Relevant bibliographies by topics / Prediction of quality

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Academic literature on the topic 'Prediction of quality'

Author: Grafiati
Published: 11 September 2023

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Journal articles on the topic "Prediction of quality"

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Liang, Yun-Chia, Yona Maimury, Angela Hsiang-Ling Chen, and Josue Rodolfo Cuevas Juarez. "Machine Learning-Based Prediction of Air Quality." Applied Sciences 10, no. 24 (December 21, 2020): 9151. http://dx.doi.org/10.3390/app10249151.

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Air, an essential natural resource, has been compromised in terms of quality by economic activities. Considerable research has been devoted to predicting instances of poor air quality, but most studies are limited by insufficient longitudinal data, making it difficult to account for seasonal and other factors. Several prediction models have been developed using an 11-year dataset collected by Taiwan’s Environmental Protection Administration (EPA). Machine learning methods, including adaptive boosting (AdaBoost), artificial neural network (ANN), random forest, stacking ensemble, and support vector machine (SVM), produce promising results for air quality index (AQI) level predictions. A series of experiments, using datasets for three different regions to obtain the best prediction performance from the stacking ensemble, AdaBoost, and random forest, found the stacking ensemble delivers consistently superior performance for R2 and RMSE, while AdaBoost provides best results for MAE.
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Muharsyah, Robi, Dian Nur Ratri, and Damiana Fitria Kussatiti. "Improving prediction quality of sea surface temperature (SST) in Niño3.4 region using Bayesian Model Averaging." IOP Conference Series: Earth and Environmental Science 893, no. 1 (November 1, 2021): 012028. http://dx.doi.org/10.1088/1755-1315/893/1/012028.

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Abstract Prediction of Sea Surface Temperature (SST) in Niño3.4 region (170 W - 120 W; 5S - 5N) is important as a valuable indicator to identify El Niño Southern Oscillation (ENSO), i.e., El Niño, La Niña, and Neutral condition for coming months. More accurate prediction Niño3.4 SST can be used to determine the response of ENSO phenomenon to rainfall over Indonesia region. SST predictions are routinely released by meteorological institutions such as the European Center for Medium-Range Weather Forecasts (ECMWF). However, SST predictions from the direct output (RAW) of global models such as ECMWF seasonal forecast is suffering from bias that affects the poor quality of SST predictions. As a result, it also increases the potential errors in predicting the ENSO events. This study uses SST from the output Ensemble Prediction System (EPS) of ECMWF seasonal forecast, namely SEAS5. SEAS5 SST is downloaded from The Copernicus Climate Change Service (C3S) for period 1993-2020. One value representing SST over Niño3.4 region is calculated for each lead-time (LT), LT0-LT6. Bayesian Model Averaging (BMA) is selected as one of the post-processing methods to improve the prediction quality of SEAS5-RAW. The advantage of BMA over other post-processing methods is its ability to quantify the uncertainty in EPS, which is expressed as probability density function (PDF) predictive. It was found that the BMA calibration process reaches optimal performance using 160 months training window. The result show, prediction quality of Niño3.4 SST of BMA output is superior to SEAS5-RAW, especially for LT0, LT1, and LT2. In term deterministic prediction, BMA shows a lower Root Mean Square Error (RMSE), higher Proportion of Correct (PC). In term probabilistic prediction, the error rate of BMA, which is showed by the Brier Score is lower than RAW. Moreover, BMA shows a good ability to discriminating ENSO events which indicates by AUC ROC close to a perfect score.
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Panchal, D. S., M. B. Shelke, S. S. Kawathekar, and S. N. Deshmukh. "Prediction of Healthcare Quality Using Sentiment Analysis." Indian Journal Of Science And Technology 16, no. 21 (June 3, 2023): 1603–13. http://dx.doi.org/10.17485/ijst/v16i21.2506.

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Martens, M., and H. Martens. "Near-Infrared Reflectance Determination of Sensory Quality of Peas." Applied Spectroscopy 40, no. 3 (March 1986): 303–10. http://dx.doi.org/10.1366/0003702864509114.

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Rapid, precise, and relevant methods for predicting the sensory quality of frozen peas were sought. Pea batches chosen to span many different types of quality variations were analyzed by a consumer test, sensory laboratory analysis, and traditional chemical and physical measurements as well as by near-infrared reflectance analysis (NIR). Partial least-squares (PLS) regression was used to reveal the relationships between the different types of measurements. A noise-compensated value, relative ability of prediction (RAP), was used to express the degree of prediction (1.0 = perfect prediction). NIR was found to predict the sensory texture variables (RAP = 0.79) better than the flavor variables (RAP = 0.67). Average consumer preference was less well predicted (RAP = 0.48) by NIR. This was interpretable since NIR gave a better description of the chemical and physical methods relevant for texture (e.g., dry matter (RAP = 0.93)) than the flavor-related variables (e.g., sucrose (RAP = 0.45)) that apparently determine the consumer preference. However, NIR was found to describe the average variation in sensory quality better than the traditional tenderometer value (TV). The highest prediction of sensory variables was obtained by a combination of NIR, TV, and chemical measurements (RAP = 0.87 and 0.80 for texture and flavor variables, respectively). We discuss the predictive validity and the meaning of the present predictive abilities in practice, leading to a conclusion that NIR has a potential for predicting the sensory quality of peas.
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Kim, Donghyun, Heechan Han, Wonjoon Wang, Yujin Kang, Hoyong Lee, and Hung Soo Kim. "Application of Deep Learning Models and Network Method for Comprehensive Air-Quality Index Prediction." Applied Sciences 12, no. 13 (July 1, 2022): 6699. http://dx.doi.org/10.3390/app12136699.

