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Tytuł:
A new approach to image-based recommender systems with the application of heatmaps maps
Autorzy:
Woldan, Piotr
Duda, Piotr
Cader, Andrzej
Laktionov, Ivan
Powiązania:
https://bibliotekanauki.pl/articles/2201330.pdf
Data publikacji:
2023
Wydawca:
Społeczna Akademia Nauk w Łodzi. Polskie Towarzystwo Sieci Neuronowych
Tematy:
feature extraction
recommender system
heatmap
Opis:
One of the fundamental issues of modern society is access to interesting and useful content. As the amount of available content increases, this task becomes more and more challenging. Our needs are not always formulated in words; sometimes we have to use complex data types like images. In this paper, we consider the three approaches to creating recommender systems based on image data. The proposed systems are evaluated on a real-world dataset. Two case studies are presented. The first one presents the case of an item with many similar objects in a database, and the second one with only a few similar items
Źródło:
Journal of Artificial Intelligence and Soft Computing Research; 2023, 13, 2; 63--72
2083-2567
2449-6499
Pojawia się w:
Journal of Artificial Intelligence and Soft Computing Research
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Klasyfikacja stanów przedkrytycznych
Classification of pre-critical states
Autorzy:
Topczewska, M.
Frischmuth, K.
Powiązania:
https://bibliotekanauki.pl/articles/154431.pdf
Data publikacji:
2012
Wydawca:
Stowarzyszenie Inżynierów i Techników Mechaników Polskich
Tematy:
klasyfikacja
ekstrakcja cech
classification
feature extraction
Opis:
Praca zawiera przykład klasyfikacji danych rzeczywistych opisujących sygnały niekrytyczne, przedkrytyczne i krytyczne. Celem jest rozpoznanie stanów niebezpiecznych tak wcześnie jak to możliwe. Ze względu na brak separowalności liniowej danych w celu separacji klas użyto klasyfikacji hierarchicznej z cięciami za pomocą klasyfikatorów liniowych oraz z podejściem one-versus-rest z wyróżnioną klasą sygnałów bezpiecznych. W wyniku ośmiu cięć uzyskano ostateczny podział przestrzeni skutkujący odseparowaniem klasy sygnałów bezpiecznych od podejrzanych, tj. przedkrytycznych i krytycznych oraz dający najmniejszą liczbę błędnie sklasyfikowanych obiektów z klasy sygnałów niekrytycznych.
The paper presents an application of classification methods to time-continuous signals (1). Signals with values that exceed a certain critical maximum are called dangerous or critical, otherwise we speak about normal or routine operation of the system under consideration, Fig. 1. The problem is to recognize pre-critical states, i.e. states preceding the actual dangerous ones, and that as early as possible. False negative classifications may have very serious consequences, while false positive verdicts cause expensive but unnecessary counter-measures. As pre-processing, the input signals are characterized by a number of features, which form sequences of vector data, indexed by the cycle number (2). In a first stage, suspicious feature vectors are selected, from which in a second sweep unlikely candidates are removed. The focus of the present paper is this second stage, i.e. the distinction between actual pre-critical and the harmless routine states among the suspicious states, indicated in the first stage by a certain preliminary test. The choice of features and the logic behind the preliminary test are beyond our present scope. Let it suffice to say that the first step is a combination of Principal Component Analysis and some statistical test, and that it is very effective but unspecific in the application at hand.For the real-world data we used to develop the method, it turned out that the obtained feature vectors were linearly non-separable. For that reason a hierarchical approach was applied, where in several steps linear cuts (4,5) of the one-versus-rest type were performed in order to single out the true pre-critical states. For the example under consideration, in eight iterations separation between pre-critical and non-pre-critical ones was achieved. We succeeded to keep the number of wrong negatives at zero while reducing the number of wrong positives to a fraction of the starting value, established by the preliminary test, Fig. 3, 4, 5. The final sensitivity, for the given data set, is 100%, and the achieved specificity is at 93.15%. Numerical experiments, using nonlinear classifiers on much larger data sets, are under way. The present aim is to find an optimal set of features and a one-step criterion which further improves the quality of the classification.
Źródło:
Pomiary Automatyka Kontrola; 2012, R. 58, nr 10, 10; 872-875
0032-4140
Pojawia się w:
Pomiary Automatyka Kontrola
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Human Detection in Thermal Images Using Low-level Features
Autorzy:
Budzan, S.
