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Статті в журналах з теми "4611 Machine learning":
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Gusev, I. V., D. V. Gavrilov, R. E. Novitsky, T. Yu Kuznetsova, and S. A. Boytsov. "Improvement of cardiovascular risk assessment using machine learning methods." Russian Journal of Cardiology 26, no. 12 (October 25, 2021): 4618. http://dx.doi.org/10.15829/1560-4071-2021-4618.
Анотація:
The increase in the prevalence of cardiovascular diseases (CVDs) specifies the importance of their prediction, the need for accurate risk stratification, preventive and treatment interventions. Large medical databases and technologies for their processing in the form of machine learning algorithms that have appeared in recent years have the potential to improve predictive accuracy and personalize treatment approaches to CVDs. The review examines the application of machine learning in predicting and identifying cardiovascular events. The role of this technology both in the calculation of total cardiovascular risk and in the prediction of individual diseases and events is discussed. We compared the predictive accuracy of current risk scores and various machine learning algorithms. The conditions for using machine learning and developing personalized tactics for managing patients with CVDs are analyzed.
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Hayles, N. Katherine. "Deeper into the machine: Learning to speak digital." Computers and Composition 19, no. 4 (December 2002): 371–86. http://dx.doi.org/10.1016/s8755-4615(02)00140-8.
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Thongprayoon, Charat, Janina Paula T. Sy-Go, Voravech Nissaisorakarn, Carissa Y. Dumancas, Mira T. Keddis, Andrea G. Kattah, Pattharawin Pattharanitima, et al. "Machine Learning Consensus Clustering Approach for Hospitalized Patients with Dysmagnesemia." Diagnostics 11, no. 11 (November 15, 2021): 2119. http://dx.doi.org/10.3390/diagnostics11112119.
Анотація:
Background: The objectives of this study were to classify patients with serum magnesium derangement on hospital admission into clusters using unsupervised machine learning approach and to evaluate the mortality risks among these distinct clusters. Methods: Consensus cluster analysis was performed based on demographic information, principal diagnoses, comorbidities, and laboratory data in hypomagnesemia (serum magnesium ≤ 1.6 mg/dL) and hypermagnesemia cohorts (serum magnesium ≥ 2.4 mg/dL). Each cluster’s key features were determined using the standardized mean difference. The associations of the clusters with hospital mortality and one-year mortality were assessed. Results: In hypomagnesemia cohort (n = 13,320), consensus cluster analysis identified three clusters. Cluster 1 patients had the highest comorbidity burden and lowest serum magnesium. Cluster 2 patients had the youngest age, lowest comorbidity burden, and highest kidney function. Cluster 3 patients had the oldest age and lowest kidney function. Cluster 1 and cluster 3 were associated with higher hospital and one-year mortality compared to cluster 2. In hypermagnesemia cohort (n = 4671), the analysis identified two clusters. Compared to cluster 1, the key features of cluster 2 included older age, higher comorbidity burden, more hospital admissions primarily due to kidney disease, more acute kidney injury, and lower kidney function. Compared to cluster 1, cluster 2 was associated with higher hospital mortality and one-year mortality. Conclusion: Our cluster analysis identified clinically distinct phenotypes with differing mortality risks in hospitalized patients with dysmagnesemia. Future studies are required to assess the application of this ML consensus clustering approach to care for hospitalized patients with dysmagnesemia.
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Wilkes, Edmund H., Gill Rumsby, and Gary M. Woodward. "Using Machine Learning to Aid the Interpretation of Urine Steroid Profiles." Clinical Chemistry 64, no. 11 (November 1, 2018): 1586–95. http://dx.doi.org/10.1373/clinchem.2018.292201.
