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Search Results: 1 - 10 of 944 matches for " Cornelia Caragea "
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Privacy Prediction of Images Shared on Social Media Sites Using Deep Features
Ashwini Tonge,Cornelia Caragea
Computer Science , 2015,
Abstract: Online image sharing in social media sites such as Facebook, Flickr, and Instagram can lead to unwanted disclosure and privacy violations, when privacy settings are used inappropriately. With the exponential increase in the number of images that are shared online every day, the development of effective and efficient prediction methods for image privacy settings are highly needed. The performance of models critically depends on the choice of the feature representation. In this paper, we present an approach to image privacy prediction that uses deep features and deep image tags as feature representations. Specifically, we explore deep features at various neural network layers and use the top layer (probability) as an auto-annotation mechanism. The results of our experiments show that models trained on the proposed deep features and deep image tags substantially outperform baselines such as those based on SIFT and GIST as well as those that use "bag of tags" as features.
Semi-supervised prediction of protein subcellular localization using abstraction augmented Markov models
Caragea Cornelia,Caragea Doina,Silvescu Adrian,Honavar Vasant
BMC Bioinformatics , 2010, DOI: 10.1186/1471-2105-11-s8-s6
Abstract: Background Determination of protein subcellular localization plays an important role in understanding protein function. Knowledge of the subcellular localization is also essential for genome annotation and drug discovery. Supervised machine learning methods for predicting the localization of a protein in a cell rely on the availability of large amounts of labeled data. However, because of the high cost and effort involved in labeling the data, the amount of labeled data is quite small compared to the amount of unlabeled data. Hence, there is a growing interest in developing semi-supervised methods for predicting protein subcellular localization from large amounts of unlabeled data together with small amounts of labeled data. Results In this paper, we present an ion Augmented Markov Model (AAMM) based approach to semi-supervised protein subcellular localization prediction problem. We investigate the effectiveness of AAMMs in exploiting unlabeled data. We compare semi-supervised AAMMs with: (i) Markov models (MMs) (which do not take advantage of unlabeled data); (ii) an expectation maximization (EM); and (iii) a co-training based approaches to semi-supervised training of MMs (that make use of unlabeled data). Conclusions The results of our experiments on three protein subcellular localization data sets show that semi-supervised AAMMs: (i) can effectively exploit unlabeled data; (ii) are more accurate than both the MMs and the EM based semi-supervised MMs; and (iii) are comparable in performance, and in some cases outperform, the co-training based semi-supervised MMs.
Entity-Specific Sentiment Classification of Yahoo News Comments
Prakhar Biyani,Cornelia Caragea,Narayan Bhamidipati
Computer Science , 2015,
Abstract: Sentiment classification is widely used for product reviews and in online social media such as forums, Twitter, and blogs. However, the problem of classifying the sentiment of user comments on news sites has not been addressed yet. News sites cover a wide range of domains including politics, sports, technology, and entertainment, in contrast to other online social sites such as forums and review sites, which are specific to a particular domain. A user associated with a news site is likely to post comments on diverse topics (e.g., politics, smartphones, and sports) or diverse entities (e.g., Obama, iPhone, or Google). Classifying the sentiment of users tied to various entities may help obtain a holistic view of their personality, which could be useful in applications such as online advertising, content personalization, and political campaign planning. In this paper, we formulate the problem of entity-specific sentiment classification of comments posted on news articles in Yahoo News and propose novel features that are specific to news comments. Experimental results show that our models outperform state-of-the-art baselines.
Keyword and Keyphrase Extraction Using Centrality Measures on Collocation Networks
Shibamouli Lahiri,Sagnik Ray Choudhury,Cornelia Caragea
Computer Science , 2014,
Abstract: Keyword and keyphrase extraction is an important problem in natural language processing, with applications ranging from summarization to semantic search to document clustering. Graph-based approaches to keyword and keyphrase extraction avoid the problem of acquiring a large in-domain training corpus by applying variants of PageRank algorithm on a network of words. Although graph-based approaches are knowledge-lean and easily adoptable in online systems, it remains largely open whether they can benefit from centrality measures other than PageRank. In this paper, we experiment with an array of centrality measures on word and noun phrase collocation networks, and analyze their performance on four benchmark datasets. Not only are there centrality measures that perform as well as or better than PageRank, but they are much simpler (e.g., degree, strength, and neighborhood size). Furthermore, centrality-based methods give results that are competitive with and, in some cases, better than two strong unsupervised baselines.