Powiązania:
https://bibliotekanauki.pl/articles/114333.pdf
Data publikacji:
2015
Wydawca:
Stowarzyszenie Inżynierów i Techników Mechaników Polskich
Tematy:
human detection
infrared
feature extraction
HOG
Opis:
In this work the human detection method in infrared has been presented. The proposed solution focuses on the use low-level features and detecting parts of the human body. Low–level processing is based on modified HOG (Histogram of Oriented Gradients) algorithm. First, the only squared cells have been used, also calculation of the gradient has been improved. Next, the model of the head from the dataset IR (Infra Red) images has been created, also the model of the human body. Finally, the probability matrix has been examined using minimal distance classifier. The novelty of the proposed solution focuses on the combination of the pixel-gradient and body parts processing, also three stage classification process (head modelling, human modelling and classifier), which has been proposed to reduce the false detection. The experiments were performed on self-created IR images database, which contains images with most of the possible difficult situations such as overlapped people, different pose, small and high resolution of the people. The performance of the proposed algorithm was evaluated using Precision and Recall quality measure.
Źródło:
Measurement Automation Monitoring; 2015, 61, 6; 191-194
2450-2855
Pojawia się w:
Measurement Automation Monitoring
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
A survey of methods for 3D model feature extraction
Autorzy:
Hlavaty, T.
Skala, V.
Powiązania:
https://bibliotekanauki.pl/articles/119225.pdf
Data publikacji:
2003
Wydawca:
Polskie Towarzystwo Geometrii i Grafiki Inżynierskiej
Tematy:
feature extraction
retrieval systems
ekstrakcja cech
systemy wyszukiwania
Opis:
This paper deals with problems that are related to a feature extraction from 3D objects. The main aim of the feature extraction is to describe a shape of 3D object by a feature vector. Then the elements of this feature vector characterize the shape of the own 3D objects and they can serve as a key in searching for similar models. In this paper are introduced current methods for the feature extraction of 3D models and their classification. These methods are based on different mathematical background and according to that they are separated into several groups.
Źródło:
Journal Biuletyn of Polish Society for Geometry and Engineering Graphics; 2003, 13; 5-8
1644-9363
Pojawia się w:
Journal Biuletyn of Polish Society for Geometry and Engineering Graphics
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Overcoming Overfitting Challenges with HOG Feature Extraction and XGBoost-Based Classification for Concrete Crack Monitoring
Autorzy:
Barkiah, Ida
Sari, Yuslena
Powiązania:
https://bibliotekanauki.pl/articles/27311909.pdf
Data publikacji:
2023
Wydawca:
Polska Akademia Nauk. Czasopisma i Monografie PAN
Tematy:
HOG
XGBoost
classification
feature extraction
concrete crack monitoring
Opis:
This study proposes a method that combines Histogram of Oriented Gradients (HOG) feature extraction and Extreme Gradient Boosting (XGBoost) classification to resolve the challenges of concrete crack monitoring. The purpose of the study is to address the common issue of overfitting in machine learning models. The research uses a dataset of 40,000 images of concrete cracks and HOG feature extraction to identify relevant patterns. Classification is performed using the ensemble method XGBoost, with a focus on optimizing its hyperparameters. This study evaluates the efficacy of XGBoost in comparison to other ensemble methods, such as Random Forest and AdaBoost. XGBoost outperforms the other algorithms in terms of accuracy, precision, recall, and F1-score, as demonstrated by the results. The proposed method obtains an accuracy of 96.95% with optimized hyperparameters, a recall of 96.10%, a precision of 97.90%, and an F1-score of 97%. By optimizing the number of trees hyperparameter, 1200 trees yield the greatest performance. The results demonstrate the efficacy of HOG-based feature extraction and XGBoost for accurate and dependable classification of concrete fractures, overcoming the overfitting issues that are typically encountered in such tasks.
Źródło:
International Journal of Electronics and Telecommunications; 2023, 69, 3; 571--577
2300-1933
Pojawia się w:
International Journal of Electronics and Telecommunications
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
A comparative study of CNN, LSTM, BiLSTM, AND GRU architectures for tool wear prediction in milling processes
Autorzy:
Zegarra, Fabio C.
Vargas-Machuca, Juan
Coronado, Alberto M.
