Browsing by Subject "support vector machine"

Now showing 1 - 12 of 12

Access status: Open Access ,
A machine learning model for improving building detection in informal areas - a case study of Greater Cairo
(Wydawnictwa AGH, 2022) Taha, Lamyaa Gamal El-deen; Ibrahim, Rania Elsayed
Building detection in Ashwa'iyyat is a fundamental yet challenging problem, mainly because it requires the correct recovery of building footprints from images with high-object density and scene complexity. A classification model was proposed to integrate spectral, height and textural features. It was developed for the automatic detection of the rectangular, irregular structure and quite small size buildings or buildings which are close to each other but not adjoined. It is intended to improve the precision with which buildings are classified using scikit learn Python libraries and QGIS. WorldView-2 and Spot-5 imagery were combined using three image fusion techniques. The Grey-Level Co-occurrence Matrix was applied to determine which attributes are important in detecting and extracting buildings. The Normalized Digital Surface Model was also generated with 0.5-m resolution. The results demonstrated that when textural features of colour images were introduced as classifier input, the overall accuracy was improved in most cases. The results show that the proposed model was more accurate and efficient than the state-of-the-art methods and can be used effectively to extract the boundaries of small size buildings. The use of a classifier ensample is recommended for the extraction of buildings.
Access status: Open Access ,
Applying Hunger Game Search (HGS) for selecting significant blood indicators for early prediction of ICU COVID-19 severity
(Wydawnictwa AGH, 2023) Sayed, Safynaz AbdEl-Fattah; ElKorany, Abeer; Sayed, Sabah
This paper introduces an early prognostic model for attempting to predict the severity of patients for ICU admission and detect the most significant features that affect the prediction process using clinical blood data. The proposed model predicts ICU admission for high-severity patients during the first two hours of hospital admission, which would help assist clinicians in decision-making and enable the efficient use of hospital resources. The Hunger Game search (HGS) meta-heuristic algorithm and a support vector machine (SVM) have been integrated to build the proposed prediction model. Furthermore, these have been used for selecting the most informative features from blood test data. Experiments have shown that using HGS for selecting features with the SVM classifier achieved excellent results as compared with four other meta-heuristic algorithms. The model that used the features that were selected by the HGS algorithm accomplished the topmost results (98.6 and 96.5%) for the best and mean accuracy, respectively, as compared to using all of the features that were selected by other popular optimization algorithms.
Access status: Restricted ,
Automatyczne rozpoznawanie tablic rejestracyjnych
(Data obrony: 2017-01-19) Zygmunt, Michał
Wydział Elektrotechniki, Automatyki, Informatyki i Inżynierii Biomedycznej
Access status: Open Access ,
Comparison of machine-learning algorithms for SPOT 7 multispectral image classification
(Wydawnictwa AGH, 2025) Morale, Davide; Parente, Claudio; Bolognesi, Salvatore Falanga
Precise and timely land-cover identification plays an important role in effective environmental monitoring and land management. This study compares the performance of five machine-learning classifiers – support vector machine (SVM), decision tree (DT), normal Bayes (NB), random forest (RF), and k-nearest neighbor (k-NN) – in the land-cover mapping of the Agro Nocerino Sarnese area (Southern Italy) using high-resolution SPOT 7 pan-sharpened multispectral images with a pixel size of 1.5 m × 1.5 m. The data set consisted of blue, green, red, and near-infrared (NIR) bands and was processed with Orfeo ToolBox (OTB) software. Two data sets were analyzed: DS-3B (which included only the visible bands [blue, green, and red]), and DS-4B (which also included the NIR band). A comparison of the classifiers’ performances across various land-cover classes was conducted in order to assess their respective classification accuracy. The results showed that SVM and k-NN achieved the highest overall accuracy levels (93% and 92%, respectively) using only the visible bands, whereas the decision tree classifier performed best when the NIR band was included. Random forest achieved excellent accuracy in vegetation classes (88–99%) but struggled with misclassifications in bare soil and man-made classes such as buildings and roads. These results emphasized the significant impact of data set characteristics on classifier performance as well as the importance of band selection and pan-sharpening techniques in high-resolution land-cover mapping.
Access status: Open Access ,
Computational intelligence methods in the problem of modelling technical wear of buildings in mining areas
(2012) Rusek, Janusz
In the work presented approach with a view to building the model of degree of technical wear of buildings in the mining areas, as well as an indication that the contribution of the consumption on technical factors interact mining and civil construction origin. Set out criteria for the selection and research methodology effects are synthetically summarised existing work in this field. Justified choice of the epsilon-SVR method confronting its advantages to the characteristics of typical neural network.
Access status: Open Access ,
Enhanced cluster merging and deep learning techniques for entity name identification from biomedical corpus
(Wydawnictwa AGH, 2025) Das, Nilanjana; Dutta, Rakesh; Mondal, Uttam Kumar; Majumder, Mukta; Mandal, Jyotsna Kumar
For mining biomedical information identifying names is the prime task. Complex and uncertain naming styles of biomedical entities are the major setbacks here. Thus, state-of-the-art accuracy of biomedical name identification is reasonably inferior compared to general domain. This study includes Machine Learning and Deep Learning techniques to recognize names from biomedical corpus. In supervised classification, a classifier is built by finding required statistics from training corpus. Accordingly, performance of the system is primarily dependent on quantity and quality of training corpus. But manually preparing a large training dataset with enriched feature samples is laborious and time-taking. Therefore, various techniques were adopted in the literature to make effective use of raw corpora. We have incorporated a novel Cluster Merging technique and Attention Mechanism with BERT embedding for boosting Machine Learning and Deep Learning classifiers respectively. The suggested results outpour that profound techniques are competent and delineate signifying improvement over surviving methods.
