Choosing funds is a general issue for investors, with the aim of balancing potential risks and returns. The this article to use super-efficiency approach analyze rank exchange-traded (ETFs) in order find best utility ETFs. range-adjusted measure (RAM)-based data envelopment analysis (DEA) model used work evaluate set ETFs inefficient funds, while RAM fully RAM-based efficient funds. Other slack...

We present a metaheuristic algorithm for creating heuristic functions for CSP, especially SAT, with as low human interaction as possible. We use the concept of genetic programming to evolve local search heuristic functions encoded as a list of RAM model-like computer instructions. However, we’ve extended the RAM model language to reflect the features of genetic programming and the random mixing...

We proposed a deep learning method for interpretable diabetic retinopathy (DR) detection. The visualinterpretable feature of the proposed method is achieved by adding the regression activation map (RAM) after the global averaging pooling layer of the convolutional networks (CNN). With RAM, the proposed model can localize the discriminative regions of an retina image to show the specific region ...

This study analyzed the economics of feeding dried poultry droppings as supplement in ram production. Gross margin analysis and profitability ratio was used to analyze the data collected. The result of the budgetary analysis revealed that highest total cost of ₦5292.98/ram was incurred, highest total revenue of ₦7565.40/ram, highest gross margin of ₦3272.41/ram and highest net farm income of ₦2...

The standard algorithm for fast generation of Erdős-Rényi random graphs only works in the Real RAM model. The critical point is the generation of geometric random variates Geo(p), for which there is no algorithm that is both exact and efficient in any bounded precision machine model. For a RAM model with word size w = Ω(log log(1/p)), we show that this is possible and present an exact algorithm...

We present an efficient algorithm to estimate large modified Kneser-Ney models including interpolation. Streaming and sorting enables the algorithm to scale to much larger models by using a fixed amount of RAM and variable amount of disk. Using one machine with 140 GB RAM for 2.8 days, we built an unpruned model on 126 billion tokens. Machine translation experiments with this model show improve...

Multiple Sclerosis (MS) is a debilitating disease which can affect general health and life' satisfaction. This study aimed to determine dimensions of adaptation, general health, and life satisfaction in MS patients. This study was a cross-sectional that samples were selected from MS patients in 2015. Data was collected by using a demographic questionnaire, Roy Adaptation Model (RAM), General He...

This paper discusses the use of a Quality-of-Service (QoS) based Resource Allocation Model (Q-RAM) as the basis for a reward-based engineering framework for performance-critical systems. Q-RAM has its roots in the problem of maximizing the use of limited resources to achieve optimal performance of a system such as distributed multi-media delivery. Q-RAM is being considered both as a tool for pr...

Parkinson disease (PD) is a chronic, progressive and debilitating disease. This study aimed to determine the relationship between the dimensions of Roy Adaptation Model (RAM) and General Health (GH) and Satisfaction with Life (SWL) in PD patients. This was a cross-sectional study. Samples were selected from the PD patients in 2016. The data was gathered by demographic, RAM, GH and SWL Scale que...