In health care and other fields, the detection and recognition of human actions or activities are essential in the context of human–robot interaction. During the last decade, many approaches for ...human activity recognition have taken advantage of high-performance computing devices. These devices make use of various sensors and improve the quality and efficiency of the results. With the aim of using a non-invasive method, we propose the design of a temporal convolutional neural network that uses spatio-temporal features to analyze and recognize human activities using only a short video as input. The proposed architecture is based on a 3D convolutional layer and a convolutional long short-term memory layer. Our methodology leverages the time-motion features with the spatial location of the activities performed by people to improve the accuracy of the classification results. This design makes optimal use of computational resources to achieve training/classification in a short period of time, and consequently, obtain real-time classification results. The computer simulations showed that our method provided superior state-of-the-art classification results for human activities even for those methods that require information from more sensors.
•Temporal convolutional neural network for leveraging spatio-temporal features.•Reduce dataset size using only short videos from one typical camera.•Proposed architecture recognizes human activity and shares the result to NAO robot.•Results demonstrate that the proposed classifier can be used to HAR in real time.
Brain tumor segmentation is a routine process in a clinical setting and provides useful information for diagnosis and treatment planning. Manual segmentation, performed by physicians or radiologists, ...is a time-consuming task due to the large quantity of medical data generated presently. Hence, automatic segmentation methods are needed, and several approaches have been introduced in recent years including the Localized Region-based Active Contour Model (LRACM). There are many popular LRACM, but each of them presents strong and weak points. In this paper, the automatic selection of LRACM based on image content and its application on brain tumor segmentation is presented. Thereby, a framework to select one of three LRACM, i.e., Local Gaussian Distribution Fitting (LGDF), localized Chan-Vese (C-V) and Localized Active Contour Model with Background Intensity Compensation (LACM-BIC), is proposed. Twelve visual features are extracted to properly select the method that may process a given input image. The system is based on a supervised approach. Applied specifically to Magnetic Resonance Imaging (MRI) images, the experiments showed that the proposed system is able to correctly select the suitable LRACM to handle a specific image. Consequently, the selection framework achieves better accuracy performance than the three LRACM separately.
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•Automatic selection of the Localized Region-based Active Contour Model (LRACM).•Statistical moment-based features as image descriptors.•Automatic Brain tumor segmentation framework.•LRACM performance depends on the image content.•Fast and reliable MRI data analysis.
The light polarization properties provide relevant information about linear-optical media quality and condition. The Stokes-Mueller formalism is commonly used to represent the polarization properties ...of the incident light over sample tests. Currently, different Stokes Polarimeters are mainly defined by resolution, acquisition rate, and light to carry out accurate and fast measurements. This work presents the implementation of an automatic Stokes dynamic polarimeter to characterize non-biological and biological material samples. The proposed system is configured to work in the He-Ne laser beam's reflection or transmission mode to calculate the Mueller matrix. The instrumentation stage includes two asynchronous photoelastic modulators, two nano-stepper motors, and an acquisition data card at 2% of accuracy. The Mueller matrix is numerically calculated by software using the 36 measures method without requiring image processing. Experiments show the efficiency of the proposed optical array to calculate the Mueller matrix in reflection and transmission mode for different samples. The mean squared error is calculated for each element of the obtained matrix using referenced values of the air and a mirror. A comparison with similar works in the literature validates the proposed optical array.
On removing conflicts for machine learning Ledesma, Sergio; Ibarra-Manzano, Mario-Alberto; Almanza-Ojeda, Dora-Luz ...
Expert systems with applications,
11/2022, Letnik:
206
Journal Article
Recenzirano
A Machine Learning (ML) system learns from a set of samples called the training set. In some cases, the training set may have learning conflicts that affect the performance of the machine learning ...system. A learning conflict is produced when two or more samples in a dataset have similar input values but different target values. We propose a method to remove learning conflicts from a dataset in this work. Our method is based on a genetic algorithm tries to keep those samples that free of conflicts and intents to remove those samples with conflicts. Each individual in the genetic algorithm represents a possible dataset. We introduce the concept of retention error in the fitness function, which describes how many samples are kept while removing learning conflicts. Additionally, the fitness function comprises the Mean-Squared Error (MSE) that validates the machine learning performance. The algorithm is designed to keep as many samples as possible while the machine learning system exhibits the highest possible performance. Therefore, the proposal consists in cleaning first the dataset that compares and highlights the individual with the best performance in the Genetic Algorithm (GA), recommending which samples must be included for training and testing. Three different datasets with learning conflicts are used to test the proposed methodology. Besides, one artificial neural network is trained using the datasets with learning conflicts for each dataset. After removing the conflicts, a second artificial neural network is trained using the cleaned datasets. A noticeable reduction in the mean-square error is observed when the neural network is trained using the cleaned dataset.
•Learning conflicts that affect machine learning system performance.•Samples in a dataset with similar values but different target.•Retention error kept meaningful samples while removing learning conflicts.•Artificial neural networks trained with learning conflicts datasets.•Generic algorithm improves the machine learning system performance.