Powiązania:
https://bibliotekanauki.pl/articles/28407329.pdf
Data publikacji:
2023
Wydawca:
Wrocławska Rada Federacji Stowarzyszeń Naukowo-Technicznych
Tematy:
tool wear
feature extraction
preprocessing
recurrent neural network
Opis:
Accurately predicting machine tool wear requires models capable of capturing complex, nonlinear interactions in multivariate time series inputs. Recurrent neural networks (RNNs) are well-suited to this task, owing to their memory mechanisms and capacity to construct highly complex models. In particular, LSTM, BiLSTM, and GRU architectures have shown promise in wear prediction. This study demonstrates that RNNs can automatically extract relevant information from time series data, resulting in highly precise wear models with minimal feature engineering. Notably, this approach avoids the need for excessively large window sizes of data points during model training, which would increase model complexity and processing time. Instead, this study proposes a procedure that achieves low prediction errors with window sizes as small as 100 data points. By employing Bayesian hyperparameter optimization and two preprocessing techniques (detrend and offset), RMSE errors consistently fall below 10. A key difference in this study is the use of boxplots to provide a better representation of result variability, as opposed to solely reporting the best values. The proposed approach matches more complex state of-the-art. methods and offers a powerful tool for wear prediction in engineering applications.
Źródło:
Journal of Machine Engineering; 2023, 23, 4; 122--136
1895-7595
2391-8071
Pojawia się w:
Journal of Machine Engineering
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Phase Autocorrelation Bark Wavelet Transform (PACWT) Features for Robust Speech Recognition
Autorzy:
Majeed, S. A.
Husain, H.
Samad, S. A.
Powiązania:
https://bibliotekanauki.pl/articles/177326.pdf
Data publikacji:
2015
Wydawca:
Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:
speech recognition
feature extraction
phase autocorrelation
wavelet transform
Opis:
In this paper, a new feature-extraction method is proposed to achieve robustness of speech recognition systems. This method combines the benefits of phase autocorrelation (PAC) with bark wavelet transform. PAC uses the angle to measure correlation instead of the traditional autocorrelation measure, whereas the bark wavelet transform is a special type of wavelet transform that is particularly designed for speech signals. The extracted features from this combined method are called phase autocorrelation bark wavelet transform (PACWT) features. The speech recognition performance of the PACWT features is evaluated and compared to the conventional feature extraction method mel frequency cepstrum coefficients (MFCC) using TI-Digits database under different types of noise and noise levels. This database has been divided into male and female data. The result shows that the word recognition rate using the PACWT features for noisy male data (white noise at 0 dB SNR) is 60%, whereas it is 41.35% for the MFCC features under identical conditions.
Źródło:
Archives of Acoustics; 2015, 40, 1; 25-31
0137-5075
Pojawia się w:
Archives of Acoustics
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
An improved algorithm for feature extraction from a fingerprint fuzzy image
Autorzy:
Surmacz, K.
Saeed, K.
Rapta, P.
Powiązania:
https://bibliotekanauki.pl/articles/174451.pdf
Data publikacji:
2013
Wydawca:
Politechnika Wrocławska. Oficyna Wydawnicza Politechniki Wrocławskiej
Tematy:
biometrics
feature extraction
fingerprints
minutiae
Gabor filter
feature vector
image processing
Opis:
Proper fingerprint feature extraction is crucial in fingerprint-matching algorithms. For good results, different pieces of information about a fingerprint image, such as ridge orientation and frequency, must be considered. It is often necessary to improve the quality of a fingerprint image in order for the feature extraction process to work correctly. In this paper we present a complete (fully implemented) improved algorithm for fingerprint feature extraction, based on numerous papers on this topic. The paper describes a fingerprint recognition system consisting of image preprocessing, filtration, feature extraction and matching for recognition. The image preprocessing includes normalization based on mean value and variation. The orientation field is extracted and Gabor filter is used to prepare the fingerprint image for further processing. For singular point detection, the Poincaré index with a partitioning method is used. The ridgeline thinning is presented and so is the minutia extraction by CN algorithm. The paper contains the comparison of obtained results to the other algorithms.
Źródło:
Optica Applicata; 2013, 43, 3; 515-527
0078-5466
1899-7015
Pojawia się w:
Optica Applicata
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Determination of the Optimal Threshold Value and Number of Keypoints in Scale Invariant Feature Transform-based Copy-Move Forgery Detection
Autorzy:
Isnanto, R. Rizal
Zahra, Ajub Ajulian
Santoso, Imam
Lubis, Muhammad Salman
Powiązania:
https://bibliotekanauki.pl/articles/227299.pdf
Data publikacji:
2020
Wydawca:
Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:
forgery
Gaussian noise
feature extraction
pattern matching
Euclidean distance
Opis:
The copy-move forgery detection (CMFD) begins with the preprocessing until the image is ready to process. Then, the image features are extracted using a feature-transform-based extraction called the scale-invariant feature transform (SIFT). The last step is features matching using Generalized 2 Nearest-Neighbor (G2NN) method with threshold values variation. The problem is what is the optimal threshold value and number of keypoints so that copy-move detection has the highest accuracy. The optimal threshold value and number of keypoints had determined so that the detection n has the highest accuracy. The research was carried out on images without noise and with Gaussian noise.