Анотація:
Abstract BACKGROUND Urine steroid profiles are used in clinical practice for the diagnosis and monitoring of disorders of steroidogenesis and adrenal pathologies. Machine learning (ML) algorithms are powerful computational tools used extensively for the recognition of patterns in large data sets. Here, we investigated the utility of various ML algorithms for the automated biochemical interpretation of urine steroid profiles to support current clinical practices. METHODS Data from 4619 urine steroid profiles processed between June 2012 and October 2016 were retrospectively collected. Of these, 1314 profiles were used to train and test various ML classifiers' abilities to differentiate between “No significant abnormality” and “?Abnormal” profiles. Further classifiers were trained and tested for their ability to predict the specific biochemical interpretation of the profiles. RESULTS The best performing binary classifier could predict the interpretation of No significant abnormality and ?Abnormal profiles with a mean area under the ROC curve of 0.955 (95% CI, 0.949–0.961). In addition, the best performing multiclass classifier could predict the individual abnormal profile interpretation with a mean balanced accuracy of 0.873 (0.865–0.880). CONCLUSIONS Here we have described the application of ML algorithms to the automated interpretation of urine steroid profiles. This provides a proof-of-concept application of ML algorithms to complex clinical laboratory data that has the potential to improve laboratory efficiency in a setting of limited staff resources.
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Bernert, Rebecca A., Amanda M. Hilberg, Ruth Melia, Jane Paik Kim, Nigam H. Shah, and Freddy Abnousi. "Artificial Intelligence and Suicide Prevention: A Systematic Review of Machine Learning Investigations." International Journal of Environmental Research and Public Health 17, no. 16 (August 15, 2020): 5929. http://dx.doi.org/10.3390/ijerph17165929.
Анотація:
Suicide is a leading cause of death that defies prediction and challenges prevention efforts worldwide. Artificial intelligence (AI) and machine learning (ML) have emerged as a means of investigating large datasets to enhance risk detection. A systematic review of ML investigations evaluating suicidal behaviors was conducted using PubMed/MEDLINE, PsychInfo, Web-of-Science, and EMBASE, employing search strings and MeSH terms relevant to suicide and AI. Databases were supplemented by hand-search techniques and Google Scholar. Inclusion criteria: (1) journal article, available in English, (2) original investigation, (3) employment of AI/ML, (4) evaluation of a suicide risk outcome. N = 594 records were identified based on abstract search, and 25 hand-searched reports. N = 461 reports remained after duplicates were removed, n = 316 were excluded after abstract screening. Of n = 149 full-text articles assessed for eligibility, n = 87 were included for quantitative synthesis, grouped according to suicide behavior outcome. Reports varied widely in methodology and outcomes. Results suggest high levels of risk classification accuracy (>90%) and Area Under the Curve (AUC) in the prediction of suicidal behaviors. We report key findings and central limitations in the use of AI/ML frameworks to guide additional research, which hold the potential to impact suicide on broad scale.
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Potty, Anish GR, Ajish S. R. Potty, Rithesh Punyamurthula, Sreeram Penna, Chris Benavides, Prithviraj Chavan, and R. Justin Mistovich. "MACHINE-LEARNING IDENTIFIES BEST MEASURES TO PREDICT ACL RECONSTRUCTION OUTCOME." Orthopaedic Journal of Sports Medicine 7, no. 3_suppl (March 1, 2019): 2325967119S0014. http://dx.doi.org/10.1177/2325967119s00144.
Анотація:
Introduction: Knee injury and Osteoarthritis Outcome Score (KOOS) is a widely used patient-reported outcome measurement to track recovery after ACL surgery. This study focuses on the function of daily living subscale (KOOS ADL), which is calculated based on 17 questions. By employing machine learning to predict KOOS ADL scores, we sought to better understand the relative importance of the survey questions and thereby identify its most critical components as well as questions that do not adequately predict outcomes. Methods: Pre- and post-operative patient reported KOOS ADL survey responses and outcomes scores following ACL surgery were obtained from the Surgical Outcome System data registry(SOS), an international patient-reported outcomes database sponsored and maintained by Arthrex. Patients with missing KOOS ADL survey responses were excluded from the study. Machine learning (ML) algorithms such as Random Forest and Gradient Boosting were used to identify the most critical survey questions that predict KOOS ADL scores with high accuracy. These decision tree-based algorithms predict patient outcomes using several decision rules and thereby determining the relative value of individual questions at predicting patient deficits (e.g., if patients have “Severe” difficulty in ascending stairs, they are more likely to have globally worse scores than those with difficulty with other tasks). Results: 4996 patients were initially identified. Based on compliance with the survey, 2407, 2407, 1817 and 1193 patients records for pre-surgery, 3 month, 6 month and 1 year post-surgery responses respectively underwent further analysis. The dataset consisted of 53.9% males and 46.1% females. Mean age was 29 (range 11 to 70 years). Results from the ML models indicated that by 6 key questions, over 80% of the variance in KOOS ADL scores could be explained instead of standard 17 survey questions (Table 1). Interestingly, the analysis provided similar accuracy at both 6 months and 1 year. Discussion and Conclusion: Most patients have similar functional deficits that can be captured using a simplified version of the KOOS ADL survey. The abbreviated survey would result in a better patient reporting experience while still obtaining quality data. Additional work on predicting post-surgery scores using ML from pre-surgery responses and other patient information would provide valuable insights; however, predicting outcome scores with high accuracy remains challenging. We advocate for novel methods to identify and measure meaningful data to assist with understanding patient outcomes and thereby proving the true value of orthopaedic interventions on functional status. [Table: see text]
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Agarwal, Manan, Khushboo K. Rao, Kaushar Vaidya, and Souradeep Bhattacharya. "ML-MOC: Machine Learning (kNN and GMM) based Membership determination for Open Clusters." Monthly Notices of the Royal Astronomical Society 502, no. 2 (February 11, 2021): 2582–99. http://dx.doi.org/10.1093/mnras/stab118.
