of AI Models and Interactive Tools for Amyotrophic Lateral Sclerosis (ALS) and Multiple Sclerosis (MS) Progression Prediction and Management." In Digital Health Transformation, Smart Ageing, and Managing Disability. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-43950-6_2.

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AbstractThe presented platform architecture and deployed implementation in real-life clinical and home care settings on four Amyotrophic Lateral Sclerosis (ALS) and Multiple Sclerosis (MS) study sites, integrates the novel working tools for improved disease management with the initial releases of the AI models for disease monitoring. The described robust industry-standard scalable platform is to be a referent example of the integration approach based on loose coupling APIs and industry open standard human-readable and language-independent interface specifications, and its successful baseline i
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Riccetti, M., M. Romano, and S. Talice. "Coastal Discharges Monitoring by an Airborne Remote Sensing System." In Water Pollution: Modelling, Measuring and Prediction. Springer Netherlands, 1991. http://dx.doi.org/10.1007/978-94-011-3694-5_26.

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Mohanty, U. C., and Akhilesh Gupta. "Deterministic Methods for Prediction of Tropical Cyclone Tracks." In Modelling and Monitoring of Coastal Marine Processes. Springer Netherlands, 2008. http://dx.doi.org/10.1007/978-1-4020-8327-3_10.

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Fusco, Terence, Yaxin Bi, Haiying Wang, and Fiona Browne. "Infectious Disease Prediction Modelling Using Synthetic Optimisation Approaches." In Communications in Computer and Information Science. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-26636-3_7.

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Kaur, Harmohanjeet, Pooja Shah, Samya Muhuri, and Suchi Kumari. "A Disease Prediction Framework Based on Predictive Modelling." In Data Science and Network Engineering. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-6755-1_21.

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Neetoo, Hudaa, Yasser Chuttur, Azina Nazurally, Sandhya Takooree, and Nooreen Mamode Ally. "Crop Disease Prediction Using Multiple Linear Regression Modelling." In Soft Computing and its Engineering Applications. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-05767-0_25.

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Thodberg, Hans Henrik, Anders Juul, Jens Lomholt, et al. "Adult Height Prediction Models." In Handbook of Growth and Growth Monitoring in Health and Disease. Springer New York, 2011. http://dx.doi.org/10.1007/978-1-4419-1795-9_3.

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Bansal, Devyanshi, Supriya Raheja, and Manoj Kumar. "Fatty Liver Disease Prediction: Using Machine Learning Algorithms." In Predictive Data Modelling for Biomedical Data and Imaging. River Publishers, 2024. http://dx.doi.org/10.1201/9781003516859-13.

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Dai, Zili, and Yu Huang. "The State of the Art of SPH Modelling for Flow-slide Propagation." In Modern Technologies for Landslide Monitoring and Prediction. Springer Berlin Heidelberg, 2015. http://dx.doi.org/10.1007/978-3-662-45931-7_8.

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Kanungo, D. P. "Ground Based Real Time Monitoring System Using Wireless Instrumentation for Landslide Prediction." In Landslides: Theory, Practice and Modelling. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-77377-3_6.

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Streszczenia konferencji na temat "Disease Prediction and Monitoring Modelling"

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Remya, R., Natham Gopi Krishna, Sammuturu Vishwanath, and Chimata Pavan Kumar. "Machine Learning Approaches for Cardiovascular Disease Prediction and Detection Modelling." In 2025 International Conference on Intelligent Computing and Control Systems (ICICCS). IEEE, 2025. https://doi.org/10.1109/iciccs65191.2025.10985674.

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Kommineni, Sivaram, Sanvitha Muddana, and Rajiv Senapati. "Explainable Artificial Intelligence based ML Models for Heart Disease Prediction." In 2024 3rd International Conference on Computational Modelling, Simulation and Optimization (ICCMSO). IEEE, 2024. http://dx.doi.org/10.1109/iccmso61761.2024.00042.

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J, Aathithya, Mohammed Basil Abdulkareem, Abdulnaser A. Hagar, Saliha Bathoo l, Dexter Woodward, and MaajidMohiUd Din Malik. "Artificial Intelligence-Enhanced Health Monitoring System via with Disease Prediction." In 2024 International Conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems (ICSES). IEEE, 2024. https://doi.org/10.1109/icses63760.2024.10910883.

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Gupta, Monika, Sonika Katta, Smitha Gayathri D, Vivek Upadhyay, Swati Nigam, and Navaneeth Nataraj. "IoT based Health Monitoring System with Disease Prediction using ML." In 2025 International Conference on Intelligent Computing and Control Systems (ICICCS). IEEE, 2025. https://doi.org/10.1109/iciccs65191.2025.10985376.

