Relevant bibliographies by topics / Machine learnings

Academic literature on the topic 'Machine learnings'

Author: Grafiati
Published: 1 June 2024

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Contents

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

Journal articles on the topic "Machine learnings":

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Li, Tianshu. "Fintech Application in Banking Operations - Application of Machine Learning in Mitigating Bank Derivatives Counterparty Risks." Asian Business Research 4, no. 3 (October 8, 2019): 1. http://dx.doi.org/10.20849/abr.v4i3.652.

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We all know that human has many psychological biases, including overconfidence, gender discrimination and so on. Although some genuine lenders may outperformance others, machine learnings have been utilized to solve this human psychological bias in many areas. By using machine learnings methods, people can make better financial decisions. This proposal tries to examine the effectiveness of several different machine learning models on predicting the ex-pose default risk, including BP neural network, decision tree, KNN, and random forest. I focus on loans on one electronic P2P lending platform, called “Paipaidai” in which lenders select and supply private loans to borrowers with different characteristics. I use machine learnings methods to predict the default risk and thus provides better ways for investors to select high-quality borrower. I will also further test how different machine learnings methods perform when there is soft information contained by using Prosper platform.
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Makarov, Vladimir, Christophe Chabbert, Elina Koletou, Fotis Psomopoulos, Natalja Kurbatova, Samuel Ramirez, Chas Nelson, Prashant Natarajan, and Bikalpa Neupane. "Good machine learning practices: Learnings from the modern pharmaceutical discovery enterprise." Computers in Biology and Medicine 177 (July 2024): 108632. http://dx.doi.org/10.1016/j.compbiomed.2024.108632.

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Kim, Jin Kook. "A Study on the Estimation Model for the Visitors to Let’s Run Park Using Machine Learning." Korean Journal of Sport Science 32, no. 3 (September 30, 2021): 411–18. http://dx.doi.org/10.24985/kjss.2021.32.3.411.

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PURPOSE The purpose of this study is to find the best model to predict the demand of visitors in Let’s Run Park by using machine learning and to provide effective data for establishing future marketing strategies.METHODS For this purpose, three methods of machine learning were applied: random forest, adaboost, and gradient boosting. The variables for predicting the audience were weather data and the number of visitors per date for four years as training data, and the accuracy was predicted by comparing the actual data for one year.RESULTS First, the performance evaluation using random forest was conducted, RMSE =1856.067, R2= .965, and error was 6.47%. Second, the performance evaluation using Adaboost was conducted, RMSE =1836.227, R2= .965, and error was 5.25%, which was the lowest among the three machine learnings. Third, the performance evaluation using gradient boosting showed that RMSE =1797.400 and R2= .967 were the most accurate among the three machine learnings and error was 6.99%.CONCLUSIONS As a result of this study, each of the three machine learning features existed, but the most efficient model was gradient boosting. In addition, the best way to utilize it in the field is to predict the number of visitors by comprehensively judging the results of the three machine learning, and it is judged that it will help efficient management decision making in the future.
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Malik, Sehrish, and DoHyeun Kim. "Improved Control Scheduling Based on Learning to Prediction Mechanism for Efficient Machine Maintenance in Smart Factory." Actuators 10, no. 2 (January 31, 2021): 27. http://dx.doi.org/10.3390/act10020027.

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The prediction mechanism is very crucial in a smart factory as they widely help in improving the product quality and customer’s experience based on learnings from past trends. The implementation of analytics tools to predict the production and consumer patterns plays a vital rule. In this paper, we put our efforts to find integrated solutions for smart factory concerns by proposing an efficient task management mechanism based on learning to scheduling in a smart factory. The learning to prediction mechanism aims to predict the machine utilization for machines involved in the smart factory, in order to efficiently use the machine resources. The prediction algorithm used is artificial neural network (ANN) and the learning to prediction algorithm used is particle swarm optimization (PSO). The proposed task management mechanism is evaluated based on multiple scenario simulations and performance analysis. The comparisons analysis shows that proposed task management system significantly improves the machine utilization rate and drastically drops the tasks instances missing rate and tasks starvation rate.
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PREETHAM S, M C CHANDRASHEKHAR, and M Z KURIAN. "METHODOLOGY FOR IMPLEMENTATION OF PREDICTION MODEL FOR STUDENTS USING MACHINE LEARNING." international journal of engineering technology and management sciences 7, no. 3 (2023): 764–66. http://dx.doi.org/10.46647/ijetms.2023.v07i03.116.

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In this era, with the continuing growth in electronic devices and internet technologies, there has been a vast rise in data storage. The word data is explaining each detail that has been interpret into a form that is further convenient to move or process. In this project machine learning data have performed. However, machine learning technology brings a vast benefit which provides a computer the potential to learn without programming it. One of the applications of machine learning is E-learning. E-learning makes many things possible especially for learners to learn anytime and anywhere as well as in online on their own. Customization on E-learnings has two steps- first part of the customization is forecasting the elegance and the second is suggesting the counsel of course selection depending upon the performance. Here the challenges in E-learning to tackle and discuss the customization classification which is the grade prediction.
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Kurniawan, Robi, and Shunsuke Managi. "Forecasting annual energy consumption using machine learnings: Case of Indonesia." IOP Conference Series: Earth and Environmental Science 257 (May 10, 2019): 012032. http://dx.doi.org/10.1088/1755-1315/257/1/012032.