Анотація:
ABSTRACT The existing open-cluster membership determination algorithms are either prior dependent on some known parameters of clusters or are not automatable to large samples of clusters. In this paper, we present ml-moc, a new machine-learning-based approach to identify likely members of open clusters using the Gaia DR2 data and no a priori information about cluster parameters. We use the k-nearest neighbour (kNN) algorithm and the Gaussian mixture model (GMM) on high-precision proper motions and parallax measurements from the Gaia DR2 data to determine the membership probabilities of individual sources down to G ∼ 20 mag. To validate the developed method, we apply it to 15 open clusters: M67, NGC 2099, NGC 2141, NGC 2243, NGC 2539, NGC 6253, NGC 6405, NGC 6791, NGC 7044, NGC 7142, NGC 752, Blanco 1, Berkeley 18, IC 4651, and Hyades. These clusters differ in terms of their ages, distances, metallicities, and extinctions and cover a wide parameter space in proper motions and parallaxes with respect to the field population. The extracted members produce clean colour–magnitude diagrams and our astrometric parameters of the clusters are in good agreement with the values derived in previous work. The estimated degree of contamination in the extracted members ranges between 2 ${{\ \rm per\ cent}}$ and 12 ${{\ \rm per\ cent}}$. The results show that ml-moc is a reliable approach to segregate open-cluster members from field stars.
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Shahbazian, Reza, and Irina Trubitsyna. "DEGAIN: Generative-Adversarial-Network-Based Missing Data Imputation." Information 13, no. 12 (December 12, 2022): 575. http://dx.doi.org/10.3390/info13120575.
Анотація:
Insights and analysis are only as good as the available data. Data cleaning is one of the most important steps to create quality data decision making. Machine learning (ML) helps deal with data quickly, and to create error-free or limited-error datasets. One of the quality standards for cleaning the data includes handling the missing data, also known as data imputation. This research focuses on the use of machine learning methods to deal with missing data. In particular, we propose a generative adversarial network (GAN) based model called DEGAIN to estimate the missing values in the dataset. We evaluate the performance of the presented method and compare the results with some of the existing methods on publicly available Letter Recognition and SPAM datasets. The Letter dataset consists of 20,000 samples and 16 input features and the SPAM dataset consists of 4601 samples and 57 input features. The results show that the proposed DEGAIN outperforms the existing ones in terms of root mean square error and Frechet inception distance metrics.
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Crowson, Matthew G., Dana Moukheiber, Aldo Robles Arévalo, Barbara D. Lam, Sreekar Mantena, Aakanksha Rana, Deborah Goss, David W. Bates, and Leo Anthony Celi. "A systematic review of federated learning applications for biomedical data." PLOS Digital Health 1, no. 5 (May 19, 2022): e0000033. http://dx.doi.org/10.1371/journal.pdig.0000033.
