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Localization is one of the most challenging requirements needed for a mobile robots. Successful localization represents success in meeting the other principal requirements, such as perception and navigation. This article proposes a new approach for the localization of autonomous mobile robots using a Kinect sensor and the concept of Transfer Learning linked to Convolutional Neural Networks (CNNs). The images acquired from the sensor are applied in a mosaic form, which consists of the three color channels provided along with the depth information of the environment. Topological maps were used for indoor environment localization. The proposed computer vision system employed the Bayesian Classifier, k-Nearest Neighbor, Random Forest, Multi-layer Perceptron, and Support Vector Machine as the classifiers. The main focus of this work is the use of a unique configuration of RGB-D images transformed into a mosaic image, combined with the descriptive power of CNNs, in order to estimate the location of a mobile robot in an indoor environments. The results show that the proposed approach proved to be a convincing method for the tasks of localization and supporting the navigation of mobile robots; the results achieved 100% in Accuracy and in the F1-Score. The values obtained for the processing times were also suitable for a computer vision system, with 31.885ms, 0.040s, and 0.044s for the extraction, training, and classification stages, respectively. All these results were from the RGB-D mosaic images. The data demonstrated the relevance of the proposed work, providing a successful localization and, consequently, a successful navigation for mobile robots.

Unmanned Aerial Vehicles are constantly being using in professional activities that require higher precision in navigating and positioning the aircraft during operation. Advanced location technologies such as Global Navigation Satellite System and Real-Time Kinematic are widely used, however, they depend on an area with transmission coverage. In this approach, this article presents a visual navigation methodology based on topological maps. We compared the performance of consolidated classifiers such as Bayesian classifier, k-nearest neighbor, Multilayer Perceptron, Optimal Path Forest and Support Vector Machines (SVM). They are evaluated with attributes returned by last generation resource extractors such as Fourier, Gray Level Co-Occurrence and Local Binary Patterns (LBP). After analyzing the results we found that the combination of LBP and SVM obtained the best values in the evaluation metrics considered, among them, 99.99% Specificity and 99.98% Precision in the navigation process. SVM reached 5.49787 s in combination with LBP completes the training in 5.49787 s. Concerning the testing time, SVM achieving 80.91 ms in association with LBP.

Huntington’s disease is a hereditary condition in which brain nerve cells rupture over time. This work proposes a new method for the detection of Huntington’s disease using digitised voice signals by diseased and healthy volunteers while they were reading Lithuanian poems. In this approach, the produced features by voice signals suffer a dimensionality reduction to optimize the prediction stage. The performance evaluation regarded 186 speech exams and 24 volunteers, combining twelve audio signal feature extractors with classification models. The results indicate an excellent performance, reaching precision and accuracy over 99 percent with prediction time below 1 second. This approach shows promising results indicating its usability to improve the medical diagnosis via computer-aided diagnosis.

The identification of different textile fabrics is a task commonly learned in practice and, therefore, is considered a very strenuous and costly form of learning, causing annoyance to the individual who performs it. Based on this context, this paper proposes a new method for classifying textile fabrics, based on the development of a computer vision system using Convolutional Neural Network (CNN). CNN works as a feature extractor by incorporating the concept of Transfer Learning. Using Transfer Learning allows a pre-trained CNN model to be reused for a new problem. In order to highlight the high performance of CNN, an analysis is performed with feature extractors established in the literature. Parameters such as Accuracy, F1-Score, and processing time are considered to evaluate the efficiency of the proposed approach. For the classification were used Bayesian Classifier, Multi-layer Perceptron (MLP), k-Nearest Neighbor (kNN), Random Forest (RF), and Support Vector Machine (SVM). The results show that the best combination is the CNN architecture DenseNet201 with SVM (RBF), obtaining an accuracy of 94% and F1-Score of 94.2%.

In the past few years, the mobile robotics has presented a growth in the research field, due to its high application area. The robot localization and its displacement in an unknown environment are fundamental to its operation. Signal processing techniques, machine learning methods, and sensors assist in the environment mapping, localization, and displacement of the mobile robot in the environment. In this paper, five machine learning techniques used: Bayes, MLP, KNN, SVM and OPF. We have used the following evaluation metrics: accuracy, sensitivity, and specificity obtained through matrices of confusion. This paper has as certain objective the analysis of the machine learning methods for later use in an embedded system. The OPF method using Canberra distance obtained an accuracy of 98.13% and is, therefore, the most appropriate method to use on the proposed location system.