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Accurate pollutant prediction is essential in fields such as meteorology, meteorological disasters, and climate change studies. In this study, long short-term memory (LSTM) and deep neural network (DNN) models were applied to six pollutants and comprehensive air-quality index (CAI) predictions from 2015 to 2020 in Korea. In addition, we used the network method to find the best data sources that provide factors affecting comprehensive air-quality index behaviors. This study had two steps: (1) predicting the six pollutants, including fine dust (PM10), fine particulate matter (PM2.5), ozone (O3), sulfurous acid gas (SO2), nitrogen dioxide (NO2), and carbon monoxide (CO) using the LSTM model; (2) forecasting the CAI using the six predicted pollutants in the first step as predictors of DNNs. The predictive ability of each model for the six pollutants and CAI prediction was evaluated by comparing it with the observed air-quality data. This study showed that combining a DNN model with the network method provided a high predictive power, and this combination could be a remarkable strength in CAI prediction. As the need for disaster management increases, it is anticipated that the LSTM and DNN models with the network method have ample potential to track the dynamics of air pollution behaviors.
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GANESAN, K., TAGHI M. KHOSHGOFTAAR, and EDWARD B. ALLEN. "CASE-BASED SOFTWARE QUALITY PREDICTION." International Journal of Software Engineering and Knowledge Engineering 10, no. 02 (April 2000): 139–52. http://dx.doi.org/10.1142/s0218194000000092.

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Highly reliable software is becoming an essential ingredient in many systems. However, assuring reliability often entails time-consuming costly development processes. One cost-effective strategy is to target reliability-enhancement activities to those modules that are likely to have the most problems. Software quality prediction models can predict the number of faults expected in each module early enough for reliability enhancement to be effective. This paper introduces a case-based reasoning technique for the prediction of software quality factors. Case-based reasoning is a technique that seeks to answer new problems by identifying similar "cases" from the past. A case-based reasoning system can function as a software quality prediction model. To our knowledge, this study is the first to use case-based reasoning systems for predicting quantitative measures of software quality. A case study applied case-based reasoning to software quality modeling of a family of full-scale industrial software systems. The case-based reasoning system's accuracy was much better than a corresponding multiple linear regression model in predicting the number of design faults. When predicting faults in code, its accuracy was significantly better than a corresponding multiple linear regression model for two of three test data sets and statistically equivalent for the third.
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Gonçalves, Mateus Teles Vital, Gota Morota, Paulo Mafra de Almeida Costa, Pedro Marcus Pereira Vidigal, Marcio Henrique Pereira Barbosa, and Luiz Alexandre Peternelli. "Near-infrared spectroscopy outperforms genomics for predicting sugarcane feedstock quality traits." PLOS ONE 16, no. 3 (March 4, 2021): e0236853. http://dx.doi.org/10.1371/journal.pone.0236853.

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The main objectives of this study were to evaluate the prediction performance of genomic and near-infrared spectroscopy (NIR) data and whether the integration of genomic and NIR predictor variables can increase the prediction accuracy of two feedstock quality traits (fiber and sucrose content) in a sugarcane population (Saccharum spp.). The following three modeling strategies were compared: M1 (genome-based prediction), M2 (NIR-based prediction), and M3 (integration of genomics and NIR wavenumbers). Data were collected from a commercial population comprised of three hundred and eighty-five individuals, genotyped for single nucleotide polymorphisms and screened using NIR spectroscopy. We compared partial least squares (PLS) and BayesB regression methods to estimate marker and wavenumber effects. In order to assess model performance, we employed random sub-sampling cross-validation to calculate the mean Pearson correlation coefficient between observed and predicted values. Our results showed that models fitted using BayesB were more predictive than PLS models. We found that NIR (M2) provided the highest prediction accuracy, whereas genomics (M1) presented the lowest predictive ability, regardless of the measured traits and regression methods used. The integration of predictors derived from NIR spectroscopy and genomics into a single model (M3) did not significantly improve the prediction accuracy for the two traits evaluated. These findings suggest that NIR-based prediction can be an effective strategy for predicting the genetic merit of sugarcane clones.
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Kouadri, Wissam Mammar, Mourad Ouziri, Salima Benbernou, Karima Echihabi, Themis Palpanas, and Iheb Ben Amor. "Quality of sentiment analysis tools." Proceedings of the VLDB Endowment 14, no. 4 (December 2020): 668–81. http://dx.doi.org/10.14778/3436905.3436924.

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In this paper, we present a comprehensive study that evaluates six state-of-the-art sentiment analysis tools on five public datasets, based on the quality of predictive results in the presence of semantically equivalent documents, i.e., how consistent existing tools are in predicting the polarity of documents based on paraphrased text. We observe that sentiment analysis tools exhibit intra-tool inconsistency , which is the prediction of different polarity for semantically equivalent documents by the same tool, and inter-tool inconsistency , which is the prediction of different polarity for semantically equivalent documents across different tools. We introduce a heuristic to assess the data quality of an augmented dataset and a new set of metrics to evaluate tool inconsistencies. Our results indicate that tool inconsistencies is still an open problem, and they point towards promising research directions and accuracy improvements that can be obtained if such inconsistencies are resolved.
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Asiah, Mat, Khidzir Nik Zulkarnaen, Deris Safaai, Mat Yaacob Nik Nurul Hafzan, Mohamad Mohd Saberi, and Safaai Siti Syuhaida. "A Review on Predictive Modeling Technique for Student Academic Performance Monitoring." MATEC Web of Conferences 255 (2019): 03004. http://dx.doi.org/10.1051/matecconf/201925503004.

