191 research outputs found
A Component Based Heuristic Search Method with Evolutionary Eliminations
Nurse rostering is a complex scheduling problem that affects hospital
personnel on a daily basis all over the world. This paper presents a new
component-based approach with evolutionary eliminations, for a nurse scheduling
problem arising at a major UK hospital. The main idea behind this technique is
to decompose a schedule into its components (i.e. the allocated shift pattern
of each nurse), and then to implement two evolutionary elimination strategies
mimicking natural selection and natural mutation process on these components
respectively to iteratively deliver better schedules. The worthiness of all
components in the schedule has to be continuously demonstrated in order for
them to remain there. This demonstration employs an evaluation function which
evaluates how well each component contributes towards the final objective. Two
elimination steps are then applied: the first elimination eliminates a number
of components that are deemed not worthy to stay in the current schedule; the
second elimination may also throw out, with a low level of probability, some
worthy components. The eliminated components are replenished with new ones
using a set of constructive heuristics using local optimality criteria.
Computational results using 52 data instances demonstrate the applicability of
the proposed approach in solving real-world problems.Comment: 27 pages, 4 figure
Improved Squeaky Wheel Optimisation for Driver Scheduling
This paper presents a technique called Improved Squeaky Wheel Optimisation
for driver scheduling problems. It improves the original Squeaky Wheel
Optimisations effectiveness and execution speed by incorporating two additional
steps of Selection and Mutation which implement evolution within a single
solution. In the ISWO, a cycle of
Analysis-Selection-Mutation-Prioritization-Construction continues until
stopping conditions are reached. The Analysis step first computes the fitness
of a current solution to identify troublesome components. The Selection step
then discards these troublesome components probabilistically by using the
fitness measure, and the Mutation step follows to further discard a small
number of components at random. After the above steps, an input solution
becomes partial and thus the resulting partial solution needs to be repaired.
The repair is carried out by using the Prioritization step to first produce
priorities that determine an order by which the following Construction step
then schedules the remaining components. Therefore, the optimisation in the
ISWO is achieved by solution disruption, iterative improvement and an iterative
constructive repair process performed. Encouraging experimental results are
reported
A novel semi-supervised algorithm for rare prescription side effect discovery
Drugs are frequently prescribed to patients with the aim of improving each
patient's medical state, but an unfortunate consequence of most prescription
drugs is the occurrence of undesirable side effects. Side effects that occur in
more than one in a thousand patients are likely to be signalled efficiently by
current drug surveillance methods, however, these same methods may take decades
before generating signals for rarer side effects, risking medical morbidity or
mortality in patients prescribed the drug while the rare side effect is
undiscovered. In this paper we propose a novel computational meta-analysis
framework for signalling rare side effects that integrates existing methods,
knowledge from the web, metric learning and semi-supervised clustering. The
novel framework was able to signal many known rare and serious side effects for
the selection of drugs investigated, such as tendon rupture when prescribed
Ciprofloxacin or Levofloxacin, renal failure with Naproxen and depression
associated with Rimonabant. Furthermore, for the majority of the drug
investigated it generated signals for rare side effects at a more stringent
signalling threshold than existing methods and shows the potential to become a
fundamental part of post marketing surveillance to detect rare side effects
Signalling paediatric side effects using an ensemble of simple study designs
Background: Children are frequently prescribed medication `o-label', meaning there has not been sucient testing of the medication to determine its safety or eectiveness. The main reason this safety knowledge is lacking is due to
ethical restrictions that prevent children from being included in the majority of clinical trials.
Methods: Multiple measures of association are calculated for each drug and medical event pair and these are used as features that are fed into a classifier to determine the likelihood of the drug and medical event pair corresponding to an adverse drug reaction. The classier is trained using known adverse drug reactions or known non-adverse drug reaction relationships.
