Academic literature on the topic 'Predictive Machine Learning'
Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles
Consult the lists of relevant articles, books, theses, conference reports, and other scholarly sources on the topic 'Predictive Machine Learning.'
Next to every source in the list of references, there is an 'Add to bibliography' button. Press on it, and we will generate automatically the bibliographic reference to the chosen work in the citation style you need: APA, MLA, Harvard, Chicago, Vancouver, etc.
You can also download the full text of the academic publication as pdf and read online its abstract whenever available in the metadata.
Journal articles on the topic "Predictive Machine Learning"
1
Mahida, Ankur. "Predictive Incident Management Using Machine Learning." International Journal of Science and Research (IJSR) 11, no. 6 (2022): 1977–80. http://dx.doi.org/10.21275/sr24401231847.
Full text
2
Tong, Tingting, and Zhen Li. "Predicting learning achievement using ensemble learning with result explanation." PLOS ONE 20, no. 1 (2025): e0312124. https://doi.org/10.1371/journal.pone.0312124.
Full textAbstract:
Predicting learning achievement is a crucial strategy to address high dropout rates. However, existing prediction models often exhibit biases, limiting their accuracy. Moreover, the lack of interpretability in current machine learning methods restricts their practical application in education. To overcome these challenges, this research combines the strengths of various machine learning algorithms to design a robust model that performs well across multiple metrics, and uses interpretability analysis to elucidate the prediction results. This study introduces a predictive framework for learning achievement based on ensemble learning techniques. Specifically, six distinct machine learning models are utilized to establish a base learner, with logistic regression serving as the meta learner to construct an ensemble model for predicting learning achievement. The SHapley Additive exPlanation (SHAP) model is then employed to explain the prediction results. Through the experiments on XuetangX dataset, the effectiveness of the proposed model is verified. The proposed model outperforms traditional machine learning and deep learning model in terms of prediction accuracy. The results demonstrate that the ensemble learning-based predictive framework significantly outperforms traditional machine learning methods. Through feature importance analysis, the SHAP method enhances model interpretability and improves the reliability of the prediction results, enabling more personalized interventions to support students.
3
Ibrahim Adedeji Adeniran, Christianah Pelumi Efunniyi, Olajide Soji Osundare, and Angela Omozele Abhulimen. "Implementing machine learning techniques for customer retention and churn prediction in telecommunications." Computer Science & IT Research Journal 5, no. 8 (2024): 2011–25. http://dx.doi.org/10.51594/csitrj.v5i8.1489.
Full textAbstract:
This review paper explores the application of machine learning techniques in predicting customer churn and enhancing customer retention within the telecommunications industry. The paper begins by discussing the significance of customer churn, its causes, and the limitations of traditional churn prediction methods. It then delves into machine learning algorithms, including decision trees, support vector machines, and ensemble methods. It highlights their effectiveness in handling large and complex datasets typical of the telecom sector. The discussion extends to the challenges faced in data quality, model selection, implementation, and ethical considerations in using customer data for predictive analytics. The paper also compares machine learning models with traditional methods, emphasizing the advantages of scalability, accuracy, and real-time processing. Furthermore, it identifies potential innovations, such as improved data integration, interpretable models, and personalized retention strategies. Finally, the paper reflects on future trends, predicting the growing role of AI and machine learning in telecommunications, particularly in customer service automation and network optimization. The review underscores the importance of adopting machine learning to reduce churn and improve customer retention while considering the field's ethical implications and future opportunities. Keywords: Customer Churn Prediction, Machine Learning, Telecommunications, Customer Retention, Predictive Analytics, AI in Telecom
4
Jogalekar, Tanmay. "Predictive Modelling for Stock Market Analysis (June 2025)." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 06 (2025): 1–9. https://doi.org/10.55041/ijsrem51062.
Full textAbstract:
The stock market is a complex and dynamic system, and predicting its behaviour is a challenging task. This research paper presents a comparative study of machine learning algorithms for predictive modelling of stock market analysis. We evaluate the performance of six machine learning algorithms, including Linear Regression, Decision Trees, Random Forest, Support Vector Machines, Artificial Neural Networks, and Gradient Boosting, on a dataset of historical stock prices. Our results show that the Gradient Boosting algorithm outperforms the other algorithms in terms of accuracy, precision, and recall. We also analyse the impact of feature engineering and hyperparameter tuning on the performance of the algorithms. The findings of this study can be used to develop predictive models for stock market analysis, which can aid investors and financial analysts in making informed decisions. Keywords - Stock Market Prediction, Predictive Modelling, Machine Learning, Deep Learning, LSTM, Financial Forecasting
5
Husam Kadhim Gharkan, Mustafa Jawad Radif. "Predicting Student Performance Using a Hybrid Model Based on Machine Learning and Feature Selection Techniques." Journal of Information Systems Engineering and Management 10, no. 4 (2025): 192–99. https://doi.org/10.52783/jisem.v10i4.8921.
Full textAbstract:
Accurately predicting student performance plays a critical role in modern educational institutions. It enables targeted interventions and enhances educational outcomes. This paper proposes a hybrid predictive model for predicting student performance employing feature selection based on standard deviation filtering, coupled with machine learning techniques. In the machine learning phase used Decision Tree (DT), Random Forest (RF), K-Nearest Neighbours (KNN), and Support Vector Machines (SVM) were used. The proposed model is tested and evaluated over the Student Performance Prediction—Multiclass Case dataset. The experimental result demonstrated robust predictive capabilities, with Decision Tree models showing the highest accuracy at 100%. KNN and Naive Bayes (NB) also exhibited strong performances, achieving accuracy rates of 98.98% and 96.94%, respectively. This work underscores the importance of selecting appropriate features and machine learning algorithms to optimise student performance prediction, significantly benefiting early identification of at-risk students.
6
Nagarjuna, N., and Dr Lakshmi HN. "Predictive Modeling of Diabetes Mellitus Utilizing Machine Learning Techniques." CVR Journal of Science and Technology 26, no. 1 (2024): 112–17. http://dx.doi.org/10.32377/cvrjst2618.
Full textAbstract:
Diabetes mellitus represents a persistent metabolic condition distinguished by elevated levels of blood sugar, which results from the inadequacy of the body to secrete and respond to insulin, leading to health risks and frequent hospitalizations. Accurate predictive models are vital for targeted interventions to reduce readmissions and improve healthcare quality and cost. Early prediction can mitigate its impact, aid in control, and potentially save lives. Machine learning algorithms show promise in medical applications, including diabetes prediction and diagnosis. Limited data quality hinders accurate diabetes prediction due to missing values and inconsistencies. This paper investigates machine learning's potential for predicting and diagnosing diabetes, aiming to enhance accuracy and efficiency in disease management. Feature engineering techniques are applied to preprocess the data and extract relevant features for model development. To address class imbalance, SMOTE (Synthetic Minority Oversampling Technique) is employed. Various machine learning algorithms, including logistic regression, Naïve Bayes, random forests, support vector machines (SVM), K-Nearest Neighbors (KNN), and eXtreme Gradient Boosting (XGBoost), are utilized to build predictive models. The performance evaluation employs standard metrics such as accuracy, recall, precision, and F1-Score. Notably, Random Forest achieves an accuracy of 82% followed by XGBoost(80%) , surpassing other ML algorithms utilized. Index Terms: Diabetes mellitus, Machine learning, Prediction, SVM, logistic regression, Accuracy.
7
Natanael, David, and Hadi Sutanto. "Machine Learning Application Using Cost-Effective Components for Predictive Maintenance in Industry: A Tube Filling Machine Case Study." Journal of Manufacturing and Materials Processing 6, no. 5 (2022): 108. http://dx.doi.org/10.3390/jmmp6050108.