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Despite of providing high quality of education, demand on predicting student academic performance become more critical to improve the quality and assisting students to achieve a great performance in their studies. The lack of existing an efficiency and accurate prediction model is one of the major issues. Predictive analytics can provide institution with intuitive and better decision making. The objective of this paper is to review current research activities related to academic analytics focusing on predicting student academic performance. Various methods have been proposed by previous researchers to develop the best performance model using variety of students data, techniques, algorithms and tools. Predictive modeling used in predicting student performance are related to several learning tasks such as classification, regression and clustering. To achieve best prediction model, a lot of variables have been chosen and tested to find most influential attributes to perform prediction. Accurate performance prediction will be helpful in order to provide guidance in learning process that will benefit to students in avoiding poor scores. The predictive model furthermore can help instructor to forecast course completion including student final grade which are directly correlated to student performance success. To harvest an effective predictive model, it requires a good input data and variables, suitable predictive method as well as powerful and robust prediction model.
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Veeramalai, S., Mr T. Praveen, and S. Pradeepa Natarajan. "Cost Based On Product Quality Prediction Using Datamining." International Journal of Trend in Scientific Research and Development Special Issue, Special Issue-Active Galaxy (June 30, 2018): 38–42. http://dx.doi.org/10.31142/ijtsrd14564.

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Dissertations / Theses on the topic "Prediction of quality"

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KUNTE, DEEPTI SHRIRAM. "Sound Quality Prediction Using Neural Networks." Thesis, KTH, Skolan för industriell teknik och management (ITM), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-283336.

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Sound quality is an important measure depicting the quality of a machine as well as the comfort in its usage. However, it being a subjective measure, not only is it difficult to capture it ahead of time but also necessitates time and cost expensive jury testing. Thus, it is worthwhile to be able to effectively predict the results of the jury study from metrics that can be objectively measured. The aim of the thesis is twofold: first, to establish neural network models to predict subjective sound quality metrics from objective metrics and second, to interpret the model to understand the relative importance of each objective metric towards a specific subjective rating. Ultimately the thesis aims to pave the way for inclusion of sound quality metrics in the early design stages. From the study, it was evident that neural networks’ performance was at least equal to or better than linear or quadratic models. The connection weights method, the profile method, the perturbation method, the improved stepwise selection method and linear regression method were the interpretation algorithms found to work well in all simulated data-sets. They also gave comparable results on the real data-sets. Neural networks were shown to have the potential for giving low prediction errors while maintaining interpretability in sound quality applications. The data scarcity study gave an idea of the scale of performance enhancement that can be achieved with more data and can act as a useful input for deciding the number data points.
Ljudkvalitet är ett viktigt mått som skildrar en maskins kvalitet såväl som bekvämlighet i dess användning. Det är emellertid ett subjektivt mått, inte bara är det svårt att fånga detta i förväg men också att det kräver både tid och dyra jurytestningar. Det är därför värdefullt att kunna effektivt förutsäga de resultaten av jurystudien från mätvärden som kan mätas objektivt. Syftet med arbetet är tvåfaldigt: det första är att etablera neuronnätsmodeller till att förutsäga subjektiva ljudkvalitetsmätvärden från objektiva mätvärden. Det andra är att tolka modellen till att kunna förstå den relativa betydelsen av varje objektivt mätvärde mot en specifik subjektiv bedömning. I sista hand syftar arbetet till att bana vägen för inkludering av mätvärden för ljudkvalitet i de tidiga designfaserna. Utifrån studien var det uppenbart att neuronnäts prestanda var åtminstone lika med eller bättre än de linjära eller kvadratiska modellerna. Anslutningsviktsmetoden, profilmetoden, störningsmetoden, den förbättrade stegvisa urvalsmetoden samt den linjära regressionsmetoden var tolkningsalgoritmerna som visade sig att fungera väl på alla simulerad datauppsättningar. De gav också jämförbara resultat på de verkliga datauppsättningarna. Neuronnät visade sig att ha potential att ge låga prediktionsfel samtidigt som de bibehåller tolkningsbarhet i applikationer för ljudkvalitet. Studien av dataknapphet gav det en uppfattning om storleken på prestandaförbättring som kan uppnås med mer data och kan fungera som en användbar input vid bestämning av antalet datapunkter.
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Steel, Donald. "Software reliability prediction." Thesis, Abertay University, 1990. https://rke.abertay.ac.uk/en/studentTheses/4613ff72-9650-4fa1-95d1-1a9b7b772ee4.

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The aim of the work described in this thesis was to improve NCR's decision making process for progressing software products through the development cycle. The first chapter briefly describes the software development process at NCR, detailing documentation review and software testing techniques. The objectives and reasons for investigating software reliability models as a tool in the decision making process are outlined. There follows a short review of software reliability models, with the Littlewood and Verrall Bayesian model considered in detail. The difficulties in using this model to obtain estimates for model parameters and time to next failure are described. These estimation difficulties exist using the model on good datasets, in this case simulated failure data, and the difficulties are compounded when used with real failure data. The problems of collecting and recording failure data are outlined, highlighting the inadequacies of these collected data, and real failure data are analysed. Software reliability models are used in an attempt to quantify the reliability of real software products. The thesis concludes by summarising the problems encountered when using reliability models to measure software products and suggests future research into metrics that are required in this area of software engineering.
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Peng, Huiping. "Air quality prediction by machine learning methods." Thesis, University of British Columbia, 2015. http://hdl.handle.net/2429/55069.

