Universities and companies have decision-making processes that allow to achieve institutional objectives. Currently, data analysis has an important role in generating knowledge, obtaining important patterns and predictions for formulating strategies. This article presents the design of a business intelligence governance framework for the Universidad de la Costa, easily replicable in other institutions. For this purpose, a diagnosis was made to identify the level of maturity in analytics. From this baseline, a model was designed to strengthen organizational culture, infrastructure, data management, data analysis and governance. The proposal contemplates the definition of a governance framework, guiding principles, strategies, policies, processes, decisionmaking body and roles. Therefore, the framework is designed to implement effective controls that ensure the success of business intelligence projects, achieving an alignment of the objectives of the development plan with the analytical vision of the institution.
Diagnosis of learning difficulties is a challenging goal. There are huge number of factors involved in the evaluation procedure that present high variance among the population with the same difficulty. Diagnosis is usually performed by scoring subjects according to results obtained in different neuropsychological (performance-based) tests specifically designed to this end. One of the most frequent disorders is developmental dyslexia (DD), a specific difficulty in the acquisition of reading skills not related to mental age or inadequate schooling. Its prevalence is estimated between 5% and 12% of the population. Traditional tests for DD diagnosis aim to measure different behavioral variables involved in the reading process. In this paper, we propose a diagnostic method not based on behavioral variables but on involuntary neurophysiological responses to different auditory stimuli. The experiments performed use electroencephalography (EEG) signals to analyze the temporal behavior and the spectral content of the signal acquired from each electrode to extract relevant (temporal and spectral) features. Moreover, the relationship of the features extracted among electrodes allows to infer a connectivity-like model showing brain areas that process auditory stimuli in a synchronized way. Then an anomaly detection system based on the reconstruction residuals of an autoencoder using these features has been proposed. Hence, classification is performed by the proposed system based on the differences in the resulting connectivity models that have demonstrated to be a useful tool for differential diagnosis of DD as well as a method to step towards gaining a better knowledge of the brain processes involved in DD. The results corroborate that nonspeech stimulus modulated at specific frequencies related to the sampling processes developed in the brain to capture rhymes, syllables and phonemes produces effects in specific frequency bands that differentiate between controls and DD subjects. The proposed method showed relatively high sensitivity above 0.6, and up to 0.9 in some of the experiments.
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Agriculture, and natural resources associated to its development like water, soils and forests, have a relevant role in the future of countries and environmental conservation. The optimization of these resources is made with the implementation of technological strategies and tools that make it possible. In this sense, we developed a monitoring prototype for agronomic variables in cassava crops (Manihot Esculenta Crantz) in the Atlántico department (Colombia) based in WSN using Z1 motes as hardware platform and the temperature and soil moisture sensor SHT11. The operating system used was Contiki, and the routing protocol was RPL. The Network Performance Metrics evaluated were packet loss, RSSI (Received Signal Strength Indicator), LQI (Link Quality Indicator) and network convergence time. Then, a deployment model using Schläfli notation to determine the location and number of nodes, also we calculated the coverage range of the nodes to keep network uniformity. With these calculations, we obtained the linkage budgets between specks, and results were validated with RadioMobile software. Then, test fields were made in a cassava crop located in the city of Manati, Atlántico. Finally, with the help of server client architecture XAMPP, all data was stored and visualized through SIMCA (Agricultural Crop Information and Monitoring System), a web application developed by authors.
Electroencephalography (EEG) signals provide an important source of information of brain activity at different areas. This information can be used to diagnose brain disorders according to different activation patterns found in controls and patients. This acquisition technology can be also used to explore the neural basis of less evident learning disabilities such as Developmental Dyslexia (DD). DD is a specific difficulty in the acquisition of reading skills not related to mental age or inadequate schooling, whose prevalent is estimated between 5% and 12% of the population. In this paper we propose a method to extract discriminative features from EEG signals based on the relationship among the spectral density at each channel. This relationship is computed by means of different correlation measures, inferring connectivity-like markers that are eventually selected and classified by a linear support vector machine. The experiments performed shown AUC values up to 0.7, demonstrating the applicability of the proposed approach for objective DD diagnosis.
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