Results: The novel ensemble framework obtained a false positive rate of 0:149, a sensitivity of 0:547 and a specificity of 0:851 when implemented on a reference set
of drug and medical event pairs. The novel framework consistently outperformed each individual simple study design.
Conclusion: This research shows that it is possible to exploit the mechanism of causality and presents a framework for signalling adverse drug reactions eectively
The effect of baroque music on the PassPoints graphical password
Graphical passwords have been demonstrated to be the possible alternatives to traditional alphanumeric passwords. However, they still tend to follow predictable patterns that are easier to attack. The crux of the problem is users’ memory limitations. Users are the weakest link in password authentication mechanism. It shows that baroque music has positive effects on human memorizing and learning. We introduce baroque music to the PassPoints graphical password scheme and conduct a laboratory study in this paper. Results shown that there is no statistic difference between the music group and the control group without music in short-term recall experiments, both had high recall success rates. But in long-term recall, the music group performed significantly better. We also found that the music group tended to set significantly more complicated passwords, which are usually more resistant to dictionary and other guess attacks. But compared with the control group, the music group took more time to log in both in short-term and long-term tests. Besides, it appears that background music does not work in terms of hotspots
An Artificial Immune System for Misbehavior Detection in Mobile Ad-Hoc Networks with Virtual Thymus, Clustering, Danger Signal and Memory Detectors
In mobile ad-hoc networks, nodes act both as terminals and information relays, and participate in a common routing protocol, such as Dynamic Source Routing (DSR). The network is vulnerable to routing misbehavior, due to faulty or malicious nodes. Misbehavior detection systems aim at removing this vulnerability. For this purpose, we use an Artificial Immune System (AIS), a system inspired by the human immune system (HIS). Our goal is to build a system that, like its natural counterpart, automatically learns and detects new misbehavior. In this paper we build on our previous work and investigate the use of four concepts: (1
Measuring agreement on linguistic expressions in medical treatment scenarios
Quality of life assessment represents a key process of deciding treatment success and viability. As such, patients’ perceptions of their functional status and well-being are important inputs for impairment assessment. Given that patient completed questionnaires are often used to assess patient status and determine future treatment options, it is important to know the level of agreement of the words used by patients and different groups of medical professionals. In this paper, we propose a measure called the Agreement Ratio which provides a ratio of overall agreement when modelling words through Fuzzy Sets (FSs). The measure has been specifically designed for assessing this agreement in fuzzy sets which are generated from data such as patient responses. The measure relies on using the Jaccard Similarity Measure for comparing the different levels of agreement in the FSs generated. Synthetic examples are provided in order to show how to calculate the measure for given Fuzzy Sets. An application to real-world data is provided as well as a discussion about the results and the potential of the proposed measure
A time predefined variable depth search for nurse rostering
This paper presents a variable depth search for the nurse rostering problem. The algorithm works by chaining together single neighbourhood swaps into more effective compound moves. It achieves this by using heuristics to decide whether to continue extending a chain and which candidates to examine as the next potential link in the chain. Because end users vary in how long they are willing to wait for solutions, a particular goal of this research was to create an algorithm that accepts a user specified computational time limit and uses it effectively. When compared against previously published approaches the results show that the algorithm is very competitive
Investigating a Hybrid Metaheuristic For Job Shop Rescheduling
Previous research has shown that artificial immune systems can be used to
produce robust schedules in a manufacturing environment. The main goal is to
develop building blocks (antibodies) of partial schedules that can be used to
construct backup solutions (antigens) when disturbances occur during
production. The building blocks are created based upon underpinning ideas from
artificial immune systems and evolved using a genetic algorithm (Phase I). Each
partial schedule (antibody) is assigned a fitness value and the best partial
schedules are selected to be converted into complete schedules (antigens). We
further investigate whether simulated annealing and the great deluge algorithm
can improve the results when hybridised with our artificial immune system
(Phase II). We use ten fixed solutions as our target and measure how well we
cover these specific scenarios
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