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article

Einsatz von Machine-Learning-Verfahren in amtlichen Unternehmensstatistiken

Florian Dumpert, Martin Beck

AStA Wirtschafts- und Sozialstatistisches Archiv > 2017 > 11 > 2 > 83-106

Aufgabe der amtlichen Unternehmensstatistiken ist die Bereitstellung von Informationen über Struktur und Entwicklung der Wirtschaft, die sie durch Erhebungen, die Nutzung von Verwaltungsdaten, den Zukauf kommerzieller Daten und die Verknüpfung von Mikrodaten gewinnt. In jüngster Zeit wurde darüber hinaus auch der Einsatz von Machine-Learning-Verfahren in amtlichen Unternehmensstatistiken experimentell...

article

Application of Support Vector Machine in the analysis of the technical state of development in the LGOM mining area

J. Rusek

Eksploatacja i Niezawodność > 2017 > Vol. 19, no. 1 > 54--61

The paper presents the results of the analysis of technical wear of buildings located within impact of mining plant in the Legnica - Głogów Copper District ( LGOM ). The study used method related to neural networks, support vector (Support Vector Machine) in regression approach ε-SVR (Support Vector Regression). The aim of the study was to assess the impact of variables describing the structural protection...

article

Parallel selective sampling method for imbalanced and large data classification

Annarita D’Addabbo, Rosalia Maglietta, A. D'Addabbo, R. Maglietta

Pattern Recognition Letters > 2015 > 62 > Complete > 61-67

Several applications aim to identify rare events from very large data sets. Classification algorithms may present great limitations on large data sets and show a performance degradation due to class imbalance. Many solutions have been presented in literature to deal with the problem of huge amount of data or imbalancing separately. In this paper we assessed the performances of a novel method, Parallel...

article

Fast data selection for SVM training using ensemble margin

Li Guo, Samia Boukir, L. Guo, S. Boukir

Pattern Recognition Letters > 2015 > 51 > Complete > 112-119

Support Vector Machine (SVM) is a powerful classification method. However, it suffers a major drawback: the high memory and time complexity of the training which constrains the application of SVM to large size classification tasks. To accelerate the SVM training, a new ensemble margin-based data selection approach is proposed. It relies on a simple and efficient heuristic to provide support vector...

article

NICeSim: An open-source simulator based on machine learning techniques to support medical research on prenatal and perinatal care decision making

Fabio Ribeiro Cerqueira, Tiago Geraldo Ferreira, Alcione de Paiva Oliveira, Douglas Adriano Augusto, more

Artificial Intelligence In Medicine > 2014 > 62 > 3 > 193-201

Objective: This paper describes NICeSim, an open-source simulator that uses machine learning (ML) techniques to aid health professionals to better understand the treatment and prognosis of premature newborns. Methods: The application was developed and tested using data collected in a Brazilian hospital. The available data were used to feed an ML pipeline that was designed to create a simulator capable...

article

Facial expression recognition based on two-stage features extraction

Chih-Chin Lai, Chung-Hung Ko, C.C. Lai, C.H. Ko

Optik - International Journal for Light and Electron Optics > 2014 > 125 > 22 > 6678-6680

Facial expression recognition plays an important role in a variety of real-world applications such as human–computer interaction, robot control, smart meeting, and visual surveillance. One critical step for facial expression recognition is to accurately extract emotional features. In this article, a facial expression recognition approach based on two-stage local facial textures extraction is proposed...

article

A real time clustering and SVM based price-volatility prediction for optimal trading strategy

Subhabrata Choudhury, Subhajyoti Ghosh, Arnab Bhattacharya, Kiran Jude Fernandes, more

Neurocomputing > 2014 > 131 > Complete > 419-426

Financial return on investments and movement of market indicators are fraught with uncertainties and a highly volatile environment that exists in the global market. Equity markets are heavily affected by market unpredictability and maintaining a healthy diversified portfolio with minimum risk is undoubtedly crucial for any investment made in such assets. Effective price and volatility prediction can...

article

Consumer Purchasing Behavior Extraction Using Statistical Learning Theory

Yi Zuo, A.B.M. Shawkat Ali, Katsutoshi Yada, Y. Zuo, more

Procedia Computer Science > 2014 > 35 > Complete > 1464-1473

Consumers classification is one of the most important task in the retail sector. RFID (Radio Frequency IDentification) - A wireless non-contact technology is made easier to classify the consumers’ in-store behavior, recently. This paper presents an extraction of consumer purchasing behavior using statistical learning theory SVM (Support Vector Machine). In this research, we present our recent investigation...