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As air pollution is a complex mixture of toxic components with considerable impact on humans, forecasting air pollution concentration emerges as a priority for improving life quality. In this study, air quality data (observational and numerical) were used to produce hourly spot concentration forecasts of ozone (O₃), particulate matter 2.5μm (PM₂.₅) and nitrogen dioxide (NO₂), up to 48 hours for six stations across Canada -- Vancouver, Edmonton, Winnipeg, Toronto, Montreal and Halifax. Using numerical data from an air quality model (GEM-MACH15) as predictors, forecast models for pollutant concentrations were built using multiple linear regression (MLR) and multi-layer perceptron neural networks (MLP NN). A relatively new method, the extreme learning machine (ELM), was also used to overcome the limitation of linear methods as well as the large computational demand of MLP NN. In operational forecasting, the continuous arrival of new data means frequent updating of the models is needed. This type of learning, called online sequential learning, is straightforward for MLR and ELM but not for MLP NN. Forecast performance of the online sequential MLR (OSMLR) and online sequential ELM (OSELM), together with stepwise MLR, all updated daily were compared with MLP NN updated seasonally, and the benchmark, updatable model output statistics (UMOS) from Environmental Canada. Overall OSELM tended to slightly outperform the other models including UMOS, being most successful with ozone forecasts and least with PM₂.₅ forecasts. MLP NN updated seasonally was generally underperforming the linear models MLR and OSMLR, indicating the need to update a nonlinear model frequently.
Science, Faculty of
Earth, Ocean and Atmospheric Sciences, Department of
Graduate
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Hollier, M. P. "Audio quality prediction for telecomunications speech systems." Thesis, University of Essex, 1995. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.282496.

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Mateus, Ana Teresa Moreirinha Vila Fernandes. "Quality management in laboratories- Effciency prediction models." Doctoral thesis, Universidade de Évora, 2021. http://hdl.handle.net/10174/29338.

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In recent years, the choice of quality tools by laboratories has increased significantly. This fact contributed to the growth of competitiveness, requiring a new organizational posture to adapt to the new challenges. In order to obtain competitive advantages in the respective sectors of activity, laboratories have increasingly invested in innovation. In this context, the main objective of this study aims to develop efficiency models for laboratories using tools from the Scientific Area of Artificial Intelligence. Throughout this work, different studies will be presented, carried out in water analysis laboratories, stem cell cryopreservation laboratories and dialysis care clinics, in which innovative solutions and better resource control were sought, without compromising quality and promoting greater sustainability This work can be seen as an investigation opportunity that can be applied not only in laboratories and clinics, but also in organizations from different sectors in order to seek to define prediction models, allowing the anticipation of future scenarios and the evaluation of ways of acting. The results show the feasibility of applying the models and that the normative references applied to laboratories and clinics can be a basis for structuring the systems; Gestão da Qualidade em Laboratórios Modelos de Previsão de Eficiência Resumo: Nos últimos anos, a adoção de ferramentas da qualidade por parte dos laboratórios tem aumentado significativamente. Este facto contribuiu para o crescimento da competitividade, exigindo uma nova postura organizacional de forma a se adaptarem aos novos desafios. Tendo em vista obter vantagens competitivas nos respetivos sectores de atividade, os laboratórios têm, cada vez mais, apostado em inovação. Neste contexto, o principal objetivo deste estudo visa o desenvolvimento de modelos de eficiência para laboratórios através do recurso a ferramentas da Área Científica da Inteligência Artificial. Ao longo deste trabalho irão ser apresentados diferentes estudos, realizados em laboratórios de análises de águas, laboratórios de criopreservação de células estaminais e clínicas de prestação de cuidados de diálise, nos quais se procuraram soluções inovadoras e um melhor controlo de recursos, sem comprometer a qualidade e promovendo uma maior sustentabilidade. Este trabalho pode ser encarado como uma oportunidade de investigação que pode ser aplicado não apenas em laboratórios e clínicas mas, também, em organizações de diversos sectores com o intuito de se procurar definir modelos de previsão, possibilitando a antecipação de cenários futuros e a avaliação de formas de atuação. Os resultados mostram a viabilidade da aplicação dos modelos e que os referenciais normativos aplicados aos laboratórios e às clínicas podem servir como base para estruturação dos sistemas.
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Taipale, T. (Taneli). "Improving software quality with software error prediction." Master's thesis, University of Oulu, 2015. http://urn.fi/URN:NBN:fi:oulu-201512042251.

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Today’s agile software development can be a complicated process, especially when dealing with a large-scale project with demands for tight communication. The tools used in software development, while aiding the process itself, can also offer meaningful statistics. With the aid of machine learning, these statistics can be used for predicting the behavior patterns of the development process. The starting point of this thesis is a software project developed to be a part of a large telecommunications network. On the one hand, this type of project demands expensive testing equipment, which, in turn, translates to costly testing time. On the other hand, unit testing and code reviewing are practices that improve the quality of software, but require large amounts of time from software experts. Because errors are the unavoidable evil of the software process, the efficiency of the above-mentioned quality assurance tools is very important for a successful software project. The target of this thesis is to improve the efficiency of testing and other quality tools by using a machine learner. The machine learner is taught to predict errors using historical information about software errors made earlier in the project. The error predictions are used for prioritizing the test cases that are most probably going to find an error. The result of the thesis is a predictor that is capable of estimating which of the file changes are most likely to cause an error. The prediction information is used for creating reports such as a ranking of the most probably error-causing commits. Furthermore, a line-wise map of probability of an error for the whole project is created. Lastly, the information is used for creating a graph that combines organizational information with error data. The original goal of prioritizing test cases based on the error predictions was not achieved because of limited coverage data. This thesis brought important improvements in project practices into focus, and gave new perspectives into the software development process
Nykyaikainen ketterä ohjelmistokehitys on monimutkainen prosessi. Tämä väittämä pätee varsinkin isoihin projekteihin. Ohjelmistokehityksessä käytettävät työkalut helpottavat jo itsessään kehitystyötä, mutta ne myös säilövät tärkeää tilastotietoa. Tätä tilastotietoa voidaan käyttää koneoppimisjärjestelmän opettamiseen. Tällä tavoin koneoppimisjärjestelmä oppii tunnistamaan ohjelmistokehitystyölle ominaisia käyttäytymismalleja. Tämän opinnäytetyön lähtökohta on ohjelmistoprojekti, jonka on määrä toimia osana laajaa telekommunikaatioverkkoa. Tällainen ohjelmistoprojekti vaatii kalliin testauslaitteiston, mikä johtaa suoraan kalliiseen testausaikaan. Toisaalta yksikkötestaus ja koodikatselmointi ovat työmenetelmiä, jotka parantavat ohjelmiston laatua, mutta vaativat paljon ohjelmistoammattilaisten resursseja. Koska ohjelmointivirheet ovat ohjelmistoprojektin edetessä väistämättömiä, on näiden työkalujen tehokkuus tunnistaa ohjelmointivirheitä erityisen tärkeää onnistuneen projektin kannalta. Tässä opinnäytetyössä testaamisen ja muiden laadunvarmennustyökalujen tehokkuutta pyritään parantamaan käyttämällä hyväksi koneoppimisjärjestelmää. Koneoppimisjärjestelmä opetetaan tunnistamaan ohjelmointivirheet käyttäen historiatietoa projektissa aiemmin tehdyistä ohjelmointivirheistä. Koneoppimisjärjestelmän ennusteilla kohdennetaan testausta painottamalla virheen todennäköisimmin löytäviä testitapauksia. Työn lopputuloksena on koneoppimisjärjestelmä, joka pystyy ennustamaan ohjelmointivirheen todennäköisimmin sisältäviä tiedostomuutoksia. Tämän tiedon pohjalta on luotu raportteja kuten listaus todennäköisimmin virheen sisältävistä tiedostomuutoksista, koko ohjelmistoprojektin kattava kartta virheen rivikohtaisista todennäköisyyksistä sekä graafi, joka yhdistää ohjelmointivirhetiedot organisaatiotietoon. Alkuperäisenä tavoitteena ollutta testaamisen painottamista ei kuitenkaan saatu aikaiseksi vajaan testikattavuustiedon takia. Tämä opinnäytetyö toi esiin tärkeitä parannuskohteita projektin työtavoissa ja uusia näkökulmia ohjelmistokehitysprosessiin
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Krishnamurthy, Janaki. "Quality Market: Design and Field Study of Prediction Market for Software Quality Control." NSUWorks, 2010. http://nsuworks.nova.edu/gscis_etd/352.