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Singh, Priyanka, Chakshu Garg, Aman Namdeo, Krishna Mohan Agarwal, and Rajesh Kumar Rai. "Development of Prediction models for Bond Strength of Steel Fiber Reinforced Concrete by Computational Machine Learning." E3S Web of Conferences 220 (2020): 01097. http://dx.doi.org/10.1051/e3sconf/202022001097.

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Sustainable construction contributed to the usage of recycled and waste materials to substitute conventional concrete. This research focuses on prediction of normalized bond strength of cement concrete substituted by large amounts of waste materials and products with strong mechanical properties and sustainability. It also emphases on using analytical model for the prediction of bond strength of the green concrete, so that there is a reduction in the cost of construction, con-serve energy, and it will lead to a reduction of CO2 production from cement industries within reliable limits. In this paper machine learning approach has been used to predict the normalized bond strength of green and sustainable concrete. Machine learning empowers machines to learn from their experiences and data provided. The system analyses the datasets and finds different patterns formed in the given data. Then, based on its learnings the machine can make certain predictions. In civil engineering application, a special computing technique called the Machine learning (ML) is in huge demand. ANN is a soft computing technique that learns from previous situations and adapts without constraints to a new environment. In this work, a ML network model for prediction of normalized bond strength of concrete has been illustrated. Different sets of data based upon several concrete design mixes were taken from technical literature and were fed to the model. The model is then trained for prediction, which are being influenced by several input attributes and were jotted down a linear regression analysis.
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Das, Aditi. "Automatic Personality Identification using Machine Learning." International Journal for Research in Applied Science and Engineering Technology 9, no. VI (June 30, 2021): 3528–34. http://dx.doi.org/10.22214/ijraset.2021.35386.

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Machine Learning has made significant changes in the world making our life more easier and comfortable .One of the most exciting applications is the prediction of Personality automatically using different algorithms. Personality computing and emotive computing, where the popularity of temperament traits is important, have gained increasing interest and a spotlight in several analysis areas recently. These applications can powerfully predict the personality of a Person. The aim of this paper is to use a more rigorous construct Validation system to extend the potential of machine learning approaches to personality assessment. We have reviewed multiple recent applications of Machine Learning to recognize personality, thus providing a broader context of fundamental principles of constructing, validating, and then providing recommendations on how to use Machine Learning to advance the level of our understanding and applying our learnings to develop advanced personality recognition applications. araphrased Text Output text rewrite / rewrite We use deep neural network learning to recognize characteristics independently and, through feature-level fusion of these networks, we obtain final predictions of obvious personalities. We use a previously trained long-term and short-term memory network to integrate time information. We train large-scale models comprised of specific subnetworks- modalities through a two-stage training process. We first train the subnets separately for and then use these trained networks to fit the overall model. We used the ChaLearn First Impressions V2 challenge dataset to evaluate the proposed method. Our method achieves the most effective overall "medium precision" score, with an average score of for 5 personality characteristics, which is compared to the state-of-the-art method.
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Malinda Sari Sembiring, Windi Astuti, Iskandar Muda,. "The Influence of Cloud Computing, Artificial Intelligence, Machine Learnings and Digital Disruption on the Design of Accounting and Finance Functions Mediated by Data Processing." International Journal on Recent and Innovation Trends in Computing and Communication 11, no. 11 (November 30, 2023): 56–62. http://dx.doi.org/10.17762/ijritcc.v11i11.9087.

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This research aims to determine the influence of cloud computing, artificial intelligence, machine learning and digital disruption on the design of accounting and financial functions mediated by data processing. This type of research is an explanatory survey, the data used is primary data on 202 respondents working in the accounting and finance sector in Indonesia drawn using the purposive random sampling method. Analysis tool using the Structural Equation Modeling approach with the WarpPLS Version 8.0 test tool. The results show that Cloud Computing, Artificial Intelligence, Machine Learnings and Digital Disruption have a significant influence on the Design of Accounting and Financial Functions Mediated by Data Processing.
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Sendak, Mark P., William Ratliff, Dina Sarro, Elizabeth Alderton, Joseph Futoma, Michael Gao, Marshall Nichols, et al. "Real-World Integration of a Sepsis Deep Learning Technology Into Routine Clinical Care: Implementation Study." JMIR Medical Informatics 8, no. 7 (July 15, 2020): e15182. http://dx.doi.org/10.2196/15182.

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Background Successful integrations of machine learning into routine clinical care are exceedingly rare, and barriers to its adoption are poorly characterized in the literature. Objective This study aims to report a quality improvement effort to integrate a deep learning sepsis detection and management platform, Sepsis Watch, into routine clinical care. Methods In 2016, a multidisciplinary team consisting of statisticians, data scientists, data engineers, and clinicians was assembled by the leadership of an academic health system to radically improve the detection and treatment of sepsis. This report of the quality improvement effort follows the learning health system framework to describe the problem assessment, design, development, implementation, and evaluation plan of Sepsis Watch. Results Sepsis Watch was successfully integrated into routine clinical care and reshaped how local machine learning projects are executed. Frontline clinical staff were highly engaged in the design and development of the workflow, machine learning model, and application. Novel machine learning methods were developed to detect sepsis early, and implementation of the model required robust infrastructure. Significant investment was required to align stakeholders, develop trusting relationships, define roles and responsibilities, and to train frontline staff, leading to the establishment of 3 partnerships with internal and external research groups to evaluate Sepsis Watch. Conclusions Machine learning models are commonly developed to enhance clinical decision making, but successful integrations of machine learning into routine clinical care are rare. Although there is no playbook for integrating deep learning into clinical care, learnings from the Sepsis Watch integration can inform efforts to develop machine learning technologies at other health care delivery systems.
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You might also be interested in the extended bibliographies on the topic 'Machine learnings' for particular source types:
Journal articles Dissertations / Theses Books