With the advancement of Unmanned Aerial Vehicles (UAVs) and their application in the most diverse areas, the demand for increasingly better precision in their positioning and navigation task has arisen. Based on this context, this article proposes a new approach for localization and navigation UAVs using topological maps and Convolutional Neural Networks (CNN). CNN is used as features extractor, according to the concept of Transfer Learning. The use of topological maps helps to guide the vehicle through the exploration environment. To evaluate the performance of the approach, parameters such as Accuracy, F1-Score, and processing time are considered. For the classification were used Bayesian Classifier, k-Nearest Neighbor (kNN), Multi-layer Perceptron (MLP), Optimum-Path Forest (OPF) and Support Vector Machine (SVM). The results show that CNN achieved 99.97% Accuracy and also F1-Score in combination with most of the considered classifiers, proving the effectiveness of our approach.

Stroke is an injury that abruptly affects brain tissues. This disease is caused by a change in the blood supply to a particular region of the brain, and it results in the loss or reduction of its related functions. Cerebral vascular accidents affect 16 million people worldwide every year, and 6 million of these people die. However, another important problem related to strokes, besides mortality, is that many survivors have chronic consequences that are complex and heterogeneous. In this paper, a new approach to identify and classify stroke from a structural co-occurrence matrix (SCM) in skull CT images. To state the efficiency of the technique considered, a comparison with other important and well-known state-of-art feature extractors was performed. In addition, SCM was evaluated with two high-pass filters (Laplacian and Sobel) and two low-pass filters (Median and Gaussian). Regarding classifiers, Bayesian classifier, Optimum-Path Forest (OPF) and Support Vector Machines were used. The proposed approach using its optimal configuration and the OPF classifier with Euclidean distance had the highest average accuracy (99.40%), and the extraction time low, similar to other ones widely used and well known.

In this paper, we propose a new localization and navigation approach for mobile robots using topological maps and classification with reject option applying convolutional neural networks (CNN) for feature extraction in omnidirectional images. The use of CNN as feature extractor is based on the concept of Transfer Learning. Reject option is used to improve the task of the classifiers, querying information from the topological map. With the objective of evidencing the high performance of the technique considered, an analysis is made between several feature extractors and classifiers, established in the literature. Parameters such as processing time and accuracy are calculated to prove the credibility and effectiveness of the approach, since these properties are fundamental in the analysis of embedded systems. Considering the proposed approach, CNN stands out among the other feature extractors, as it generated the best results in extraction time and accuracy. It obtained an average accuracy of 99.86% and an extraction time of 0.1517s, proving to be a relevant method for the localization and navigation activities.

Parkinson’s disease affects millions of people around the world and consequently various approaches have emerged to help diagnose this disease, among which we can highlight handwriting exams. Extracting features from handwriting exams is an important contribution of the computational field for the diagnosis of this disease. In this paper, we propose an approach that measures the similarity between the exam template and the handwritten trace of the patient following the exam template. This similarity was measured using the Structural Cooccurrence Matrix to calculate how close the handwritten trace of the patient is to the exam template. The proposed approach was evaluated using various exam templates and the handwritten traces of the patient. Each of these variations was used together with the Naïve Bayes, OPF, and SVM classifiers. In conclusion the proposed approach was proven to be better than the existing methods found in the literature and is therefore a promising tool for the diagnosis of Parkinson’s disease.

Parkinson’s disease affects millions of people worldwide. Nowadays there are several ways to help diagnose this disease. Among which we can highlight handwriting exams. One of the main contributions of the computational field to help diagnose this disease is the feature extraction of handwriting exams. This paper proposed a similarity extraction approach which was applied to the exam template and the handwritten trace of the patient. The similarity metrics used in this work were: structural similarity, mean squared error and peak signal-to-noise ratio. The proposed approach was evaluated with variations in obtaining the exam template and the handwritten trace generated by the patient. Each of these variations was used together with the Nave Bayes, OPF, and SVM classifiers. In conclusion, the proposed approach demonstrated that it was better than the other approach found in the literature, and is therefore a potential aid in the detection and monitoring of Parkinson’s disease.