127 research outputs found
Detection of Malaria Parasite Based on Thick and Thin Blood Smear Images Using Local Binary Pattern
No full textHuman skin detection using histogram processing and Gaussian Mixture Model based on color spaces
No full textFinancial Planning Recommendation System Using Content-Based Collaborative and Demographic Filtering
No full textGene co-expression network construction and analysis for identification of genetic biomarkers associated with glioblastoma multiforme using topological findings
No full textAbstract Background Glioblastoma multiforme (GBM) is one of the most malignant types of central nervous system tumors. GBM patients usually have a poor prognosis. Identification of genes associated with the progression of the disease is essential to explain the mechanisms or improve the prognosis of GBM by catering to targeted therapy. It is crucial to develop a methodology for constructing a biological network and analyze it to identify potential biomarkers associated with disease progression. Methods Gene expression datasets are obtained from TCGA data repository to carry out this study. A survival analysis is performed to identify survival associated genes of GBM patient. A gene co-expression network is constructed based on Pearson correlation between the gene’s expressions. Various topological measures along with set operations from graph theory are applied to identify most influential genes linked with the progression of the GBM. Results Ten key genes are identified as a potential biomarkers associated with GBM based on centrality measures applied to the disease network. These genes are SEMA3B, APS, SLC44A2, MARK2, PITPNM2, SFRP1, PRLH, DIP2C, CTSZ, and KRTAP4.2. Higher expression values of two genes, SLC44A2 and KRTAP4.2 are found to be associated with progression and lower expression values of seven gens SEMA3B, APS, MARK2, PITPNM2, SFRP1, PRLH, DIP2C, and CTSZ are linked with the progression of the GBM. Conclusions The proposed methodology employing a network topological approach to identify genetic biomarkers associated with cancer
The efficacy of emamectin benzoate against infestations of Lepeophtheirus salmonis on farmed Atlantic salmon (Salmo salar L) in Scotland, 2002-2006
No full textInfestations of the parasitic copepod Lepeophtheirus salmonis, commonly referred to as sea lice, represent a major challenge to commercial salmon aquaculture. Dependence on a limited number of theraputants to control such infestations has led to concerns of reduced sensitivity in some sea lice populations. This study investigates trends in the efficacy of the in-feed treatment emamectin benzoate in Scotland, the active ingredient most widely used across all salmon producing regions. Study data were drawn from over 50 commercial Atlantic salmon farms on the west coast of Scotland between 2002 and 2006. An epi-informatics approach was adopted whereby available farm records, descriptive epidemiological summaries and statistical linear modelling methods were used to identify factors that significantly affect sea lice abundance following treatment with emamectin benzoate (SLICEH, Schering Plough Animal Health). The results show that although sea lice infestations are reduced following the application of emamectin benzoate, not all treatments are effective. Specifically there is evidence of variation across geographical regions and a reduction in efficacy over time. Reduced sensitivity and potential resistance to currently available medicines are constant threats to maintaining control of sea lice populations on Atlantic salmon farms. There is a need for on-going monitoring of emamectin benzoate treatment efficacy together with reasons for any apparent reduction in performance. In addition, strategic rotation of medicines should be encouraged and empirical evidence for the benefit of such strategies more fully evaluated
An improved conversation emotion detection using hybrid f-nn classifier
No full textEmotion recognition from text is a crucial task in natural language processing (NLP) with applications in sentiment analysis, human-computer interaction, and psychological research. In this study, we present a novel approach for text-based emotion recognition using a modified firefly algorithm (MFA). The firefly algorithm is a swarm intelligence method inspired by the bioluminescent communication of fireflies, and it is known for its simplicity and efficiency in optimization tasks. In this paper MFAbased model is evaluated on the international survey on emotion antecedents and reactions (ISEAR) dataset, which includes text entries categorized by various emotions. Experimental results indicate that our approach achieved promising outcomes. Specifically, the proposed method, which combines the firefly algorithm with a multilayer perceptron (MLP), attained an accuracy of 92.07%, surpassing most other approaches reported in the literature
Comparative Analysis of Neural Network and Machine Learning Techniques for Air Quality Prediction
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