Dissertations / Theses on the topic "Machine learnings":

1

Algohary, Ahmad. "PROSTATE CANCER RISK STRATIFICATION USING RADIOMICS FOR PATIENTS ON ACTIVE SURVEILLANCE: MULTI-INSTITUTIONAL USE CASES." Case Western Reserve University School of Graduate Studies / OhioLINK, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=case1599231033923829.

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Stohr, Daniel Christoph [Verfasser]. "Die beruflichen Anforderungen der Digitalisierung hinsichtlich formaler, physischer und kompetenzspezifischer Aspekte : Eine Analyse von Stellenanzeigen mittels Methoden des Text Minings und Machine Learnings / Daniel Christoph Stohr." Frankfurt a.M. : Peter Lang GmbH, Internationaler Verlag der Wissenschaften, 2019. http://d-nb.info/1185347240/34.

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Tebbifakhr, Amirhossein. "Machine Translation For Machines." Doctoral thesis, Università degli studi di Trento, 2021. http://hdl.handle.net/11572/320504.

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Traditionally, Machine Translation (MT) systems are developed by targeting fluency (i.e. output grammaticality) and adequacy (i.e. semantic equivalence with the source text) criteria that reflect the needs of human end-users. However, recent advancements in Natural Language Processing (NLP) and the introduction of NLP tools in commercial services have opened new opportunities for MT. A particularly relevant one is related to the application of NLP technologies in low-resource language settings, for which the paucity of training data reduces the possibility to train reliable services. In this specific condition, MT can come into play by enabling the so-called “translation-based” workarounds. The idea is simple: first, input texts in the low-resource language are translated into a resource-rich target language; then, the machine-translated text is processed by well-trained NLP tools in the target language; finally, the output of these downstream components is projected back to the source language. This results in a new scenario, in which the end-user of MT technology is no longer a human but another machine. We hypothesize that current MT training approaches are not the optimal ones for this setting, in which the objective is to maximize the performance of a downstream tool fed with machine-translated text rather than human comprehension. Under this hypothesis, this thesis introduces a new research paradigm, which we named “MT for machines”, addressing a number of questions that raise from this novel view of the MT problem. Are there different quality criteria for humans and machines? What makes a good translation from the machine standpoint? What are the trade-offs between the two notions of quality? How to pursue machine-oriented objectives? How to serve different downstream components with a single MT system? How to exploit knowledge transfer to operate in different language settings with a single MT system? Elaborating on these questions, this thesis: i) introduces a novel and challenging MT paradigm, ii) proposes an effective method based on Reinforcement Learning analysing its possible variants, iii) extends the proposed method to multitask and multilingual settings so as to serve different downstream applications and languages with a single MT system, iv) studies the trade-off between machine-oriented and human-oriented criteria, and v) discusses the successful application of the approach in two real-world scenarios.
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Dinakar, Karthik. "Lensing Machines : representing perspective in machine learning." Thesis, Massachusetts Institute of Technology, 2017. http://hdl.handle.net/1721.1/112523.

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Thesis: Ph. D., Massachusetts Institute of Technology, School of Architecture and Planning, Program in Media Arts and Sciences, 2017.
Cataloged from PDF version of thesis. Due to the condition of the original material with text runs off the edges of the pages, the reproduction may have unavoidable flaws.
Includes bibliographical references (pages 167-172).
Generative models are venerated as full probabilistic models that randomly generate observable data given a set of latent variables that cannot be directly observed. They can be used to simulate values for variables in the model, allowing analysis by synthesis or model criticism, towards an iterative cycle of model specification, estimation, and critique. However, many datasets represent a combination of several viewpoints - different ways of looking at the same data that leads to various generalizations. For example, a corpus that has data generated by multiple people may be mixtures of several perspectives and can be viewed with different opinions by others. It isn't always possible to represent the viewpoints by clean separation, in advance, of examples representing each perspective and train a separate model for each point of view. In this thesis, we introduce lensing, a mixed-initiative technique to (i) extract lenses or mappings between machine-learned representations and perspectives of human experts, and (2) generate lensed models that afford multiple perspectives of the same dataset. We explore lensing of latent variable model in their configuration, parameter and evidential spaces. We apply lensing to three health applications, namely imbuing the perspectives of experts into latent variable models that analyze adolescent distress and crisis counseling.
by Karthik Dinakar.
Ph. D.
5

Roderus, Jens, Simon Larson, and Eric Pihl. "Hadoop scalability evaluation for machine learning algorithms on physical machines : Parallel machine learning on computing clusters." Thesis, Högskolan i Skövde, Institutionen för informationsteknologi, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:his:diva-20102.

