The number of people diagnosed with autism spectrum disorder (ASD) has been increasing significantly. However, the early treatment has become restricted, reducing the potential benefits from this intervention and increasing family and social expenses as a result of late interventions. Underdeveloped countries have proportionally fewer people diagnosed with ASD. Thus, the accessibility to mechanisms used to identification of ASD is important in theses countries. The aim of this paper is to evaluate the use of convolutional neural networks to assist in the identification of ASD, using static two-dimensional facial images as input. The results show success for the studied approach. MobileNet and DenseNet201 obtained the best results with an average of 90.7% accuracy and standard deviations of 0.68% and 1.64%, respectively. DenseNet201 achieved an accuracy of 93.5% in the best cases.

Melanoma is considered the worst type of skin cancer. The early diagnosis of this disease is still a complex task due to many variables that must be analyzed. Because of this, new methodologies are becoming common in the literature due to the good results obtained. Convolutional Neural Networks are Deep Learning techniques capable of providing effective solutions in the classification of medical images. In this sense, this work developed a disease detection system using AlexNet and VGG-F convolutional architectures, trained with images of skin lesions to create feature descriptors, not classifiers. Other conventional descriptors of skin lesions were used to assess the quality of data obtained from the last layers of convolutional architectures. Data from all feature extraction processes were submitted to the conventional classifiers Support Vector Machine, Multilayer Perceptron, and K-Nearest Neighbor. The results obtained in the approach show that the feature extracting models are viable and can offer a more accurate melanoma diagnosis possibility. The VGG-F architecture obtained the best result, with an accuracy of 91.54% and a precision of 91.64% given by the K-Nearest Neighbor. It is possible to see that this result highlights the quality of data in convolutional architectures and can provide a sense of further research.

The analysis of pollen grains is a prominent task in areas such as ecology, food engineering, and others that have different purposes, such as identifying the origin of honey, as well as helping in the development of new products or evaluating the quality of the products. This research presents a CNN architecture to classify pollen grains that can have performance equal to or superior to those found in the literature. Using POLEN23E database. Two experiments were performed with this database, one of which used data augmentation to improve accuracy. Promising results were obtained, as the experiments achieved 92% accuracy in the worst case and 100% accuracy in the best case. Two experiments were performed where one of them used data augmentation to improve accuracy. Promising results were obtained, as the experiments achieved 92% accuracy in the worst case and 100% accuracy in the best case.

Motorcycle accidents have been rapidly growing throughout the years in many countries. Due to various social and economic factors, this type of vehicle is becoming increasingly popular. The helmet is the main safety equipment of motorcyclists, but many drivers do not use it. If an motorcyclist is without helmet an accident can be fatal. This paper aims to explain and illustrate an automatic method for motorcycles detection and classification on public roads and a system for automatic detection of motorcyclists without helmet. For this, a hybrid descriptor for features extraction is proposed based in Local Binary Pattern, Histograms of Oriented Gradients and the Hough Transform descriptors. Traffic images captured by cameras were used. The best result obtained from classification was an accuracy rate of 0.9767, and the best result obtained from helmet detection was an accuracy rate of 0.9423.

In recent years have increased the use of automated mechanisms for monitoring and enforcement of fines for traffic violations, such as radar, electronic spines and photosensors. Due to various economic and social factors use of motorcycles is gaining increasingly popular acceptance. Increasing the number of such vehicle with carelessness by conductors made to grow abruptly the number of accidents. The main security equipment of motorcyclists is the helmet but many conductors do not use it or use incorrectly. This work aims to study and implement methods for automatic detection of motorcyclists on public roads in order to, in a future work, the detection of non-use of helmets. For this purpose, transit images were used captured by video cameras. From these images different attributes have been taken through the SURF, HAAR, HOG, Fourier and K-means descriptors. And for classification of images were used classifiers as Multilayer Perceptron and Support Vector Machine.