article

Support Vector Machine based modeling of an industrial natural gas sweetening plant

Hooman Adib, Fatemeh Sharifi, Nasir Mehranbod, Nooshin Moradi Kazerooni, more

Journal of Natural Gas Science and Engineering > 2013 > 14 > Complete > 121-131

In this study Support Vector Machine (SVM) is employed to develop a model to estimate process output variables of an industrial natural gas sweetening plant. The developed model is evaluated by process operating data of Hashemi Nejad natural gas refinery in Khorasan/Iran. A set of 13 input/output plant data each consisting of 145 data has been used to train, optimize, and test the model. Model development...

article

Optimal design of an in-situ bioremediation system using support vector machine and particle swarm optimization

Sudheer ch, Deepak Kumar, Ram Kailash Prasad, Shashi Mathur, more

Journal of Contaminant Hydrology > 2013 > 151 > Complete > 105-116

A methodology based on support vector machine and particle swarm optimization techniques (SVM–PSO) was used in this study to determine an optimal pumping rate and well location to achieve an optimal cost of an in-situ bioremediation system. In the first stage of the two stage methodology suggested for optimal in-situ bioremediation design, the optimal number of wells and their locations was determined...

chapter

Robust recognition of high-frequency CW signal corrupted by impulsive noise

Guojun Li, Jinzhao Lin, Xiaona Zhou

2012 9th International Conference on Fuzzy Systems and Knowledge Discovery > 1929 - 1933

2012 9th International Conference on Fuzzy Systems and Knowledge Discovery (FSKD)

High-frequency Continue Wave (CW) telegraph has been an important tactical communication mode. However, this telegraph signal is often corrupted by impulsive disturbance and exhibits serious frequency deviation due to the poor High-frequency channel. As a result, the conventional fixed frequency tracking method based on Gaussian noise assumption may lose the tracking of time-varying CW signal. To...

article

A predictive model combining connectomics and entropy biomarkers to discriminate long‐term vagus nerve stimulation efficacy for pediatric patients with drug‐resistant epilepsy

Tung‐yang Cheng, Yingbing Hu, Xiaoya Qin, Jiayi Ma, more

CNS Neuroscience & Therapeutics > 30 > 7 > n/a - n/a

Aims To predict the vagus nerve stimulation (VNS) efficacy for pediatric drug‐resistant epilepsy (DRE) patients, we aim to identify preimplantation biomarkers through clinical features and electroencephalogram (EEG) signals and thus establish a predictive model from a multi‐modal feature set with high prediction accuracy. Methods Sixty‐five pediatric DRE patients implanted with VNS were included...

article

Construction and application of a bearing fault diagnosis model based on improved GWO algorithm

Lingbo Jiang

Diagnostyka > 2024 > Vol. 25, No. 3 > art. no. 224302

In mechanical equipment, if bearing components fail, it can cause serious equipment damage and even threaten human life safety. Therefore, equipment bearings fault diagnosis is of great meaning. In the study of bearing fault diagnosis, an improved gray wolf optimization algorithm is put forward to optimize the support vector machine model. The model improves the convergence factor of the algorithm,...

article

A framework for estimating the matric suction in unsaturated soils using multiple artificial intelligence techniques

Junjie Wang, Sai Vanapalli

International Journal for Numerical and Analytical Methods in Geomechanics > 48 > 11 > 2854 - 2879

Implementation of the state‐of‐the‐art understanding of the mechanics of unsaturated soils into geotechnical engineering practice is partly limited due to the lack of quick, reliable, and economical techniques for matric suction measurement. Matric suction is one of the key stress state variables that significantly influences the hydro‐mechanical behavior of unsaturated soils. In this paper, to address...

article

Suitability of machine learning for atrophy and fibrosis development in neovascular age‐related macular degeneration

Jesus Fuente, Sara Llorente‐González, Patricia Fernandez‐Robredo, María Hernandez, more

Acta Ophthalmologica > 102 > 5 > e831 - e841

Purpose To assess the suitability of machine learning (ML) techniques in predicting the development of fibrosis and atrophy in patients with neovascular age‐related macular degeneration (nAMD), receiving anti‐VEGF treatment over a 36‐month period. Methods An extensive analysis was conducted on the use of ML to predict fibrosis and atrophy development on nAMD patients at 36 months from start of...

article

Assessing kiwifruit quality in storage through machine learning

Mohsen Azadbakht, Shaghayegh Hashemi Shabankareh, Ali Kiapey, Abbas Rezaeiasl, more