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Given the increasing competition in the software industry and the critical consequences of software errors, it has become important for companies to achieve high levels of software quality. While cost reduction and timeliness of projects continue to be important measures, software companies are placing increasing attention on identifying the user needs and better defining software quality from a customer perspective. Software quality goes beyond just correcting the defects that arise from any deviations from the functional requirements. System engineers also have to focus on a large number of quality requirements such as security, availability, reliability, maintainability, performance and temporal correctness requirements. The fulfillment of these run-time observable quality requirements is important for customer satisfaction and project success. Generating early forecasts of potential quality problems can have significant benefits to quality improvement. One approach to better software quality is to improve the overall development cycle in order to prevent the introduction of defects and improve run-time quality factors. Many methods and techniques are available which can be used to forecast quality of an ongoing project such as statistical models, opinion polls, survey methods etc. These methods have known strengths and weaknesses and accurate forecasting is still a major issue. This research utilized a novel approach using prediction markets, which has proved useful in a variety of situations. In a prediction market for software quality, individual estimates from diverse project stakeholders such as project managers, developers, testers, and users were collected at various points in time during the project. Analogous to the financial futures markets, a security (or contract) was defined that represents the quality requirements and various stakeholders traded the securities using the prevailing market price and their private information. The equilibrium market price represents the best aggregate of diverse opinions. Among many software quality factors, this research focused on predicting the software correctness. The goal of the study was to evaluate if a suitably designed prediction market would generate a more accurate estimate of software quality than a survey method which polls subjects. Data were collected using a live software project in three stages: viz., the requirements phase, an early release phase and a final release phase. The efficacy of the market was tested with results from prediction markets by (i) comparing the market outcomes to final project outcome, and (ii) by comparing market outcomes to results of opinion poll. Analysis of data suggests that predictions generated using the prediction market are significantly different from those generated using polls at early release and final release stages. The prediction market estimates were also closer to the actual probability estimates for quality compared to the polls. Overall, the results suggest that suitably designed prediction markets provide better forecasts of potential quality problems than polls.
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Wallner, Björn. "Protein Structure Prediction : Model Building and Quality Assessment." Doctoral thesis, Stockholm University, Department of Biochemistry and Biophysics, 2005. http://urn.kb.se/resolve?urn=urn:nbn:se:su:diva-649.

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Proteins play a crucial roll in all biological processes. The wide range of protein functions is made possible through the many different conformations that the protein chain can adopt. The structure of a protein is extremely important for its function, but to determine the structure of protein experimentally is both difficult and time consuming. In fact with the current methods it is not possible to study all the billions of proteins in the world by experiments. Hence, for the vast majority of proteins the only way to get structural information is through the use of a method that predicts the structure of a protein based on the amino acid sequence.

This thesis focuses on improving the current protein structure prediction methods by combining different prediction approaches together with machine-learning techniques. This work has resulted in some of the best automatic servers in world – Pcons and Pmodeller. As a part of the improvement of our automatic servers, I have also developed one of the best methods for predicting the quality of a protein model – ProQ. In addition, I have also developed methods to predict the local quality of a protein, based on the structure – ProQres and based on evolutionary information – ProQprof. Finally, I have also performed the first large-scale benchmark of publicly available homology modeling programs.

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Wallner, Björn. "Protein structure prediction : model building and quality assessment /." Stockholm : Stockholm Bioinformatics Center, Department of Biochemistry and Biophysics, Stockholm University, 2005. http://urn.kb.se/resolve?urn=urn:nbn:se:su:diva-649.

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Brun, Daniel, and Colin Lawless. "Quality Prediction in Jet Printing Using Neural Networks." Thesis, KTH, Skolan för industriell teknik och management (ITM), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-278882.