Źródło:
International Journal of Electronics and Telecommunications; 2020, 66, 3; 561-569
2300-1933
Pojawia się w:
International Journal of Electronics and Telecommunications
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
A Classification Method Related to Respiratory Disorder Events Based on Acoustical Analysis of Snoring
Autorzy:
Wang, Can
Peng, Jianxin
Zhang, Xiaowen
Powiązania:
https://bibliotekanauki.pl/articles/176601.pdf
Data publikacji:
2020
Wydawca:
Polska Akademia Nauk. Czasopisma i Monografie PAN
Tematy:
acoustical analysis
feature extraction
support vector machine
snoring sound
Opis:
Acoustical analysis of snoring provides a new approach for the diagnosis of obstructive sleep apnea hypopnea syndrome (OSAHS). A classification method is presented based on respiratory disorder events to predict the apnea-hypopnea index (AHI) of OSAHS patients. The acoustical features of snoring were extracted from a full night’s recording of 6 OSAHS patients, and regular snoring sounds and snoring sounds related to respiratory disorder events were classified using a support vector machine (SVM) method. The mean recognition rate for simple snoring sounds and snoring sounds related to respiratory disorder events is more than 91.14% by using the grid search, a genetic algorithm and particle swarm optimization methods. The predicted AHI from the present study has a high correlation with the AHI from polysomnography and the correlation coefficient is 0.976. These results demonstrate that the proposed method can classify the snoring sounds of OSAHS patients and can be used to provide guidance for diagnosis of OSAHS.
Źródło:
Archives of Acoustics; 2020, 45, 1; 141-151
0137-5075
Pojawia się w:
Archives of Acoustics
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Brain-computer interface as measurement and control system The review paper
Autorzy:
Rak, R. J.
Kołodziej, M.
Majkowski, A.
Powiązania:
https://bibliotekanauki.pl/articles/221747.pdf
Data publikacji:
2012
Wydawca:
Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:
EEG
brain-computer interface
feature extraction
feature selection
measurement and control
Opis:
In the last decade of the XX-th century, several academic centers have launched intensive research programs on the brain-computer interface (BCI). The current state of research allows to use certain properties of electromagnetic waves (brain activity) produced by brain neurons, measured using electroencephalographic techniques (EEG recording involves reading from electrodes attached to the scalp - the non-invasive method - or with electrodes implanted directly into the cerebral cortex - the invasive method). A BCI system reads the user's "intentions" by decoding certain features of the EEG signal. Those features are then classified and "translated" (on-line) into commands used to control a computer, prosthesis, wheelchair or other device. In this article, the authors try to show that the BCI is a typical example of a measurement and control unit.
Źródło:
Metrology and Measurement Systems; 2012, 19, 3; 427-444
0860-8229
Pojawia się w:
Metrology and Measurement Systems
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
An application of machine learning methods to cutting tool path clustering and rul estimation in machining
Autorzy:
Zegarra, Fabio C.
Vargas-Machuca, Juan
Roman-Gonzalez, Avid
Coronado, Alberto M.
Powiązania:
https://bibliotekanauki.pl/articles/28407324.pdf
Data publikacji:
2023
Wydawca:
Wrocławska Rada Federacji Stowarzyszeń Naukowo-Technicznych
Tematy:
feature extraction
k-means clustering
time series
unsupervised learning
Opis:
Machine learning has been widely used in manufacturing, leading to significant advances in diverse problems, including the prediction of wear and remaining useful life (RUL) of machine tools. However, the data used in many cases correspond to simple and stable processes that differ from practical applications. In this work, a novel dataset consisting of eight cutting tools with complex tool paths is used. The time series of the tool paths, corresponding to the three-dimensional position of the cutting tool, are grouped according to their shape. Three unsupervised clustering techniques are applied, resulting in the identification of DBA-k-means as the most appropriate technique for this case. The clustering process helps to identify training and testing data with similar tool paths, which is then applied to build a simple two-feature prediction model with the same level of precision for RUL prediction as a more complex four-feature prediction model. This work demonstrates that by properly selecting the methodology and number of clusters, tool paths can be effectively classified, which can later be used in prediction problems in more complex settings.
Źródło:
Journal of Machine Engineering; 2023, 23, 4; 5--17
1895-7595
2391-8071
Pojawia się w:
Journal of Machine Engineering
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
An improved feature extraction method for rolling bearing fault diagnosis based on MEMD and PE
Autorzy:
Zhang, H.
Zhao, L.
Liu, Q.
Luo, J.
Wei, Q.