Full textAbstract:
Maintenance is an activity that cannot be separated from the context of product manufacturing. It is carried out to maintain the components’ or machines’ function so that no failure can reduce the machine’s productivity. One type of maintenance that can mitigate total machine failure is predictive maintenance. Predictive maintenance, along with the times, no longer relies on visuals or other senses but can be combined into automated observations using machine learning methods. It can be applied to a toothpaste factory with a tube filling machine by combining the results of sensor observations with machine learning methods. This research aims to increase the Overall equipment effectiveness (OEE) to 10% by predicting the components that will be damaged. The machine learning methods tested in this study are random forest regression and linear regression. This study indicates that the prediction accuracy of machine learning with the random forest regression method for PHM predictive is 88%of the actual data, and linear regression has an accuracy of 59% of the actual data. After implementing the system on the machine for three months, the OEE value increased by 13.10%, and unplanned machine failure decreased by 62.38% in the observed part. Implementation of the system can significantly reduce the failure factor of unplanned machines.
8
MISHRA,, SAURABH. "HEALTH PREDICTION USING MACHINE LEARNING." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 05 (2024): 1–5. http://dx.doi.org/10.55041/ijsrem34438.
Full textAbstract:
Machine learning techniques have transformed healthcare by enabling precise and timely disease prediction. The capacity to forecast multiple diseases simultaneously can greatly enhance early diagnosis and treatment, leading to improved patient outcomes and lower healthcare expenses. This research paper delves into the use of machine learning algorithms for predicting various diseases, highlighting their advantages, challenges, and prospects. It provides a comprehensive overview of different machine learning models and the data sources frequently employed in disease prediction. Furthermore, it emphasises the importance of feature selection, model evaluation, and the integration of diverse data types to improve prediction accuracy. The findings underscore the significant potential of machine learning in predicting multiple diseases and its impact on public health. Specifically, the study demonstrates the application of a machine learning model to determine if an individual is affected by certain diseases. This model is trained using sample data to enhance its predictive capabilities. Key Words: Disease Prediction, Disease data, Machine Learning.
9
Sreshta, Maradapu Ananya. "Analyzing Cancer Prognosis with Advanced Machine Learning Models." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 04 (2024): 1–5. http://dx.doi.org/10.55041/ijsrem30443.
Full textAbstract:
This study investigates the use of Support Vector Machines (SVM) and other machine learning algorithms for predicting the prognosis of lung, breast, and cervical cancer patients. The research evaluates the predictive accuracy, influential features, algorithm performance, and model generalization across different cancer types. Using publicly available datasets, the study highlights SVM's exceptional accuracy in prognosis prediction, with findings indicating its superiority over alternative algorithms. Notably, it identifies the key clinical, molecular, and pathological features that significantly impact predictive accuracy. The study also discusses the clinical applicability of these models, emphasizing their potential to aid healthcare professionals in making more informed treatment decisions. Acknowledging limitations, including data availability and computational resources, the study suggests future directions, encouraging the exploration of additional techniques, diverse datasets, and real-world clinical trials to validate the model’s effectiveness. Keywords Cancer, Medical Diagnosis, Markers, Learning, Patients, Machine Learning, Support Vector Machine
10
Fokkema, Marjolein, Dragos Iliescu, Samuel Greiff, and Matthias Ziegler. "Machine Learning and Prediction in Psychological Assessment." European Journal of Psychological Assessment 38, no. 3 (2022): 165–75. http://dx.doi.org/10.1027/1015-5759/a000714.
Full textAbstract:
Abstract. Modern prediction methods from machine learning (ML) and artificial intelligence (AI) are becoming increasingly popular, also in the field of psychological assessment. These methods provide unprecedented flexibility for modeling large numbers of predictor variables and non-linear associations between predictors and responses. In this paper, we aim to look at what these methods may contribute to the assessment of criterion validity and their possible drawbacks. We apply a range of modern statistical prediction methods to a dataset for predicting the university major completed, based on the subscales and items of a scale for vocational preferences. The results indicate that logistic regression combined with regularization performs strikingly well already in terms of predictive accuracy. More sophisticated techniques for incorporating non-linearities can further contribute to predictive accuracy and validity, but often marginally.
Dissertations / Theses on the topic "Predictive Machine Learning"
1
Romano, Donato. "Machine Learning algorithms for predictive diagnostics applied to automatic machines." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2021. http://amslaurea.unibo.it/22319/.
Full textAbstract:
In questo lavoro di tesi è stato analizzato l'avvento dell'industria 4.0 all'interno dell' industria nel settore packaging. In particolare, è stata discussa l'importanza della diagnostica predittiva e sono stati analizzati e testati diversi approcci per la determinazione di modelli descrittivi del problema a partire dai dati. Inoltre, sono state applicate le principali tecniche di Machine Learning in modo da classificare i dati analizzati nelle varie classi di appartenenza.
2
Korvesis, Panagiotis. "Machine Learning for Predictive Maintenance in Aviation." Thesis, Université Paris-Saclay (ComUE), 2017. http://www.theses.fr/2017SACLX093/document.
Full textAbstract:
L'augmentation des données disponibles dans presque tous les domaines soulève la nécessité d'utiliser des algorithmes pour l'analyse automatisée des données. Cette nécessité est mise en évidence dans la maintenance prédictive, où l'objectif est de prédire les pannes des systèmes en observant continuellement leur état, afin de planifier les actions de maintenance à l'avance. Ces observations sont générées par des systèmes de surveillance habituellement sous la forme de séries temporelles et de journaux d'événements et couvrent la durée de vie des composants correspondants. Le principal défi de la maintenance prédictive est l'analyse de l'historique d'observation afin de développer des modèles prédictifs.Dans ce sens, l'apprentissage automatique est devenu omniprésent puisqu'il fournit les moyens d'extraire les connaissances d'une grande variété de sources de données avec une intervention humaine minimale. L'objectif de cette thèse est d'étudier et de résoudre les problèmes dans l'aviation liés à la prévision des pannes de composants à bord. La quantité de données liées à l'exploitation des avions est énorme et, par conséquent, l'évolutivité est une condition essentielle dans chaque approche proposée.Cette thèse est divisée en trois parties qui correspondent aux différentes sources de données que nous avons rencontrées au cours de notre travail. Dans la première partie, nous avons ciblé le problème de la prédiction des pannes des systèmes, compte tenu de l'historique des Post Flight Reports. Nous avons proposé une approche statistique basée sur la régression précédée d'une formulation méticuleuse et d'un prétraitement / transformation de données. Notre méthode estime le risque d'échec avec une solution évolutive, déployée dans un environnement de cluster en apprentissage et en déploiement. À notre connaissance, il n'y a pas de méthode disponible pour résoudre ce problème jusqu'au moment où cette thèse a été écrite.La deuxième partie consiste à analyser les données du livre de bord, qui consistent en un texte décrivant les problèmes d'avions et les actions de maintenance correspondantes. Le livre de bord contient des informations qui ne sont pas présentes dans les Post Flight Reports bien qu'elles soient essentielles dans plusieurs applications, comme la prédiction de l'échec. Cependant, le journal de bord contient du texte écrit par des humains, il contient beaucoup de bruit qui doit être supprimé afin d'extraire les informations utiles. Nous avons abordé ce problème en proposant une approche basée sur des représentations vectorielles de mots. Notre approche exploite des similitudes sémantiques, apprises par des neural networks qui ont généré les représentations vectorielles, afin d'identifier et de corriger les fautes d'orthographe et les abréviations. Enfin, des mots-clés importants sont extraits à l'aide du Part of Speech Tagging.Dans la troisième partie, nous avons abordé le problème de l'évaluation de l'état des composants à bord en utilisant les mesures des capteurs. Dans les cas considérés, l'état du composant est évalué par l'ampleur de la