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Surface mount   technology   is   widely   used   in   the   manufacturing   of   commercial  electronics, and  the  demands  on  the  machines  increase  as  the  complexity of  the electronics increases and the size of the components decreases. Mycronic is a company that focuses on addressing those demands with their high-technology jet printing and pick-and-place machines. This master's thesis has been performed at Mycronic and has focused on the MY700 jet printer. Due to unknown factors, the quality of the ejected  solder paste droplets from the machine can vary over time. It was therefore of interest to monitor variables of the MY700 in order to gain more knowledge about the cause of the varying quality, and also to be able to detect substantial changes in deposit quality. In this project, the temperature has been measured at three key locations  on the ej ector as well as the current going through the piezoelectric actuator. This data was fed to a neural network in order to make  quality predictions with respect to the diameter of the solder paste deposits. Different combinations of sensor data were used to evaluate how the different sensors affected the performance of the neural network. Thereby, a better understanding of how big an  impact the different variables had on the quality of the deposits could be achieved.  The results indicate that the current was more significant than the temperature for making quality predictions. Using only the temperature data, the neural network was not able to accurately predict quality deviations, whereas with the piezo current data or both of them  combined,  better predictions could be made. The current data also significantly improved the performance of the neural network when printing jobs with varying diameter were  used. The conclusion is that none of the  three  temperature sensors  significantly  improved  the  performance, and there were no considerable differences between them, while the current did improve it.
Ytmonteringsteknologi är en väletablerad metod som används inom tillverkningen av kommersiell elektronik, och kravet på dessa maskiner ökar i takt med att elektronikens komplexitet  ökar  och  storleken  på  komponenterna  minskar.  Mycronic är ett företag vars fokus ligger i att möta dessa krav med deras högteknologiska jet printing - och pick-and-place-maskiner. Detta examensarbete  har utförts på Mycronic och har fokuserat på jet printing-maskinen MY700. På  grund av  okända faktorer kan kvaliteten på den deponerade lodpastan från maskinen variera över tid. Det var därför intressant att övervaka variabler hos maskinen för att få mer kunskap om orsaken till den varierande kvaliteten och också för att kunna upptäcka förändringar i kvaliteten.  I det här projektet har temperaturen mätts på tre kritiska positioner på ejektorn samt även strömmen som går genom  det  piezoelektriska  ställdonet. Dessa data  gavs till ett neuralt nätverk för att göra kvalitetsprognoser med avseende på diametern på deponeringarna av lodpasta. Olika  kombinationer av sensordata användes för att utvärdera  hur de olika sensorerna påverkade det neurala nätverkets prestanda. Därigenom kunde en bättre förståelse av hur stor påverkan de olika variablerna hade på kvaliteten på deponeringarna uppnås. Resultaten indikerar att strömmen var mer betydelsefull än temperaturen för att göra kvalitetsprognoser. Om bara temperaturdata användes lyckades inte det neurala nätverket göra exakta förutsägelser för kvalitetsavvikelser, medan med bara strömdata eller  båda  kombinerade kunde bättre förutsägelser  göras. Strömdatan  förbättrade också prestandan hos det neurala nätverket när jobb med olika diametrar användes. Slutsatsen är att ingen av de tre temperatursensorerna förbättrade prestandan signifikant, och det fanns inga betydande skillnader  mellan  dem, medan strömmen förbättrade prestandan.
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Books on the topic "Prediction of quality"

1

Mittag, Gabriel. Deep Learning Based Speech Quality Prediction. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-91479-0.

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

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Belmudez, Benjamin. Audiovisual Quality Assessment and Prediction for Videotelephony. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-14166-4.

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Holman, Thomas B., Paul James Birch, Jason S. Carroll, Cynthia Doxey, Jeffry H. Larson, and Steven T. Linford. Premarital Prediction of Marital Quality or Breakup. Boston, MA: Springer US, 2002. http://dx.doi.org/10.1007/b107947.

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Handbook of image quality: Characterization and prediction. New York: Marcel Dekker, 2002.

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A, Kupecz Julie, Gluyas J. G, and Bloch S, eds. Reservoir quality prediction in sandstones and carbonates. Tulsa, Okla: American Association of Petroleum Geologists, 1997.

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D, Meshri Indu, Ortoleva Peter J, and American Association of Petroleum Geologists., eds. Prediction of reservoir quality through chemical modeling. Tulsa, Okla., U.S.A: American Association of Petroleum Geologists, 1990.

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Prediction and regulation of air pollution. Dordrecht: Kluwer Academic Publishers, 1991.

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Möller, Sebastian. Assessment and Prediction of Speech Quality in Telecommunications. Boston, MA: Springer US, 2000. http://dx.doi.org/10.1007/978-1-4757-3117-0.

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Schiffner, Falk Ralph. Dimension-Based Quality Analysis and Prediction for Videotelephony. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-56570-1.

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Book chapters on the topic "Prediction of quality"

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Möller, Sebastian. "Quality Prediction." In Quality Engineering, 163–90. Berlin, Heidelberg: Springer Berlin Heidelberg, 2023. http://dx.doi.org/10.1007/978-3-662-65615-0_9.

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McGuffin, Liam J. "Model Quality Prediction." In Introduction to Protein Structure Prediction, 323–42. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2010. http://dx.doi.org/10.1002/9780470882207.ch15.

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Aggarwal, K. K. "Reliability Prediction." In Topics in Safety, Reliability and Quality, 107–21. Dordrecht: Springer Netherlands, 1993. http://dx.doi.org/10.1007/978-94-011-1928-3_5.

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Möller, Sebastian. "Quality of Prediction Models." In Assessment and Prediction of Speech Quality in Telecommunications, 159–87. Boston, MA: Springer US, 2000. http://dx.doi.org/10.1007/978-1-4757-3117-0_7.

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Schiffner, Falk Ralph. "Quality Modeling and Prediction." In T-Labs Series in Telecommunication Services, 83–88. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-56570-1_5.

