New Media E-marketing Campaign. Case Study for a Romanian Press Trust

AbstractThe impact of new media on the organization of the companies has increased a lot in the recent years. This was achieved either through the implementation of new media tools for the business management, or by opening up to clients or other companies using social media. Attracting new customers and communicate with them by using social media platforms such as Facebook or Twitter that have become a necessity. In this work we have done a case study applied to press trust in Romania. For a period of 2 years will be done a total of 5 marketing campaigns on the social network platform Facebook. In this paper we will present the results of the first three campaigns, the other two still being in progress. We will see the link between campaigns and various specific metrics, such as the number of users, the number of likes on the company's Facebook page or revenues from online advertising

NEAR-LOSSLESS LZW IMAGE COMPRESSION

Abstract: Classical image compression methods are based on measuring the error only at entire image level. In some areas there is an obvious need for getting an upper bound for the error at the pixel level. In the paper we propose such a near-lossess method based on LZW dictionary algorithm. The modifications needed to adapt LZW to become a near-lossless method are presented. As far as we know this is the first attempt to use LZW as a near-lossless method. Experimental results done and presented in the paper prove that the method gives better that the one based on quadtree partitioning so the proposed method is promising

Methodological Approach for Messages Classification on Twitter Within E-Government Area

© 2018, Springer Nature Switzerland AG.The constant growth in the numbers of Social Media users is a reality of the past few years. Companies, governments and researchers focus on extracting useful data from Social Media. One of the most important things we can extract from the messages transmitted from one user to another is the sentiment—positive, negative or neutral—regarding the subject of the conversation. There are many studies on how to classify these messages, but all of them need a huge amount of data already classified for training, data not available for Romanian language texts. We present a case study in which we use a Naïve Bayes classifier trained on an English short text corpus on several thousand Romanian texts. We use Google Translate to adapt the Romanian texts and we validate the results by manually classifying some of them

A Decision-Tree Approach to Assist in Forecasting the Outcomes of the Neonatal Brain Injury

Neonatal brain injury or neonatal encephalopathy (NE) is a significant morbidity and mortality factor in preterm and full-term newborns. NE has an incidence in the range of 2.5 to 3.5 per 1000 live births carrying a considerable burden for neurological outcomes such as epilepsy, cerebral palsy, cognitive impairments, and hydrocephaly. Many scoring systems based on different risk factor combinations in regression models have been proposed to predict abnormal outcomes. Birthweight, gestational age, Apgar scores, pH, ultrasound and MRI biomarkers, seizures onset, EEG pattern, and seizure duration were the most referred predictors in the literature. Our study proposes a decision-tree approach based on clinical risk factors for abnormal outcomes in newborns with the neurological syndrome to assist in neonatal encephalopathy prognosis as a complementary tool to the acknowledged scoring systems. We retrospectively studied 188 newborns with associated encephalopathy and seizures in the perinatal period. Etiology and abnormal outcomes were assessed through correlations with the risk factors. We computed mean, median, odds ratios values for birth weight, gestational age, 1-min Apgar Score, 5-min Apgar score, seizures onset, and seizures duration monitoring, applying standard statistical methods first. Subsequently, CART (classification and regression trees) and cluster analysis were employed, further adjusting the medians. Out of 188 cases, 84 were associated to abnormal outcomes. The hierarchy on etiology frequencies was dominated by cerebrovascular impairments, metabolic anomalies, and infections. Both preterms and full-terms at risk were bundled in specific categories defined as high-risk 75–100%, intermediate risk 52.9%, and low risk 0–25% after CART algorithm implementation. Cluster analysis illustrated the median values, profiling at a glance the preterm model in high-risk groups and a full-term model in the inter-mediate-risk category. Our study illustrates that, in addition to standard statistics methodologies, decision-tree approaches could provide a first-step tool for the prognosis of the abnormal outcome in newborns with encephalopathy