Glycosylation site prediction using ensembles of Support Vector Machine classifiers
Cornelia Caragea, Jivko Sinapov, Adrian Silvescu, Drena Dobbs, Vasant Honavar
BMC Bioinformatics , 2007, DOI: 10.1186/1471-2105-8-438
Abstract: We explore machine learning methods for training classifiers to predict the amino acid residues that are likely to be glycosylated using information derived from the target amino acid residue and its sequence neighbors. We compare the performance of Support Vector Machine classifiers and ensembles of Support Vector Machine classifiers trained on a dataset of experimentally determined N-linked, O-linked, and C-linked glycosylation sites extracted from O-GlycBase version 6.00, a database of 242 proteins from several different species. The results of our experiments show that the ensembles of Support Vector Machine classifiers outperform single Support Vector Machine classifiers on the problem of predicting glycosylation sites in terms of a range of standard measures for comparing the performance of classifiers. The resulting methods have been implemented in EnsembleGly, a web server for glycosylation site prediction.Ensembles of Support Vector Machine classifiers offer an accurate and reliable approach to automated identification of putative glycosylation sites in glycoprotein sequences.Glycosylation is one of the most complex and ubiquitous post-translational modifications (PTMs) of proteins in eukaryotic cells. It is a dynamic enzymatic process in which saccharides are attached to proteins or lipoproteins, usually on serine (S), threonine (T), asparagine (N), and tryptophan (W) residues. Glycosylation, like phosphorylation, is clinically important because of its role in a wide variety of cellular, developmental and immunological processes, including protein folding, protein trafficking and localization, cell-cell interactions, and epitope recognition [1-8].Glycosylation can be classified into four types based on the nature of chemical linkage between specific acceptor residues in the protein and sugar: N-linked and O-linked glycosylation, C-mannosylation, and GPI (glycosylphosphatidylinositol) anchors. The acceptor residues represent the glycosylation sites.In N-lin
Empirical Analysis of Time in Relation to Economic Development. A System of Time Accounts
Revista Romana de Economie , 2010,
Abstract: The paper proposes a new approach to the relation between socio-economic development and time. Measuring the economic development of a country by GDP it is obvious that the indicator is an insufficient measure in order to illustrate the progress of the society. National Time Accounting is a set of methods for measuring, comparing and analyzing how people spend and experience their time. The approach is based on evaluated time use or the flow of emotional experience during daily activities. In order to determine the level of development an international system of new statistical indicators was elaborated to express development trough the quality of life growing. The indicators are related to the economic level of the country, living and environmental conditions, employment and the quality of human capital in labour market, but also they reflect the household activities, the balance between professional and private life of people, health condition. The U-index helps to overcome some of the limitations of interpersonal comparisons of subjective well-being.
Time Allocation in Economics and the Implications for Economic Development
Nicoleta CARAGEA
Revista Romana de Economie , 2009,
Abstract: In a modern and more complex society, in which there are not borders between the professional family and social life of individuals, time should be viewed as an economic resource and it has to be optimized, rationalized and controlled.In this paper I will try to identify the main coordinates and the dominant points of view of economic thinking focused on time allocation, at both national and international level. The paper also presents an analysis of the correlation between time use and economic development, on the basis of some regression models.
Challenges of the Knowledge Society , 2011,
Abstract: Health inequality is met everywhere in the world, including in countries with a high level of economic development, or those with strong social protection systems. In this paper I analyzed certain methods to measure health inequalities between population groups and also I presented some empirical results regarding health disparities between European Union countries. My research is focussed on three health areas: health status of population, access to health care services and resource allocation and population spending on health care.
Protein-RNA interface residue prediction using machine learning: an assessment of the state of the art
Rasna R Walia, Cornelia Caragea, Benjamin A Lewis, Fadi G Towfic, Michael Terribilini, Yasser El-Manzalawy, Drena Dobbs, Vasant Honavar
BMC Bioinformatics , 2012, DOI: 10.1186/1471-2105-13-89
Abstract: We provide a review of published approaches for predicting RNA-binding residues in proteins and a systematic comparison and critical assessment of protein-RNA interface residue predictors trained using these approaches on three carefully curated non-redundant datasets. We directly compare two widely used machine learning algorithms (Na?ve Bayes (NB) and Support Vector Machine (SVM)) using three different data representations in which features are encoded using either sequence- or structure-based windows. Our results show that (i) Sequence-based classifiers that use a position-specific scoring matrix (PSSM)-based representation (PSSMSeq) outperform those that use an amino acid identity based representation (IDSeq) or a smoothed PSSM (SmoPSSMSeq); (ii) Structure-based classifiers that use smoothed PSSM representation (SmoPSSMStr) outperform those that use PSSM (PSSMStr) as well as sequence identity based representation (IDStr). PSSMSeq classifiers, when tested on an independent test set of 44 proteins, achieve performance that is comparable to that of three state-of-the-art structure-based predictors (including those that exploit geometric features) in terms of Matthews Correlation Coefficient (MCC), although the structure-based methods achieve substantially higher Specificity (albeit at the expense of Sensitivity) compared to sequence-based methods. We also find that the expected performance of the classifiers on a residue level can be markedly different from that on a protein level. Our experiments show that the classifiers trained on three different non-redundant protein-RNA interface datasets achieve comparable cross-validation performance. However, we find that the results are significantly affected by differences in the distance threshold used to define interface residues.Our results demonstrate that protein-RNA interface residue predictors that use a PSSM-based encoding of sequence windows outperform classifiers that use other encodings of sequence windows. Whi
Time allocation differences among human generations in Romania
Nicoleta Caragea,Carmen Armstrong
Perspectives of Innovations, Economics and Business , 2010,
Abstract: Daily time is limited to 24 hours, but the quality of life depends by the individual time allocation structure and also by the social values and norms, traditions and the economic development of the country. For the individuals, the structure of time is different across particular stage of the life cycle. The main objective of this paper is to investigate the essential differences between the patterns of time allocation of Romanian population in some stages of the life cycles, especially in the early childhood education, higher education, and labor market entry.
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