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Pourbafrani, Mahsa, Shreya Kar, Sebastian Kaiser, and Wil M. P. van der Aalst. "Remaining Time Prediction for Processes with Inter-case Dynamics." In Lecture Notes in Business Information Processing, 140–53. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-98581-3_11.

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AbstractProcess mining techniques use event data to describe business processes, where the provided insights are used for predicting processes’ future states (Predictive Process Monitoring). Remaining Time Prediction of process instances is an important task in the field of Predictive Process Monitoring (PPM). Existing approaches have two key limitations in developing Remaining Time Prediction Models (RTM): (1) The features used for predictions lack process context, and the created models are black-boxes. (2) The process instances are considered to be in isolation, despite the fact that process states, e.g., the number of running instances, influence the remaining time of a single process instance. Recent approaches improve the quality of RTMs by utilizing process context related to batching-at-end inter-case dynamics in the process, e.g., using the time to batching as a feature. We propose an approach that decreases the previous approaches’ reliance on user knowledge for discovering fine-grained process behavior. Furthermore, we enrich our RTMs with the extracted features for multiple performance patterns (caused by inter-case dynamics), which increases the interpretability of models. We assess our proposed remaining time prediction method using two real-world event logs. Incorporating the created inter-case features into RTMs results in more accurate and interpretable predictions.
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Pandey, Ajeet Kumar, and Neeraj Kumar Goyal. "Background: Software Quality and Reliability Prediction." In Early Software Reliability Prediction, 17–33. India: Springer India, 2013. http://dx.doi.org/10.1007/978-81-322-1176-1_2.

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Michael, Thilo. "Conversational Quality Predictions." In Simulating Conversations for the Prediction of Speech Quality, 101–21. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-31844-3_6.

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Duzbayev, Nurzhan, and Iman Poernomo. "Runtime Prediction of Queued Behaviour." In Quality of Software Architectures, 78–94. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11921998_10.

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Du, Shichang, and Lifeng Xi. "Surface Prediction." In High Definition Metrology Based Surface Quality Control and Applications, 265–91. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-15-0279-8_7.

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Conference papers on the topic "Prediction of quality"

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Padilla, Dionis A., Glenn V. Magwili, Luis Benjamin Z. Mercado, and Jean Tristan L. Reyes. "Air Quality Prediction using Recurrent Air Quality Predictor with Ensemble Learning." In 2020 IEEE 12th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management (HNICEM). IEEE, 2020. http://dx.doi.org/10.1109/hnicem51456.2020.9400051.

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Shihab, Emad. "Practical Software Quality Prediction." In 2014 IEEE International Conference on Software Maintenance and Evolution (ICSME). IEEE, 2014. http://dx.doi.org/10.1109/icsme.2014.114.

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Sazzad, Z. M. Parvez, Shouta Yamanaka, Yoshikazu Kawayokeita, and Yuukou Horita. "Stereoscopic image quality prediction." In 2009 International Workshop on Quality of Multimedia Experience (QoMEx 2009). IEEE, 2009. http://dx.doi.org/10.1109/qomex.2009.5246956.

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Kodama, S., and I. Kataoka. "Study on Analytical Prediction of Forced Convective CHF in the Wide Range of Quality." In 10th International Conference on Nuclear Engineering. ASMEDC, 2002. http://dx.doi.org/10.1115/icone10-22128.

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For the purpose of predicting CHF for a wide range in quality, we developed the analytical CHF prediction method. Two-phase flow analysis code based on multi-fluid model was developed and, by using typical dryout model and DNB model, CHF predictions for a wide range in quality were carried out, firstly. The dryout model and DNB model gave good predictions for high quality conditions and low quality conditions respectively. The boundary between high quality and low quality seemed to be about 0.1 to 0.2, which corresponds to the annular mist flow transition criterion. Based on this result, secondly, we carried out CHF predictions by using dryout model or DNB model selectively depending on the flow regime and got agreement to some extent with CHF data for a wide range in quality.
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Lincke, Rüdiger, Tobias Gutzmann, and Welf Löwe. "Software Quality Prediction Models Compared." In 2010 10th International Conference on Quality Software (QSIC). IEEE, 2010. http://dx.doi.org/10.1109/qsic.2010.9.

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Guirguis, Shawket, Fatma Zada, and Tawfik Khattab. "Quality Controlled Stock Prediction Model." In 2013 23rd International Conference on Computer Theory and Applications (ICCTA). IEEE, 2013. http://dx.doi.org/10.1109/iccta32607.2013.9529781.

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Nagorny, Pierre, Maurice Pillet, Eric Pairel, Ronan Le Goff, Jerome Loureaux, Marlene Wali, and Patrice Kiener. "Quality prediction in injection molding." In 2017 IEEE International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications (CIVEMSA). IEEE, 2017. http://dx.doi.org/10.1109/civemsa.2017.7995316.

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S, Abhinav, Sahana Srinivasan, Aishwarya Ganesan, Anala M R, and Mamatha T. "Wireless Water Quality Monitoring and Quality Deterioration Prediction System." In 2019 26th International Conference on High Performance Computing, Data and Analytics Workshop (HiPCW). IEEE, 2019. http://dx.doi.org/10.1109/hipcw.2019.00013.

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Wu, Dazhong, Yupeng Wei, and Janis Terpenny. "Surface Roughness Prediction in Additive Manufacturing Using Machine Learning." In ASME 2018 13th International Manufacturing Science and Engineering Conference. American Society of Mechanical Engineers, 2018. http://dx.doi.org/10.1115/msec2018-6501.

