Search results for: Oliva H. K. Chu

Authors: U. Bottigli, M.Carpinelli, P.L. Fiori, B. Golosio, A. Marras, G. L. Masala, P. Oliva

Abstract:

The counting process of cell colonies is always a long and laborious process that is dependent on the judgment and ability of the operator. The judgment of the operator in counting can vary in relation to fatigue. Moreover, since this activity is time consuming it can limit the usable number of dishes for each experiment. For these purposes, it is necessary that an automatic system of cell colony counting is used. This article introduces a new automatic system of counting based on the elaboration of the digital images of cellular colonies grown on petri dishes. This system is mainly based on the algorithms of region-growing for the recognition of the regions of interest (ROI) in the image and a Sanger neural net for the characterization of such regions. The better final classification is supplied from a Feed-Forward Neural Net (FF-NN) and confronted with the K-Nearest Neighbour (K-NN) and a Linear Discriminative Function (LDF). The preliminary results are shown.

Keywords: Automatic cell counting, neural network, region growing, Sanger net.

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Authors: Rafenomananjara Tsinjo Nirina, Tomoo Sekito, Andrianaivoravelona Jaconnet Oliva

Abstract:

Manufacturing a composite material from post-consumer bottles is an interesting outlet since Madagascar is still facing the challenges of managing plastic waste on the one hand and appropriate waste treatment facilities are not yet developed on the other hand. New waste management options are needed to divert End-Of-Life (EOL) soft plastic wastes from landfills and incineration. Waste polyethylene terephthalate (PET) bottles might be considered as a valuable resource and recovered into polymer concrete. The methodology is easy to implement and appropriate to the local context in Madagascar. This approach will contribute to the production of ecological building materials that might be profitable for the environment and the construction sector. This work aims to study the feasibility of using the post-consumer PET bottles as an alternative binding agent instead of the conventional Portland cement and water. Then, the mechanical and physical properties of the materials were evaluated.

Keywords: PET recycling, polymer concrete, ecological building materials, pollution mitigation.

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Authors: N. W. U. D. Chathurani, Shlomo Geva, Vinod Chandran, Proboda Rajapaksha

Abstract:

Selecting an appropriate image representation is the most important factor in implementing an effective Content-Based Image Retrieval (CBIR) system. This paper presents a multi-feature fusion approach for efficient CBIR, based on the distance distribution of features and relative feature weights at the time of query processing. It is a simple yet effective approach, which is free from the effect of features' dimensions, ranges, internal feature normalization and the distance measure. This approach can easily be adopted in any feature combination to improve retrieval quality. The proposed approach is empirically evaluated using two benchmark datasets for image classification (a subset of the Corel dataset and Oliva and Torralba) and compared with existing approaches. The performance of the proposed approach is confirmed with the significantly improved performance in comparison with the independently evaluated baseline of the previously proposed feature fusion approaches.

Keywords: Feature fusion, image retrieval, membership function, normalization.

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Authors: U. Bottigli, D.Cascio, F. Fauci, B. Golosio, R. Magro, G.L. Masala, P. Oliva, G. Raso, S.Stumbo

Abstract:

Purpose of this work is the development of an automatic classification system which could be useful for radiologists in the investigation of breast cancer. The software has been designed in the framework of the MAGIC-5 collaboration. In the automatic classification system the suspicious regions with high probability to include a lesion are extracted from the image as regions of interest (ROIs). Each ROI is characterized by some features based on morphological lesion differences. Some classifiers as a Feed Forward Neural Network, a K-Nearest Neighbours and a Support Vector Machine are used to distinguish the pathological records from the healthy ones. The results obtained in terms of sensitivity (percentage of pathological ROIs correctly classified) and specificity (percentage of non-pathological ROIs correctly classified) will be presented through the Receive Operating Characteristic curve (ROC). In particular the best performances are 88% ± 1 of area under ROC curve obtained with the Feed Forward Neural Network.

Keywords: Neural Networks, K-Nearest Neighbours, SupportVector Machine, Computer Aided Diagnosis.