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The amount of available data has allowed the field of machine learning to flourish. But with growing data set sizes comes an increase in algorithm execution times. Cluster computing frameworks provide tools for distributing data and processing power on several computer nodes and allows for algorithms to run in feasible time frames when data sets are large. Different cluster computing frameworks come with different trade-offs. In this thesis, the scalability of the execution time of machine learning algorithms running on the Hadoop cluster computing framework is investigated. A recent version of Hadoop and algorithms relevant in industry machine learning, namely K-means, latent Dirichlet allocation and naive Bayes are used in the experiments. This paper provides valuable information to anyone choosing between different cluster computing frameworks. The results show everything from moderate scalability to no scalability at all. These results indicate that Hadoop as a framework may have serious restrictions in how well tasks are actually parallelized. Possible scalability improvements could be achieved by modifying the machine learning library algorithms or by Hadoop parameter tuning.
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Kent, W. F. "Machine learning for parameter identification of electric induction machines." Thesis, University of Liverpool, 2003. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.399178.

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This thesis is concerned with the application of simulated evolution (SE) to the steady-state parameter identification problem of a simulated and real 3-phase induction machine, over the no-load direct-on-line start period. In the case of the simulated 3-phase induction machine, the Kron's two-axis dynamic mathematical model was used to generate the real and simulated system responses where the induction machine parameters remain constant over the entire range of slip. The model was used in the actual value as well as the per-unit system, and the parameters were estimated using both the genetic algorithm (GA) and the evolutionary programming (EP) from the machine's dynamic response to a direct-on-line start. Two measurement vectors represented the dynamic responses and all the parameter identification processes were subject to five different levels of measurement noise. For the case of the real 3-phase induction machine, the real system responses were generated by the real 3-phase induction machine whilst the simulated system responses were generated by the Kron's model. However, the real induction machine's parameters are not constant over the range of slip, because of the nonlinearities caused by the skin effect and saturation. Therefore, the parameter identification of a real3-phase induction machine, using EP from the machine's dynamic response to a direct-on-line start, was not possible by applying the same methodology used for estimating the parameters of the simulated, constant parameters, 3-phase induction machine.
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Thorén, Daniel. "Radar based tank level measurement using machine learning : Agricultural machines." Thesis, Linköpings universitet, Programvara och system, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-176259.

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Agriculture is becoming more dependent on computerized solutions to make thefarmer’s job easier. The big step that many companies are working towards is fullyautonomous vehicles that work the fields. To that end, the equipment fitted to saidvehicles must also adapt and become autonomous. Making this equipment autonomoustakes many incremental steps, one of which is developing an accurate and reliable tanklevel measurement system. In this thesis, a system for tank level measurement in a seedplanting machine is evaluated. Traditional systems use load cells to measure the weightof the tank however, these types of systems are expensive to build and cumbersome torepair. They also add a lot of weight to the equipment which increases the fuel consump-tion of the tractor. Thus, this thesis investigates the use of radar sensors together witha number of Machine Learning algorithms. Fourteen radar sensors are fitted to a tankat different positions, data is collected, and a preprocessing method is developed. Then,the data is used to test the following Machine Learning algorithms: Bagged RegressionTrees (BG), Random Forest Regression (RF), Boosted Regression Trees (BRT), LinearRegression (LR), Linear Support Vector Machine (L-SVM), Multi-Layer Perceptron Re-gressor (MLPR). The model with the best 5-fold crossvalidation scores was Random For-est, closely followed by Boosted Regression Trees. A robustness test, using 5 previouslyunseen scenarios, revealed that the Boosted Regression Trees model was the most robust.The radar position analysis showed that 6 sensors together with the MLPR model gavethe best RMSE scores.In conclusion, the models performed well on this type of system which shows thatthey might be a competitive alternative to load cell based systems.
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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/.

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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.
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Schneider, C. "Using unsupervised machine learning for fault identification in virtual machines." Thesis, University of St Andrews, 2015. http://hdl.handle.net/10023/7327.

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Self-healing systems promise operating cost reductions in large-scale computing environments through the automated detection of, and recovery from, faults. However, at present there appears to be little known empirical evidence comparing the different approaches, or demonstrations that such implementations reduce costs. This thesis compares previous and current self-healing approaches before demonstrating a new, unsupervised approach that combines artificial neural networks with performance tests to perform fault identification in an automated fashion, i.e. the correct and accurate determination of which computer features are associated with a given performance test failure. Several key contributions are made in the course of this research including an analysis of the different types of self-healing approaches based on their contextual use, a baseline for future comparisons between self-healing frameworks that use artificial neural networks, and a successful, automated fault identification in cloud infrastructure, and more specifically virtual machines. This approach uses three established machine learning techniques: Naïve Bayes, Baum-Welch, and Contrastive Divergence Learning. The latter demonstrates minimisation of human-interaction beyond previous implementations by producing a list in decreasing order of likelihood of potential root causes (i.e. fault hypotheses) which brings the state of the art one step closer toward fully self-healing systems. This thesis also examines the impact of that different types of faults have on their respective identification. This helps to understand the validity of the data being presented, and how the field is progressing, whilst examining the differences in impact to identification between emulated thread crashes and errant user changes – a contribution believed to be unique to this research. Lastly, future research avenues and conclusions in automated fault identification are described along with lessons learned throughout this endeavor. This includes the progression of artificial neural networks, how learning algorithms are being developed and understood, and possibilities for automatically generating feature locality data.
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SOAVE, Elia. "Diagnostics and prognostics of rotating machines through cyclostationary methods and machine learning." Doctoral thesis, Università degli studi di Ferrara, 2022. http://hdl.handle.net/11392/2490999.