fluctuation du capteur et une tendance à l'augmentation monotone. Dans notre approche, nous avons formulé un problème de décomposition des séries temporelles afin de séparer les fluctuations de la tendance en résolvant un problème convexe. Pour quantifier l'état du composant, nous calculons à l'aide de Gaussian Mixture Models une fonction de risque qui mesure l'écart du capteur par rapport à son comportement normal<br>The increase of available data in almost every domain raises the necessity of employing algorithms for automated data analysis. This necessity is highlighted in predictive maintenance, where the ultimate objective is to predict failures of hardware components by continuously observing their status, in order to plan maintenance actions well in advance. These observations are generated by monitoring systems usually in the form of time series and event logs and cover the lifespan of the corresponding components. Analyzing this history of observation in order to develop predictive models is the main challenge of data driven predictive maintenance.Towards this direction, Machine Learning has become ubiquitous since it provides the means of extracting knowledge from a variety of data sources with the minimum human intervention. The goal of this dissertation is to study and address challenging problems in aviation related to predicting failures of components on-board. The amount of data related to the operation of aircraft is enormous and therefore, scalability is a key requirement in every proposed approach.This dissertation is divided in three main parts that correspond to the different data sources that we encountered during our work. In the first part, we targeted the problem of predicting system failures, given the history of Post Flight Reports. We proposed a regression-based approach preceded by a meticulous formulation and data pre-processing/transformation. Our method approximates the risk of failure with a scalable solution, deployed in a cluster environment both in training and testing. To our knowledge, there is no available method for tackling this problem until the time this thesis was written.The second part consists analyzing logbook data, which consist of text describing aircraft issues and the corresponding maintenance actions and it is written by maintenance engineers. The logbook contains information that is not reflected in the post-flight reports and it is very essential in several applications, including failure prediction. However, since the logbook contains text written by humans, it contains a lot of noise that needs to be removed in order to extract useful information. We tackled this problem by proposing an approach based on vector representations of words (or word embeddings). Our approach exploits semantic similarities of words, learned by neural networks that generated the vector representations, in order to identify and correct spelling mistakes and abbreviations. Finally, important keywords are extracted using Part of Speech Tagging.In the third part, we tackled the problem of assessing the health of components on-board using sensor measurements. In the cases under consideration, the condition of the component is assessed by the magnitude of the sensor's fluctuation and a monotonically increasing trend. In our approach, we formulated a time series decomposition problem in order to separate the fluctuation from the trend by solving a convex program. To quantify the condition of the component, we compute a risk function which measures the sensor's deviation from it's normal behavior, which is learned using Gaussian Mixture Models
3
Karlsson, Lotta. "Predictive Maintenance for RM12 with Machine Learning." Thesis, Högskolan i Halmstad, Akademin för ekonomi, teknik och naturvetenskap, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:hh:diva-42283.
Full textAbstract:
Few components within mechanical engineering possess the fatigue resistance as of high-pressure turbine blades found in jet engines. This as they are designed to perform in extensively high temperatures under severe loading which causes degradation to be an important aspect despite a design, optimized for its environment. This study aims to find a method for predicting life consumption of those blades belonging to the turbine section of the jet engine in JAS 39 Gripen C/D called RM12. This was performed at GKN Aerospace, which holds the military type certificate for this engine as well as a patented solution that determines life consumption in components depending on operational history. With the help of machine learning in Matlab, flight sensor data and loading results, the method was to explore a variety of prediction models and find a selection of blades with varied utilization before reaching end of life for comparison. Followed by a search of understanding the life limiting fatigue conditions and the factors involved in the deterioration process. A similarity finding approach gave valuable meaning to the accuracy of regression analysis from flight data towards output in form of temperature predictions. Comparing known and reliable fatigue calculation results gave however no clear picture as inspected blades had reach their limit at very diverse accumulated values. The next approach was therefore to investigate if an initialization point of degradation could be found, from where the result could give an answer that matched for all blades and their different utilization. The result was that an accelerated degradation after high loading could give a prediction that could explain the total life consumption with an accuracy of 87% for 19 out of 21 investigated blades. The accelerated deterioration could in theory be explained by the fact that the fatigue resistance as well as different types of degradation, propagates each other and originates from thermal loading making them all contributors, whereas the conventional numerical methods only handles them separately. In order to get confidence, valuable and reliable predictions, the models do however need to be accompanied with more testing and adding of contributing factors before assumed as a proven method for life consumption determination of the high-pressure turbine blades.
4
Darwiche, Aiman A. "Machine Learning Methods for Septic Shock Prediction." Diss., NSUWorks, 2018. https://nsuworks.nova.edu/gscis_etd/1051.
Full textAbstract:
Sepsis is an organ dysfunction life-threatening disease that is caused by a dysregulated body response to infection. Sepsis is difficult to detect at an early stage, and when not detected early, is difficult to treat and results in high mortality rates. Developing improved methods for identifying patients in high risk of suffering septic shock has been the focus of much research in recent years. Building on this body of literature, this dissertation develops an improved method for septic shock prediction. Using the data from the MMIC-III database, an ensemble classifier is trained to identify high-risk patients. A robust prediction model is built by obtaining a risk score from fitting the Cox Hazard model on multiple input features. The score is added to the list of features and the Random Forest ensemble classifier is trained to produce the model. The Cox Enhanced Random Forest (CERF) proposed method is evaluated by comparing its predictive accuracy to those of extant methods.
5
Pienaar, Celia. "Machine learning in predictive analytics on judicial decision-making." Master's thesis, Faculty of Science, 2021. http://hdl.handle.net/11427/33925.
Full textAbstract:
Legal professionals globally are under pressure to provide ‘more for less' – not an easy challenge in the era of big data, increasingly complex regulatory and legislative frameworks and volatile financial markets. Although largely limited to information retrieval and extraction, Machine Learning applications targeted at the legal domain have to some extent become mainstream. The startup market is rife with legal technology providers with many major law firms encouraging research and development through formal legal technology incubator programs. Experienced legal professionals are expected to become technologically astute as part of their response to the ‘more for less' challenge, while legal professionals on track to enter the legal services industry are encouraged to broaden their skill sets beyond a traditional law degree. Predictive analytics applied to judicial decision-making raise interesting discussions around potential benefits to the general public, over-burdened judicial systems and legal professionals respectively. It is also associated with limitations and challenges around manual input required (in the absence of automatic extraction and prediction) and domain-specific application. While there is no ‘one size fits all' solution when considering predictive analytics across legal domains or different countries' legal systems, this dissertation aims to provide an overview of Machine Learning techniques which could be applied in further research, to start unlocking the benefits associated with predictive analytics on a greater (and hopefully local) scale.
6
Gligorijevic, Djordje. "Predictive Uncertainty Quantification and Explainable Machine Learning in Healthcare." Diss., Temple University Libraries, 2018. http://cdm16002.contentdm.oclc.org/cdm/ref/collection/p245801coll10/id/520057.