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Authors: U. Bottigli, R.Chiarucci, B. Golosio, G.L. Masala, P. Oliva, S.Stumbo, D.Cascio, F. Fauci, M. Glorioso, M. Iacomi, R. Magro, G. Raso

Abstract:

Purpose of this work is to develop an automatic classification system that could be useful for radiologists in the breast cancer investigation. The software has been designed in the framework of the MAGIC-5 collaboration. In an automatic classification system the suspicious regions with high probability to include a lesion are extracted from the image as regions of interest (ROIs). Each ROI is characterized by some features based generally on morphological lesion differences. A study in the space features representation is made and some classifiers are tested to distinguish the pathological regions from the healthy ones. The results provided in terms of sensitivity and specificity will be presented through the ROC (Receiver Operating Characteristic) curves. In particular the best performances are obtained with the Neural Networks in comparison with the K-Nearest Neighbours and the Support Vector Machine: The Radial Basis Function supply the best results with 0.89 ± 0.01 of area under ROC curve but similar results are obtained with the Probabilistic Neural Network and a Multi Layer Perceptron.

Keywords: Neural Networks, K-Nearest Neighbours, Support Vector Machine, Computer Aided Detection

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Authors: A. Bárbara Cattozatto Fortunato, D. de Lucca Soave, E. Pinheiro de Mello, M. Piasentini Oliva, V. Tavares de Moraes, G. Wolf Lebrão, D. Fernandes Parra, S. Marraccini Giampietri Lebrão

Abstract:

Due to increasing environmental pressure for biodegradable products, especially in polymeric materials, in order to meet the demands of the biological cycles of the circular economy, new materials have been developed as a sustainability strategy. This study proposes a composite material developed from the biodegradable polymer PLA Ecovio® (polylactic acid - PLA) with natural sisal fibers, where the soybean ester was used as a plasticizer, which can aid in adhesion between the materials and fibers, making the most attractive final composite from an environmental point of view. The composites were obtained by extrusion. The materials tests were produced and submitted to biodegradation tests. Through the biodegradation tests, it can be seen that the biodegradable polymer composition with 5% sisal fiber presented about 12.4% more biodegradability compared to the polymer without fiber addition. It has also been found that the plasticizer was not a compatible with fibers and the polymer. Finally, fibers help to anticipate the decomposition process of the material when subjected to conditions of a landfill. Therefore, its intrinsic properties are not affected during its use, only the biodegradation process begins after its exposure to landfill conditions.

Keywords: Biocomposites, sisal, polylactic acid, PLA.

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Authors: Kim H. K. Choy, Oliva H. K. Chu, W. Y. Keung, B. Lim, Winnie P. Y. Tang

Abstract:

Background: The prevalence of workplace obesity is rising worldwide; therefore, the workplace is an ideal venue to implement weight control intervention. This pilot randomized controlled trial aimed to develop, implement, and evaluate a nutritional wellness program for obese health care providers working in a hospital. Methods: This hospital-based nutritional wellness program was an 8-week pilot randomized controlled trial for obese health care providers. The primary outcomes were body weight and body mass index (BMI). The secondary outcomes were serum fasting glucose, fasting cholesterol, triglyceride, high-density (HDL) and low-density (LDL) lipoprotein, body fat percentage, and body mass. Participants were randomly assigned to the intervention (n = 20) or control (n = 22) group. Participants in both groups received individual nutrition counselling and nutrition pamphlets, whereas only participants in the intervention group were given mobile phone text messages. Results: 42 participants completed the study. In comparison with the control group, the intervention group showed approximately 0.98 kg weight reduction after two months. Participants in intervention group also demonstrated clinically significant improvement in BMI, serum cholesterol level, and HDL level. There was no improvement of body fat percentage and body mass for both intervention and control groups. Conclusion: The nutritional wellness program for obese health care providers was feasible in hospital settings. Health care providers demonstrated short-term weight loss, decrease in serum fasting cholesterol level, and HDL level after completing the program.

Keywords: Health care provider, hospital, weight management, weight control.

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