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In the last decades, the vibration analysis has been exploited for monitoring many mechanical systems for industrial applications. Although several works demonstrated how the vibration based diagnostics may reach satisfactory results, the nowadays industrial scenario is deeply changing, driven by the fundamental need of time and cost reduction. In this direction, the academic research has to focus on the improvement of the computational efficiency for the signal processing techniques applied in the mechanical diagnostics field. In the same way, the industrial word requires an increasing attention to the predictive maintenance for estimating the system failure avoiding unnecessary machine downtimes for maintenance operations. In this contest, in the recent years the research activity has been moved to the development of prognostic models for the prediction of the remaining useful life. However, it is important to keep in mind how the two fields are strictly connected, being the diagnostics the base on which build the effectiveness of each prognostic model. On these grounds, this thesis has been focused on these two different but linked areas for the detection and prediction of possible failures inside rotating machines in the industrial framework. The first part of the thesis focuses on the development of a blind deconvolution indicator based on the cyclostationary theory for the fault identification in rotating machines. The novel criterion aims to decrease the computational cost of the blind deconvolution through the exploitation of the Fourier-Bessel series expansion due to its modulated nature more comparable with the fault related vibration pattern. The proposed indicator is extensively compared to the other cyclostationary one based on the classic Fourier transform, taking into account both synthesized and real vibration signals. The comparison proves the improvement given by the proposed criterion in terms of number of operations required by the blind deconvolution algorithm as well as its diagnostic capability also for noisy measured signals. The originality of this part regards the combination of cyclostationarity and Fourier-Bessel transform that leads to the definition of a novel blind deconvolution criterion that keeps the diagnostic effectiveness of cyclostationarity reducing the computational cost in order to meet the industrial requirements. The second part regards the definition of a novel prognostic model from the family of the hidden Markov models constructed on a generalized Gaussian distribution. The target of the proposed method is a better fitting quality of the data distribution in the last damaging phase. In fact, the fault appearance and evolution reflects on a modification of the observation distribution within the states and consequently a generalized density function allows the changes on the distribution form through the values of some model parameters. The proposed method is compared in terms of fitting quality and state sequence prediction to the classic Gaussian based hidden Markov model through the analysis of several run to failure tests performed on rolling element bearings and more complex systems. The novelty of this part regards the definition of a new iterative algorithm for the estimation of the generalized Gaussian model parameters starting from the observations on the physical system for both monovariate and multivariate distributions. Furthermore, the strictly connection between diagnostics and prognostics is demonstrated through the analysis of a not monotonically increasing damaging process proving how the selection of a suitable indicator enables the correct health state estimation.
Negli ultimi decenni, l’analisi vibrazionale è stata sfruttata per il monitoraggio di molti sistemi meccanici per applicazioni industriali. Nonostante molte pubblicazioni abbiano dimostrato come la diagnostica vibrazionale possa raggiungere risultati soddisfacenti, lo scenario industriale odierno è in profondo cambiamento, guidato dalla necessità di ridurre tempi e costi produttivi. In questa direzione, la ricerca deve concentrarsi sul miglioramento dell’efficienza computazionale delle tecniche di analisi del segnale applicate a fini diagnostici. Allo stesso modo, il mondo industriale richiede una sempre maggior attenzione per la manutenzione predittiva, al fine di stimare l’effettivo danneggiamento del sistema evitando così inutili fermi macchina per operazioni manutentive. In tale ambito, negli ultimi anni l’attività di ricerca si sta spostando verso lo sviluppo di modelli prognostici finalizzati alla stima della vita utile residua dei componenti. Tuttavia, è importante ricordare come i due ambiti siano strettamente connessi, essendo la diagnostica la base su cui fondare l’efficacia di ciascun modello prognostico. Su questa base, questa tesi è stata incentrata su queste due diverse, ma tra loro connesse, aree al fine di identificare e predire possibile cause di cedimento su macchine rotanti per applicazioni industriali. La prima parte della tesi è concentrata sullo sviluppo di un nuovo indicatore di blind deconvolution per l’identificazione di difetti su organi rotanti sulla base della teoria ciclostazionaria. Il criterio presentato vuole andare a ridurre il costo computazionale richiesto dalla blind deconvolution tramite l’utilizzo della serie di Fourier-Bessel grazie alla sua natura modulata, maggiormente affine alla tipica firma vibratoria del difetto. L’indicatore proposto viene accuratamente confrontato con il suo analogo basato sulla classica serie di Fourier considerando sia segnali simulati che segnali di vibrazione reali. Il confronto vuole dimostrare il miglioramento fornito dal nuovo criterio in termini sia di minor numero di operazioni richieste dall’algoritmo che di efficacia diagnostica anche in condizioni di segnale molto rumoroso. Il contributo innovativo di questa parte riguarda la combinazione di ciclostazionarietà e serie di Furier-Bessel che porta alla definizione di un nuovo criterio di blind deconvolution in grado di mantenere l’efficacia diagnostica della ciclostazionarietà ma con un minor tempo computazionale per venire incontro alle richieste del mondo industriale. La second parte riguarda la definizione di un nuovo modello prognostico, appartenente alla famiglia degli hidden Markov models, costruito partendo da una distribuzione Gaussiana generalizzata. L’obbiettivo del metodo proposto è una miglior riproduzione della reale distribuzione dei dati, in particolar modo negli ultimi stadi del danneggiamento. Infatti, la comparsa e l’evoluzione del difetto comporta una modifica della distribuzione delle osservazioni fra i diversi stati. Di conseguenza, una densità di probabilità generalizzata permette la modificazione della forma della distribuzione tramite diversi valori dei parametri del modello. Il metodo proposto viene confrontato con il classico hidden Markov model di base Gaussiana in termini di qualità di riproduzione della distribuzione e predizione della sequenza di stati tramite l’analisi di alcuni test di rottura su cuscinetti volventi e sistemi complessi. L’innovatività di questa parte è data dalla definizione di un algoritmo iterativo per la stima dei parametri del modello nell’ipotesi di distribuzione Gaussiana generalizzata, sia nel caso monovariato che multivariato, partendo dalle osservazioni sul sistema fisico in esame.
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Books on the topic "Machine learnings":