Zhou, Z.
Qu, Y.
Powiązania:
https://bibliotekanauki.pl/articles/259770.pdf
Data publikacji:
2018
Wydawca:
Politechnika Gdańska. Wydział Inżynierii Mechanicznej i Okrętownictwa
Tematy:
improved feature extraction method
rolling bearing fault diagnosis
MEMD
PE
Opis:
The health condition of rolling bearing can directly influence to the efficiency and lifecycle of rotating machinery, thus monitoring and diagnosing the faults of rolling bearing is of great importance. Unfortunately, vibration signals of rolling bearing are usually overwhelmed by external noise, so the fault frequencies of rolling bearing cannot be readily obtained. In this paper, an improved feature extraction method called IMFs_PE, which combines the multivariate empirical mode decomposition with the permutation entropy, is proposed to extract fault frequencies from the noisy bearing vibration signals. First, the raw bearing vibration signals are filtered by an optimal band-pass filter determined by SK to remove the irrelative noise which is not in the same frequency band of fault frequencies. Then the filtered signals are processed by the IMFs_PE to get rid of the relative noise which is in the same frequency band of fault frequencies. Finally, a frequency domain condition indicator FFR(Fault Frequency Ratio), which measures the magnitude of fault frequencies in frequency domain, is calculated to compare the effectiveness of the feature extraction methods. The feature extraction method proposed in this paper has advantages of removing both irrelative noise and relative noise over other feature extraction methods. The effectiveness of the proposed method is validated by simulated and experimental bearing signals. And the results are shown that the proposed method outperforms other state of the art algorithms with regards to fault feature extraction of rolling bearing.
Źródło:
Polish Maritime Research; 2018, S 2; 98-106
1233-2585
Pojawia się w:
Polish Maritime Research
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
A Multistage Procedure of Mobile Vehicle Acoustic Identification for Single-Sensor Embedded Device
Autorzy:
Astapov, S.
Riid, A.
Powiązania:
https://bibliotekanauki.pl/articles/227146.pdf
Data publikacji:
2013
Wydawca:
Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:
vehicle identification
acoustic signal analysis
feature extraction
classification
fuzzy logic
Opis:
Mobile vehicle identification has a wide application field for both civilian and military uses. Vehicle identification may be achieved by incorporating single or multiple sensor solutions and through data fusion. This paper considers a single-sensor multistage hierarchical algorithm of acoustic signal analysis and pattern recognition for the identification of mobile vehicles in an open environment. The algorithm applies several standalone techniques to enable complex decision-making during event identification. Computationally inexpensive procedures are specifically chosen in order to provide real-time operation capability. The algorithm is tested on pre-recorded audio signals of civilian vehicles passing the measurement point and shows promising classification accuracy. Implementation on a specific embedded device is also presented and the capability of real-time operation on this device is demonstrated.
Źródło:
International Journal of Electronics and Telecommunications; 2013, 59, 2; 151-160
2300-1933
Pojawia się w:
International Journal of Electronics and Telecommunications
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Vein Biometric Recognition Methods and Systems: A Review
Autorzy:
Al-Khafaji, Ruaa S.S.
Al-Tamimi, Mohammed S.H.
Powiązania:
https://bibliotekanauki.pl/articles/2022496.pdf
Data publikacji:
2022
Wydawca:
Stowarzyszenie Inżynierów i Techników Mechaników Polskich
Tematy:
biometric technology
finger vein recognition
pre-processing
feature extraction
matching
Opis:
The Finger-vein recognition (FVR) method has received increasing attention in recent years. It is a new method of personal identification and biometric technology that identifies individuals using unique finger-vein patterns, which is the first reliable and suitable area to be recognized. It was discovered for the first time with a home imaging system; it is characterized by high accuracy and high processing speed. Also, the presence of patterns of veins inside one’s body makes it almost difficult to repeat and difficult to steal. Based on the increased focus on protecting privacy, that also produces vein biometrics safer alternatives without forgery, damage, or alteration over time. Fingerprint recognition is beneficial because it includes the use of low-cost, small devices which are difficult to counterfeit. This paper discusses preceding finger-vein recognition approaches systems with the methodologies taken from other researchers’ work about image acquisition, pretreatment, vein extraction, and matching. It is reviewing the latest algorithms; continues to critically review the strengths and weaknesses of these methods, and it states the modern results following a key comparative analysis of methods.
Źródło:
Advances in Science and Technology. Research Journal; 2022, 16, 1; 36-46
2299-8624
Pojawia się w:
Advances in Science and Technology. Research Journal
Dostawca treści:
Biblioteka Nauki
Artykuł
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