Анотація:
Objectives Federated learning (FL) allows multiple institutions to collaboratively develop a machine learning algorithm without sharing their data. Organizations instead share model parameters only, allowing them to benefit from a model built with a larger dataset while maintaining the privacy of their own data. We conducted a systematic review to evaluate the current state of FL in healthcare and discuss the limitations and promise of this technology. Methods We conducted a literature search using PRISMA guidelines. At least two reviewers assessed each study for eligibility and extracted a predetermined set of data. The quality of each study was determined using the TRIPOD guideline and PROBAST tool. Results 13 studies were included in the full systematic review. Most were in the field of oncology (6 of 13; 46.1%), followed by radiology (5 of 13; 38.5%). The majority evaluated imaging results, performed a binary classification prediction task via offline learning (n = 12; 92.3%), and used a centralized topology, aggregation server workflow (n = 10; 76.9%). Most studies were compliant with the major reporting requirements of the TRIPOD guidelines. In all, 6 of 13 (46.2%) of studies were judged at high risk of bias using the PROBAST tool and only 5 studies used publicly available data. Conclusion Federated learning is a growing field in machine learning with many promising uses in healthcare. Few studies have been published to date. Our evaluation found that investigators can do more to address the risk of bias and increase transparency by adding steps for data homogeneity or sharing required metadata and code.
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Mehravaran, Shiva, Iman Dehzangi, and Md Mahmudur Rahman. "Interocular Symmetry Analysis of Corneal Elevation Using the Fellow Eye as the Reference Surface and Machine Learning." Healthcare 9, no. 12 (December 16, 2021): 1738. http://dx.doi.org/10.3390/healthcare9121738.
Анотація:
Unilateral corneal indices and topography maps are routinely used in practice, however, although there is consensus that fellow-eye asymmetry can be clinically significant, symmetry studies are limited to local curvature and single-point thickness or elevation measures. To improve our current practices, there is a need to devise algorithms for generating symmetry colormaps, study and categorize their patterns, and develop reference ranges for new global discriminative indices for identifying abnormal corneas. In this work, we test the feasibility of using the fellow eye as the reference surface for studying elevation symmetry throughout the entire corneal surface using 9230 raw Pentacam files from a population-based cohort of 4613 middle-aged adults. The 140 × 140 matrix of anterior elevation data in these files were handled with Python to subtract matrices, create color-coded maps, and engineer features for machine learning. The most common pattern was a monochrome circle (“flat”) denoting excellent mirror symmetry. Other discernible patterns were named “tilt”, “cone”, and “four-leaf”. Clustering was done with different combinations of features and various algorithms using Waikato Environment for Knowledge Analysis (WEKA). Our proposed approach can identify cases that may appear normal in each eye individually but need further testing. This work will be enhanced by including data of posterior elevation, thickness, and common diagnostic indices.
Дисертації з теми "4611 Machine learning":
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Bridge, Christopher. "Computer-aided analysis of fetal cardiac ultrasound videos." Thesis, University of Oxford, 2017. https://ora.ox.ac.uk/objects/uuid:c9cad151-6f08-461a-acd6-9fd63477b91a.
Анотація:
This thesis addresses the task of developing automatic algorithms for analysing the two-dimensional ultrasound video footage obtained from fetal heart screening scans. These scans are typically performed in the second trimester of pregnancy to check for congenital heart anomalies and require significant training and anatomical knowledge to perform. The aim is to develop a tool that runs at high frame rates with no user initialisation and infers the visibility, position, orientation, view classification, and cardiac phase of the heart, and additionally the locations of cardiac structures of interest (such as valves and vessels) in a manner that is robust to the various sources of variation that occur in real-world ultrasound scanning. This is the first work to attempt such a detailed automated analysis of these videos. The problem is posed as a Bayesian filtering problem, which provides a principled framework for aggregating uncertain measurements across a number of frames whilst exploiting the constraints imposed by anatomical feasibility. The resulting inference problem is solved approximately with a particle filter, whose state space is partitioned to reduce the problems associated with filtering in high-dimensional spaces. Rotation-invariant features are captured from the videos in an efficient way in order to tackle the problem of unknown orientation. These are used within random forest learning models, including a novel formulation to predict circular-valued variables. The algorithm is validated on an annotated clinical dataset, and the results are compared to estimates of inter- and intra-observer variation, which are significant in both cases due to the inherent ambiguity in the imagery. The results suggest that the algorithm's output approaches these benchmarks in several respects, and fall slightly behind in others. The work presented here is an important first step towards developing automated clinical tools for the detection of congenital heart disease.