1

Ertekin, Şeyda. Algorithms for efficient learning systems: Online and active learning approaches. Saarbrücken: VDM Verlag Dr. Müller, 2009.

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Campbell, Colin. Learning with support vector machines. San Rafael, Calif. (1537 Fourth Street, San Rafael, CA 94901 USA): Morgan & Claypool, 2011.

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Boyle, Brandon H. Support vector machines: Data analysis, machine learning, and applications. Hauppauge, N.Y: Nova Science Publishers, 2011.

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Zhou, Zhi-Hua. Machine Learning. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-15-1967-3.

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Jung, Alexander. Machine Learning. Singapore: Springer Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-8193-6.

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Mitchell, Tom M., Jaime G. Carbonell, and Ryszard S. Michalski. Machine Learning. Boston, MA: Springer US, 1986. http://dx.doi.org/10.1007/978-1-4613-2279-5.

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Fernandes de Mello, Rodrigo, and Moacir Antonelli Ponti. Machine Learning. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-94989-5.

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Bell, Jason. Machine Learning. Indianapolis, IN, USA: John Wiley & Sons, Inc, 2014. http://dx.doi.org/10.1002/9781119183464.

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Huang, Kaizhu, Haiqin Yang, Irwin King, and Michael Lyu. Machine Learning. Berlin, Heidelberg: Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-79452-3.

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Jebara, Tony. Machine Learning. Boston, MA: Springer US, 2004. http://dx.doi.org/10.1007/978-1-4419-9011-2.

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Book chapters on the topic "Machine learnings":

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Heesen, Bernd. "Grundlagen des Machine Learnings mit R." In Künstliche Intelligenz und Machine Learning mit R, 111–398. Wiesbaden: Springer Fachmedien Wiesbaden, 2023. http://dx.doi.org/10.1007/978-3-658-41576-1_6.

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August, Stephanie E., and Audrey Tsaima. "Artificial Intelligence and Machine Learning: An Instructor’s Exoskeleton in the Future of Education." In Innovative Learning Environments in STEM Higher Education, 79–105. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-58948-6_5.

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AbstractThe role of artificial intelligence in US education is expanding. As education moves toward providing customized learning paths, the use of artificial intelligence (AI) and machine learning (ML) algorithms in learning systems increases. This can be viewed as growing metaphorical exoskeletons for instructors, enabling them to provide a higher level of guidance, feedback, and autonomy to learners. In turn, the instructor gains time to sense student needs and support authentic learning experiences that go beyond what AI and ML can provide. Applications of AI-based education technology support learning through automated tutoring, personalizing learning, assessing student knowledge, and automating tasks normally performed by the instructor. This technology raises questions about how it is best used, what data provides evidence of the impact of AI and ML on learning, and future directions in interactive learning systems. Exploration of the use of AI and ML for both co-curricular and independent learnings in content presentation and instruction; interactions, communications, and discussions; learner activities; assessment and evaluation; and co-curricular opportunities provide guidance for future research.
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Han, Haihang, Tianjie Zhang, Qiao Dong, Xueqin Chen, and Yangyang Wang. "Pavement roughness level classification based on logistic and decision tree machine learnings." In Green and Intelligent Technologies for Sustainable and Smart Asphalt Pavements, 400–405. London: CRC Press, 2021. http://dx.doi.org/10.1201/9781003251125-63.

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Awotunde, Joseph Bamidele, Sunday Adeola Ajagbe, Matthew A. Oladipupo, Jimmisayo A. Awokola, Olakunle S. Afolabi, Timothy O. Mathew, and Yetunde J. Oguns. "An Improved Machine Learnings Diagnosis Technique for COVID-19 Pandemic Using Chest X-ray Images." In Communications in Computer and Information Science, 319–30. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-89654-6_23.

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Bringsjord, Selmer, Naveen Sundar Govindarajulu, Shreya Banerjee, and John Hummel. "Do Machine-Learning Machines Learn?" In Studies in Applied Philosophy, Epistemology and Rational Ethics, 136–57. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-96448-5_14.

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Dai, Anni. "Co-creation: Space Reconfiguration by Architect and Agent Simulation Based Machine Learning." In Computational Design and Robotic Fabrication, 304–13. Singapore: Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-19-8637-6_27.

