Medical imaging analysis with artificial neural networks

Given that neural networks have been widely reported in the research community of medical imaging, we provide a focused literature survey on recent neural network developments in computer-aided diagnosis, medical image segmentation and edge detection towards visual content analysis, and medical image registration for its pre-processing and post-processing, with the aims of increasing awareness of how neural networks can be applied to these areas and to provide a foundation for further research and practical development. Representative techniques and algorithms are explained in detail to provide inspiring examples illustrating: (i) how a known neural network with fixed structure and training procedure could be applied to resolve a medical imaging problem; (ii) how medical images could be analysed, processed, and characterised by neural networks; and (iii) how neural networks could be expanded further to resolve problems relevant to medical imaging. In the concluding section, a highlight of comparisons among many neural network applications is included to provide a global view on computational intelligence with neural networks in medical imaging

Evaluating the Potential Effects of Deicing Salts on Roadside Carbon Sequestration

This project sought to document patterns of road deicing salts and the effects of these salts on the amount of carbon being sequestered passively along Montana Department of Transportation roads; it was designed collaboratively with a related roadside project that tested three different highway right-of-way management techniques (mowing height, shrub planting, disturbance) to determine whether they have the capacity to increase soil organic carbon. Our sampling did not reveal elevated salt levels at any of the nine locations sampled at each of the three I-90 sites. The greatest saline concentrations were found at the sample locations farthest from the road. This pattern was consistent across all three sites. The range of soil organic matter (SOM) was broad, from ~1% to >10%. Generally, SOM values were lowest adjacent to the road and highest farthest from the road. We found no or weak evidence of a relationship between our indices of soil salinity and SOM levels, with electrical conductivity, exchangeable calcium, and cation exchange capacity. Results imply that if road deicing salts are altering patterns of roadside SOM and potential carbon sequestration, this effect was not captured by our experimental design, nor did deicing salts appear to have affected roadside vegetation during our most recent sampling effort. Our findings highlight the value of experimentally separating the multiple potentially confounding effects of winter maintenance operations on roadside soils: roads could focus the flow of water, salts, and sands to roadside soils. How these types of mass inputs to roadside soils might influence medium- or long-term carbon dynamics remains an open question, but their fuller characterization and possible flow paths will be essential to clarifying the role of roadside soils in terrestrial soil organic carbon sequestration strategies

Prediction of Thermo-Physical Properties of TiO2-Al2O3/Water Nanoparticles by Using Artificial Neural Network

In this paper, an artificial neural network is implemented for the sake of predicting the thermal conductivity ratio of TiO2-Al2O3/water nanofluid. TiO2-Al2O3/water in the role of an innovative type of nanofluid was synthesized by the sol–gel method. The results indicated that 1.5 vol.% of nanofluids enhanced the thermal conductivity by up to 25%. It was shown that the heat transfer coefficient was linearly augmented with increasing nanoparticle concentration, but its variation with temperature was nonlinear. It should be noted that the increase in concentration may cause the particles to agglomerate, and then the thermal conductivity is reduced. The increase in temperature also increases the thermal conductivity, due to an increase in the Brownian motion and collision of particles. In this research, for the sake of predicting the thermal conductivity of TiO2-Al2O3/water nanofluid based on volumetric concentration and temperature functions, an artificial neural network is implemented. In this way, for predicting thermal conductivity, SOM (self-organizing map) and BP-LM (Back Propagation-Levenberq-Marquardt) algorithms were used. Based on the results obtained, these algorithms can be considered as an exceptional tool for predicting thermal conductivity. Additionally, the correlation coefficient values were equal to 0.938 and 0.98 when implementing the SOM and BP-LM algorithms, respectively, which is highly acceptable. View Full-Tex

Forecasting Long-Term Government Bond Yields: An Application of Statistical and AI Models