Journal of Food Process Engineering > 47 > 7 > n/a - n/a

Today, diets rich in fruits and vegetables are highly recommended since they contribute to health. The high deterioration rate of fruits distinguishes them from other crops. This study aimed to evaluate the effects of various storage conditions on some quality attributes of kiwifruit through machine learning (ML) using support vector machine (SVM) models. Kiwifruits were subjected to quasi‐static...

article

3D mask based lung function monitoring system using machine learning for early identification of lung disorders

Ashok Bhansali, P. M. Rekha, Nagamani H. Shahapure, G. B. Pallavi, more

International Journal of Imaging Systems and Technology > 34 > 4 > n/a - n/a

Early identification of illness can aid in lowering the death rate related to lung illnesses. Asthma, Chronic Obstructive Pulmonary Disease (COPD), and bronchiectasis are all chronic respiratory illnesses that cause irritation and oedema of the airway due to increased mucus discharge. Monitoring the asthmatic patient's physiological state is vital to avoiding dangerous circumstances. This study offers...

article

Development of a comprehensive classification model for determining the storage day of frigate tuna (Auxis thazard) for freshness evaluation using a portable electronic nose

M. Nerandi Madhubhashini, Chamara P. Liyanage, Amali U. Alahakoon, Rumesh Prasanga Liyanage

International Journal of Food Science & Technology > 59 > 7 > 4672 - 4679

Ensuring the freshness of fish is essential to guaranteeing safer products for consumers. A classification model was developed using the PEN3 electronic nose to predict fish freshness with its storage day. Frigate tuna (Auxis thazard) was used for odour sampling along with the total volatile base nitrogen (TVB‐N), at 4 °C for 14 days every other day. Random forest and support vector machine (SVM)...

article

Using multivariate pattern analysis to increase effect sizes for event‐related potential analyses

Carlos Daniel Carrasco, Brett Bahle, Aaron Matthew Simmons, Steven J. Luck

Psychophysiology > 61 > 7 > n/a - n/a

Multivariate pattern analysis (MVPA) approaches can be applied to the topographic distribution of event‐related potential (ERP) signals to “decode” subtly different stimulus classes, such as different faces or different orientations. These approaches are extremely sensitive, and it seems possible that they could also be used to increase effect sizes and statistical power in traditional paradigms that...

article

Modeling and solving a multi‐objective optimal portfolio of upstream oil and gas assets

Wei Yan

Optimal Control Applications and Methods > 45 > 3 > 1166 - 1181

This paper focuses on optimizing project investments in oil and gas companies. It proposes a multi‐objective method for investing in oil and gas assets, considering factors such as scale and efficiency. The model takes into account the presence of nonlinear equations and integer constraints, and establishes a nonlinear multi‐objective mixed integer programming portfolio model for oil and gas. The...

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Einsatz von Machine-Learning-Verfahren in amtlichen Unternehmensstatistiken

Application of Support Vector Machine in the analysis of the technical state of development in the LGOM mining area

Parallel selective sampling method for imbalanced and large data classification

Fast data selection for SVM training using ensemble margin

NICeSim: An open-source simulator based on machine learning techniques to support medical research on prenatal and perinatal care decision making

Facial expression recognition based on two-stage features extraction

A real time clustering and SVM based price-volatility prediction for optimal trading strategy

Consumer Purchasing Behavior Extraction Using Statistical Learning Theory

Support Vector Machine based modeling of an industrial natural gas sweetening plant

Optimal design of an in-situ bioremediation system using support vector machine and particle swarm optimization

Robust recognition of high-frequency CW signal corrupted by impulsive noise

A predictive model combining connectomics and entropy biomarkers to discriminate long‐term vagus nerve stimulation efficacy for pediatric patients with drug‐resistant epilepsy

Construction and application of a bearing fault diagnosis model based on improved GWO algorithm

A framework for estimating the matric suction in unsaturated soils using multiple artificial intelligence techniques

Suitability of machine learning for atrophy and fibrosis development in neovascular age‐related macular degeneration

Assessing kiwifruit quality in storage through machine learning

3D mask based lung function monitoring system using machine learning for early identification of lung disorders

Development of a comprehensive classification model for determining the storage day of frigate tuna (Auxis thazard) for freshness evaluation using a portable electronic nose

Using multivariate pattern analysis to increase effect sizes for event‐related potential analyses

Modeling and solving a multi‐objective optimal portfolio of upstream oil and gas assets

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