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Li, Zhengrong. "Aerial image analysis using spiking neural networks with application to power line corridor monitoring." Thesis, Queensland University of Technology, 2011. https://eprints.qut.edu.au/46161/1/Zhengrong_Li_Thesis.pdf.
Анотація:
Trees, shrubs and other vegetation are of continued importance to the environment and our daily life. They provide shade around our roads and houses, offer a habitat for birds and wildlife, and absorb air pollutants. However, vegetation touching power lines is a risk to public safety and the environment, and one of the main causes of power supply problems. Vegetation management, which includes tree trimming and vegetation control, is a significant cost component of the maintenance of electrical infrastructure. For example, Ergon Energy, the Australia’s largest geographic footprint energy distributor, currently spends over $80 million a year inspecting and managing vegetation that encroach on power line assets. Currently, most vegetation management programs for distribution systems are calendar-based ground patrol. However, calendar-based inspection by linesman is labour-intensive, time consuming and expensive. It also results in some zones being trimmed more frequently than needed and others not cut often enough. Moreover, it’s seldom practicable to measure all the plants around power line corridors by field methods. Remote sensing data captured from airborne sensors has great potential in assisting vegetation management in power line corridors. This thesis presented a comprehensive study on using spiking neural networks in a specific image analysis application: power line corridor monitoring. Theoretically, the thesis focuses on a biologically inspired spiking cortical model: pulse coupled neural network (PCNN). The original PCNN model was simplified in order to better analyze the pulse dynamics and control the performance. Some new and effective algorithms were developed based on the proposed spiking cortical model for object detection, image segmentation and invariant feature extraction. The developed algorithms were evaluated in a number of experiments using real image data collected from our flight trails. The experimental results demonstrated the effectiveness and advantages of spiking neural networks in image processing tasks. Operationally, the knowledge gained from this research project offers a good reference to our industry partner (i.e. Ergon Energy) and other energy utilities who wants to improve their vegetation management activities. The novel approaches described in this thesis showed the potential of using the cutting edge sensor technologies and intelligent computing techniques in improve power line corridor monitoring. The lessons learnt from this project are also expected to increase the confidence of energy companies to move from traditional vegetation management strategy to a more automated, accurate and cost-effective solution using aerial remote sensing techniques.
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You, Mingshan. "An Adaptive Machine Learning Framework for Access Control Decision Making." Thesis, 2022. https://vuir.vu.edu.au/43688/.
Анотація:
With the increasing popularity of information systems and digital devices, data
leakage has become a serious threat on a global scale. Access control is recognised
as the first defence to guarantee that only authorised users can access sensitive
data and thus prevent data leakage. However, currently widely used attributebased
access control (ABAC) is costly to configure and manage for large-scale
information systems. Furthermore, misconfiguration and policy explosion are
two significant challenges for ABAC strategies.
In recent years, machine learning technologies have been more applied in access
control decision-making to improve the automation and performance of access
control decisions. Nevertheless, existing studies usually fail to consider the
dynamic class imbalance problem in access control and thus achieve poor performance
on minority classes. In addition, the concept drift problem caused by the
evolving user and resource attributes, user behaviours, and access environments
is also challenging to tackle.
This thesis focuses on leveraging machine learning algorithms to make more
accurate and adaptive access control decisions. Specifically, a minority class
boosted framework is proposed to address the possible concept drifts caused by
evolving users’ behaviours and system environments. Its basic idea is to adopt an
incremental batch learning strategy to update the classifier continuously. Within
this framework, a boosting window (BW) algorithm is specially designed to boost
the performance of the minority class since the minority class is fatal for data
protection in access control problems. Furthermore, to improve the overall performance
of access control, this study adopts a knowledge graph to mine the
interlinked relationships between users and resources. A knowledge graph construction algorithm is designed to build a domain-specific knowledge graph. The
constructed knowledge graph is also adopted into an online learning framework
for access control decision-making.
The proposed frameworks and algorithms are evaluated and verified through
two open-source real-world Amazon datasets. Experimental results show that the
proposed BW algorithm effectively boosts the performance of the minority class.
