A unified learning framework for content based medical image retrieval using a statistical model

AbstractThis paper presents a unified learning framework for heterogeneous medical image retrieval based on a Full Range Autoregressive Model (FRAR) with the Bayesian approach (BA). Using the unified framework, the color autocorrelogram, edge orientation autocorrelogram (EOAC) and micro-texture information of medical images are extracted. The EOAC is constructed in HSV color space, to circumvent the loss of edges due to spectral and chromatic variations. The proposed system employed adaptive binary tree based support vector machine (ABTSVM) for efficient and fast classification of medical images in feature vector space. The Manhattan distance measure of order one is used in the proposed system to perform a similarity measure in the classified and indexed feature vector space. The precision and recall (PR) method is used as a measure of performance in the proposed system. Short-term based relevance feedback (RF) mechanism is also adopted to reduce the semantic gap. The Experimental results reveal that the retrieval performance of the proposed system for heterogeneous medical image database is better than the existing systems at low computational and storage cost

Object Classification Techniques using Tree Based Classifiers

Object recognition is presently one of the most active research areas in computer vision, pattern recognition, artificial intelligence and human activity analysis. The area of object detection and classification, attention habitually focuses on changes in the location of anobject with respect to time, since appearance information can sensibly describe the object category. In this paper, feature set obtained from the Gray Level Co-Occurrence Matrices (GLCM), representing a different stage of statistical variations of object category. The experiments are carried out using Caltech 101 dataset, considering sevenobjects viz (airplanes, camera, chair, elephant, laptop, motorbike and bonsai tree) and the extracted GLCM feature set are modeled by tree based classifier like Naive Bayes Tree and Random Forest. In the experimental results, Random Forest classifier exhibits the accuracy and effectiveness of the proposed method with an overall accuracy rate of 89.62%, which outperforms the Naive Bayes classifier

Thermodynamic stability of metallurgical coke relative to graphite

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A comprehensive approach to quality management of intensive care services

Purpose – The purpose of this paper is to develop a comprehensive framework for improving intensive care unit performance. Design/methodology/approach – The study introduces a quality management framework by combining cause and effect diagram and logical framework. An intensive care unit was identified for the study on the basis of its performance. The reasons for not achieving the desired performance were identified using a cause and effect diagram with the stakeholder involvement. A logical framework was developed using information from the cause and effect diagram and a detailed project plan was developed. The improvement projects were implemented and evaluated. Findings – Stakeholders identified various intensive care unit issues. Managerial performance, organizational processes and insufficient staff were considered major issues. A logical framework was developed to plan an improvement project to resolve issues raised by clinicians and patients. Improved infrastructure, state-of-the-art equipment, well maintained facilities, IT-based communication, motivated doctors, nurses and support staff, improved patient care and improved drug availability were considered the main project outputs for improving performance. The proposed framework is currently being used as a continuous quality improvement tool, providing a planning, implementing, monitoring and evaluating framework for the quality improvement measures on a sustainable basis. Practical implications – The combined cause and effect diagram and logical framework analysis is a novel and effective approach to improving intensive care performance. Similar approaches could be adopted in any intensive care unit. Originality/value – The paper focuses on a uniform model that can be applied to most intensive care units

Rice-based pasta: a comparison between conventional pasta-making and extrusion- cooking

Good quality gluten-free products continue to be in demand among the celiac community and the production of pasta from non-conventional raw materials is a major technological challenge. In this work, the effects of two different pasta-making processes (conventional and extrusion-cooking) were investigated on parboiled brown and milled rice flours. The two processes differentiated for extrusion temperature (conventional extrusion: 50 C, max; extrusion-cooking: 115 C), whereas the drying diagram was the same. Starch modifications induced by each pasta-making process were analyzed by using a Micro-ViscoAmylo-graph (MVAG), Differential Scanning Calorimetry (DSC), and X-ray Diffraction. The cooking quality was evaluated by weight increase, solid loss into the cooking water, and texture analysis. Pasta obtained from milled rice using the extrusion-cooking process was characterized by the best cooking behavior. In this sample, starch presented the highest peak and final viscosities, the highest gelatinization temperature and lower enthalpy value, and the lowest crystallinity. The cooking quality of pasta obtained from brown rice appeared less affected by the processing conditions. Therefore, the nature and intensity of starch modifications can be modulated by the processing conditions and might explain the different cooking behaviour of rice pasta

Effect of iodine in semolina matrices

The effect of starch-protein interactions on the ability of linear starch chains to bind iodine was investigated in 4 types of semolina. Based on K/S (absorption/scattering coefficient) spectra, obtained after equilibration above K 2SO 4 and exposure to iodine vapor, and X-ray diffraction, semolina samples showed differences in chain mobility, iodine-binding capacity and crystalline order. After removing protein from the samples, starch exhibited a higher iodine-binding capacity, suggesting greater starch chain mobility, and low crystalline order. The results suggest that protein and/or starch-protein affect the packing arrangement of starch polymers within the granule

Number of adaptive steps to a local fitness peak

We consider a population of genotype sequences evolving on a rugged fitness landscape with many local fitness peaks. The population walks uphill until it encounters a local fitness maximum. We find that the statistical properties of the walk length depend on whether the underlying fitness distribution has a finite mean. If the mean is finite, all the walk length cumulants grow with the sequence length but approach a constant otherwise. Experimental implications of our analytical results are also discussed

Evolutionary dynamics on strongly correlated fitness landscapes

We study the evolutionary dynamics of a maladapted population of self-replicating sequences on strongly correlated fitness landscapes. Each sequence is assumed to be composed of blocks of equal length and its fitness is given by a linear combination of four independent block fitnesses. A mutation affects the fitness contribution of a single block leaving the other blocks unchanged and hence inducing correlations between the parent and mutant fitness. On such strongly correlated fitness landscapes, we calculate the dynamical properties like the number of jumps in the most populated sequence and the temporal distribution of the last jump which is shown to exhibit a inverse square dependence as in evolution on uncorrelated fitness landscapes. We also obtain exact results for the distribution of records and extremes for correlated random variables

Managing healthcare performance in analytical framework

Purpose – The purpose of the paper is to develop an integrated framework for performance management of healthcare services. Design/methodology/approach – This study develops a performance management framework for healthcare services using a combined analytic hierarchy process (AHP) and logical framework (LOGFRAME). The framework is then applied to the intensive care units of three different hospitals in developing nations. Numerous focus group discussions were undertaken, involving experts from the specific area under investigation. Findings – The study reveals that a combination of outcome, structure and process-based critical success factors and a combined AHP and LOGFRAME-based performance management framework helps manage performance of healthcare services. Practical implications – The proposed framework could be practiced in hospital-based healthcare services. Originality/value – The conventional approaches to healthcare performance management are either outcome-based or process-based, which cannot reveal improvement measures appropriately in order to assure superior performance. Additionally, they lack planning, implementing and evaluating improvement projects that are identified from performance measurement. This study presents an integrated approach to performance measurement and implementing framework of improvement projects