Breast cancer is one the main death causes for women worldwide, as 16% of the diagnosed malignant lesions worldwide are its consequence. In this sense, it is of paramount importance to diagnose these ...lesions in the earliest stage possible, in order to have the highest chances of survival. While there are several works that present selected topics in this area, none of them present a complete panorama, that is, from the image generation to its interpretation. This work presents a comprehensive state-of-the-art review of the image generation and processing techniques to detect Breast Cancer, where potential candidates for the image generation and processing are presented and discussed. Novel methodologies should consider the adroit integration of artificial intelligence-concepts and the categorical data to generate modern alternatives that can have the accuracy, precision and reliability expected to mitigate the misclassifications.
Early detection of different levels of tremors helps to obtain a more accurate diagnosis of Parkinson's disease and to increase the therapy options for a better quality of life for patients. This ...work proposes a non-invasive strategy to measure the severity of tremors with the aim of diagnosing one of the first three levels of Parkinson's disease by the Unified Parkinson's Disease Rating Scale (UPDRS). A tremor being an involuntary motion that mainly appears in the hands; the dataset is acquired using a leap motion controller that measures 3D coordinates of each finger and the palmar region. Texture features are computed using sum and difference of histograms (SDH) to characterize the dataset, varying the window size; however, only the most fundamental elements are used in the classification stage. A machine learning classifier provides the final classification results of the tremor level. The effectiveness of our approach is obtained by a set of performance metrics, which are also used to show a comparison between different proposed designs.
Short-circuit in three-phase engines are highly destructive faults, which overheat and damage internal elements reducing efficiency and lifetime. New multi-class approaches are best trained with ...measurements from three-phase motors instrumented with short-circuit faults because it offers natural and physical signal behavior. This work overcomes the lack of datasets by acquiring current signals from an instrumented induction motor to create a dataset of inter-turn short-circuit (ITSC) faults at four levels per phase. The dataset generated consists of 13 categories with five repetitions per trial for a squirrel cage motor induction. The proposed classification method is based on quaternions that simultaneously model the three-phase signals as pure quaternions. Three statistical features are extracted from quaternions, and a decision tree classifier is trained per feature. Thereby, a boosting scheme is used to calculate the resulting category. Boosting method improves the classification results of decision tree models, showing fast, accurate, and robust performance.
•Classification of Inter-turn short circuit faults in induction motors.•Modeling and characterizing three-phase currents in quaternion space.•Detection of four levels of inter-turn short circuit faults per phase and healthy.•The decision tree and the boosting scheme predict the fault category.•A multiclass dataset with raw and filtered three-phase currents of an induction motor.
Popular comments suggest that continuous exposure of children and adolescents to video games yields a non-benefit behavior in the players' mental health. Contrarily, several studies have proven that ...commercial and serious games improve mental activity; some are used in treating psychological and physical disorders. This paper presents a method based on electroencephalogram signals analysis to classify multiple emotions recorded from subjects' gameplay seasons. In the core of this study, a self-assessed labeling method is evaluated using the Force, EEG, and Emotion Labelled Dataset (FEEL) for emotion recognition tasks. Besides, a 1-D Local Binary Pattern (LBP) method transforms the EEG temporal behavior to extract time-frequency features. Complementarily, the database artifacts were removed using a novel Conflict Learning approach for machine learning models, associating the extracted samples with the subjects' emotion labeling. A semi-supervised clustering method was employed to show the similarity between self-assessed subjects' labels. Finally, numerical results suggested a conflict between 23 original labels, improving the classification by over 92% in accuracy for 19 self-assessed classes.
The lifetime of induction motors can be significantly extended by installing diagnostic systems for monitoring their operating conditions. In particular, detecting broken bar failures in motors is ...important for avoiding the risk of short circuits or other accidents with serious consequences. In the literature, many approaches have been proposed for motor fault detection; however, additional generalized methods based on local and statistical analysis could provide a low-complexity and feasible solution in this field of research. The proposed work presents a methodology for detecting one or two broken rotor bars using the sums and differences histograms (SDH) and machine learning classifiers in this context. From the SDH computed in one phase of the motor’s current, nine texture features are calculated for different displacements. Then, all features are used to train two classifiers and to find the best displacements for faults and health identification in the induction motors. A final experimental evaluation considering the best displacements shows an accuracy of 98.16% for the homogeneity feature and a few signal samples used in a decision tree classifier. Additionally, a polynomial regression curve validates the use of 50 samples to obtain an accuracy of 88.15%, whereas the highest performance is achieved for 250 samples.
Short circuits occurring between turns within the windings are widely known as one of the primary causes of damage in electrical transformers; as a result, early detection plays a fundamental role in ...preventing further and more serious damage. This study introduces a novel approach that relies on the analysis of current and vibration signals, specifically employing the analysis of quaternion signals, to effectively detect short circuits within electrical transformers., offering an identification of conditions ranging from a healthy state to six levels of short circuit turns. in a no-load transformer, i.e., 0, 5, 10, 15, 20, 25 and 30 SCT. This proposed method employs quaternion rotation to extract statistical features that can be used to classify the condition of the transformer. To evaluate the effectiveness of the proposed methodology, an experimental validation is carried out using a 1.5 kVA transformer, comparing its performance against other existing methods. The results demonstrate the feasibility of the proposal, accurately identifying various levels of SCT, achieving an accuracy of 97.5%, using only 100 samples with the k nearest neighbors method.
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