Furthermore, using topological features extracted from our constructed access
control knowledge graph can improve access control performance in both offline
and online learning scenarios.
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Kou, Jiaying. "Analysing Housing Price in Australia with Data Science Methods." Thesis, 2022. https://vuir.vu.edu.au/43940/.
Анотація:
Housing market price prediction is a major and important challenge in economics. Since the 2008 global financial crisis, researchers, economists, and politicians around the world have increasingly drawn attention to the need of better understanding housing market behaviour, since the failure to predict housing market crisis ahead of time had led to catastrophic global damage. On the other hand, around the same time, we have seen the revolution of information technology and artificial intelligence in the last two decades. The advent of powerful cloud and high performance computing systems, big data, and advanced machine learning algorithms have demonstrated new applications and advantages in cutting-edge research and technology areas such as pattern recognition, bioinformatics, natural language processing, and product recommendation systems.
Can we make the leap of improving our understanding of housing market behaviour by leveraging these recent advances in artificial intelligence and newly available big data? This is the main theme of the thesis. There is strong motivation to explore the application of data science methods, including new large datasets and advanced machine learning algorithm, to accelerate our understanding of housing market problems for the benefit of the common good.
In order to understand housing market behaviour, we divide the problem into two major steps: first, to improve understanding of housing appraisal (at microlevel), which is to predict housing price at the point level given a fixed timeframe; second, to improve understanding of the trend prediction (at macro level), which is to predict the housing price trend for a specific place during a time interval.
For these two major steps, we improve upon traditional economic modelling by:
• Adding new, non-traditional variables/features to our models, such as location-based Point of Interests, regional economic clusters, qualitative index, searching index, and newspaper articles
• Applying machine learning algorithms for data analysis, such as non-linear algorithms, K-Nearest-Neighbour, Support Vector Machine, Gradient Boost, and sentiment analysis
Specifically, in Chapter 3, we focus on the development of Location-Based Social
Network (LBSN) for our micro-level housing appraisal modelling. A good location
goes beyond the direct benefits from its neighbourhood. By leveraging housing
data, neighbourhood data, regional economic cluster data and demographic data,
we build a housing appraisal model, named HNED. Unlike most previous statistical and machine learning based housing appraisal research, which limit their
investigations to neighbourhoods within 1km radius of the house, we expand the
investigation beyond the local neighbourhood and to the whole metropolitan area,
by introducing the connection to significant influential economic nodes, which we
term Regional Economic Clusters. Specifically, we introduce regional economic
clusters within the metropolitan range into the housing appraisal model, such as
the connection to CBD, workplace, or the convenience and quality of big shopping
malls and university clusters. When used with the gradient boosting algorithm
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XGBoost to perform housing price appraisal, HNED reached 0.88 in R . In addition, we found that the feature vector from Regional Economic Clusters alone reached 0.63 in R2, significantly higher than all traditional features. Chapter 3 focuses on the exploration and validation of HNED modelling.
In Chapter 4 and Chapter 5, we focus on macro-level housing price trend prediction. We fill the gap between the traditional macro-level housing market modelling and new developments of the concept of irrationality in microeconomic theories, by collecting and analysing economic behavioural data, such as real estate opinions in local newspaper articles, and people’s web searching behaviour as captured by Google Trend Index. In Chapter 4, we discuss the usage of micro-level behavioural data for understanding macro-level housing market behaviour. We use sentiment analysis to examine local newspaper articles discussing real estate at a suburb level in inner-west Sydney, Australia. We then calculate the media sentiment index by using two different methods, and compare them with each other and the housing price index. The use of media sentiment index can serve as a finer-grained guiding tool to facilitate decision-making for home buyers, investors, researchers and policy makers. In Chapter 5, we discuss how new developments of behavioural economic theory indicate that the information from decision-making at the micro-level will bring a new solution to the age-old problem of economic forecasting. It provides the theoretical link between irrationality and big data methods. Specifically, Google Trend Index is included as a new variable in a time series auto-regression model to forecast housing market cycles.
To summarise the contributions of the thesis, we conclude that this is a successful early attempt to study housing price problems using data science methods, by leveraging newly available data sets and applying novel machine learning methods. Specifically, location-based social data improves the housing appraisal modelling. Human behaviour for housing market is analysed by introducing local newspaper articles and Google Trend Index into the modelling and analysis.