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AbstractThis research is a manifestation of architectural co-creation between agent simulation based machine learning and an architect’s tacit knowledge. Instead of applying machine learning brains to agents, the author reversed the idea and applied machine learning to buildings. The project used agent simulation as a database, and trained the space to reconfigure itself based on its distance to the nearest agents. To overcome the limitations of machine learning model’s simplified solutions to complicated architectural environments, the author introduced a co-creation method, where an architect uses tacit knowledge to overwatch and have real-time control over the space reconfiguration process. This research combines both the strength of machine learning’s data-processing ability and an architect’s tacit knowledge. Through exploration of emerging technologies such as machine learning and agent simulation, the author highlights limitations in design automation. By combining an architect’s tacit knowledge with a new generation design method of agent simulation based machine learning, the author hopes to explore a new way for architects to co-create with machines.
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Södergård, Caj. "Summary of Potential and Exploitation of Big Data and AI in Bioeconomy." In Big Data in Bioeconomy, 417–23. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-71069-9_32.

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AbstractIn this final chapter, we summarize the DataBio learnings about how to exploit big data and AI in bioeconomy. The development platform for the software used in the 27 pilots was a central tool. The Enterprise Architecture model Archimate laid a solid basis for the complex software in the pilots. Handling data from sensors and earth observation were shown in numerous pilots. Genomic data from crop species allows us to significantly speed up plant breeding by predicting plant properties in-silico. Data integration is crucial and we show how linked data enables searches over multiple datasets. Real-time processing of events provides insights for fast decision-making, for example about ship engine conditions. We show how sensitive bioeconomy data can be analysed in a privacy-preserving way. The agriculture pilots show with clear numbers the impact of big data and AI on precision agriculture, insurance and subsidies control. In forestry, DataBio developed several big data tools for forest monitoring. In fishery, we demonstrate how to reduce maintenance cost and time as well as fuel consumption in the operation of fishing vessels as well as how to accurately predict fish catches. The chapter ends with perspectives on earth observation, machine learning, data sharing and crowdsourcing.
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Puigbò, Jordi-Ysard, Xerxes D. Arsiwalla, and Paul F. M. J. Verschure. "Challenges of Machine Learning for Living Machines." In Biomimetic and Biohybrid Systems, 382–86. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-95972-6_41.

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Wehenkel, Louis A. "Machine Learning." In Automatic Learning Techniques in Power Systems, 99–144. Boston, MA: Springer US, 1998. http://dx.doi.org/10.1007/978-1-4615-5451-6_5.

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Cios, Krzysztof J., Witold Pedrycz, and Roman W. Swiniarski. "Machine Learning." In Data Mining Methods for Knowledge Discovery, 229–308. Boston, MA: Springer US, 1998. http://dx.doi.org/10.1007/978-1-4615-5589-6_6.

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Conference papers on the topic "Machine learnings":

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Gaber, Ayman, Mohamed Mahmoud Zaki, Ahmed Maher Mohamed, and Mohamed Abdellatif Beshara. "Cellular Network Power Control Optimization Using Unsupervised Machine Learnings." In 2019 International Conference on Innovative Trends in Computer Engineering (ITCE). IEEE, 2019. http://dx.doi.org/10.1109/itce.2019.8646611.

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Guajardo, Marco, Ahmed S. Omran, and Howard Clark. "Fast model-driven target optimization methods using machine learnings." In Design-Technology Co-optimization XV, edited by Chi-Min Yuan and Ryoung-Han Kim. SPIE, 2021. http://dx.doi.org/10.1117/12.2587122.

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Eshita, Kakeru, Kousei Nishizono, Ryusei Kunitake, Hirohumi Miyazima, Kenichi Arai, and Toru Kobayashi. "Surface Roughness Prediction System for Blade Machining Using Machine Learnings." In 2023 IEEE 12th Global Conference on Consumer Electronics (GCCE). IEEE, 2023. http://dx.doi.org/10.1109/gcce59613.2023.10315442.

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Powney, M., J. Masi, D. Austin, T. Citraningtyas, M. Dyrendahl, B. Alaei, S. Cornelius, F. Dias, and P. Emmet. "Legacy Learnings to Future Insight – Characterising CCUS Sites Using Legacy Data with Machine Learning." In First EAGE Workshop on Hydrogen & CCS in LATAM. European Association of Geoscientists & Engineers, 2023. http://dx.doi.org/10.3997/2214-4609.202382004.

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Sato, Keita, Masafumi Chida, Yoshihiro Hayakawa, and Nahomi Miyamoto Fujiki. "Automatic Feature Extraction from Wearable Sensor Data by Use of Machine Learnings." In The 7th International Conference on Intelligent Systems and Image Processing 2019. The Institute of Industrial Application Engineers, 2019. http://dx.doi.org/10.12792/icisip2019.067.

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Gera, Saksham, Mr Mridul, and Kireet Joshi. "Regression Analysis And Future Forecasting Of COVID-19 Using Machine Learnings Algorithm." In 2021 11th International Conference on Cloud Computing, Data Science & Engineering (Confluence). IEEE, 2021. http://dx.doi.org/10.1109/confluence51648.2021.9377065.

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Jusman, Yessi, Muhammad Khoirul Anam, Sartika Puspita, and Edwyn Saleh. "Machine Learnings of Dental Caries Images based on Hu Moment Invariants Features." In 2021 International Seminar on Application for Technology of Information and Communication (iSemantic). IEEE, 2021. http://dx.doi.org/10.1109/isemantic52711.2021.9573208.