Access status: Open Access ,
Ensemble machine learning methods to predict the balancing of ayurvedic constituents in the human body
(Wydawnictwa AGH, 2022) Rajasekar Vani; Krishnamoorthi Sathya; Saračević Muzafer; Pepic Dzenis; Zajmovic Mahir; Zogic Haris
In this paper, we demonstrate the result of certain machine-learning methods like support vector machine (SVM), naive Bayes (NB), decision tree (DT), k-nearest neighbor (KNN), artificial neural network (ANN), and AdaBoost algorithms for various performance characteristics to predict human body constituencies. Ayurveda-dosha studies have been used for a long time, but the quantitative reliability measurement of these diagnostic methods still lags. The careful and appropriate analysis leads to an effective treatment to predict human body constituencies. From an observation of the results, it is shown that the AdaBoost algorithm with hyperparameter tuning provides enhanced accuracy and recall (0.97), precision and F-score (0.96), and lower RSME values (0.64). The experimental results reveal that the improved model (which is based on ensemble-learning methods) significantly outperforms traditional methods. According to the findings, advancements in the proposed algorithms could give machine learning a promising future.
Access status: Open Access ,
Machine learning methods for diagnosing the causes of die-casting defects
(Wydawnictwa AGH, 2023) Okuniewska, Alicja; Perzyk, Marcin; Kozłowski, Jacek
The research was focused on analyzing the causes of high-pressure die-casting defects, more specifically on casting leakage, which is considered perhaps the most important and common defect. The real data used for modelling was obtained from a high-pressure die-casting foundry that manufactures aluminum cylinder blocks for the world's leading automotive brands. This paper compares and summarizes the results of applying advanced modelling using artificial neural networks, regression trees, and support vector machines methods to select artificial neural networks as the most effective method to perform a multidimensional optimization of process parameters to diagnose the causes of die-casting defects and to indicate the future research scope in this area. The developed system enables the prediction of the level of defects in castings with satisfactory accuracy and is therefore a highly relevant reference for process engineers of high-pressure foundries. This article indicates exactly which process parameters significantly influence the formation of a defect in a casting.
Access status: Open Access ,
Support vector machine for susceptibility modeling of dengue fever in Kendari, Southeast Sulawesi
(Wydawnictwa AGH, 2024) Widayani, Prima; Sahitya, Abhista Fawwaz; Saputri, Agatha Andriantari
Dengue fever (DF) is an infectious disease that is still a problem in Indonesia. The total death rate due to DF was 705 people in 2021, in 2022, this increased to 1183 (Indonesian Ministry of Health, 2023). Seeing this fact, prevention efforts are still needed when handling DF cases in all of the regions of Indonesia. This research was conducted in the Kendari area of Southeast Sulawesi, where there are still cases of DF. The purpose of this study was to create a spatial model of dengue susceptibility using a support vector machine. Landsat 8 imagery was used to intercept data on building density, vegetation density, land use, and land surface temperatures. Rainfall and humidity variables were obtained from the Meteorological, Climatological, and Geophysical Agency (BMKG). Based on the modeling results, the districts of Wua-wua, Kadia, Barunga, Poasi, and Puuwatu are areas with high susceptibility. The results of testing the susceptibility model to dengue hemorrhagic fever (DHF) in Kendari obtained an area under the curve (AUC) of 0.75, meaning that this model was well-accepted.
Access status: Open Access ,
Tenfold bootstrap procedure for support vector machines
(Wydawnictwa AGH, 2020) Vrigazova, Borislava; Ivanov, Ivan
Cross validation is often used to split input data into training and test sets in support vector machines. The two most commonly used cross validation versions are the tenfold and leave-one-out cross validation. Another commonly used resampling method is the random test/train split. The advantage of these methods is that they avoid overﬁtting in a model and perform model selection. However, they can increase the computational time for ﬁtting support vector machines by increasing the size of the dataset. In this research, we propose an alternative for ﬁtting SVM, which we call the tenfold bootstrap for support vector machines. This resampling procedure can signiﬁcantly reduce execution time despite the large number of observations, while preserving a model’s accuracy. With this ﬁnding, we propose a solution to the problem of slow execution time when ﬁtting support vector machines on big datasets.
Access status: Restricted ,
Wpływ doboru parametrów maszyny wektorów nośnych (Support Vector Machine) na jakość klasyfikacji binarnej
(Data obrony: 2017-01-26) Kapusta, Grzegorz
Wydział Elektrotechniki, Automatyki, Informatyki i Inżynierii Biomedycznej
Access status: Restricted ,
Zastosowanie obrazowania hiperspektralnego w diagnostyce
(Data obrony: 2020-10-07) Stopiński, Michał
Wydział Inżynierii Mechanicznej i Robotyki