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Khomh, Foutse. "Patterns and quality of object-oriented software systems." Thèse, 2010. http://hdl.handle.net/1866/4601.
Анотація:
Lors de ces dix dernières années, le coût de la maintenance des systèmes orientés objets s'est accru jusqu' à compter pour plus de 70% du coût total des systèmes. Cette situation est due à plusieurs facteurs, parmi lesquels les plus importants sont: l'imprécision des spécifications des utilisateurs, l'environnement d'exécution changeant rapidement et la mauvaise qualité interne des systèmes. Parmi tous ces facteurs, le seul sur lequel nous ayons un réel contrôle est la qualité interne des systèmes. De nombreux modèles de qualité ont été proposés dans la littérature pour contribuer à contrôler la qualité. Cependant, la plupart de ces modèles utilisent des métriques de classes (nombre de méthodes d'une classe par exemple) ou des métriques de relations entre classes (couplage entre deux classes par exemple) pour mesurer les attributs internes des systèmes. Pourtant, la qualité des systèmes par objets ne dépend pas uniquement de la structure de leurs classes et que mesurent les métriques, mais aussi de la façon dont celles-ci sont organisées, c'est-à-dire de leur conception, qui se manifeste généralement à travers les patrons de conception et les anti-patrons.
Dans cette thèse nous proposons la méthode DEQUALITE, qui permet de construire systématiquement des modèles de qualité prenant en compte non seulement les attributs internes des systèmes (grâce aux métriques), mais aussi leur conception (grâce aux patrons de conception et anti-patrons). Cette méthode utilise une approche par apprentissage basée sur les réseaux bayésiens et s'appuie sur les résultats d'une série d'expériences portant sur l'évaluation de l'impact des patrons de conception et des anti-patrons sur la qualité des systèmes. Ces expériences réalisées sur 9 grands systèmes libres orientés objet nous permettent de formuler les conclusions suivantes:
• Contre l'intuition, les patrons de conception n'améliorent pas toujours la qualité des systèmes; les implantations très couplées de patrons de conception par exemple affectent la structure des classes et ont un impact négatif sur leur propension aux changements et aux fautes.
• Les classes participantes dans des anti-atrons sont beaucoup plus susceptibles de changer et d'être impliquées dans des corrections de fautes que les autres classes d'un système.
• Un pourcentage non négligeable de classes sont impliquées simultanément dans des patrons de conception et dans des anti-patrons. Les patrons de conception ont un effet positif en ce sens qu'ils atténuent les anti-patrons.
Nous appliquons et validons notre méthode sur trois systèmes libres orientés objet afin de démontrer l'apport de la conception des systèmes dans l'évaluation de la qualité.Maintenance costs during the past decades have reached more than 70% of the overall costs of object-oriented systems, because of many factors, such as changing software environments, changing users' requirements, and the overall quality of systems. One factor on which we have a control is the quality of systems. Many object-oriented software quality models have been introduced in the literature to help assess and control quality. However, these models usually use metrics of classes (such as number of methods) or of relationships between classes (for example coupling) to measure internal attributes of systems. Yet, the quality of object-oriented systems does not depend on classes' metrics solely: it also depends on the organisation of classes, i.e. the system design that concretely manifests itself through design styles, such as design patterns and antipatterns. In this dissertation, we propose the method DEQUALITE to systematically build quality models that take into account the internal attributes of the systems (through metrics) but also their design (through design patterns and antipatterns). This method uses a machine learning approach based on Bayesian Belief Networks and builds on the results of a series of experiments aimed at evaluating the impact of design patterns and antipatterns on the quality of systems. These experiments, performed on 9 large object-oriented open source systems enable us to draw the following conclusions: • Counter-intuitively, design patterns do not always improve the quality of systems; tangled implementations of design patterns for example significantly affect the structure of classes and negatively impact their change- and fault-proneness. • Classes participating in antipatterns are significantly more likely to be subject to changes and to be involved in fault-fixing changes than other classes. • A non negligible percentage of classes participate in co-occurrences of antipatterns and design patterns in systems. On these classes, design patterns have a positive effect in mitigating antipatterns. We apply and validate our method on three open-source object-oriented systems to demonstrate the contribution of the design of system in quality assessment.
