Tool for filtering PubMed search results by sample size.

Objective: The most used search engine for scientific literature, PubMed, provides tools to filter results by several fields. When searching for reports on clinical trials, sample size can be among the most important factors to consider. However, PubMed does not currently provide any means of filtering search results by sample size. Such a filtering tool would be useful in a variety of situations, including meta-analyses or state-of-the-art analyses to support experimental therapies. In this work, a tool was developed to filter articles identified by PubMed based on their reported sample sizes. Materials and Methods: A search engine was designed to send queries to PubMed, retrieve results, and compute estimates of reported sample sizes using a combination of syntactical and machine learning methods. The sample size search tool is publicly available for download at http://ihealth.uemc.es. Its accuracy was assessed against a manually annotated database of 750 random clinical trials returned by PubMed. Results: Validation tests show that the sample size search tool is able to accurately (1) estimate sample size for 70% of abstracts and (2) classify 85% of abstracts into sample size quartiles. Conclusions: The proposed tool was validated as useful for advanced PubMed searches of clinical trials when the user is interested in identifying trials of a given sample size.

An intelligent surveillance platform for large metropolitan areas with dense sensor deployment.

This paper presents an intelligent surveillance platform based on the usage of large numbers of inexpensive sensors designed and developed inside the European Eureka Celtic project HuSIMS. With the aim of maximizing the number of deployable units while keeping monetary and resource/bandwidth costs at a minimum, the surveillance platform is based on the usage of inexpensive visual sensors which apply efficient motion detection and tracking algorithms to transform the video signal in a set of motion parameters. In order to automate the analysis of the myriad of data streams generated by the visual sensors, the platform's control center includes an alarm detection engine which comprises three components applying three different Artificial Intelligence strategies in parallel. These strategies are generic, domain-independent approaches which are able to operate in several domains (traffic surveillance, vandalism prevention, perimeter security, etc.). The architecture is completed with a versatile communication network which facilitates data collection from the visual sensors and alarm and video stream distribution towards the emergency teams. The resulting surveillance system is extremely suitable for its deployment in metropolitan areas, smart cities, and large facilities, mainly because cheap visual sensors and autonomous alarm detection facilitate dense sensor network deployments for wide and detailed coverage.

Temperature and relative humidity estimation and prediction in the tobacco drying process using Artificial Neural Networks.

This paper presents a system based on an Artificial Neural Network (ANN) for estimating and predicting environmental variables related to tobacco drying processes. This system has been validated with temperature and relative humidity data obtained from a real tobacco dryer with a Wireless Sensor Network (WSN). A fitting ANN was used to estimate temperature and relative humidity in different locations inside the tobacco dryer and to predict them with different time horizons. An error under 2% can be achieved when estimating temperature as a function of temperature and relative humidity in other locations. Moreover, an error around 1.5 times lower than that obtained with an interpolation method can be achieved when predicting the temperature inside the tobacco mass as a function of its present and past values with time horizons over 150 minutes. These results show that the tobacco drying process can be improved taking into account the predicted future value of the monitored variables and the estimated actual value of other variables using a fitting ANN as proposed.

A semantic autonomous video surveillance system for dense camera networks in Smart Cities.

This paper presents a proposal of an intelligent video surveillance system able to detect and identify abnormal and alarming situations by analyzing object movement. The system is designed to minimize video processing and transmission, thus allowing a large number of cameras to be deployed on the system, and therefore making it suitable for its usage as an integrated safety and security solution in Smart Cities. Alarm detection is performed on the basis of parameters of the moving objects and their trajectories, and is performed using semantic reasoning and ontologies. This means that the system employs a high-level conceptual language easy to understand for human operators, capable of raising enriched alarms with descriptions of what is happening on the image, and to automate reactions to them such as alerting the appropriate emergency services using the Smart City safety network.

Performance study of the application of Artificial Neural Networks to the completion and prediction of data retrieved by underwater sensors.

This paper presents a proposal for an Artificial Neural Network (ANN)-based architecture for completion and prediction of data retrieved by underwater sensors. Due to the specific conditions under which these sensors operate, it is not uncommon for them to fail, and maintenance operations are difficult and costly. Therefore, completion and prediction of the missing data can greatly improve the quality of the underwater datasets. A performance study using real data is presented to validate the approach, concluding that the proposed architecture is able to provide very low errors. The numbers show as well that the solution is especially suitable for cases where large portions of data are missing, while in situations where the missing values are isolated the improvement over other simple interpolation methods is limited.