Clasificación de sentimientos semi-supervisada y dependiente de objetivo para micro- blogs

The wealth of opinions expressed in micro-blogs, such as tweets, motivated researchers to develop techniques for automatic opinion detection. However, accuracies of such techniques are still limited. Moreover, current techniques focus on detecting sentiment polarity regardless of the topic (target) discussed. Detecting sentiment towards a specific target, referred to as target-dependent sentiment classification, has not received adequate researchers’ attention. Literature review has shown that all target-dependent approaches use supervised learning techniques. Such techniques need a large number of labeled data. However, labeling data in social media is cumbersome and error prone. The research presented in this paper addresses this issue by employing semi-supervised learning techniques for target-dependent sentiment classification. Semisupervised learning techniques make use of labeled as well as unlabeled data. In this paper, we present a new semi-supervised learning technique that uses less number of labeled micro-blogs than that used by supervised learning techniques. Experiment results have shown that the proposed technique provides comparable accuracy.Facultad de Informátic

Clasificación de sentimientos semi-supervisada y dependiente de objetivo para micro- blogs

The wealth of opinions expressed in micro-blogs, such as tweets, motivated researchers to develop techniques for automatic opinion detection. However, accuracies of such techniques are still limited. Moreover, current techniques focus on detecting sentiment polarity regardless of the topic (target) discussed. Detecting sentiment towards a specific target, referred to as target-dependent sentiment classification, has not received adequate researchers’ attention. Literature review has shown that all target-dependent approaches use supervised learning techniques. Such techniques need a large number of labeled data. However, labeling data in social media is cumbersome and error prone. The research presented in this paper addresses this issue by employing semi-supervised learning techniques for target-dependent sentiment classification. Semisupervised learning techniques make use of labeled as well as unlabeled data. In this paper, we present a new semi-supervised learning technique that uses less number of labeled micro-blogs than that used by supervised learning techniques. Experiment results have shown that the proposed technique provides comparable accuracy.Facultad de Informátic

Metaheuristic Clustering Algorithm

In this thesis we describe an essential problem in data clustering and present some solutions for it. We investigate using distance measures other than Euclidean type for improving the performance of clustering. We also develop a new point symmetry-based distance measure and prove its efficiency. We develop a novel effective k-means algorithm which improves the performance of the k-mean algorithm. We develop a dynamic linkage clustering algorithm using kd-tree and we prove its high performance. The Automatic Clustering Differential Evolution (ACDE) is specific to clustering simple data sets and finding the optimal number of clusters automatically. We improve ACDE for classifying more complex data sets using kd-tree. The proposed algorithms do not have a worst-case bound on running time that exists in many similar algorithms in the literature. Experimental results shown in this thesis demonstrate the effectiveness of the proposed algorithms. We compare the proposed algorithms with other famous similar algorithms. We present the proposed algorithms and their performance results in detail along with promising avenues of future research

Interest rates volatility and its consequences on stock returns: The case study from Amman Stock Exchange, Jordan

Abstract. This paper examines the special effects of interest rates on the stock market return by using monthly time series data for the economy of Jordan over the period of 2006 to 2016. An extensive variety of econometric procedures have been involved to analyze the relationship between the interest rate and stock market return. The study exposes a constant and significant long-run relationship between the variables. By using Cointegration methods the experimental in the long run represents that a one percent rise in interest rate causes (12.3459 %)reduction in market index. The assessed error correction coefficient highlight that (-0.678522) percent deviation of stock returns are corrected in the short run. Impulse response function of the study furthermore sustains the positive relationship between the variables. The result of Variance decompositions recommends that about (99.99705%) of the variation in stock market returns is referring to its own shock which denotes that stock market returns are mostly independent of the other variables in the structure. To go over the main points, Granger causality analysis yield that there is no presence of a unidirectional causality as of interest rate to the market index.Keywords. Stock market, Cointegration, Granger causality, Interest rate, ASE.JEL. E40, E43, G12

Unusual case of pancreatic inflammatory myofibroblastic tumor associated with spontaneous splenic rupture

<p>Abstract</p> <p>Background</p> <p>Spontaneous splenic rupture considered a relatively rare but life threatening. The three commonest causes of spontaneous splenic rupture are malignant hematological diseases, viral infections and local inflammatory and neoplastic disorders. We describe a unique and unusual case of inflammatory myofibroblastic tumor of the tail of pancreas presented with massively enlarged spleen and spontaneous splenic rupture.</p> <p>Case presentation</p> <p>A 19 years old male patient with no significant past medical history presented to emergency room with abdominal pain and fatigue. Massively enlarged spleen was detected. Hypotension and rapid reduction of hemoglobin level necessitated urgent laparatomy. About 1.75 liters of blood were found in abdominal cavity. A large tumor arising from the tail of pancreas and local rupture of an enlarged spleen adjacent to the tumor were detected. Distal pancreatectomy and splenectomy were performed. To our knowledge, we report the first case of massively enlarged spleen that was complicated with spontaneous splenic rupture as a result of splenic congestion due to mechanical obstruction caused by an inflammatory myofibroblastic tumor of the tail of pancreas. A review of the literature is also presented.</p> <p>Conclusion</p> <p>Inflammatory myofibroblastic tumor of the tail of pancreas should be included in the differential diagnosis of the etiological causes of massively enlarged spleen and spontaneous splenic rupture.</p

Clasificación de sentimientos semi-supervisada y dependiente de objetivo para micro- blogs

The wealth of opinions expressed in micro-blogs, such as tweets, motivated researchers to develop techniques for automatic opinion detection. However, accuracies of such techniques are still limited. Moreover, current techniques focus on detecting sentiment polarity regardless of the topic (target) discussed. Detecting sentiment towards a specific target, referred to as target-dependent sentiment classification, has not received adequate researchers’ attention. Literature review has shown that all target-dependent approaches use supervised learning techniques. Such techniques need a large number of labeled data. However, labeling data in social media is cumbersome and error prone. The research presented in this paper addresses this issue by employing semi-supervised learning techniques for target-dependent sentiment classification. Semisupervised learning techniques make use of labeled as well as unlabeled data. In this paper, we present a new semi-supervised learning technique that uses less number of labeled micro-blogs than that used by supervised learning techniques. Experiment results have shown that the proposed technique provides comparable accuracy.Facultad de Informátic

Fenofibrate for COVID-19 and related complications as an approach to improve treatment outcomes: the missed key for Holy Grail

Financiado para publicación en acceso aberto: Universidade de Vigo/CISUGIntroduction Fenofbrate is an agonist of peroxisome proliferator activated receptor alpha (PPAR-α), that possesses antiinfammatory, antioxidant, and anti-thrombotic properties. Fenofbrate is efective against a variety of viral infections and diferent infammatory disorders. Therefore, the aim of critical review was to overview the potential role of fenofbrate in the pathogenesis of SARS-CoV-2 and related complications. Results By destabilizing SARS-CoV-2 spike protein and preventing it from binding angiotensin-converting enzyme 2 (ACE2), a receptor for SARS-CoV-2 entry, fenofbrate can reduce SARS-CoV-2 entry in human cells Fenofbrate also suppresses infammatory signaling pathways, which decreases SARS-CoV-2 infection-related infammatory alterations. In conclusion, fenofbrate anti-infammatory, antioxidant, and antithrombotic capabilities may help to minimize the infammatory and thrombotic consequences associated with SARSCoV-2 infection. Through attenuating the interaction between SARS-CoV-2 and ACE2, fenofbrate can directly reduce the risk of SARS-CoV-2 infection. Conclusions As a result, fenofbrate could be a potential treatment approach for COVID-19 control