Книги з теми "4611 Machine learning":
1
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.
2
Utgoff, Paul E. Machine Learning of Inductive Bias. Boston, MA: Springer US, 1986. http://dx.doi.org/10.1007/978-1-4613-2283-2.
3
Grefenstette, John J., ed. Genetic Algorithms for Machine Learning. Boston, MA: Springer US, 1994. http://dx.doi.org/10.1007/978-1-4615-2740-4.
4
Fielding, Alan H., ed. Machine Learning Methods for Ecological Applications. Boston, MA: Springer US, 1999. http://dx.doi.org/10.1007/978-1-4615-5289-5.
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Berendt, Bettina, Björn Bringmann, Élisa Fromont, Gemma Garriga, Pauli Miettinen, Nikolaj Tatti, and Volker Tresp, eds. Machine Learning and Knowledge Discovery in Databases. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-46131-1.
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Частини книг з теми "4611 Machine learning":
1
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Yao, Xin, and Yong Liu. "Machine Learning." In Search Methodologies, 477–517. Boston, MA: Springer US, 2013. http://dx.doi.org/10.1007/978-1-4614-6940-7_17.
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Galakatos, Alex, Andrew Crotty, and Tim Kraska. "Distributed Machine Learning." In Encyclopedia of Database Systems, 1196–201. New York, NY: Springer New York, 2018. http://dx.doi.org/10.1007/978-1-4614-8265-9_80647.
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Muggleton, Stephen, and Flaviu Marginean. "Logic-Based Machine Learning." In Logic-Based Artificial Intelligence, 315–30. Boston, MA: Springer US, 2000. http://dx.doi.org/10.1007/978-1-4615-1567-8_14.
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Saitta, Lorenza, and Jean-Daniel Zucker. "Abstraction in Machine Learning." In Abstraction in Artificial Intelligence and Complex Systems, 273–327. New York, NY: Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4614-7052-6_9.
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Arbib, Michael A. "Learning Networks." In Brains, Machines, and Mathematics, 91–120. New York, NY: Springer US, 1987. http://dx.doi.org/10.1007/978-1-4612-4782-1_5.
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Stevens-Wood, Barry. "Real learning." In Machine Learning Methods for Ecological Applications, 225–46. Boston, MA: Springer US, 1999. http://dx.doi.org/10.1007/978-1-4615-5289-5_9.
Тези доповідей конференцій з теми "4611 Machine learning":
1
Vu, Thanh, Dat Quoc Nguyen, and Anthony Nguyen. "A Label Attention Model for ICD Coding from Clinical Text." In Twenty-Ninth International Joint Conference on Artificial Intelligence and Seventeenth Pacific Rim International Conference on Artificial Intelligence {IJCAI-PRICAI-20}. California: International Joint Conferences on Artificial Intelligence Organization, 2020. http://dx.doi.org/10.24963/ijcai.2020/461.
Анотація:
ICD coding is a process of assigning the International Classification of Disease diagnosis codes to clinical/medical notes documented by health professionals (e.g. clinicians). This process requires significant human resources, and thus is costly and prone to error. To handle the problem, machine learning has been utilized for automatic ICD coding. Previous state-of-the-art models were based on convolutional neural networks, using a single/several fixed window sizes. However, the lengths and interdependence between text fragments related to ICD codes in clinical text vary significantly, leading to the difficulty of deciding what the best window sizes are. In this paper, we propose a new label attention model for automatic ICD coding, which can handle both the various lengths and the interdependence of the ICD code related text fragments. Furthermore, as the majority of ICD codes are not frequently used, leading to the extremely imbalanced data issue, we additionally propose a hierarchical joint learning mechanism extending our label attention model to handle the issue, using the hierarchical relationships among the codes. Our label attention model achieves new state-of-the-art results on three benchmark MIMIC datasets, and the joint learning mechanism helps improve the performances for infrequent codes.
Звіти організацій з теми "4611 Machine learning":
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Herling, Darrell. Machine Learning for Automated Weld Quality Monitoring and Control - CRADA 461. Office of Scientific and Technical Information (OSTI), February 2021. http://dx.doi.org/10.2172/1827788.