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To realize high quality, additively manufactured parts, real-time process monitoring and advanced predictive modeling tools are crucial for accelerating quality assurance and quality control in additive manufacturing. While previous research has demonstrated the effectiveness of physics- and model-based diagnosis and prognosis for additive manufacturing, very little research has been reported on real-time monitoring and prediction of surface roughness in fused deposition modeling (FDM). This paper presents a new data-driven approach to surface roughness prediction in FDM. A real-time monitoring system is developed to monitor the health condition of a 3D printer and FDM processes using multiple sensors. A predictive model is built by random forests (RFs). Experimental results have shown that the predictive model is capable of predicting the surface roughness of a printed part with very high accuracy.
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Shruthi, P. "Wine Quality Prediction Using Data Mining." In 2019 International Conference on Advanced Technologies in Intelligent Control, Environment, Computing & Communication Engineering (ICATIECE). IEEE, 2019. http://dx.doi.org/10.1109/icatiece45860.2019.9063846.

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Reports on the topic "Prediction of quality"

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Wei, Jie. Magnet Quality and Collider Performance Prediction. Office of Scientific and Technical Information (OSTI), November 1996. http://dx.doi.org/10.2172/1119510.

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Murphy, D. D., W. M. Thomas, W. M. Evanco, and W. W. Agresti. Procedures for Applying Ada Quality Prediction Models. Fort Belvoir, VA: Defense Technical Information Center, December 1992. http://dx.doi.org/10.21236/ada264730.

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Agresti, W. W., W. M. Evanco, M. C. Smith, and D. R. Clarson. An Approach to Software Quality Prediction from Ada Designs. Fort Belvoir, VA: Defense Technical Information Center, December 1992. http://dx.doi.org/10.21236/ada264731.

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McDonald, M. J. Quality prediction and mistake proofing: An LDRD final report. Office of Scientific and Technical Information (OSTI), March 1998. http://dx.doi.org/10.2172/650152.

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Di, Jiaqi, Xuanlin Li, Jingjing Yang, Luguang Li, and Xueqing Yu. Critical appraisal of the reporting quality of risk prediction models for idiopathic pulmonary fibrosis. INPLASY - International Platform of Registered Systematic Review and Meta-analysis Protocols, November 2020. http://dx.doi.org/10.37766/inplasy2020.11.0105.

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Vlek, R. J., D. J. M. Willems, and H. Rijgersberg. Requirements for implementation : a quality prediction system for soft fruit based on a Bayesian Belief Network. Wageningen: Wageningen Food and Biobased Research, 2018. http://dx.doi.org/10.18174/563391.

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Thegeya, Aaron, Thomas Mitterling, Arturo Martinez Jr, Joseph Albert Niño Bulan, Ron Lester Durante, and Jayzon Mag-atas. Application of Machine Learning Algorithms on Satellite Imagery for Road Quality Monitoring: An Alternative Approach to Road Quality Surveys. Asian Development Bank, December 2022. http://dx.doi.org/10.22617/wps220587-2.

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This paper examines the feasibility of using satellite imagery and artificial intelligence to develop an efficient and cost-effective way to determine and predict the condition of roads in the Asia and Pacific region. The paper notes that collecting information on road quality is difficult, particularly in harder to reach middle- and low-income areas, and explains why this method offers an alternative. It shows how the study’s preliminary algorithm was created using satellite imagery and existing road roughness data from the Philippines. It assesses the accuracy rate and finds it sufficient for the preliminary identification of poor to bad roads. It notes that additional enhancements are needed to increase its prediction accuracy and make it more robust.
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Vecherin, Sergey, Stephen Ketcham, Aaron Meyer, Kyle Dunn, Jacob Desmond, and Michael Parker. Short-range near-surface seismic ensemble predictions and uncertainty quantification for layered medium. Engineer Research and Development Center (U.S.), September 2022. http://dx.doi.org/10.21079/11681/45300.

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To make a prediction for seismic signal propagation, one needs to specify physical properties and subsurface ground structure of the site. This information is frequently unknown or estimated with significant uncertainty. This paper describes a methodology for probabilistic seismic ensemble prediction for vertically stratified soils and short ranges with no in situ site characterization. Instead of specifying viscoelastic site properties, the methodology operates with probability distribution functions of these properties taking into account analytical and empirical relationships among viscoelastic variables. This yields ensemble realizations of signal arrivals at specified locations where statistical properties of the signals can be estimated. Such ensemble predictions can be useful for preliminary site characterization, for military applications, and risk analysis for remote or inaccessible locations for which no data can be acquired. Comparison with experiments revealed that measured signals are not always within the predicted ranges of variability. Variance-based global sensitivity analysis has shown that the most significant parameters for signal amplitude predictions in the developed stochastic model are the uncertainty in the shear quality factor and the Poisson ratio above the water table depth.
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Johnson, Paul C. Prediction of Groundwater Quality Improvement Down-Gradient of In Situ Permeable Treatment Barriers and Fully Remediated Source Zones. Fort Belvoir, VA: Defense Technical Information Center, December 2008. http://dx.doi.org/10.21236/ada602219.

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Peterson, Warren. PR-663-19600-Z01 Develop Guidance for Calculation of HCDP in Pipelines. Chantilly, Virginia: Pipeline Research Council International, Inc. (PRCI), March 2020. http://dx.doi.org/10.55274/r0011659.

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To maintain the integrity and reliability of natural gas transportation systems, system operators ensure that products in transit remain in the gas phase under foreseeable operating conditions. Compliance with pipeline hydrocarbon dew point (HCDP) specifications are demonstrated though in-situ testing or predictive models based on Equations of State (EOS) calculations. Numerical prediction of HCDP is a product of contributing elements, including gas chromatography, calibration gas quality, thermophysical science and the experimental data that underpins equations of state. Some hydrocarbon mixtures, such as those from non-traditional gas supplies, are more difficult to sample and assess than others. The methods described in this paper and accompanying spreadsheet examples are designed to assist persons in making technically defendable decisions with respect to predictive methods and the operational impacts of liquid dropout. The primary focus of this work is to connect the over-all performance of HCDP prediction to its operational implications. The secondary objective of the work is to provide tools for assessing the potential benefit from using C9+ versus C6+ gas chromatographs.
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