Full textAbstract:
Computer and Information Science<br>Ph.D.<br>Predictive modeling is an ever-increasingly important part of decision making. The advances in Machine Learning predictive modeling have spread across many domains bringing significant improvements in performance and providing unique opportunities for novel discoveries. A notably important domains of the human world are medical and healthcare domains, which take care of peoples' wellbeing. And while being one of the most developed areas of science with active research, there are many ways they can be improved. In particular, novel tools developed based on Machine Learning theory have drawn benefits across many areas of clinical practice, pushing the boundaries of medical science and directly affecting well-being of millions of patients. Additionally, healthcare and medicine domains require predictive modeling to anticipate and overcome many obstacles that future may hold. These kinds of applications employ a precise decision--making processes which requires accurate predictions. However, good prediction by its own is often insufficient. There has been no major focus in developing algorithms with good quality uncertainty estimates. Ergo, this thesis aims at providing a variety of ways to incorporate solutions by learning high quality uncertainty estimates or providing interpretability of the models where needed for purpose of improving existing tools built in practice and allowing many other tools to be used where uncertainty is the key factor for decision making. The first part of the thesis proposes approaches for learning high quality uncertainty estimates for both short- and long-term predictions in multi-task learning, developed on top for continuous probabilistic graphical models. In many scenarios, especially in long--term predictions, it may be of great importance for the models to provide a reliability flag in order to be accepted by domain experts. To this end we explored a widely applied structured regression model with a goal of providing meaningful uncertainty estimations on various predictive tasks. Our particular interest is in modeling uncertainty propagation while predicting far in the future. To address this important problem, our approach centers around providing an uncertainty estimate by modeling input features as random variables. This allows modeling uncertainty from noisy inputs. In cases when model iteratively produces errors it should propagate uncertainty over the predictive horizon, which may provide invaluable information for decision making based on predictions. In the second part of the thesis we propose novel neural embedding models for learning low-dimensional embeddings of medical concepts, such are diseases and genes, and show how they can be interpreted to allow accessing their quality, and show how can they be used to solve many problems in medical and healthcare research. We use EHR data to discover novel relationships between diseases by studying their comorbidities (i.e., co-occurrences in patients). We trained our models on a large-scale EHR database comprising more than 35 million inpatient cases. To confirm value and potential of the proposed approach we evaluate its effectiveness on a held-out set. Furthermore, for select diseases we provide a candidate gene list for which disease-gene associations were not studied previously, allowing biomedical researchers to better focus their often very costly lab studies. We furthermore examine how disease heterogeneity can affect the quality of learned embeddings and propose an approach for learning types of such heterogeneous diseases, while in our study we primarily focus on learning types of sepsis. Finally, we evaluate the quality of low-dimensional embeddings on tasks of predicting hospital quality indicators such as length of stay, total charges and mortality likelihood, demonstrating their superiority over other approaches. In the third part of the thesis we focus on decision making in medicine and healthcare domain by developing state-of-the-art deep learning models capable of outperforming human performance while maintaining good interpretability and uncertainty estimates.<br>Temple University--Theses
7
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.
Full textAbstract:
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 de réseaux sans défaillance et sans pertes, et permettre aux applications critiques d'être exécutées sur des réseaux de plus grandes dimensions et hétérogènes. Dans ce manuscrit, nous nous proposons de contribuer au domaine du NFP en nous focalisant sur la prédiction des alertes réseau. Dans un premier temps, nous présentons une étude de l'état de l'art complet du NFP en utilisant des méthodes d'apprentissage machine (ML) entièrement dédiée aux réseaux de télécommunications. Ensuite, nous établissons de futures directions de recherche dans le domaine. Dans un deuxième temps, nous proposons et étudions un couple de métriques (Réduction des coûts de maintenance, et mesure des gains de Qualité de Service) de performances de ML adaptées au NFP dans le cadre de la maintenance des réseaux. Dans un troisième temps, nous décrivons l'architecture complète de traitement des données, incluant l'infrastructure réseau et logicielle, et la chaîne de prétraitement des données nécessaires au ML qui ont été mis en œuvre chez SPIE ICS, société d'intégration de réseaux et de systèmes. Nous décrivons également avec précision le modèle du problème d'alarme et d'incidents. Dans un quatrième temps, nous établissons une comparaison des différentes méthodes de ML appliquées à notre jeu de données. Nous considérons des méthodes conventionnelles de ML, basés sur des arbres de décision, des perceptrons multicouches et des Séparateurs à Vastes Marges. Nous testons la généralisation des performances des modèles par rapport aux différents types d'équipements, ainsi que les généralisations en ML des modèles de ML et des paramètres proposés. Ensuite, nous étudions avec succès les architectures de ML à entrée séquentielle - Réseaux de neurones convolutifs et Long Short Term Memory - dans le cas de données SNMP séquentielles sur notre ensemble de données. Finalement, nous étudions l'impact de la définition de l'horizon de prédiction (et des variables arbitraires associées) sur la performance de prédiction des modèles ML<br>With the improvement of computation power necessary for advanced applications of Machine Learning (ML), Network Fault Prediction (NFP) experiences a renewed scientific interest. The ability to predict network equipment failure is increasingly identified as an effective means to improve network reliability. This predictive capability can be used, to mitigate or to enact predictive maintenance on incoming network failures. This could contribute to establishing zero-failure networks and allow safety-critical applications to run over higher dimension and heterogeneous networks.In this PhD thesis, we propose to contribute to the NFP field by focusing on network alarm prediction. First, we present a comprehensive survey on NFP using Machine Learning (ML) methods entirely dedicated to telecommunication networks, and determine new directions for research in the field. Second, we propose and study a set of Machine Learning performance metrics (maintenance cost reduction and Quality of Service improvement) adapted to NFP in the context of network maintenance. Third, we describe the complete data processing architecture, including the network and software infrastructure, and the necessary data preprocessing pipeline that was implemented at SPIE ICS, Networks and Systems Integrator. We also describe the alarm or failure prediction problem model precisely. Fourth, we establish a benchmark of the different ML solutions applied to our dataset. We consider Decision Tree-based methods, Multi-Layer Perceptron and Support Vector Machines. We test the generalization of performance prediction across equipment types as well as normal ML generalization of the proposed models and parameters.Then, we apply sequential - Convolutional Neural Networks (CNN) and Long Short Term Memory (LSTM) - ML architectures with success on our sequential SNMP dataset. Finally, we study the impact of the definition of the prediction horizon (and associated arbitrary timeframes) on the ML model prediction performance
8
PROVEZZA, LUCA. "Predictive diagnostics through machine learning on the injection group of a diecasting machine." Doctoral thesis, Università degli studi di Brescia, 2022. http://hdl.handle.net/11379/559976.
Full textAbstract:
L'analisi dei big data ha sempre più preso un ruolo rilevante nello scenario industriale degli ultimi decenni. Il "Data Lake" rappresenta una nuova frontiera in data science. Non si vuole solo immagazzinare dati, ma analizzarli con il fine di applicare procedure correttive in tempo per poter evitare stop di produzione e far crescere la produttività di un'azienda.
Ogni campo della catena produttiva è importante per poter crescere la produttività. In particolare, la manutenzione è uno dei tasks più importanti di cui tenere in considerazione per queste analisi. Infatti, i costi dovuti alla manutenzione sono la maggior parte dei costi totali di un impianto di produzione. Questi costi devono essere ridotti mediante diverse strategie.
La nuova frontiera nella strategia di manutenzione è rappresentata dalla Manutenzione Predittiva (PM) o anche definita, Predictive Health Management (PHM). La PHM è una strategia di manutenzione dove vengono applicati algoritmi statistici o algoritmi di machine learning per ottenere la "Remaining Useful Life" (RUL) di un componente. Questo progetto è focalizzato nell'applicazione della PHM nel gruppo iniezione di una macchina di pressocolata. Per sua definizione, il processo di pressocolata ad alta pressione (HPDC) presenta aspetti differenti che possono andare ad inficiare sull'analisi dei dati. Per esempio, il malfunzionamento di un componente è un evento raro e l'analisi non può essere eseguita andando ad investigare ampi dataset, oppure investigando i dati di fault basandoci sui registri di manutenzione di un'azienda. Questo rende difficoltoso indentificare condizioni di fault dei componenti utilizzando i tradizionali algoritmi di machine learning. Un ulteriore problema legato con il processo di HPDC sta nel frequente cambio di produzione, che porta a cambiamenti radicali nei parametri di processo. Inoltre, può capitare che le aziende di piccole dimensioni non facciano il corretto update dei dati di produzione (cambio di stampo, cambio ricetta nell'iniezione).
Per risolvere queste problematiche, viene proposto un nuovo metodo per determinare le condizioni di fault di componenti in una macchina di pressocolata. Il metodo proposto è in grado di determinare automaticamente un cambio di produzione e resettare il dataset utilizzato per il training. Il metodo si basa sulla peculiarità del processo di pressocolata di avere differenti fasi che risultano essere eguali per ogni tipo di macchina e produzione. Queste fasi sono, l'avanzamento lento del pistone per l'iniezione in modo da evitare bolle d'aria nella camera di iniezione, l'avanzamento veloce del pistone con il completo riempimento dello stampo, e la fase di moltiplica dando una maggiore pressione al processo di pressofusione. Questo aumento di pressione serve per compensare il ritiro del materiale dovuto al raffreddamento dopo l'iniezione.