This paper evaluates several artificial intelligence and classical algorithms on their ability of forecasting the monthly yield of the US 10-year Treasury bonds from a set of four economic indicators. Due to the complexity of the prediction problem, the task represents a challenging test for the algorithms under evaluation. At the same time, the study is of particular significance for the important and paradigmatic role played by the US market in the world economy. Four data-driven artificial intelligence approaches are considered, namely, a manually built fuzzy logic model, a machine learned fuzzy logic model, a self-organising map model and a multi-layer perceptron model. Their performance is compared with the performance of two classical approaches, namely, a statistical ARIMA model and an econometric error correction model. The algorithms are evaluated on a complete series of end-month US 10-year Treasury bonds yields and economic indicators from 1986:1 to 2004:12. In terms of prediction accuracy and reliability of the modelling procedure, the best results are obtained by the three parametric regression algorithms, namely the econometric, the statistical and the multi-layer perceptron model. Due to the sparseness of the learning data samples, the manual and the automatic fuzzy logic approaches fail to follow with adequate precision the range of variations of the US 10-year Treasury bonds. For similar reasons, the self-organising map model gives an unsatisfactory performance. Analysis of the results indicates that the econometric model has a slight edge over the statistical and the multi-layer perceptron models. This suggests that pure data-driven induction may not fully capture the complicated mechanisms ruling the changes in interest rates. Overall, the prediction accuracy of the best models is only marginally better than the prediction accuracy of a basic one-step lag predictor. This result highlights the difficulty of the modelling task and, in general, the difficulty of building reliable predictors for financial markets.interest rates; forecasting; neural networks; fuzzy logic.

Evaluating Management Options to Increase Roadside Carbon Sequestration

We estimated the amount of carbon sequestered along Montana Department of Transportation (MDT) roads and tested 3 different highway right-of-way (ROW) management techniques to increase carbon stocks. Using Geographic Information System techniques, the total ROW acreage owned by MDT was found to sequester 75,292 metric tons of carbon per year and to consist mostly of grasslands (70%). From 2016-2018 we tested 3 ROW management techniques to increase carbon stocks- increase mowing height, plant woody shrubs, or add legumes to reclamation seed mixes of disturbed soils - at 3 sites (Three Forks [3F], Bear Canyon [BC], and Bozeman Pass [BP]) along Interstate 90 in southwestern Montana. Soil samples generally averaged 0.75–1.5% soil organic carbon (SOC) at the 3F site, 2.5–4% SOC at the BC site, and 1.5–2.5% SOC at the BP site. Average SOC levels were always lower in 2018 than in 2016. Soil respiration rates were generally highest in June or July at the BC site, averaging ~4 μmol CO2 m-2 second-1. Soil respiration rates were lower at the BC site in November 2016, at the BP site in June 2018, and at the 3F site in July 2018 (all ~2–3 μmol CO2 m-2 s-1). Aboveground biomass carbon estimates generally mirrored belowground SOC estimates. Taken together, our findings suggest that of the three treatments implemented (raised mowing height, shrub planting, and disturbance), minimizing disturbance to soils likely makes the greatest contribution to the medium- and long-term carbon-storage potential of these roadside soils

Strategic Human Resource Management Practices: An Exploratory Survey of French Organisations

Strategic Human Resource Management (SHRM) have been amply discussed in both academic circles and business press. Most of our notion of SHRM are from the work done in the US and from the body of literature known as "High Performance Work Practices". This paper tries to contribute to the debate by understanding the changes in strategic HRM practices (Role and Structure of HR Department, Recruitment, Retraining & Redeployment, Performance Appraisal, Compensation, and Rightsizing) in France in the last 5 years and try to answer specifically the question of how strategic HRM practices have changed in French organizations to enhance corporate performance. A multi-respondent survey of 28 French organizations are analyzed to find the changes in SHRM in French organizations. The responses yielded a variety of HRM variables relating to role and structure of the HRM department, recruitment, performance appraisal, retraining and redeployment and rightsizingSHRM; Performance; Change; France