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Srivastava, Priyank, Mainak Bandyopadhyay, Shantanu Chakraborty, Samarth Patwardhan, and Huy Tran. "Classification of Wireline Formation Testing Responses Using Unsupervised Machine Learning Methods." In Offshore Technology Conference. OTC, 2022. http://dx.doi.org/10.4043/31892-ms.

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Abstract This paper presents a novel technique for planning and execution of Wireline Formation Testing (WFT) jobs using recent applications of machine learning. WFT measurements provide a link between the static petrophysical measurements and dynamic rock-fluid properties for enhanced formation evaluation. However, despite the availability of newer generation tools for these services, there are still obstacles related to formation test job design and real-time optimization. Skilled geoscientists traditionally use quicklook analysis and theoretical models in designing WFT jobs to acquire quality dataset. In practice, operators have access to a detailed data repository. However, the learnings from historical jobs may be overlooked during dynamic real time operations and engineers may typically regress to simpler or experienced based models. Some of the methods are self-made to reduce time and energy in data acquisition. Digitization and automation of data provide an opportunity for application of data analytics to support these real-time measurements for an objective evaluation of the dynamic data. In this paper we propose use of unsupervised machine learning models, by which routinely recorded real-time measurements can be used to optimize the test design and real-time operations. Basic principles of interpretation of WFT are used to develop quality parameters and classification based unsupervised machine learning models (Self organized maps, K-means and Hierarchical Clustering). These machine based recommendations can aid in designing and optimizing the real-time job while ensuring quality of the results. In this way, highest quality testing results can be acquired to improve the integration of the dynamic data into the petrophysical analysis. This also will enable standards to be established for real-time data acquisition that can save testing time while improving data and quality. Results show that machine learning models can be a powerful tool to develop a machine based recommendation systems for classification of WFT responses that are dependent on petrophysical parameters.
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Qumsiyeh, Emma, Miar Yousef, and Malik Yousef. "ReScore Disease Groups Based on Multiple Machine Learnings Utilizing the Grouping-Scoring-Modeling Approach." In 15th International Conference on Bioinformatics Models, Methods and Algorithms. SCITEPRESS - Science and Technology Publications, 2024. http://dx.doi.org/10.5220/0012379400003657.

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Emery, David J., Marcelo Guarido, Brian Russell, and Daniel Trad. "Machine learnings and lessons learned on improvements to Castagna’s mudrock, Gardner’s density, and Faust’s velocity estimation." In Second International Meeting for Applied Geoscience & Energy. Society of Exploration Geophysicists and American Association of Petroleum Geologists, 2022. http://dx.doi.org/10.1190/image2022-3749277.1.

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Reports on the topic "Machine learnings":

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Giannoulakis, Stylianos, and Arrigo Beretta. PR-471-18210-R01 Pump Failure and Performance Degradation Prediction. Chantilly, Virginia: Pipeline Research Council International, Inc. (PRCI), September 2020. http://dx.doi.org/10.55274/r0011801.

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Sulzer Pumps Incorporation is performing fundamental research for developing an early pump failure prediction method, for better supporting its customers. Target is to protect critical equipment and reduce unplanned outages. This effort focuses on combining modern machine learning anomaly detection techniques with pump physical know-how. The developed approach was tested with real life failure datasets, provided by Pipeline Research Council International members. In addition, a performance degradation technique was inspired by anomaly detection learnings and tested at this project.
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Vesselinov, Velimir Valentinov. Machine Learning. Office of Scientific and Technical Information (OSTI), January 2019. http://dx.doi.org/10.2172/1492563.

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Valiant, L. G. Machine Learning. Fort Belvoir, VA: Defense Technical Information Center, January 1993. http://dx.doi.org/10.21236/ada283386.

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Chase, Melissa P. Machine Learning. Fort Belvoir, VA: Defense Technical Information Center, April 1990. http://dx.doi.org/10.21236/ada223732.

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Kagie, Matthew J., and Park Hays. FORTE Machine Learning. Office of Scientific and Technical Information (OSTI), August 2016. http://dx.doi.org/10.2172/1561828.

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Lin, Youzuo, Shihang Feng, and Esteban Rougier. Machine Learning Tutorial. Office of Scientific and Technical Information (OSTI), July 2022. http://dx.doi.org/10.2172/1876777.

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Caplin, Andrew, Daniel Martin, and Philip Marx. Modeling Machine Learning. Cambridge, MA: National Bureau of Economic Research, October 2022. http://dx.doi.org/10.3386/w30600.

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Kelly, Bryan, and Dacheng Xiu. Financial Machine Learning. Cambridge, MA: National Bureau of Economic Research, July 2023. http://dx.doi.org/10.3386/w31502.

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Vassilev, Apostol. Adversarial Machine Learning:. Gaithersburg, MD: National Institute of Standards and Technology, 2024. http://dx.doi.org/10.6028/nist.ai.100-2e2023.

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Junttila, Jukka, Ville Lämsä, Leonardo Espinosa Leal, and Anssi Sillanpää. Feature engineering –based machine learning models for operational state recognition of rotating machines. Peeref, March 2023. http://dx.doi.org/10.54985/peeref.2303p8483224.

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