Ogni fase viene interpolata in modo da estrarre parametri significativi per la futura predizione del fault. Per ogni parametro, viene calcolato uno stimatore dell'incertezza, che viene combinato con l'incertezza della strumentazione per ottenere un'incertezza estesa che tenga in considerazione dei due contributi. Il core di questo metodo si concretizza proprio nel calcolo della metrica finale per poter monitorare lo stato di salute di un componente. Infatti, il metodo si basa sulla combinazione della classica analisi statistica con delle matrici peso date dagli esperti del settore della manutenzione di questa tipologia di macchina. Il risultato finale è l'Health Index (HI) che rappresenta la probabilità di avere una condizione di fault per la macchina di pressocolata. Ogni matrice peso che viene combinata con i parametri estratti si traduce in un HI per quel componente. In questo modo, è possibile creare tanti Heath Index possibili utilizzando una specifica matrice peso, che è possibile costruire mediante le interviste degli esperti.<br>In the last decades, data analysis becomes relevant in the industrial scenario. The data lake represents the new frontier in the data science. The new concept is not only the data storage anymore, but the possibility to analyse the historical data in order to optimize the production by finding bottle necks in the production chain and solving the problem by applying corrective procedures to increase the productivity of a company.
Every field in the production chain is important to increase the productivity. Maintenance is one of the most important tasks to take into in account. Indeed, maintenance costs are a major part of the total operating costs of all manufacturing or production plants. These costs must be reduced by applying different strategies.
The new frontier in the maintenance strategy is represented by the Predictive Maintenance (PM) or Predictive Health Management (PHM). PHM is a maintenance strategy in which different statistical algorithms or machine learning algorithms can be applied to obtain the Remaining Useful Life (RUL) of a component.
This project is focused on the application of the PHM on an injection group of a die casting machine. By this own definition, the High Pressure Die Casting (HPDC) process presents different aspects that can affect the analysis. For instance, the fault of components is a rare event, and the analysis cannot be performed by investigating large datasets or fault data based on maintenance records. This makes very difficult to detect the fault of components with traditional machine learning algorithms. A further problem, however, linked with HPDC process is in the frequent change in production, which leads to changes in the process parameters. Moreover, sometimes small companies do not correctly update the production identifiers. To solve these problems, a new method is proposed to detect the fault of components in a diecasting machine. The proposed method automatically detects a production change and resets each time the dataset used for training.
The method is based on the peculiarity of the die casting process that presents different phases equal to each machine and production considered. These phases are the slow motion of the piston to avoid air bubbles inside the injection chamber, the stroke with the filling of the die, and the multiplication phase to compensate the shrinkage of the material due to the cooling by giving more pressure in the process. Each phase is interpolated to extract sensitive parameters to perform the prediction of fault. For each parameter, an uncertainty estimator is recorded and combined with the uncertainty of the instrumentation to obtain an uncertainty that considers the two contributions.
The core of this method is in the combination of the classical prediction analysis with a weighing matrix given by the experts. The weights are determined in a series of formal interviews for each phase and quantity recorded. The result is the Health Index (HI) representing the probability of different types of faults in the diecasting machine. Each weighing matrix combined with the parameters extracted is a HI for that component and it is possible to create how many HIs as possible by using a proper weighing matrix that can be constructed through the interview of the experts.
9
PROVEZZA, LUCA. "Predictive diagnostics through machine learning on the injection group of a diecasting machine." Doctoral thesis, Università degli studi di Brescia, 2022. http://hdl.handle.net/11379/559959.
Full textAbstract:
L'analisi dei big data ha sempre più preso un ruolo rilevante nello scenario industriale degli ultimi decenni. Il "Data Lake" rappresenta una nuova frontiera in data science. Non si vuole solo immagazzinare dati, ma analizzarli con il fine di applicare procedure correttive in tempo per poter evitare stop di produzione e far crescere la produttività di un'azienda.
Ogni campo della catena produttiva è importante per poter crescere la produttività. In particolare, la manutenzione è uno dei tasks più importanti di cui tenere in considerazione per queste analisi. Infatti, i costi dovuti alla manutenzione sono la maggior parte dei costi totali di un impianto di produzione. Questi costi devono essere ridotti mediante diverse strategie.
La nuova frontiera nella strategia di manutenzione è rappresentata dalla Manutenzione Predittiva (PM) o anche definita, Predictive Health Management (PHM). La PHM è una strategia di manutenzione dove vengono applicati algoritmi statistici o algoritmi di machine learning per ottenere la "Remaining Useful Life" (RUL) di un componente. Questo progetto è focalizzato nell'applicazione della PHM nel gruppo iniezione di una macchina di pressocolata. Per sua definizione, il processo di pressocolata ad alta pressione (HPDC) presenta aspetti differenti che possono andare ad inficiare sull'analisi dei dati. Per esempio, il malfunzionamento di un componente è un evento raro e l'analisi non può essere eseguita andando ad investigare ampi dataset, oppure investigando i dati di fault basandoci sui registri di manutenzione di un'azienda. Questo rende difficoltoso indentificare condizioni di fault dei componenti utilizzando i tradizionali algoritmi di machine learning. Un ulteriore problema legato con il processo di HPDC sta nel frequente cambio di produzione, che porta a cambiamenti radicali nei parametri di processo. Inoltre, può capitare che le aziende di piccole dimensioni non facciano il corretto update dei dati di produzione (cambio di stampo, cambio ricetta nell'iniezione).
Per risolvere queste problematiche, viene proposto un nuovo metodo per determinare le condizioni di fault di componenti in una macchina di pressocolata. Il metodo proposto è in grado di determinare automaticamente un cambio di produzione e resettare il dataset utilizzato per il training. Il metodo si basa sulla peculiarità del processo di pressocolata di avere differenti fasi che risultano essere eguali per ogni tipo di macchina e produzione. Queste fasi sono, l'avanzamento lento del pistone per l'iniezione in modo da evitare bolle d'aria nella camera di iniezione, l'avanzamento veloce del pistone con il completo riempimento dello stampo, e la fase di moltiplica dando una maggiore pressione al processo di pressofusione. Questo aumento di pressione serve per compensare il ritiro del materiale dovuto al raffreddamento dopo l'iniezione.
Ogni fase viene interpolata in modo da estrarre parametri significativi per la futura predizione del fault. Per ogni parametro, viene calcolato uno stimatore dell'incertezza, che viene combinato con l'incertezza della strumentazione per ottenere un'incertezza estesa che tenga in considerazione dei due contributi. Il core di questo metodo si concretizza proprio nel calcolo della metrica finale per poter monitorare lo stato di salute di un componente. Infatti, il metodo si basa sulla combinazione della classica analisi statistica con delle matrici peso date dagli esperti del settore della manutenzione di questa tipologia di macchina. Il risultato finale è l'Health Index (HI) che rappresenta la probabilità di avere una condizione di fault per la macchina di pressocolata. Ogni matrice peso che viene combinata con i parametri estratti si traduce in un HI per quel componente. In questo modo, è possibile creare tanti Heath Index possibili utilizzando una specifica matrice peso, che è possibile costruire mediante le interviste degli esperti.<br>In the last decades, data analysis becomes relevant in the industrial scenario. The data lake represents the new frontier in the data science. The new concept is not only the data storage anymore, but the possibility to analyse the historical data in order to optimize the production by finding bottle necks in the production chain and solving the problem by applying corrective procedures to increase the productivity of a company.
Every field in the production chain is important to increase the productivity. Maintenance is one of the most important tasks to take into in account. Indeed, maintenance costs are a major part of the total operating costs of all manufacturing or production plants. These costs must be reduced by applying different strategies.
