Studies stochastic models of queueing, reliability, inventory, and sequencing in which random influences are considered. One stochastic mode--rl is approximated by another that is simpler in structure or about which simpler assumptions can be made. After general results on comparison properties of random variables and stochastic processes are given, the properties are illustrated by application to various queueing models and questions in experimental design, renewal and reliability theory, PERT networks and branching processes.

https://link.springer.com/content/pdf/10.1057%2Fjors.1984.224.pdf

Comparison Methods for Queues and Other Stochastic Models

http://ieeexplore.ieee.org/iel5/5/31196/01451889.pdf

Random differential equations in science and engineering

Extreme Value Theory is the branch of statistics that is used to model extreme events. The topic is of interest to meteorologists because much of the recent literature on climate change has focussed on the possibility that extreme events (very high or low temperatures, high precipitation events, droughts, hurricanes etc.) may be changing in parallel with global warming. As a specific example, the paper by Stott, Stone and Allen (2004) used the generalized Pareto distribution (see Section 2) to estimate the probability of the European heatwave event of 2003 under two conditions, (a) based on climate model data without an anthropogenic signal, (b) including anthropogenic effects (greenhouse gases etc.). They estimated a probability of about 1/1000 under (a) but about 1/250 under (b). Although even the probability under (b) is low, the increase in probability compared with (a) led them to conclude that the fraction of attributable risk due to the anthropogenic influence is about 75%. Another example of the use of statistics to examine trends in probabilities of extreme events is the recent paper by Elsner et al. (2008), which is highly relevant to the question of whether there is an increasing trend in severe hurricanes that may possibly be associated with anthropogenic global warming.

Extreme Value Theory

Recently, food recommender systems have received increasing attention due to their relevance for healthy living. Most existing studies on the food domain focus on recommendations that suggest proper food items for individual users on the basis of considering their preferences or health problems. These systems also provide functionalities to keep track of nutritional consumption as well as to persuade users to change their eating behavior in positive ways. Also, group recommendation functionalities are very useful in the food domain, especially when a group of users wants to have a dinner together at home or have a birthday party in a restaurant. Such scenarios create many challenges for food recommender systems since the preferences of all group members have to be taken into account in an adequate fashion. In this paper, we present an overview of recommendation techniques for individuals and groups in the healthy food domain. In addition, we analyze the existing state-of-the-art in food recommender systems and discuss research challenges related to the development of future food recommendation technologies.

/pdf/an-overview-of-recommender-systems-in-the-healthy-food-xwromyi9dp.pdf

An overview of recommender systems in the healthy food domain

In recent algorithmic family simulates different biological processes observed in Nature in order to efficiently address complex optimization problems. In the last years the number of bio-inspired optimization approaches in literature has grown considerably, reaching unprecedented levels that dark the future prospects of this field of research. This paper addresses this problem by proposing two comprehensive, principle-based taxonomies that allow researchers to organize existing and future algorithmic developments into well-defined categories, considering two different criteria: the source of inspiration and the behavior of each algorithm. Using these taxonomies we review more than three hundred publications dealing with nature-inspired and bio-inspired algorithms, and proposals falling within each of these categories are examined, leading to a critical summary of design trends and similarities between them, and the identification of the most similar classical algorithm for each reviewed paper. From our analysis we conclude that a poor relationship is often found between the natural inspiration of an algorithm and its behavior. Furthermore, similarities in terms of behavior between different algorithms are greater than what is claimed in their public disclosure: specifically, we show that more than one-third of the reviewed bio-inspired solvers are versions of classical algorithms. Grounded on the conclusions of our critical analysis, we give several recommendations and points of improvement for better methodological practices in this active and growing research field.

Comprehensive Taxonomies of Nature- and Bio-inspired Optimization: Inspiration Versus Algorithmic Behavior, Critical Analysis Recommendations

In this paper, A new distribution called Exponential Lomax distribution is introduced It is seemed that the parameter values of our new distribution are depending on decreasing and upside-down bathtub failure rate function Also, the statistical properties of this model are studied, such as, quantiles, moments, mean deviation Moreover, maximum likelihood estimators of its parameters are discussed Finally, the procedure is illustrated by real data set It is shown that the introduced model is more competitive than other models

/pdf/exponential-lomax-distribution-rrbh6bcvsk.pdf

Exponential Lomax Distribution

Estimation of components reliability in a parallel system using masked system life data

Exponentiated Generalized Weibull-Gompertz Distribution with Application in Survival Analysis

Recommender systems are needed to find food items of one’s interest. This paper reviews recommender systems and recommendation methods, then propose a food personalization framework based on adaptive hypermedia and extend Hermes framework with food recommendation functionality. Moreover, it combines TF-IDF term extraction method with cosine similarity measure. Healthy heuristics and standard food database are incorporated into the knowledgebase. Based on the performed evaluation, we conclude that semantic recommender systems in general outperform traditional recommenders systems with respect to accuracy, precision, and recall, and that the proposed recommender has a better F-measure than existing semantic recommenders.

Food Recommendation Using Ontology and Heuristics

Recommender systems are needed to find food items of ones interest. We review recommender systems and recommendation methods. We propose a food personalization framework based on adaptive hypermedia. We extend Hermes framework with food recommendation functionality. We combine TF-IDF term extraction method with cosine similarity measure. Healthy heuristics and standard food database are incorporated into the knowledgebase. Based on the performed evaluation, we conclude that semantic recommender systems in general outperform traditional recommenders systems with respect to accuracy, precision, and recall, and that the proposed recommender has a better F-measure than existing semantic recommenders.

A. H. El-Bassiouny

Papers

Exponential Lomax Distribution

Estimation of components reliability in a parallel system using masked system life data

Exponentiated Generalized Weibull-Gompertz Distribution with Application in Survival Analysis

Food Recommendation Using Ontology and Heuristics

Food Recommendation using Ontology and Heuristics