based on the Social Spider Optimization(SSO) algorithm for optimizing clusters of data, taking as metric the sum of euclidean distances. Other important algorithms of the literature were implemented in order to make comparisons: K-means algorithm, and a Genetic Algorithm (GA) for Clustering. Experiments were performed using 5 datasets taken from the UCI Machine Learning Repository, each algorithm was executed many times and then the following measures were calculated: mean, median, minimum, and maximum values of the results. These experiments showed that the SSO algorithm outperforms the K- means algorithm, and it has results equally competitive as the GA. All these results were confirmed by statistical tests performed over the outputs of the algorithm.

adverse activity. In this study, we show a comparative analysis of the performance of two descriptors to detect anomaly trajectories. We define Wavelet and Fourier transforms as trajectory descriptors to generate characteristics and subsequently detect anomalies. The experiments emphasize performance in the description in the coefficient feature space. For that, we used unsupervised learning, specifically clustering techniques, to generate subsets and identify which are irregular. The implications of the study demonstrate that it is possible to use descriptors in trajectories for automatic anomaly detection and the use of unsupervised learning methods that automatically segment the required information. The performance and comparative analysis of our study are demonstrated through experiments and a case study considering synthetic and real data sets that leave evidence of our contribution.

applications to inform passengers. ITS are a reliable source of data collection for further analysis. This research aims to perform a clustering analysis to detect traffic jams, for which the Parallel Social Spider Optimization (P-SSO) algorithm was used. The choice of the P-SSO algorithm was made based on the previous results where the P-SSO algorithm was compared with the K-means and Social Spider Optimization (SSO) algorithms to solve clustering problems. On the first stage of this research, the P-SSO algorithm was used to define traffic jam states. In the second stage, the P-SSO algorithm was used to detect traffic jam areas based on the geospatial data generated by public transport buses in Cusco-Perú. This clustering analysis was performed on the main streets of the city between June and August 2019. Further, an application was developed that allows to check graphically the clusters obtained.

interactions. However, available datasets do not cover near-accident scenarios that our dataset covers. We introduce I see you, a new vehicle-pedestrian interaction dataset that tackles the lack of trajectory data in near-accident scenarios using YOLOv5 and camera calibration methods. I see you consist of 170 near-accident occurrences in seven intersections in Cusco-Peru. This new dataset and pipeline code are available on GitHub.

in diverse areas such as surveillance, tracking devices, traffic, and people flow. This study presents a methodology to detect anomaly trajectories based on their morphology features. For that, we follow two stages: (1) comparative analysis of the performance of two descriptors to group similar trajectories, and (2) trajectory anomaly detection based on their similarities. We define Wavelet and Fourier transforms as trajectory descriptors to generate characteristics and subsequently detect anomalies. Our experiments emphasize the measure of the performance in the description of the coefficient feature space using unsupervised learning, specifically clustering techniques, to create subsets and identify irregular ones. The study's implications demonstrate that it is possible to use descriptors in trajectories for automatic anomaly detection and the use of unsupervised learning to segment required information. Our study's performance and comparative analysis have been demonstrated throughout multiple experiments. We present some quantitative results using synthetic data sets as well as qualitative analysis throughout two case studies considering real data sets that leave evidence of our contribution.

be capable of identifying if a given image does not belong to any of the classes of interest. We propose a method based on convolutional neural networks that obtain effective feature vectors to perform accurate classification of buildings. Additionally, we analyze and propose methods for the problem of classification with rejection.

simplify the variety of traditional channels. The objective of the research is determined what extent applications in the cloud of Google Apps for Education improve internal communication between members of the university community. The results of the research reveal that the use of the Software as a Service Google Apps does not substantially improve internal communication among members of the university community. However, we have identified some factors that prevent the use of Google Apps for Education, such as limitations in computer infrastructure, low level of knowledge in computing, limited use of digital tools in academic activities and the absence of a strategic plan for internal communication.

noise, to improve the contrast under a variant illumination, to separate the hand from the background of the image and finally detect and cut the region containing the hand gesture. We use of convolutional neural networks (CNN) to classify the 24 hand gestures. Two CNN architectures were developed with different amounts of layers and parameters per layer. The tests showed that the first CNN has an accuracy of 95.37% and the second CNN has an accuracy of 96.20% in terms of recognition of the 24 static hand gestures using the database developed. We compared the two architectures developed in accuracy level for each type of invariance presented in this paper. We compared the two architectures developed and usual techniques of machine learning in results of accuracy.

necessary to consider due to the complexity of the gestures. We built a model for Peruvian sign language recognition (PSL) to Spanish composed by 4 phases; first, the preprocessing phase in charge to process RGB, depth and skeleton streams obtained through the Kinect sensor v.1; second, the feature extraction which learn spatial information through 3 types of convolutional neural network (CNN); third, the bidirectional long short term memory (BLSTM) with residual connections in charge to reduced and encode the information. Finally, a decoder with attention mechanism and maxout network which learn the temporal information. Our proposed model is evaluated in LSA64 and ourself-built dataset. The experimental results show significant improvement compared to other models evaluated in these dataset .

Lloyd’s algorithm to approximately solve (EMAX algorithm) a more robust variation of the k-means problem, namely the EMAX problem. Also, a new center-based clustering algorithm (SSO-C) is proposed, which is based on a swarm intelligence technique called Social Spider Optimization. This algorithm minimizes a multi-objective optimization function defined as a weighted combination of the objective functions of the k-means and EMAX problems. Also, an approximation algorithm for the discrete k-center problem is used as a local search strategy for initializing the population. Results of the experiments showed that SSO-C algorithm is suitable for finding maximum best values, however EMAX algorithm is better in finding median and mean values.

Social Spider Optimization (SSO). In this work, we propose parallel island models of the SSO algorithm for the Clustering problem, using 24 processors for each parallel algorithm. Such models were implemented using static and dynamic topologies, and datasets from the UCI Machine Learning Repository used for the stage of experiments. The achieved average speedups range from 15 to 28 times faster than the SSO algorithm for large and small datasets, respectively, and a parallel model with static ring topology performs a little bit faster than the other parallel models. The parallel algorithms provide results with similar precision to the ones computed with the SSO algorithm.