The new frontier in the maintenance strategy is represented by the Predictive Maintenance (PM) or Predictive Health Management (PHM). PHM is a maintenance strategy in which different statistical algorithms or machine learning algorithms can be applied to obtain the Remaining Useful Life (RUL) of a component.
This project is focused on the application of the PHM on an injection group of a die casting machine. By this own definition, the High Pressure Die Casting (HPDC) process presents different aspects that can affect the analysis. For instance, the fault of components is a rare event, and the analysis cannot be performed by investigating large datasets or fault data based on maintenance records. This makes very difficult to detect the fault of components with traditional machine learning algorithms. A further problem, however, linked with HPDC process is in the frequent change in production, which leads to changes in the process parameters. Moreover, sometimes small companies do not correctly update the production identifiers. To solve these problems, a new method is proposed to detect the fault of components in a diecasting machine. The proposed method automatically detects a production change and resets each time the dataset used for training.
The method is based on the peculiarity of the die casting process that presents different phases equal to each machine and production considered. These phases are the slow motion of the piston to avoid air bubbles inside the injection chamber, the stroke with the filling of the die, and the multiplication phase to compensate the shrinkage of the material due to the cooling by giving more pressure in the process. Each phase is interpolated to extract sensitive parameters to perform the prediction of fault. For each parameter, an uncertainty estimator is recorded and combined with the uncertainty of the instrumentation to obtain an uncertainty that considers the two contributions.
The core of this method is in the combination of the classical prediction analysis with a weighing matrix given by the experts. The weights are determined in a series of formal interviews for each phase and quantity recorded. The result is the Health Index (HI) representing the probability of different types of faults in the diecasting machine. Each weighing matrix combined with the parameters extracted is a HI for that component and it is possible to create how many HIs as possible by using a proper weighing matrix that can be constructed through the interview of the experts.
10
PROVEZZA, LUCA. "Predictive diagnostics through machine learning on the injection group of a diecasting machine." Doctoral thesis, Università degli studi di Brescia, 2022. http://hdl.handle.net/11379/559956.
Full textAbstract:
L'analisi dei big data ha sempre più preso un ruolo rilevante nello scenario industriale degli ultimi decenni. Il "Data Lake" rappresenta una nuova frontiera in data science. Non si vuole solo immagazzinare dati, ma analizzarli con il fine di applicare procedure correttive in tempo per poter evitare stop di produzione e far crescere la produttività di un'azienda.
Ogni campo della catena produttiva è importante per poter crescere la produttività. In particolare, la manutenzione è uno dei tasks più importanti di cui tenere in considerazione per queste analisi. Infatti, i costi dovuti alla manutenzione sono la maggior parte dei costi totali di un impianto di produzione. Questi costi devono essere ridotti mediante diverse strategie.
La nuova frontiera nella strategia di manutenzione è rappresentata dalla Manutenzione Predittiva (PM) o anche definita, Predictive Health Management (PHM). La PHM è una strategia di manutenzione dove vengono applicati algoritmi statistici o algoritmi di machine learning per ottenere la "Remaining Useful Life" (RUL) di un componente. Questo progetto è focalizzato nell'applicazione della PHM nel gruppo iniezione di una macchina di pressocolata. Per sua definizione, il processo di pressocolata ad alta pressione (HPDC) presenta aspetti differenti che possono andare ad inficiare sull'analisi dei dati. Per esempio, il malfunzionamento di un componente è un evento raro e l'analisi non può essere eseguita andando ad investigare ampi dataset, oppure investigando i dati di fault basandoci sui registri di manutenzione di un'azienda. Questo rende difficoltoso indentificare condizioni di fault dei componenti utilizzando i tradizionali algoritmi di machine learning. Un ulteriore problema legato con il processo di HPDC sta nel frequente cambio di produzione, che porta a cambiamenti radicali nei parametri di processo. Inoltre, può capitare che le aziende di piccole dimensioni non facciano il corretto update dei dati di produzione (cambio di stampo, cambio ricetta nell'iniezione).
Per risolvere queste problematiche, viene proposto un nuovo metodo per determinare le condizioni di fault di componenti in una macchina di pressocolata. Il metodo proposto è in grado di determinare automaticamente un cambio di produzione e resettare il dataset utilizzato per il training. Il metodo si basa sulla peculiarità del processo di pressocolata di avere differenti fasi che risultano essere eguali per ogni tipo di macchina e produzione. Queste fasi sono, l'avanzamento lento del pistone per l'iniezione in modo da evitare bolle d'aria nella camera di iniezione, l'avanzamento veloce del pistone con il completo riempimento dello stampo, e la fase di moltiplica dando una maggiore pressione al processo di pressofusione. Questo aumento di pressione serve per compensare il ritiro del materiale dovuto al raffreddamento dopo l'iniezione.
Ogni fase viene interpolata in modo da estrarre parametri significativi per la futura predizione del fault. Per ogni parametro, viene calcolato uno stimatore dell'incertezza, che viene combinato con l'incertezza della strumentazione per ottenere un'incertezza estesa che tenga in considerazione dei due contributi. Il core di questo metodo si concretizza proprio nel calcolo della metrica finale per poter monitorare lo stato di salute di un componente. Infatti, il metodo si basa sulla combinazione della classica analisi statistica con delle matrici peso date dagli esperti del settore della manutenzione di questa tipologia di macchina. Il risultato finale è l'Health Index (HI) che rappresenta la probabilità di avere una condizione di fault per la macchina di pressocolata. Ogni matrice peso che viene combinata con i parametri estratti si traduce in un HI per quel componente. In questo modo, è possibile creare tanti Heath Index possibili utilizzando una specifica matrice peso, che è possibile costruire mediante le interviste degli esperti.<br>In the last decades, data analysis becomes relevant in the industrial scenario. The data lake represents the new frontier in the data science. The new concept is not only the data storage anymore, but the possibility to analyse the historical data in order to optimize the production by finding bottle necks in the production chain and solving the problem by applying corrective procedures to increase the productivity of a company.
Every field in the production chain is important to increase the productivity. Maintenance is one of the most important tasks to take into in account. Indeed, maintenance costs are a major part of the total operating costs of all manufacturing or production plants. These costs must be reduced by applying different strategies.
The new frontier in the maintenance strategy is represented by the Predictive Maintenance (PM) or Predictive Health Management (PHM). PHM is a maintenance strategy in which different statistical algorithms or machine learning algorithms can be applied to obtain the Remaining Useful Life (RUL) of a component.
This project is focused on the application of the PHM on an injection group of a die casting machine. By this own definition, the High Pressure Die Casting (HPDC) process presents different aspects that can affect the analysis. For instance, the fault of components is a rare event, and the analysis cannot be performed by investigating large datasets or fault data based on maintenance records. This makes very difficult to detect the fault of components with traditional machine learning algorithms. A further problem, however, linked with HPDC process is in the frequent change in production, which leads to changes in the process parameters. Moreover, sometimes small companies do not correctly update the production identifiers. To solve these problems, a new method is proposed to detect the fault of components in a diecasting machine. The proposed method automatically detects a production change and resets each time the dataset used for training.
The method is based on the peculiarity of the die casting process that presents different phases equal to each machine and production considered. These phases are the slow motion of the piston to avoid air bubbles inside the injection chamber, the stroke with the filling of the die, and the multiplication phase to compensate the shrinkage of the material due to the cooling by giving more pressure in the process. Each phase is interpolated to extract sensitive parameters to perform the prediction of fault. For each parameter, an uncertainty estimator is recorded and combined with the uncertainty of the instrumentation to obtain an uncertainty that considers the two contributions.
The core of this method is in the combination of the classical prediction analysis with a weighing matrix given by the experts. The weights are determined in a series of formal interviews for each phase and quantity recorded. The result is the Health Index (HI) representing the probability of different types of faults in the diecasting machine. Each weighing matrix combined with the parameters extracted is a HI for that component and it is possible to create how many HIs as possible by using a proper weighing matrix that can be constructed through the interview of the experts.