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Meena, Jaishree, Vivek Shukla, Amit Jain, Vishan Kumar Gupta, and Paras Jain. "IoT and BSN Applications for Real Time Monitoring and Disease Prediction." 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/otcon60325.2024.10688283.

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Santha Raju, N., R. Tamilkodi, Vanaparthi Chandra Shekar, Bogadi Jaya Bharathi, Koviri Dinesh Kumar, and Yanamala Sumanth. "AI-Powered Crop Suggestion, Yield Prediction, Disease Detection, and Soil Monitoring." In 2024 3rd International Conference on Automation, Computing and Renewable Systems (ICACRS). IEEE, 2024. https://doi.org/10.1109/icacrs62842.2024.10841754.

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Sutton, Jacob, Ayan Dutta, O. Patrick Kreidl, Ladislau Bölöni, and Swapnoneel Roy. "Robotic Crop Disease Monitoring Using Neural Network-Based Prediction and Weighted Path Planning." In 2024 IEEE International Conference on Systems, Man, and Cybernetics (SMC). IEEE, 2024. https://doi.org/10.1109/smc54092.2024.10831889.

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H S, Gunavathi, Ishani Priya, Kothakota Bindhu Sree, and Aparna Kushwaha. "Enhancing Crop Health Monitoring: A ResNet50 Approach to Automated Plant Disease Severity Prediction." In 2025 IEEE International Students' Conference on Electrical, Electronics and Computer Science (SCEECS). IEEE, 2025. https://doi.org/10.1109/sceecs64059.2025.10940498.

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Tuteja, Gaurav, Parul Nasra, and Mohhamied Husaein Fallaah. "IoT-Enabled Health Monitoring System for Cardiovascular Disease Prediction Using Deep Learning Models." In 2025 IEEE International Conference on Interdisciplinary Approaches in Technology and Management for Social Innovation (IATMSI). IEEE, 2025. https://doi.org/10.1109/iatmsi64286.2025.10984495.

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S, Mohideen Badhusha, Gayatri C. Bhagavantnavar, Akshatha, Deeksha Subrahmanya Shet, and Abhishek BK. "Smart Crop Health Monitoring and Disease Prediction System using IoT and Machine Learning." In 2025 3rd International Conference on Inventive Computing and Informatics (ICICI). IEEE, 2025. https://doi.org/10.1109/icici65870.2025.11069859.

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Raporty organizacyjne na temat "Disease Prediction and Monitoring Modelling"

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Van Lancker, V., L. Kint, G. Montereale-Gavazzi, et al. How subsurface voxel modelling and uncertainty analysis contribute to habitat-change prediction and monitoring. Natural Resources Canada/ESS/Scientific and Technical Publishing Services, 2017. http://dx.doi.org/10.4095/305937.

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Stebbing, Nicola, Claire Witham, Frances Beckett, Helen Webster, Lois Huggett, and David Thomson. © Crown copyright 2024, Met Office Page 1 of 43 Can we improve plume dispersal modelling for fire related emergency response operations by utilising short-range dispersion schemes? Met Office, 2024. http://dx.doi.org/10.62998/wnnr5415.

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Large fires that produce plumes of smoke and other contaminants can cause harm to both people and the environment. To support UK emergency responders, the Met Office Environmental Monitoring and Response Centre (EMARC) provides dedicated weather advice and forecasts of the plume in the form of CHEmical METeorological (CHEMET) reports. The plume’s expected location, extent and relative air concentrations of pollutants are predicted using the Numerical Atmospheric-dispersion Modelling Environment (NAME), which simulates the transport and dispersion of pollutants using numerical weather predictio
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Rankin, Nicole, Deborah McGregor, Candice Donnelly, et al. Lung cancer screening using low-dose computed tomography for high risk populations: Investigating effectiveness and screening program implementation considerations: An Evidence Check rapid review brokered by the Sax Institute (www.saxinstitute.org.au) for the Cancer Institute NSW. The Sax Institute, 2019. http://dx.doi.org/10.57022/clzt5093.

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Background Lung cancer is the number one cause of cancer death worldwide.(1) It is the fifth most commonly diagnosed cancer in Australia (12,741 cases diagnosed in 2018) and the leading cause of cancer death.(2) The number of years of potential life lost to lung cancer in Australia is estimated to be 58,450, similar to that of colorectal and breast cancer combined.(3) While tobacco control strategies are most effective for disease prevention in the general population, early detection via low dose computed tomography (LDCT) screening in high-risk populations is a viable option for detecting asy
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