Books on the topic "Predictive Machine Learning"
1
Joshi, Amit, Mahdi Khosravy, and Neeraj Gupta, eds. Machine Learning for Predictive Analysis. Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-15-7106-0.
Full text
2
Barga, Roger, Valentine Fontama, and Wee Hyong Tok. Predictive Analytics with Microsoft Azure Machine Learning. Apress, 2015. http://dx.doi.org/10.1007/978-1-4842-1200-4.
Full text
3
Barga, Roger, Valentine Fontama, and Wee Hyong Tok. Predictive Analytics with Microsoft Azure Machine Learning. Apress, 2014. http://dx.doi.org/10.1007/978-1-4842-0445-0.
Full text
4
Quiñonero-Candela, Joaquin, Ido Dagan, Bernardo Magnini, and Florence d’Alché-Buc, eds. Machine Learning Challenges. Evaluating Predictive Uncertainty, Visual Object Classification, and Recognising Tectual Entailment. Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11736790.
Full text
5
Gama, Joao, Sepideh Pashami, Albert Bifet, et al., eds. IoT Streams for Data-Driven Predictive Maintenance and IoT, Edge, and Mobile for Embedded Machine Learning. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-66770-2.
Full text
6
Joaquin, Quiñonero-Candela, ed. Machine learning challenges: Evaluating predictive uncertainty visual object classification and recognizing textual entailment : First PASCAL Machine Learning Challenges Workshop, MLCW 2005, Southampton, UK, April 11-13, 2005 : revised selected papers. Springer, 2006.
Find full text
7
Džeroski, Sašo, Hendrik Blockeel, Jan Struyf, and Bernard Zenko. Predictive Clustering. Springer, 2012.
Find full text
8
Barga, Roger, Wee Hyong Tok, and Valentine Fontama. Predictive Analytics with Microsoft Azure Machine Learning 2nd Edition. Apress, 2015.
Find full text
9
Vidales, A. Machine Learning with Matlab: Supervised Learning Using Predictive Models. Regression. Independently Published, 2019.
Find full text
10
Classen, Laronda. Python How to Build Predictive Machine Learning Models. Independently Published, 2022.
Find full textBook chapters on the topic "Predictive Machine Learning"
1
Webb, Geoffrey I., Eamonn Keogh, Risto Miikkulainen, Risto Miikkulainen, and Michele Sebag. "Negative Predictive Value." In Encyclopedia of Machine Learning. Springer US, 2011. http://dx.doi.org/10.1007/978-0-387-30164-8_582.
Full text
2
Zeugmann, Thomas, Pascal Poupart, James Kennedy, et al. "Positive Predictive Value." In Encyclopedia of Machine Learning. Springer US, 2011. http://dx.doi.org/10.1007/978-0-387-30164-8_645.
Full text
3
Zeugmann, Thomas, Pascal Poupart, James Kennedy, et al. "Predictive Software Models." In Encyclopedia of Machine Learning. Springer US, 2011. http://dx.doi.org/10.1007/978-0-387-30164-8_660.
Full text
4
Dinov, Ivo D. "Specialized Machine Learning Topics." In Data Science and Predictive Analytics. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-72347-1_16.
Full text
5
Kumar, Rohit. "Predictive Analytics." In Machine Learning and Cognition in Enterprises. Apress, 2017. http://dx.doi.org/10.1007/978-1-4842-3069-5_6.
Full text
6
Li, Jin. "Tree-based machine learning methods." In Spatial Predictive Modeling with R. Chapman and Hall/CRC, 2022. http://dx.doi.org/10.1201/9781003091776-8.
Full text
7
Zeugmann, Thomas, Pascal Poupart, James Kennedy, et al. "Predictive Techniques in Software Engineering." In Encyclopedia of Machine Learning. Springer US, 2011. http://dx.doi.org/10.1007/978-0-387-30164-8_661.
Full text
8
Thulin, Måns. "Predictive modelling and machine learning." In Modern Statistics with R, 2nd ed. Chapman and Hall/CRC, 2024. http://dx.doi.org/10.1201/9781003401339-11.
Full text
9
Afroze, Lameya, Silke Merkelbach, Sebastian von Enzberg, and Roman Dumitrescu. "Domain Knowledge Injection Guidance for Predictive Maintenance." In Machine Learning for Cyber-Physical Systems. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-47062-2_8.
Full textAbstract:
AbstractWith the integration of Industry 4.0 technologies, overall maintenance costs of industrial machines can be reduced by applying predictive maintenance. Unique challenges that often occur in real-time manufacturing environments require the use of domain knowledge from different experts. However, there is hardly any guidance that suggests data scientists how to inject knowledge from predictive maintenance use cases in machine learning models. This paper addresses this lack and presents a guidance for the injection of domain knowledge in machine learning models for predictive maintenance by analyzing 50 use cases from the literature. The guidance is based on the informed machine learning framework by von Rueden et al. [1]. Finally, the guidance gives a recommendation to data scientists on how domain knowledge can be injected into different phases of model development and suggests promising machine learning models for specific use cases. The guidance is applied exemplarily to two predictive maintenance use cases.
10
Dutta, Abhishek, Gopu Pooja, Neeraj Jain, Rama Ranjan Panda, and Naresh Kumar Nagwani. "A Hybrid Deep Learning Approach for Stock Price Prediction." In Machine Learning for Predictive Analysis. Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-7106-0_1.
Full textConference papers on the topic "Predictive Machine Learning"
1
Kshirsagar, Vanita, Digvijay G. Bhosale, Shantanu Gilbile, Anchal Kharade, Harshal Sasane, and Pratik Dhembare. "Predictive Maintenance in Industrial Machinery using Machine Learning." In 2024 Intelligent Systems and Machine Learning Conference (ISML). IEEE, 2024. https://doi.org/10.1109/isml60050.2024.11007348.
Full text
2
Suresh Dahake, Parihar, and Nihar Suresh Dahake. "Predicting Buyer Behaviour: A Reconnaissance of Retail Predictive Analytics Model." In 2024 Intelligent Systems and Machine Learning Conference (ISML). IEEE, 2024. https://doi.org/10.1109/isml60050.2024.11007411.
Full text
3
Shaikh, Mobin B., Pratik J. Patil, Prashant V. Thokal, and Dipesh B. Pardeshi. "Implementing Machine Learning for Predictive Maintenance in Industrial Machinery." In 2024 15th International Conference on Computing Communication and Networking Technologies (ICCCNT). IEEE, 2024. http://dx.doi.org/10.1109/icccnt61001.2024.10724004.
Full text
4
Sood, Gourav. "Utilizing Machine Learning for Predictive Sales Forecasting." In 2024 15th International Conference on Computing Communication and Networking Technologies (ICCCNT). IEEE, 2024. http://dx.doi.org/10.1109/icccnt61001.2024.10725596.
Full text
5
Jing, Junwen, Yinuo Du, Weijie Jin, and Qian Xie. "Machine Learning Predictive Analytics for Insurance Premiums." In 2024 International Conference on Interactive Intelligent Systems and Techniques (IIST). IEEE, 2024. http://dx.doi.org/10.1109/iist62526.2024.00142.
Full text
6
Li, Xiang. "Machine Learning-Based Wine Quality Predictive Modelling." In International Conference on Innovations in Applied Mathematics, Physics and Astronomy. SCITEPRESS - Science and Technology Publications, 2024. http://dx.doi.org/10.5220/0012998000004601.
Full text
7
N, Balakrishnan, Ashwini B, Karthiga K, and Monikaa Sri B. "Predictive Modeling of Landslide Using Machine Learning." In 2025 8th International Conference on Trends in Electronics and Informatics (ICOEI). IEEE, 2025. https://doi.org/10.1109/icoei65986.2025.11013468.
Full text
8
Satheeskumar, R., Er Vandana Dutt, Ch Srinivasa Reddy, Zatin Gupta, Sundarapandiyan Natarajan, and Muralidhar L B. "Machine Learning Models for Predictive Marketing Analytics." In 2024 Second International Conference Computational and Characterization Techniques in Engineering & Sciences (IC3TES). IEEE, 2024. https://doi.org/10.1109/ic3tes62412.2024.10877468.
Full text
9
Horvath, Krisztian, and Ambrus Zelei. "Using Machine Learning Models to Predict and Reduce Noise Levels in Gear Systems." In 10th International Scientific Conference on Advances in Mechanical Engineering. Trans Tech Publications Ltd, 2025. https://doi.org/10.4028/p-0gdarj.
Full textAbstract:
Machine learning models are effective tools for predicting and reducing noise levels in industrial gear systems. In this study, we compare different machine learning methods to investigate the effects of different gear modification parameters on noise levels. Four different predictive models was used. Random Forest Regressor, XGBoost, Gradient Boosting Machines and neural network. The study concluded that Random Forest and Gradient Boosting Machines models were the most effective. Both models achieved low mean squared error values 6.10 and 6.67. Further tests with synthetic data confirmed the stability of these models. Current sustainability trends show that the integration of machine learning into industrial applications fits well with manufacturers' objectives. However, it is currently challenging to determine which machine learning methods are most effective in optimizing noise reduction. This paper seeks to address this gap by comparing the accuracy and reliability of these models. Based on the results, the use of machine learning models is recommended to reduce noise levels in geared systems.
10
Gupta, Anand Kumar, Srijana Karnatak, Suchi Jain, Mamta Bisht, Pushpendra Singh Kharayat, and Gajanan Ankatwar. "Monitoring and Predictive Maintenance using Machine Learning for Industrial Machine." In 2024 2nd International Conference on Self Sustainable Artificial Intelligence Systems (ICSSAS). IEEE, 2024. https://doi.org/10.1109/icssas64001.2024.10760557.
Full textReports on the topic "Predictive Machine Learning"
1
Friedman, J. Recent Advances in Predictive (Machine) Learning. Office of Scientific and Technical Information (OSTI), 2004. http://dx.doi.org/10.2172/826695.
Full text
2
Flaxman, Seth. Statistical Machine Learning for Researchers. Instats Inc., 2023. http://dx.doi.org/10.61700/3sz8pzpbpsg2i469.
Full textAbstract:
This workshop is designed to empower researchers with the fundamentals of machine learning using R. Participants will learn the key principles that make machine learning so effective, powering the modern AI and deep learning revolution. Through hands-on exercises, participants will gain experience applying a variety of flexible and scalable statistical machine learning methods to analyze datasets and build effective predictive models. An official Instats certificate of completion is provided along with 2 ECTS Equivalent points.
3
Flaxman, Seth. Statistical Machine Learning for Researchers. Instats Inc., 2023. http://dx.doi.org/10.61700/wu1mihoap95h0469.
Full textAbstract:
This workshop is designed to empower researchers with the fundamentals of machine learning using R. Participants will learn the key principles that make machine learning so effective, powering the modern AI and deep learning revolution. Through hands-on exercises, participants will gain experience applying a variety of flexible and scalable statistical machine learning methods to analyze datasets and build effective predictive models. An official Instats certificate of completion is provided along with 2 ECTS Equivalent points.
4
Mahajan, Aprajit, Shekhar Mittal, Ofir Reich, and Taha Barwahwala. Using Machine Learning to Catch Bogus Firms. Institute of Development Studies, 2024. http://dx.doi.org/10.19088/ictd.2024.050.
Full textAbstract:
We investigate the use of a machine learning algorithm to identify non-existent(fraudulent) firms that are used for tax evasion. Using a rich dataset of tax returns in an Indian state over several years, we train a machine learning-based model to predict fraudulent firms. We then use the model predictions to carry out field inspections of firms identified as suspicious by the machine learning tool. We find that the machine learning model is accurate in both simulated and field settings in identifying non-existent firms. Withholding a randomly selected group of firms from inspection, we estimate the causal impact of machine learning-driven inspections. Despite the strong predictive performance, our model-driven inspections do not yield a significant increase in enforcement, as shown by the cancellation of fraudulent firm registrations and tax recovery. We provide two reasons for this discrepancy, based on a close analysis of the tax department's operating protocols – selection bias, and institutional friction in integrating the model into existing administrative systems. Our study serves as a cautionary tale for the application of machine learning in public policy contexts, and relying solely on test set performance as an effectiveness indicator. Field evaluations are critical in assessing the real-world impact of predictive models.
5
Belianinov, Alex. Machine Learning for Predictive Performance Analysis in Charged Particle Beam Tools. Office of Scientific and Technical Information (OSTI), 2023. http://dx.doi.org/10.2172/2430302.
Full text
6
Wang, Jianxun, Jinlong Wu, Julia Ling, Gianluca Iaccarino, and Heng Xiao. Physics-Informed Machine Learning for Predictive Turbulence Modeling: Towards a Complete Framework. Office of Scientific and Technical Information (OSTI), 2016. http://dx.doi.org/10.2172/1562229.
Full text
7
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.
Full textAbstract:
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 human productivity, AI-driven sustainability practices for energy and resource efficiency, and predictive maintenance models that reduce downtime. Addressing ethical challenges, the Article highlights the importance of data privacy, unbiased algorithms, and the environmental responsibility of intelligent automation. Through case studies across manufacturing, healthcare, and energy sectors, readers gain insights into real-world applications of AI and ML, showcasing their impact on efficiency, quality, and safety. The Article concludes with future directions, emphasizing emerging technologies like quantum computing, human-machine synergy, and the sustainable vision for Industry 5.0, where intelligent automation not only drives innovation but also aligns with ethical and social values for a resilient industrial future. Keywords: Industry 5.0, intelligent automation, AI, machine learning, sustainability, human- machine collaboration, cobots, predictive maintenance, quality control, ethical AI, data privacy, Industry 4.0, computer vision, natural language processing, energy efficiency, adaptive logistics, environmental responsibility, industrial ecosystems, quantum computing.
8
Chernozhukov, Victor, Kaspar Wüthrich, and Yinchu Zhu. Exact and robust conformal inference methods for predictive machine learning with dependent data. The IFS, 2018. http://dx.doi.org/10.1920/wp.cem.2018.1618.
Full text
9
Lalisse, Matthias. Measuring the Impact of Campaign Finance on Congressional Voting: A Machine Learning Approach. Institute for New Economic Thinking Working Paper Series, 2022. http://dx.doi.org/10.36687/inetwp178.
Full textAbstract:
How much does money drive legislative outcomes in the United States? In this article, we use aggregated campaign finance data as well as a Transformer based text embedding model to predict roll call votes for legislation in the US Congress with more than 90% accuracy. In a series of model comparisons in which the input feature sets are varied, we investigate the extent to which campaign finance is predictive of voting behavior in comparison with variables like partisan affiliation. We find that the financial interests backing a legislator’s campaigns are independently predictive in both chambers of Congress, but also uncover a sizable asymmetry between the Senate and the House of Representatives. These findings are cross-referenced with a Representational Similarity Analysis (RSA) linking legislators’ financial and voting records, in which we show that “legislators who vote together get paid together”, again discovering an asymmetry between the House and the Senate in the additional predictive power of campaign finance once party is accounted for. We suggest an explanation of these facts in terms of Thomas Ferguson’s Investment Theory of Party Competition: due to a number of structural differences between the House and Senate, but chiefly the lower amortized cost of obtaining individuated influence with Senators, political investors prefer operating on the House using the party as a proxy.
10
Lu, Michael, and Tina Gibson. Development of Predictive Tools for Anti-Cancer Peptide Candidates using Generative Machine Learning Models. Journal of Young Investigators, 2021. http://dx.doi.org/10.22186/jyi.39.5.60-64.
Full text